diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
index f87fc89fa..3e2e1d18f 100644
--- a/.github/workflows/main.yml
+++ b/.github/workflows/main.yml
@@ -1,15 +1,19 @@
 name: Py 3.10, 3.11, 3.12 | Windows Mac Linux
 
 on:
-  push:
+  pull_request:
     branches:
       - main
       - develop
-  pull_request:
+  push:
     branches:
       - main
       - develop
 
+concurrency:
+  group: ${{ github.event.pull_request.number }}  # Use the pull request number as the concurrency group
+  cancel-in-progress: true # Cancel any in-progress jobs for the same group. Prevents duplicate workflows on PRs from develop into main or from main into develop.
+
 jobs:
   set-os:
     runs-on: ubuntu-latest
@@ -83,7 +87,7 @@ jobs:
         shell: bash -l {0}
         run: |
           conda activate mhkit-env
-          pip install -e . --no-deps
+          pip install -e  ".[all,dev]" --no-deps
 
       - name: Prepare non-hindcast API data
         shell: bash -l {0}
@@ -134,7 +138,7 @@ jobs:
         shell: bash -l {0}
         run: |
           conda activate mhkit-env
-          pip install -e . --no-deps
+          pip install -e  ".[all,dev]" --no-deps
 
       - name: Prepare Wave Hindcast data
         shell: bash -l {0}
@@ -183,7 +187,7 @@ jobs:
         shell: bash -l {0}
         run: |
           conda activate mhkit-env
-          pip install -e . --no-deps
+          pip install -e  ".[all,dev]" --no-deps
 
       - name: Prepare Wind Hindcast data
         shell: bash -l {0}
@@ -286,7 +290,7 @@ jobs:
         shell: bash -l {0}
         run: |
           python -m pip install --upgrade pip wheel
-          pip install coverage pytest coveralls .
+          pip install -e  ".[all,dev]"
 
       - name: Install setuptools for Python 3.12
         if: matrix.python-version == '3.12'
@@ -354,7 +358,7 @@ jobs:
         shell: bash -l {0}
         run: |
           conda activate mhkit-env
-          pip install -e . --no-deps
+          pip install -e  ".[all,dev]" --no-deps
 
       - name: Download Wave Hindcast data from artifact
         uses: actions/download-artifact@v4
@@ -390,6 +394,41 @@ jobs:
           parallel: true
           path-to-lcov: ./coverage.lcov
 
+  test-optional-pip-dependencies:
+    needs: [set-os, prepare-nonhindcast-cache]
+    runs-on: ubuntu-latest
+    strategy:
+      matrix:
+        module:
+          [wave, tidal, river, dolfyn, power, loads, mooring, acoustics, utils]
+        python-version: ['3.12']
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Download non-hindcast data
+        uses: actions/download-artifact@v4
+        with:
+          name: data
+          path: ~/.cache/mhkit
+
+      - name: Install MHKiT with optional dependency
+        run: |
+          python -m pip install --upgrade pip
+          pip install "mhkit[${{ matrix.module }}]"
+          pip install pytest
+
+      - name: Run tests for ${{ matrix.module }}
+        env:
+          MPLBACKEND: Agg
+        run: |
+          python -m pytest -c .github/workflows/pytest.ini mhkit/tests/${{ matrix.module }}/
+
   notebook-matrix:
     runs-on: ubuntu-latest
     needs:
@@ -455,7 +494,8 @@ jobs:
         with:
           miniconda-version: 'latest'
           auto-update-conda: true
-          python-version: '3.11'
+          python-version: '3.12'
+          channels: conda-forge
           activate-environment: TESTconda
           use-only-tar-bz2: false
 
@@ -475,7 +515,7 @@ jobs:
         shell: bash -l {0}
         run: |
           conda activate mhkit-env
-          pip install -e . --no-deps
+          pip install -e  ".[all,dev]" --no-deps
 
       - name: Download non-hindcast data
         uses: actions/download-artifact@v4
diff --git a/.github/workflows/pylint.yml b/.github/workflows/pylint.yml
index ecf7cae76..27d52e520 100644
--- a/.github/workflows/pylint.yml
+++ b/.github/workflows/pylint.yml
@@ -18,8 +18,7 @@ jobs:
       - name: Install dependencies
         run: |
           python -m pip install --upgrade pip wheel
-          pip install pylint
-          pip install .
+          pip install ".[all,dev]"
 
       - name: Run Pylint on mhkit/loads/
         run: |
@@ -36,3 +35,15 @@ jobs:
       - name: Run Pylint on mhkit/acoustics/
         run: |
           pylint mhkit/acoustics/
+
+      - name: Run Pylint on mhkit/tidal/
+        run: |
+          pylint mhkit/tidal/
+
+      - name: Run Pylint on mhkit/river/
+        run: |
+          pylint --extension-pkg-allow-list=netCDF4 mhkit/river/
+
+      - name: Run Pylint on mhkit/wave/io/hindcast/
+        run: |
+          pylint  mhkit/wave/io/hindcast/
diff --git a/README.md b/README.md
index f38a8c3a3..85151bd56 100644
--- a/README.md
+++ b/README.md
@@ -32,19 +32,54 @@ See the [MHKiT documentation](https://mhkit-software.github.io/MHKiT) for more i
 
 ## Installation
 
-MHKiT-Python requires Python (3.10, 3.11, 3.12) along with several Python
-package dependencies. MHKiT-Python can be installed using the Conda package manager:
+[MHKiT-Python](https://github.com/MHKiT-Software/MHKiT-Python) requires [Python (3.10-3.12)](https://www.python.org/).
+It is recommended to use the [Anaconda Python Distribution](https://www.anaconda.com/distribution/) (a fully featured Python installer with a GUI) 
+or [Miniconda](https://docs.anaconda.com/miniconda/#quick-command-line-install) (a lightweight installer with the ``conda`` command line utility).  
+Both will include most of MHKiT-Python's package dependencies. 
+MHKiT can be installed several ways:
+
+### Option 1: Install from Python
+
+This option is recommended as a fast installation for MHKiT-Python users.
+To install MHKiT-Python using ``conda``, in an Anaconda Prompt:
+
+```bash
+	conda install -c conda-forge mhkit
+```
+
+### Option 2: Clone Repository from GitHub
+
+This option is recommended for MHKiT-Python users who want access to example notebooks and developers.
+Download and install your preferred version of [git](https://git-scm.com/).
+To clone MHKiT-Python:
 
 ```bash
-conda install -c conda-forge mhkit
+	git clone https://github.com/MHKiT-Software/MHKiT-Python
+	cd MHKiT-Python
 ```
 
-MHKiT can alternatively be installed from PyPi using pip:
+To install a local, editable version of MHKiT-Python using [pip](https://pip.pypa.io/en/stable/):
 
 ```bash
-pip install mhkit
+	pip install -e .["all"]
 ```
 
+An [environment YAML file](https://github.com/MHKiT-Software/MHKiT-Python/blob/main/environment.yml) is also provided that can create the base environment required by MHKiT. 
+MHKiT can then be installed into that environment using any of the provided methods.
+
+### Option 3: Module-specific Install from Python
+
+A slim version of MHKiT-Python can be installed to reduce the number of dependencies and potential conflicts with other software. 
+This installation utilizes pip's optional dependencies installation. 
+To install a single MHKiT module, e.g. the wave module, and its dependencies, use:
+
+	pip install mhkit["wave"]
+
+Note that ``pip install mhkit`` only installs the base MHKiT dependencies and not the entire software.
+To install all MHKiT dependencies use:
+
+	pip install mhkit["all"]
+
 See [installation instructions](https://mhkit-software.github.io/MHKiT/installation.html) for more information.
 
 ## Copyright and license
diff --git a/environment.yml b/environment.yml
index 60c96123c..884a2e01d 100644
--- a/environment.yml
+++ b/environment.yml
@@ -3,7 +3,7 @@ channels:
   - conda-forge
   - defaults
 dependencies:
-  - python>=3.10
+  - python>=3.10,<3.13
   - pip
   - numpy>=2.0.0
   - pandas>=2.2.2
@@ -25,3 +25,4 @@ dependencies:
   - matplotlib>=3.9.1
   - fatpack
   - nrel-rex
+  - cartopy
diff --git a/examples/ADCP_Delft3D_TRTS_example.ipynb b/examples/ADCP_Delft3D_TRTS_example.ipynb
index 26d22c383..6347594ff 100644
--- a/examples/ADCP_Delft3D_TRTS_example.ipynb
+++ b/examples/ADCP_Delft3D_TRTS_example.ipynb
@@ -14,7 +14,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "447c788c-a042-4649-81c6-532fcd464c0e",
    "metadata": {},
    "outputs": [],
@@ -67,7 +67,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "079e0248-bb41-4c99-a954-19735dbe5e90",
    "metadata": {},
    "outputs": [
@@ -104,28 +104,76 @@
        " */\n",
        "\n",
        ":root {\n",
-       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
-       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
-       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
-       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
-       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
-       "  --xr-background-color: var(--jp-layout-color0, white);\n",
-       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
-       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base rgba(0, 0, 0, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(0, 0, 0, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, white)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
+       "  );\n",
        "}\n",
        "\n",
-       "html[theme=dark],\n",
-       "html[data-theme=dark],\n",
-       "body[data-theme=dark],\n",
+       "html[theme=\"dark\"],\n",
+       "html[data-theme=\"dark\"],\n",
+       "body[data-theme=\"dark\"],\n",
        "body.vscode-dark {\n",
-       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
-       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
-       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-       "  --xr-border-color: #1F1F1F;\n",
-       "  --xr-disabled-color: #515151;\n",
-       "  --xr-background-color: #111111;\n",
-       "  --xr-background-color-row-even: #111111;\n",
-       "  --xr-background-color-row-odd: #313131;\n",
+       "  --xr-font-color0: var(\n",
+       "    --jp-content-font-color0,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
+       "  );\n",
+       "  --xr-font-color2: var(\n",
+       "    --jp-content-font-color2,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
+       "  );\n",
+       "  --xr-font-color3: var(\n",
+       "    --jp-content-font-color3,\n",
+       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
+       "  );\n",
+       "  --xr-border-color: var(\n",
+       "    --jp-border-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
+       "  );\n",
+       "  --xr-disabled-color: var(\n",
+       "    --jp-layout-color3,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
+       "  );\n",
+       "  --xr-background-color: var(\n",
+       "    --jp-layout-color0,\n",
+       "    var(--pst-color-on-background, #111111)\n",
+       "  );\n",
+       "  --xr-background-color-row-even: var(\n",
+       "    --jp-layout-color1,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
+       "  );\n",
+       "  --xr-background-color-row-odd: var(\n",
+       "    --jp-layout-color2,\n",
+       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
+       "  );\n",
        "}\n",
        "\n",
        ".xr-wrap {\n",
@@ -166,7 +214,7 @@
        ".xr-sections {\n",
        "  padding-left: 0 !important;\n",
        "  display: grid;\n",
-       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
        "}\n",
        "\n",
        ".xr-section-item {\n",
@@ -174,11 +222,14 @@
        "}\n",
        "\n",
        ".xr-section-item input {\n",
-       "  display: none;\n",
+       "  display: inline-block;\n",
+       "  opacity: 0;\n",
+       "  height: 0;\n",
        "}\n",
        "\n",
        ".xr-section-item input + label {\n",
        "  color: var(--xr-disabled-color);\n",
+       "  border: 2px solid transparent !important;\n",
        "}\n",
        "\n",
        ".xr-section-item input:enabled + label {\n",
@@ -186,6 +237,10 @@
        "  color: var(--xr-font-color2);\n",
        "}\n",
        "\n",
+       ".xr-section-item input:focus + label {\n",
+       "  border: 2px solid var(--xr-font-color0) !important;\n",
+       "}\n",
+       "\n",
        ".xr-section-item input:enabled + label:hover {\n",
        "  color: var(--xr-font-color0);\n",
        "}\n",
@@ -207,7 +262,7 @@
        "\n",
        ".xr-section-summary-in + label:before {\n",
        "  display: inline-block;\n",
-       "  content: '►';\n",
+       "  content: \"►\";\n",
        "  font-size: 11px;\n",
        "  width: 15px;\n",
        "  text-align: center;\n",
@@ -218,7 +273,7 @@
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label:before {\n",
-       "  content: '▼';\n",
+       "  content: \"▼\";\n",
        "}\n",
        "\n",
        ".xr-section-summary-in:checked + label > span {\n",
@@ -290,15 +345,15 @@
        "}\n",
        "\n",
        ".xr-dim-list:before {\n",
-       "  content: '(';\n",
+       "  content: \"(\";\n",
        "}\n",
        "\n",
        ".xr-dim-list:after {\n",
-       "  content: ')';\n",
+       "  content: \")\";\n",
        "}\n",
        "\n",
        ".xr-dim-list li:not(:last-child):after {\n",
-       "  content: ',';\n",
+       "  content: \",\";\n",
        "  padding-right: 5px;\n",
        "}\n",
        "\n",
@@ -315,7 +370,9 @@
        ".xr-var-item label,\n",
        ".xr-var-item > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
        "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
        "}\n",
        "\n",
        ".xr-var-item > .xr-var-name:hover span {\n",
@@ -326,6 +383,7 @@
        ".xr-var-list > li:nth-child(odd) > label,\n",
        ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
        "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
        "}\n",
        "\n",
        ".xr-var-name {\n",
@@ -375,8 +433,15 @@
        ".xr-var-data,\n",
        ".xr-index-data {\n",
        "  display: none;\n",
-       "  background-color: var(--xr-background-color) !important;\n",
-       "  padding-bottom: 5px !important;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
        "}\n",
        "\n",
        ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
@@ -389,6 +454,12 @@
        "  float: right;\n",
        "}\n",
        "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
        ".xr-var-name span,\n",
        ".xr-var-data,\n",
        ".xr-index-name div,\n",
@@ -448,6 +519,14 @@
        "  stroke: currentColor;\n",
        "  fill: currentColor;\n",
        "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
        "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 4MB\n",
        "Dimensions:              (time: 1227, time_gps: 1227, range: 47, beam: 4,\n",
        "                          dir: 4, x1: 4, x2: 4, earth: 3, inst: 3)\n",
@@ -475,30 +554,35 @@
        "    hdwtime_gps          (time_gps) datetime64[ns] 10kB 2010-08-10T14:28:15.5...\n",
        "    beam2inst_orientmat  (x1, x2) float32 64B 1.462 -1.462 0.0 ... -1.034 -1.034\n",
        "    orientmat            (earth, inst, time) float32 44kB -0.9037 ... 1.0\n",
-       "Attributes: (12/38)\n",
+       "Attributes: (12/39)\n",
+       "    firmware_ver:             10.16\n",
        "    inst_make:                TRDI\n",
        "    inst_type:                ADCP\n",
        "    rotate_vars:              [&#x27;vel&#x27;, &#x27;vel_bt&#x27;]\n",
        "    has_imu:                  0\n",
-       "    prog_ver:                 10.16\n",
        "    inst_model:               Rio Grande\n",
        "    ...                       ...\n",
-       "    false_target_threshold:   50\n",
-       "    transmit_lag_m:           0.08\n",
        "    bandwidth:                0\n",
-       "    sourceprog:               WINRIVER\n",
-       "    fs:                       1.92\n",
-       "    vel_gps_corrected:        0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c664d336-71bd-4303-aa63-1547f9b0e5fa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c664d336-71bd-4303-aa63-1547f9b0e5fa' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1227</li><li><span class='xr-has-index'>time_gps</span>: 1227</li><li><span class='xr-has-index'>range</span>: 47</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-842e2bb9-110e-4d52-afdb-04ff8c6029d1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-842e2bb9-110e-4d52-afdb-04ff8c6029d1' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-08-10T14:28:15.559999942 .....</div><input id='attrs-02ffa456-e898-457b-a8fb-0ec53dfbd3f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-02ffa456-e898-457b-a8fb-0ec53dfbd3f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a65dec45-626b-4e70-acfb-52f45595f23a' class='xr-var-data-in' type='checkbox'><label for='data-a65dec45-626b-4e70-acfb-52f45595f23a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-10T14:28:15.559999942&#x27;, &#x27;2010-08-10T14:28:16.319999933&#x27;,\n",
+       "    power_level:              128\n",
+       "    serialnum:                134877318\n",
+       "    source_program:           WINRIVER\n",
+       "    range_offset:             0.07\n",
+       "    fs:                       1.92</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-578127cd-b7cf-4d63-91f5-72d5d43d2429' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-578127cd-b7cf-4d63-91f5-72d5d43d2429' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1227</li><li><span class='xr-has-index'>time_gps</span>: 1227</li><li><span class='xr-has-index'>range</span>: 47</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3b3ff946-efbb-4554-ad63-3bd08aa87899' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3b3ff946-efbb-4554-ad63-3bd08aa87899' class='xr-section-summary' >Coordinates: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-08-10T14:28:15.559999942 .....</div><input id='attrs-6f699fd2-9283-4c36-a4f9-8affda9178d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f699fd2-9283-4c36-a4f9-8affda9178d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-748cabd6-4c0c-476c-a0ea-946e8174f704' class='xr-var-data-in' type='checkbox'><label for='data-748cabd6-4c0c-476c-a0ea-946e8174f704' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-10T14:28:15.559999942&#x27;, &#x27;2010-08-10T14:28:16.319999933&#x27;,\n",
        "       &#x27;2010-08-10T14:28:17.069999933&#x27;, ..., &#x27;2010-08-10T14:39:59.190000057&#x27;,\n",
        "       &#x27;2010-08-10T14:39:59.910000085&#x27;, &#x27;2010-08-10T14:40:00.630000114&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-08-10T22:28:17.200000047 .....</div><input id='attrs-28b50a84-b356-491b-9610-625ef996fab4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28b50a84-b356-491b-9610-625ef996fab4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39e46d3d-d6a1-4e6c-940e-ec954cfe1377' class='xr-var-data-in' type='checkbox'><label for='data-39e46d3d-d6a1-4e6c-940e-ec954cfe1377' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-10T22:28:17.200000047&#x27;, &#x27;2010-08-10T22:28:18.000000000&#x27;,\n",
+       "      shape=(1227,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-08-10T22:28:17.200000047 .....</div><input id='attrs-645c471c-2cf9-4022-9054-79bc12763726' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-645c471c-2cf9-4022-9054-79bc12763726' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d99364c0-18c3-44cb-b839-8615ddedb585' class='xr-var-data-in' type='checkbox'><label for='data-d99364c0-18c3-44cb-b839-8615ddedb585' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time gps</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-10T22:28:17.200000047&#x27;, &#x27;2010-08-10T22:28:18.000000000&#x27;,\n",
        "       &#x27;2010-08-10T22:28:18.599999904&#x27;, ..., &#x27;2010-08-10T22:40:00.799999952&#x27;,\n",
        "       &#x27;2010-08-10T22:40:01.400000095&#x27;, &#x27;2010-08-10T22:40:02.200000047&#x27;],\n",
-       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.57 0.82 1.07 ... 11.82 12.07</div><input id='attrs-c7717bc4-9c30-4231-9d5d-ad517eba07e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c7717bc4-9c30-4231-9d5d-ad517eba07e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fd34888-b6b5-45d4-8ab6-6de41604ef75' class='xr-var-data-in' type='checkbox'><label for='data-6fd34888-b6b5-45d4-8ab6-6de41604ef75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.57,  0.82,  1.07,  1.32,  1.57,  1.82,  2.07,  2.32,  2.57,  2.82,\n",
+       "      shape=(1227,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.57 0.82 1.07 ... 11.82 12.07</div><input id='attrs-9dc6856a-64b6-432c-a221-71c0d9b2f0da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9dc6856a-64b6-432c-a221-71c0d9b2f0da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-922d5d6d-b4d9-4676-80c5-08c0e1695166' class='xr-var-data-in' type='checkbox'><label for='data-922d5d6d-b4d9-4676-80c5-08c0e1695166' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.57,  0.82,  1.07,  1.32,  1.57,  1.82,  2.07,  2.32,  2.57,  2.82,\n",
        "        3.07,  3.32,  3.57,  3.82,  4.07,  4.32,  4.57,  4.82,  5.07,  5.32,\n",
        "        5.57,  5.82,  6.07,  6.32,  6.57,  6.82,  7.07,  7.32,  7.57,  7.82,\n",
        "        8.07,  8.32,  8.57,  8.82,  9.07,  9.32,  9.57,  9.82, 10.07, 10.32,\n",
-       "       10.57, 10.82, 11.07, 11.32, 11.57, 11.82, 12.07], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-2574d167-71d6-472c-9a80-5a9190f07a92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2574d167-71d6-472c-9a80-5a9190f07a92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc32c5ef-aa97-44ce-a00b-a07e3041a07b' class='xr-var-data-in' type='checkbox'><label for='data-bc32c5ef-aa97-44ce-a00b-a07e3041a07b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27; &#x27;err&#x27;</div><input id='attrs-efc90ddc-0b03-4c4a-8899-39ccf0ddfa6f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-efc90ddc-0b03-4c4a-8899-39ccf0ddfa6f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2963d6b6-00fd-4170-94af-3516679a8f06' class='xr-var-data-in' type='checkbox'><label for='data-2963d6b6-00fd-4170-94af-3516679a8f06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>ship</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;, &#x27;err&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-abf633ab-3df0-48a6-bc71-046cad600f9e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-abf633ab-3df0-48a6-bc71-046cad600f9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-288a1198-d4bd-46c8-aa71-812276a00927' class='xr-var-data-in' type='checkbox'><label for='data-288a1198-d4bd-46c8-aa71-812276a00927' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-3369a98b-d046-4ceb-882b-ebafb81cecdd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3369a98b-d046-4ceb-882b-ebafb81cecdd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-892a389f-a256-48ff-98c3-cad87397d824' class='xr-var-data-in' type='checkbox'><label for='data-892a389f-a256-48ff-98c3-cad87397d824' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-211efbdd-d5a5-420c-baaf-43a1be7e3820' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-211efbdd-d5a5-420c-baaf-43a1be7e3820' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5891c197-314b-4b04-96d9-9b70c44812a3' class='xr-var-data-in' type='checkbox'><label for='data-5891c197-314b-4b04-96d9-9b70c44812a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-e234f9e6-b66e-4094-9a6f-0fd72add2126' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e234f9e6-b66e-4094-9a6f-0fd72add2126' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bffb17eb-a0dd-4bcf-9956-be2d8be6672a' class='xr-var-data-in' type='checkbox'><label for='data-bffb17eb-a0dd-4bcf-9956-be2d8be6672a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-730811ee-728d-4aa5-9ea4-ac5a6b6335c8' class='xr-section-summary-in' type='checkbox'  ><label for='section-730811ee-728d-4aa5-9ea4-ac5a6b6335c8' class='xr-section-summary' >Data variables: <span>(36)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.652e+03 3.653e+03 ... 4.904e+03</div><input id='attrs-98656c86-ca17-4a52-addc-d0e3e1fc513b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98656c86-ca17-4a52-addc-d0e3e1fc513b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-824e555b-eabe-4d6a-97f9-15ed849e1eee' class='xr-var-data-in' type='checkbox'><label for='data-824e555b-eabe-4d6a-97f9-15ed849e1eee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([3652., 3653., 3654., ..., 4902., 4903., 4904.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>builtin_test_fail</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-bda91e73-ccef-48bc-b8cb-3628f8b8d5be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bda91e73-ccef-48bc-b8cb-3628f8b8d5be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c928c676-9e8f-44aa-9d4c-e2551e884d0c' class='xr-var-data-in' type='checkbox'><label for='data-c928c676-9e8f-44aa-9d4c-e2551e884d0c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Built-In Test Failures</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.466e+03 1.466e+03 ... 1.466e+03</div><input id='attrs-8d37cedc-0f70-4cfe-ba00-a63f9c84017f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d37cedc-0f70-4cfe-ba00-a63f9c84017f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24f52276-918d-40bf-a64f-669661a2ef53' class='xr-var-data-in' type='checkbox'><label for='data-24f52276-918d-40bf-a64f-669661a2ef53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1466., 1466., 1466., ..., 1466., 1466., 1466.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-bae99a81-0f8e-4bc4-8f51-175c7b573f29' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bae99a81-0f8e-4bc4-8f51-175c7b573f29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7405dd75-5684-4b5a-9a9b-243129cc31c8' class='xr-var-data-in' type='checkbox'><label for='data-7405dd75-5684-4b5a-9a9b-243129cc31c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.1 -0.09 -0.21 ... 0.2 0.07 0.56</div><input id='attrs-1a31d4ec-0125-45a0-8a72-50e4c24a9488' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a31d4ec-0125-45a0-8a72-50e4c24a9488' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74747f63-e85c-4de7-a8ad-431d20f447d6' class='xr-var-data-in' type='checkbox'><label for='data-74747f63-e85c-4de7-a8ad-431d20f447d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.1 , -0.09, -0.21, ...,  0.2 ,  0.07,  0.56], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.33 3.32 3.33 ... 2.24 2.65 2.33</div><input id='attrs-b0b2ecaf-3d13-43ef-b461-32493580abbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0b2ecaf-3d13-43ef-b461-32493580abbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5d6c9ba9-0294-4106-ac15-bde1283660c2' class='xr-var-data-in' type='checkbox'><label for='data-5d6c9ba9-0294-4106-ac15-bde1283660c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([3.33, 3.32, 3.33, ..., 2.24, 2.65, 2.33], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>154.6 154.5 155.0 ... 145.8 147.8</div><input id='attrs-4bb2381a-1a26-4579-90ed-348b792d1744' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bb2381a-1a26-4579-90ed-348b792d1744' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f17ee38-3c84-4649-b2e0-699a5be0f406' class='xr-var-data-in' type='checkbox'><label for='data-1f17ee38-3c84-4649-b2e0-699a5be0f406' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([154.65, 154.49, 155.01, ..., 146.32, 145.75, 147.81], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>15.2 15.25 15.23 ... 15.21 15.22</div><input id='attrs-992fd7fc-7f71-4140-b4f6-cd3683bd652f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-992fd7fc-7f71-4140-b4f6-cd3683bd652f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba9fcd55-2649-40f8-aeb9-64763f2a9565' class='xr-var-data-in' type='checkbox'><label for='data-ba9fcd55-2649-40f8-aeb9-64763f2a9565' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([15.2 , 15.25, 15.23, ..., 15.2 , 15.21, 15.22], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-76e72405-99ba-4d78-baf8-e429a518f37b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-76e72405-99ba-4d78-baf8-e429a518f37b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17733a4f-05e7-42cc-ba1d-eb34ce54a32d' class='xr-var-data-in' type='checkbox'><label for='data-17733a4f-05e7-42cc-ba1d-eb34ce54a32d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>psu</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>min_preping_wait</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-7e491920-d66f-4bef-a65a-b4722e0e22b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7e491920-d66f-4bef-a65a-b4722e0e22b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4cb454f-7158-4f73-8ed7-0b587c6895e9' class='xr-var-data-in' type='checkbox'><label for='data-c4cb454f-7158-4f73-8ed7-0b587c6895e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Minimum Pre-Ping Wait Time Between Measurements</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-ba438acd-0871-41b8-a8b9-2c11fd1acc72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba438acd-0871-41b8-a8b9-2c11fd1acc72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97b7564b-fc68-4797-8797-9769013c2479' class='xr-var-data-in' type='checkbox'><label for='data-97b7564b-fc68-4797-8797-9769013c2479' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-85e88eb3-3acf-4bd6-bc1c-52fc5d109349' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85e88eb3-3acf-4bd6-bc1c-52fc5d109349' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80c0fbe6-e419-4e8e-b23a-85ac91c2bcc1' class='xr-var-data-in' type='checkbox'><label for='data-80c0fbe6-e419-4e8e-b23a-85ac91c2bcc1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-52f02e79-1b2e-4266-905e-d7d90b9ea08b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52f02e79-1b2e-4266-905e-d7d90b9ea08b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24ba7586-2b7b-47a6-8668-8d3728dd7e78' class='xr-var-data-in' type='checkbox'><label for='data-24ba7586-2b7b-47a6-8668-8d3728dd7e78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-3a0e545c-a309-46f1-995d-15835c5f65e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a0e545c-a309-46f1-995d-15835c5f65e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69d68705-0450-4af1-b140-6935f12fecf5' class='xr-var-data-in' type='checkbox'><label for='data-69d68705-0450-4af1-b140-6935f12fecf5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-0d327be1-2365-43e2-a76c-80ce29e5c007' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d327be1-2365-43e2-a76c-80ce29e5c007' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-928245b6-7e25-4783-a82b-48a024620716' class='xr-var-data-in' type='checkbox'><label for='data-928245b6-7e25-4783-a82b-48a024620716' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.057 -0.192 0.121 ... nan nan nan</div><input id='attrs-ea13c769-52fa-468d-9a47-9a024dc2fc58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea13c769-52fa-468d-9a47-9a024dc2fc58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66cf73ca-9f32-45d7-b99c-4320f39841b8' class='xr-var-data-in' type='checkbox'><label for='data-66cf73ca-9f32-45d7-b99c-4320f39841b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.057, -0.192,  0.121, ..., -0.01 , -0.629,  0.134],\n",
+       "       10.57, 10.82, 11.07, 11.32, 11.57, 11.82, 12.07], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-599765d6-77ae-4a61-a905-2324ac5d1f78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-599765d6-77ae-4a61-a905-2324ac5d1f78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-964ea246-25bb-44f0-901d-04ffbd82c4ac' class='xr-var-data-in' type='checkbox'><label for='data-964ea246-25bb-44f0-901d-04ffbd82c4ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U3</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27; &#x27;err&#x27;</div><input id='attrs-64de5b59-19c4-4851-b272-5ff3de8aed9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64de5b59-19c4-4851-b272-5ff3de8aed9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbbe2bd1-daae-44ea-94e2-d537e74eb20c' class='xr-var-data-in' type='checkbox'><label for='data-dbbe2bd1-daae-44ea-94e2-d537e74eb20c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>ship</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;, &#x27;err&#x27;], dtype=&#x27;&lt;U3&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-b205455b-7133-47f7-85fe-ecca371d709c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b205455b-7133-47f7-85fe-ecca371d709c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9330cc18-117d-49b7-ba6a-d3a71969f4c0' class='xr-var-data-in' type='checkbox'><label for='data-9330cc18-117d-49b7-ba6a-d3a71969f4c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-1066768f-a31d-4528-a7e9-97f8496ef754' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1066768f-a31d-4528-a7e9-97f8496ef754' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-56d38387-e9d7-4749-95f4-5a160110d81c' class='xr-var-data-in' type='checkbox'><label for='data-56d38387-e9d7-4749-95f4-5a160110d81c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-38d98e31-61ae-4207-b70d-6fabcb89b255' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38d98e31-61ae-4207-b70d-6fabcb89b255' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f82843d4-052d-4df7-a07a-220c972fe11e' class='xr-var-data-in' type='checkbox'><label for='data-f82843d4-052d-4df7-a07a-220c972fe11e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-c44f7188-bc78-4278-bf17-a7f81e65bea1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c44f7188-bc78-4278-bf17-a7f81e65bea1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-04a3d2fc-1128-4c10-a5ed-0e3c2fa7708f' class='xr-var-data-in' type='checkbox'><label for='data-04a3d2fc-1128-4c10-a5ed-0e3c2fa7708f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-86463766-f297-4ebf-8c1a-1f5100ba6d0f' class='xr-section-summary-in' type='checkbox'  ><label for='section-86463766-f297-4ebf-8c1a-1f5100ba6d0f' class='xr-section-summary' >Data variables: <span>(36)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.652e+03 3.653e+03 ... 4.904e+03</div><input id='attrs-97ddec0b-186d-4a85-8f26-ed11197cb843' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97ddec0b-186d-4a85-8f26-ed11197cb843' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c33d8902-2e4c-47f6-ba42-1eee9bc54c58' class='xr-var-data-in' type='checkbox'><label for='data-c33d8902-2e4c-47f6-ba42-1eee9bc54c58' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([3652., 3653., 3654., ..., 4902., 4903., 4904.], shape=(1227,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>builtin_test_fail</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-b2e55464-b80c-4679-99e7-12c356690b22' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b2e55464-b80c-4679-99e7-12c356690b22' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82a21a40-e3bd-4842-95ae-6bc74eadf327' class='xr-var-data-in' type='checkbox'><label for='data-82a21a40-e3bd-4842-95ae-6bc74eadf327' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Built-In Test Failures</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.466e+03 1.466e+03 ... 1.466e+03</div><input id='attrs-6fbd405f-7f47-43c5-9751-03b9666ef931' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fbd405f-7f47-43c5-9751-03b9666ef931' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0d08dc1-0342-42b5-ae7b-e49152c3710e' class='xr-var-data-in' type='checkbox'><label for='data-d0d08dc1-0342-42b5-ae7b-e49152c3710e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1466., 1466., 1466., ..., 1466., 1466., 1466.],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-ba550007-9240-4057-9406-e444918287cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba550007-9240-4057-9406-e444918287cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69d6ab2b-06a0-40f2-b269-1b7ceeae35e2' class='xr-var-data-in' type='checkbox'><label for='data-69d6ab2b-06a0-40f2-b269-1b7ceeae35e2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.1 -0.09 -0.21 ... 0.2 0.07 0.56</div><input id='attrs-bd690552-6634-4c77-9cdf-23167bb2efec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd690552-6634-4c77-9cdf-23167bb2efec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47d52669-fed0-4c63-8a27-466ae8f6b301' class='xr-var-data-in' type='checkbox'><label for='data-47d52669-fed0-4c63-8a27-466ae8f6b301' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.1 , -0.09, -0.21, ...,  0.2 ,  0.07,  0.56],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.33 3.32 3.33 ... 2.24 2.65 2.33</div><input id='attrs-8d2385a3-41bf-472e-a0c0-862100cf2c5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d2385a3-41bf-472e-a0c0-862100cf2c5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57ca0aa5-f45f-474c-bf3c-8490d40f796a' class='xr-var-data-in' type='checkbox'><label for='data-57ca0aa5-f45f-474c-bf3c-8490d40f796a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([3.33, 3.32, 3.33, ..., 2.24, 2.65, 2.33],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>154.6 154.5 155.0 ... 145.8 147.8</div><input id='attrs-8dd0c484-23c0-42ae-beeb-06c6c937fcb0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8dd0c484-23c0-42ae-beeb-06c6c937fcb0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-059aac28-3f57-48de-9c0b-5d09ab084401' class='xr-var-data-in' type='checkbox'><label for='data-059aac28-3f57-48de-9c0b-5d09ab084401' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([154.65, 154.49, 155.01, ..., 146.32, 145.75, 147.81],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>15.2 15.25 15.23 ... 15.21 15.22</div><input id='attrs-4617d5f5-9ccd-4b39-b273-6cc0df68d8da' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4617d5f5-9ccd-4b39-b273-6cc0df68d8da' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9883e43f-bc11-446d-b45c-20456e967c8e' class='xr-var-data-in' type='checkbox'><label for='data-9883e43f-bc11-446d-b45c-20456e967c8e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([15.2 , 15.25, 15.23, ..., 15.2 , 15.21, 15.22],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-a4e2f45e-f579-462c-a1f2-3f6be538ec04' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4e2f45e-f579-462c-a1f2-3f6be538ec04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a0f8759-a484-4206-ad9c-82731899e954' class='xr-var-data-in' type='checkbox'><label for='data-0a0f8759-a484-4206-ad9c-82731899e954' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>psu</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>min_preping_wait</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-11cad2d2-671a-460b-8aee-db407f9afb43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11cad2d2-671a-460b-8aee-db407f9afb43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1bea08cd-80ec-41aa-8fbb-ffc75fc3ee7a' class='xr-var-data-in' type='checkbox'><label for='data-1bea08cd-80ec-41aa-8fbb-ffc75fc3ee7a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Minimum Pre-Ping Wait Time Between Measurements</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-ab02f46a-4482-40b1-a718-e0c75a36162d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab02f46a-4482-40b1-a718-e0c75a36162d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10a46c39-a816-438b-b58a-90309262c89a' class='xr-var-data-in' type='checkbox'><label for='data-10a46c39-a816-438b-b58a-90309262c89a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-f916e17c-0566-4775-9823-bdd787fb8b89' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f916e17c-0566-4775-9823-bdd787fb8b89' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f940130b-baa8-4ded-9871-a4aab5c8d1c7' class='xr-var-data-in' type='checkbox'><label for='data-f940130b-baa8-4ded-9871-a4aab5c8d1c7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-405e2f6f-7b11-4a08-8537-8ab17ece385f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-405e2f6f-7b11-4a08-8537-8ab17ece385f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-180d019c-0ce7-4470-b3ab-ef7253e9a9e6' class='xr-var-data-in' type='checkbox'><label for='data-180d019c-0ce7-4470-b3ab-ef7253e9a9e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-41000da5-77e5-4aa9-82a2-198f45e78775' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41000da5-77e5-4aa9-82a2-198f45e78775' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-149a6432-df9b-40bc-bef6-f041e0bf878b' class='xr-var-data-in' type='checkbox'><label for='data-149a6432-df9b-40bc-bef6-f041e0bf878b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-bf175105-ef42-47b5-b53c-6f66438aeaea' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf175105-ef42-47b5-b53c-6f66438aeaea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aac5104a-4b37-4268-84e8-52c7470d4b73' class='xr-var-data-in' type='checkbox'><label for='data-aac5104a-4b37-4268-84e8-52c7470d4b73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., ..., 0., 0., 0.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.057 -0.192 0.121 ... nan nan nan</div><input id='attrs-350671e0-1533-40cb-a5f3-8187f36b2c54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-350671e0-1533-40cb-a5f3-8187f36b2c54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4f334ad-743e-4fb3-80d3-3d9928096c82' class='xr-var-data-in' type='checkbox'><label for='data-e4f334ad-743e-4fb3-80d3-3d9928096c82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.057, -0.192,  0.121, ..., -0.01 , -0.629,  0.134],\n",
        "        [-0.058, -0.131, -0.119, ...,  0.048, -0.146, -0.078],\n",
        "        [ 0.059,  0.406,  0.067, ..., -0.123, -0.524,  0.057],\n",
        "        ...,\n",
@@ -529,7 +613,7 @@
        "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
        "        [   nan,    nan,    nan, ...,    nan,    nan,    nan],\n",
        "        [   nan,    nan,    nan, ...,    nan,    nan,    nan]]],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>224.0 229.0 225.0 ... 63.0 49.0</div><input id='attrs-fdaa4db3-c52e-49e8-8869-39993513bdd2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdaa4db3-c52e-49e8-8869-39993513bdd2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-841fe3c7-52e3-4d9e-9cde-16b17b4df258' class='xr-var-data-in' type='checkbox'><label for='data-841fe3c7-52e3-4d9e-9cde-16b17b4df258' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[224., 229., 225., ..., 228., 229., 227.],\n",
+       "      shape=(4, 47, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>224.0 229.0 225.0 ... 63.0 49.0</div><input id='attrs-4f37e2fe-ed0f-4d69-93b4-88f8608f406a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f37e2fe-ed0f-4d69-93b4-88f8608f406a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50afdb7d-af77-4499-a825-23388b176da8' class='xr-var-data-in' type='checkbox'><label for='data-50afdb7d-af77-4499-a825-23388b176da8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[224., 229., 225., ..., 228., 229., 227.],\n",
        "        [222., 222., 221., ..., 222., 228., 226.],\n",
        "        [217., 215., 214., ..., 223., 219., 224.],\n",
        "        ...,\n",
@@ -559,7 +643,8 @@
        "        ...,\n",
        "        [ 51.,  57.,  52., ...,  61.,  92.,  49.],\n",
        "        [ 52.,  58.,  53., ...,  59.,  66.,  52.],\n",
-       "        [ 51.,  60.,  50., ...,  74.,  63.,  49.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>124.0 118.0 130.0 ... 16.0 8.0 21.0</div><input id='attrs-7b9cf91b-eb58-4f9a-8538-c2caaa0c4161' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7b9cf91b-eb58-4f9a-8538-c2caaa0c4161' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-818c7630-4501-4d2d-a9bd-63bee458329b' class='xr-var-data-in' type='checkbox'><label for='data-818c7630-4501-4d2d-a9bd-63bee458329b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[124., 118., 130., ..., 125., 111., 133.],\n",
+       "        [ 51.,  60.,  50., ...,  74.,  63.,  49.]]],\n",
+       "      shape=(4, 47, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>124.0 118.0 130.0 ... 16.0 8.0 21.0</div><input id='attrs-f567a3e8-0fac-4a0c-ba63-3880a9cfafab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f567a3e8-0fac-4a0c-ba63-3880a9cfafab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca3e4071-d4a1-46e7-a3a8-2ee3287fbbb3' class='xr-var-data-in' type='checkbox'><label for='data-ca3e4071-d4a1-46e7-a3a8-2ee3287fbbb3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[124., 118., 130., ..., 125., 111., 133.],\n",
        "        [128., 140., 128., ..., 118., 140., 120.],\n",
        "        [132., 119., 119., ..., 109., 116., 122.],\n",
        "        ...,\n",
@@ -589,7 +674,8 @@
        "        ...,\n",
        "        [ 19.,  13.,  17., ...,  18.,  23.,  11.],\n",
        "        [  7.,  25.,  16., ...,  26.,  23.,  27.],\n",
-       "        [ 13.,  25.,  13., ...,  16.,   8.,  21.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcnt_gd</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-3c1e0ed5-eca6-4276-8a1c-bfd60d62e3cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c1e0ed5-eca6-4276-8a1c-bfd60d62e3cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99f565ce-5915-4619-9e56-9cdf9e35859f' class='xr-var-data-in' type='checkbox'><label for='data-99f565ce-5915-4619-9e56-9cdf9e35859f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Percent Good</dd><dt><span>standard_name :</span></dt><dd>proportion_of_acceptable_signal_returns_from_acoustic_instrument_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
+       "        [ 13.,  25.,  13., ...,  16.,   8.,  21.]]],\n",
+       "      shape=(4, 47, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcnt_gd</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-1d21a950-b980-45d7-b2eb-5b092f6c9168' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d21a950-b980-45d7-b2eb-5b092f6c9168' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5de435e-8387-4b8d-b170-2f524765f536' class='xr-var-data-in' type='checkbox'><label for='data-e5de435e-8387-4b8d-b170-2f524765f536' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Percent Good</dd><dt><span>standard_name :</span></dt><dd>proportion_of_acceptable_signal_returns_from_acoustic_instrument_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
        "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
        "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
        "        ...,\n",
@@ -619,38 +705,44 @@
        "        ...,\n",
        "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
        "        [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
-       "        [  0.,   0.,   0., ...,   0.,   0.,   0.]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dist_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.95 3.95 3.96 ... 1.93 1.95 2.0</div><input id='attrs-97c1effa-5f94-4564-a5d6-6d316e4db905' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97c1effa-5f94-4564-a5d6-6d316e4db905' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4b4c7d0-4973-4842-a314-2558b413f593' class='xr-var-data-in' type='checkbox'><label for='data-b4b4c7d0-4973-4842-a314-2558b413f593' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Bottom Track Measured Depth</dd></dl></div><div class='xr-var-data'><pre>array([[3.95, 3.95, 3.96, ..., 2.65, 2.64, 2.6 ],\n",
+       "        [  0.,   0.,   0., ...,   0.,   0.,   0.]]],\n",
+       "      shape=(4, 47, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dist_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.95 3.95 3.96 ... 1.93 1.95 2.0</div><input id='attrs-18d28ea0-92c5-476d-8ce2-18edc604e463' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-18d28ea0-92c5-476d-8ce2-18edc604e463' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6fafaf1-cff5-4704-907f-0db346fb8b66' class='xr-var-data-in' type='checkbox'><label for='data-e6fafaf1-cff5-4704-907f-0db346fb8b66' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Bottom Track Measured Depth</dd></dl></div><div class='xr-var-data'><pre>array([[3.95, 3.95, 3.96, ..., 2.65, 2.64, 2.6 ],\n",
        "       [2.55, 2.56, 2.57, ..., 1.61, 1.59, 1.59],\n",
        "       [3.31, 3.32, 3.33, ..., 1.93, 1.95, 1.96],\n",
-       "       [2.87, 2.88, 2.88, ..., 1.93, 1.95, 2.  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_bt</span></div><div class='xr-var-dims'>(dir, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.063 -0.004 0.039 ... 0.01 0.0</div><input id='attrs-4bd616e5-0bca-43c1-96b1-0fd24bc964c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bd616e5-0bca-43c1-96b1-0fd24bc964c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-487d6993-8e5e-4ed6-bd10-59624e8afd3e' class='xr-var-data-in' type='checkbox'><label for='data-487d6993-8e5e-4ed6-bd10-59624e8afd3e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 6.3000e-02, -4.0000e-03,  3.9000e-02, ...,  2.3000e-02,\n",
+       "       [2.87, 2.88, 2.88, ..., 1.93, 1.95, 2.  ]],\n",
+       "      shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_bt</span></div><div class='xr-var-dims'>(dir, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.063 -0.004 0.039 ... 0.01 0.0</div><input id='attrs-0a14f9ef-3b14-4690-acae-a60c465076ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a14f9ef-3b14-4690-acae-a60c465076ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e8688af7-56f3-44b9-b1f1-97077730fb2c' class='xr-var-data-in' type='checkbox'><label for='data-e8688af7-56f3-44b9-b1f1-97077730fb2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 6.3000e-02, -4.0000e-03,  3.9000e-02, ...,  2.3000e-02,\n",
        "         2.7000e-02,  0.0000e+00],\n",
        "       [-3.7000e-02,  1.9000e-02,  1.5000e-02, ..., -6.9000e-02,\n",
        "        -1.0100e-01, -1.5800e-01],\n",
        "       [ 9.0000e-03,  0.0000e+00,  8.0000e-03, ...,  0.0000e+00,\n",
        "        -1.7000e-02, -2.0000e-03],\n",
        "       [-1.0000e-02, -6.0000e-03, -3.2768e+01, ...,  7.0000e-03,\n",
-       "         1.0000e-02,  0.0000e+00]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>231.0 243.0 242.0 ... 249.0 246.0</div><input id='attrs-d7d4a207-c40a-44d2-8b75-50c1f78b50bb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7d4a207-c40a-44d2-8b75-50c1f78b50bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7090847-d5a3-401b-9b60-190b9b375cf9' class='xr-var-data-in' type='checkbox'><label for='data-a7090847-d5a3-401b-9b60-190b9b375cf9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[231., 243., 242., ..., 252., 255., 248.],\n",
+       "         1.0000e-02,  0.0000e+00]], shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>231.0 243.0 242.0 ... 249.0 246.0</div><input id='attrs-ed1ddc5b-2f3a-4742-bac6-facf5e7402ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed1ddc5b-2f3a-4742-bac6-facf5e7402ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83911fb1-feeb-40b8-bf83-c3d4550b73eb' class='xr-var-data-in' type='checkbox'><label for='data-83911fb1-feeb-40b8-bf83-c3d4550b73eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[231., 243., 242., ..., 252., 255., 248.],\n",
        "       [229., 239., 246., ..., 249., 248., 254.],\n",
        "       [223., 237.,   0., ..., 249., 252., 251.],\n",
-       "       [248., 244., 250., ..., 248., 249., 246.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>40.0 40.0 36.0 ... 40.0 64.0 49.0</div><input id='attrs-faf93f99-698b-402e-88fc-2354d1789e58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-faf93f99-698b-402e-88fc-2354d1789e58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caa9f7a0-e4fc-4a41-a6ee-2631e077162c' class='xr-var-data-in' type='checkbox'><label for='data-caa9f7a0-e4fc-4a41-a6ee-2631e077162c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[40., 40., 36., ..., 64., 66., 51.],\n",
+       "       [248., 244., 250., ..., 248., 249., 246.]],\n",
+       "      shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>40.0 40.0 36.0 ... 40.0 64.0 49.0</div><input id='attrs-93bc6915-f449-4344-ab4e-92149df0d5d4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-93bc6915-f449-4344-ab4e-92149df0d5d4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-798c2c32-d456-4bb6-98e8-b36c003ae1dd' class='xr-var-data-in' type='checkbox'><label for='data-798c2c32-d456-4bb6-98e8-b36c003ae1dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[40., 40., 36., ..., 64., 66., 51.],\n",
        "       [45., 38., 47., ..., 78., 78., 83.],\n",
        "       [40., 43., 36., ..., 55., 71., 71.],\n",
-       "       [55., 54., 52., ..., 40., 64., 49.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcnt_gd_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 100.0 ... 100.0 100.0 100.0</div><input id='attrs-db6b6afb-c324-4772-9690-4de91dd3c4e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db6b6afb-c324-4772-9690-4de91dd3c4e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ece8dec-2630-44b2-a8e1-286d69a10a78' class='xr-var-data-in' type='checkbox'><label for='data-7ece8dec-2630-44b2-a8e1-286d69a10a78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Percent Good</dd><dt><span>standard_name :</span></dt><dd>proportion_of_acceptable_signal_returns_from_acoustic_instrument_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[  0.,   0., 100., ...,   0.,   0.,   0.],\n",
+       "       [55., 54., 52., ..., 40., 64., 49.]],\n",
+       "      shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcnt_gd_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 100.0 ... 100.0 100.0 100.0</div><input id='attrs-1408b995-55a5-4be7-b140-1e5705f9c189' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1408b995-55a5-4be7-b140-1e5705f9c189' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-678b44f7-d6f3-4bc0-a050-af367c37baa0' class='xr-var-data-in' type='checkbox'><label for='data-678b44f7-d6f3-4bc0-a050-af367c37baa0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Percent Good</dd><dt><span>standard_name :</span></dt><dd>proportion_of_acceptable_signal_returns_from_acoustic_instrument_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[  0.,   0., 100., ...,   0.,   0.,   0.],\n",
        "       [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
        "       [  0.,   0.,   0., ...,   0.,   0.,   0.],\n",
-       "       [100., 100.,   0., ..., 100., 100., 100.]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>64.56 64.56 64.56 ... 64.56 64.56</div><input id='attrs-dce48c3f-e23a-41ab-9d9d-5d3e56442bf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dce48c3f-e23a-41ab-9d9d-5d3e56442bf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3db1cbe9-a5fb-4545-a6d7-66c364c3f63f' class='xr-var-data-in' type='checkbox'><label for='data-3db1cbe9-a5fb-4545-a6d7-66c364c3f63f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([64.560905, 64.560905, 64.560905, ..., 64.56094 , 64.56094 ,\n",
-       "       64.56094 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-149.1 -149.1 ... -149.1 -149.1</div><input id='attrs-0a800b93-0409-4f26-80ba-e131bb806c90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a800b93-0409-4f26-80ba-e131bb806c90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37d2cd0a-3e0b-42f8-a08f-71dd3061d23a' class='xr-var-data-in' type='checkbox'><label for='data-37d2cd0a-3e0b-42f8-a08f-71dd3061d23a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-149.06679, -149.06679, -149.06679, ..., -149.06682, -149.06682,\n",
-       "       -149.0668 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fix_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.0 9.0 9.0 9.0 ... 9.0 9.0 9.0 9.0</div><input id='attrs-63754f9c-dad9-42c7-aef4-d118e5c69377' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63754f9c-dad9-42c7-aef4-d118e5c69377' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3edd3710-d95f-498e-883b-0f1b6e44ce2c' class='xr-var-data-in' type='checkbox'><label for='data-3edd3710-d95f-498e-883b-0f1b6e44ce2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>GPS Fix</dd></dl></div><div class='xr-var-data'><pre>array([9., 9., 9., ..., 9., 9., 9.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_sat_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.0 9.0 9.0 9.0 ... 10.0 10.0 10.0</div><input id='attrs-ef738b60-6e0f-4fe7-b640-4c0fdf008854' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef738b60-6e0f-4fe7-b640-4c0fdf008854' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfc31f69-22dc-48fe-9725-d1008884ef09' class='xr-var-data-in' type='checkbox'><label for='data-dfc31f69-22dc-48fe-9725-d1008884ef09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>count</dd><dt><span>long_name :</span></dt><dd>Number of Satellites</dd></dl></div><div class='xr-var-data'><pre>array([ 9.,  9.,  9., ..., 10., 10., 10.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdop_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.9 0.9 0.9 0.9 ... 0.8 0.8 0.8 0.8</div><input id='attrs-e416fad2-1eab-406f-b378-f2f280dca6c7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e416fad2-1eab-406f-b378-f2f280dca6c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a8102952-021e-4925-9027-215213de50c8' class='xr-var-data-in' type='checkbox'><label for='data-a8102952-021e-4925-9027-215213de50c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Horizontal Dilution of Precision</dd></dl></div><div class='xr-var-data'><pre>array([0.9, 0.9, 0.9, ..., 0.8, 0.8, 0.8], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>108.5 108.2 108.2 ... 108.6 108.6</div><input id='attrs-1cdf3e9b-6603-4e61-9fa6-690419b8fe35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1cdf3e9b-6603-4e61-9fa6-690419b8fe35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca475c4f-ddaa-4f1e-9ee7-7d3082a15af9' class='xr-var-data-in' type='checkbox'><label for='data-ca475c4f-ddaa-4f1e-9ee7-7d3082a15af9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Elevation above MLLW</dd></dl></div><div class='xr-var-data'><pre>array([108.483, 108.196, 108.208, ..., 108.644, 108.629, 108.606],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rtk_age_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 6.0 2.0 3.0 ... 6.0 2.0 3.0 3.0</div><input id='attrs-b062239d-9dd6-4ca0-92b3-a83bbcbba8db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b062239d-9dd6-4ca0-92b3-a83bbcbba8db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efa94880-8931-400a-96d0-a91dfee31fa5' class='xr-var-data-in' type='checkbox'><label for='data-efa94880-8931-400a-96d0-a91dfee31fa5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Age of Received Real Time Kinetic Signal</dd></dl></div><div class='xr-var-data'><pre>array([5., 6., 2., ..., 2., 3., 3.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_over_grnd_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.03549 0.03395 ... 0.1096 0.1672</div><input id='attrs-e301777a-71f9-478a-b689-7866de4e0692' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e301777a-71f9-478a-b689-7866de4e0692' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2e7e684-ad1a-41f1-ad90-67f32e1f1a2e' class='xr-var-data-in' type='checkbox'><label for='data-b2e7e684-ad1a-41f1-ad90-67f32e1f1a2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Platform Speed over Ground</dd><dt><span>standard_name :</span></dt><dd>platform_speed_wrt_ground</dd></dl></div><div class='xr-var-data'><pre>array([0.03549382, 0.03395062, 0.03858025, ..., 0.11985596, 0.1095679 ,\n",
-       "       0.16718106], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dir_over_grnd_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>115.5 94.08 6.931 ... 160.5 182.1</div><input id='attrs-ccddc641-414e-4964-8e1c-14d602074a9e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ccddc641-414e-4964-8e1c-14d602074a9e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-864ecbe7-8462-4fc7-9657-4e00da942df8' class='xr-var-data-in' type='checkbox'><label for='data-864ecbe7-8462-4fc7-9657-4e00da942df8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Platform Direction over Ground</dd><dt><span>standard_name :</span></dt><dd>platform_course</dd></dl></div><div class='xr-var-data'><pre>array([115.501,  94.077,   6.931, ..., 140.963, 160.468, 182.087],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdwtime_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-08-10T14:28:15.559999942 .....</div><input id='attrs-3430fdd0-f1fa-4e2e-99c0-07b288f3e29e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3430fdd0-f1fa-4e2e-99c0-07b288f3e29e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b4fbebb-c27a-4957-8fbb-a7d8aadfcdc3' class='xr-var-data-in' type='checkbox'><label for='data-6b4fbebb-c27a-4957-8fbb-a7d8aadfcdc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-10T14:28:15.559999942&#x27;, &#x27;2010-08-10T14:28:16.319999933&#x27;,\n",
+       "       [100., 100.,   0., ..., 100., 100., 100.]],\n",
+       "      shape=(4, 1227), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>64.56 64.56 64.56 ... 64.56 64.56</div><input id='attrs-4955270e-8fce-45c7-a350-67eee8d83063' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4955270e-8fce-45c7-a350-67eee8d83063' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74a7d630-4ef4-4ee0-a26f-a11a9ec78455' class='xr-var-data-in' type='checkbox'><label for='data-74a7d630-4ef4-4ee0-a26f-a11a9ec78455' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([64.560905, 64.560905, 64.560905, ..., 64.56094 , 64.56094 ,\n",
+       "       64.56094 ], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-149.1 -149.1 ... -149.1 -149.1</div><input id='attrs-4304c44f-dfea-43b9-b5b6-e59f00833f93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4304c44f-dfea-43b9-b5b6-e59f00833f93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d23bdbd6-8fad-4a08-b5b3-91a02baaab04' class='xr-var-data-in' type='checkbox'><label for='data-d23bdbd6-8fad-4a08-b5b3-91a02baaab04' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-149.06679, -149.06679, -149.06679, ..., -149.06682, -149.06682,\n",
+       "       -149.0668 ], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fix_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.0 9.0 9.0 9.0 ... 9.0 9.0 9.0 9.0</div><input id='attrs-48a1e437-d460-421d-9720-f09a8c23c220' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48a1e437-d460-421d-9720-f09a8c23c220' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80f8d7a4-e81a-41db-8f2f-1326bd2cb1e9' class='xr-var-data-in' type='checkbox'><label for='data-80f8d7a4-e81a-41db-8f2f-1326bd2cb1e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>GPS Fix</dd></dl></div><div class='xr-var-data'><pre>array([9., 9., 9., ..., 9., 9., 9.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_sat_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.0 9.0 9.0 9.0 ... 10.0 10.0 10.0</div><input id='attrs-953512d9-4ba2-47ef-9011-53c88a53fa55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-953512d9-4ba2-47ef-9011-53c88a53fa55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-beacc763-8095-4709-b13b-e5336494c2ac' class='xr-var-data-in' type='checkbox'><label for='data-beacc763-8095-4709-b13b-e5336494c2ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>count</dd><dt><span>long_name :</span></dt><dd>Number of Satellites</dd></dl></div><div class='xr-var-data'><pre>array([ 9.,  9.,  9., ..., 10., 10., 10.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdop_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.9 0.9 0.9 0.9 ... 0.8 0.8 0.8 0.8</div><input id='attrs-aa04ea87-18a5-42f5-9dad-ff98e2d976a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aa04ea87-18a5-42f5-9dad-ff98e2d976a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd8671b9-2d9e-4fa8-9cb8-e121e1af71e6' class='xr-var-data-in' type='checkbox'><label for='data-bd8671b9-2d9e-4fa8-9cb8-e121e1af71e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Horizontal Dilution of Precision</dd></dl></div><div class='xr-var-data'><pre>array([0.9, 0.9, 0.9, ..., 0.8, 0.8, 0.8], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>108.5 108.2 108.2 ... 108.6 108.6</div><input id='attrs-fcc07695-d8f4-4f7d-a4f9-58f1d8bbad58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fcc07695-d8f4-4f7d-a4f9-58f1d8bbad58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31ac4442-7df8-46dd-bfa9-a8f100eb36c8' class='xr-var-data-in' type='checkbox'><label for='data-31ac4442-7df8-46dd-bfa9-a8f100eb36c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Elevation above MLLW</dd></dl></div><div class='xr-var-data'><pre>array([108.483, 108.196, 108.208, ..., 108.644, 108.629, 108.606],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rtk_age_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.0 6.0 2.0 3.0 ... 6.0 2.0 3.0 3.0</div><input id='attrs-1ffe38ff-187d-4a46-96ca-ba105be74222' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ffe38ff-187d-4a46-96ca-ba105be74222' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35efa5b0-985c-424e-b510-667668d3e6f2' class='xr-var-data-in' type='checkbox'><label for='data-35efa5b0-985c-424e-b510-667668d3e6f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Age of Received Real Time Kinetic Signal</dd></dl></div><div class='xr-var-data'><pre>array([5., 6., 2., ..., 2., 3., 3.], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_over_grnd_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.03549 0.03395 ... 0.1096 0.1672</div><input id='attrs-aab67b6c-2791-4215-be33-56398b668fb6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aab67b6c-2791-4215-be33-56398b668fb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7500a405-dc71-4b65-8672-8959c959c1c3' class='xr-var-data-in' type='checkbox'><label for='data-7500a405-dc71-4b65-8672-8959c959c1c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Platform Speed over Ground</dd><dt><span>standard_name :</span></dt><dd>platform_speed_wrt_ground</dd></dl></div><div class='xr-var-data'><pre>array([0.03549382, 0.03395062, 0.03858025, ..., 0.11985596, 0.1095679 ,\n",
+       "       0.16718106], shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dir_over_grnd_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>115.5 94.08 6.931 ... 160.5 182.1</div><input id='attrs-01872462-a56d-402f-8d58-c0b320652190' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01872462-a56d-402f-8d58-c0b320652190' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-635a854c-72b9-49f5-b10a-bb6b13d2a24c' class='xr-var-data-in' type='checkbox'><label for='data-635a854c-72b9-49f5-b10a-bb6b13d2a24c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Platform Direction over Ground</dd><dt><span>standard_name :</span></dt><dd>platform_course</dd></dl></div><div class='xr-var-data'><pre>array([115.501,  94.077,   6.931, ..., 140.963, 160.468, 182.087],\n",
+       "      shape=(1227,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdwtime_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2010-08-10T14:28:15.559999942 .....</div><input id='attrs-854f6af0-9af6-4929-85d8-b15d3e7b834c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-854f6af0-9af6-4929-85d8-b15d3e7b834c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-467689e9-7155-43f2-8079-ce0b5295f27e' class='xr-var-data-in' type='checkbox'><label for='data-467689e9-7155-43f2-8079-ce0b5295f27e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2010-08-10T14:28:15.559999942&#x27;, &#x27;2010-08-10T14:28:16.319999933&#x27;,\n",
        "       &#x27;2010-08-10T14:28:17.069999933&#x27;, ...,\n",
        "       &#x27;2010-08-10T14:39:59.190000057&#x27;, &#x27;2010-08-10T14:39:59.910000085&#x27;,\n",
-       "       &#x27;2010-08-10T14:40:00.630000114&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.462 -1.462 0.0 ... -1.034 -1.034</div><input id='attrs-5c817838-c880-4f3a-ba00-7f9927c4cec1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c817838-c880-4f3a-ba00-7f9927c4cec1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4a74a2dc-9a61-48b7-9dfc-419eccab869c' class='xr-var-data-in' type='checkbox'><label for='data-4a74a2dc-9a61-48b7-9dfc-419eccab869c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.4619021 , -1.4619021 ,  0.        ,  0.        ],\n",
+       "       &#x27;2010-08-10T14:40:00.630000114&#x27;],\n",
+       "      shape=(1227,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.462 -1.462 0.0 ... -1.034 -1.034</div><input id='attrs-0113a027-7e0f-426c-a742-872ad1713938' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0113a027-7e0f-426c-a742-872ad1713938' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a03d6ad-4c2a-48c4-84f9-2644cd1a919d' class='xr-var-data-in' type='checkbox'><label for='data-9a03d6ad-4c2a-48c4-84f9-2644cd1a919d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.4619021 , -1.4619021 ,  0.        ,  0.        ],\n",
        "       [ 0.        ,  0.        , -1.4619021 ,  1.4619021 ],\n",
        "       [ 0.26604444,  0.26604444,  0.26604444,  0.26604444],\n",
        "       [ 1.033721  ,  1.033721  , -1.033721  , -1.033721  ]],\n",
-       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.9037 -0.9025 -0.9064 ... 1.0 1.0</div><input id='attrs-28e83e44-6b25-4a1c-9534-70ed9ae8e833' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-28e83e44-6b25-4a1c-9534-70ed9ae8e833' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3176ae24-a02e-46d5-b7d1-1ee478d7e9b8' class='xr-var-data-in' type='checkbox'><label for='data-3176ae24-a02e-46d5-b7d1-1ee478d7e9b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.9037092 , -0.9025101 , -0.9063815 , ..., -0.8321477 ,\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.9037 -0.9025 -0.9064 ... 1.0 1.0</div><input id='attrs-66663342-742c-4476-8963-b17dfc7bcc92' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-66663342-742c-4476-8963-b17dfc7bcc92' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b36e2e53-263a-4699-b309-3d6dd3524d9a' class='xr-var-data-in' type='checkbox'><label for='data-b36e2e53-263a-4699-b309-3d6dd3524d9a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.9037092 , -0.9025101 , -0.9063815 , ..., -0.8321477 ,\n",
        "         -0.8265897 , -0.84628606],\n",
        "        [-0.4281468 , -0.43066862, -0.42246014, ..., -0.554554  ,\n",
        "         -0.56280506, -0.53272873],\n",
@@ -669,7 +761,7 @@
        "        [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
        "          0.        ,  0.        ],\n",
        "        [ 1.        ,  1.        ,  1.        , ...,  1.        ,\n",
-       "          1.        ,  1.        ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c7ed2ff5-11bb-412c-949c-592833a4ed0e' class='xr-section-summary-in' type='checkbox'  ><label for='section-c7ed2ff5-11bb-412c-949c-592833a4ed0e' class='xr-section-summary' >Indexes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3874fc33-2a15-4067-8a1f-3a894c3eac79' class='xr-index-data-in' type='checkbox'/><label for='index-3874fc33-2a15-4067-8a1f-3a894c3eac79' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2010-08-10 14:28:15.559999942&#x27;,\n",
+       "          1.        ,  1.        ]]], shape=(3, 3, 1227), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-715323bb-8a05-4f9d-9ed8-9c97206fb923' class='xr-section-summary-in' type='checkbox'  ><label for='section-715323bb-8a05-4f9d-9ed8-9c97206fb923' class='xr-section-summary' >Indexes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e5cbe076-06f8-4d0e-b075-481c131181e2' class='xr-index-data-in' type='checkbox'/><label for='index-e5cbe076-06f8-4d0e-b075-481c131181e2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2010-08-10 14:28:15.559999942&#x27;,\n",
        "               &#x27;2010-08-10 14:28:16.319999933&#x27;,\n",
        "               &#x27;2010-08-10 14:28:17.069999933&#x27;,\n",
        "               &#x27;2010-08-10 14:28:17.799999952&#x27;,\n",
@@ -690,7 +782,7 @@
        "               &#x27;2010-08-10 14:39:59.190000057&#x27;,\n",
        "               &#x27;2010-08-10 14:39:59.910000085&#x27;,\n",
        "               &#x27;2010-08-10 14:40:00.630000114&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1227, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_gps</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6eaf8fea-f659-486a-b60e-9437a612d0e9' class='xr-index-data-in' type='checkbox'/><label for='index-6eaf8fea-f659-486a-b60e-9437a612d0e9' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2010-08-10 22:28:17.200000047&#x27;,\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=1227, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_gps</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-2886a302-36d8-4d56-8351-9f96f18e870c' class='xr-index-data-in' type='checkbox'/><label for='index-2886a302-36d8-4d56-8351-9f96f18e870c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2010-08-10 22:28:17.200000047&#x27;,\n",
        "                         &#x27;2010-08-10 22:28:18&#x27;,\n",
        "               &#x27;2010-08-10 22:28:18.599999904&#x27;,\n",
        "               &#x27;2010-08-10 22:28:19.400000095&#x27;,\n",
@@ -711,7 +803,7 @@
        "               &#x27;2010-08-10 22:40:00.799999952&#x27;,\n",
        "               &#x27;2010-08-10 22:40:01.400000095&#x27;,\n",
        "               &#x27;2010-08-10 22:40:02.200000047&#x27;],\n",
-       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_gps&#x27;, length=1227, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-48552d78-f36e-4daa-bca1-e71785909868' class='xr-index-data-in' type='checkbox'/><label for='index-48552d78-f36e-4daa-bca1-e71785909868' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5699999928474426, 0.8199999928474426, 1.0700000524520874,\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_gps&#x27;, length=1227, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-0250f22b-7f25-4d4d-a112-eaaad29de8c5' class='xr-index-data-in' type='checkbox'/><label for='index-0250f22b-7f25-4d4d-a112-eaaad29de8c5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.5699999928474426, 0.8199999928474426, 1.0700000524520874,\n",
        "       1.3200000524520874, 1.5700000524520874, 1.8200000524520874,\n",
        "        2.069999933242798,  2.319999933242798,  2.569999933242798,\n",
        "        2.819999933242798,  3.069999933242798,  3.319999933242798,\n",
@@ -727,7 +819,7 @@
        "       10.319999694824219, 10.569999694824219, 10.819999694824219,\n",
        "       11.069999694824219, 11.319999694824219, 11.569999694824219,\n",
        "       11.819999694824219, 12.069999694824219],\n",
-       "      dtype=&#x27;float32&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f87cc829-7b65-43ec-9e71-ae0786197a35' class='xr-index-data-in' type='checkbox'/><label for='index-f87cc829-7b65-43ec-9e71-ae0786197a35' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2095873f-942d-4c90-a258-ef1bf22af952' class='xr-index-data-in' type='checkbox'/><label for='index-2095873f-942d-4c90-a258-ef1bf22af952' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;, &#x27;err&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8ed519c-a834-4313-ab4e-00428ea83219' class='xr-index-data-in' type='checkbox'/><label for='index-b8ed519c-a834-4313-ab4e-00428ea83219' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;x1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-735eab89-666b-48f9-a3da-6f0984578e5f' class='xr-index-data-in' type='checkbox'/><label for='index-735eab89-666b-48f9-a3da-6f0984578e5f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;x2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3708a764-6d78-4e2b-bd0d-a5b33b223057' class='xr-index-data-in' type='checkbox'/><label for='index-3708a764-6d78-4e2b-bd0d-a5b33b223057' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3123afc8-d5dc-480e-92e9-7c2777644b05' class='xr-index-data-in' type='checkbox'/><label for='index-3123afc8-d5dc-480e-92e9-7c2777644b05' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d98f8d2d-5a22-42f5-86e2-8df0ec83c3d3' class='xr-section-summary-in' type='checkbox'  ><label for='section-d98f8d2d-5a22-42f5-86e2-8df0ec83c3d3' class='xr-section-summary' >Attributes: <span>(38)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>inst_make :</span></dt><dd>TRDI</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;vel_bt&#x27;]</dd><dt><span>has_imu :</span></dt><dd>0</dd><dt><span>prog_ver :</span></dt><dd>10.16</dd><dt><span>inst_model :</span></dt><dd>Rio Grande</dd><dt><span>beam_angle :</span></dt><dd>20</dd><dt><span>freq :</span></dt><dd>1200</dd><dt><span>beam_pattern :</span></dt><dd>convex</dd><dt><span>orientation :</span></dt><dd>down</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>n_cells :</span></dt><dd>47</dd><dt><span>pings_per_ensemble :</span></dt><dd>1</dd><dt><span>cell_size :</span></dt><dd>0.25</dd><dt><span>blank_dist :</span></dt><dd>0.25</dd><dt><span>profiling_mode :</span></dt><dd>12</dd><dt><span>min_corr_threshold :</span></dt><dd>64</dd><dt><span>n_code_reps :</span></dt><dd>4</dd><dt><span>min_prcnt_gd :</span></dt><dd>0</dd><dt><span>max_error_vel :</span></dt><dd>1.5</dd><dt><span>sec_between_ping_groups :</span></dt><dd>0.09</dd><dt><span>coord_sys :</span></dt><dd>ship</dd><dt><span>use_pitchroll :</span></dt><dd>yes</dd><dt><span>use_3beam :</span></dt><dd>yes</dd><dt><span>bin_mapping :</span></dt><dd>yes</dd><dt><span>heading_misalign_deg :</span></dt><dd>0.0</dd><dt><span>magnetic_var_deg :</span></dt><dd>0.0</dd><dt><span>sensors_src :</span></dt><dd>01011101</dd><dt><span>sensors_avail :</span></dt><dd>00011101</dd><dt><span>bin1_dist_m :</span></dt><dd>0.57</dd><dt><span>transmit_pulse_m :</span></dt><dd>0.3</dd><dt><span>water_ref_cells :</span></dt><dd>[0, 5]</dd><dt><span>false_target_threshold :</span></dt><dd>50</dd><dt><span>transmit_lag_m :</span></dt><dd>0.08</dd><dt><span>bandwidth :</span></dt><dd>0</dd><dt><span>sourceprog :</span></dt><dd>WINRIVER</dd><dt><span>fs :</span></dt><dd>1.92</dd><dt><span>vel_gps_corrected :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
+       "      dtype=&#x27;float32&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a60c54d3-df25-42a7-8ef8-2253c9892a87' class='xr-index-data-in' type='checkbox'/><label for='index-a60c54d3-df25-42a7-8ef8-2253c9892a87' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b5a9ace4-c990-46a0-abfd-93d43eade1ff' class='xr-index-data-in' type='checkbox'/><label for='index-b5a9ace4-c990-46a0-abfd-93d43eade1ff' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;, &#x27;err&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-08b08bc7-5644-4126-858f-18dffc747812' class='xr-index-data-in' type='checkbox'/><label for='index-08b08bc7-5644-4126-858f-18dffc747812' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;x1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x2</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-a18de09c-9449-4985-b77e-86c4cdfb3f2b' class='xr-index-data-in' type='checkbox'/><label for='index-a18de09c-9449-4985-b77e-86c4cdfb3f2b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int64&#x27;, name=&#x27;x2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-444909e6-dced-4025-a032-e062865e9f49' class='xr-index-data-in' type='checkbox'/><label for='index-444909e6-dced-4025-a032-e062865e9f49' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-bf421842-96bc-4c53-9d66-2f033ee263fc' class='xr-index-data-in' type='checkbox'/><label for='index-bf421842-96bc-4c53-9d66-2f033ee263fc' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-735e5954-b400-49c9-8606-fc374e7b760d' class='xr-section-summary-in' type='checkbox'  ><label for='section-735e5954-b400-49c9-8606-fc374e7b760d' class='xr-section-summary' >Attributes: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>firmware_ver :</span></dt><dd>10.16</dd><dt><span>inst_make :</span></dt><dd>TRDI</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;vel_bt&#x27;]</dd><dt><span>has_imu :</span></dt><dd>0</dd><dt><span>inst_model :</span></dt><dd>Rio Grande</dd><dt><span>beam_angle :</span></dt><dd>20</dd><dt><span>carrier_freq :</span></dt><dd>1200</dd><dt><span>beam_pattern :</span></dt><dd>convex</dd><dt><span>orientation :</span></dt><dd>down</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>n_cells :</span></dt><dd>47</dd><dt><span>pings_per_ensemble :</span></dt><dd>1</dd><dt><span>cell_size :</span></dt><dd>0.25</dd><dt><span>blank_dist :</span></dt><dd>0.25</dd><dt><span>profiling_mode :</span></dt><dd>12</dd><dt><span>min_corr_threshold :</span></dt><dd>64</dd><dt><span>n_code_reps :</span></dt><dd>4</dd><dt><span>min_prcnt_gd :</span></dt><dd>0</dd><dt><span>max_error_vel :</span></dt><dd>1.5</dd><dt><span>sec_between_ping_groups :</span></dt><dd>0.09</dd><dt><span>coord_sys :</span></dt><dd>ship</dd><dt><span>use_pitchroll :</span></dt><dd>yes</dd><dt><span>use_3beam :</span></dt><dd>yes</dd><dt><span>bin_mapping :</span></dt><dd>yes</dd><dt><span>heading_misalign_deg :</span></dt><dd>0.0</dd><dt><span>magnetic_var_deg :</span></dt><dd>0.0</dd><dt><span>sensors_src :</span></dt><dd>01011101</dd><dt><span>sensors_avail :</span></dt><dd>00011101</dd><dt><span>transmit_pulse_m :</span></dt><dd>0.3</dd><dt><span>water_ref_cells :</span></dt><dd>[0, 5]</dd><dt><span>false_target_threshold :</span></dt><dd>50</dd><dt><span>transmit_lag_m :</span></dt><dd>0.08</dd><dt><span>bandwidth :</span></dt><dd>0</dd><dt><span>power_level :</span></dt><dd>128</dd><dt><span>serialnum :</span></dt><dd>134877318</dd><dt><span>source_program :</span></dt><dd>WINRIVER</dd><dt><span>range_offset :</span></dt><dd>0.07</dd><dt><span>fs :</span></dt><dd>1.92</dd></dl></div></li></ul></div></div>"
       ],
       "text/plain": [
        "<xarray.Dataset> Size: 4MB\n",
@@ -757,23 +849,23 @@
        "    hdwtime_gps          (time_gps) datetime64[ns] 10kB 2010-08-10T14:28:15.5...\n",
        "    beam2inst_orientmat  (x1, x2) float32 64B 1.462 -1.462 0.0 ... -1.034 -1.034\n",
        "    orientmat            (earth, inst, time) float32 44kB -0.9037 ... 1.0\n",
-       "Attributes: (12/38)\n",
+       "Attributes: (12/39)\n",
+       "    firmware_ver:             10.16\n",
        "    inst_make:                TRDI\n",
        "    inst_type:                ADCP\n",
        "    rotate_vars:              ['vel', 'vel_bt']\n",
        "    has_imu:                  0\n",
-       "    prog_ver:                 10.16\n",
        "    inst_model:               Rio Grande\n",
        "    ...                       ...\n",
-       "    false_target_threshold:   50\n",
-       "    transmit_lag_m:           0.08\n",
        "    bandwidth:                0\n",
-       "    sourceprog:               WINRIVER\n",
-       "    fs:                       1.92\n",
-       "    vel_gps_corrected:        0"
+       "    power_level:              128\n",
+       "    serialnum:                134877318\n",
+       "    source_program:           WINRIVER\n",
+       "    range_offset:             0.07\n",
+       "    fs:                       1.92"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -804,7 +896,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "158b216f-f71a-44aa-93ec-a81e5c1f45c9",
    "metadata": {},
    "outputs": [],
@@ -828,7 +920,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "c3440ed6-d655-4f6c-bb0e-6c5e533d5bac",
    "metadata": {},
    "outputs": [],
@@ -850,7 +942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "bead5b60-b704-4cda-97b7-7d35a045c3ce",
    "metadata": {},
    "outputs": [],
@@ -871,7 +963,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "afa77384-5c3f-424b-a775-16e03c2a6699",
    "metadata": {},
    "outputs": [
@@ -881,7 +973,7 @@
        "Text(0, 0.5, '$UTM_y (m)$')"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -896,7 +988,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -969,10 +1061,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "67f37e4f-a889-4676-9797-82d7c13fbe94",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\akeeste\\Documents\\Software\\GitHub\\MHKiT-Python\\mhkit\\dolfyn\\adp\\clean.py:90: UserWarning: The 'range_offset' is either already known or can be calculated from 'bin1_dist_m': 0.07000000029802322 m. If you would like to override this value with 0 m, ignore this warning. If you do not want to override this value, you do not need to use this function.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
    "source": [
     "# Adjust the range offset, included here for reference\n",
     "offset = 0\n",
@@ -992,23 +1093,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 8,
    "id": "87eb43c7-486f-497f-b1b6-dd93330a2d18",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.QuadMesh at 0x1c42c7a4e90>"
+       "<matplotlib.collections.QuadMesh at 0x24186f3d5b0>"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 2 Axes>"
       ]
@@ -1037,7 +1138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 9,
    "id": "88c8358f-d05f-47c9-9ce3-66f5c9a491e7",
    "metadata": {},
    "outputs": [],
@@ -1078,7 +1179,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "id": "609dc780-401c-4814-a3ca-46d0bcdcb3be",
    "metadata": {},
    "outputs": [
@@ -1246,7 +1347,7 @@
        "[22018 rows x 6 columns]"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1290,7 +1391,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 11,
    "id": "7902c289",
    "metadata": {},
    "outputs": [
@@ -1298,12 +1399,10 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sterl\\AppData\\Local\\Temp\\ipykernel_4716\\1528100513.py:7: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] = (ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] - min_coords) / (max_coords - min_coords)\n"
+      "C:\\Users\\akeeste\\AppData\\Local\\Temp\\ipykernel_31724\\3234039020.py:7: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[0. 0. 0. ... 1. 1. 1.]' has dtype incompatible with float32, please explicitly cast to a compatible dtype first.\n",
+      "  ADCP_points.loc[:,[\"utm_x\", \"utm_y\", \"waterdepth\"]] = (ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] - min_coords) / (max_coords - min_coords)\n",
+      "C:\\Users\\akeeste\\AppData\\Local\\Temp\\ipykernel_31724\\3234039020.py:9: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '[0.         0.         0.         ... 1.48387099 1.48387099 1.48387099]' has dtype incompatible with float32, please explicitly cast to a compatible dtype first.\n",
+      "  ADCP_ideal_points.loc[:,[\"utm_x\", \"utm_y\", \"waterdepth\"]] = (ADCP_ideal_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] - min_coords) / (max_coords - min_coords)\n"
      ]
     }
    ],
@@ -1314,30 +1413,17 @@
     "# Normalize the data to avoid precision issues\n",
     "min_coords = ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]].min()\n",
     "max_coords = ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]].max()\n",
-    "ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] = (ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] - min_coords) / (max_coords - min_coords)\n",
+    "ADCP_points.loc[:,[\"utm_x\", \"utm_y\", \"waterdepth\"]] = (ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] - min_coords) / (max_coords - min_coords)\n",
     "\n",
-    "ADCP_ideal_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] = (ADCP_ideal_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] - min_coords) / (max_coords - min_coords)\n"
+    "ADCP_ideal_points.loc[:,[\"utm_x\", \"utm_y\", \"waterdepth\"]] = (ADCP_ideal_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] - min_coords) / (max_coords - min_coords)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 12,
    "id": "767587a8-2248-4ad5-850e-68e7dda56441",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sterl\\AppData\\Local\\Temp\\ipykernel_4716\\1835236727.py:33: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
-      "Try using .loc[row_indexer,col_indexer] = value instead\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] = (ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] *  (max_coords - min_coords) + min_coords)\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Project velocity onto ideal tansect\n",
     "ADCP_ideal = pd.DataFrame()\n",
@@ -1371,9 +1457,8 @@
     ")\n",
     "\n",
     "# Denormalize the ideal points for plotting\n",
-    "ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] = (ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] *  (max_coords - min_coords) + min_coords)\n",
-    "ADCP_ideal_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] = ADCP_ideal_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] * (max_coords - min_coords) + min_coords\n",
-    "\n"
+    "ADCP_points.loc[:,[\"utm_x\", \"utm_y\", \"waterdepth\"]] = (ADCP_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] *  (max_coords - min_coords) + min_coords)\n",
+    "ADCP_ideal_points.loc[:,[\"utm_x\", \"utm_y\", \"waterdepth\"]] = ADCP_ideal_points[[\"utm_x\", \"utm_y\", \"waterdepth\"]] * (max_coords - min_coords) + min_coords"
    ]
   },
   {
@@ -1386,7 +1471,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 13,
    "id": "c70be4c3-3082-4ec0-bacf-e40592838abd",
    "metadata": {},
    "outputs": [
@@ -1406,13 +1491,13 @@
        "  Text(401100.0, 0, '401,100')])"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x440 with 2 Axes>"
       ]
@@ -1463,23 +1548,23 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 14,
    "id": "3870861e-3476-4e47-95e8-ed9f4534dc0b",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x1c445525450>"
+       "<matplotlib.legend.Legend at 0x241a4281280>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1536,7 +1621,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 15,
    "id": "fd9d270d-0b41-4fff-9b3e-e5626197b24a",
    "metadata": {},
    "outputs": [
@@ -1546,13 +1631,13 @@
        "Text(0, 0.5, '$ Depth [m]$')"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x440 with 1 Axes>"
       ]
@@ -1617,7 +1702,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 16,
    "id": "fc62335c",
    "metadata": {},
    "outputs": [
@@ -1625,7 +1710,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sterl\\AppData\\Local\\Temp\\ipykernel_4716\\3546222366.py:4: SettingWithCopyWarning: \n",
+      "C:\\Users\\akeeste\\AppData\\Local\\Temp\\ipykernel_31724\\3546222366.py:4: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -1645,7 +1730,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 17,
    "id": "c854c7d9-9efe-4540-a533-f7d7ab39486c",
    "metadata": {},
    "outputs": [
@@ -1653,7 +1738,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "C:\\Users\\sterl\\AppData\\Local\\Temp\\ipykernel_4716\\14157162.py:29: SettingWithCopyWarning: \n",
+      "C:\\Users\\akeeste\\AppData\\Local\\Temp\\ipykernel_31724\\14157162.py:29: SettingWithCopyWarning: \n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
       "Try using .loc[row_indexer,col_indexer] = value instead\n",
       "\n",
@@ -1707,7 +1792,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 18,
    "id": "69b668e7-8a41-40bb-841e-de8fc62e7b01",
    "metadata": {},
    "outputs": [],
@@ -1750,7 +1835,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 19,
    "id": "e90c6c52-3af2-4a06-ba5f-ff364256c2c5",
    "metadata": {},
    "outputs": [
@@ -1770,13 +1855,13 @@
        "  Text(401100.0, 0, '401,100')])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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Y60HPYtDIndqg8ayMw6wYPigrvXv3xurVq+XrXbt2xcaNG3WsSD31O40zcNis3bt34/Lly7h8+TJKlixpcptw0tn+ZDssETSywl4P9Rg8iEEjd5YKGllhrwc9q2fPniZDps6ePYtq1arpWBFZk13ErH79+kEIkeWFSC9ZTQTXQkSyDyeYq8QJ5s6HE8Fzlz4B3Bo4wZwAYPDgwSZhIzEx0e7DhhGS6oszsovAQWQr0kOGNYLGs9KDB8OHcunBg+HDMaWHDAaN7GVcaUoP6cGD4cP57NixA0uWLJGvJyUlwcPDQ8eKLCN9SJWaixIzZ87E888/D29vbxQpUgSdO3fGpUuXcnzMihUrMq36qneb28WQKiK96REwcsLhVupxuJXjYMDInV4BIyccbuU8kpKS0L59e/l6VFSUw6weqn4fDmWPOXDgAN555x08//zzSE1NxYQJE9CqVSucP38eBQoUyPZxPj4+JsFEkvR9v2TgIMqBrQWNZzF4qMfgYb8YNHJni0HjWQwejs/f318+Pn78OHx9HWfTSKOQYFSzLK7Cx+zatcvk+ooVK1CkSBGcPHkSjRs3zvZxkiTZ1MJKDBxEz7D1kJEVrm6lHpfVtQ8MGbmzh5CRFQYPx/Tll18iKSkJANC2bVvUr19f54ocQ3R0NACgUKFCOd4vLi4OpUuXhtFoRJ06dTBjxgw899xz1igxSwwcRP/PHoNGVtjroR57PWwPg0bu7DVoPIvL6jqGGzduYOzYsdi0aZN8bvv27TpWpA2jyiFV6ftwxMSYfkabs1m10WjEqFGj8MILL+Q46b5SpUpYtmwZatSogejoaHz++edo1KgR/v7770yrvVoLAwc5PUcJGs9i8FCPwUN/DBq5c5SgkRX2etin8PBwtG7dGsnJyfK5f/75R/f5A1owChcYVeypkf6Y4OBgk/OTJ0/GlClTcnzsO++8g3PnzuHQoUM53i80NBShoaHy9UaNGqFKlSpYvHgxpk2bprhmS2DgIKfkqCEjKxxupR6HW1kXQ0buHDlkZIXBw34IITBixAiTsLFp0yZUqFBBx6q0o3ZlvPTH3Lp1Cz4+/30+59a7MWzYMGzbtg0HDx5U3Evh5uaG2rVr4/Lly4rrtRQGjlyk7/XhiOncGTlT0MgKez3UY6+Hdhg0cudsQeNZDB6276effsLZs2fl62vWrMErr7yiY0W2zcfHxyRwZEcIgeHDh+Pnn39GeHg4ypQpo/i10tLScPbsWbRr105NqRbBwJELF5enXV8NGzbE4cOH5etkX5w9aDyLwUM9Bg/LYdDInbMHjWdxnoft6tq1q3y8Z88eNG/eXMdqtJfXIVXmeuedd7BmzRps2bIF3t7eiIiIAAD4+vrC09MTANCnTx+UKFECM2fOBABMnToVDRs2RPny5REVFYXZs2fjxo0bGDhwoOJ6LYWBw0zHjh2Dq6sr5s+fj5EjR+pdDpmBISN3HG6lHodbqcegkTOGDPOw18N2LFiwQD7evHmzw4cNAEiDuveyNIX3X7hwIQAgLCzM5Pzy5cvRr18/AMDNmzdN/iD+5MkTDBo0CBEREfD390fdunVx5MgRVK1aVXG9lsLAkQuDwYCyZcvizp07AIBRo0ahQIECuqZEyhmDhjrs9VCPvR65Y8jIHYOGOgwe+hs+fLh83KlTJx0rsR5r9XCkD+3PSXh4uMn1efPmYd68eYpeR2scH5SL/Pnz4/bt2/j444/lc4MGDdKxIsqOd4lYhg0LiEj2Men5IPP55ksw6fkg9RMrnUl8an6GDQvw9jDIF7KejMvf/vTTTzpWQraMgcNM77//vt4lUDYYNLSRHjwYPpRj8GDQMAeDhnYYPKwn49yNLl266FiJdaUJF9UXZ8QhVWZyc3MzuZ6cnIz8+flBoRcGDOvicCt1nG2eBwNG7hgwrIvDrbS1a9cu+fjbb7/VsRLrE5BgVPGeJ5z0fdI5Y5YCycnJWLp0KapUqWJyfunSpTpV5NzYm6Ev9nqo58i9HuzNyB17M/TFHg9ttG3bVj52tuHm7OFQxjl/agXS0tLw4Ycf4t9//zU5n3GCFGmPQcP2MHio40jBg0EjdwwatoXzPCxny5Yt8vHcuXN1rITsAYdU5cLT0xPTpk1DbGws+vfvj8KFCwMAjEajzpU5PgYM+8DhVurY63ArBozcMWDYBw63ypvOnTvLx6NHj9avEJ0YhQSjUP5+qOYxjoCBwwyDBw/WuwSnwqBhn7inh3r2sKwug0buGDTsE4OHctOmTZOP161bp2Ml+kmDC9JUDBRS8xhHwMCRB0ajkTuPWxCDhuNgr4c6thg8GDRyx6DhGBg8zHPt2jVMmjRJvt6jRw8dq9EPeziU4bdlhdauXSsfZ1ydgdRJn5vBsOGYOM9DnfR5HnrN9Uifm8Gwkb30uRkMG46H8zyyFxcXh7Jly8rXb968qWM1ZE8YOBTq1q2bfNy+fXsdK7FvDBnOhatbqWfN4MGQkTuGDOfC4PGfpKQkeHt7y9fHjRuH4OBgHSvSlxEuqi/OiEOqFHJ1ddW7BLvGkEEcbqWOlsOtGDJyx5Dh3Jx9uNWNGzcQEhIiX2/dujVmzJihX0E2IE1ISFMxPErNYxyBc8Yssjr2aNCz2OOhjiV7PNijkTv2aFBGzjjc6sCBAyZho3///ti1axckybnfO9LncKi5OCP2cKjg6uqKtLQ0ANxxPCcMGGQOrm6ljtpldRkwcseAQeZwhl6PRYsWYejQofL1ZcuWoX///jpWRPaKPRwq7Nu3Tz7+/vvvdazENrE3g9Rir4c65vR6sDcjd+zNIDUctcejf//+JmHj2LFjDBsZCOECo4qLcNKdxtnDoULjxo3l44EDB2LAgAE6VmM78sW6wrNylN5lkAPgPA910kNHVMp/PR4MGbljyDBfXIrj/jU/rxylx+P69esoU6aMybnbt2+jRIkSOlVkm9T+EcdZ35MZOMiiEi/6AQDcKvCLYm588iXJx0Xd2F45yfgGfSupkI6V2IeYVA+9S7B5Ucme8rGLJHSsxPalGk3/Inus9UydKiGtpKSkYPv27Rg8eDAePHhgcltCQgI8PT2zeaTzMgp1e2oYnfTtxjn7dSwg47yNpKSkHO7pnFL+9ZEvlLv7KT64n8K2Mkewx2MEezzWuwyyU1HJniZhg7KXanTJFDbIsdy7dw+dOnVC/vz50aVLF5Ow0bVrVxiNRoYNsgi+k6iUcQPAo0eP6liJ7WPwMB+Dh/kYPEgJBg3zMWg4vqioKAQEBKB48eLYunWryW1169bFhQsXsHHjRqdfiSonauZvpF+ckXP+1BZQs2ZN+Zg7jpuHwcN86cGD4SN36cGD4YOelR4yGDRylx4yGDQcmxAC/fv3h7+/Px49emRy26pVqxAXF4cTJ06gcuXKOlVoP4yQVF+cEd9ZVCpbtqx8/Nlnn+lYif3hcCtlGDzMx+BBAHszlGDIcB7bt2+Hi4sLVqxYIZ9r3LgxHj16BCEEevfujQIFCuhXoJ1J3/hPzcUZcdK4SpIk4fnnn8cff/wBAIiLi0PBggV1rsr+pIcOTjLPXXro4ATz3GUMHZxk7hwYMJRhyHAeQggEBATg8WPTP8ZERESgaNGiOlVFzobvOHnw5ptvysfcjyNv2ONhPg63Uoa9Ho6NvRnm47Ap52M0GuHi4mISNnbs2AEhBMNGHnEOhzLs4ciDpk2byscjRoww2SCH1MkYOtjrkTv2epgvPXSwx8MxMGSYjwHDeVWqVMnkenJyMtzc3HSqxrEYIalbFpdzOGxbx44dUapUKXh4eKBYsWLo3bs37t69q2tNFStWlI9TU1N1rMQxsdfDfOzxMB8nmdsvTgJXhr0Zzm3UqFG4fPkyAMDDwwNpaWkMGxYkVE4YFwwctq1p06bYsGEDLl26hE2bNuHKlSvo2rWrrjVJkoQKFSrI14Vw0t1cNMbgYT4Ot1KGwcM+MGSYj8OmCAD27NmDL774Qr4eHx8PFxf+TpB+7Oa3b/To0WjYsCFKly6NRo0aYdy4cTh27BhSUlJ0rWvMmDHy8b///qtjJY6Pq1spw+BhPgYP28SgYT6GDEp34cIFtGzZUr4eExPDsKEBo5BUX5yRXc7hePz4MX744Qc0atRI9+7BVq1ayce7d+82GWZF2uHqVubjPA/zcXUr/TFgKMOQQRmdOXPGZJ+wY8eOwdvbW8eKHJfaCeDOOmncrn7qDz74AAUKFEDhwoVx8+ZNbNmyJcf7GwwGxMTEmFwsrUyZMvLx7NmzLf78lDP2eJiPw62UYa+HdbE3w3wcNkVZWbJkiUnY2LhxIxo0aKBjRY6NPRzK6PpuNW7cOEiSlOPl4sWL8v3fe+89/Pnnn/j111/h6uqKPn365DhvYubMmfD19ZUvwcHBFv8ZJEmCn58fAODGjRsWf34yD4dbKcPgYT5OMtcOJ4Erw5BBWUlOToavry8GDx4sn1u9erXu81yJMtJ1SNXYsWPRr1+/HO+TcUfvgIAABAQEoGLFiqhSpQqCg4Nx7NgxhIaGZvnY8ePHm8yxiImJ0SR0fPjhh3jvvfcAANHR0fD19bX4a5D5ONzKfBlDB4dc5Y5L61oGA4YyDBmUnfDwcJMl+gHg8uXLKFeunE4VOY/0VafUPM4Z6Ro4AgMDERgYqOqxRqMRwNNhU9lxd3eHu7u7qudX4rXXXpMDx86dO/Haa69p/pqUOwYPZTjXw3wMHuowaJiPIYNyYjQa0bx5c4SHh8vnmjRpgv3790OSnPMLrbWpHR7FIVU27Pjx41iwYAFOnz6NGzduYN++fXj99ddRrly5bHs3rKlkyZLy8aRJk3SshLLC4VbKcLiV+TjcKnccNqUMh01RToQQ+Prrr+Hq6moSNvbs2YPw8HCGDSviHA5l7GKVKi8vL/z000+YPHky4uPjUaxYMbRp0wYfffSRVXowzOHj44OYmBgujWvj2OthPg63Uoa9HqYYMJRhyKCcREVF4eWXX8bhw4dNzvv6+uL+/fs2812IKDt2ETiqV6+Offv26V1Gjj755BMMHz4cwNMJXPnz59e5IsoJg4cyHG5lPmcPHgwa5mPIoNzExsaidevWOHr0aKbbtm3bhvbt2+tQFQEcUqUU3+0sZNCgQfLxkiVLdKyElOBwK2U43Mp8zjTcisOmlOGwKcrNgwcPEBYWBh8fH5Ow4eXlhf/9738QQjBs6IxDqpThO56FZOzOHDZsmI6VkFoMHubjnh7KOGrwYMgwH/fOIHNt374dRYoUwYEDB+RzxYoVw/379xEfH4+XX35Zx+ooncB/K1UpuWS/mYNjs4shVfbipZdewm+//aZ3GZRHHG6lDIdbmc8RdjJnwFCGAYPM9fjxY5QvXx5PnjyRz/n6+uLs2bOaLOlPecMhVcrwndCCvvnmG/k4q/GWZF843EoZ9ngoY2+9HuzNUIa9GWSuHTt2QJIkFC5c2CRsbNmyBVFRUQwb5BD4bmhB1apVk4+HDBmiYyVkaQwe5uNwK2VsPXgwaJiPw6bIHEajERs2bEDRokUhSVKmuRgvvfQSUlNT0bFjR50qJHNwDocyHFJlYb6+voiOjsaZM2f0LoU0wOFWynC4lflsabgVA4YyDBhkjt27d6NVq1bZ3v7GG29g6dKl8PTkvz97wCFVyvBd0sKWLl0qH9+9e1fHSkhLHG6lDHs8lNGr14O9GcqwN4PMtXfv3izDRqVKlfDXX39BCIEffviBYcOOsIdDGb5TWtirr74qH48ePVrHSshaGDzMx+FWylgreDBomI/Dpkip+/fvo0WLFvL1Nm3aIDo6GkIIXLx4ETVq1NCxOiLr4DumhUnSf8l1w4YNOlZC1sbgoQyDh/m02NODe2cow5BBaqSmpiIoKEi+/v3332Pnzp3w8eF7n70TQlJ9cUZ899TArFmz5OOEhAQdKyE9cLiVMgweyuQ1eDBkKMOgQXnRp08f+XjUqFHo1auXjtWQJanZgyP94oz4LqqBMWPGyMfTpk3TsRLSG4OH+TjcShklvR7szVCGw6bIEpYsWYK1a9fK1+fMmaNjNWRpnMOhDN9NNeDm5iYfZ+ztIOfFXg9lGDyUyS54MGQow5BBljJx4kQMHjxYvn7mzBm4uPB3i5wXf/s1kvGNxmg06lgJ2RoGD/MxeCiTHjwYNJRh0CBLuXfvHiRJwvTp0+Vzx44dQ/Xq1XWsirRgrTkcM2fOxPPPPw9vb28UKVIEnTt3xqVLl3J93MaNG1G5cmV4eHigevXq2LFjh9of1SK4D4dGZs2ahW+//RYA8N1332HQoEE6V0S2xhUMojmp7PnfstIG4ZbDPelZIQUe4a/HJfQuwy5EJf0XzAq6G3SshOyZEAJvvfUWlixZYnL+3r17JpPGyXFYax+OAwcO4J133sHzzz+P1NRUTJgwAa1atcL58+dRoECBLB9z5MgRvP7665g5cyZefvllrFmzBp07d8apU6dMNqm2JkkIIXR5ZR3ExMTIG/NZY4WIjCtWOUMzV/1wnt4l2KyESsmZzvWsdVyHSmxfxqCRjoHDPGfjS8rH56KKIY1/tc9VxsCREcNH9g61+EzvEmzKF198gVGjRpmc++CDDzikOg+s/X1NifTa6m4ajXwF3BU/PjXegJOvzlP9sz148ABFihTBgQMH0Lhx4yzv06NHD8THx2Pbtm3yuYYNG6JWrVpYtGiR4te0BPZwaKhFixbYs2eP3mWQjrIKGpS1rIIGmSdj0MjI1eVpLxqDh3JxhqdfJBg8KDurV69G7969M52PiIhA0aJFdaiI7ElMTIzJdXd3d7i75x5goqOjAQCFChXK9j5Hjx41WcAIAFq3bo3NmzcrL9RCFAeOa9eu4bfffsONGzeQkJCAwMBA1K5dG6GhofDw8NCiRrv1zTffoGLFigCAPXv2mGz8Q46LIcN8DBnqZRcyspIePACGD6UYPOhZ27dvx8svv5zp/L59+9C0aVMdKiI9CJVDqtLncAQHB5ucnzx5MqZMmZLjY41GI0aNGoUXXnghx6FRWYXeokWLIiIiQnG9lmJ24Pjhhx/wxRdf4MSJEyhatCiKFy8OT09PPH78GFeuXIGHhwd69uyJDz74AKVLl9ayZrtRoUIF+fjNN9/EzZs3dayGtMagYT4GDfWUBI2ssNdDnfTgATB8OKtDhw7hpZdeynR+8+bN6NSpkw4VkZ4EADWj5dMfcuvWLZMhVeb0brzzzjs4d+4cDh06pPyFdWZW4Khduzby58+Pfv36YdOmTZlSmcFgwNGjR7Fu3TrUq1cP33zzDbp166ZJwfambNmyuHr1Km7duqV3KaQRBg3zMWiok9eQkRX2eqjHXg/n8vfff2f51+Tly5ejX79+1i+IbIIREiQVm/ilb/zn4+OjaA7HsGHDsG3bNhw8eBAlS+b8mRAUFIT79++bnLt//76uCxiY9Skza9YsHD9+HG+//XamsAE8TWVhYWFYtGgRLl68iLJly1q8UHu1bNky+fjixYs6VkKWlFApWb5Qzip73pUvpMzZ+JKahI1nuboYTQIImSfO4G7S80GOJSoqCn5+fpnCxpw5cyCEYNggqxBCYNiwYfj555+xb98+lClTJtfHhIaGYu/evSbndu/ejdDQUK3KzJVZPRytW7c2+wkLFy6MwoULqy7I0TRp0kQ+HjJkCMLDw/UrhvKMAcN8DBjqWSNkZIXDrdThcCvHIoRA79698cMPP5ic/+CDDzBz5kyTFSjJeanZUyP9cUq88847WLNmDbZs2QJvb295Hoavry88PZ+ustenTx+UKFECM2fOBACMHDkSTZo0wZw5c9C+fXusW7cOJ06ckLdr0IPqVaoiIyMRGRmZaVO7GjVq5LkoR3XgwAG9SyAVGDKUYdBQR6+QkRUOt1KPw63sW1YrT7Vq1Qrbt29Hvnxc2JP+YxQSJCvsw7Fw4UIAQFhYmMn5jEP6bt68abKTfaNGjbBmzRp89NFHmDBhAipUqIDNmzfrtgcHoCJwnDx5En379sWFCxfkvSUkSYIQApIkIS0tzeJF2rvly5ejf//+AICHDx8iICBA54rIHAwa5mPIUM+WgkZW2OuhDoOHfblw4QKqVq2a6fz9+/dRpEgRHSoiWyeEyknjCh9jzj5uWY2e6datm03Np1b8CfLmm2+iYsWKOHLkCK5evYpr166Z/Jcy69u3r3z8/vvv61gJmYNzM8zHuRnqWWt+hqVwnoc66fM8ONfDNsXHxyMkJCRT2AgPD4cQgmGDyEIU93BcvXoVmzZtQvny5bWoxyFlHO+5fPlyk4nkZBsYMJRhyFDHngJGdjjcSj32etgOIQTGjh2LefPmmZyfNm0aPvroI52qIntirTkcjkJx4GjevDn++usvBg6FJk6ciGnTpgF4uoywOestk/YYNMzHkKGeIwSNrHC4lToMHvqKiYmBr6+vybn69evjt99+Q/78+XWqiuwNA4cyigPH0qVL0bdvX5w7dw7VqlWDm5ubye0dO3a0WHGOZMKECXLgmD17Nv+CojMGDfMxaKjjqCEjK+z1UIerW1mfECJT2Lhx4wZKlSqlU0Vkr6w1adxRKA4cR48exeHDh7Fz585Mt3HSePY8PDzk44kTJzJw6IAhw3wMGeo5U9DICns91GGvh3VkXMmnSZMm2Ldvn8k5ImdVqFAhRfeXJAmnTp1C6dKlzbq/4sAxfPhw9OrVCxMnTkTRokWVPtyp9ezZU17XO31VL9Ieg4b5GDTUc/ag8SwGD3XY66GdkSNHyseVK1fmvliUJ9ZapcpaoqKiMH/+/Ew9gFkRQuDtt99W1MmgOHA8evQIo0ePZthQYc6cOXLgWLt2Ld544w2dK3JsDBrmY9BQhyEjdxxupR57PSzn8OHD+PLLL+Xr58+f17EacgRPA4eaORwaFGMhr732mtkrsw0fPlzRcysOHK+88gr279+PcuXKKX2o08sY0vr378/AoQGGDPMxZKjHoKEOez3UYfDIm1u3buHFF1+Ur0dFRXGEAeWZo00af3Yj79zExsYqur/iwFGxYkWMHz8ehw4dQvXq1TNNGh8xYoTSp3QqoaGhOHr0KJKT+cXYkhg0zMegoR6DhmUweKjD4VbK3blzx2RC+L59+8waMkJElqVqlaqCBQviwIEDOHDggMltkiQxcORi8eLFqFGjBgDgyJEjaNSokc4V2TcGDfMxaKjDkKEdDrdSj70euVu2bBkGDBggX580aRKaNm2qY0XkSMT/X9Q8ztatXLkSAQEBaN++PYCnm1Z/++23qFq1KtauXWv2RPGMFAeOa9euKX4R+k/16tXl44EDB3IcqQoMGeZjyFCPQcO62OuhDoNHZk+ePMm04s64ceMwZcoUfQoih+RoQ6oymjFjBhYuXAjg6eq0X3/9NebNm4dt27Zh9OjR+OmnnxQ/p+LAQXlXtGhR3L9/HxcuXNC7FLvCoGE+Bg11GDL0x14PdTjc6qktW7agc+fOJueOHz+O+vXr61MQOS4H7uK4deuWvMH35s2b8eqrr2Lw4MF44YUXEBYWpuo5zXo3nzVrFhITE816wuPHj2P79u2qijGHwWBArVq1IEkSTp8+rdnraGnZsmXy8fXr1/UrxE4kVEpm2DBTZc+7DBsqnI0vybBhg1xdjCYBhMwTZ3A3CSDOYsWKFSZhY+DAgTAajQwbRAoVLFgQjx49AgD8+uuvaNmyJYCne8qZmweeZVYPx/nz51GqVCl069YNHTp0QL169RAYGAgASE1Nxfnz53Ho0CGsXr0ad+/exapVq1QVY473338fxYsXx19//aXZa2itXbt28vGwYcOwbds2HauxTQwY5mPAUI8hwz5wuJU6zjTc6vPPP8d7770nX//tt99MVqYisjiVQ6pgB0OqWrZsiYEDB6J27dr4559/5O+tf//9N0JCQlQ9p1mBY9WqVfjrr7+wYMECvPHGG4iJiYGrqyvc3d2RkJAAAKhduzYGDhyIfv36meyqbUk7d+7Er7/+ik2bNmW507k90rI3yB4xaJiPQUMdhgz7xeFW6jj6cKt3330Xc+bMka9fvHgRlSpV0rEicgaOtvFfRl9//TU++ugj3Lp1C5s2bULhwoUBACdPnsTrr7+u6jnNnsNRs2ZNLFmyBIsXL8aZM2dw48YNJCYmIiAgALVq1UJAQICqAsx1//59DBo0CJs3b4aXl5dZjzEYDDAY/ntzjYmJ0ao8xb766it505SYmBj4+PjoXJG+GDTMx6ChDoOGY2GvhzqO1uvRo0cPbNiwQb4eERHBjYnJKhxx0viyZcvQsWNHBAQEYMGCBZlu//jjj1U/t+J3ahcXF9SqVQudOnXCa6+9hhYtWmgeNoQQ6NevH4YMGYJ69eqZ/biZM2fC19dXvgQHB2tYpTJDhw6Vjz/66CMdK9FP+twMho3cpc/NYNhQjvMzHBvneahj7/M8hBBo0KCBSdiIjY1l2CDKg9WrV6NkyZJo1KgRPv30U1y8eNFiz63rn4bGjRsHSZJyvFy8eBFfffUVYmNjMX78eEXPP378eERHR8uXW7duafSTKOfq6ioff/XVVzpWYn0MGeZjyFAnPWQwaDiP9ODB8KFMevCwp/Bx/fp1uLi44Pfff5fPGQwGFCxYUMeqyOkISf3FRu3btw/37t3D22+/jZMnT6J+/fqoUKECxo4di4MHDyrejTwjXZfFHTt2LPr165fjfcqWLYt9+/bh6NGjcHc3fUOsV68eevbsiZUrV2b5WHd390yPsSWjR4/GvHnzADydfJ8vn2OvUsyQYT6GDHUYMAjgcCu1bH241ZEjR/DCCy9kOp+SkuLwn59kexx1Doe/vz969eqFXr16ITk5Gfv27cPWrVvRs2dPJCYmol27dujYsSPatm2LAgUKmP28khC2/qMDN2/eNJl/cffuXbRu3Ro//vgjGjRogJIlzfuSERMTA19fX0RHR9vEnIm4uDh4e3sDAObNm4dRo0bpW1AeVf1wXqZzDBnZ61nruMl1hozsGYRbtrcxZGTtXFQxvUuwKdmFj6gkTytXYj+yCx6HWnxm1ToMBgMCAgIQFxdncn7UqFHyH+3Isdja97WM0msrvWQiXLyUL5JkTEjCjUHTbPJny82JEyewdetWbNmyBV27dsXEiRPNfqxd/EmgVKlSJtfTu03LlStndtiwRRm7f0ePHm33gSMjBg3zMWiow6BBSrDXQzlbWN3q3LlzqF69usm5YcOGYe7cuXBzy/4PEURkefXq1UO9evUwdepUpKSkKHqsXQQOR9apUyds2bJF7zIshkHDfAwa6jBoUF4weKijx3Cr48ePo2HDhvL1l156Cfv37zeZA0mkF0dcpSqdEAI//vgj9u/fj8jISJO5G5IkYdOmTYoDv+LAER8fj1mzZmHv3r2ZigCAq1evKn1KxUJCQmAHI8HM8tVXX8mBY8uWLejUqZPOFalnzAd4XMmvdxk2LaVqgnx8L8Vfx0rsyx2Dn94lkINJDx6JBv6VXAlrtdeZM2dMwsZPP/2ELl26WOW1iczmGF9FMxk1ahQWL16Mpk2bomjRopCkvIckxYFj4MCBOHDgAHr37o1ixYpZpAhnlnGp3jfffFPeSp4cS8agkS79S3QJ9yjrFmOHCrvF612Czfo3vkimc1djnm7SlJTKTmyyrEuvTNL8Nc6ePYuaNWvK1w8cOIDGjRtr/rpESjhyD8f333+Pn376Sd5h3BIUfxrt3LkT27dvz3KlCFKnevXqOHv2LB4/fqx3KWRhWQWNZzF4kBpZBY1neeRLBcDgQXljjZCRbufOnSZfcn755ReGDbJNAup6OOygV8TX1xdly5a16HMqHsTq7++PQoUKWbQIZ7d06VL5+M8//9SxErKElKoJ8kWJOwY/Dh2iHP0bX0S+KOGRL1W+EJnr0iuTrBY2hBB4+eWXTcLGtm3b0KpVK6u8PhH9Z8qUKfj444+RmJhosedU/GevadOmYdKkSVi5ciW8vLwsVogzq1+/vnw8ePBg/PHHHzpWQ2opDRjZyRg62OtBgHm9GeZirwflxpo9GgCwbNkyDBgwwOTckSNHEBoaatU6iJSR/v+i5nG2rXv37li7di2KFCmCkJCQTBPET506pfg5zfrEqV27tslcjcuXL6No0aIWK4KAAgUKID4+HidOnNC7FFLIUkEjKxxu5dwsGTSexeBBGVk7ZNy7dw9z5szBnDlzTM5LkoSEhAR4eCjf34DIqhx4SFXfvn1x8uRJ9OrVy7qTxjt37pznF6KcLV++HN27dwfwdGPD4sWL61wR5UbLoPEsBg/nomXQeBaDh3OzZtBIS0vDoEGDsHz58ixv37NnD5o3b261eojyxIEDx/bt2/HLL7/gxRdftNhzmvUJM3nyZIu9IGWta9eu8vHo0aOxfv16Hauh7FgzZGSFw60clzVDRlYyzu9g+HB81u7RSExMzHYY9po1a/D6669btR4iyl5wcLDFd0FXPGm8bNmyWS7dGhUVZfEZ7c4kY3fVhg0bdKyEsqJmErjWOMncMaiZBK41TjB3TOmTwK0dNm7fvp0pbGzbtg0pKSkQQjBskH0SkvqLjZszZw7ef/99XL9+3WLPqfjPWNevX0daWlqm8waDAbdv37ZIUc5q1qxZGDduHAAgISGBk/JtgK2FjKxwuJV9srWQkRUOt3IM1g4YGV2/fh1lypSRr3fr1g3r16/nHl5k94R4elHzOFvXq1cvJCQkoFy5cvDy8so0X1vNNg5mf4ps3bpVPv7ll1/g6+srX09LS8PevXtN3lRIuTFjxsiBY9q0aZg5c6bOFTknewgZWWHwsH32EDKywuFW9kfPkJHuypUrKF++vHx9yZIlGDhwoI4VEVmQA8/hmD9/vsWf0+xPjvSJ45IkoW/fvia3ubm5ISQkJNNqE6RMxgQ5a9YsBg4rs9eg8SzO87A99ho0ssJeD9tmC0EjOTkZX3zxBd5//3353NKlSzMtfUtEtunZ7/mWYPYnhtFoBACUKVMGf/zxBwICAixeDD3dh+Pbb78F8LTNXVwUT7MhhRwlaGSFvR76cqSg8SwGD9tiC0EDeLrS1PDhw3Hx4kX53IQJExg2yPGonY9ho3M4YmJiFE0Uj42Nhbe3t9n3V/xJce3aNaUPIQVmzZolB47vvvsOgwYN0rkix+TIISMrDB7W48ghIyscbqUfWwkZAPDvv/+iTp06iIuLMznft29fTJ8+XaeqiLQjiacXNY+zRf7+/rh37x6KFDHvM6xEiRI4ffq02QtGqfp02Lt3L+bNm4cLFy4AAKpUqYJRo0ahRYsWap6OMvD395ePBw8ezMBhYc4WNJ7F4KEdZwsaWWGvh3XYUtA4efIk6tWrl+VtPXv2xKJFi2A0GpGSkmLlysiWubm5wdXVVe8y8sbB5nAIIbB06VIULFjQrPsr/Tet+FPhm2++wciRI9G1a1eMHDkSAHDs2DG0a9cO8+bNwzvvvKP0KekZLVq0wJ49e/Quw6E4e9B4Fud5WA6DRmYMHtqwpaABABEREZnCRsOGDTF+/Hg0b94cXl5eiIiIQFRUlD4Fkk3z8/NDUFAQVyyzEaVKlcKSJUvMvn9QUFCm1atyovjTYMaMGZg3bx6GDRsmnxsxYgReeOEFzJgxg4HDAhYuXIgKFSoAeNqbxJ1X1WHIMA97PZRjyDAPg0fe2VrISCeEQLFixeTrrVq1ws6dO03mHd67dw9RUVEoUqQIvLy8+MWSADz93UlISEBkZCQAmPwe2RUHm8NhyT03sqL4UyAqKgpt2rTJdL5Vq1b44IMPLFKUs8u4jGD//v1x8+ZNHauxPwwa6jB45I5BQx3O81DOVoMG8HQVqtq1a8vXmzdvjl27dpkEirS0NDlsFC5cWI8yyYZ5enoCACIjI1GkSBH7HF7lYEOqtKZ4CaSOHTvi559/znR+y5YtePnlly1SFEGehHPr1i2dK7EftrgbuD1K38Gcu5j/xxZ3A7dX3MU8Z3rsBG4uIQQ2bNgAd3d3nD9/Xj6/e/fuTL0X6eO7uYEtZSf9d8Nu5/eIPFyckOI/NVWtWhWffPIJwsPDERoaCuDpHI7Dhw9j7Nix+PLLL+X7jhgxwnKVOplly5YhLCwMAHDx4kVUrlxZ34JsGEOGdpy914MhQzscbvUfWw0YGSUnJ6Nly5Y4ePCgyfmUlJQch0pxGBVlh78bzkXxO/13330Hf39/nD9/3uQvHH5+fvjuu+/k65IkMXDkQZMmTeTjAQMG4PDhwzpWY3sYMqzLmYIHQ4Z1OfNwK3sIGgDw5MkTFCpUyORczZo18b///Q/58jnX/zMiGYdUKcJ9OGyYh4cHkpKScOTIEb1LsRkMGvpy5ODBoKE/Z+n1sJegAQA7d+5Eu3btTM7t2bMHzZo1c6q/UEuShJ9//hmdO3e26uuGh4ejadOmePLkCfz8/Kz62pQLB5s0rjXV21gnJyfj0qVLSE3lWFytrFmzRj529qDH+Rm2xZHmeXB+hu1xxHke6XMz7CVsXLt2DZIkmYSNd999F0ajEc2bN3eqsAE8XXGrbdu2epehWkhICObPn29ybsWKFQwxeZC+8Z+ai60LCQnB1KlTLbpokeLAkZCQgAEDBsDLywvPPfecXMzw4cMxa9YsixVGMPlLyltvvaVfITpJDxkMGrbNHoNHeshg0LBt6cHDnsOHPYWM5ORkfPXVV5AkKdPuweHh4Zg9e7bTBY3k5GQAT/cccHd31+x10tLSYDQaNXt+sm8HDx5Ehw4dULx4cUiShM2bN+d4//DwcEiSlOkSERFh1uuNGjUKP/30E8qWLYuWLVti3bp1MBgMefoZFAeO8ePH46+//kJ4eDg8PDzk8y1atMD69evzVAyZyvjGvnv3bh0rsS6GDPtkD8GDIcN+2VvwsKegcfjwYUiSBHd390xzL9966y0YjUaTeYWOLCwsDMOGDcOoUaMQEBCA1q1bA4DJl7xGjRpl2gbgwYMHcHNzkyfVGwwGvPvuuyhRogQKFCiABg0aIDw8XL5/eu/C1q1bUbVqVbi7u+f41+TDhw+jRo0a8PDwQMOGDXHu3DmT2zdt2oTnnnsO7u7uCAkJwZw5c0x+phs3bmD06NHyF8/w8HD0798f0dHR8rkpU6YAeDpnp0+fPvD394eXlxfatm2Lf//9N1Pt27ZtQ6VKleDl5YWuXbsiISEBK1euREhICPz9/TFixAikpaUp/n9gN6y4SlV8fDxq1qyJr7/+WtHjLl26hHv37smXIkXM+/wbNWoUTp8+jd9//x1VqlTB8OHDUaxYMQwbNgynTp1S/gNAxRyOzZs3Y/369WjYsKHJF+LnnnsOV65cUVUEZW/VqlXo06cPAODGjRsoXbq0zhVphyHDMdjiPA+GDMdhy/M87CVgpEtKSkLFihWzXH69T58+WLp0qaKdhHOTvuGbtSnddHDlypUYOnRotou19OzZE5999hlmzZolP+/69etRvHhxvPTSSwCAYcOG4fz581i3bh2KFy+On3/+GW3atMHZs2fljX0TEhLw6aefYunSpShcuHCOXwbfe+89fPHFFwgKCsKECRPQoUMH/PPPP3Bzc8PJkyfRvXt3TJkyBT169MCRI0fw9ttvo3DhwujXrx9++ukn1KxZE4MHD8agQYMAAIUKFcL8+fMxadIkXLp0CQBQsGBBAEC/fv3w77//YuvWrfDx8cEHH3yAdu3a4fz58/LvQ0JCAr788kusW7cOsbGxeOWVV9ClSxf4+flhx44duHr1Kl599VW88MIL6NGjh9ltT1lr27atqiF9RYoUydOwuTp16qBOnTqYM2cOvvnmG3zwwQdYuHAhqlevjhEjRqB///5m/9tS/I794MGDLP9RxMfHO11XqzW8/vrrcuAYOXJkrt1o9oYhw3Fl7O3QI3wwZDg2W1rdyt6CRnJyMvr06ZNpVMLQoUPx+eefa7Z3RkJCgvyl1pri4uJQoEABs+9foUIFfPbZZ9ne3r17d4waNQqHDh2SA8aaNWvw+uuvQ5Ik3Lx5E8uXL8fNmzdRvHhxAE/nv+zatQvLly/HjBkzADxdUvibb75BzZo1c61p8uTJaNmyJYCngahkyZL4+eef0b17d8ydOxfNmzfHxIkTAQAVK1bE+fPnMXv2bPTr1w+FChWCq6srvL29ERQUJD+nr68vJEkyOZceNA4fPoxGjRoBAH744QcEBwdj8+bN6Natm1z7woULUa5cOQBA165d8f333+P+/fsoWLAgqlatiqZNm2L//v0OGzgkqJuPYc1vyrVq1YLBYEC1atUwZcoUvPDCC4oen5KSgp9//hnLly/H7t270bBhQwwYMAC3b9/GhAkTsGfPHpP5xjlR/C5dr149bN++HcOHDwfw37CfpUuXyvtykOVkXHJwy5YtOlZiWQwazsWavR4MGs5Hr14Pewsad+/eRYMGDXD79m2T8/Xr18fBgwc1naNgT+rWrZvj7YGBgWjVqhV++OEHvPTSS7h27RqOHj2KxYsXAwDOnj2LtLQ0VKxY0eRxBoPBZNf1/Pnzo0aNGmbVlPH7VaFChVCpUiVcuHABAHDhwgV06tTJ5P4vvPAC5s+fj7S0NEW7eF+4cAH58uVDgwYN5HOFCxc2eT3gaa9RetgAgKJFiyIkJMQkUBYtWhSRkZFmv7aziYmJMbnu7u5usX+DxYoVw6JFi1CvXj0YDAYsXboUYWFhOH78OOrUqZPr40+dOoXly5dj7dq1cHFxQZ8+fTBv3jyTPeG6dOmC559/3uyaFL87z5gxA23btsX58+eRmpqKL774AufPn8eRI0dw4MABpU9HZli9ejV69eoF4Ol4vEqVKulckXoMGs5Ny+DBoEHWCB72FjKApyssVa1aFVFRUZluu3DhgtU2lvXy8kJcXJxVXuvZ11XCnN6Qnj17YsSIEfjqq6+wZs0aVK9eHdWrVwfwtEfF1dUVJ0+ezPRlP+MXck9PT7sdGfLsUDtJkrI859AT4fO4LG5wcLDJ6cmTJ8vzaPKqUqVKJt8VGzVqhCtXrmDevHn4/vvvc338888/j5YtW2LhwoXo3LlzlkMry5Qpg9dee83smhS/K7/44os4ffo0Zs2aherVq+PXX39FnTp1cPToUfkfG1nWa6+9JgeOypUrQwg7WFPtGQwalJElh1sxaNCztAge9hg0bt++jcqVKyM+Pt7kfPpIBXMnkFqKJEmKhjbZsk6dOmHw4MHYtWsX1qxZIw99BoDatWsjLS0NkZGR8pCrvDp27BhKlSoF4Omk7n/++QdVqlQBAFSpUiXTfJPDhw+jYsWKcuDJnz9/pgncWZ2rUqUKUlNTcfz4cXlI1aNHj3Dp0iVUrVrVIj+Lw8jjxn+3bt2Cj4+PfFrrHsb69evj0KFDZt336tWruc4ZLlCgAJYvX27266t6Ny5XrhyWLFmi5qGkgpLuUFvCkEHmUNPrwZBB5rDEPA97DBopKSkIDQ3FyZMnTc63bNkSW7Zsgaenp06VOY4CBQqgc+fOmDhxIi5cuIDXX39dvq1ixYro2bMn+vTpgzlz5qB27dp48OAB9u7dixo1aqB9+/aKX2/q1KkoXLgwihYtig8//BABAQHy0vljx47F888/j2nTpqFHjx44evQoFixYgG+++UZ+fEhICA4ePIjXXnsN7u7uCAgIQEhICOLi4rB3717UrFkTXl5eqFChAjp16oRBgwZh8eLF8Pb2xrhx41CiRIlMw7acXh4Dh4+Pj0ng0Nrp06dRrFgxs+7btGlT/PHHHyZDAAEgKioKderUwdWrVxW/vuJ34OjoaOzevRvXr1+X1+pu3ry5VRvNGdWrVw8nTpwA8HSX1xYtWuhcUfYYNEgNc4IHgwappaTXwx5DRrolS5Zg8ODBJufatm2LzZs3I3/+/DpV5Zh69uyJdu3aoXHjxnLvQ7rly5dj+vTpGDt2LO7cuYOAgAA0bNgQL7/8sqrXmjVrFkaOHIl///0XtWrVwv/+9z/5/2edOnWwYcMGTJo0CdOmTUOxYsUwdepU9OvXT3781KlT8dZbb6FcuXIwGAwQQqBRo0YYMmQIevTogUePHslDepYvX46RI0fi5ZdfRnJyMho3bowdO3ZYdMUyUiYuLg6XL1+Wr1+7dg2nT59GoUKFUKpUKYwfPx537tzBqlWrAADz589HmTJl8NxzzyEpKQlLly7Fvn378Ouvv5r1etevX89ySWODwYA7d+6o+hkkoWB8zurVqzFs2LBME118fX2xaNEim1+JICYmBr6+voiOjra7gJSammryj90Wh1WVW/+J3iXYlVcrn9a7BLvg5ZqMK/GBepdhV67GFM79Tk4uPXjEJf43jMGeg8aNGzcQEhJici4kJATnzp3TZRhTUlISrl27hjJlypjs2UWULqffEVv+vpZeW8gnn8BFxe+2MSkJ1z/8UNHPFh4ejqZNm2Y637dvX6xYsQL9+vXD9evX5b1ePvvsM3z77be4c+cOvLy8UKNGDUyaNCnL58ho69atAJ5uPL1y5Ur4+vrKt6WlpWHv3r3YvXu3vJSyEmb3cJw6dQr9+/dHz549MXr0aHkuwfnz5zF//nz07t0blStXNmt5N1Iu42pVNus2u+kVsc48Tbvl5ZosH8elcvUcJRJT+JdIcxXwSMapdtP1LkM1o9GI1q1bY8+ePSbnT506hdq1a+tUFZETyOOQKiXCwsJy/EPzihUrTK6///77eP/99xW/TvowPUmS0LdvX5Pb3NzcMm0qqYTZO41/9dVX6Ny5M1asWIGaNWvC3d0dHh4eqFOnDlatWoWOHTviiy++UFUEmefTTz+Vj6dPt98PSGfnW+UxfKs81rsMm+XlmmwSNgDAL38C/PJzqB5Z1ql20+06bKxZswaurq4mYWP8+PEQQjBsEGnNijuNW4vRaITRaESpUqUQGRkpXzcajTAYDLh06ZLqYYFmB47Dhw/jrbfeyvb2IUOGmD37XY2QkBBIkmRymTVrlmavZ4syptX0DX7IfjBoZC89ZDwbNJ7F4EGWYO9B49dff4UkSejZs6d8zs/PD7GxsfKmckREal27dg0BAQEWfU6zx+ncvXs30yY2GVWsWFH1RBJzTZ06FYMGDZKve3t7a/p6tk7phj5kfQwYOcstYGQnY+iIStZmV2RyLPYWMIQQuHHjBvbv34/9+/dj37592X7GHjp0SPEOwkSUN5JQudO4jfZwfPnllxg8eDA8PDzw5Zdf5njfESNGKH5+swNHQkJCjhO/3N3dkZSUpLgAJby9vREUFKTpa9i6CxcuyGtvv//++6rH0pG2GDRypjZoZCU9fDB4UFbsJWg8evRI8V8UZ8yYgXHjxtn05nG2uMAJ2Qa7/93I48Z/tmbevHno2bMnPDw8MG/evGzvJ0mStoEDAH755ReTGesZZbWDqaXNmjUL06ZNQ6lSpfDGG29g9OjROU6mNhgMMBgM8vVnV9eyRxl3hJ07dy4Dhw1hyMiZJUNGVtjrQensJWQAT5eaL1GiRKbN+bLTuXNnTJ061eY32k1fVTEhIYH7flCWEhKevmfb7XK7Vpw0bg3Xrl3L8thSFAWOZ2esP0vLv7KMGDECderUQaFChXDkyBGMHz8e9+7dw9y5c7N9zMyZM/Hxxx9rVpNetmzZIm/As2vXLrRp00bnipwbg0bOtA4aWWGvh3Oyp6ABAEuXLjUZJpyuf//+aNOmDZo0aYKiRYvqUFneubq6ws/PD5GRkQAALy8vm+6JIesRQiAhIQGRkZHw8/Pj0HAnoWgfDksbN26cycpLWblw4YLJX/XTLVu2DG+99Rbi4uKy3Q4+qx6O4OBgm1zXWQkhBFxcXEyu24Jyc7IPf44or0GjRQnl61jbE0sGjRuJhfL0eGcLHjei/fUuwarsLWgYDAaUKFECjx49ks+1adMGW7ZscajN+YQQiIiIsMoICLI/fn5+CAoKyjKI2sM+HGUnz1C9D8fVjyfY5M+W7tVXX0X9+vXxwQcfmJz/7LPP8Mcff2Djxo2Kn1PXzR3Gjh1rshNmVsqWLZvl+QYNGiA1NRXXr19HpUqVsryPu7t7tmHEnkmShCFDhmDRokUAgKtXr2bbTmRZ7M3ImR69GebgcCvHY28hI92BAwcQFhZmcu6ff/5BhQoV9ClIQ5IkoVixYihSpAhSUlL0LodsiJubm/33bDjYkKqMDh48iClTpmQ637ZtW9VD+XUNHIGBgQgMVLeD8OnTp+Hi4oIiRYpYuCr7MHPmTDlwdOnSBX/99ZfOFTk2Bo2c2WrQyAqHW9k3ew0aBoMB3bt3l3fyBYDQ0FAcPnzY4Ycaubq62v+XS6JnqVylyh4CR1xcXJa9rW5ubqrnQ9vB9tXA0aNHcfz4cTRt2hTe3t44evQoRo8ejV69esHf37mGDqTz8/NDxYoV8c8//+DMmTN49OgRChcurHdZDodBI2f2FDSexeBhX+w1aADA9u3bM22WtWXLFnTs2FGnioiIsle9enWsX78ekyZNMjm/bt06VK1aVdVz2kXgcHd3x7p16zBlyhQYDAaUKVMGo0ePxpgxY/QuTVfh4eEoXrw4AKB9+/Y4duyYzhU5BoaMnNlzyMgKh1vZLnsOGUIITJs2DZMnT85024MHDyy+qRYRWZkDD6maOHEiXnnlFVy5cgXNmjUDAOzduxdr165VNX8DUBg40tLScPjwYdSoUQN+fn6qXlCNOnXq8Mt0FooVK4bSpUvjxo0bOH78OBISEuDlxS9MajFo5MzRgkZW2OthG+w5aNy6dQutWrXCxYsXM902f/58DB8+3GTRDyKyUw4cODp06IDNmzdjxowZ+PHHH+Hp6YkaNWpgz549aNKkiarnVBQ4XF1d0apVK1y4cMGqgYOyd+LECXkeTJs2bXDw4EGdK7IvDBm5c4ag8Sz2elifPYcMIQQWL16MoUOHZnn78uXLc10ghYjsi6PtNP6s9u3bo3379hZ7PsVDqqpVq4arV6+iTJkyFiuC1AsICEBQUBAiIiLw22+/ITExkZssmYFBI2fOGDKyw14Pbdlr0DAYDFiwYAHefffdLG+vXLkyfv31VwQHB1u5MiIiyzh58iQuXLgAAHjuuedQu3Zt1c+lOHBMnz4d7777LqZNm4a6deuiQIECJrfb6prCjuz06dMICgoCAHTs2BG7d+/WuSLbxaCRMwaN7DF4WJa9BY2oqCgcPnwY8+bNw969e7O939SpU/HRRx85/MpTROS4IiMj8dprryE8PFwe0RQVFYWmTZti3bp1qlaYVRw42rVrB+DpF9uMb6hCCEiShLS0NMVFUN4ULVoU/v7+ePLkCfbs2YOoqCgOecuAISN3DBrm43Ar9ewtZEREROCdd97BTz/9lOP9Xn75ZSxevFhexIOInIADz+EYPnw4YmNj8ffff6NKlSoAgPPnz6Nv374YMWIE1q5dq/g5FQeO/fv3K34R0t6ZM2fkrvsaNWrg5s2bOlekPwaNnDFk5B17PcxjL0FDCIHw8HDMnj0bO3fuzPG+s2bNwsiRI+GhYqdhIrJ/jjyHY9euXdizZ48cNgCgatWq+Prrr9GqVStVz6k4cKidnU7aKlmyJEJDQ3H06FHcunULly5dynYHdkfHoJEzBg3LY/DImq0HDYPBgO+++w6zZs3CrVu3crzv3Llz8cYbb6Bo0aJWqo6IbJ4dhAc1jEYj3NzcMp13c3OD0WhU9Zyq9uH47bffsHjxYly9ehUbN25EiRIl8P3336NMmTJ48cUXVRVCebd//375r22VK1eGEA76LyELDBm5Y9DQHodb2X7IAIANGzagR48eud7vvffew4QJEzhElYicSrNmzTBy5EisXbtWHip6584djB49Gs2bN1f1nIoXA9+0aRNat24NT09PnDp1CgaDAQAQHR2NGTNmqCqCLMPd3R3jx4+Xr2/atEnHaqzDt8pjho0ceLkmyxeyLr/8CSYBxNGdajfd5sOGwWBAgQIFsgwbfn5++OSTT/Do0SMIISCEwGeffcawQURZE3m42LgFCxYgJiYGISEhKFeuHMqVK4cyZcogJiYGX331larnVLVK1aJFi9CnTx+sW7dOPv/CCy9g+nTb/rBxBjNmzMDMmTMBAF27doXRaHS41VIYMHLHgGE7HLnXw9YDRkbHjx9Hw4YNTc59/PHH+OCDD+Du7q5TVURkrxx5DkdwcDBOnTqFPXv2yJuYVqlSBS1atFD9nIoDx6VLl9C4ceNM5319fREVFaW6ELKc3bt3o2XLlgCAPn364Pvvv9e5Istg0Mgdg4Ztc5S5HvYUNIQQ6NOnD1avXi2fa968OX755Re4urrqWBkR2TUHXqUKACRJQsuWLeXvk3mlOHAEBQXh8uXLCAkJMTl/6NAhlC1b1iJFUd5kTKCrV6/G/PnzUbhwYR0ryhsGjZwxZNgfew0e9hQ0AODo0aNo1KiRyblffvlF9SorRESO6ssvvzT7viNGjFD8/IoDx6BBgzBy5EgsW7YMkiTh7t27OHr0KN59911MnDhRcQGkjQcPHsgbswQEBNjdBHKGjNwxaNg/exhuZW8hAwAeP36MoKAgpKSkmJyPiYmBt7e3TlURkSNxtCFV8+bNM+t+kiRZJ3CMGzcORqMRzZs3R0JCAho3bgx3d3e8++67GD58uOICSBsBAQHo3r07NmzYAODpClZNmzbVuarcMWjkjkHDMdlar4c9Bg0AWLVqFfr27WtybtGiRXjrrbd0qoiIHJKDDam6du2aps+vOHBIkoQPP/wQ7733Hi5fvoy4uDhUrVoVBQsW1KI+yoN169bJgaNZs2Y23cvBoJEzhgznoXfwsNegATztgV+6dKl8vXPnzti0aRNcXBQvyEhElDMHCxxZSU5OxrVr11CuXDnky6dqJw2Z4nfhN998E7GxscifPz+qVq2K+vXro2DBgoiPj8ebb76Zp2LIsiRJkgMHALz77rs6VpNZ+pK2DBvZ45K2zit9WV1rLK2bvqStPYeNl156ySRs/P333/j5558ZNoiIFEpISMCAAQPg5eWF5557Djdv3gQADB8+HLNmzVL1nIrfiVeuXInExMRM5xMTE7Fq1SpVRZB2unXrJh/PmTMH8fHxOlbzFENG7hg0KCOtgoe9h4x0LVu2xKFDh+TrCQkJqFq1qo4VEZGjS5/DoeZi68aPH4+//voL4eHh8obSwNNFidavX6/qOc3uH4mJiZE3Q4qNjTUpIC0tDTt27ECRIkVUFUHaunHjBkqXLg0AKF++PO7du2f1GhgwcseAQbmxxCRzRwgYGb355pvYs2ePfD01NZXL3RKR9hx4SNXmzZuxfv16NGzY0GQvt+eeew5XrlxR9ZxmBw4/Pz9IkgRJklCxYsVMt0uShI8//lhVEaStUqVKoXHjxjh48CAiIiJw6tQp1KlTxyqvzaCROwYNUkPpXA9HCxoAMHnyZCxfvly+zrBBRFbjwIHjwYMHWXYixMfHq95M2uzAsX//fggh0KxZM2zatAmFChWSb8ufPz9Kly6N4sWLqyqCtLdv3z55wk/dunU1n0DOoJE7Bg2yhNyChyMGDQCYOHEipk//72dLTExk2CAisoB69eph+/bt8uqz6SFj6dKlCA0NVfWcZgeOJk2aAHi6bFZwcDAn4tkZV1dXfPPNN3j77bcBAJ9//rnFJ5GXqn3Hos9HlO7U/ZJ6l2B3/np5mt4laMJoNKJWrVo4e/asfO7Jkycmw3yJiLTmaPtwZDRjxgy0bdsW58+fR2pqKr744gucP38eR44cwYEDB1Q9p+LUULp0abi4uCAhIQEXL17EmTNnTC5ku4YOHSofv/fee5k2xcqra5eDcO1ykEWf05G5u6RkuqQYXXnJ4lLIKwGFvLRfrYls2y+//AJXV1eTsPHo0SP4+fnpVxQROSeRh4uNOnfuHADgxRdfxOnTp5Gamorq1avj119/RZEiRXD06FHUrVtX1XMrXlT3wYMH6N+/P3bu3Jnl7WlpaaoKIes4d+4cqlWrBgBo2LAhTp48afHXSA8dZcpHWPy5HV3BfEkAgLhU/rU2KxlDx+ME29ggj7R37NixTN34Pj4+ePLkCXvbiUgXjtjDUaNGDTz//PMYOHAgXnvtNSxZssRiz634nXrUqFGIiorC8ePH4enpiV27dmHlypWoUKECtm7darHCSBvPPfccypcvDwA4deoUrl+/rtlrpfd4sNdDuYL5kuQLZY29Ho7v+PHjqFChQqaw8cMPPyA6Opphg4jIgg4cOIDnnnsOY8eORbFixdCvXz/89ttvFnluxe/W+/btw9y5c1GvXj24uLigdOnS6NWrFz777DPMnDnTIkWRtjIOR6hQoYJVXpPBQz0Gj5wxeDiW5ORkrFmzBg0bNkTDhg1x+fJl+bZ58+ZBCIE33nhDxwqJiOCQQ6peeuklLFu2DPfu3cNXX32Fa9euoUmTJqhYsSI+/fRTRESoH7miOHDEx8fLS2X5+/vjwYMHAIDq1avj1KlTqgsh6/Hw8ECnTp0APF1GMi+/QEqx10M9Bo+cpQcPhg/7lJCQgOnTp6NkyZLo2bMnjh8/Lt8WHByM6OhojBo1Sr8CiYgycsDAka5AgQLo378/Dhw4gH/++QfdunXD119/jVKlSqFjx46qnlNx4KhUqRIuXboEAKhZsyYWL16MO3fuYNGiRShWrJiqIsj6Mu4U+fzzz+tSA4OHOhxulTsGD/ty//59lC9fHhMnTpT/iJVu8uTJuH79Onx8fHSqjogoMykPF3tSvnx5TJgwAR999BG8vb2xfft2Vc+jeNL4yJEj5Z2qJ0+ejDZt2uCHH35A/vz5sWLFClVFkPW5u7ujYcOGOHbsGG7fvo3IyEjddornJHP1OMk8Z+mhgxPMbdeWLVvQuXNnk3O+vr546623MGjQIHnOGRERWdfBgwexbNkybNq0CS4uLujevTsGDBig6rnMDhzXrl1DmTJl0KtXL/lc3bp1cePGDVy8eBGlSpVCQECAqiJIH9u3b0fhwoUBAO3atcOJEyd0rSdjbwfDhzIMHjnj6la2qUePHtiwYYN8vUSJEvj888/RpUsXuLu761gZEVEuHHSn8bt372LFihVYsWIFLl++jEaNGuHLL79E9+7dUaBAAdXPa3bgKFeuHEqXLo2mTZuiWbNmCAsLQ8mSJeHl5YU6deqoLoD0U6hQIYSEhOD69es4efIkjEajzaz6wl4PdTIOs2L4yBp7PfR3//59BAWZDqc8ePAgXnrpJZ0qIiJSxhGXxW3bti327NmDgIAA9OnTB2+++SYqVapkkec2+9vlvn370LdvX1y9ehWDBg1C6dKlUaFCBbz11ltYt24d7t+/b5GCyLp27NghH//yyy86VpI1zvNQj/M8csZ5HvqYOXNmprARHx/PsEFE9sUBJ427ubnhxx9/xO3bt/Hpp59aLGwACno4wsLCEBYWBgBISkrCkSNHEB4ejvDwcKxcuRIpKSmoXLky/v77b4sVR9qrUqWKfDxv3jy0bdtWx2qyx+FW6nG4Vc443Mo6oqKi4O/vb3Ju8uTJmDJlij4FERGRCS3301M8aRx4uqxqs2bN8OKLL6Jp06bYuXMnFi9ejIsXL1q6PrKi3bt3612CWTjcSh0Ot8odh1tpY9GiRRg6dKjJuZs3byI4OFinioiILMCGeytsjaLAkZycjGPHjmH//v0IDw/H8ePHERwcjMaNG2PBggVo0qSJVnWShlq3bm2Tw6lyw14P9djrkTMGD8uIi4uDt7e3ybnBgwdj8eLFOlVERGQZjjiHQ0tmB45mzZrh+PHjKFOmDJo0aYK33noLa9as4d4bDmDo0KFy4DAYDHa5Ogx7PdRhr0fOONxKvVWrVqFv374m5y5duoSKFSvqVBERkQU56CpVWjF70vhvv/2GwoULo1mzZmjevDlatmxp9bCxfft2NGjQAJ6envD398+0djup0759e/l406ZNOlaSd5xkrh4nmeeMk8zNEx8fDw8PD5Ow0b17dwghGDaIiFQ4ePAgOnTogOLFi0OSJGzevDnXx4SHh6NOnTpwd3dH+fLldd8rz+zAERUVhW+//RZeXl749NNPUbx4cVSvXh3Dhg3Djz/+mGl3WEvbtGkTevfujf79++Ovv/7C4cOH8cYbb2j6ms4iX77/OroWLVqkYyWWkx48GD6UY/DIGYNH1oQQGD58OAoWLAiDwSCfP3PmDNavX69jZURElpc+pErNRan4+HjUrFkTX3/9tVn3v3btGtq3b4+mTZvi9OnTGDVqFAYOHKjr8Hmzh1QVKFAAbdq0QZs2bQAAsbGxOHToEPbv34/PPvsMPXv2RIUKFXDu3DmLF5mamoqRI0di9uzZJjscVq1a1eKv5ex+++03vUuwOA63UofDrXLG4VZPGY1GjBs3DrNnzzY537ZtW2zfvh2SJOlUGRGRhqw4pKpt27aKVhFdtGgRypQpgzlz5gB4uiLpoUOHMG/ePLRu3Vp5ARagepe3AgUKoFChQihUqBD8/f2RL18+XLhwwZK1yU6dOoU7d+7AxcUFtWvXRrFixdC2bdtcw43BYEBMTIzJhbLWpUsXvUvQHHs81GOvR86csdcjJiYGjRs3hqura6aw8e+//2LHjh0MG0TksPLaw/Hs99OMPcN5dfToUbRo0cLkXOvWrXH06FGLvYZSZgcOo9GI33//HZ999hnatm0LPz8/NGrUCN988w2CgoLw9ddf4+rVq5oUmf68U6ZMwUcffYRt27bB398fYWFhePz4cbaPmzlzJnx9feULl2DM3pAhQ+TjuLg4HSvRHodbqcfgkTNnCB6RkZEoX748fH19M/WIHjp0CEIIlC9fXqfqiIjsQ3BwsMl31JkzZ1rsuSMiIlC0aFGTc0WLFkVMTAwSExMt9jpKmD2kys/PD/Hx8QgKCkLTpk0xb948hIWFoVy5cqpffNy4cfj0009zvM+FCxdgNBoBAB9++CFeffVVAMDy5ctRsmRJbNy4EW+99VaWjx0/fjzGjBkjX4+JiWHoyEbGJLxu3ToMHDhQx2qsh8Ot1OFwq5w56nCrt99+GwsXLjQ5FxAQgN9//x1lypTRqSoiIh3kcUjVrVu34OPjI5+2xxVClTA7cMyePRtNmza16CojY8eORb9+/XK8T9myZXHv3j0ApnM23N3dUbZsWdy8eTPbx7q7uzv8/0BLcXH5r7Nr0aJFThM40nFPD/W4p0fOHGFPj+PHj6Nhw4Ym52rXro2DBw+iYMGCOlVFRKSjPAYOHx8fk8BhSUFBQbh//77Jufv378PHxweenp6avGZuzA4c2fUi5EVgYCACAwNzvV/dunXh7u6OS5cu4cUXXwQApKSk4Pr16yhdurTF63J2J0+e1LsEXbHXQx0Gj5zZY/BIS0tDkyZNcPjwYZPzd+7cQfHixXWqiohIf7a88V9oaCh27Nhhcm737t0IDQ3V/sWzoXrSuDX5+PhgyJAhmDx5Mn799VdcunQJQ4cOBQB069ZN5+ocR8+ePfUuwaZwnoc66fM8ONcja+nzPGx9rseDBw+QL18+k7CxbNkyCCEYNoiIrCguLg6nT5/G6dOnATxd9vb06dPyKJ/x48ejT58+8v2HDBmCq1ev4v3338fFixfxzTffYMOGDRg9erQe5QNQ0MOht9mzZyNfvnzo3bs3EhMT0aBBA+zbtw/+/v56l+YwhgwZgh9++AEA8OTJE7bt/+NwK/XY65EzW+31OHDgAMLCwuTrXl5eiIiIgLe3t35FERHZEisui3vixAk0bdpUvp4+P7lv375YsWIF7t27ZzLFoEyZMti+fTtGjx6NL774AiVLlsTSpUt1WxIXsKPA4ebmhs8//xyff/653qU4rBdeeEE+/v777zFixAgdq7FNHG6lDoNHzmxpkvmMGTPw4YcfytcnTJiATz75RMeKiIhsjyQEJKE8Pah5TFhYGEQOj8tqF/GwsDD8+eefil9LK3YTOEh7GdfMX7RoEQNHDhg81OHqVrnTs9ejfv36+OOPP+Trhw4dMvlDBBER/T8r9nA4AgYOypJWmzg6Gg63Uo+9HjmzZvBISEhAgQIFTM49ePAAAQEBmr82EZE9suVJ47bILiaNk/U423K4lsRJ5upwgnnOtJ5kfuHCBZOw4e7ujrS0NIYNIiKyGAYOMpFxx/HIyEgdK7Ff3MlcHa5ulTtLB4/vvvvOZH+jN998E0lJSSb78hARURZEHi5OiJ8qZKJu3bry8fLly3WsxDEweKjD4JGzvAaPtLQ01KlTx6RHc+PGjfjuu+8sUR4RkcNLH1Kl5uKMGDgoWwsXLtS7BIfB4KEOg0fOlA63iomJwaBBg5AvXz6T1UuuXLmCrl27alUmEZHjYQ+HIpw0Tpm4ubkhJSUFN27cUPQ4n2KxGlXkOB7FPx0r/1xgBM7ElNS5Gvtx57Gf3iXYrbS0NEyZMgXTp0/PdFtAQAAiIiLg6uqqQ2VEROQs2MNBmaTv4q5UwgVuFGiuvx8EISE1v95lkAP6p+tE/NN1Iq5evQpfX1/ky5cvy7Dxyy+/4MGDBwwbREQqcEiVMgwclMlbb70lH9++fVvRYxMu+DN4KJCQmp/BgywiPWg8ePAAkiShXLlyiImJMbnPJ598guTkZAgh0KpVK50qJSJyABxSpQiHVFEmGVetWbp0KaZMmaL4OTKGDq8qTyxRlkNLDx1e+ZJ1roTsyT9dJ5pcnzRpEqZNm2ZyrlmzZti4cSMKFSpkzdKIiByes/ZWqMHAQTlauHChqsCRUXr4YPDIXcbeDoYPys6zQSM2NhY+Pj4m57p27Yq1a9ciXz6+zRMRkb74SURZ8vX1RXR0tEX34mDwUIa9HvSsZ4MG8HTYY3BwsMm5e/fuISiIq6IREWlGiKcXNY9zQgwclKUhQ4bg008/1eS5OdxKGfZ6OLesQka6lJQUk7AxYMAALF261BplERE5NbUTwJ11GBYnjVOWMk4cv3z5smavw0nmynCSufNInwSeHSEE8uf/73dh1qxZDBtERNbCSeOKMHBQlsqUKSMff/vtt5q/HoOHMgwejiu3oJGuV69e8nGHDh3wwQcfaFkWERGRahxSRblatGgRPvvsM6u8FodbKcPhVo7BnICR0ZEjR7BmzRr5+tatWy1dEhER5UAyPr2oeZwzYuCgbBUrVgz37t1DbKw+O4hzkrkynGRuf5QGDQBITk7GCy+8IF+Pjo62ZElERGQOtcOjOKSKyNSQIUP0LgHAf8OtOOTKPBxuZfvMHTaVlYCAAPl427ZtmZbDJSIi7XGncWXYw0HZGjBgACZPngwAOHfuHKpVq6ZzRez1UILDrWyL2oCR0YIFC+QexyZNmqB9+/Z5fk4iIiKtsYeDslWiRAn5eNGiRTpWkhl7PJRhr4d+8tKbkdG9e/cwfPhw+fr+/fvz/JxERKRS+j4cai5OiD0cZJZFixZhwYIFepeRCSeZK8N5HtZjiZCRUfHixeXja9euQZIkiz4/ERGZj/twKMPAQTkqW7Ysrl69irS0NL1LyRWHW5mPw620YemQka5fv37y8bRp0xASEqLJ6xARkZk4aVwRDqmiHA0dOlTvEhTjcCtlONwq7yw1bCor//77L1auXClf/+ijjzR5HSIiIq0wcFCOMv5l9Y8//tCvEBW4upUyDB7KaRk0gKe7iVesWFG+HhMTo9lrERGR+bhKlTIcUkU5yrgE56JFi/D888/rWI16HG5lPg63ypmWAeNZy5Ytk48/+eQTeHt7W+21iYgoB2ongDvppHH2cJDZMn75sVfs8VCGvR7/0bo341lCCAwcOFC+Pn78eKu9NhER5Yw9HMqwh4NyVa1aNZw7d07vMiyKq1sp48yrW1kzZGT0ySefyMfbtm3jqlRERGS3GDgoV++//z769OkDAIiKioKfn5++BVkYh1uZz1mGW+kVMtKlpKRg4sT/auAGf0RENoarVCnCIVWUqzfeeEM+njFjho6VaIvDrZRxxOFW1h42lZ3atWvLxydOnNCxEiIiygqHVCnDwEG5cnV1lY9nz56tYyXWwdWtlHGE4GErQQMANm/ejL///hsAUK5cOdStW1fnioiIKBOjUH9xQhxSRWbp3r07NmzYAACIi4tDwYIFda7IOjjcynz2NtzKVgJGRhs2bECPHj3k68ePH9exGiIiIstgDweZZdiwYfLx9u3bdaxEH+zxUMaWez1sqTcjJSUFP/74I0JDQyFJkknY2LRpEwoXLqxjdURElC2Rh4sTYg8HmSXj/hszZsww+WLkTLi6lTK2tLqVLYQMIQS+++47DBo0KMf7nThxgkOpiIhsmAR18zGcdb1BBg4yi4eHBwoWLIi4uDicOXMG8fHxKFCggN5l6YrDrcyn13ArWwgZABAdHY327dvj8OHDOd5v7ty5GDlyJFxc2PlMRGTTuPGfInbxqRYeHg5JkrK8/PHHH3qX5zRGjBghH+/YsUPHSmwLh1spY43hVrYybCo2Nha1atWCn59fprDRoEEDrF+/HgaDAUIICCEwevRohg0iInI4dtHD0ahRI9y7d8/k3MSJE7F3717Uq1dPp6qcz0svvSQf//jjj+jWrZuO1dgeDrdSxtLDrWwhYGS0b98+NG/e3ORcYGAgDh48iMqVK+tUFRERWYLaJW6ddVlcuwgc+fPnR1BQkHw9JSUFW7ZswfDhw7n7rhU1atRIPt6wYQOWL18OLy8vHSuyXRxuZb68DreytaABAPPmzcOYMWPk6+XLl8fJkyfh4+OjY1VERGQx3PhPEbvsu9+6dSsePXqE/v3753g/g8GAmJgYkwup5+PjY7Ih2a5du3Ssxj5wuJUySoZb2cqwqWdNmDDBJGzs27cP//77L8MGEZEDkYRQfXFGdtHD8azvvvsOrVu3RsmSJXO838yZM/Hxxx9bqSrn0LhxY/z5558AgI0bN+KVV17RuSL78OxwKyEkXH3MJU+VssWAkdHatWsxc+ZM+fr9+/dRpEgRHSsiIiLSnySEflFr3Lhx+PTTT3O8z4ULF0zGO9++fRulS5fGhg0b8Oqrr+b4WIPBAIPBIF+PiYlBcHAwoqOj+ddGlQ4cOICwsDD5ekJCAjw9PfUryE5V3zpZ7xLsSszDArjx5vt6l5GjiIgIFCtWTL5++/ZtlChRQseKiIjsU0xMDHx9fW3y+1p6bS81nox8+TwUPz41NQm/HfzYJn82LenawzF27Fj069cvx/uULVvW5Pry5ctRuHBhdOzYMdfnd3d3h7u7e15KpGc0adIETZs2xf79+wEAv/zyCzp37qxvUeSwYh7az9LLH374oXz83nvvMWwQETkwtcOjOKRKB4GBgQgMDDT7/kIILF++HH369IGbm5uGlVFOZsyYgdDQUADA9OnTGTjIouwpZKQ7ffo0li1bJl//4IMPdKyGiIg0x0njitjVpPF9+/bh2rVrGDhwoN6lOLWGDRvKQfHkyZNISkrSuSJyBDEPC9hl2BBCYNSoUfL1SZMmoXBhzs8hIiJKZ1eB47vvvkOjRo24hr0N2L17t3z82Wef6VgJ2Tt7DRrptm7digMHDsjXR48erWM1RERkFek7jau5OCG7Chxr1qzJtFsv6aNmzZry8eTJnABNyqSHDHsOGgCQnJyMLl26yNdnz54NPz8//QoiIiKrSN/4T83FGdlV4CDbsnPnTvl49uzZOlZC9sIRQkZG8+bNQ/pCf3Xq1GHvBhGRs7ByD8fXX3+NkJAQeHh4oEGDBvj999+zve+KFSsgSZLJxcND+YpalsTAQaq1adNGPn7//feh4wrLZOMcLWgAwMOHDzFu3Dj5+ubNm+Hq6qpjRUREZC2SUf1FqfXr12PMmDGYPHkyTp06hZo1a6J169aIjIzM9jE+Pj64d++efLlx40Yeftq8Y+CgPDl69Kh8/N577+lYCdkaRxk2lZ2mTZvKx+vXr0dwcLCO1RARkaOaO3cuBg0ahP79+6Nq1apYtGgRvLy8TFZHfJYkSQgKCpIvRYsWtWLFmTFwUJ40bNhQPp4zZw6MRhXRnRyKI4eMdIcPH8a5c+cAPN0rqHv37jpXREREVmWlIVXJyck4efIkWrRoIZ9zcXFBixYtTP7o+6y4uDiULl0awcHB6NSpE/7++2/VP6olMHBQnmX8JX7ttdd0rIT05AxBAwCMRiNefPFF+fpff/2lYzVERKQLkYcLnu5YnvFiMBiyfJmHDx8iLS0tUw9F0aJFERERkeVjKlWqhGXLlmHLli1YvXo1jEYjGjVqhNu3b+fxh1aPgYPyrGrVqvLxxo0bcebMGR2rIWty9GFTWalbt658vH79ehQsWFDHaoiISA/pO42ruQBAcHAwfH195cvMmTMtVltoaCj69OmDWrVqoUmTJvjpp58QGBiIxYsXW+w1lNJ1p3FyHFFRUfJyoDVr1kRKSgry5eOvl6NypoCR0Y4dO3D69GkAQKlSpTiUioiIVLl16xZ8fHzk6+7u7lneLyAgAK6urrh//77J+fv37yMoKMis13Jzc0Pt2rVx+fJl9QXnEXs4yCJ8fX2xbt06+Xq1atV0rIa04my9GRklJyejffv28vWrV6/qWA0REekqj3M4fHx8TC7ZBY78+fOjbt262Lt3r3zOaDRi7969CA0NNavUtLQ0nD17FsWKFcv7z60SAwdZTI8ePVCxYkUAwKVLlyBJEt544w2cOHFC58ooL5xx2FRWihcvLh//9ttvXAKXiMiZCQBGFRcVOwiMGTMGS5YswcqVK3HhwgUMHToU8fHx6N+/PwCgT58+GD9+vHz/qVOn4tdff8XVq1dx6tQp9OrVCzdu3MDAgQPV/7x5xDEvZFFnzpwx2Vxm7dq1WLt2LQDg5MmTqFOnjnxbYmIiDAYDzpw5g7Jly6JkyZJWr5ey5+wBI6P169fj0aNHAICXXnrJZNI4ERE5n4zzMZQ+TqkePXrgwYMHmDRpEiIiIlCrVi3s2rVLnkh+8+ZNuLj814fw5MkTDBo0CBEREfD390fdunVx5MgRkzm31iYJJ9qtLSYmBr6+voiOjjYZN0eW9fjxY7zyyis4cOBAptt8fX1RoUKFLHs9+vbti2XLlpn8o3FU1bdO1ruEbNlq0Ljx5vu6vG5CQgIKFPivTYxGIyRJ0qUWIiJnYMvf19Jra1Z7HPK5Kt+9OzUtCfv+nGWTP5uWHP+bHVldoUKFEB4eDiEEhBBYsmSJfFt0dHS2Q6xWrlwJV1dXbNy40Vql0v/jsKnsZQwbf/75J8MGERH9/xK3auZw6F24Phg4SHMDBw6EwWDINJF8+PDh2LlzJ7777juT8927d4ckSWjRogWuXbtmzVKdDkNGzjIuIfjKK6+gVq1a+hVDRES2w0ob/zkKzuEgq8ifPz/Onj2Le/fuwcPDA/7+/ia3v/nmm9i6dSs6deokn9u7dy/Kli2LIkWK4Pfff0fp0qWtXbbDYsjIXVRUFIYMGSJf37Rpk47VEBGRTTECUNPhbbR0IfaBPRxkVcWKFcsUNtJ17NgRQggcPXoU+fPnl89HRkYiJCQEPj4+7PHIAw6bUibj7+mlS5d0rISIiMi+MXCQzWnYsCEMBgMiIiLQuHFj+XxsbCzKli0LSZLw888/w4nWO8gThgzlJk2aJB8PHjxYXu6ZiIgIyPtO486GgYNsVtGiRXHgwAFERUWhdevWJre98sorcHFxgSRJGDRoEJKTk3Wq0nYxaKhz6dIlTJs2Tb6ecR4HERERAM7hUIiBg2yer68vdu3ahdTUVIwYMSLT7UuXLoW7uzu6d++OlJQUHSq0HRw2lTcGgwGVK1eWr9+/f1/HaoiIyGYxcCjCwEF2w9XVFV988QWEEHj48CHef990X4aNGzcif/78kCQJvXv3xtSpUzFw4EC0aNEC5cuXh5ubGyRJQvv27fHkyROdfgptMGRYRsZNK5ctW4YiRYroWA0REZFj4CpVZJcKFy6MTz/9FJ9++imuXbuGsmXLmty+evXqbB+7Y8cOFCpUCABw6tQp1K5dW9NatcSQYTkZf4e6deuG/v3761gNERHZNLW9FezhILJPZcqUgRACd+/eRe/evRU9tk6dOpAkCX/88YdG1Vkeh01ZlhAC5cuXl1dA8/T0xIYNG3SuioiIbJoxDxcnxMBBDqNYsWJYtWqVvMN5dpfExET06dPH5LH169eHJEmYO3cujEbbfDdgyLC8hIQEuLi44MqVK/K5+Ph4HSsiIiJ7wFWqlGHgIKfj4eGBlStXwmg04oMPPjC5bezYsXB1dYWvry/Cw8P1KfAZDBra2LRpEwoU+K9dvby8YDQaIUlqdnIiIiKi7DBwkNOSJAmzZs2C0WjE7NmzTW6LiYlB06ZNIUkS6tevb/XVijhsSjv379+HJEno2rWrfO7ll19GXFwcwwYREZmHq1QpwsBBTk+SJLz77rsQQuDGjRto1KiRye1//PEHgoKCIEkSTp8+rWktDBnaEUKgZ8+eCAoKMjl/8OBB/O9//2PYICIi8xmF+osTYuAgyqBUqVI4fPgwhBA4evQoAgMDTW6vXbs22rZta9Fdztmbob2dO3fCxcUFa9askc8NHjwYQgi89NJLOlZGRER2iT0cijBwEGWjYcOGiIyMREpKCkaOHCmf37VrF1xcXHDixAnVz82QYR13796FJElo166dyflHjx5xB3EiIiIrkYQl/1Rr42JiYuDr64vo6Gj4+PjoXQ7ZmaSkJPj5+cFgMMjn3N3dcevWrUw9IaSvuLg41K1bF//884/J+R07dqBt27Y6VUVEROaw5e9r6bW1KDsC+VzcFT8+1WjAnqtf2uTPpiX2cBCZycPDA0lJSVi+fLl8zmAwoEiRIujRo4dFh1mROqmpqXj55Zfh7e1tEjYGDRoEo9HIsEFERJbBIVWKMHAQKdSvXz+kpqaiU6dO8rkNGzbAxcUFDx480LEy55WWloZevXrBzc0N27dvl8/XqlUL8fHx+PbbbzkpnIiILIeTxhVh4CBSwdXVFZs3b8aTJ09MzhcpUgT79u3TqSrnk5iYiFatWiFfvnz44Ycf5PMFChRAZGQk/vzzT3h5eelYIRERETFwEOWBn58fhBCYPHmyfK558+ackKyRtWvXIiAgAJIkQZIkeHl5Yffu3Sb3+eeffxAXF8d5NUREpB1hVH9xQgwcRBYwZcoUHD9+XL4+ZMgQTJo0SceKHEtycjL8/Pzwxhtv4NGjR5lu9/X1xY0bNyCEQIUKFXSokIiInArncCjCwEFkIfXr18edO3fk69OmTcPEiRN1rMgxPHnyBO7u7oiOjs50W4cOHfDo0SNERUWhVKlSOlRHREROiXM4FLGbwPHPP/+gU6dOCAgIgI+PD1588UXs379f77KITBQvXtxkXsf06dPxzTff6FiR/dmyZQu8vLzkYVOFChWSbwsICMCDBw+QmpoKIQS2bt1qcjsRERHZHrsJHC+//DJSU1Oxb98+nDx5EjVr1sTLL7+MiIgIvUsjMuHn54eoqCj5+jvvvIODBw/qV5Cd+N///gdJktC5c2ckJiZmur1hw4aIiIhAQEAAXF1ddaiQiIjo/3FIlSJ2ETgePnyIf//9F+PGjUONGjVQoUIFzJo1CwkJCTh37pze5RFl4uvrazK8qkmTJrh9+7aOFdmu9N3AO3bsmOm2kiVLokmTJjh79iyOHj3KoEFERLZBQGXg0LtwfdhF4ChcuDAqVaqEVatWIT4+HqmpqVi8eDGKFCmCunXrZvs4g8GAmJgYkwuRtRQvXhxHjx6VrwcHByM5OVnHimzLn3/+iSJFiqBEiRIm5zds2AAhBIQQuHXrFsLDw1GtWjWdqiQiIsoCezgUyad3AeaQJAl79uxB586d4e3tDRcXFxQpUgS7du2Cv79/to+bOXMmPv74YytWSmSqYcOGWLhwIYYOHQoAcHd3R1paGlxc7CLr50lSUhJu3bqFmJgYREdH48GDB9izZw82bdqUaf8SABgwYACWLFnCDfqIiIgcjCSEflFr3Lhx+PTTT3O8z4ULF1CpUiV07twZKSkp+PDDD+Hp6YmlS5di69at+OOPP1CsWLEsH2swGGAwGOTrMTExCA4ORnR0NHx8fCz6sxDlpGfPnlizZg0AwMXFBampqQ79xXrnzp3o0aMHYmNjc71v/fr1sX37dgQEBFihMiIisnUxMTHw9fW1ye9r6bW1KDIQ+VzyK358qjEZeyKX2uTPpiVdA8eDBw+yXFM/o7Jly+K3335Dq1at8OTJE5P/ORUqVMCAAQMwbtw4s17Pln+ByfFVqVIFFy9elK87Yk9HamoqgoKCcv13XaJECaxatQrNmjWzUmVERGQvbPn7mhw4AgeoDxwPvrPJn01Lug6pCgwMNGs34ISEBADI9OXMxcUFRqNz7thI9uf8+fMIDAyUv4y7urriwYMHDvOX/X///RcVK1Y0Oefn54dWrVqhSZMmCAsLQ5UqVRy6Z4eIiJyE2vkYTjqHwy7+vBoaGgp/f3/07dsXf/31F/755x+89957uHbtGtq3b693eURmkSQJDx8+RGhoqHwuMDBQHmplz5YsWWISNqpXr47z58/j8ePHWL9+Pd5++21UrVqVYYOIiMgJ2UXgCAgIwK5duxAXF4dmzZqhXr16OHToELZs2YKaNWvqXR6RIkeOHMG0adPk6z179kSFChVyHYZkqxo1aoTBgwfL11euXIkzZ86wN4OIiBwXdxpXRNc5HNZmy2MCyfn8+eefqFOnjsm5FStWoE+fPnbxRf3JkyeZdvm+du0aQkJC9CmIiIgcgi1/X0uvrbl/X9VzOPY+WWmTP5uW7KKHg8gR1a5dG/Hx8ShdurR8rl+/fnBxcUGPHj3wxx9/6FidqejoaKxfvx7vvvsuOnbsCB8fH5OwUbBgQaSmpjJsEBGRcxAqezec5+/8Jhg4iHTk5eWFa9euYdasWSbnN2zYgPr160OSJOzevdvqdQkh8OOPP6JIkSKQJAl+fn547bXXMGfOHPzvf/8zWe72/fffR2xsLHcBJyIi58GN/xRh4CDSmSRJ+OCDD3D8+HH06dMn0+2tWrVCWFiY5iuypaWlYefOnXB1dYWLiwu6deuGBw8eZHv/ggUL4vz587nupUNERETOjYGDyEbUr18fK1euhBACaWlpmDFjhnzbgQMH4OrqilWrVmny2kIIvPzyy2jXrl2mYFOpUiVs3boVkZGRMBgMEEJACIHY2FhUqVJFk3qIiIhsmtGo/uKEGDiIbJCLiwvGjx+P2NhY+Pr6yuf79u0LSZLQvXt3xMXFWez1PvzwQ+zatUu+HhYWhsjISAghcPHiRXTo0AGBgYHIn1/5BDkiIiKHwyFVijBwENmwggULIioqCjt37jQ5v3HjRnh7e0OSJGzZsiVPr7F48WLMnDlTvn748GHs37/frE05iYiInJEwGlVfnBEDB5EdaNOmDYQQ2L9/f6bbOnfuDEmSTEKDOYQQmDVrFoYMGSKfu3PnDho1apTneomIiIjSMXAQ2ZGwsDAIIZCYmIiBAwea3DZhwgSULl0aN2/ezPV5EhMT8fLLL2P8+PHyudOnT6N48eIWr5mIiMjhcEiVIgwcRHbIw8MDS5YsydTrcfPmTfTu3Rs57ed56dIleHl5YceOHfK5zz//HDVr1tS0ZiIiIofBncYVYeAgsnNhYWEmvRoHDx7Epk2bMt0vMTERLVu2ROXKlU3Ob968GWPGjNG8TiIiIochBCCMKi4MHERkp4KDg7F69Wr5erdu3RAfHw8AuHr1KooWLQovLy/s2bNHvs+0adOQmpqKTp06QZIkq9dMREREziGf3gUQkWW88cYbWLduHbZt2wbg6QpXWfH29saNGzfg7+9vzfKIiIgchjAKCEl5b0VOQ54dGXs4iByEJElYuXIlXF1ds7w9NDQUDx8+RExMDMMGERFRXqgaTvX/FxW+/vprhISEwMPDAw0aNMDvv/+e4/03btyIypUrw8PDA9WrVzeZt6kHBg4iB1KoUCGkpKSgSZMm8rkBAwYgJSUFR44cQeHChXWsjoiIyDEIo1B9UWr9+vUYM2YMJk+ejFOnTqFmzZpo3bo1IiMjs7z/kSNH8Prrr2PAgAH4888/0blzZ3Tu3Bnnzp3L64+tmiScqG8nJiYGvr6+iI6Oho+Pj97lEGkmNjYW4eHhaNGiBTw9PfUuh4iIyGy2/H0tvbYwqQvySW6KH58qUhAuflb0szVo0ADPP/88FixYAAAwGo0IDg7G8OHDMW7cuEz379GjB+Lj4+Uh1gDQsGFD1KpVC4sWLVJcsyU41RyO9GwVExOjcyVE2mvSpAlSUlKQkpKidylERERmS/+eZst/E08VBlXDo1Lx9DP52e+i7u7ucHd3z3T/5ORknDx50mTfLBcXF7Ro0QJHjx7N8jWOHj2aafXJ1q1bY/PmzYrrtRSnChyxsbEAnq7oQ0RERES2KzY2Fr6+vnqXYSJ//vwICgrCoQj1cyIKFiyY6bvo5MmTMWXKlEz3ffjwIdLS0lC0aFGT80WLFsXFixezfP6IiIgs7x8REaG65rxyqsBRvHhx3Lp1C97e3naxDGhMTAyCg4Nx69Ytm+tSdBRsY22xfbXHNtYW21d7bGNt2WP7CiEQGxuL4sWL611KJh4eHrh27RqSk5NVP4cQItP30Kx6NxyJUwUOFxcXlCxZUu8yFPPx8bGbNwl7xTbWFttXe2xjbbF9tcc21pa9ta+t9Wxk5OHhAQ8PD6u8VkBAAFxdXXH//n2T8/fv30dQUFCWjwkKClJ0f2vgKlVERERERDYof/78qFu3Lvbu3SufMxqN2Lt3L0JDQ7N8TGhoqMn9AWD37t3Z3t8anKqHg4iIiIjInowZMwZ9+/ZFvXr1UL9+fcyfPx/x8fHo378/AKBPnz4oUaIEZs6cCQAYOXIkmjRpgjlz5qB9+/ZYt24dTpw4gW+//Va3n4GBw4a5u7tj8uTJDj+uT09sY22xfbXHNtYW21d7bGNtsX3tX48ePfDgwQNMmjQJERERqFWrFnbt2iVPDL958yZcXP4btNSoUSOsWbMGH330ESZMmIAKFSpg8+bNqFatml4/gnPtw0FERERERNbFORxERERERKQZBg4iIiIiItIMAwcREREREWmGgYOIiIiIiDTDwEFERERERJph4HBgXIBMW2xfIiL98b1YW2xfsgQGDgeU/uYgSZLOlTgmti8Rkf74Xqwtti9ZEjf+czBxcXH4+OOPcevWLZQpUwYtW7ZEs2bN9C7LYbB9tRcXF4evvvoKN27cQN26dVGnTh3UrVtX77IcCttYW2xf7fG9WFtsX7I0bvznQOLj41GzZk1UqFABBQsWRExMDMLDwzFz5kyMGTNG7/LsHttXe3FxcahVqxaCg4ORlpaGxMRE3L17F7Nnz8Ybb7yhd3kOgW2sLbav9vherC22L2mBPRwOZNGiRfD398fmzZvh7u6OR48eYfXq1Rg7diyio6Px8ccf612iXWP7au+zzz5DsWLFsGPHDnh6euL8+fNYsWIFevXqhYSEBAwcOFDvEu0e21hbbF/t8b1YW2xf0gIDhwOJjIyEj48P3N3dAQCFCxfGyJEj4ePjgwEDBqBEiRIYPHiwzlXaL7av9m7fvo1ixYrB09MTAFC1alV8+OGH8PLywpAhQ1CoUCG88sorOldp39jG2mL7ao/vxdpi+5ImBDmMVatWCW9vb3Hu3LlMt3388ceiWLFi4vz58zpU5hjYvtr7/PPPRbly5cSNGzdMzj948EAMGTJE1K9fX9y+fVun6hwD21hbbF/t8b1YW2xf0gJXqbJjiYmJOHz4sHw9NDQUDRs2xKeffoqrV68C+G+ViVdffRVubm7yeVKO7au9unXrwt/fH0uXLsWDBw/k8wEBAejatSuuX7+Oe/fu6Vih/WMbW5bRaDT5b/369dm+FsbPOuti+5IWGDjsVExMDJo0aYJly5bhypUrAIDy5cujW7duOH/+PD7//HP8888/8nJ25cuXh5+fH2JjY/Us227ExcVhzJgxeP311/HOO+8gOTkZ5cuXR9euXdm+FpKQkIBVq1Zh1apV2LZtGwAgLCwMnTp1wurVq7F8+XLcvXtXvn/t2rXh5+eHqKgonSq2P0lJSdi9eze2bNmC48ePA3jaxu3atcMPP/zANs6j2NhYvPjii9i5cydcXJ5+nL700kvo0KED29dC+FmnLX7WkdXo28FCasTHx4uqVauKzp07i+joaGE0Gk1unz9/vqhfv75o27atOHTokLh165b49ttvhb+/vzh16pROVduPmJgYUalSJdG8eXMxePBgERAQIHr37i3f/vnnn4sGDRqwffMgJiZGVKhQQdSpU0cEBweLQoUKiQ4dOoh79+4JIYQYN26cqFixonj77bfF2bNnRUxMjFi8eLEIDAxkV76ZoqOjRZ06dUSDBg2Ej4+PqFu3rvjyyy/l2999911RqVIltnEezJ49W0iSJAoUKCD+97//mdw2duxY/g7nET/rtMXPOrImBg47tHbtWtGiRQv5zXfVqlXi888/F1OnThWPHj0SQgixefNm8corrwhJkkSVKlVEUFCQWLdunZ5l24XU1FTRpUsX8eqrr8rnli1bJrp3727yYffzzz+LLl26sH1VSEtLE6+99ppo166dSE5OFnfu3BGHDh0SISEhonbt2uKff/4RQggxd+5c0bRpU+Hq6irq1Kkj/P392cZmio+PF3Xq1BHdunUTkZGR4s8//xSjRo0SHTt2FI8fP5bvN3fuXNGsWTO2sUo///yz6NGjh5g4caLIly+f2Lp1q8ntCxcu5O9wHvCzTjv8rCNr4ypVdujGjRsQQkCSJLz++us4e/YsAgMDcebMGfz444+YN28eOnXqhHbt2uHs2bOQJAkFChRAxYoVuXNoLpKSkvDw4UOTFTju37+PS5cuoX379vD09MTo0aPRuXNndOjQAX/99RfbVyEXFxdER0fjxRdfhJubG4oXL47ixYvjxIkTCA0NRa9evRAeHo7Ro0ejR48eOH/+PACgaNGiqF69uvy7T9lbtGgR/Pz88M033yAgIACBgYF4+PAh2rdvj/v378Pf3x8AMHr0aPTs2RNnzpwBwDZWKiQkBFevXsXs2bMRERGBV199FYcPH8b58+fh6uqKIUOGoEuXLjh79iwAtq9S/KzTDj/ryNoYOOyQu7s7XFxccO7cOdy8eRPbtm1DyZIlAQDNmjXDe++9h2PHjsHNzQ116tQxeSzfHHLm4uKCGzduYPfu3WjSpAnCw8Px0UcfYfDgwXjuueewatUqDB06FLt27UKJEiXYvioIIfDw4UNcunRJPpeSkoLChQvjwIEDqFu3LoYNG4bvvvtODiNkvrS0NPj7+6NFixbw8/OTzz///PMoXrw4kpKS5Pu5urqiSJEiaNGihU7V2rdChQrBaDSiYMGCmD9/PgoVKoTQ0FAYjUZ5zgzbVz1+1mmHn3VkdXp1rZB6165dE15eXqJRo0aiTZs2IiEhQaSlpQkhno7J9PT0FD/++KPOVdqvnTt3igIFCoiwsDDh6+srpk2bZnK7l5eXmDt3rk7VOYb169eLIkWKiOXLl8vnDAaDEOLpsImKFSuKK1euZBqzTea5fv26SExMFEIIuQ1TU1NFxYoVxfHjx+X7/f3337rUZ+9SU1Pl4xYtWshtOn78eJE/f37h6uoqfvnlFyGE4O9wHvCzTlv8rCNr4ipVNs5gMOCff/7B6dOnkZqaCuBpN/7EiRPx77//4tGjR8ifPz9cXFxgNBqRlJSEKlWqIDAwUOfK7UPG9k1JSQEAtGnTBrdu3cL69etRo0YN+a+TiYmJiImJQc2aNVG0aFE9y7YrSUlJOHnyJA4cOCD/db1Zs2bo0KEDFi5ciPXr1wMA8ufPDwAIDAxEXFwc8uXLx7+imSm9jcPDw5GUlITSpUvDw8MDRqMRkiRBCIHo6Gg8efJE/j1fvXo1atWqhQsXLsjDIyhrz/4Ou7q6mtx++/ZtLFiwAPPnz8fy5csxYsQItGnTBtu2bePvsJmyep/gZ53lZNW+/Kwja+KQKhsWExODNm3aICEhAbGxsciXLx/mzJmD5s2b480330RkZCTmz5+Pt99+G7NmzYKLiwv27t2Lhw8fymO0KXvZtW/Dhg0REBCApKQkRERE4NixY2jYsCE8PT2xZcsWXL9+HRUrVtS7fLuQvqRlfHw8Hj9+DE9PT0ybNg3dunXD+++/j48++gifffYZHj58iHfeeQdpaWm4c+cOfH19+UXNTM+2sZeXF6ZOnYrWrVujWLFiAJ4OY8uXLx9cXV3h7++PTZs2oW/fvvj+++9RpUoVnX8C25Zd+7Zo0QIlS5ZE27ZtMXLkSERFRWHdunXo2LEj2rVrl2Uwoaxl18adOnXCoEGD+FmXR7n9DvOzjqxC5x4WykZiYqKoV6+e6NWrlzh37py4fPmy6Nq1q/D29hbTp08XUVFR4tGjR2L+/PnC29tblC5dWtSsWVMULlxYrF+/Xu/ybV527evr6yumT58ubt26JYQQYsqUKaJIkSKiefPmomfPnsLf319s2LBB5+rtQ3JysmjevLno1q2buHTpkrh+/bp46623RNmyZcW7774rnjx5Ii5fvizGjBkjvLy8RPXq1UVYWJgoUKAAf4fNlF0blytXTrz77rvi+vXr8n3T0tJEw4YNxcCBA4WLi4tYvXq1EOLpkB8O+8labu17//59cfjwYVG7dm2xY8cOk8empKQIITikKje5vU9ERkaKqKgoftaplNvv8OXLl4UQTz/rAgMD+VlHmmHgsFGHDh0S9evXl7/4CiHEH3/8IQoUKCBCQkLEvHnz5PO3bt0SS5YsERs3bhQnTpwQQvBDLjc5tW/ZsmXl9r1+/bpYtWqVaNOmjRgzZozYs2ePEILta47IyEhRpUoV8fPPP5ucnzVrlqhataqYMGGCSEpKEgkJCeLPP/8UEyZMEF9++aU4ePCgEIJtbI6c2rhatWriww8/lJcPjYyMFJ6enkKSJLFx40YhBMNGbnJq3+eee05MmDBBCCFMlhomZcxp49jYWCEEP+vUMOc9IiEhQdy9e5efdaQpDqmyUbdv38aFCxdMxqf6+vqiQ4cOEEJg0qRJ6NatG0qUKIGSJUti4MCBJo/ncJScmdu+pUuXRu/evdGzZ09IkiSPh6fcpaSkwGg0Ii4uDsDTMcQeHh744IMPkJKSgm+++QZhYWFo2bIlatWqhVq1aulbsB3KrY0XLVqEJk2aoGXLlvDw8MDgwYPRvn17tGzZkstamkHJ77DRaJR3Gyfz5dbGCxculNuYn3XKmdO+6e8R/KwjLUmCv1E2KSIiAmFhYWjSpAlmzpyJpKQkNGvWDC+99BKWLFmC2rVro2PHjvj444/1LtUumdO+HTp0wNSpU7lmfh68+uqruHDhAv7++29IkgSDwQB3d3cAQJcuXRAZGYnDhw/rXKV9U9LGcXFxKFiwIMOGAvwd1h7bWFu5te/9+/dx5MgRnaskR8c/x9gof39/jBkzBgcOHED58uVRu3ZtVKxYEUuWLAEAFC5cGA8ePNC5SvtlTvs+fPgQAL+UqWE0GgEA8+fPR0pKClq2bAng6br66asktWzZEklJSUhOTtatTnumpI0NBgMAoGDBggAg/wWTssffYe2xjbVlbvsaDAa2L2mOgcNGubu7o1+/fti3bx+WLVuG1atXY+vWrQCebtgVEBCAcuXKAQC7PVVg+2orfWhJiRIl8OWXX+Ly5cto2rQpYmNj4ebmBgB4+PAhChQogOTkZLaxCkraOCUlhW2sEH+Htcc21hbbl2wJh1TZgPQhO+YM3UlJScH333+PMWPGYPv27XjhhResVKX9YvvqKyUlBb/99huGDx+O2NhYNGrUCN7e3li5ciXWr1+PLl266F2i3WMba4vtqz22sbbYvqQ3Bg4dpaWlyeMpPT09c73/v//+iwULFmD16tX45ptv0KNHDytUab/YvtaTlpaW654DycnJmDp1Km7evAk3Nzd0794drVu35hwZM7GNtcX21R7bWFtsX7JlDBw6iY2NxeDBg3Hr1i0UKFAArVu3xpgxY3J93PLly1G2bFk0adKEEz9zwPbVXnx8PH766Sf07t0bAHJcpefZD7OMvU4A2zg7bGNtsX21xzbWFtuX7AXncOggISEBzz//PJKSktC6dWtUrVoVH374Ibp27Ypr166Z3Dd90lf6BK/+/fujSZMm8u18g8iM7au9+Ph4vPDCC+jbty8++eQTAE/HC6e3Z0ZGoxGSJCExMVE+l/EDjm2cNbaxtti+2mMba4vtS/aEgUMHO3fuhIuLC5YtW4aJEydi3rx5OHz4MA4cOIChQ4fizp07AJ6+Gbi4uODYsWNYuHAhAJi8kfANImtsX22lpKRg1KhR8Pb2xvDhw/HDDz9g6tSpALL+sHNxccHRo0cxceJEOdhxv4KcsY21xfbVHttYW2xfsjf8bdNBTEwMDAYD/P39ATx946hTpw6OHj2KkydPYsKECQD++8K7d+9ejBo1ChcuXOAbhBnYvtpKTExEWloa+vfvj/feew+vvfYa1q5dK+8Jk9WH3cWLFzF37lz88ccfepRsd9jG2mL7ao9trC22L9kdC+5aTmb666+/RL58+cT3338vn0tOThZCCLFv3z7h6ekpNm3aJN+WlpYmevfuLVauXGn1Wu0R21c7aWlpQgghHj16JOLj44UQQty+fVtMnjxZVK5cWUyePFm+b2pqqjAajfL14cOHi48//tjkHGXGNtYW21d7bGNtsX3JHuXTO/A4o8qVK2Po0KGYN28eAgMD0bp1a7i5uUEIgeeffx5Vq1bFpUuX5Pu7uLigdevWaNasmY5V2w+2r3bSe4AKFSoE4OmqKCVKlMBbb70FAFi/fj0kScLkyZMxf/58PHr0CNOnT4eLiwteeeUVVKlShUPVcsE21hbbV3tsY22xfckeMXBoLC4uDpMmTUJcXBxcXFwwduxYVKhQAYMGDcL169cxY8YMpKWloV27dpAkCQULFkRgYCBSU1MB/LfMXc+ePXX+SWwT21d7WbVxqVKl4O7uLq9wUqxYMfnD7scff8TBgwexf/9+rFixQv5wDAsL0/GnsG1sY22xfbXHNtYW25fsno69Kw4vLi5OlClTRoSFhYmePXuK5557TpQqVUrMmzdPJCUlid9//1288soromzZsmLOnDkiPDxcfPXVV8LT01McOHBA7/JtHttXe1m1cUhIiJg7d66IjIwUQvzXvS+EEHfv3hVhYWFCkiSxefNmIYRg130u2MbaYvtqj22sLbYvOQIGDg1NmzZNNG7c2OTcO++8I6pWrSqmTJkiDAaDuHz5spg+fbrw9/cXFStWFJUrVxYbNmzQqWL7wvbVXnZtXK1aNTFp0iTx8OFDk9sWL14sJEkSP/30kxDi6YccP+hyxjbWFttXe2xjbbF9yRFwSR4NJSUlwWg0IikpCWlpaQCABQsWoEOHDli7di3WrVuHcuXK4cMPP8SVK1dw8OBB7Nu3D926dYN4GgZ1/glsG9tXe9m1cfv27fHjjz9i27ZtAJ4uMZyQkIDdu3dj/fr16NKlCzeTMhPbWFtsX+2xjbXF9iWHoEfKcWQZ/4owffp0Ua5cOREXFyeEECIpKUm+rVevXqJs2bIiISHB6jXaM7av9pS0cbly5UzaOP12/kUtZ2xjbbF9tcc21hbblxwNA4cFxcbGinHjxoljx44JIYRITEwUJUuWFF26dJHvk5iYKIR4OiazUKFCYvXq1brUao/YvtrLSxvzw808bGNtsX21xzbWFtuXHBGHVFmIEAKjR4/GZ599hu+++w7Hjx+Hh4cHvv32Wxw4cEBeBcnDwwMAEB8fj2LFismb01HO2L7ay2sbS5LEbvtcsI21xfbVHttYW2xfclQMHBYiSRK8vLxQoUIFnDt3DgsWLMCff/6Jtm3b4uuvv8bBgwfRtGlTnDt3DpcvX8Yvv/yCe/fuISgoSO/S7QLbV3tsY+2xjbXF9tUe21hbbF9yVJIQnDmbV0IISJKEtWvX4sGDB6hcuTLGjRuH5557Dl988QW2b9+OcuXKYdiwYYiMjES+fPmQkpKCOXPm4LXXXtO7fJvH9tUe21h7bGNtsX21xzbWFtuXHBk3/rOA9O5LHx8fLF68GOHh4Xj06BG++uortGjRAqdPn8bFixdx6tQp7N+/H+7u7vD390eVKlW4goQZ2L7aYxtrj22sLbav9tjG2mL7kiNj4LAQIQRKly4Ng8EAAHj99dexdetWbNy4EWFhYYiPjwcANG3a1ORxfHMwD9tXe2xj7bGNtcX21R7bWFtsX3JUnMNhIZIkoXLlynBzc0NUVBSmT5+OrVu3YsyYMUhKSsKMGTNw8uRJvcu0W2xf7bGNtcc21hbbV3tsY22xfclRsYfDglJTU5EvXz60a9cOZ86cwbp169ChQwesWbMGs2fPRr58bO68YPtqj22sPbaxtti+2mMba4vtSw7JGmvvOpMFCxaIkJAQsXnzZpPzd+/e1akix8L21R7bWHtsY22xfbXHNtYW25ccDVepsrD4+Hg8ePAAISEhAP5bdSL9v5Q3bF/tsY21xzbWFttXe2xjbbF9ydEwcBARERERkWY4aZyIiIiIiDTDwEFERERERJph4CAiIiIiIs0wcBARERERkWYYOIiIiIiISDMMHEREREREpBkGDiIiIiIi0gwDBxERERERaYaBg4iIiIiINMPAQUTkRB49eoQiRYrg+vXreXqe1157DXPmzLFMUURE5NAYOIiILEAIAT8/P3z11VeZbnv77bdRv359/PLLL5AkKcfLr7/+iiZNmkCSJMyYMSPTazRo0ACSJGHq1Kmq6vzkk0/QqVMnhISEqHp8uo8++giffPIJoqOj8/Q8RETk+Bg4iIgs4MqVK4iOjka9evUy3Xby5EnUrVsXjRs3xr179+RL4cKFMXHiRJNzzZs3x59//onSpUvj7NmzJs+zcuVK3L17FwBQp04dxTUmJCTgu+++w4ABA9T9kBlUq1YN5cqVw+rVq/P8XERE5NgYOIiILODkyZPIly8fatWqZXI+JSUFZ86cQd26deHp6YmgoCAEBQUhLS0Njx49wksvvSSfCwoKwpUrVxAbG4u+ffuaBI7Y2FiMHz8e/fr1AwDUrVtXcY07duyAu7s7GjZsKJ8LCwvD8OHDMWrUKPj7+6No0aJYsmQJ4uPj0b9/f3h7e6N8+fLYuXNnpufr0KED1q1bp7gOIiJyLgwcREQWcPLkSVStWhWenp4m5//++28kJSVlCgh//vkngMw9FSdPnoSXlxdef/11XLp0CcnJyQCAadOmoV69eggMDERQUBCKFSumuMbffvsty6CycuVKBAQE4Pfff8fw4cMxdOhQdOvWDY0aNcKpU6fQqlUr9O7dGwkJCSaPq1+/Pn7//XcYDAbFtRARkfNg4CAisoCTJ09mO5zK3d0d1apVMzl/6tQpBAcHo3DhwpnO16hRA5UqVYKHhwcuXryIf//9FwsXLsTcuXNx6tQpVcOpAODGjRsoXrx4pvM1a9bERx99hAoVKmD8+PHw8PBAQEAABg0ahAoVKmDSpEl49OgRzpw5Y/K44sWLIzk5GREREarqISIi58DAQURkAadOnco2cFSvXh1ubm6Z7p9VcEg/L0kSatSogbNnz2L06NEYOvT/2rl/kMbBOIzjTyoVF3EwpYsORQRBK8ZRwVFFR5GCi4Io3Ojg0KGToE4qFBTFydHFLln8MzmJJVKoIggOli4qtYPQgqI3Xbia844zF47zvp+p/JL3zZvx6e9980Xt7e3ueZCPqFQqamho8NS7u7vd33V1dWpublY8Hndr0WhUknR7e1sz7ls3523nAwCA7xE4AMCnQqGgcrmszs5Oz7XDw0P19fV56r8KHJLU09OjtbU1ZbNZpVIpVatVXV5e1ozr7+/XycmJJGl6elqrq6vvrtM0TT08PHjqb8OQYRg1NcMwJEkvLy8195VKJUlSJBJ595kAABA4AMCnp6cnSdLj42NN/eDgQFdXV0okEjX1+/t7FQoFT+C4vr5WuVx265ZlKZvNamlpSY2Njcrlcnp+fq7pcKRSKS0vL2tlZUWhUEhzc3PvrtOyLF1cXPh61+/l83m1tLTINM0/NicA4PMhcACAT7FYTB0dHUomkzo6OlIul9PW1pYmJiY0NTXl6XA4jiPpxwfG6+vr3fMek5OTuru7c79M5TiOIpGIWltb3THDw8O6ubmRbdtaX1//6TqHhoZ0fn7+wy7HRxwfH2twcPCPzAUA+LwIHADgk2EYsm1bbW1tSiQSGhgY0ObmphYXF7W9ve25/+zsTNFo1HOA23EcdXV1uduZwuGwTNN0tzQ5jiPLsmrGnJ6eqlQqqampybM16q14PK7e3l7t7u76eV1JUrVaVSaT0czMjO+5AACfm/H6+vr6txcBAPh9xWJRIyMjymQyGhsb087OjudrWG/Ztq35+Xnl83mFQh//z2ljY0N7e3va39//8BwAgP8DHQ4A+AdVKhWNj48rnU4rFospmUxqYWHhl+NGR0c1OzurYrHo6/nhcFjpdNrXHACA/wMdDgAAAACBocMBAAAAIDAEDgAAAACBIXAAAAAACAyBAwAAAEBgCBwAAAAAAkPgAAAAABAYAgcAAACAwBA4AAAAAASGwAEAAAAgMAQOAAAAAIEhcAAAAAAIDIEDAAAAQGC+AoYocaEk6nsEAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 1000x440 with 2 Axes>"
       ]
@@ -1836,7 +1921,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 20,
    "id": "2727bb7b-9994-450f-8cf7-43bdf0934b2e",
    "metadata": {},
    "outputs": [
@@ -1846,13 +1931,13 @@
        "Text(0, 0.5, 'Dischage ($f^3/s$)')"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1862,7 +1947,7 @@
     },
     {
      "data": {
-      "image/png": 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6iMGgUqkwZswY4XleXh4SExPx3XffYezYsfjmm2+wc+dOiRvPZrOhvb0dra2tQh0m98/ZnZtvvhnZ2dno27cvpk6diqlTp+KOO+5ATEwMSkpK/J6Tr/e2sbERFy9eRGFhoWR7YWEhvvnmG8m2YcOGCf9nhbgvX76MvLw8fPfdd7jjjjsk4/Pz8yWuw46ABFUXhQmqNgv1MiOIrs53332HnJwc2X3XXnstzp49iy1btuDzzz/HXXfdhUmTJuH9998XKjz7wv3GxXGcpA7f7NmzUVNTg+XLlyM7OxtarRb5+fkejadjY2ODOqfm5mYAwJo1azBu3DjJvmAKJ/o7R4VCITkfIPxg6kDeV380Nzfjhz/8IV5++WWPfRkZGTh27BgA4L///S969eol2d8RCQnNzc148cUX8aMf/chjn7hMgL/POS4uDocOHcJXX32Fzz77DC+88AIWLFiA/fv3C5+5r3OKxHsLSL/X7MdItHt7kqDqoggWKgpKJ65y9GolTrw0JWqvHS5ffPEFvv32Wzz11FNex8THx+Puu+/G3XffjTvvvBNTp05FbW0thg0bhgsXLuDUqVM+rVS+2LlzJ/7+97/j1ltvBQCUlZVJAqe9MWDAAOzfvx8/+clPhG379+8X/p+WloaePXvizJkzQRVkdmfYsGHYtm2b12bzPXr0kATZNzY24uzZsx7j9uzZI1nrnj17vFqChg0bhrfeegsWiyVkK9W1116LDz74AH369JHNHhw0aBC0Wi1KS0sxYcIE2TlYNwDWHcAXVqsVBw4cwNixYwE4LIj19fUYOHCgsJ7i4mLk5uaGdD5iVCoVJk2ahEmTJuEPf/gDEhMT8cUXX+Dmm2/2e06+3tv4+Hj07NkTO3fulBy/c+dO4bwCYeDAgdi7d69k2549e4I4w9AgQdVFaWSCykqCiri64TguIm63K4HJZEJlZSVsNhsuXbqETz/9FIsXL8a0adMkN3sxS5cuRUZGBkaOHAmFQoGNGzciPT0diYmJmDBhAm644Qb8+Mc/xtKlS5Gbm4uTJ0+C4zhMnTo1oDX1798f//rXvzB69Gg0NjZi3rx5AVkRHn/8cTzyyCMYPXo0CgoK8N577+Ho0aPo27evMObFF1/E3LlzkZCQgKlTp8JkMuHAgQOoq6uTNI/3xfz58zF06FD84he/wM9//nNoNBp8+eWXmDlzJlJSUnDjjTfizTffxA9/+EMkJibihRdekLWAbdy4EaNHj8b48ePx9ttvY9++fVi7dq3sa/7qV7/CX//6V9xzzz2YP38+EhISsGfPHowdO1ZobeaPX/7yl1izZg1mzZqF3/zmN0hOTkZJSQneffddvPHGG4iLi8Ovf/1rPPXUU7Db7Rg/fjwaGhqwc+dOxMfHY/bs2cjOzgbHcfj4449x6623Qq/Xy7oQAYfF5vHHH8df/vIXqFQq/OpXv8J1110nCJEXXngB06ZNQ1ZWFu68804oFAp88803OHbsGP74xz8GdE4A8PHHH+PMmTO44YYbkJSUhE8++QR2ux0DBgwI6Jz8vbfz5s3DH/7wB/Tr1w8jRozA+vXrceTIEbz99tsBr3Hu3LkoLCzEa6+9httvvx1bt27tcHcfAMryu1JEOij9h3/dzmc/+zE/5o9FEZmPILoKXTnLDwAPgFepVHyPHj34SZMm8evWreNtNptkLERB6atXr+ZHjBjBx8bG8vHx8fxNN93EHzp0SBhbU1PD//SnP+WNRiOv0+n4IUOG8B9//DHP845gZfdA3E2bNvHiS/+hQ4f40aNH8zqdju/fvz+/ceNGj0Bv8XrEvPTSS3xKSgpvMBj4hx56iJ87dy5/3XXXSca8/fbb/IgRI3iNRsMnJSXxN9xwA//hhx8K+ydMmMDPnj3b53v31Vdf8QUFBbxWq+UTExP5KVOmCIHaDQ0N/N13383Hx8fzmZmZ/JtvvikblP63v/2Nv/nmm3mtVsv36dOHf++994T97kHpPM/z33zzDT958mQ+JiaGj4uL46+//nr+9OnTXtfoHpTO8zx/6tQp/o477uATExN5vV7P5+Xl8U8++aSQYGC32/lly5bxAwYM4NVqNd+jRw9+ypQp/P/+9z/Je5yens5zHOf1fWKf8wcffMD37duX12q1/KRJkzyy9D799FO+oKCA1+v1fHx8PD927Fh+9erVkvdJ7nMWs337dn7ChAl8UlISr9fr+WHDhkney0DOydd7a7PZ+AULFvC9evXi1Wo1P3z4cH7Lli3CsXKfVV1dHQ+A//LLL4Vta9eu5Xv37s3r9Xr+hz/8If/aa691eFA6tZ65QkS69cyEV7/E+ZpWxOtUOLogOu4OgogGV0Prma7KzTffjPT0dPzrX/8K+Jjs7Gy8+OKLXmssEf6hFkPhE4nWM13DTk544Mryo6B0giCuPK2trVi1ahWmTJkCpVKJd955B59//jmKiooCnuP48eNISEjw6u4kiK4ECaouiN3OCzFUZpsdNjsPpSL8TtkEQRCBwnEcPvnkE/zpT39Ce3s7BgwYgA8++ACTJk0KeI7Bgwfj6NGjHbhKgrhykKDqgrSYrbCLHLXtFhtitfRREgRx5dDr9fj888+jvQwCjlpl5DKNPtTLrwvC3H2MNiruSRAEQRBRhQRVF8RDUFEtKoIgCIKIKiSouiDugspEtaiIqxBKUCYIIlJE4npCgqoL0uhhoaJMP+LqgRVtdG+NQhAEESqtra0APFs1BQNFMndBKIaKuJpRqVSIiYlBVVUV1Go1FAr6XUgQRGjwPI/W1lZcvnwZiYmJQfWZdIcEVRfEXVC1k6AiriI4jkNGRgbOnj2L8+fPR3s5BEF0AxITE5Genh7WHCSouiBkoSKudjQaDfr3709uP4IgwkatVodlmWKQoOqCkIWKIACFQkGtZwiC6DRQ8EEU+eTbCnx+4lLQxzW0WSXPSVARBEEQRHQhQRUlGtstePydw/jlhkOw2ILL0vPM8iNBRRAEQRDRhARVlLjU0A6bnYfJakeLyer/ABHM5Revc3hs26hBMkEQBEFEFRJUUaK62RVM29QenKBiFqr0BEf8CLn8CIIgCCK6kKCKEtXNJuH/LebQLFRp8SSoCIIgCKIzQIIqStSIBFVzEBYqnuc9BBWVTSAIgiCI6EKCKkqIXX7NQcRQtZptsNodPYfSyUJFEARBEJ0CElRRoqZFZKEKQlAx65RaySExxtFziILSCYIgCCK6kKCKElVNLgtVMFl+TFAl6NWI0Tiz/KhsAkEQBEFEFRJUUUIclB5Mlp9QMkGvhk7t+PhMVhJUBEEQBBFNSFBFCbHLr8UUuCASW6j0akfvIbJQEQRBEER0IUEVJaqbxEHpFh8jpYgFlU7jFFQUlE4QBEEQUYUEVRRoNVslIqg5CAtVo1AlXQ2dyiGoKMuPIAiCIKILCaooILZOAcFl+TWKXX4aJqgoy48gCIIgogkJqihQLYqfAkLP8mMxVGShIgiCIIjoQoIqClQ3SQVVMJXSJTFUziw/iqEiCIIgiOhCgioK1LQ4XH4alePtD6WwpyTLz2IDz/MRXiVBEARBEIHSqQVVeXk57r//fhiNRuj1egwdOhQHDhyQHfvzn/8cHMdh2bJlku21tbW47777EB8fj8TERMyZMwfNzc2SMUePHsX1118PnU6HzMxMvPLKKx7zb9y4EXl5edDpdBg6dCg++eSTkM+LWaiyk2MAhCao4kVZfjwPmG0UR0UQBEEQ0aLTCqq6ujoUFhZCrVZjy5YtOHHiBJYsWYKkpCSPsZs2bcKePXvQs2dPj3333Xcfjh8/jqKiInz88cf4+uuv8bOf/UzY39jYiMmTJyM7OxsHDx7Eq6++igULFmD16tXCmF27dmHWrFmYM2cODh8+jBkzZmDGjBk4duxYSOfGLFTZxlgAoVuoWJYfALSbSVARBEEQRLRQRXsB3nj55ZeRmZmJ9evXC9tycnI8xpWXl+Pxxx/H1q1bcdttt0n2fffdd/j000+xf/9+jB49GgDw17/+Fbfeeitee+019OzZE2+//TbMZjPWrVsHjUaDwYMH48iRI1i6dKkgvJYvX46pU6di3rx5AICFCxeiqKgIK1aswKpVq4I+typnlfQ+xlAsVI6xCXo11EoOSgUHm51Hm8WGBKiDXgtBEARBEOHTaS1UH330EUaPHo2ZM2ciNTUVI0eOxJo1ayRj7HY7HnjgAcybNw+DBw/2mGP37t1ITEwUxBQATJo0CQqFAnv37hXG3HDDDdBoNMKYKVOmoLi4GHV1dcKYSZMmSeaeMmUKdu/e7XX9JpMJjY2NkgdDcPmlOCxUZqsdZqt/CxPP866yCTFqcBxHmX4EQRAE0QnotILqzJkzWLlyJfr374+tW7fisccew9y5c/HWW28JY15++WWoVCrMnTtXdo7KykqkpqZKtqlUKiQnJ6OyslIYk5aWJhnDnvsbw/bLsXjxYiQkJAiPzMxMYZ/g8nPGUAGBlU5ot9iFWKl4ncO4SJl+BEEQBBF9Oq3Lz263Y/To0Vi0aBEAYOTIkTh27BhWrVqF2bNn4+DBg1i+fDkOHToEjuOivFpP5s+fj6efflp43tjYKIgq1hg5PUEHnVqBdosdzSYrkmI1snMxWPyUUsHBoGWCitrPEARBEES06bQWqoyMDAwaNEiybeDAgSgtLQUAbN++HZcvX0ZWVhZUKhVUKhXOnz+PZ555Bn369AEApKen4/Lly5I5rFYramtrkZ6eLoy5dOmSZAx77m8M2y+HVqtFfHy85AEAFpsd9a0OYWSM1QjCKJA4qsZ21nZGJYhIcvkRBEEQRPTptIKqsLAQxcXFkm2nTp1CdnY2AOCBBx7A0aNHceTIEeHRs2dPzJs3D1u3bgUA5Ofno76+HgcPHhTm+OKLL2C32zFu3DhhzNdffw2LxdWguKioCAMGDBAyCvPz87Ft2zbJWoqKipCfnx/0edU53X0KDkiKCU5QiTP8GDoSVARBEAQRdTqty++pp55CQUEBFi1ahLvuugv79u3D6tWrhXIGRqMRRqNRcoxarUZ6ejoGDBgAwGHRmjp1Kh555BGsWrUKFosFv/rVr3DPPfcIJRbuvfdevPjii5gzZw6effZZHDt2DMuXL8frr78uzPvEE09gwoQJWLJkCW677Ta8++67OHDggKS0QqDUONvOJMdqoVBwMOiCEFStnoJKKO5JZRMIgiAIImp0WgvVmDFjsGnTJrzzzjsYMmQIFi5ciGXLluG+++4Lap63334beXl5uOmmm3Drrbdi/PjxEiGUkJCAzz77DGfPnsWoUaPwzDPP4IUXXpDUqiooKMCGDRuwevVqDB8+HO+//z42b96MIUOGBH1eNS0OUZRicMRLxWqcgiqA9jPiop4MnYYsVARBEAQRbTqthQoApk2bhmnTpgU8/ty5cx7bkpOTsWHDBp/HDRs2DNu3b/c5ZubMmZg5c2bAa/FGrTMgPcWgBQDEOS1UgWT5ybr8VJTlRxAEQRDRptNaqLorzOUnWKjCjKHSk4WKIAiCIKIOCaorjMvl57BQhRuUTll+BEEQBBF9SFBdYWqcLj+ju6AKIIaq0UeWH7n8CIIgCCJ6kKC6wrAq6czlxwRViznEoHTK8iMIgiCIqEOC6gpTKwgqh4WKxVA1BZHlJ+vys5KFiiAIgiCiBQmqK4x7lp8hiCw/Vild6vJzfITtZhJUBEEQBBEtSFBdYWqdQelGN5dfuFl+FENFEARBENGDBNUVxmrnAcgJKv+CiFrPEARBEETnhARVFIjTqaBVOYSQqw6VxdchMFltaLc4As9lg9JJUBEEQRBE1CBBFQV6OOOnAHGldN+CiFmnOA6I07oK3Au9/CyU5UcQBEEQ0YIEVRRg7j5AZKHyk+XHalDF69RQKDhhOxNUJrJQEQRBEETUIEEVBVJEFioWQ2W22WHyUfpALn4KcGX5kcuPIAiCIKIHCaooIBZUsc4sPcC328+7oKKgdIIgCIKINiSoooDY5adSKgS3nS+3n6tKukqyXSibQHWoCIIgCCJqkKCKAmILFSDO9PMhqFr9WagoKJ0gCIIgogUJqiiQIrJQAa5MP5+Cqs2xz11QMeuW2WaHzVnjiiAIgiCIKwsJqijgbqESGiT7EFSs7Uy8F0EFUBwVQRAEQUQLElRRwOjh8nOIoiafFip5l59W5foIKdOPIAiCIKIDCaoo4O7yM2gdIsmXhcqboFIoOEFUkYWKIAiCIKIDCaorjFqlEFx8DIM28Cw/d0EFuDL9SFARBEEQRHQgQXWFSYnVgOM4yTZDAEHpjT4ElU7FSidQph9BEARBRAMSVFeY5FiNx7aAyiYEYqHyUWmdIAiCIIiOgwTVFcZo8BRUcQFk+fkSVKwWFRX3JAiCIIjoQILqCmP0YaHyluVnsdnR6hRL8To5QUX9/AiCIAgimpCgusIkx2o9tvmrQ8WsU4BnHSrAVYuKgtIJgiAIIjqQoLrCyLn8mKDyluXHBFWcVgWlgvPYT4KKIAiCIKILCaorjJzLz1+WX2ObfJV0BsVQEQRBEER0IUF1hTHKuPz8Zfn5CkgHRA2SrVQ2gSAIgiCiAQmqK0yywVMU+cvy8yeo9BpnUDpZqAiCIAgiKpCgusKEYqHyVdQTcBX2pBgqgiAIgogOJKiuMIkx3mOoLDYeJpninP4tVCSoCIIgCCKakKC6wshl6cVqXL395DL9BEEV4ycoPQhB1W6xoay2NaCxF+payZ1IEARBED4gQdUJUCo4xDitTC0mT+FS3xpYUHqbJfCg9Bf/7wQmvPol9p+r9Tnu6IV6THj1K/z2w6MBz00QBEEQVxskqDoJBqFausVjX0VDOwAgLV4ne2wodaiOlNXDzgP/PVrhc9yWY5Ww2XnsO+tbeBEEQRDE1QwJqk6Cr+KepU7XXFZyjOyxLMsvGEFV3WwCAOwsqfY5ju2vbGyXje8iCIIgCIIEVaeBBaa3mKWCymqzo7y+DYB3QcWy/AKNc7LbedS2mAEA319uxuXGdtlx9a1mfFveAADgeaC8ri2g+QmCIAjiaoMEVSeBBaY3uVmoKhraYbPz0KgUSI3zLLkAADqW5RegBam+zQKbnRee7zwtb6Xac6YGvGuYYCkjCIIgCEIKCapOgmChcgtKZyImM0kPhUyGIOCKoQrUQlXjdPcxdnxfIztuh5s7MNCsQIIgCIK42ujUgqq8vBz3338/jEYj9Ho9hg4digMHDgj7FyxYgLy8PMTGxiIpKQmTJk3C3r17hf1fffUVOI6Tfezfvx8AcO7cOdn9e/bskaxl48aNyMvLg06nw9ChQ/HJJ59E9FyFGCq3oHQmYjK9uPsAUeuZALP8qtwE1a7T1eDFpii2vcQhtLKNjtcmCxVBEARByNNpBVVdXR0KCwuhVquxZcsWnDhxAkuWLEFSUpIw5pprrsGKFSvw7bffYseOHejTpw8mT56MqqoqAEBBQQEqKiokj4cffhg5OTkYPXq05PU+//xzybhRo0YJ+3bt2oVZs2Zhzpw5OHz4MGbMmIEZM2bg2LFjETtfl6CSt1B5i58Cgs/yq2l2xE8N750AjUqBioZ2nKlukYwpr2/DmeoWKDhg5qjeAICyWoqhIgiCIAg5VP6HRIeXX34ZmZmZWL9+vbAtJydHMubee++VPF+6dCnWrl2Lo0eP4qabboJGo0F6erqw32Kx4D//+Q8ef/xxcJzUfWY0GiVjxSxfvhxTp07FvHnzAAALFy5EUVERVqxYgVWrVoV1noxYL1l+wQiqQAt7sgy/Xkl6xGpV2HW6BrtKqtGvh0EYw7L7hvVOxOCeCZK1EARBEAQhpdNaqD766COMHj0aM2fORGpqKkaOHIk1a9Z4HW82m7F69WokJCRg+PDhXuesqanBT3/6U49906dPR2pqKsaPH4+PPvpIsm/37t2YNGmSZNuUKVOwe/fuEM5MnjidfIPkwFx+rrIJcq47d5iFKsWgRWFuCgDPeKldzufjc1OE1y6rbQ1ofoIgCIK42ui0gurMmTNYuXIl+vfvj61bt+Kxxx7D3Llz8dZbb0nGffzxxzAYDNDpdHj99ddRVFSElJQU2TnXrl2LKVOmoHfv3sI2g8GAJUuWYOPGjfjvf/+L8ePHY8aMGRJRVVlZibS0NMlcaWlpqKys9Lp+k8mExsZGycMXsc5MPfcGyYFYqFiWn50HzDb/cVTMQmWMdQmq3adrhMw/nuexwxk/VZibgt5JegBAk8kqVG0nCIIgCMJFp3X52e12jB49GosWLQIAjBw5EseOHcOqVaswe/ZsYdzEiRNx5MgRVFdXY82aNbjrrruwd+9epKamSua7cOECtm7din//+9+S7SkpKXj66aeF52PGjMHFixfx6quvYvr06SGvf/HixXjxxRcDHm/QOdrKiAVVU7sFdU4B48tCxVx+ANButkOrUnodCwDVzEIVp8HQXgmI06nQ2G7FsfIGDM9MxKlLzahuNkGnVuDa7ERoVUqkxWtxqdGE0tpWJMV6NngmCIIgiKuZTmuhysjIwKBBgyTbBg4ciNLSUsm22NhY5Obm4rrrrsPatWuhUqmwdu1aj/nWr18Po9EYkEgaN24cSkpKhOfp6em4dOmSZMylS5e8xlwBwPz589HQ0CA8ysrKfL6mQetpoWJB4MmxGiFoXQ61UiE0XQ6kFhWzUKUYtFAqOOT3NQJwuf1Y/NSYPsmCOGMWMoqjIgiCIAhPOq2gKiwsRHFxsWTbqVOnkJ2d7fM4u90Ok0laFoDneaxfvx4/+clPoFbLNxgWc+TIEWRkZAjP8/PzsW3bNsmYoqIi5Ofne51Dq9UiPj5e8vCFQetYlziGqjSA+ClGMLWoalqYoHJYmsb3d7j9dp2WCqrxuS7XqRBHVUeCiiAIgiDc6bQuv6eeegoFBQVYtGgR7rrrLuzbtw+rV6/G6tWrAQAtLS3405/+hOnTpyMjIwPV1dX429/+hvLycsycOVMy1xdffIGzZ8/i4Ycf9nidt956CxqNBiNHjgQAfPjhh1i3bh3eeOMNYcwTTzyBCRMmYMmSJbjtttvw7rvv4sCBA8JaIkGs00IlrpReFkD8FEOnVqLZZA0o06+6yRWUDgAF/RzCaf+5OjSbrNhzxhU/xcgSBaYTBEEQBCGl0wqqMWPGYNOmTZg/fz5eeukl5OTkYNmyZbjvvvsAAEqlEidPnsRbb72F6upqGI1GjBkzBtu3b8fgwYMlc61duxYFBQXIy8uTfa2FCxfi/PnzUKlUyMvLw3vvvYc777xT2F9QUIANGzbg+eefx3PPPYf+/ftj8+bNGDJkSMTON06ml58rIF3v93hxpp8vWkSiy+gUVP16xCI9XofKxnas23EWLWYbEmPUGJThsqplJpHLjyAIgiC80WkFFQBMmzYN06ZNk92n0+nw4YcfBjTPhg0bvO6bPXu2JMjdGzNnzvSwfEUScR0qnufBcZzgXgvEQhVoLSpWMkGnVgiZhRzHoTA3BR8cuoDVX58BABT2S5G0usmiaukEQRAE4ZVOG0N1tcGCzq12Hiaro/SBq49fAIJKE1i19CpRyQRxcdPCXEdgOguKL3A+ZzBRd7G+HZYASjMQBEEQxNUECapOQqzGZSxsNllht/O44MzyCyQoXacKrJ8fa4ycEqeVbBfHSwHSgHQA6GHQQqtSwGbnUVHf7nc9BEEQBHE1QYKqk6BQcIILrsVkxaWmdphtdqgUHDISdH6PZ8U9/WX5CTWo3GpJpcXr0D/V0Xqmd5Lew82oUHAhZfpdbmzH23vPB5R9SBAEQRBdFRJUnQiDMzC9qd2K0hqHaOmVpIdK6f9j0juD0v3HULlqULlzff8ezn9TPHodAkCms2J6MHFUSz47hd9tOoaNB33X4SIIgiCIrkynDkq/2nAEppvQYrKirM7h7gskIB1wlE0A/MdQCW1nDJ7Vzp+4qT+SYtS4d1yW7LGhFPf8/nITAKDkcnPAxxAEQRBEV4MEVScijmX6maxBFfUEXFl+fgVVi7QGlZiEGDUev6m/12MzQxBUpc44MMoOJAiCILoz5PLrRMSKBFVZEBl+gMtC5c/lV90kH5QeCMEW92w1WwWLGAkqgiAIojtDgqoTYZCxUAXv8vOT5dciH5QeCMFaqFgvQgC4UNsGu50P+jUJgiAIoitAgqoTYRAV9wxWUAVa2LPaS9mEQGCCqr7VgsZ2i9/xYuFlttlxqYnKLRAEQRDdExJUnQiW5VfdbEKV0zUXsKDSOFvP+ChPYLHZUd/qEELGECxUBq1KOC4Qt5/7GJa5SBAEQRDdDRJUnQgWQ3Wy0pEZF69TISFGHdCxgsvP6l1Q1TrdfQoOSIoJXlABLitVIILK3TXIMhcJgiAIortBgqoTwVx+31U0Agg8ww8QBaX7sFAxq1dyrFbSpy8YgimdwESXRqUI+BiCIAiC6IqQoOpEMEHFqpkH6u4DAouhEgLSZWpQBUpmcuDFPdmY0dlJAALPDiQIgiCIrgYJqk4EE1SMYARVIFl+QskEmRpUgeKyUPl23/E8L7SoYX0CyUJFEARBdFdIUHUiYt0EVTAuv0AKe9a0MEEVjoXKsaYLfsRRVbMJ7RY7FBxwXV8jABJUBEEQRPeFBFUnIk4XuoWKZfn5cvkxV6IxAhaqC3VtsPmoK8XcexkJeuT2cDRdrmoyUZNkgiAIoltCgqoT4W6hCkZQaVX+LVTVPhojB0pGgh4qBeeoK9Xova6Uq3WOHgkxakEsXqgjKxVBEATR/SBB1YkQx1BxHNAzUR/wsXqN/yw/ZqEKx+WnVHDoleQ/ML20RtrcOZTGygRBEATRVSBB1YkQC6qeCXqh3EAguOpQeQ9Kr4mAhQoITBy5V3onQUUQBEF0Z0hQdSIMohgqVp4gUFhQutlq9xrbFAmXn2Nt/gPTWYZfJgkqgiAI4iqABFUnIsYpioDg4qcAl6AC5OOoeJ5HjRCUHrrLT7w2X+KozM1C5aqwTtXSCYIgiO4HCapOhELBCW6/YAWVVuQelBNUDW0WWJ2Wq44WVO0WGyqdAeuZHoKKLFQEQRBE9yMigspisaCsrAzFxcWora2NxJRXLUxQBVODCnCIMSaq5EonsID0OJ1KyAgMlcwk38U9y+vbwPNAjEYpNFMWizCe915ugSAIgiC6IiELqqamJqxcuRITJkxAfHw8+vTpg4EDB6JHjx7Izs7GI488gv3790dyrVcFTHgMyogP+liW6SdnoWLxUz3CjJ8CgOyUGHCcY87LMqUTxAHpHOfoGdgrUQ+Oc4g9Ju4IgiAIorsQkqBaunQp+vTpg/Xr12PSpEnYvHkzjhw5glOnTmH37t34wx/+AKvVismTJ2Pq1Kn4/vvvI73ubsvf7rsWm35RgP5pcUEfq1N5bz/DBFW47j4AiNepMbinQ/DtOl3jsf9CrTQgHXA0SO6ZEHgfQIIgCILoSqj8D/Fk//79+PrrrzF48GDZ/WPHjsVDDz2EVatWYf369di+fTv69+8f1kKvFnrEadEjLjQrklCLSsZCVSPUoArfQgUAhf1ScKy8ETtLqjFjZC/JPveSCYzMZD3K69tQVtuKUc6GyQRBEATRHQhJUL3zzjsBjdNqtfj5z38eyksQIcBqUckV94ykhQpwNDz+x9dnsLOkGjzPC649QFQlPUla+iEzKQZ7UEuB6QRBEES3I+yg9Iceeghvvvmm8Pz8+fPYsmULGhoawp2aCBKd2vFxysdQRdZCNaZPMjRKBS42tONsdYtkHwtWzzJKLVRUi4ogCILoroQtqD755BPk5eUBAOrr6zFq1CjMmDEDgwYNQnFxcdgLJAKH1aKSz/KLTFFP4bU0SlybnQgA2CmKo+J53qMGFYMJLBJUBEEQRHcjbEHV0NCAXr0cMTQffPAB0tPT0djYiLvvvhvz588Pe4FE4DBBJWehcrWdiYzLDwDG56YAAHZ+Xy1sq2+1oNlkBQD0TnKPoaJaVARBEET3JGxBlZmZibNnzwIANm7ciAcffFCIndq5c2fYCyQCR+jnJ5vlF1mXHwAUOAXVrtPVQrsbZn1Ki9cK62Ewi1VFYztMVu9NnAmCIAiiqxG2oHrwwQcxd+5c/P73v8e2bdswY8YMAIDdbkdzc3O40xNBoPPh8qsRgtIjJ6iG9UpAnFaFxnYrjl90xMx5y/ADAGOsBjEaJXgeuFjvWb+KIAiCILoqYQuq+fPnY+bMmfj666/x5z//Gbm5uQAcpRWysrLCXiAROHqNs1K6W5Zfm9mGFue2SLr8VEoFxvU1AgB2lDjcfq4MP09BxXGcqMo6uf0IgiCI7kPYgorjOPzud7/D//73PzzzzDPC9srKStx7773hTk8EgVDY082dxgLSNSqF0NomUozPdQiqXSWOwPQymaKeYjIp048gCILohoR8d33hhRdw++23Y9SoUbL7582bF/KiiNAQWs+Y5QVVD4NWUi8qEozv74ij2neuFu0WG8rqvLv8xNspMJ0gCILoToRsobpw4QJuueUW9O7dG4899hi2bNkCs5l6tEUTbzFUrEp6pIp6iunXw4DUOC3MVjsOnq9zxVAZvQkqZ/uZGhJUBEEQRPchZEG1bt06VFZW4p133kFcXByefPJJpKSk4Mc//jH++c9/ora2NpLrJALAW5ZfpGtQieE4Tiif8L9TVUKwuVcLlVNoMUsWQRAEQXQHwoqhUigUuP766/HKK6+guLgYe/fuxbhx4/CPf/wDPXv2xA033IDXXnsN5eXlkVov4QNvhT2FtjOxkbdQAa7yCR8eKofNzkOjUqCHF/EmBKXXtILn+Q5ZD0EQBEFcacIOShczcOBA/OY3v8HOnTtRVlaG2bNnY/v27QH3/iPCg2X5uRf2FGpQhdh02R+FzsB0Jtwyk/RQKORjtVixzyaTFQ1tlg5ZD0EQBEFcaSIqqMT06NEDc+bMwX/+8x/8+te/DmmO8vJy3H///TAajdDr9Rg6dCgOHDgg7F+wYAHy8vIQGxuLpKQkTJo0CXv37pXM0adPH3AcJ3n8+c9/low5evQorr/+euh0OmRmZuKVV17xWMvGjRuRl5cHnU6HoUOH4pNPPgnpnDoSIcvPi4WqI1x+AJCRoEe/HrHCc2/uPsAROJ/qFHaU6UcQBEF0F4IWVHV1dUJ8VFVVFT788EMcP3484gurq6tDYWEh1Go1tmzZghMnTmDJkiVISkoSxlxzzTVYsWIFvv32W+zYsQN9+vTB5MmTUVVVJZnrpZdeQkVFhfB4/PHHhX2NjY2YPHkysrOzcfDgQbz66qtYsGABVq9eLYzZtWsXZs2ahTlz5uDw4cOYMWMGZsyYgWPHjkX8vMNBp/EdlB7JGlTuFDrdfoBvQSXeT4KKIAiC6C4EJajeeOMNjBo1CqNHj8bKlStxxx13YNu2bbjnnnvwxhtvRHRhL7/8MjIzM7F+/XqMHTsWOTk5mDx5Mvr16yeMuffeezFp0iT07dsXgwcPxtKlS9HY2IijR49K5oqLi0N6errwiI11WVPefvttmM1mrFu3DoMHD8Y999yDuXPnYunSpcKY5cuXY+rUqZg3bx4GDhyIhQsX4tprr8WKFSsies7hIsRQeSmb0FEWKkAqqLzVoGK4Sie0ddh6CIIgCOJKElQdqr/85S84fvw42trakJWVhbNnz6JHjx5oaGjAhAkT8PDDD0dsYR999BGmTJmCmTNn4n//+x969eqFX/ziF3jkkUdkx5vNZqxevRoJCQkYPny4ZN+f//xnLFy4EFlZWbj33nvx1FNPQaVynPru3btxww03QKNxWW+mTJmCl19+GXV1dUhKSsLu3bvx9NNPS+acMmUKNm/e7HX9JpMJJpNJeN7Y2BjsWxA0LMvvdFULBjy/xbUWqyPrryPKJjCu62uEggPsvH9B1du5/7XPirHs81PCdo4D7hmThQXTB/s8/q1d5/DOvlKse3AMeibqvY6rbGjHT9/cjx9f2wsPX983iLPxzqlLTfj5/zuIJyddg+nDe0ZkToIgCMLBks+K8cb2s7C7JS3F69VY85PRGJGZGJ2FBUBQFiqVSgW9Xo/k5GTk5uaiR48eAICEhISIF4w8c+YMVq5cif79+2Pr1q147LHHMHfuXLz11luScR9//DEMBgN0Oh1ef/11FBUVISXFZS2ZO3cu3n33XXz55Zd49NFHsWjRIvzmN78R9ldWViItLU0yJ3teWVnpcwzbL8fixYuRkJAgPDIzM0N7I4Kgb49YJDsz+UxWu/AAgIwEHfoYY30dHhYJejVuG9YTiTFqjMpO8jm2sJ8RSgUHm52XrLPdYse/D5T5zf57Z18pTlY2YdNh39mjm4+U47uKRryzrzTo8/HG23vO40xVCzYeKIvYnARBEISDTYfL0WaxSe4NJqsdVU0mfFV8OdrL80lQFiqlUon29nbodDr873//E7Z3RBNku92O0aNHY9GiRQCAkSNH4tixY1i1ahVmz54tjJs4cSKOHDmC6upqrFmzBnfddRf27t2L1NRUAJBYloYNGwaNRoNHH30UixcvhlbbcS6w+fPnS167sbGxw0VVvE6NXb+9ETUtngVWUwwaaJ1B6x3F8rtHgOPgV1yP62vEod/fjGaTVdhmtdnxg9e+QqvZhtoWs9cmzjzPC1XWd3xfjV9OzPX6Ojud/QUv1LXBbue9Zh4Gww7RnARBEERkYdnf7zxynVC38B//O41/7j7f6TPDg7JQff7559BoNPjNb34jESOtra2SIO5IkJGRgUGDBkm2DRw4EKWlUmtDbGwscnNzcd1112Ht2rVQqVRYu3at13nHjRsHq9WKc+fOAQDS09Nx6dIlyRj2PD093ecYtl8OrVaL+Ph4yeNKoFMr0StR7/HoaDEFAAoFF7ClMkGvlqwv2xiL9HgdAN/B6rUtZqHR88HzdR7xYox2iw37zjqSJ0xWO6qaTbLjgqGyoR2nq1oAABfqWmGzUx0tgiCISGGz82hqd/zQzk01CPcHFtrRrQRVQkICFAoFli1bhoaGBgDAgw8+CIPBgDFjxkR0YYWFhSguLpZsO3XqFLKzs30eZ7fbJbFL7hw5cgQKhUKwYOXn5+Prr7+GxeL6oIqKijBgwAAhozA/Px/btm2TzFNUVIT8/PygzonwTSCNk8X7zDY7DpyXr8h/qLROcHf6mzNQmMULACw2HpWN7WHPSRAEQThoanfdhxP0ao//N3YnQcXo2bMnjhw5AgD417/+1SEuv6eeegp79uzBokWLUFJSgg0bNmD16tX45S9/CQBoaWnBc889hz179uD8+fM4ePAgHnroIZSXl2PmzJkAHAHny5YtwzfffIMzZ87g7bffxlNPPYX7779fEEv33nsvNBoN5syZg+PHj+O9997D8uXLJe66J554Ap9++imWLFmCkydPYsGCBThw4AB+9atfRfy8r2YCaZzsLox2iESOmJ1u2yPRO7Aj5iQIgiAcMAuUXq2ERuWSJ0xQdSsLFeOZZ57BD3/4Q1x//fUAHKUH9u3bh7a2yMWVjBkzBps2bcI777yDIUOGYOHChVi2bBnuu+8+AI54rpMnT+LHP/4xrrnmGvzwhz9ETU0Ntm/fjsGDHVliWq0W7777LiZMmIDBgwfjT3/6E5566imJezIhIQGfffYZzp49i1GjRuGZZ57BCy+8gJ/97GfCmIKCAkHQDR8+HO+//z42b96MIUOGROx8icDqUzGxFad1hP+5ixzGzpIaybhwLVQ8zwvijc3pS/gRBEEQwcEEk9g6JX7e2QVVUEHpjMcffxwTJkzA//3f/2Hnzp3429/+hnnz5oHjOOTm5mL48OEYMWIEhg8fjltuuSXkxU2bNg3Tpk2T3afT6fDhhx/6PP7aa6/Fnj17/L7OsGHDsH37dp9jZs6cKVi+iI4hM9nhJ/ctqByi/faRPfH/9pTi+MVG1LWYkSTqU9jQZsHRC/WSceE2Yz5d1YzLTSZoVQpMGZKO9w9eoAbPBEEQEaSrC6qQW88MGzYMv/vd79CvXz/s2bMHTU1N2LFjB5588kkkJSXhP//5D+66665IrpXo5gRS8JOJrVHZSbgmzQCeB3afqZGM2XumBnYe6JsSi3E5Ruec4YmfHd87rFNj+iSjf6pBshaCIAgifLq6oArJQiXm+++/F/4/btw4jBs3Tnjur54QQYhhQekVDW0wW+0SHzqDiZis5BgU5qbg1KVm7Cipxq1DM4QxzA1YmJsSsTY3O0/XRHxOgiAIwkVjmyPDL95NULHn7RY7TFbbFclaD4WQLFTupQu8wVLoy8t9F2AkCADoYdBCp1bAzgMX6z2tVGarHRUNju2ZyTEo7Oco4LrLLY5qhyCojIL4udRo8mgaHShWmx17BEFlFIQfxVARBEFEDm8WqjitCqwiDxNdnZGQBNWYMWPw6KOPYv/+/V7HNDQ0YM2aNRgyZAg++OCDkBdIXD1wHOfT+nOxvg12HtCpFehh0GJc32QoFRzO1bTigjOeidWK4jggv28KEmPUMDiDyC+EGPN0tLwBTSYrEvRqDO6ZIAiq6mYzWkyd94+bIAiiK+FNUCkUHOJ1nd/tF5LL78SJE/jTn/6Em2++GTqdDqNGjULPnj2h0+lQV1eHEydO4Pjx47j22mvxyiuv4NZbb430uoluSmZSDE5dapYVVCwIPDMpBhzHIU6nxojMRBw8X4ddJTW4a0yM4O4b1isBCTGOP8DM5Bh8V9GIsto25KbGBb0mZgErcLbMSdCrkaBXo6HNggt1bRiQHvycBEEQhBRvgopta2izdGpBFZKFymg0YunSpaioqMCKFSvQv39/VFdXC/FU9913Hw4ePIjdu3eTmCKCwpc7TRw/xSjs5wg6Z26+naed4ifX1c8xK4DsQV+wuaVzUhwVQRBEJGGFO+P1nrYetq0zF/cMKyhdr9fjzjvvxJ133hmp9RBXOUKmn4x7jomXTLGgyk3BX74owa7T1bDbecFCNT5C4qfNbMOh8/Wyc35b3kCCiiAIIkL4s1CJx3RGQi6bQBAdgS/xUyZjoRqZlQS9WonqZjO2HKvEpUYTNCoFRmUnBTSnP/afq4XZZkfPBB36GF2vS4HpBEEQkYUEFUFEENZdXK6ti5yFSqNSYGxOMgBgyWeO3o9j+iRBp3al1fYOQ/yISzCIGz8HUoSUIAiCCBwSVAQRQTKTHOKnsd2KhlbpHw4r+Cm2UAEuV9yZ6hYADvEjRtwjMNjaaCwma3x/73MSBEEQ4eNLUMWToCKI4NBrlEgxaAFIrT8Nra7sDmYdYhTkGiXPWX0qRq9EPTgOaDHbUNtiDngttS1mHL/YCADI7yd9DbEbkQrYEgRBhIfdzqOxvWtbqMKulE4QkSYrWY/qZhNKa1sxtHcCAFeQeopBixiN9Gs7MD0eybEa1LaYEa9TYUivBMl+nVqJ9HgdKhraUVrbCqNTsIlpt9jw/aVm8HCJo31na8HzwIC0OKTG6STjeybqoeAAk9WOqiYTUuN17lPKYrXZUdtiDmj85cZ2GA1aKBWc37EEQXQfeJ5HVbMJPQxaSahBV+ZyUzuSYjRQK+XtOM1mK9hvU/dK6cCVEVQ8z+NMdYtHfcHmpsaAjo+IoNq+fTv+8Y9/4PTp03j//ffRq1cv/Otf/0JOTg7Gjx8fiZcgriKykmNwqLReYqFylUzQe4xXKDjk9zPiv0crkO+sFeVOZnKMIKhGZiV57J+9bh/2nq2VXY+7BQwA1EoFeibqcaGuDaW1rQELqmc/+BYfHLqAjx8f7yH8xBwrb8C0v+7AnaN647WZwwOamyCI7sGnxyrx2NuH8PtpgzBnfE60lxM2JZebMfn1/+GWIRn4233Xyo5hIR5alUISA8tggqojyya8f/AC5r1/1GO73RRYaEfYLr8PPvgAU6ZMgV6vx+HDh2EymQA4KqUvWrQo3OmJqxC50glyNajEPHpDX4zITMSjE/r5nPNCnWdLm+pmkyCmeiboJI+89DjMGpvlc85gAtP3n3O8zqHSOp/jDp537P/2QkPAcxME0T1gte82Hb4Q5ZVEhkOldbDzwNHyeq9jfMVPibd3pIXqwDnHdTdOp5LcB9LjPb0acoRtofrjH/+IVatW4Sc/+QneffddYXthYSH++Mc/hjs9cRUiV5JALsNPzLDeidj8y0LvcyZ5zx7c5ezTNzAjHlueuD7wdSbFAKgRguX9YbXZhR6F/oLZ2f6aFlPA6yEIontQ5vzhd/xiI2pbzEiO1UR5ReFxgV3Pmr3HsDYGKKg60kLFfsS/OH0wfnRtb9faGhuR8JL/48O2UBUXF+OGG27w2J6QkID6+vpwpyeuQjJlLD9lfgSVP7KM3ssc7PyeFQP1dO35njM4C1VFQzusdj6gY9j+2hYzbHYKeieIqwl2veN5YLfzB19Xhl3PWs02tJrl+592BguVP0+IP8IWVOnp6SgpKfHYvmPHDvTt2zfc6YmrEPZlLq9rg9VmByBf1DOUOd2FDM/zsq1lAiHY4p5Si5tvqxZbp50H6loDz0wkCKJrY7PzkkburHRLV0Z83fVmpWoQ2s7ICyrWHLnFbIPFeV+IJBaRByFqguqRRx7BE088gb1794LjOFy8eBFvv/02fv3rX+Oxxx4Ld3riKiQtXgeNUgGrnUdFQ7vzAhPeF52Jn4qGNskfY2ltK8rr26BWchjbJzmoOYONoXK3uHkrt8DzvER8VTeT248grhYqG9thsbmuDay4cFdG/AOyysv1zJ+FSiy0OsLtd7G+DXbeERTfIy6wmCl3wo6h+u1vfwu73Y6bbroJra2tuOGGG6DVavHrX/8ajz/+eLjTE1chSgWH3kl6nKluQVltKzgOsNp5aJQKpAWYTedOD4MWOrUC7RbHr5BsYywAV/DnyKwkxGqD+3NggqqysR3tFptsZooYsaBqNllR12qRjY2obTGjxWwTnvuKOyAIonvBfkylxmlR22LG+ZpWlNW2hhzuEG1azVbJj0J/Fipvgkqp4BCnVaHJZEVDm0W2/E04iN19oZaqCNtCxXEcfve736G2thbHjh3Dnj17UFVVhYULF4Y7NXEVkynK9GNf9N5J+pBrMnEcJ2tR2lXiiE9wLwYaCEkxahicIqy83n9geplbhqE3V6H7OLJQEcTVA7s+DUiPw/DMRADAri7s9nPPrPZ2PfPn8hPv64g4KpZcFI5wjVildI1Gg0GDBmHs2LEwGAyRmpa4ShH3yrvg/KL3DvMXmpDpx+KT7LyotUxwAemAQ6T1Tgq8p5/7GG/HuG+vaiJBRRBXC+J4UdZGa0dJ1w1Md8+srgnR5Sfe1xGCKtyAdCACLr+nn35adjvHcdDpdMjNzcXtt9+O5OTg4lOIqxuXNakNHDjnNs+insHgnj14oqIR9a0WxGqUGNY7MeR1nqxsCigwnY3JS4/Dycomr4LKfa6aINrlEATRtRHf2EdmJeEv277HrpJq2O08FF2wa4L7da7ai8uvsd2R/RctQRVuJjkQAUF1+PBhHDp0CDabDQMGDAAAnDp1CkqlEnl5efj73/+OZ555Bjt27MCgQYPCfTniKkHsnuPctoU7J/vDYcGe1/U1em2HEPA6ZepbiWlqtwh9BAtzU3yKMDaXXq1Em8WGarJQEcRVg7jm3ojMROjVStS0mFF8qQkDM+KjvLrgYefDrmehBqWL9zHxFUkiYaEK2+V3++23Y9KkSbh48SIOHjyIgwcP4sKFC7j55psxa9YslJeX44YbbsBTTz0V7ksRVxHikgSR+KKLj2e+chaQXhhkuQTJnAHWomKvmRyrwSDnRdGfy2+Ys48hWagI4uqBXSuykmOgUSkwrq/Du9NVs/3K3K9nXgSVv8Ke4n0dkeXXKQTVq6++ioULFyI+3qWcExISsGDBArzyyiuIiYnBCy+8gIMHD4b7UsRVBBNUtS1mfH+pSbItVMTix2S1CW1gwhFUruD5wOpKZSbHCOsQt9YRw7aznoMUlE4QVwfijDh2bWEJM11WUHlcz0LL8gOAhJiOcfk1tFmEOVlcbCiELagaGhpw+fJlj+1VVVVobHR0aE5MTITZTL+yicCJ16mRFOMq5AaEL6jYH0pDmwVfnqxCu8WOFIMW16SFnkTBAt191ZUCIBTqy0zSC7+ALta3exSoExeXuzYrEQCVTSCIqwVmnUrQqwVhwX7w7T1bC7M18gUtOxKe54UfkyOF65nnD0Se54MLSm+NrKBiVrQUgybo8jliIuLye+ihh7Bp0yZcuHABFy5cwKZNmzBnzhzMmDEDALBv3z5cc8014b4UcZUhFlCJMWqhUm6oxGhUSHHWLnl3fykAoDDXGHLNEcAl0lhdKW+Izck9DFpoVArY7Dwq6tsl48TF5Vi8RFWzyadYIwiieyDndspLj4MxVoNWsw3fXKiP0spCo6rZhHaLHQoOGO5M/KlrtXj8kGwx24QWW/F674ImXufYF2kLVSQC0oEICKp//OMfuOmmm3DPPfcgOzsb2dnZuOeee3DTTTdh5cqVAIC8vDy88cYb4b4UcZUh/nKHGz/lmschgP53qgpAeO4+ANCplUh3Fhv1FUclvlAqFBwyvZRbELsGWbVes9WOZlPkgzAJguhcyAkqhYJDfj9HWZcd33cttx8TKhkJevSI04IlKda5xYUygaRWctD7KJDcUXWoIhWnG7agMhgMWLNmDWpqanD48GEcPnwYNTU1WL16tVCPasSIERgxYkS4L0VcZYi/3JGqEszmZAafcAWVeM5ABZWvY8TjdGqlUDjUW9wBQRDdByZAeruViBmf2zXjqMTXM6WCQ3Ks40eie6Yfc+El6NU+PQYdVTYhUoIq7LIJjNLSUly8eBFmsxnnzp0Ttk+fPj1SL0FcZWR1iIXKNU9OSix6JYZX2wpwiL1952q9lkGw23mhOGmmm6ByD0wXZ/gAgNGgQbPJippmE3JSYsNeK0EQnRdvTeDZD78jZfVoNlmFH1qdHffrWYpBg+pmk0dcaCBV0oGOF1Th/nAP+1M5c+YM7rjjDnz77bfgOE6I9WAq02az+TqcILzSEYJK/AdTmBt8dXQ53OtbuXOpqR1mmx1KBYeMBJ1kHe4WKndffopBi/M1rZTpRxBXAd4sJZnJMchKjkFpbSv2na3BjXlp0Vhe0LiEiuOHqyOGtcnjehZIQLp4f6TLJrD2OCzJKFTCFlRPPPEEcnJysG3bNuTk5GDv3r2ora3FM888g9deey3c6YmrGPGXO9wvujCP6EI1PgLuPsecvtvPsF9pvRL1UDkLiHoTYe4XVKOzeXIVufwIosvz/aUm1LVaMDbHs3OIOCNO7gdkYW4KSveV4s1d53G2WnrdGJGZgFHZna8bibvlx2hwXM/cLVSN7cEJqiaTFTY7H3JvVzE2Oy9kYbOSNqEStqDavXs3vvjiC6SkpEChUECpVGL8+PFYvHgx5s6di8OHD4f7EsRVSkaiDhqlAmabHX1SIiOomNtMwTkqpEeCbOcfYcnlZvA87xEDIHeR9Gah8vhF5wxM91YMjyCIroHNzuO+N/aipsWMz5+e4OHCr2oywWR1ZMT1lAlFGJ+bgnf2leLrU1X42plUw9CqFNj3u0l+BcmVxt2FybKs3S1UgRT1BKQuwcY2C5KcPzjDobKxHRYbD7WSExKMQiVsQWWz2RAXFwcASElJwcWLFzFgwABkZ2ejuLg43OmJqxi1UoFXZw5DdbMZvSNkoUqL1+HlHw9FjEaFxJjw/xgBYFBGAjRKBS43mXC2ugV9e0jrWsn559n/61staGy3IF6nlhSXYxa5FOcFg1x+BNG1OX6xAZedbaS+PlXlIahYPGXPRL1sK6zJg9Pw6IS+qGyQllrZ/n01alvM2HumBpMHp3fQ6oPHZLWhstGx1iw3C5VHUHqAgkqtVCBWo0SL2YbG9sgIKtbqq3dSTNgWr7AF1ZAhQ/DNN98gJycH48aNwyuvvAKNRoPVq1ejb9++4U5PXOXcPqJXxOe8e0xWROfTa5QYlZ2E3WdqsLOk2kNQyQWaGrQqGGM1qGkxo6y2FYN7JsgWl3NZqMjlRxBdmR2iDL0dJdWYXdBHsl/44eXlx6NaqcD8WwZ6bH9+87f4f3tKsbOkulMJqvK6NvA8EKtRItkpfJiFyltQeiAWtgS9Gi1mW8QC0yNVgwqIQNmE559/Hna7o0jXSy+9hLNnz+L666/HJ598gr/85S9hL5AgugIswH1nSY3HPl+BpoDrD1ruD9sYK28iJwiia7FLdG3Yc7oGVrfilqU10oy4QBFKKpz2vPZEE7FlnoVBpBjkLe7BCKpI16IqE3WxCJewBdWUKVPwox/9CACQm5uLkydPorq6GpcvX8aNN94Y9gIJoivA0pp3na4WKv4yytziohjutajkhFeKlyBOgiC6Du0WG/Y5e4eqlRyaTFZ8W94gGSP8/QcZGH1dXyM4zhHD6e4OjCZyPxD9Waj8lU0AIl86IVI1qIAICCo5kpOTw2rnQRBdjaG9EhCnVaGx3YrjF10XyjazTYibcP+DDURQGQ3yhfAIgug6HDxfB7PVjrR4LW7MSwXgWaQzVNdTYowGQ3slyM4ZTXxdz2papO20BEEVQHuxSFuoOp2g2rZtG5577jk8/PDDeOihhySPcCgvL8f9998Po9EIvV6PoUOH4sCBA8L+BQsWIC8vD7GxsUhKSsKkSZOwd+9eYf+5c+cwZ84c5OTkQK/Xo1+/fvjDH/4gadR87tw5cBzn8dizZ49kLRs3bkReXh50Oh2GDh2KTz75JKxzI7oXKqUC17H2EKKLGkvHjdOpPMzZLkHV5vzX84Law3kBamq3wmSlmm4E0RVhQqcwN0Vw0e1wF1R1od/YCwW3XycXVM5YKouNR2Obq51WsDFU4mPCpVPFUL344ouYPHkytm3bhurqatTV1UkeoVJXV4fCwkKo1Wps2bIFJ06cwJIlS5CUlCSMueaaa7BixQp8++232LFjB/r06YPJkyejqsqRUnry5EnY7Xb84x//wPHjx/H6669j1apVeO655zxe7/PPP0dFRYXwGDVqlLBv165dmDVrFubMmYPDhw9jxowZmDFjBo4dOxby+RHdD7n2EOKLirvVlrWXuOAcI1dcLl6vglrpOI7cfgTRNWHXhPG5KYL4OXS+Hm1mx4+kdotnRlwwFPZzXXs6SyN19yrpgKP3aZyzwbHY6h5o2QTxmEgIqhaTVWjrFW4NKiACWX6rVq3Cm2++iQceeCDsxYh5+eWXkZmZifXr1wvbcnJyJGPuvfdeyfOlS5di7dq1OHr0KG666SZMnToVU6dOFfb37dsXxcXFWLlypUfRUaPRiPR0+QyJ5cuXY+rUqZg3bx4AYOHChSgqKsKKFSuwatWqsM6T6D6wwPT95+rQbrFBp1b6NCezbRfq2mCx2WWLy3EcB2OsFpWN7ahuNsnWpyEIovPS0GrBUWe8VGFuClLjtMhI0KGioR37z9Xihmt6oLzelRGXFBN8LanRfZKgUSlwqdGE01UtyE01+D+oA+F53qvlJ8WgRVO7o51WbqoBPM+7LFQBnHskq6WzH7EJenVA7kZ/hG2hMpvNKCgoCHsh7nz00UcYPXo0Zs6cidTUVIwcORJr1qzxuY7Vq1cjISEBw4cP9zquoaEBycmeFWWnT5+O1NRUjB8/Hh999JFk3+7duzFp0iTJtilTpmD37t1eX8dkMqGxsVHyILo3/XoYkBavhdlqx8HzDuusrx5RGQl6qBQczDY7vimr91pcLiWOAtMJoquy+0wNeB7ITTUgLV4HjuNcLjqn5UouIy4YdGolRmcnSeaMJvWtFjSZHC693m7Zc65MP8f1rN1ih8XmsKpdaQtVJOOngAgIqocffhgbNmyIxFoknDlzBitXrkT//v2xdetWPPbYY5g7dy7eeustybiPP/4YBoMBOp0Or7/+OoqKipCSIt9SpKSkBH/961/x6KOPCtsMBgOWLFmCjRs34r///S/Gjx+PGTNmSERVZWUl0tKkvZPS0tJQWVnpdf2LFy9GQkKC8MjMzAzlbSC6EHIXyjK3pshilApOuNiweAq54nJGLx3aCYLo/AjxU/1cnRnGu8U8eWuKHAyFXmKzogETKmnxWujUSsk+dj2raXFcz5gwUio4xGqkY+XozIIqJJff008/Lfzfbrdj9erV+PzzzzFs2DCo1VKFuXTp0pAWZrfbMXr0aCxatAgAMHLkSBw7dgyrVq3C7NmzhXETJ07EkSNHUF1djTVr1uCuu+7C3r17kZqaKpmvvLwcU6dOxcyZM/HII48I21NSUiTnM2bMGFy8eBGvvvoqpk+fHtLaAWD+/PmSeRsbG0lUXQUU9kvBh4fKRYLK9x9sZnIMztW0CuPlhJe3VGOCIDo/4oB0RoFTXB2/2IhaZ3FfILwb+/jcFLy6tRh7zjhqXKlkqq1fKXwJFWZxr26SCqoEvTog65zL5Wf1M9I/kQxIB0IUVO79+UaMGAEAHkHa4ZROyMjIwKBBgyTbBg4ciA8++ECyLTY2Frm5ucjNzcV1112H/v37Y+3atZg/f74w5uLFi5g4cSIKCgqwevVqv689btw4FBUVCc/T09Nx6dIlyZhLly55jbkCAK1WC61W6/e1iO4Fu2geLW9AfavZ7y8gtv1wab3zuWeMlLdieARBdG4u1rfhTHULFBwwTtQ7NDVeh2vSDDh1qRm7T9eEXINKzJBeCYjXOUq3fFvegJFZSf4P6iCEYpky1z2Xxd3xAzGYDD8gsmUTOoWF6ssvv4zIi/uisLDQoxfgqVOnkJ2d7fM4u90Ok8l14ykvL8fEiRMxatQorF+/HgqFf9V+5MgRZGRkCM/z8/Oxbds2PPnkk8K2oqIi5OfnB3g2xNVCeoIOuakGlFxuxsdHK9BmsYHjgF5egsnZBcfqLAYq13bCZaEiQUUQXQlmnRrWO9FDMBTmpuDUpWbsPF0tlE7x1nYmEJQKDvn9jNh6/BJ2na6JrqDyaaGSXs+CKeoJdEOX35XgqaeeQkFBARYtWoS77roL+/btw+rVqwULU0tLC/70pz9h+vTpyMjIQHV1Nf72t7+hvLwcM2fOBOAQUz/4wQ+QnZ2N1157TSinAECwLr311lvQaDQYOXIkAODDDz/EunXr8MYbbwhjn3jiCUyYMAFLlizBbbfdhnfffRcHDhwIyNpFXH0U9jOi5HIz3tlXCgDIiNdBo5IX8t6KfYoxugVxEgTRNRCXS3CnsF8K1u88hx3O5sZA+K6nwtwUbD1+CTu+r8YvJ+aGNVc4+OpL6N7wPVgLleDya7fAbuehCLGhsTQTMTLZ02ELqsWLFyMtLc2jiOe6detQVVWFZ599NqR5x4wZg02bNmH+/Pl46aWXkJOTg2XLluG+++4DACiVSpw8eRJvvfUWqqurYTQaMWbMGGzfvh2DBw8G4LAilZSUoKSkBL1795bML67VsXDhQpw/fx4qlQp5eXl47733cOeddwr7CwoKsGHDBjz//PN47rnn0L9/f2zevBlDhgwJ6dyI7k1hbgre2n0exy86Mjt9XSS99fcTwyxU5PIjiK4Dz/NCf72CXKPH/nF9k6FUcIL4ADwz4oKFhRwcPF+HNrMN+gCCvDsCXy5MwULVInX5xesCkyPxesc4ngeaTNaAhZg7VU0mmKx2KDhErBxN2ILqH//4h2yW3+DBg3HPPfeELKgAYNq0aZg2bZrsPp1Ohw8//NDn8Q8++CAefPBBn2Nmz54tCXL3xsyZMwXLF0H44rp+Rig4gLX082VOdhdQchcgslARRNfj+8vNqGoyQadW4FoZ91ucTo0RmYlCiZX0eJ1HRlyw9E2JFWpcHThfi+v79whrvlCw2uy4WO+9SCmrli4XlB4IWpUSOrUC7RY7GtssIQsqJvp6JuqhjlAAf9izVFZWSuKNGD169EBFRUW40xNElyNep8aw3onCc1+CKkGvFi4IiTHyxeVY+5naFhPs9s5RBZkgCN/s+N7h7hvTJ9mrUBKXUohEHA/HcSjoF93yCRUN7bDZeWhVCuHaJYZZqFrMNrSZbUFVSWdEIo4q0vFTQAQEVWZmJnbu3OmxfefOnejZs2e40xNEl0QcM+Evc4f9QXv7w05y/qKz80BdK1mpCKIrsOu0Z7kEd8T7IpW6P76/Q6TtKqmJyHzBIi5SKhffFKdVQeO0CFU3m4K2UInHdjZBFbbL75FHHsGTTz4Ji8WCG2+8EYCjWfJvfvMbPPPMM2EvkCC6IgW5Rqz4sgSAo1inLzKT9fi2vMFrho9aqUBSjBp1rRZUN5uFju2+KK5swsYDZXhiUn/EhdBS4d19pWhos+DRCf2CPrbNbMOrW4sxdUg6xuZ4diXwx7HyBrx/8AKeuKm/ICaJ6HC5qR0rvijBA9dlo39aXLSXEzZWmx2vbi1Gfj8jfjAg1efYXSXV2Hu2FnNv6u9RbNcfFpsde87UAnD12ZNjZFYS9Gol2iy2iAVGMwvVsYsNqGsxB/03xPM8ln3+PU5davLYNyA9Dk/c1N9nSSRXQLr8+XAchxSDBhcb2lHTYo6ohYrneSz57BROVzX7nSOQGNdgCVtQzZs3DzU1NfjFL34Bs9nx61mn0+HZZ5+V1IIiiKuJUdlJSIpRo91iR78esT7HDkyPxyffViIv3fsNK8WgRV2rxZlq7P/GtuzzU9hyrBJxOjWemNQ/qLXXtpgxf9O34Hngx6N6C0HxgbLt5CWs23kW31U04p2fXRfUsQCwfNv3KDpxCbmpBtx/ne8yKUTH8t6+Mvxz93mYrXb8+cfDor2csNl7thb/+PoMviqu8iuoXvr4BE5WNqF/mgHThgXnbTl6oR7NJisSY9QY1DPe6ziNSoEJ1/TAp8crMaRnQlCv4Y20eB36pxrw/eVm7D5Tg1uHeobk+OJkZROWb/tedt+WY5X4wYBUjMhM9H58hUOoZBu9X/dS4rS42NCO6qbIWqj2na0VfsgGiq/rbrCELag4jsPLL7+M3//+9/juu++g1+vRv39/KmpJXNVoVUps/Hk+2i12JMb4/oU45/oc9E+Lww3XeP8lazRo8P3lwNvPnK1uAeBI2w5WUO0+7eg9BgB1LeagBdWlRsca60M0x5+vcaw9EnVmiPA4V+OwNtS3do/P4lKjI1j6UlN7wGN3llQHLah2Ot1tBf2Mfq1bi380FPeMzcSEayIXQF6Ym4LvLzdjZ0l10IKK/f31McZgzvV9he0fHLyAI2X12FlS7VNQsczG6/p6t06zwPSaltAElbfinqxMxZg+SZg+opffeVJiNZjoR1gHQ9iCqq2tDTzPw2AwYMyYMTh//jxWrlyJQYMGYfLkyZFYI0F0SXJTA/vlE6NRYeoQ71X3geDaz4jrqxwqrUOLyYpYbeB/6uJg1lBEDSvv0GIKvjUEz/OCy6A5hOOJyMK+Ry3m7vFZsO9mfasFFpvda3aXxWZHnVNEhhLczY4p8OHuYyTFavxay4KlMDcFb+46F1KjZPb3N6x3Ih4QWYh5nhcElbcaV5ca21FyuRkcB1zX17NUBMNVCsYcdGFPQNx+Rnp9Yu/7zFGZuGvMlW/1FnZQ+u23345//vOfAID6+nqMGzcOS5Yswe23346VK1eGvUCCIIKrRVXbYkaL2QbAUYF937naoF6LBdMCoQkqVgE5FEFU1WxCu8XuOL69e9zEuzLs5trUTT4L8Q8SVkxTjjrRvrLaNpTWtHod606r2YrDpY5SCHIFPa8ErMbVuZpWXKgLfO2A92BtFkB/4Hwd2i022WOZgBvaK8GnZd4oup5FyuXX1G7BNxcaHGvtH533PWxBdejQIVx//fUAgPfffx9paWk4f/48/vnPf+Ivf/lL2AskCMLVzy8QC5W4UCAA7Pw+8F+pZbWtOC+6eYRmoXKsMRRBVSZaeygWLiJytFtsqHS6vbrLZyF2mVc1ef9x4u5a33k68L+hfWdrYbHx6JWoR3YYvfnCIV6nxvDejpisYLP9WBscd0HVNyUW6fE6mK12HDhXJ3vsDplG0HKw61l5XRtMVscPqISY8ATV3jO1sNl59DHGeG311dGELahaW1sRF+dwbXz22Wf40Y9+BIVCgeuuuw7nz58Pe4EEQUh/0fmDCSoWuhGMy8LdRRCOhcpstcPsvFgGilgMNnWTm3hX5UJdm/D/7uJ+Ff8gqfFhoXL/4RLK31BhrtFnNlxHw0RNsC7LMlHZAzEcx/mck+d5Qbz5ymwEXBb3M85YT44DDJrAwxJYvT7x9SlQMdeRhC2ocnNzsXnzZpSVlWHr1q1C3NTly5cRH+89u4EgiMARXH4+bgIMdkFkQa4nK5sCblvDAkoZ4ViogOAtG2W1rpt4d7GKdFXKRK6i7iKoxH8H1T4sVGxcnLMdyu7TNQEX1WUB6dG8sYtff9fpakmrNV/Y7LzgIpSrn1fobKGzS8Zid7qqBZWN7dCoFBjdx3djZnY9YwHw8Tp1UD355GKodnYHQfXCCy/g17/+Nfr06YNx48YhPz8fgMNaxRoOEwQRHkL7GR83AQYTJSMykzAww/GjZtdp/2Z/u53HLudFaXS244IYrKDieV5y0wr2Riy2UHWXm3hXxd39GuhNuTMjEVQ+fmSwfTf074FYjRK1LWZ8V9nod/6aZhNOOMsGBBKQ3pGMzEqEXq1EdbMZxTI1peS41NgOi42HWskhPV7nsZ+JlW/LG1DvVmRYnGHnr4UOu55ZbI7vVLDtY5h7kF2fLje243tnMHy+j2D4jiZsQXXnnXeitLQUBw4cwKeffipsv+mmm/D666+HOz1BEHC1n6luNvm9sbkak+oxnv2iDMDsX3ypCTUtZujVSsG6FaygajZZhZgI9jwYSFB1HsSB2HYeaPMSiNxV4Hk+aJdfeoJOKE4bSCzS7jOOMXnpcegRF93SQVqVEmOca98RYBwl+/vrlaiXLffAalzxvMNqJ2ZnEJmN7qVYghZUbjFULMZtSM+EqBYDjkhHwPT0dIwcORIKhWu6sWPHIi8vLxLTE8RVD/tFZ7LahQw+b4izdNgvyu3f+zf7swviuL7JQr+txrbgRI17A+dgRZHYKkJZftHFPbmhq38eDW0WWEVuO1/WXhaUbjRogopF6gxuJzHCD6oALNSAtG2MN9i5iQP1rTa7ICYDyWxMilFDHF4WqqBqbHdYTnd876z7lRs96xQQYh2qp59+GgsXLkRsbCyefvppn2OXLl0a0sIIgnARo1EhRqNEq9mG6iYTDF7qSpmtdlQ0OFx+mUkxMOhUUCs5lNe3obS21Wf1YiGos1+K1zov/qhxc6MEcxM2WV1ZZQDFUEUbd0HVZLIistWSrizuYt9XPCKzUKUYtBjmzJbbd7YWZqsdGpV3OwT7G4pWuQR3mPjZc6bGZ90tRlkA/e1cNa5cIu3b8gY0tVsRr1NhSC//Fd9VSgWSYzSClTBUQWWz82g2WYWYrmi/7yEJqsOHD8NisQj/90Y0MxwIoruRYtCitLYVNS0m9EmRF0YX69tg5+Ho9B6nBcdxGJmVhH1na7GzpMaroDJb7dh31tl7LDdFaMIcrMvPPS4lGAtVeV0beN6R8cPzjm70NjsfdB81InzExWGFz6OLC1z372YgQekpBg0GpMUhxaBBdbMZh0vrMM5LjE5pTSvKatugUnAh9bDsCAamxyM5VoPaFjOOlNVjTB/f6wqkYTCrcXW2ugXl9W3olagXLGD5AVSGZ6QYtIKgCqaoJwDo1EpoVAqYrXYcLq1HRYMjGN7f+XU0IQmqL7/8Uvb/BEF0HEaDBqW1rahq8v7LmmVmZSXHCD9oCvulOAVVNe4dlyV73JGyerSabTDGapCXHicE1gYvqEJ3+bGLeR9jrNA6p8VsFVKkiStHXasFLWYbOM7xXTpf09rlXX7M6sQsvTUt3gWV2ELFcRwK+qXgo28uYmdJtVdBxVxgI7MSg+pM0JEoFBzy+xnx36MV2FlS7VdweCuZICZep8aw3gk4XOqomn7X6EwhRisYC5HRoAEuOf4frIWKHVPVZMKWYxUAgFFZ/oPhO5qwYqjsdjvWrVuHadOmYciQIRg6dCimT5+Of/7zn90iI4QgOhNC+xkfNwK5X5jj+7tSnb2lfjNXRX4/IxQKTrbOSyC4WwGCsWqwi3m/HgaolVzQxxORg32P0uN1SHYG+Xb1JAH23bwmzVE3sabZLPv3wPO88DfG6r+NDyCOqjPUQZKDrT2QNjTeinr6mrPNbMPB845Cn8GcuzgwPVRBBQBbjztU2fgoVUcXE7Kg4nke06dPx8MPP4zy8nIMHToUgwcPxvnz5/Hggw/ijjvuiOQ6CeKqJ0UoneDdQiUXVDqsdyIMWhXqWi2C5cmdXW6xH+xi1WaxBVWc070gYjAtS8RikP3C7+pWka6K8D1KihHi9bq6oGLxfQOcgspq59HY7vmDobHNKqTzsya+LNj5mwsNaJI5xm7nhay3ziaoWJHNw6X1Pn+gtJqtguj0ZaECXJl8O0tqsP9cLcw2OzISdMjxEoogB0u0AcITVKyFUEG/6AakA2EIqjfffBNff/01tm3bhsOHD+Odd97Bu+++i2+++Qaff/45vvjiC6HHH0EQ4ROIhUouqFStVGCcM6ZD7ldqs8mKI2X1AFw3gzidSsjCCcZKxS7IeqfpPRgLk0tQ6bvNTbyrInb9sM+iq1sLq5xiPyNRJ9yM5WpRsQy/OK1KcCH1TopBH2MMbHYee8949sb8rrIRtS1mxGqUGJGZ2EFnEBpZxhhkJusdfT3Peu/ryerXJejVfgXOtdmJ0KkVqG42Yf3OswAc145g4qYjZaECHNeroQEEw3c0IQuqd955B8899xwmTpzose/GG2/Eb3/7W7z99tthLY4gCBfs17KvgoTe0p5dqc6e6dP7ztbAaueRlRwjHKdQcIhz3kiDEVTMQsV6mAUjiNgFPcvYfawiXRVWg0psLezqrYBqml1uPKFQrkxvTDYuxa2OlFy5AAarUTWur9FvJl00CMRlGUhAOkOrUmJsjsMi9GVxleQ1AiVFZKGK1wcfcxavcx2T39cIVSd430NewdGjRzF16lSv+2+55RZ88803oU5PEIQb7AIvdxNgiG+EYtjNYN/ZGpis0jpWrIaLu6vCvRpxIDCx18eZTRioIBJnlWV1I6tIV0VcHLa7fBbsu9nDoHG1cpL5cVItBKRLC0QW+ohF2iEUtYy+20kOl4vOv6DKTA6ssXCh27kGe+6RtFB1FjdryIKqtrYWaWlpXvenpaWhrk6+IzVBEMFjjPXdILmh1YJGZ8yR+0XxmjQDUgxatFvsOHS+XrJvp5faOaHUomJry04JzkJV32oRLCC9k0RWEYqhigribFFDN4lnY0LJaNCK4hHlBJXTkhUrtVDl9zWC44BTl5pxWVQvTVxypDMERsvBxM7JyiZUeSkXEUiGnxixiLkmzYBUmVY1vjB2Q0EVcm6nzWaDSuX9cKVSCau1a/8BEkRnokec735+7CaYYtAixq1zO8dxGJ9rxOYjF/HIPw9I0ourm02OHlhuvzDd2zv4w2S1CYKOWagCtWqwX8dp8Vro1EoYdNGxilQ2tOORfx7AveOyMGusfImJYPmuohHP/PsbPDP5Gtw00PuP0M6CxWbHxXppcVgAaDb5rtD/9L+PwGS1Y8WskRGpQWi12fHQWweQ28OAF344yOfYTYcv4I3tZ7HyvlGyTX0BkSvPoBXFI/py+UktVEmxGgzpmYBvyxtw8+tfC649O8+jzWITalZ1RowGLQZlxONERSN2na7G7SN6eYwJpKinmEEZ8UiKUaOu1RKSoEkJMyid1a5Kj9ehX4/Ag+E7kpAFFc/zePDBB6HVyvcrMpkC625PEERgZCTooeAc7RZYQT0x4qBuOW4b1hObj1xEs8nqYTkq6GcU0uMZrvYOgQkqlm2jUnDo6VxboBYmcVYZABg00Ymh2lFSjW/LG/D23vMRE1Rbj1fiREUjNh+52CUElXtxWCHj0uT9e9DUbsGHh8oBAL+7daDw+YfD95eb8fWpKuwsqcZzt+b5jJHZsLcUxy824rMTlXj4+r4e+9vMNqFlk9Gg8WntZcHr7hYqALhtWAa+LW+Q/ZFx69CMTl3MujDX6BBUJTWygiqYGCrAEWd5x8jeeGv3OfxweM+g15MWr0PPBB3UKkVIteaG9EoAxwHThnWe9z1kQTV79my/Y37yk5+EOj1BEG7EalUYnpkoKagnxt8F8eZBadj52xs90r45cLLpzoKFqjUwQcXKORgNGsQxC5M5MEEkdjEBCNgqEmnYjZIFyEcCdtMOtqZXtBB/jzjOlZzQ4uOzEJ9bWW1rRAQVe99sdh4VDe0+XVHs87pQJ/+5sbk0KgXitCrB+hRMUDoAPHpDX0wZnO4Rh6hScOibYvB3SlGlMDcFa7afxY4SR19PsQjheT5oQQUAz92ahycm9Q/JwqRWKvD5MxPAgYMihG4I1/U14sDvJiEpJnrNkN0JWVCtX78+kusgCCIACvul+BVUvm48DqtWYDe7+CBdftUtLpdKsHE37vEbgVhFOgJ2rg1tFjS0WoTA/HBgmY9dUVABCCjLT3xupbWtXquJB4O4pllpbavX73W7xdUD0r3/IENoJROrAcdxPi1U4rHucJz8j4+uwNicZKGv5/maVkn7qqomE0xWOxQcghLDKqUCCfrQs+vcQxOCRRyH1RmIfp4hQRAB48o0qvHoRhBsUKk/gq2WzmK7jCJB5cuqIcb9Jh6IVaQjEAfgM6tZuLAbdLCNpqMFs/aw71EgWX7uFqpIIBY73oQSILVKeRsntJJxWp1YPKJ7IVrAFVclZ6HqysRoVBiZlQTAs/QDe98yEvSdsuxDV4HeOYLoQogL6p261CzZF2xQqT+CDUoXp5szq4bZZvdwj8jhStN3s4pc4cwyd0tLJOhqFip3YR6ItbGxA963ajcLlTfEAq6stlW2nYwrc0/j/NeHhapJOrY74a0NjbvLnQgNElQE0YXQqpRCg1NxkT6bnRd+qUdLUImzqAyi5rD+rExWmx0X6x0uG/cYqiud5dcRgqpKFEPVFXqculsLA/ksOuJ9C9RCJd5nstqF91uMYHVyuoiY9anVbEOrKM5PHLze3SxUgCMwHQB2na6RCM/SmsheO65WSFARRBeD/crcJRJUFQ1tsNp5aJQKpAVZD8YbwVuomKDSQKnghPYz/uKoKhraYbPz0KgU6OG84cVFqVJ6pF1XJqtNsLLZ7Lxws+7MeAgq9lmYrV4FoVRQRSagv0YkjC4EaKGSew5AqL3ERFKsRgmdWuF8HZclTAheVyqE72B3gvX1rHfr6+luISZCgwQVQXQxWBzVnjM1sNgcjYvZBbFXkh7KEDJm5Ai2sCezAjB3iitTz7cocpVM0AvZPrFRqs4daUuLe4xOZ3f7NbRahDWy4rBMUPG8w6Ije5zovKqbTWiLgHAM1OXnvk9urOu76XDjeQtMd1myNJ0mFT+SqJUKXNfX08Id6fjLqxUSVATRxWAF9VrMNhy9UA+gYy6IwVqo3K0Agfbjk0vXNkSpf1ykLVQegirAEhTRQq44rE6tEES6t8/S/TsSiYB+sYWqrtXitR6auCis+LkYFhfVQ+TGk2vlVN3kvWRCd0GuDY34Rw0ROiSoCKKLoVBwwkWR9eHzV9QzFJigajHbBEuYL8S/7gGxoPItInwJqittoRJb4y7UtcEmE+AcDO5Bz4EWSY0WLmHu+h5xHIdYjcN96y1JoLFNup31lAwVnucFocMMrnICV9wDkllu5S1Unu1kUmSajbsHr3dHWHuc/edqYbLaJGUnKIYqPEhQEUQXxL1RK0t1j+QFMV5UrM9ftp3dzguV0lngb6zWGUPlJyhdrn4Wcxe2mm1hi5pAabfYYLI6hCPHAVY7j4qG8OKB3AVVZ3f5eSvuGOcsoeFN4LLzYl6ycN2lje1WmJ0i/hpnOxc5QVXbYhbi0tiPDLlxQgaqqJ2M0H5G1uXXfS1U/VMN6BHn6utZ7mwzFKtRenRLIIKDBBVBdEFYts7hsjq0mKwhVTn2h1LhqpLtTwjUt1kE4cMuygat4ybsLyhdzl3JxBgQeLX1cGkUiQL2PoZbMd29EndXFVQucexbUOU4eziGK6iYyInTqtBfEFSen0WZM7M1PV6H/qkG2XFWmx11rZ7tZIysQbLoM6oS1VLrrnAch0Jn386dJdWSHzTdMW7sSkKCiiC6IFnJMeidpIfFxmPfudoOCyoNtFo6s8QkxqiFwoAG503Yn9tOrn6WVqWExjlPoNXWw4WdY7xOjWynMAg3jsrD5ddFBJX798hfPBw7ryG9EgAAF8KMoWIix2jQCG5sOZEmFoBszZWN7Wi3uKyita1m8LxDKIstMMwK5S0ovTvDLNw7SqojXr/uaoYEFUF0QTiOE8onfHb8knAjiLSgCjQwvVpUg4rB3Ha+Assb2y2oa2VZZcFZRSINO8cEvdrnTTwYarqYy8/bzTXWT3FPdl5DnYIqUu9bikErNMyWm5Ott3eyHkkxakH4iaunsx6TyTEaSQasy0IliqFq8vwed0eYoDp6oR7Hyx3lEyjDL3xIUBFEF6XAeVH86Eg5AId1KJSu7b6I1wfm8hMsCiILQCClD9gN0RirkRQDBQIvuxAppILK+008GNj70svZH60zCyqbnRfiadxvrr6aXfM8L5zXEJGgCqeIqRAcbtCI3K8yFqoaaSPnTJmxNS3yIqmHEENl9ju2u9EzUY++KbGw88DHRy8CIAtVJOjUgqq8vBz3338/jEYj9Ho9hg4digMHDgj7FyxYgLy8PMTGxiIpKQmTJk3C3r17JXPU1tbivvvuQ3x8PBITEzFnzhw0N0tbdhw9ehTXX389dDodMjMz8corr3isZePGjcjLy4NOp8PQoUPxySefdMxJE0SAFDjjIFhQbkdcEAO1UAkWBVG6eVwALUt8uSpjNcE1WA4XsaDyZRUJBiYM+vaIlbxGZ6SysR0WGw+1kkO6W3FY9lnIJSe0mm2wOuPnBmXEQ8EB7Rb5iuWB4mpjpBW+G3JZl+4xX3KWRbE4E2OUcfmJXY3dHWal6sjrx9VGpxVUdXV1KCwshFqtxpYtW3DixAksWbIESUlJwphrrrkGK1aswLfffosdO3agT58+mDx5MqqqqoQx9913H44fP46ioiJ8/PHH+Prrr/Gzn/1M2N/Y2IjJkycjOzsbBw8exKuvvooFCxZg9erVwphdu3Zh1qxZmDNnDg4fPowZM2ZgxowZOHbs2JV5MwhChhSDFnnpccLzjjDZB1rcU3D5yViomn0ElfsKpo+7wu1nJIJKuIlHxkLVr4dB8hqdEWbt6Z0U41Ec1lf7GXZOKgWHeL0KGQkOURNO/JlLBGmRkaCDSsHBbLPjkjO9X1izh6CSsVCJxJkYFidV12qB1WaXBK93dwsV4EpsYZDLL3w6bW39l19+GZmZmVi/fr2wLScnRzLm3nvvlTxfunQp1q5di6NHj+Kmm27Cd999h08//RT79+/H6NGjAQB//etfceutt+K1115Dz5498fbbb8NsNmPdunXQaDQYPHgwjhw5gqVLlwrCa/ny5Zg6dSrmzZsHAFi4cCGKioqwYsUKrFq1qiPfBoLwyfjcFJysbAIQbQuV540okKa6LCMrU6Z+VuwVLu4pBKXr1UILjupmM1pMVmEtweAoJeEQBv26gIXKl7XQV1C6WIhyHIes5BiU17ehrLYNo7JDWwv7PvUwaKBSKtArSY/zNa0oq21FT6f71GKzC2Ut3AWV2EJVJRPfBwBJMRooOMDOO8ovgIMQvJ4UE1nXeWckv28KOOc5A0BvKuoZNp3WQvXRRx9h9OjRmDlzJlJTUzFy5EisWbPG63iz2YzVq1cjISEBw4cPBwDs3r0biYmJgpgCgEmTJkGhUAiuwd27d+OGG26ARuP6ZT1lyhQUFxejrq5OGDNp0iTJ602ZMgW7d++O2PkSRCgUOov0ARDcVJFEEFR+KnyLLQqMQCxMvixUoRb3tNjsMFv9FyJ1RywM4nVqJDpvqt6qfltsdp8FT+tazWAeqpyUyFuoxJlskcBXcdhABZXj+PDdpe5JDnJzXqxvg50HtCqFUAG9t8y4Gi9uPIWCQ7LQfsYsjEuOcYi47k5CjBrDnDFvafFa6NRKP0cQ/ui035ozZ85g5cqV6N+/P7Zu3YrHHnsMc+fOxVtvvSUZ9/HHH8NgMECn0+H1119HUVERUlIcN5nKykqkpqZKxqtUKiQnJ6OyslIYk5aWJhnDnvsbw/bLYTKZ0NjYKHkQRKQZ2ycZKqd7JpoWKlfMi4zLL4CgdJ9WkSBiqGx2Hj/86w7c/Pr/ghYc7sJAiKPyUvX7Z/88gHGLtqG+1Sy7n2VeJsaohXT9SJVNeP/gBeT9/lNs+bYiIvMB4vYjMvFsPj4LsWUPcFkbwxFUQu89p6CSCzYXi3FWP0ns8mNB8Uyc9ZBx46WIMv3kMlW7OyyxheKnIkOnFVR2ux3XXnstFi1ahJEjR+JnP/sZHnnkEQ8X28SJE3HkyBHs2rULU6dOxV133YXLly9HadUuFi9ejISEBOGRmZkZ7SUR3ZBYrQqPTuiL/L5GjMpO8n9AkLCbpL+WKXIWqkB6+bGYGBZ3I8YQQAyWOycuNuJkZRPO17Ri/7nagI8DXO1T3C0tZXVtHmMvN7bjy+Iq1LaYcfRCg+x84hT8hBgWi2YNK/uNsctZIX/jwQthz8VgGW6p8Z6CwleWX6O7EI2EhUp47xyCRy5JQK5mVq9EPTjOEWjNKvd7s1A55ndm+rWYfI7rrswak4XBPeNx77isaC+lW9BpBVVGRgYGDRok2TZw4ECUlpZKtsXGxiI3NxfXXXcd1q5dC5VKhbVr1wIA0tPTPcSV1WpFbW0t0tPThTGXLl2SjGHP/Y1h++WYP38+GhoahEdZWVmgp04QQTFvSh7e+dl10Gsib7IPxELl6LvmaQXwJ6jazDYhw0iukKK/2kdy7BA1fBX/PxC8CQO54Oqdp11ze3MJVjMrS6xGmNNss6PdErw70h0WF7TnTE1AfRYDwd1CJ8ZXxqU3l1+oQentFpsQN2f04fKTcxfr1EohQ5Ht92V5EixUTear0kKVZYzBf+dejztG9o72UroFnVZQFRYWori4WLLt1KlTyM72HeVot9thMjn+MPLz81FfX4+DBw8K+7/44gvY7XaMGzdOGPP111/DYnHdMIqKijBgwAAhozA/Px/btm2TvE5RURHy8/O9rkOr1SI+Pl7yIIiuRiCCqtVsE0SC+Ne9ODNMzirDbmAalcKjBhUQWpbfTpGI2hmkoHK5rhyv6ysWiDWl9rYfEFlZ4rSI1SiFzLlIxFExa0qr2YYjZfVhzwf4FlS+aoI1ennfKhvbYbIGH+fF3H0apQLxOumcYmvhBSGhQequyhSN5Xnep+VJXDqhykt5BYIIlE4rqJ566ins2bMHixYtQklJCTZs2IDVq1fjl7/8JQCgpaUFzz33HPbs2YPz58/j4MGDeOihh1BeXo6ZM2cCcFi0pk6dikceeQT79u3Dzp078atf/Qr33HMPevbsCcCRKajRaDBnzhwcP34c7733HpYvX46nn35aWMsTTzyBTz/9FEuWLMHJkyexYMECHDhwAL/61a+u/BtDEFeQQAQVE0Z6tVKSDcf+b7HxQtNhueN6GLSyPcQCicES026xSdx8xy82oq5FPr5JjkCDq3mexy6xhcqboBKVkuA4LuB4tEAQ104KVjh6gyUeyAqqIILSk2M1iNUowfNAuYy71B/VTS5h4x4bVdVkQpvTquktoYG5B8tqWyVNluUtVJ5B6VeThYqILJ1WUI0ZMwabNm3CO++8gyFDhmDhwoVYtmwZ7rvvPgCAUqnEyZMn8eMf/xjXXHMNfvjDH6Kmpgbbt2/H4MGDhXnefvtt5OXl4aabbsKtt96K8ePHS2pMJSQk4LPPPsPZs2cxatQoPPPMM3jhhRcktaoKCgoEQTd8+HC8//772Lx5M4YMGXLl3hCCiAIshqqp3epRVJEhBKTHSX/ZMzcRIG9l8hezEkgMlphD5+tgstqRFq9F/1QDeB7YfabG/4FOPILSk131lMQWtjPVLahocNVD8mahcr9BR0pQ2e28YMUBIiOo7HZecLPF+xBULSZPi5P7+yauWB5KHJVctfKEGLVgrWIuVm+CShDCNa2C8DRoVbJZbEbZoHSyUBGh0WnrUAHAtGnTMG3aNNl9Op0OH374od85kpOTsWHDBp9jhg0bhu3bt/scM3PmTMHyRRBXC2JrRVO7BYkxnjcbISA9VvrLXqngEKNRotVsQ7PJKglYFx/nzSIQrKBiMVOF/VIQr1fj+8vN2FFSjVuHZvg91my1o82ZFcjOuWeiHgoOMFntqGoyIdUZm8METFq8FpcaTV6zAN0D9QNtNO2PhjaLRNweLq0PuVYWo8lkFeoRycZQiT4Lu52HQlT4U85VmJkcg5OVTSHFUbHee+5CO8sYg2PljSitaUVanE54XfcaZllGV5ZhjUz2qZgeoqB0DpxzLFmoiNDotBYqgiCij1qpQIwz2N2bEPDlKmGiSK5lSY0oaFsOVwxWYHE4O087rFGFuSlC4+hdAVpvxOcW5+yHqFYqhCKSYksLE1R3jnIE8ja2W2XrdLGgdHYzj5SFigm1eJ0Kmcl6WO089p0NLqPRHRYHpVMroFV5WnJYPBsAtLqVo5ATVOHUoqr20k9PnOnHrFQpBg1iNFIhKX5tuexTMUaZoHRvYwnCHySoCILwiT8h4MtV4qs4Z5UoaFsOX2LMnYY2C769UA/AIajG9U2GUsHhXE1rQO1j2LnF6VSStivuwsBm57HLKdxuHpQu3PTlMv1csUBOC5UusEbT/nC5WLWCcAw2o9EdXwHpgKN4Jqt35p7p516HCghTUHmzUAnB5q0+65exbRUNbah0uma9WajkyiaQy48IFRJUBEH4xJ+gqvHhuvOVHVbT4jsIOJhK6XvO1MDOO1q8pCfoEKdTY3hvRxXoXSX+46i8CQp3YfBteQOa2q2I06kwtFeCbDNewBG4zmKBekQ4hsoV7K5FQT+HoAo3jsqfoOI4TuT2k66/wa1+FyAunRB8ULr7+8YQl7HwVWG/h0ELnVoBOw8cdYpsb98xJtosNt5n8DpBBAIJKoIgfOIv9qfaR3C5UL9IRhS5F290hwmqNosNVj+1lpigYBYb8f8Dsd6416BiuAdXs9fJ72uEUuE9+LpFVEqCBesH2mjaH4KAjdOgoJ+jwe3JyibB4hcK/gQVII5pc7n8eJ6Xfe+8BfQHgrfYOrG49SWoOI4T3IOHnSUlvLnxtCqlxJ3pLXidIAKBBBVBED4J3OUXrIXKd1C6OMiaFQD1BhNNBSJBVcjiqE5X+72ps0rw3gQVq3kkCLf+0pYd7oKqRlRKgsX4REpQCQI2VgujQYtBGY4ad+JSDsESlKASufzaLXbBsiM+trdT0DSZrKj30wfSHW/Zn3KCSs7lJx573pkw0MOHG09sCSN3HxEOJKgIgvCJSwjIu96qfRRE9OW282XZAhwFPzUqxyXKV6ZfRUMbzlS1QMEB1/U1CttHZiVBr1aiutmM4ktNXo8HAnP5tVtsOHDe0TCdudq8VVOvFlmRGJFy+bkL0cJcxzkH4tr0hhAHpfMhqGTEMROiCg6S4qw6tRJpzhY2wcZReRPoLOuy3WIXipl660HnLrR8BZqLv38UkE6EAwkqgiB84jeGyhkLJdd81luDY6vNjrpW/4UUA4mj2ukUEkN7J0oEkUalwNicZADAju99W29Ylp67oBBX/d5ZUg2z1Y70eB369YiV7PcUVC4rEiNSgqrKLWi7UOTaDLVPoFxguTtyhVbFx7kXZw0lMN1m54UefO5CW6NSCD0fWaKCPwsVw9d3LIUsVESEIEFFEIRPfAkBi80uuHTkft3HysTdAEBtqxk877BsJMnUtmIEkunnip8yeuxjcVT+grYFC1WMVFAkxaiFNby739GPszA3xaOC94W6NkltKDkrS0dZqMbmJEOt5FBe3xZyQ+JAXH5xMuLW13GZoqy8QKlrNcPOAxwHJMt8L8Q1p9RKTujb5+21Gb7ayZCFiogUJKgIgvCJr9gfZk1QKjgkytxU43TymWEsTiY5ViMpU+BOrB8LFc/zglgqFMVPMQqcImvv2VqfTYS9CQOO49A7yXET/+LkZefruIRbWrwOGqUCVjuPigZXRptcCn6kCnsKLXuc7sQYjQojsxx9R0MtnxCIoIrVOoK1JRYqH+1qxC1gAoWdW1KMBiql5+1JbHnqnRTj9bsTuoWKBBUROiSoCILwCWt6KycEWGZZcqxGUj2bEessCupenNNbdXV34vxUSy+53IzLTSZoVQpc6xQVYgamxyM5VuO3ibAvlxe7OTMLlFi4KRUceiV5lk7oUAuVjDsxUEucN7xlOYoxaB375Fx+cseF4vLzVwtKLJS8ufsc+1yWLHGTZTmM5PIjIgQJKoIgfOJLCPhtH+OMSWpyE0RyQdtyCFYRLy4/ZpEZm5Msm+6uUHBCaQFfcVSBCAMAyE01IM3NzeSeCQjIB+ozd6LJake7JbDq7+60mq1odWY8iguiCoHpp2tg99Jz0ReBCSrPz8KnEDUGL6j8CW2xiMpyazkjJkajEr6T4ibLcogzAMlCRYQDCSqCIHziS1D5syh4CyqXs7LIHq/ztIqIYQHpLOtODnH5BG/4FFRG1018vIxbUa64Z7VMOx6DRgV2Xw+1dAKrIq5VKQTrHwAM650Ig1aF+lYLTlQ0Bj2vtxgyMa5WQMFZqC7Wt/utI8YQV4GXI0siqLxbqBz7HZ+Lr/gpx37Xa3lrg0QQgdCpmyMTBBF9wrJQecnyq/JznOt4z7gdhtVmx54zDkElJ3QYbN/h0no0m6yS9H6GLwsNiwUC5OO05FxbchYqhYJDvE6NhjYLGtosQrPlYBD3uRNbXdRKBa7rm4zPv7uMtTvOIr+fNEB/TJ9k5KTEep03sBgqZ4JAgIKqh0ELjUoBs9WON3edk1ixYjUqTBqU6tE30GWhkhc2YguV+HPxNvZQab3f75gkhsqLkCOIQCBBRRCET9iNsLHdArudl8RKVfjpleatsKe34o0ex/sISv+uognNJisS9GoM6hnvdY7M5BhkJutRVtuGQ+frcMM1PTzG+BIGfZxCRKngMK5vssd+OUHFzs+9lESC3iWoQsFXdfnC3BR8/t1lbDpcjk2HyyX7eiXqsfO3N8rOyfM8Gts928e4I/dZ+BKiCgWH7OQYfH+5GX/873ce+397Sx5+PqGfZFuNEHDvpVVMrAYGrQrNJqvEcihHttHxuaX6EUk94rTgOIDnvb8uQQQCCSqCIHzCbpY877BOiG+e+87WAnDUgJLDm4VJyFTzYz2Qs4owzta0AAAGpMf5zBQEHMHpZbVtOF/TAkAqqCw2u1CJXU4Y5KTE4re35CHFoJUtfOle3NNstQuCyT0NP9zAdF/9D388qjeOX2wUMi8BRyD9/05Voby+De0Wm2ycWbPJKgTcB9Z6JjALFQDMvzUPG/aWQhzWVdVkwrflDfji5GUPQeWq3yUvtDmOw4vTB6OkqlmoEO+Ne8Zk4lJDO346vo/PcQatCr+7dSB43ndhU4LwBwkqgiB8olUpoVMr0G6xo7HNItw8a5pNQrxOQT/PGlCAKzOsxWQFz/OCm0qIvfITlO7LQlXmo5+bO74yzsQ1rrxlg7nf+MUwQVXTYkaLySrMJ1dKIkFk7QsFZqGSs+zF69R4beZwyTae5zHg+U9httlR02JGr0TPQG4mijQqhc8+dnKCylvLHsaNeWm4MS9Nsu18TQsmvPoVDpfWodVsFVrzAP5dyIBDOAZCz0Q9Xr5zWEBjH76+b0DjCMIXFJROEIRf5Cwru0474pfy0uN89ONz3KCtdh4mqyswOdCyCd5isACg1NmnzV8sDeDZ5FgMOyeDViVb+8gf8To1Ep3B3GV1rZI4IPdSEsL7GGR/O4YvC5UcHMcJ4qvaS/PkQNrOAKIirTJZfr4sW+5kJcegd5IeFhsvWDgZNX6C0gmiM0OCiiAIv8gV92RZc74CwmNF1gdm2eB5PuAbp6/mykwcZRm9p88zXBaqNo99LkERusFemL9GJKhkRI+ruKf3yu++CDSYXwwTVKzCujsuUeT7/ON8ZPkF4yrjOA6F/TzrZvE8L5wfZdsRXRESVARB+EXOQrXDR4VyhkLBCen9zLLR2G6F2ZlG7+/GKdc/jlEahMtPHOfk3u8ukD52gc5fWtsqKpngeW5hx1D5aETtDSa+WMkFdwKpQQWIqtabbUKtq1AsVABQ2J/1H3Q1dG42WWF2WjGpHhTRFSFBRRCEX5gFgt1AS2taUVbbBpWCExoQe8PdysREQZxW5TNmh40BPGOoLDa70OrFV8VsBmsf02yyos7N3RaqKBAjbpJc48OKFK6gqvaSPegL5lat9mKhamzzn+EHQFJuosVshclqQ7vFHtCx7rCYu+8qGoX3i51brEYJvcb394IgOiMkqAiC8Iu7ENjpdPeNzEoULBfecLcyVQdYMkHuWMbF+jbYeUCnVgQkLnRqpdBI1723XCQEldC3rq5NFFjdkRaqwAUVC/z3ZqEK9Py1KgVUzpiwZpNVOI7jXO7AgNdk0CIvPQ6AKxZPEKIUP0V0UUhQEQThF/fGvoG4+xhxbsHMgWRyMeQyywCXuy8zKcZnWxExmTIVzYHAXV6+yJJx+cmJHl+Npv1hsdkF61owPedSmIWq2V8Mle/z5zjOZW1stwrnEKdVyfZx9Id7/0F/RT0JorNDgoogCL+ILSt2O49dJf4D0hmu2Bupyy8QCxUTVO0Wu6R9STDxUwxvmX6RdvlVNXkXjL4aTfuD1ZdScEBiTBCCKi7QoHT/58+SDMQWKl/tanzBxDizdsq16yGIrgQJKoIg/CIWVCcqGlHXakGsRonhmYl+j2WiiNVnqgrixil2J7aYXA2FBQtVEIIqy60AJ4OVMAhHUGUk6qBUcDBZ7ThZ6ajNJScYw3H5MQtOcqzWbyFTMUIMlR+XXyBB+XE6GUEV4vs2NicZKgWHsto2v9mRBNEVIEFFEIRfxEKAlUsY19cIdQB1m9yLcwYTB6RRKaBVOV6jyeQSIcEU9WR4K+4ZrqUFcPTS65noiNHyFTgenqDy3YjaG0y4RsJCJf4swxVUsVoVrs1KAuBwIbva9ZDLj+iakKAiCMIv4tgflururTq6O+5Zfq62M4HdOF03cU8LVUQFVRgWKrm1+LJQtVlsQomAQPGVPegL5vKrbTELLWbEBOXyE1kbI2HZK8h1fId2nq4mCxXR5SFBRRCEX5j1prrZjH1nHYJqfH//8VOAZ6ZeTZCxMi5B5rLqCFXSQ4ihqmhoh0UUj8Xap4RThwrwrNguVwU+TlQAM9j2M76yB32RHKMBxwF2Hqhr9XT7NQbh8jOIinsG0lDZHywGb1dJNS77iD0jiK4ACSqCIPzCbpqOJrt2pBg0GJAWF9Cx7u1jgrVEuAKhHRaqhlaLcDNnmXuB0MOghValgM3Oo6K+XdgeKQuVWNwl6NXQqDwvr0oFJ8QhBev2q/GRPegLlVKBJGcQu1ymX1AuP5mg9HCE6PDMRMRqlKhrteDohXoAwQtGgugskKAiCMIv7jfbgn4pAZcrEFqWmN0tVAG6/HRSQVZW1+o8XitprOsPhYKTzfQLpX2KHGKXn68MxlDjqEJpOyOsx1mKgL33DJ7ngxNUOpe4jcT7plYqMK6vw+1nsTnckeTyI7oqJKgIgvCL+00zkHIJDGZhamq3ot1iQ5PT9RfojdM9qN0VPxW4dYrhHkdls/NC9mEkY6h8iZ5QBVVNEAVR3RHaz7hZqFrNNlidcVXBxFA1mywRs+y51zILpgo8QXQmSFARBOEXnVoBjSijjwUTB4I47qbGWUtJo1QE3IxYKLvgIagCj59iuAuqJlEcU2QFlX8LVbDFPV3B/CFYqAzM5Se1ULE4LpWCQ0wA7V7iRAkCkRNUru+SWskJtboIoqtBgoogCL9wHCfEyvQxxqB3UuBiRlztvLrJVdQzUJdhrBcLVTAB6QzW04+5DZko0KuVsjFPwZAYoxYER1exUIlFUSCfhzjLLxIV5gFgQFqcIECNsdqAvxcE0dkgQUUQREAkOC0HgbSbESMuexBM2xlGnFvZhbIwBJV7cc9IWVkAh+js7ZxfLsOPITSabg1cUPE8L9SRCiWGigmWGndBFWTpA7G1MVLvHcdxKOjn+E6FIhYJorNAgoogiIDomeiw7ky4pkdQx7msGpaQrCziGCwgtKKejCyj1OUXSUEFAAPSDM7X8R7fxUpQBGOhamyzCkHbySH0unNZqKQuv2Az9Qxah1swEpXSxdyYlwoAyEmJDXsugogW5KwmCCIg/jhjCA6X1uPmQWlBHefK8rOFlKkmtorY7Dwu1LUBCE1QsVpR9a2OoOpIC6r5tw7E9f174LahPb2OCcXlx963OJ0KOrX/WCd3jAG4/ALBoHWMq28zo9VsC+pYX9w+oie0KgVGOiunE0RXhAQVQRABkW2MRbYxeAsCs1CJxVAwFiqxVaSioQ1WOw+NUoG0eF1Ia0kxaFDdbEZZbWtEaimJSYvX4cejevscEx+CoAonIB0Qu/zkLVSBiqJY52dR2eCq4xWJ947jONwyNCPseQgimpDLjyCIDiVGrQSLMz5f0wIgOGHArCLNJqvgquudpA+qQbCYTFEcVWNbZEomBIOQ5RdEpfRwAtIBl0WwqtkEnne1nwk2sDzO+Vkw92OcVhXy50AQ3Q0SVARBdCgKBSfEQZ13towJKoaKWajarUL8VO8Q3H0M5vYrq2uNuMsvEFwuP2vAx4QSzC+GHWe22oXgfscamIUuMGcF+ywYkbLsEUR3gAQVQRAdDsv0u9jgcPmFkuXXYraGVdSTIa5FFaygiASh1KGqaXaVmwgFvUaJWGedKXFgetAxVG61w0hQEYSLTi2oysvLcf/998NoNEKv12Po0KE4cOAAAMBiseDZZ5/F0KFDERsbi549e+InP/kJLl68KBz/1VdfgeM42cf+/fsBAOfOnZPdv2fPHslaNm7ciLy8POh0OgwdOhSffPLJlXsjCKKLwywbzNsUjKCKFfUCLKsNPSCd4RJUbRGrpRQMoQWlB9dQWg4WmC4unRCsoNKqlFArXS6+BCrCSRACnVZQ1dXVobCwEGq1Glu2bMGJEyewZMkSJCU5skBaW1tx6NAh/P73v8ehQ4fw4Ycfori4GNOnTxfmKCgoQEVFheTx8MMPIycnB6NHj5a83ueffy4ZN2rUKGHfrl27MGvWLMyZMweHDx/GjBkzMGPGDBw7duzKvBkE0cUxuLWuCS4o3VWHKpwq6QxxDFU0XX7NJiusNntAx9QE2VBajhShWrpLUDWG0HaHfR7BHkcQ3Z1O+/Pi5ZdfRmZmJtavXy9sy8nJEf6fkJCAoqIiyTErVqzA2LFjUVpaiqysLGg0GqSnpwv7LRYL/vOf/+Dxxx/3qMZrNBolY8UsX74cU6dOxbx58wAACxcuRFFREVasWIFVq1aFfa4E0d0xiGJvOA5IjgleUJmsdpypagYQWlFPBqtFdaGuVWi3ciWFgbjlTmO7NaC6Uq4sv9ALXxplalGFkuUYq1WhLsiCoARxNdBpLVQfffQRRo8ejZkzZyI1NRUjR47EmjVrfB7T0NAAjuOQmJjodc6amhr89Kc/9dg3ffp0pKamYvz48fjoo48k+3bv3o1JkyZJtk2ZMgW7d+/2uhaTyYTGxkbJgyCuVsRWjaQYDVTKwC89sVqpAAHCE1Tp8TqolRwsNh4llx0C7UoKA5VSIbwfgbr9WA/E8CxUnrWoQrHQkYWKIOTptILqzJkzWLlyJfr374+tW7fisccew9y5c/HWW2/Jjm9vb8ezzz6LWbNmIT4+XnbM2rVrMWXKFPTu7aoTYzAYsGTJEmzcuBH//e9/MX78eMyYMUMiqiorK5GWJi1mmJaWhsrKSq/rX7x4MRISEoRHZmZmMKdPEN0KsSjy1ThYDrVSAa2oz15ijFpo3xIKSgWHXs6q7yarw+V2pYUBs1IFKqhYD8RwYqjkalGRoCKIyNFpXX52ux2jR4/GokWLAAAjR47EsWPHsGrVKsyePVsy1mKx4K677gLP81i5cqXsfBcuXMDWrVvx73//W7I9JSUFTz/9tPB8zJgxuHjxIl599VVJPFawzJ8/XzJvY2MjiSriqiVOdBP21efO6/E6FUxOIRBO/BQjMzkG55wlHIAoCCq9Ghcb2gMSVG1mG1qcVcmDFaNi3C1U7RYbzCEISnGmHwkqgnDRaS1UGRkZGDRokGTbwIEDUVpaKtnGxNT58+dRVFTk1Tq1fv16GI3GgETSuHHjUFJSIjxPT0/HpUuXJGMuXbrkNeYKALRaLeLj4yUPgrhakVio4oIXVGKrSDjuPoa7KLvS6f/BZPoxAaRRKSTvQ7AY3SxU7LWVCi6oecVjqWwCQbjotIKqsLAQxcXFkm2nTp1Cdna28JyJqe+//x6ff/45jEaj7Fw8z2P9+vX4yU9+ArXa/wXgyJEjyMhwtUHIz8/Htm3bJGOKioqQn58fzCkRxFWL2KphDKG5r1iQRcJCJZ5Dq1KE1B8vHIIRVCx+qodB65FMEwzuFiohIF2nCmpecvkRhDyd1uX31FNPoaCgAIsWLcJdd92Fffv2YfXq1Vi9ejUAh5i68847cejQIXz88cew2WxCTFNycjI0GtdF+4svvsDZs2fx8MMPe7zOW2+9BY1Gg5EjRwIAPvzwQ6xbtw5vvPGGMOaJJ57AhAkTsGTJEtx222149913ceDAAWEtBEH4Ruzy6xGuhSopMi4/RjREQTDFPVn8VKhFPRnMXVjlLqiCPH8SVAQhT6cVVGPGjMGmTZswf/58vPTSS8jJycGyZctw3333AXAU/WSB4yNGjJAc++WXX+IHP/iB8Hzt2rUoKChAXl6e7GstXLgQ58+fh0qlQl5eHt577z3ceeedwv6CggJs2LABzz//PJ577jn0798fmzdvxpAhQyJ70gTRTYnVhmehMnSgharTC6ow284w2PFN7VaYrDY0hFj6IJYEFUHI0mkFFQBMmzYN06ZNk93Xp08fSZNPX2zYsMHrvtmzZ3sEucsxc+ZMzJw5M6DXIwhCikGS5ReChUoXWUEltlBFIw4oFJdfKEJUTLxODZWCg9XOo6bZHHJR0zgdxVARhBydNoaKIIjug1hQheK6YlYRpYJDRqIu7PUk6NWCkIiKhSomcEFVxUomhOAqFaNQcJLA9FBdfmShIgh5SFARBNHhiC1MoVioWAxWz0Qd1EEUBfUFs3RF0+V3JS1UgDQwvbE9NAsVE8cxGmXEPguC6A7QXwNBEB1ObJguP3Z8JALSGZnJjuKe0RBU8cGUTXBaqEIJ5nfHKBJUobr8mKAi6xRBSCFBRRBEh5ORoEOcToW+KbHQa4IvUXBNWhwAYFR2UsTWdG2WY64B6XERmzNQmLWJufN8camxHUBkBJWrQXLoMVS5qQYoOCAvCu8bQXRmOnVQOkEQ3YMYjQpf/voHkhYywTB1SDq+njcRvZL0EVvTQ4U5mDQwDdnGyFm9AoVZ2i43mdBusXmtg2W387hQ1wYgMsH4zDpY02wSMgyDFVSZyTHY+dsbkRREg2uCuBogCxVBEFeEFIMWcWH04MsyxkCpCL2wpTsKBYc+KbFhFcsMlcQYtRAXdqGu1eu4S03tMNvsUCk4ZCSELyZdFqrQXX4AkJGgv+LFUAmis0OCiiAI4grDcZxQuqG01rugKnX2G+yVpI+ImGR9FGtaQnf5EQQhDwkqgiCIKMCC4ktrfAgqp9iKhLsPcJVeqGoKz0JFEIQnFENFEAQRBbIEC1Wb1zFlTkEViYbQgCsYvrrZjGYT6+VHgoogIgEJKoIgiCjABFWZjxiqSFuoWKZgTYsJrNEEWagIIjKQy48gCCIKMKtTmY8YqrIIZvgBQLLTQsXEFMdJW8kQBBE6JKgIgiCiQJYoKN1bX1JmoYpUQVO1UoHEGJdFKk6rgiKCmZMEcTVDgoogCCIK9ErSg+OAVrNNaC8jps1sEwp/RspCBUhb2CTEkLuPICIFCSqCIIgooFUpkR7vaPQsVzqBxVbF61QRFT7i1j8UP0UQkYMEFUEQRJTwFUfFyilkRbiSe0ocCSqC6AhIUBEEQUSJLB+CilmoIunuA4AUscuPBBVBRAwSVARBEFEiy0e19EgHpDPI5UcQHQMJKoIgiCghVEuXs1BFuKgnwygSVPEkqAgiYpCgIgiCiBIul59ntfRIF/VksAbJAFmoCCKSkKAiCIKIEsz6VNHQBrPVLmzneV4QWZEWVBILFbWdIYiIQYKKIAgiSvQwaKFTK2DngYv1LitVdbMZbRYbFBzQM1Ef8ddkkIWKICIHCSqCIIgowXGcbGA6+39Ggh4aVWQv00Zy+RFEh0CCiiAIIoqwLD6xoHIFpEfWOgUAsVoV9GolABJUBBFJSFARBEFEEbninh0VkM6YPrwnrkkz4Jq0uA6ZnyCuRqjNOEEQRBTx5fLrKEH18p3DOmRegriaIQsVQRBEFBFKJ9TJufw6RlARBBF5SFARBEFEEdarj/XuA0hQEURXhAQVQRBEFOmd5Ag8b2y3oqHVApPVhorGdgAd5/IjCCLykKAiCIKIIjEaldBfr7S2FeV1beB5IEajhFHUyJggiM4NCSqCIIgokyXq6ScOSOc4LprLIggiCEhQEQRBRBlxYHpZnaNiOsVPEUTXgsomEARBRBlx6QSD1nFZZgU/CYLoGpCgIgiCiDK9RcU9YzWOy3JWB1RJJwii4yBBRRAEEWXEFipBUBnJQkUQXQkSVARBEFGGCaryujbonH32qGQCQXQtKCidIAgiyqTF66BRKmC182g2WQEAvSmGiiC6FCSoCIIgooxSwaFXkitmKjVOK1iqCILoGpCgIgiC6ASIyySQu48guh6dWlCVl5fj/vvvh9FohF6vx9ChQ3HgwAEAgMViwbPPPouhQ4ciNjYWPXv2xE9+8hNcvHhRMkefPn3AcZzk8ec//1ky5ujRo7j++uuh0+mQmZmJV155xWMtGzduRF5eHnQ6HYYOHYpPPvmk406cIIirDnFWHwkqguh6dFpBVVdXh8LCQqjVamzZsgUnTpzAkiVLkJSUBABobW3FoUOH8Pvf/x6HDh3Chx9+iOLiYkyfPt1jrpdeegkVFRXC4/HHHxf2NTY2YvLkycjOzsbBgwfx6quvYsGCBVi9erUwZteuXZg1axbmzJmDw4cPY8aMGZgxYwaOHTvW8W8EQRBXBWIRRUU9CaLr0Wmz/F5++WVkZmZi/fr1wracnBzh/wkJCSgqKpIcs2LFCowdOxalpaXIysoStsfFxSE9PV32dd5++22YzWasW7cOGo0GgwcPxpEjR7B06VL87Gc/AwAsX74cU6dOxbx58wAACxcuRFFREVasWIFVq1ZF7JwJgrh6ySKXH0F0aTqtheqjjz7C6NGjMXPmTKSmpmLkyJFYs2aNz2MaGhrAcRwSExMl2//85z/DaDRi5MiRePXVV2G1WoV9u3fvxg033ACNxtWEdMqUKSguLkZdXZ0wZtKkSZI5p0yZgt27d4d5lgRBEA7EWX1koSKIrkentVCdOXMGK1euxNNPP43nnnsO+/fvx9y5c6HRaDB79myP8e3t7Xj22Wcxa9YsxMfHC9vnzp2La6+9FsnJydi1axfmz5+PiooKLF26FABQWVkpsXwBQFpamrAvKSkJlZWVwjbxmMrKSq/rN5lMMJlMwvPGxsbg3wSCIK4axIU8yUJFEF2PTiuo7HY7Ro8ejUWLFgEARo4ciWPHjmHVqlUegspiseCuu+4Cz/NYuXKlZN/TTz8t/H/YsGHQaDR49NFHsXjxYmi12g5b/+LFi/Hiiy922PwEQXQv4nVqPHPzNWi12JCeoIv2cgiCCJJO6/LLyMjAoEGDJNsGDhyI0tJSyTYmps6fP4+ioiKJdUqOcePGwWq14ty5cwCA9PR0XLp0STKGPWdxV97GeIvLAoD58+ejoaFBeJSVlflcF0EQxOM39cezU/OivQyCIEKg0wqqwsJCFBcXS7adOnUK2dnZwnMmpr7//nt8/vnnMBqNfuc9cuQIFAoFUlNTAQD5+fn4+uuvYbFYhDFFRUUYMGCAkFGYn5+Pbdu2SeYpKipCfn6+19fRarWIj4+XPAiCIAiC6J50WkH11FNPYc+ePVi0aBFKSkqwYcP/b+/eg6I6zzCAPwuyuwgBVARExFsQFEXFimKNOmpBk+IlTUgwEnBSjBbSqhMlFwpO2opaozFeGnUEY6pRSaNJGoNjECXRFcUAFhUvyGirLlYNiwoihbd/OJxx5bLgCsvK85vZSc75Du/53nM+d989ey7bsXHjRsTGxgJ4UEy99NJLyMnJwbZt21BdXQ29Xg+9Xo/79+8DeHAy+UcffYT8/HxcvHgR27Ztw/z58zFz5kylWJoxYwbUajXeeOMNnDp1Cjt37sTq1auNfir8wx/+gPT0dHz44YcoLCzE4sWLkZOTg7i4uNbfMERERNT2SBv2zTffyMCBA0Wj0Yifn59s3LhRaSsuLhYA9b4yMzNFROTEiRMyYsQIcXZ2Fq1WK/3795clS5bIvXv3jNaTn58vo0ePFo1GI927d5elS5fW6cuuXbukX79+olarxd/fX7799ttm5WIwGASAGAyG5m8IIiIisoimfn6rREQsV861H2VlZXB2dobBYODPf0RERFaiqZ/fbfYnPyIiIiJrwYKKiIiIyEwsqIiIiIjMxIKKiIiIyEwsqIiIiIjMxIKKiIiIyEwsqIiIiIjMxIKKiIiIyEwsqIiIiIjMxIKKiIiIyEwdLN2B9qL2CT9lZWUW7gkRERE1Ve3ntqkn9bGgaiU3b94EAPTo0cPCPSEiIqLmunnzJpydnRtsZ0HVSjp37gwAuHz5cqM7pLmGDx+O48ePP7F41qK95g0w9/aYe3vNu6ysDD169MC///3vdvlQ+fa639ta3gaDAd7e3srneENYULUSG5sHp6s5Ozs/0TcGW1vbdvlG017zBph7e8y9veZdy8nJqV3m3173e1vNu/ZzvMH2VuoHtZDY2FhLd8Ei2mveAHNvj9pr3u1de93v1pq3SkydZUVPRFlZGZydnWEwGNpk5U1E1NbwfZPagqaOQx6haiUajQZJSUnQaDSW7goRkVXg+ya1BU0dhzxCRURERGQmHqEiIiIiMhMLKgtat24devXqBa1WixEjRuDYsWNKW1FREaZPn46uXbvCyckJ4eHhKCkpMRnz4MGDCAwMhEajwbPPPostW7Y0a70tLSsrC2FhYfD09IRKpcKePXuUtqqqKsTHx2PQoEFwcHCAp6cnXn/9dVy9etVk3LaeN9B47gAQHR0NlUpl9Jo0aZLJuG09d1N537lzB3FxcfDy8oK9vT0GDBiATz75xGTckydP4rnnnoNWq0WPHj2wfPnyOsukpaXBz88PWq0WgwYNwt69e59UWiYlJydj+PDheOaZZ+Dm5oZp06bh7NmzRsts3LgR48aNg5OTE1QqFUpLS5sUu63vc2p8+7/55pvo27cv7O3t0bVrV0ydOhWFhYUmY5oazyKCxMREdOvWDfb29pg4cSLOnz//xHNrjKlxp9PpMH78eDg4OMDJyQljxoxBRUVFozGtZrwLWcSOHTtErVZLSkqKnDp1SmJiYsTFxUVKSkrkzp070qdPH5k+fbqcPHlSTp48KVOnTpXhw4dLdXV1gzEvXrwoHTt2lAULFsjp06dlzZo1YmtrK+np6U1ab2vYu3evvP/++/Lll18KANm9e7fSVlpaKhMnTpSdO3dKYWGh6HQ6CQoKkmHDhjUa0xryFmk8dxGRqKgomTRpkly7dk153bp1q9GY1pC7qbxjYmKkb9++kpmZKcXFxbJhwwaxtbWVr776qsGYBoNB3N3d5bXXXpOCggL5/PPPxd7eXjZs2KAsc/jwYbG1tZXly5fL6dOnJSEhQezs7ORf//pXS6VqJDQ0VFJTU6WgoEDy8vLk+eefF29vb7lz546yzKpVqyQ5OVmSk5MFgPz8888m41rDPm/vTG3/DRs2yKFDh6S4uFhOnDghYWFh0qNHD/nf//7XYMymjOelS5eKs7Oz7NmzR/Lz82XKlCnSu3dvqaioaPGcRUznfeTIEXFycpLk5GQpKCiQwsJC2blzp9y7d6/BmNY03llQWUhQUJDExsYq09XV1eLp6SnJycmyb98+sbGxEYPBoLSXlpaKSqWS/fv3Nxhz0aJF4u/vbzTvlVdekdDQ0Catt7XV9+H6qGPHjgkAuXTpUoPLWFveIvXnHhUVJVOnTm1WHGvLvb68/f395YMPPjCaFxgYKO+//36DcdavXy+dOnWSyspKZV58fLz4+voq0+Hh4fLCCy8Y/d2IESPkzTffNCODx3f9+nUBIIcOHarTlpmZ2eSCypr2+dq1a6Vnz56i0WgkKChIsrOzlbaKigr53e9+J507dxYHBwd58cUXRa/Xm4y5a9cu8fX1FY1GIwMHDpRvv/3WqL2mpkb++Mc/ioeHh2i1WpkwYYKcO3fuiefWmOZu//z8fAEgFy5caDCmqfFcU1MjHh4e8te//lVpLy0tFY1GI59//rk56TSZqbxHjBghCQkJzYppTeOdP/lZwP3793HixAlMnDhRmWdjY4OJEydCp9OhsrISKpXK6IoCrVYLGxsb/Pjjj8q8cePGITo6WpnW6XRGMQEgNDQUOp2uSettiwwGA1QqFVxcXJR5T3PeBw8ehJubG3x9fTF37lzlkUW1nsbcR40aha+//hpXrlyBiCAzMxPnzp1DSEiIskx0dDTGjRunTOt0OowZMwZqtVqZFxoairNnz+Lnn39Wlmls27Q2g8EAACbvtvwoa93nO3fuxIIFC5CUlISffvoJgwcPRmhoKK5fvw4AmD9/Pr755hukpaXh0KFDuHr1Kl588cVGYx45cgQRERF44403kJubi2nTpmHatGkoKChQllm+fDk+/vhjfPLJJ8jOzoaDgwNCQ0Nx7969Fs23VnO3/927d5GamorevXsbPZqsV69eWLx4sTJtar8XFxdDr9cbLePs7IwRI0a0yn43lff169eRnZ0NNzc3jBo1Cu7u7hg7dqzRZxpgveMd4DlUFnHjxg1UV1fD3d3daL67uzv0ej1GjhwJBwcHxMfHo7y8HHfv3sXbb7+N6upqXLt2TVne29sb3bp1U6b1en29McvKylBRUWFyvW3NvXv3EB8fj4iICKN7fzyteU+aNAlbt25FRkYGli1bhkOHDmHy5Mmorq5Wlnkac1+zZg0GDBgALy8vqNVqTJo0CevWrcOYMWOUZbp16wZvb29luqG8a9saW8YSedfU1GDevHn45S9/iYEDBzbrb611n69cuRIxMTGYNWuWcl5cx44dkZKSAoPBgM2bN2PlypUYP348hg0bhtTUVBw5cgRHjx5tMObq1asxadIkLFy4EP3798ef/vQnBAYGYu3atQAenEP00UcfISEhAVOnTkVAQAC2bt2Kq1ev1jl3r6U0dfuvX78ejo6OcHR0xHfffYf9+/cbfUHo27cvXF1dlWlT47n2v5ba76byvnjxIgBg8eLFiImJQXp6OgIDAzFhwgSj87ysdbwDfPRMm9S1a1ekpaVh7ty5+Pjjj2FjY4OIiAgEBgYa3fp+69atFuxly6qqqkJ4eDhEBH/729+M2p7WvF999VXl/wcNGoSAgAD07dsXBw8exIQJEwA8nbmvWbMGR48exddff42ePXsiKysLsbGx8PT0VL51JicnW7iX5omNjUVBQUGdb+NNYY37vPaowbvvvqvMe/ioQVBQEKqqqoyOKvj5+cHb2xs6nQ4jR44E8OAoTXR0tHKkRqfTYcGCBUbrCg0NVYolU0dpHv43ZmmvvfYafvWrX+HatWtYsWIFwsPDcfjwYWi1WgBARkaGhXv4ZNXU1AB4cEL+rFmzAABDhw5FRkYGUlJSlH/j1jjea7GgsgBXV1fY2trWuWqvpKQEHh4eAICQkBAUFRXhxo0b6NChA1xcXODh4YE+ffo0GNfDw6PemE5OTrC3t4etra3J9bYFtcXUpUuXcODAAZN3SH5a8n5Unz594OrqigsXLigF1aOsPfeKigq899572L17N1544QUAQEBAAPLy8rBixYo6h/prNZR3bVtjy7R23nFxcfjnP/+JrKwseHl5mR3PGvZ5Y0cNCgsLodfroVarjX7Kr21/+KiCtR2lAZr2/g48KPScnZ3h4+ODkSNHolOnTti9ezciIiLqjWtqPNf+t6SkxOgIT0lJCYYMGfIkUmuUqbxr+zRgwACj9v79++Py5csNxrWG8V6LP/lZgFqtxrBhw4y+gdTU1CAjIwPBwcFGy7q6usLFxQUHDhzA9evXMWXKlAbjBgcH1/lWs3//fiVmc9ZrKbXF1Pnz5/H999+jS5cuJv/maci7Pv/5z39w8+ZNozfHR1l77lVVVaiqqqrz0FFbW1vlG219goODkZWVhaqqKmXe/v374evri06dOinLNLZtWpqIIC4uDrt378aBAwfQu3fvJxLX2vd5c2RkZCAuLs7S3WiWx9n+8uACMVRWVjYY19R+7927Nzw8PIyWKSsrQ3Z2dqvsd1N59+rVC56ennVuHXLu3Dn07NmzwbhWNd5b9RR4UuzYsUM0Go1s2bJFTp8+LbNnzxYXFxflKpeUlBTR6XRy4cIF+eyzz6Rz586yYMECoxiRkZHyzjvvKNO1l5cuXLhQzpw5I+vWrav38tLG1tvSbt++Lbm5uZKbmysAZOXKlZKbmyuXLl2S+/fvy5QpU8TLy0vy8vKMbh/w8NVc1pi3qdxv374tb7/9tuh0OikuLpbvv/9eAgMDxcfHx+iSYmvMvbG8RUTGjh0r/v7+kpmZKRcvXpTU1FTRarWyfv16JcY777wjkZGRynRpaam4u7tLZGSkFBQUyI4dO6Rjx451bpvQoUMHWbFihZw5c0aSkpJa9bYJc+fOFWdnZzl48KDRWC4vL1eWuXbtmuTm5sqmTZsEgGRlZUlubq7cvHlTWcYa93llZaXY2trWuaLz9ddflylTpkhGRka9VzV6e3vLypUrG4zbo0cPWbVqldG8xMRECQgIEBGRoqIiASC5ublGy4wZM0Z+//vfP246zdbY9i8qKpIlS5ZITk6OXLp0SQ4fPixhYWHSuXNno8v8x48fL2vWrFGmmzKely5dKi4uLvLVV18pt9tp7dsmNDbuVq1aJU5OTpKWlibnz5+XhIQE0Wq1Rlc3WuN4r8WCyoLWrFkj3t7eolarJSgoSI4ePaq0xcfHi7u7u9jZ2YmPj498+OGHUlNTY/T3Y8eOlaioKKN5mZmZMmTIEFGr1dKnTx9JTU1t1npbWu3l4Y++oqKipLi4uN42AJKZmanEsMa8a/vYUO7l5eUSEhIiXbt2FTs7O+nZs6fExMTUeUOwxtwby1vkQVERHR0tnp6eotVqxdfXt854j4qKkrFjxxrFzc/Pl9GjR4tGo5Hu3bvL0qVL66x7165d0q9fP1Gr1eLv71/nEvuW1NBYfnj/JCUlmVzGGve5yINL2ePi4pTp6upq6d69uyQnJ0tpaanY2dnJF198obQXFhYKANHpdA3GDA8Pl1//+tdG84KDg+vcOmDFihVKu8FgaNVbB9RqaPtfuXJFJk+eLG5ubmJnZydeXl4yY8YMKSwsNPr7nj17SlJSktE8U+O59pYR7u7uotFoZMKECXL27NkWzfNRpsZdcnKyeHl5SceOHSU4OFh++OEHo3ZrHe8iLKiIiKgFmDpqMGfOHPH29pYDBw5ITk6OBAcHS3BwsFEMazxKQ+0XT0onIqIn7pVXXsF///tfJCYmQq/XY8iQIUhPT1dOGF+1ahVsbGzwm9/8BpWVlQgNDcX69euNYtRemFNr1KhR2L59OxISEvDee+/Bx8cHe/bsMboVxaJFi3D37l3Mnj0bpaWlGD16NNLT05Wr54haikpExNKdICIiIrJmvMqPiIiIyEwsqIiIiIjMxIKKiIiIyEwsqIiIiIjMxIKKiIiIyEwsqIiIiIjMxIKKiIjMkpWVhbCwMHh6ekKlUmHPnj1G7V9++SVCQkLQpUsXqFQq5OXlNSu+n58fNBpNqz3gmOhxsKAiIiKz3L17F4MHD8a6desabB89ejSWLVvW7Ng//vgjKioq8NJLL+HTTz81t6tELYYFFRERmWXy5Mn485//jOnTp9fbHhkZicTEREycOLHZsTdv3owZM2YgMjISKSkpddrrOyLm4uKCLVu2KNNHjhzBkCFDoNVq8Ytf/AJ79ux5rCNlRI3ho2eIiKhNun37NtLS0pCdnQ0/Pz8YDAb88MMPeO6555oco6ysDGFhYXj++eexfft2XLp0CfPmzWu5TlO7xSNURETUJu3YsQM+Pj7w9/eHra0tXn31VWzevLlZMbZv3w6VSoVNmzZhwIABmDx5MhYuXNhCPab2jAUVERFZ1Jw5c+Do6Ki8aqWkpGDmzJnK9MyZM5GWlobbt283OfbZs2cREBBg9HDkoKCgJ9NxooewoCIiIov64IMPkJeXp7wA4PTp0zh69CgWLVqEDh06oEOHDhg5ciTKy8uxY8cO5W9VKhVExCheVVVVa3afCADPoSIiIgtzc3ODm5ub0bzNmzdjzJgxda4cTE1NxebNmxETEwMA6Nq1K65du6a0nz9/HuXl5cq0r68v/v73v6OyshIajQYAcPz48ZZKhdoxHqEiIiKz3Llzx+joUnFxMfLy8nD58mUAwK1bt5CXl4fTp08DePAzXF5eXoP3laqqqsJnn32GiIgIDBw40Oj129/+FtnZ2Th16hQAYPz48Vi7di1yc3ORk5ODOXPmwM7OTok1Y8YM1NTUYPbs2Thz5gz27duHFStWAHhwdIvoiREiIiIzZGZmCoA6r6ioKBERSU1Nrbc9KSmp3nhffPGF2NjYiF6vr7e9f//+Mn/+fBERuXLlioSEhIiDg4P4+PjI3r17xdnZWVJTU5XlDx8+LAEBAaJWq2XYsGGyfft2ASCFhYVPcjNQO6cSeeTHZyIioqfYtm3bMGvWLBgMBtjb21u6O/SU4DlURET0VNu6dSv69OmD7t27Iz8/H/Hx8QgPD2cxRU8UCyoiInqq6fV6JCYmQq/Xo1u3bnj55Zfxl7/8xdLdoqcMf/IjIiIiMhOv8iMiIiIyEwsqIiIiIjOxoCIiIiIyEwsqIiIiIjOxoCIiaoLo6GhMmzbN0t0gojaKt00gonbP1CNIkpKSsHr16joP4SUiqsWCiojavYcfrrtz504kJibi7NmzyjxHR0c4OjpaomtEZCX4kx8RtXseHh7Ky9nZGSqVymieo6NjnZ/8xo0bh7feegvz5s1Dp06d4O7ujk2bNuHu3buYNWsWnnnmGTz77LP47rvvjNZVUFCAyZMnw9HREe7u7oiMjMSNGzdaOWMietJYUBERPaZPP/0Urq6uOHbsGN566y3MnTsXL7/8MkaNGoWffvoJISEhiIyMRHl5OQCgtLQU48ePx9ChQ5GTk4P09HSUlJQgPDzcwpkQkblYUBERPabBgwcjISEBPj4+ePfdd6HVauHq6oqYmBj4+PggMTERN2/exMmTJwEAa9euxdChQ7FkyRL4+flh6NChSElJQWZmJs6dO2fhbIjIHDyHiojoMQUEBCj/b2triy5dumDQoEHKPHd3dwDA9evXAQD5+fnIzMys93ysoqIi9OvXr4V7TEQthQUVEdFjsrOzM5pWqVRG82qvHqypqQEA3LlzB2FhYVi2bFmdWN26dWvBnhJRS2NBRUTUSgIDA/GPf/wDvXr1QocOfPsleprwHCoiolYSGxuLW7duISIiAsePH0dRURH27duHWbNmobq62tLdIyIzsKAiImolnp6eOHz4MKqrqxESEoJBgwZh3rx5cHFxgY0N346JrJlKeOtfIiIiIrPwKxERERGRmVhQEREREZmJBRURERGRmVhQEREREZmJBRURERGRmVhQEREREZmJBRURERGRmVhQEREREZmJBRURERGRmVhQEREREZmJBRURERGRmVhQEREREZnp/zAv2dSV67iJAAAAAElFTkSuQmCC",
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ZwXbBiRviAcICkUNEDPFeomsTghfvi1MKUUds8J6g6ByCB6/hoIMOUtsMsF/wOUNjA7ry8FlCGg2RMnRrugXbApGMzx26D9977z21/X379nX1mkLtW3z+UNOFNDU6/KZNm6bShLqJwg34zCFNiHqs3/72t6rLtanTfQqMniFNz86dOxGTVv/HI/PW7PD0uO4ddfnbG4tbenMICcju3bs9S5cuVf8nEpMnT1a/AbikpqZ62rVr5xk/frzn2Wef9dTV1fkti2XeeOMN9feTTz7pOfDAAz05OTme/Px8z5FHHumZP3++tWxJSYnn3HPP9RQXF3syMzM9gwYN8rzzzjvqvmnTpnkKCgr81o31mj/9WNfIkSPVY/v06eOZPn26p0ePHp4HHnjAcXtMbr31Vk/btm09ubm5nvPOO89zxRVXeA466CC/ZV544QW1/enp6Z6ioiLPYYcd5nn99det+w8//HC1b4Lx+eefe8aNG+fJyMjwFBYWeiZOnOgpLS1V9+E39fe//73aN926dfM899xznqFDh3puvvlmv+1/9NFHPUcddZRaR8+ePT2vvPKKdf+qVavUMgsWLLBuW7RokWfChAme7OxsT15enufQQw/1/PLLLwG3EfsZ+9tk+fLlnpNOOkltc1ZWlqdfv36eK6+80lNfX6/ux/8PPvigp2/fvp60tDT1mcBr++KLL/z2cceOHT2tWrUKuJ/0+/zf//7Xs99++6nXiM/WmjVr/JZ7//331X7EtmB/jR49Wn2+Qr3PJl999ZV6z/BeYj1Dhgzx25f1Ll5TsH2L78Itt9zi6dKli3o83ssZM2YEfa/wWcBtn332mXXbM8884+natavaxuOPP95z7733NvouuP1dcXv85iy/ZiLeZ/l9vmyrnDPNG64/ZURXuffUoS29SYQ4wll+8QuKkRE9Q8TLLYjCIJIVyGOJhAYRuiuvvJKO6i08y48pP+JnmQBg7kkIIcFANxfsGGAgiRTbSy+9pIrmYUfgFtQY4UAVKN1JSCJBQUUaCSrWUBFCQoHaHtTPoA0fZ/eof0GxPIqV3YIi+ED+W4QkGhRURMGidEJIOKBAGhEp0vIgXcqUactD2wTSyDahmrYJJAFg+SchJJ5+TyioiEMNFSNUJH7RLfF240lCCImmJhBE447PlB9R6LEzgMaeJJ6Bxw7GXGzbtk39+IVrSkgIIWZkCmIKY2vgYxaOi78dCirSuCidESoS58XQMLpEi/OaNWtaenMIIUlAYWGhNdg6UiioiIJdfiSRgFs2HL6Z9iOERAsi3dFEpjQUVMShy48pPxL/INVHY09CSLzA4oMkZPX2XbJsc0VYj2FROiGEEBI5FFRJRn29R055fKac+Og3UrW3QSSFW5TOlnRCCCHEPRRUSQYiTdsrq2V3TZ2UVO51LcIqbeKL42cIIYQQ91BQJRklu6qtv3e5jFBBTNkDUjT3JIQQQtxDQZVklFY1RKV2VdeFle5LT2ktrVt5b2MdFSGEEOIeCqokw0zzua2h0gXp+VmpkpnmbR1lpx8hhBDiHgqqJCOSCJUWVHmZaQ2CihEqQgghxDUUVElGyS5TUNWGlfLLz0yVzFTvR4LmnoQQQoh7KKiSjNJdEaT8qmusCFUGU36EEEJI2FBQJXOEam+4Kb9UyfBFqFiUTgghhLiHgiqJI1Thp/yMGipGqAghhBDXUFAlGTv8BFX4EarMNNZQEUIIIeFCQZVk7KgKv4ZKD0ZWNVSpOkJFQUUIIYS4hYIqydhRGX4NVbmfD5WuoWLKjxBCCHELBVUSgaiSKaLc1lA5+lAxQkUIIYS4hoIqSU09I/eh8goqRqgIIYQQ91BQJWlBOqhybZvQUEPFonRCCCEkfCioklhQhZ/ySzWMPSmoCCGEkKQQVBs2bJCzzjpLiouLJSsrSwYPHixz5851XPbiiy+WVq1ayYMPPuh3+44dO+TMM8+U/Px8KSwslPPPP18qKyv9llm8eLEceuihkpmZKd26dZO777670fqnT58u/fr1U8tgO9577z2JV0GF1B3YFWaXX0FWmjV6hik/QgghJAkEVWlpqRx88MGSlpYmM2bMkKVLl8p9990nRUVFjZZ94403ZNasWdK5c+dG90FM/fDDD/LRRx/JO++8I19++aVcdNFF1v3l5eUyYcIE6dGjh8ybN0/uueceueWWW+TJJ5+0lvn222/l9NNPV2JswYIFcuKJJ6rLkiVLJB4FVdeibPV/lQsfqpq6esvEkxEqQgghJDK8oYw45K677lLRomnTplm39erVyzGKdfnll8sHH3wgxx57rN99P/74o7z//vsyZ84cGTlypLrt4Ycflt/85jdy7733KgH2wgsvyN69e+XZZ5+V9PR0GThwoCxcuFDuv/9+S3g99NBDcvTRR8u1116rrt92221KoD3yyCPy+OOPO25/dXW1upjCrblc0ru1yZKlm8pVhMrj8ajIXah0H8jNgG0CndIJIYSQpIlQvf3220oEnXrqqdK+fXsZNmyYPPXUU37L1NfXy9lnn62EDoSQnZkzZ6o0nxZTYPz48dK6dWv57rvvrGUOO+wwJaY0EydOlGXLlqkomV4GjzPBMrg9EHfccYcUFBRYF4jD5prj180Xoar3hBZGusMvJz1FUlNaW7P8GKEihBBCkkBQrVy5Uh577DHp06ePij5dcsklcsUVV8jzzz/vF8VKTU1VtzuxefNmJcZMsHybNm3UfXqZDh06+C2jr4daRt/vxPXXXy87d+60LuvWrZPmsk3oUpRl3Raqjsr0oAJWhIo1VIQQQkjip/wQfUJkaerUqeo6IlSoWUKKbfLkyareCam4+fPnB01ptRQZGRnq0pyU+FzSi3MzJDs9RdkmqDqq3NCWCXBJB5ZTOiNUhBBCSOJHqDp16iQDBgzwu61///6ydu1a9fdXX30lW7dule7du6uoEy5r1qyRa665Rnr27KmW6dixo1rGpLa2VnX+4T69zJYtW/yW0ddDLaPvjxd0hKo4J12y070CqTKEdYI5xw9oY09GqAghhJAkEFTo8EMdk8ny5ctVNx5A7RTsDlBAri8oMkc9FVKEYOzYsVJWVqaiWZpPP/1URb/GjBljLYPOv5oar7AAKDjv27ev1VGIZT755BO/bcEyuD0eu/yKstMlNyPF1YBkPccPHX5myo8RKkIIISQJUn5XXXWVjBs3TqX8Jk2aJLNnz1ZWBtrOAN5UuJjAYgFRI4ghHdFCd96FF16oUoUQTZdddpmcdtpplsXCGWecIf/4xz+UJcJ1112n0opIJT7wwAPWeqdMmSKHH364sm1AJ+HLL7+s/LBMa4WWpr7eI6VVXlFYnNsQoQo1ILlh7Iw3QpVBp3RCCCEkeSJUo0aNUv5SL730kgwaNEhZFcC0E75S4QBbBBhyHnnkkcou4ZBDDvETQujA+/DDD2XVqlUyYsQIlTK86aab/LyqIOxefPFF9bihQ4fKa6+9Jm+++abarngBqbs6tPWJSGF2muToCFWIlJ/pkg44y48QQghJoggVOO6449TFLatXr250Gzr6IIaCMWTIEFWTFQzYN+ASr+h0X15GqmSkpkhOhrsaqsZdfoxQEUIIIUkToSIR1k/leP20cnwpv1ADkssbdfnR2JMQQggJFwqqJBNUbXyCCrYJ7nyo/Lv8rBqq2jrlsk4IIYSQ0FBQJamg0im/XS5TfnqgMtKFAFpqbx2jVIQQQogbKKiShB1VdkHli1BVu0z52WqoAAvTCSGEEHdQUCUJOyrtKT9dQxVel196SmvRxvMsTCeEEELcQUGVrBEqq4aqzl3KL8sbocIYH8s6gYXphBBCiCsoqJKthirbfQ0Vis61saeOUAGaexJCCCHhQUGVJJQGKEpXw5EDAGuEWp8ZqO7yAzT3JIQQQsKDgipJKLH5ULmxTdAF6a1bNaQIAc09CSGEkPCgoEqyCFVxGLYJpgcVaqc0NPckhBBCwoOCKglAJEkXn9ud0oMVpZfbOvw0GZagYoSKEEIIcQMFVRJQ6uvwS23dyjLodDMcWRekaw8qTUZqg1s6IYQQQkJDQZUElPg8qBCd0qk7y4eqpk7qfYXnoTyo7Ck/2iYQQggh7qCgSqIIla6fArm+GiqMkNkdIHVn96DSZDJCRQghhIQFBVUSeVAV+TyodKeerjMP1Omnu/wCRahYlE4IIYS4g4IqmUw9cxsEFVJ/OenBvah0l1/AGioWpRNCCCGuoKBKQpd0jS5MrwxQmG6l/ALVUNHYkxBCCHEFBVUyCSqjhgrkWAOSnSNNDWNnbDVUPmPPakaoCCGEEFdQUCWxoMr2RagC1VCF6vJjyo8QQghxBwVVMguqEDVUuii9UZcfi9IJIYSQsKCgSmJBlRti/EygCBWNPQkhhJDwoKBKIh+qxhEqdym/Rl1+NPYkhBBCwoKCKsGBC3ppVU2URem2GipGqAghhJCwoKBKcFAHVecbLVOY7R9pyvGl/JxsEyDEKn2Rq8ZdfixKJ4QQQsKBgirBKfHVT+VlpEpGqlcIaYINSIaYwliaoDVUTPkRQgghrqCgSnBK9dgZW7rP7PLb5ZDy0+m+9NTWVkRKQ2NPQgghJDwoqJIkQmWvn/KLUDkUpQdySfcTVEz5EUIIIa6goEqSCJWjoPJFqCodfKgCdfiZTumsoSKEEELcQUG1L0SoHGqoAnX4+RWlM+VHCCGEuIKCKokjVMFqqCqqnV3S/YvSGaEihBBC3EBBlaQu6aZtgpNTevluZ5f0SIvStXUDIYQQsi9CQZXg7NAu6dnhFqX7Un4ZDjVUPvsFiKSautCi6qOlW2Tgze/Lmws2BFwGvleTHp8pJz/2LcUXIYSQpIOCKsGp9BWXBytK3+VQlL61olr9X5zb+HEZvqJ0t2m/b1ZsV55VrwcRVD9trpDZq3fIvDWlsrFsd8h1EkIIIYlE43wPSSheu2Sc7N5bJymtWwVM+e2uqVNRIXOZDaVeUdOlKCtgDRWAUMrLdJd2nL+mVGrr6iU1pbFOn7N6R8Nzl+2Wbm2y3b1AQgghJAFghCoJyEpPUQaddvRwZKe0H0QN6FLYWFC1atUqrMJ0PZwZI25+3FThuMzsVTsaiTlCCCEkWaCgSmIginRUyj4gOZigCrcwvaTSK6gA0np2PB6P3+36uQkhhJBkgYIqiUGkSUepzE4/DFTWxp5OKb9wzT11hArMXlXS6P7VJVWyzVezBRihIoQQkmxQUCU5uZZ1Ql0jQVOUnWZ5VQWOUAUXVIg+6RoqMGd1qbrNZI6R7gMbd1JQEUIISS7iWlBt2LBBzjrrLCkuLpasrCwZPHiwzJ0717r/lltukX79+klOTo4UFRXJ+PHj5bvvvrPu//zzz1WUxukyZ84ctczq1asd7581a5bftkyfPl09V2ZmptqO9957TxIBK0Jl1FDpLrtA0SnQUEMVPOWHVKJOC6altFLi6pdtlX7LfOcTVMO7F6r/GaEihBCSbMStoCotLZWDDz5Y0tLSZMaMGbJ06VK57777lHDSHHDAAfLII4/I999/L19//bX07NlTJkyYINu2bVP3jxs3TjZt2uR3ueCCC6RXr14ycuRIv+f7+OOP/ZYbMWKEdd+3334rp59+upx//vmyYMECOfHEE9VlyZIlEu/oTj+zKF3XMHUuCCyo3EaodHQKAmxED+97M3tVqWOH30nDuljPb49iEUIIIYlM3Aqqu+66S7p16ybTpk2T0aNHKxEEsbT//vtby5xxxhkqKrXffvvJwIED5f7775fy8nJZvHixuj89PV06duxoXRDpeuutt+Tcc89VUSgT3GcuCyGneeihh+Too4+Wa6+9Vvr37y+33XabDB8+XIm5eCfHYUByMMsEu7lnqAiV6dQ+uldxozqqzTv3yNodVYLa+OOGdBbsdkS0thuF7IQQQkiiE7eC6u2331ZRpFNPPVXat28vw4YNk6eeeirg8nv37pUnn3xSCgoKZOjQoQHXWVJSogSVnRNOOEE9zyGHHKKWM5k5c6YSbiYTJ05UtweiurpaiTvz0hI4DUheH6LDzzT3DFWUbjm1Q1D1bGPVUWl0d9/AzgVSlJMu7fMy1HWaexJCCEkm4lZQrVy5Uh577DHp06ePfPDBB3LJJZfIFVdcIc8//7zfcu+8847k5uaq2qYHHnhAPvroI2nbtq3jOp955hklhLp27WrdhscilYgaqXfffVcJKqTzTFG1efNm6dChg9+6cB23B+KOO+5Q4k5fEG1rCZwGJGsx07UodMovZISqskFQDe9RKKmtW6mU3vrSKr9o1Sif2NIijtYJhBBCkom4dUqvr69XEaqpU6eq64hQoWbp8ccfl8mTJ1vLHXHEEbJw4ULZvn27imBNmjRJFaYj2mSyfv16JcxeffVVv9shvq6++mrr+qhRo2Tjxo1yzz33qKhVpFx//fV+60WEqiVElVOESqf8OgeLULk09tSWCRBUEG8DuxTIonVlqm6qa1G2zPHVU43uVWQ95/y1ZSxMJ4QQklTEbYSqU6dOMmDAAL/bUL+0du1av9vQ4de7d2856KCDVAQqNTVV/W8HtViok3IjksaMGSMrVqywrqOmasuWLX7L4DpuD0RGRobk5+f7XVqCHF1D5StKR5G5nuMXLOXn1tizxFdDVeQbzjymVxurML2saq8s21LhH6HyRcUYoSKEEJJMxK2gQoffsmXL/G5bvny59OjRI2RkC/VLJugog6D6wx/+4FdsHghEvCDoNGPHjpVPPvnEbxmkFnF7vJOtu/x8RembyvZYxp1OA5XDNfYs9QmqYt+6tHBCqk/XUu3fLkeKc721U12Z8iOEEJKExG3K76qrrlK2B0j5IY03e/ZsVXSOC9i1a5fcfvvtKuIE8YOU36OPPqq8q1DIbvLpp5/KqlWrlGWCHdRkoRsQKUXw+uuvy7PPPitPP/20tcyUKVPk8MMPV7VWxx57rLz88svKD0tvSzyTY/OhsjyoCrMadTo6dvmFsE2wIlSWoPKm9n7ZtktmLNmk/tbdf34RKqb8CCGEJBFxK6hQy/TGG2+oWqRbb71V2SY8+OCDcuaZZ6r7U1JS5KefflKCCGIK6Tw85quvvlIWCiZIAUKcwZjTCdggrFmzRqULscwrr7wip5xyinU/Hvviiy/K3//+d7nhhhtUofybb74pgwYNkkTxodKjZ3SHX7D6KbPLrzpEUbo9QlWYnS59O+SpVN9bCzf61U+Zz8sIFSGEkGQibgUVOO6449TFCXT1IZrkBoihQKDA3SxyDwSiXvbIVyJgFaX7uvx0ZChYh58ZoXJr7KkjVGB0rzZKUNXVexpHqHyCaufuGqmsrrVG4xBCCCGJTNzWUJEY2yb4IlQ6MhSsID0s24Qq/wgVGOUrTNfPYz5XXmaa5Gd6t4leVIQQQpIFCqokJ8cnqHSEys0cP7dF6bV19VJWVdM4QuUrTFd/G+KqUdrPZR1VxZ4a+XFTyxijEkIIIW6goEpydMoP6TW3c/xAhhWhCiyoynZ7xRRq2wuzGronOxZkSvc22X5dfyY63ajruUJx9auL5JiHvpLv1+90tTwhhBDS3FBQJTkNw5HrpL7eY9kmhIpQNRh71oesnyrISpPUFP+P0g2/6S8nHthZfntg50aP0ylAtym/JRu8QmrJRgoqQggh8QkrgpOcbG2bUF0r2yqrZW9dvRpU3DE/M+jjGow961wNRrZz9KCO6uJEONYJNXX1sqXcKwJptUAIISReYYQqydFddHA8X7vDO18PYsoeUYqkKN0SVD6XdLeEY52weece8TULsoidEEJI3EJBtY90+YHlvjEwodJ9IFOn/CKMUAXDGpDsIuJkii63NVeEEEJIc0NBleSkp7aWtBSvI/rPWypdWSaYRenBjD0jFlQ+QbelYo9K6QXDFF1M+RFCCIlXKKj2ocL0sCJU2im9CSJUbXMylNDzeLwpPbcRqs3le5RVAyGEEBJvUFDtA+T40n7LfRGqUGNn/Gb5NUGEqnXrVtK5wFsUvz5E1Mmsm4Lz+tYK/8HXhBBCSDxAQbUPdfptr6x2nfJrKEoPHKEqrYpMUJlRslCF5vbCdc4AJIQQEo9QUO0DZNvm5YWa42em/GrrPQHTbCWVjef4hV2YHkpQ+SJY6b6uRNZREUIIiUcoqPYBcn1u6Ro3Kb8MX8oP7KmtDxqhMuf4uaVLYXZIgeTxeCzBdWC3Qu/yjFARQgiJQyio9jHrhKLsNL/roZzSQbVD2g9ip8RXQ1UUpg8V6FyYGVIgYf3wz8JomxE9i0IuTwghhLQUFFT7ADm+Giq3HX66cBydeIEiVBhls9d3e3Fu09RQ6ehVh7xM6VkcOqJFCCGEtBQUVPuQbYLbgvRG5p4OESrd4YdIVpavgD0cuuqUX9luFe1yQkejIL6sFCEjVIQQQuIQCqp9TlB5hYkbtLlnMEGF+qlWyMmFSceCTJXKQ0pvu6+43Y6ORqHmy0oRlgYWYIQQQkhLQUG1D9kmAC1M3NBg7lkfUFBF0uEHkE5sn5cRNOpkRaiUoPJG1nbX1ElZVU1Ez0kIIYQ0FRRU+wA5RhG6G8uExuaegSNUkXhQ2dOPgeqozJQffLHa5gYXYIQQQkhLQUG1DxBpyi8zyDy/mAiqouCF5vr2Lr6omi5kD+WuTgghhDQ3FFT7ADkZ4Xf5mSk/xwhVVeSWCW7NPTfu1IIq209YhXJXJ4QQQpobCqp9AO07BYEEHyq3aHPPPQ4DkkuNovRI0QLJKeK0q7rWqpXSItCtuzohhBDS3IR2eHRBTU2NbN68WaqqqqRdu3bSpk2bWKyWxIi2Pp+oXm1zw+rIs4rSHVJ+lqlnFIKqaxtv5GnVdu/QZhMtmgqy0iTXl7K0BBVTfoQQQpJFUFVUVMh//vMfefnll2X27Nmyd+9e1c6OA3bXrl1lwoQJctFFF8moUaNiu8UkbDC25bbfDpShvvEtsbBNiEWEamhX7/b8sm2Xqsky67FMy4RGNVeMUBFCCEmGlN/9998vPXv2lGnTpsn48ePlzTfflIULF8ry5ctl5syZcvPNN0ttba0SVUcffbT8/PPPsd9y4hqI3LPH9pQhPgETdpdfE9gmAAioPu1z1d9zVu8IaJngtiuQEEIISagI1Zw5c+TLL7+UgQMHOt4/evRoOe+88+Txxx9Xouurr76SPn36RLutpJnJcFGUHk2ECozq1UZ+3lopc1btkIkDOzYSVKbNgxZUSDfu3lsnWYa/FiGEEJJwguqll15ytVxGRoZcfPHFkTwFiQMafKj8I1S1dfVWwXg0ESowplcbefG7tTLbHqGyLBMaBFV+Vqqqp6qsrlWCq7cvukUIIYQkfJcfIlHPPfecdX3NmjUyY8YM2blzZ7SrJi1Mg1O6f4SqbLdXTKG+vTDLfdegE6N6ehsYfthYroSSPUJl1lAhdclOP0IIIUkpqN577z3p16+f+rusrExGjBghJ554ogwYMECWLVsWi20kLYQ29rRHqHT9FDrwUlOi+whBMEEk1dV7ZP6aUut2XSdl983S11lHRQghJKkEFSJRXbp0UX//97//lY4dO0p5ebn8/ve/l+uvvz4W20haiAbbhLqYu6Tb035mYXpNXb1sKd/TKOUHzCHJhBBCSNIIqm7dusmqVavU39OnT5dzzjnHqp365ptvYrGNpIUIZOxpCaooXNLthengu1VeQbV55x6p93gHKNuL3rVrOlN+hBBCksrYEwLqiiuukOOPP14++eQTeeSRR9Tt9fX1UlnZ2LCRJA6BjD1jHaEa7RNUC9eVqXot7ZyO6FTr1q0cU36MUBFCCEkqQYW0Hgw9P/zwQ7nzzjuld+/elrVC9+7dY7GNpKVrqAJFqGIkqPZrm6Pc3LdX7pXF63c21E/Z0n3mbYxQEUIISSpBhc6rv/3tb+piglE0Z5xxRrSrJ/GQ8mviCBU+Q+j2m7Fks8xetUMVqIcSVJvL9yj7hmiL4gkhhJBYEPHR6KabbpJ58+YFvP/aa6+VG2+8MdLVkzhK+dmNPWMtqEz7BAgqp7EzmvZ5GZKW0kqJri0V1TF7fkIIIaRFBNX69evlmGOOUXP7LrnkEuU9hXl+JBkjVP6CqrQq9oJK11HNW1Mqa3dUOVomANRUdSpgHRUhhJAkEVTPPvusSuvBNT0vL0+uvPJKadu2rZx88snyr3/9S3bs8He+Jols7Omf8iupjH6On53+nfIlz+eCPnfNjoApP/N2elERQgiJF6IqQGndurUceuihcvfddysTz++++07GjBkjTzzxhHTu3FkOO+wwuffee2XDhg2x22LS4saepTGa42eS0rqVjOhZpP6uqfM0muNnolOBLEwnhBASL8S0ord///7yl7/8RflPrVu3TiZPnqwGI7ud/UfiU1CZxp7o6MRwYlAUIx8qex2VHmvTId9r4mlHpwK1vQIhhBDS0jRZi1S7du3k/PPPl7feekv+/Oc/R7QORLbOOussKS4ulqysLBk8eLDMnTvXuv+WW25RY29ycnKkqKhIxo8fr6JkJj179lRdZOYF9g4mixcvVpG2zMxMZVSKiJsdmJbiubAMtgMjd5KdjNTWjWwTqvbWyV5fCrA4N7aCSjumgw55mcrY04mujFARQghJdEFVWlpq1Udt27ZNXn/9dfnhhx9ivmF4noMPPljS0tJUwfvSpUvlvvvuU8JJc8ABBygj0e+//16+/vprJZ4mTJigtsvk1ltvlU2bNlmXyy+/3LoPY3LwmB49eqiuxXvuuUcJtSeffNJa5ttvv5XTTz9dCcQFCxaoWYW4LFmyRPaFCBVScNrKQHf4QWxl+e6PFYO7FlgiyqkgXcN5foQQQuKNVh7kcFzy9NNPy9SpUy1bhBdeeEGGDh0qX375pUyZMkUuuOCCmG3YX//6V5U6RMrQLRBHBQUF8vHHH8uRRx6pboPIQsE8Lk489thjykMLBfbp6enWc7/55pvy008/qeuYS7hr1y555513rMcddNBBcuCBB8rjjz/uuN7q6mp1MbcN0S/MPszPz5dEoGpvrQy46QP19/DuhdK6VSsVoVq6qVw6F2TKt9d793Es+f0TM9UImhOGdpZ/nj7McZlV23fJEfd+LqmtW8mB3Qr9OgDPHddTjhncKeD6P/xhs3y2bKvcfPxASzDa2b23Tm5+e4kcPaij/Lpfh6hez67qWrnl7R/kN0M6yRF920e1LkIIiUfu/WCZzFpZ4ncbfo8vOKSXTBjYURIdrS1CHb/DMvb85z//qaJRu3fvVi7omOGH1B6e5PDDD4+poHr77bdl4sSJcuqpp8oXX3yhBjD/6U9/kgsvvNBxeVg2IKqEFw2RZ4IU32233aa2GWajV111laSmel/6zJkzVfG8FlMAz3vXXXepKBkiYljm6quv9lsnloHoCsQdd9wh//jHPySRyUxNkba5GbK9slrmry3zu2//9rlN8pxHDeigBJUplOxgQHJhdpqUVdXI3DWlfveV764JKqju+WCZ/Ly1Ukb2aCMnj+jquMxbCzfIq3PXy5IN5VELqjcWbJDp89bLsi0VFFSEkKSjrGqvPPLZCsf7MOg+GQSVW8ISVBAhqGXCBSNmIKYARAxqk2LJypUrVfQIQuaGG25Qo2wwMxDCB8XuGkSNTjvtNKmqqpJOnTrJRx99pOwbNHjM8OHDpU2bNip1h1E5SPvdf//96n5Epnr16uX33B06dLDug6DC//o2cxncHgg8jynCdIQqkcAZxtuXHazGwfjd3gr1TsVN8pznHdxLeVIN6JQf1B/r7UsPUZEyzbbKarnxzSVBvakQjNWF7HNW7wgoqGAuGqsaLWtdLKAnhCQhOLHVZSAPnebNKqzYWiH3frhcdvru21cIS1ClpKTInj17VGE2okaaphiCjOHKI0eOtFKMw4YNUzVLSLGZguqII46QhQsXyvbt2+Wpp56SSZMmqcL09u290QBT1AwZMkQJsj/+8Y8qgpSRkSFNBdbdlOtvLmBR4ORY3pQibkjXwNEpTffibHUx03QQVBXVtbJzd40UZKU1ekxpVY3s9nUsaqHjBCJkAOuBL1ZuRmQTmiDg9POgMxLbmJUe27ozQghpSSr21Kr/C7PTVJkE+GFjlhJU5b779hXCKkpHbRIECawRTLGA6JBZxB0LEG0aMGBAI1uGtWvX+t2GDj9Ey1DT9Mwzz6goGv4PBHyyamtrZfXq1ep6x44dZcuWLX7L6Ou4L9gy+n7S8kCoaOf2QNEg8/aV23fJNofRNYhKmZGpaCJLiIZh5qC5bkIISSYq9nijUPmZDSex+m99375CWIIKqT2YeT744IOqbgqcc845kpubK6NGjYrphqHDD2ahJsuXL1fdeKEiW2YxuB1Es/AadARr7Nixqqi+pqbhjUfasG/fvlZHIZb55JNP/NaDZXA7iR+0g3og4WK/HWk/O3NskasNZd4xOJFgj4KxK5EQkmyU+0RTXmZqI0GFKRvVhu1OshORDxVc0CFMwL///e8mSfmhcHzWrFkq5bdixQp58cUXVRTs0ksvVfej6w61VVhmzZo1yvLgvPPOU95VKGQHKCaH+Fu0aJGqyUJXItYLbystllCkjqgbLBFQcP/KK6/IQw895JcqRAfj+++/r2wb0PkHWwX4YV122WUxf90kckKNpLELKqe032ybyNpQ1hBhChf7+hmhIoQkGzqtl2dEqHINcaVTgvsCEQmqa665Ro4//nhlhgkgVGbPnq26/2IFIl5vvPGGclkfNGiQ6tKDODrzzDOtei6IG8wOhB8VtqekpETZLAwcOFAtg7Tkyy+/rDoQcdvtt9+uBJWZnkTU7cMPP1QdiyNGjFCv7aabbpKLLrrIWmbcuHGWoEMH4WuvvaY6/LBdJH4INZJGp++08HIUVL7brGhXFCk/HQGLxboIISQe0YIp36hbxSgxXXu6LwmqiKptYYwJkfK///1PeUU9+uijypcKnX6oZ4LogEcT/j/mmGMi3rjjjjtOXZxAYTxMRYOB7j5EsEKBYvVQfleIeunIF4lPtOFnwBoqX/ruxGGd5dHPfpEfN5ercLUOT5dUVsuKrd5o6wkHdpbHPv8l4qjS1oo9qk4Lza/HD+0sj38R+boIISRegVWNPeWnr6OpR9+/LxDx6BmIEBhi7r///kq0VFRUKLdyGGginYaRM+i4I6S50JGg9QGEy0Zf+m5YtyLpUZwtsLSdt7rBx2qO7+8DOuTKoM4FvsdEJoLm+tbVr2O+9O+Up/6moCKEJG2EKtO/s7qhMJ0RKtf8/PPPfh10uGjCMGEnJGq6hhhJowUNIlmje7aRNSVVqmbqiH7t/VJ08MEKFe0KhU4dYj6h3i6m/Agh+0JRunld378vEFGEym5dEAht9olCcUKaq4YKdgh7fH5T5hgdPYcQYmmUbxCz2dWnBdWonm2saNeWij3WMOhIBJV3XV6/LFgo1NaFvy5CCIl/24RUR0G1L1kntI60YBzmmHAvDwRsFWC0icLt//73v9FsIyGuKMpOswY2b9rp352no1Z5GakqFI0IFVi0vkyJL+T6l2zYaUWoinPS1aBmBFk329YVChiCoj4LjOpVJO3yMtTcQQyY3uLgfUUIIclUlG5eZ8ovBEuXLlUdc0cddZQqDkd3HKwU8Dfm3+F+WBCgKPzuu++W3/zmN7HfckIcIqKIPqGwHOm1Xm1zGtkf6FQeaqja52XI1opqWbiuTEWh6j0i3dpkSacC7zKIUmEQM1KFpit7KOavKVVCDM/fPi9T3dapMFPW7dithJ2OfhFCSNKn/HYzQhWU4uJiNQsPM/EeeeQR6dOnjxr9ouupYG0AXyj4QFFMkXjworJbJkB86bQf0nNmis6+rnCLyfXomlE9ixqvi3VUhJAkosLBh8q8vi+Nn4mqKB1Dkk855RR1ISSe6qjsnX7aMsGcS4iC8XcXb1K1U3D01bc1rCszIhHUUNxe7NojixBCEhF2+cWwy4+QeCJQR50VofLdb0aj5q0plVrk+xpFqLLDHj+DeqzF68vU37pOS22XFnqMUBFCkgR08gfzodrXuvwoqEhS0ZCmq3L0oDLrl/p2yFOdKTok3TY3w6/uSosv/Vg3LFhbJjV1HumYn6nqsRqvi4KKEJIc7Kmpt05G8wMWpe87gipiY09C4hHLP6pRyq9xhKp161Z+EanRvYosqw+1bARpOqsWq1cb27p0tIuCihCSHOjoU+tWIjnp3g7rxkXp+07Kj4KKJBW6VglWB7ApAPB+ggcUsHfYwSLB+tsQV+ayEEH1vnWFwjQH9d+uhnosGt4SQpIBHX3C3L5Wxgmk6UtVUb3vRKiY8iNJRYe8DDWYE2k3GHx2LMhU3k8QV+kpraVdbobf8rrTz/43wGPxGwFLhe27qi0LBM3U936Up75aqSwS7NjFmRZ6u2vqpKyqRopy0oO+DojASU/MlMLsdHn2nFEBl0NR/c1v/yAPnz5Mxu7fUARPCGl5cPJ08X/mSdXeOpl2zihJTUmsGMbHS7fIX1//Xu49dYj8qq93ooSJLpewp/uauih9xveb5KpXF6qUo52XLjyoxX4LY/LuYrDwWWedJWPHjrVc0f/973+r2X6ENCf4wUL9kllHpQvS4QWFNJ/J4C4FMqhLvgztVqjm7pnA2LODT0TZ66gg0F78bq2jmBrYOV/6tM/1uy0zLUXVaLlN+63dUSXz15bJpz9tVUahgZixZJNsr6yWL5ZvC7lOQkjzgukMH/ywRb76ebv8sNFr9ptIfPDDZvX78vky59+XhoL0xoIqzxBUsY7Kz1iy2VFMJXyECi7oZ599tvKeWrBggVRXV1tO6VOnTpX33nsvFttJiGtQJwXRgo66ET0ahJWToWZaSmt55/JDg64L6UKIsgO7FVq3/7ipXLmro07g02t+pWoINEXZ6Y2Em14XfpywXYO6eIcvB8IUXXjuAoczQHO5Ut9YHUJI/GB+j1EOgBO3RNx+PbYrsAdVYymhb8PJJyJ0ORmpMd+uu04eLOP7d7A9r/NvZUJEqP7v//5PHn/8cTVmJi2t4YUcfPDBMn/+/GhXT0gU5p57/CJUpgeVWzoH6BrU5p0je3hHyxTnNlycxJR3uzJdR6jMbsBgnYH6vhIKKkLiDvO7q38zEnH7QwkquwcVyE5PUeUXTZH209t1QIc8v99eXJBZaCmifuZly5bJYYcd1uj2goICKSvz+vEQ0pLWCdbYmQgEVSCHcz1U2TTvdLsuN9YJ5vMFEmCo7cLoHFBaRUFFSLxh+s7NXb3DdXNLPIBt1SelgQRVeYDByABF6k3hRVVTVy9bAjQZtTRRC6qOHTvKihUrGt2O+qn99tsv2tUTErl1gu/HzMkyIex1GTVUqAdo6OYrcr+uMMbPmE7vgQTVpp3oGJSgP3iEkJbD/O6WVtXIim2VkiigPGFvXX2ICJWzqadG3x5LLyp0cEOXoslI16UmjaC68MILZcqUKfLdd98pRbpx40Z54YUX5M9//rNccsklsdlKQsLA7h+1obTKz608HPRjzB/GX7btUim2jNTWMriL+5qILkXuvajcRKjMZSioCIk/7CdP2qcuETBP6nZU7XUsLNceU05dfmYqMJZeVDrqByuaQOUVLUXUVWJ//etfpb6+Xo488kipqqpS6b+MjAwlqC6//PLYbCUhkdQ9+TyftCCJqobKJ8rMH8Vh3QvDytd3DqeGaqd/UboT5nrQCYhQOIrsCSHxgf4eD+laIIvX71S/HWcd1EMSAbM0AeUFu/bWKb+pSCJU5TGMUG2MIuPQ1ET964uo1N/+9jfZsWOHLFmyRGbNmiXbtm2T2267LTZbSEiEESr8AKwuqbLaa2GbEPa6fF9a+K3oHw+n4cdu6OpzS0c0affeuoDLoStmk5FiDBihst0OfytCSPygT4ZOPLCL+h+CKlGMfe0nck6dxMGK0pvKi8o6QS5IQkGlSU9PlwEDBsjo0aMlN9ffg4eQ5iQrPUWKfcaZuni8fV6GZKT6j0ZwA87ItGWBLtDUESq7eWco8rNSrTO8YFGqrRV7rPlYAAalGLoc6gePaT9C4oeqvbWqbgocN6STpLZupSxYEmVAuv03yqmTWEeeAlkV5OmUXwwjVE6D7pMm5Xf11VcHjFxlZmZK79695be//a20aRPewYeQaMCXDT8AulU5mi8fIl5IqaFrMCcjRf3Q4MdxeI/wPGXwncC6lm2pUOvobTP/tP9gdPX5ViHChkLMnsbgZqcfPAoqQuIH/T1G2qt9fqYM7lqghqfjhKxbG2+0OlkiVKGL0mtjt1065RdnHX4xEVQw84TfVF1dnfTt21fdtnz5cklJSZF+/frJ//t//0+uueYa1fWHCBYhzQHCwahZ0Om5SOqnrHUVZsnSTeXqB0a7lg/sUiDZ6eF/fVBHpQRVkLNU8wcDhe8ogsdtdkGlawnSUryjdiioCIkf7Ad+RLS1oDp5RFdJlO3Xvy8lwVJ+gYrSs3TKjzVUrkD0afz48aq7b968eeqyfv16Oeqoo+T0009Xo2hQqH7VVVfFZosJcYH+smGES6QdfhpEinTXy+xVpervMba5f+FuVzAvKtPmweoMtAkw0yOmb8c8qxOHEBKngsr3m6FP8hJl+/Xvi1OEqmH0jPPJpfanilWXn9lkFI8RqqgF1T333KMK0PPz8/1MPW+55Ra5++67JTs7W2666SYltAhpLuxftmhTfgACZvaqEvX3qDDrpxrWFdo6wUr5FWZZz222MJseMega7u+bQbijkoKKkHjBXuszskcbNWx95fZdqk4ynkHNk44+DersHZNlj1DhpK5yr9uUX01Mtmt75V6prq1X+7FTMhalY2bf1q1bG92OTr/ycu8wyMLCQtm7lz/2pPmwp/ii6QjR61qyYadKv4FRPYsiXJfPOiFIyk9Hr/C8elyNPaKlBRYGQbfP95rb0S2dkPjBSk35fj8KstOkbwdvtGfuam+kO17Rv09F2WlWhN4eoYKY0g2Lobr8ymNUQ6VPRNFk1JIjZpo05XfeeefJG2+8oVJ9uODv888/X0488US1zOzZs+WAAw6IxfYS4gr9IxCTCJXvsau2e8UUfhQLs9Oj2q4NrlN+zu7q5tkvhjEDzvMjJH5wmtCg037xbvBp1ikV5Tj/vuh0HxzLM9OcO6h1l1+sIlR2kZp0RelPPPGEqo867bTTpLbWq0JTU1Nl8uTJcv/996vrKE5/+umno99aQlow5Wf/UYwm5Yf26dq6ekm1GXGqGoFSsyg9xVGAmT8sxbnpAWscCCEtg9NQdvx2/GvmmrgXVGadkragsUfAGwrSA8uI/KzYdvlFM+g+IQQVPKeeeuopeeCBB2TlypXqNszwM72oDjzwwGifhpCwKMxOU9POq/bWqTx+oJC0G9rmpqvwMtyCwagoBBVC1bprZktFdSOxhi5CGJLqH40M35kf5vahZkGPWjDd3xmhIiS+wNQCnDTZG2K0d92Pm8vVd1173MUbpnDRvy/2LuKGgvTAryHPGj0TmwhVNHNZm4OYJSHXrl2rOv1Wr14tn376qbz99tvqQkhLAM8nfRYTbXhY+0dpwjX0NIEg6lgQuI5K/2BAxCGM3iEvQ1JaewXYtspqx5RfcY6vhoqCipC4YEu58wBf+FH1LM5WtUfz18RvHdV6hwi4XVCF8qAy78NJIiZARIv+fYymazuuI1SISp100kny/fffqwOPttXH3wD+VIS0BPgxWLG1Mib5dqwDNVTd22Rbgiiada3bsdvROsFM9wGkBFF4jh8SOCx3yPefB4jlinLSrB88fP/0d48Q0nTgu4bvMeoi7UN6NwQZ4IsOYYzEevf7TZJjzMbLSkuRgZ3z42Lgr/5twmvTESr7vNCKam/UKVj0P88QW5V7alVhfrK6pMckQjVlyhTp1auX6vSDRQLm+X355ZcycuRI+fzzz2OzlYREgP7SxeLLZ/eSiW5d2X4eWaFC2vq5zToq60zNiFDBRkGnCwkhTcv0eevlsHs+k2e/WdXovmAD2fVvyGvz1sukJ2Zal+Mf+Vqe/tpbNtPSNJzYZasGHH2OZs4L1d5SwSJUGakpypxYLR+DwvRoBt0nhKCaOXOm3HrrrdK2bVtp3bq1ckg/5JBD5I477pArrrgiNltJSARMGtlN/XidPDx6V+Kzx/aQQ3q3lYsO2y/qdfXv5G2dXriurNF91pmtYfNgNwM1PWLww4LZhZlp3q8yvagIaR5mfL/J+/+SzSEjzSYTB3WUg3sXy35tc6xLB5/1ycc/NrYgam6qa+tka0W1FWFDyUGhr9bLTPvpzr1ggiqW8/wqq2utSRVJ2+WHlF5envcAAVGFOiqMoOnRo4csW7YsFttISEQc2K1QXv3j2Jisa1CXAvnPBWNisq4xvYotx2TUFeAHS7NxZ+MIld27yvSI0eNvEKXC2Rvc0rsXx/+cMEISGXxvtZfU4vVlani5aR3g9D3WIEX2wgUH+d32y7ZKOfK+L9RJFgRNJIPcY8Um3wQGnKS18XX44X8MevYXVL4uvxANP/lZqcqIOFq3dH1CCff1YIXwLUnUEapBgwbJokWL1N9jxoxR7ujffPONilqh248Q0jhClZOeon6Qlm2uCHlma3dXd6ojaKijaihcJ4Q0DT9tLpeKaq9AQMMIZvSZoN4xnEgKolRoREEnMWaQtiRmfaaux9TCyhRUOuIUStzkxciLquF3L35PGKMWVH//+9+lvt7bTg4RtWrVKjn00EPlvffek3/+85+x2EZCkgoUmo/o6TzXy7GGymbuqc9+zbRgG18d1Y5dsRtCSghxxu4jZb8e7rw5CBc9zqqlPaqc6pQsQWV4UZW78KEy5/lF60XV0HkYXVNQXAuqiRMnyu9+9zv1d+/eveWnn36S7du3qyL1X//617HYRkKSjtG+0TXmjyfSBphV1ThC5V9D5RShauPrnmGEipCmR58I6ckH5okRuv9Mp3G3xIuLujVLtMhBUBk1mm58qPzHz8QoQhWn9VOgSYbhtGmDIZAt3/pJSLxinY2u3mFZjegfYaQDTcM/XUNV4SvKND1iNIxQEdI84PuqRc/Fh++v/p+3plRZCui02J6a8Af46t8ErCsWnk2R4hRda+Pglu7Gh8p/QHJsaqji1TIhZoLqk08+kRtuuEEuuOACNdfPvETDhg0b5KyzzpLi4mLJysqSwYMHy9y5c637b7nlFjXWJicnR4qKimT8+PHy3XffWffDZBQzBWHrgMfvv//+cvPNN/sNasYyEH/2y6xZs/y2Zfr06eq5MjMz1XYgpUlIpAztVqhM/7ZVVCtPGnu6zzwhQeG5/kHDWZrzGSQjVIQ0B/CjQyQZ0xNOGdFVnfzsrqmTHzaWRzXAt3+nfMnLSFXdbD9u8q6rJXCs0XSYxqBrokIXpaf5LZ+slgkxEVT/+Mc/ZMKECUpUIdVXWlrqd4kUPPbggw+WtLQ0mTFjhixdulTuu+8+JZw0GLj8yCOPKFPRr7/+Wnr27Km2Zdu2bep+pB9R34V5gz/88IMaj/P4448r8Wfn448/lk2bNlmXESNGWPd9++23cvrppytxtmDBAjX0GRd4bhESCegIGtqtQP09x3e2G2xOlZn22+hY48AIFSHNgY5OoYsY3+NRVvq+JKp5c+j2HeFb13ctmPZzqtF0mhda7jZCleG9P9ouv0RI+UVtmwCB8txzz8nZZ58tseSuu+6Sbt26ybRp06zbEGkyOeOMM/yuYxjzM888I4sXL5YjjzxSjj76aHXRoOsQVg6PPfaY3HvvvX6PRRSsY8eOjtvy0EMPqfVce+216vptt90mH330kRJzeP2ERAJqJuasLlU/npNGdQtayIrbvt+wU1aX7LI8YvxD8oxQEdIcIE1vjqDC9xj+UbNXlcpFh4VfkG6CdX2+bJs6yTr/EP/jXXOAeaHaNsEpQrUjgghVnk75+ZzVIwHdj1sqGm9X0kWokD4bN26cxBrMAYTb+qmnnirt27eXYcOGqSHMwbbjySeflIKCAhk6dGjA5Xbu3KlqvOyccMIJ6nlgSmqfQQjzUqQT7cX4uD0Q1dXVUl5e7nchxKlmQhe0Bhv8qW/Ty5oeMWaECl4xhJCmj1DpInLzewxBEs0A39HGunRtZXOCeaGYuIBoGUZeafQ0Bi2oIHBQJ+aqyy9Lp/xqo5qNiN2BFGpb37YkpaBC3dSLL74osQYzAhFJ6tOnj3zwwQdyySWXKOf1559/3m+5d955R3Jzc1VtE1J6iBzBYNSJFStWyMMPPyx//OMfrdvwWKQSUSP17rvvKkGFdJ4pqjZv3iwdOnTwWxeu4/ZAwCke4k5fEG0jxGREjyKBpydG0GzeuSdoSFunDxDR0suYdVY6QlViDFAmhMQWpNvhMYXv7fAeRZbpL+bwoWHk562VUaWmBnctUKNaUKv0y7Zd0txo/yyIKdi7NPK5q/LOCzXroXKNeYRBndJ9XYHRbFfngsazERM+5Xf11Vdbf6NGCZEh1CANGTJE1TzZ03CRgPUiQjV16lR1HREq1CwhxTZ58mRruSOOOEIWLlyo6rcQwZo0aZIqTEe0yV7gjrQdIl4XXnihdTvEl/l6Ro0apdze77nnHhW1ipTrr7/eb72IUFFUEfsPzYDO+bJkQ7lKI4RK+ZlniPb6DB2hQl2DOcCUEBI7dIQYIkoLCXzXhvcolG9WlKg6qmhSfnBIR20WygAQCevdPleak4b6TH+vJ2teaK13XqiONmWnp/gJr6bq8osm6hf3ggqF2SYHHnig+t9epB2NdUKnTp1kwIABfrf1799f/vvf//rdhg4/+F/hctBBB6mIFuqoIGg0EEgQXkhNQvyFAo7viHRpUFu1ZcsWv2VwPVDNFcjIyFAXQoIxumexElSzVpaoKFWgHw2zo8/pOjqN8HVDWBytze3z4tf8jpBET/fpNJ/5PVaCanVp1O39Y3q1UYIK4u2MMd2lOQkkBvW8UKT5UJhe7rJ+KlY+VIlQkB6xoPrss8+kqUGHn30W4PLly9WMwFCRLdQvmZEpiCl07aHAHQOcQ4GIFwSdZuzYsaqL8corr7Rug+DC7YREw+heRWpa/ftLNkttvUdSW7dyFEP2HxL7ddQ8oHAUEazSXTUUVIQ0o6Aa1cub/vtmxXarjjHSg/+oFjT4dLJM0Oh5oUhH7qp21+FnLqO7AiPBEqm+MVxJ2+XXVFx11VUqooSUH9J4s2fPVtElHWHatWuX3H777SotB/GDlN+jjz6qBBTSegB//+pXv1IiDF192k4B6OgSarLS09NVShG8/vrr8uyzz8rTTz9tLTtlyhQ5/PDDVa3VscceKy+//LLyw3IT7SIkGPqHWafyOvmmu9spzE5TdRrwuwnUko1hyVhPier08w4sJ4TEBny3UCMFtFWCZli3IklLaWV9j/OiGOA7vHuR+g2AeFlfWiVdm3F2XTCvJ9RR4X5EqDDA2Sw4D0a+bxlvIbv/EOnwtyu+TxSjLrRA8TUEiB3cBuuDSEEt0xtvvCEvvfSSGsAMq4IHH3xQzjzzTHV/SkqK8pk6+eSTlR/V8ccfLyUlJfLVV1/JwIEDrSgSCtERXeratasSXvpignUjgoVU31tvvSWvvPKKnHvuudb9EHYovIeAQgfha6+9Jm+++abaLkKioTg3Q/Zvl2NdN71f7Olzv/l+Dj94us4BESpCSNPUT6GuCd9be0pscBevr1y0qamcjFQZ1Dnf7zmbi4ZIUOPt13WaiFBpTyk3Eapco2g90jqqpK6hMoFpplOXH0TNaaedJtddd13E6z7uuOPUxQl09SGaFIxzzjlHXYKBAnezyD0QiHrpyBchsWR0r2KroyfYDwZ+5Fb4zpCdlrM6cehFRUjMmWOzS3D6Hs9fW+ZY4xgueI5F63cqb6uThnWV5sJpCoN9XigiVLo82k0ULqV1KyWq4ACP7sB2eeHVFptWFF3jPOUXdYQK1gH2iA9o166dchwnhISuo9J0DXJmq0UUMoKmR4yGbumENJ+hZ7DvcbTF09asT5/7enMA2wfMCw2U8jMjVDrSlO8iQmUuF0mECs+HdCFEXMeCzOSOUMEK4JtvvmnkYo7bOnfuHO3qCUl6cGarCTauQv9I2z1iInFLh5fMe99vVl5Y4f5Ifb5sqzozxWPDYdnmClm1vVKOHtT4BCwYmHeI5zx+aOeI6i+IO1Df8r9FG+WIfu2lrS2llQhgqDCKpQ87oF3AZXD/u4s3yVEDOkiRYYwbCkRX9Ky+QBGqET3aWJ22nWMkqBC53l5ZHdb7gW661+etV/YGJh3yM+Xk4V0Cdt/r6BRqMTE/NNDvCyJU2Rne76HbOrE8LLdzT6NOvxVbK+SjpVulPoiJKb7/kcxGTEhBBU8ndL/V1NTIr3/9a3Ubapb+8pe/yDXXXBOLbSQkqYFQ6tYmS9bt2C092zbUU9npUewNd/codl7GilC5cEv/5MetcumL8+XwA9rJ8+eNdr2tCL2f99wcKcxOl/k3HiXhcPlL82X5lkr5+OrDw/LXeeDj5fLid2tVF+Tpo5u3jXxfYvrcdXLjWz/I5LE95B+/Taz6UKSFzpk2W3bvrZO5fx+vPp9OvPDdGpn63k/yh4095NYwXuP8NaVSV+9R39VAYgnWJQN9vnLBvsdugNg7oEOu+r7MXb0jrJOQZ75aJQ998rPjfZjJd0Rff49GzZoSb9lBoCJ4M0JV50lzXUMVzIvqshcXyE+bK8QNgX73kkpQYb4disH/9Kc/qfEvur4JtVOmFxQhJDAP/n6YLFpXpjxoAjG+fweZcmQf9b8T4USolm3x/ojB/wrhdLdnfrN+KZF6j7fjCZ0+MCJ0CwQj2Fq+JyxBpevGMH6CNB0N+znxavAQ+dAH683lewIKKu24DXuDcNDF4cG+n+Cuk4fIVz9vD/gdDQdEwiCoUEcVjqBasc37PiKC3Lud93u2aH2ZEi4zfykJKKjmrvFOYRjStaG43jFCVbVXdIDcTZefuZzplo6pDlpMTRrZVVpJYN9KuKOfNqpb8gsqhA/RzXfjjTfKjz/+KFlZWcpck6aWhLgHP36hUmhId1111AEB77fOICsbBpgGQhd5VtfWy/cbylS6wg2mNw4OYBm57gQVIgfa8sGeigi5rb6DYFWYjyPhoT8Tu/ZG7hfUUphDe82/Ay0XbioNRpumR1QgBnYuUJdYgLTff2atldmrSyL6vlx4aC9LiL2xYL1c9coi63W4mVEYuEZzr6T7FFV+FBGqOb4xWojE3X1K4Pm7iUTUCcndu3dLVVWVmokHq4O8vDw1g+/DDz+MzRYSQlzRxndWjjNItz+6AGfAbjHbuMOZzYUZYJqqMA7YtXX1KuIAtJkgaRrWJ7BwDVdQAaTS3IBI7MJ1ZUHFRlOgn2vpxnK/2XmReEnpmqwfNux0/B55a8R2+i0bOAK+Vyqq3Tulm8uZryOQSeo+Lah++9vfyr/+9S/1d1lZmfJyggEmboewIoQ0D21y060fvFCT6vWPbjheNygOXbm9YWBrOB07KGQ1f7zdsqWiWtWuAAqqZopQJeB+NoWS+VkLtpzbE4nv1+9UafG2uemyX5S1UeHQqcBbW4mPv7ZjcNNYoIu4zU5D1EXhOuoQFzisCwX9eB7YJQSqEdMRKnQDaq+7cGuoyv0iVMEjYvukoJo/f74ceuih6m8YXnbo0EHWrFmjRNY///nPWGwjISSMCFVNnSeoaIHY0gZ++odNi5Zg2IVXOLO5zANZVbX7CIi5neGmCklkNUiJmPIzo7LBbEPM5dym0qx0X882Uc2njQTMCAzHPkHPA8XcvTa2Lkbt7q7tH8Lx2DLnhYJNO3eH3+UnDb8ZiFTpiBgFlQHSfUjzAaT5fve736l5eRhUDGFFCGke4NaM8TSh0h5lVTVWWgfT4nEghaVBKOyzxcKJUJnbE84B20xNhpMqJOFhCtdwBG+8gM4zTaCmDJxImJ9Dt6k0fSLREqkp7W01x2U0zUz32cWftmdxEmdW/VSQ16jnhQJ9/pWf5dKHKsu/hgoRN6wDEThE4pKFqAVV79691RiWdevWyQcffCATJkxQt2/dulXy8732+YSQ5kGflQYTVPpHFwW5uhDezRmw/tHFAGdQEWmEKoxIk5marEzAA32iYArXcFKy8YKZ5gtkG4LXhegt6JCfoQ7oSHUFA5Hbeb7i6ZaIpGgRhBoupPNcDzd2SNtpcYaUH1KYGqx34Xp3NWLwqDIJO0K1u8YvIpZM9VMxEVQ33XST/PnPf5aePXuq+qmxY8da0So9cJgQEn+CCs7rug1cd9wEAqH6Hzf7GxvqeV5uMLcnnAO2LpQGVQl4oE/ECBU6P9EMkGwRKv0ZRBT3kN7tXNUP/ripXLmH52WkSv9OzR8g6FmcrU589tbVy+L13hRZMKwRLQ6jY/Zvl6t+H7ydvQ3rWmzUiPUKUSOm54UCnFflpKdE1OU3e5U7G4p9TlCdcsopsnbtWpk7d668//771u1HHnmkPPDAA9GunhASBkVuBJWe11WYZZ0Bo04kWCE7ztJxN37g8cMcdoTK7PILQxj5paJYQ9VkrDf2M6hyEQ2J2whVgBoq/Z2AqNAHcnsa246+f0TPIpXyam6QtmvY1tBRZOtkySFChXVZdVTG6zaLw0PViOl5oTrq5LamLN+ooTIjYoxQOdCxY0cVjULtlGb06NHSr1+/WKyeEOKS4jAiVJ0LM5WJHzxl4MmzuqQq9ByzXm2segizYycUOwxvrF0RpvwSsVg6EVN+iVhHtSOMCBUElfaTWrRuZ9BUWkvWTzUuJi91/T4G6tTTJ1BmZM4sug+F7vQLp8PPPsuvISKWETIilmhEZOx59dVXy2233SY5OTnq72Dcf//9kW4bISRMdNGoGREKVmcBs9ADuxUqwYQz4EA/cKZnjE6vhNXlZ2yP27Z8RMzMA30itvMnCqZwTcQ6KvPzhZZ+fHbs0RNTUCHS2i4vQ1kMqAkF+zXM09RgHfGQmtIiaN7qHSoV6zTH002ESq3LJ5rMzl6M1fE+jxtB5R+hckue4UOlI22o6Wrursm4FFQLFixQs/v034FItp1FSLyDWV32iJCdjb6W5y6+mV2jehX5BFWp/H5U41l5OINf7AvRj+lVLF+t2BZll5+76Ae6EbW7OkBBcThjcohElFpNxI5K8/OOeiMIQvsB3xRUODZBXLz7/SYlLpwEFTzXcPKAz9vgAONYmoO+HfNUNAjftx83VQTcFswz1HYGqI90on+nPMnNSLU6ezGUWO2rjFTp1zE/rAiVW5d0oKPa0HCfL9sWsqNwnxJUn332mePfhJD4iFAFc0u3dwLhDPjRz34J6MuDriCIGXRGoc3ZqocIwyndrHFxW0Olz7bRWVTq69zCgT491XlOG4kMiNStPjNIiA0Ij10JlPKD4NciHefwqPVDlKqRoPJ9J3TjBiIyEFRIeV3msF4dnRrWrTCsmZWxBrVbiAx/+tNW+W5VSUBBta2yWn1PUerVMT/TcRlEt4b3KJIvl29TkSJf06OMdFkjFmmEKistRa0fUbH5a0tdjfFJRKI61auvr5dnn31WjjvuOBk0aJAMHjxYTjjhBGXqGcqpmRASe/TBwux6ss/U0/dpQTW8e6H6EcbwYn2Ga9JQtFqszuwDTY4PBM6cTYHnNnWnO/y6F+dIhi8qlWipqEQA7zl+rrGPu/kiG4mUXtWfLdh5dPIJiRKHOiodxdLfEV0zhJSXU1ejG7PL5kJva7CuRP19gZgKlhY0O3st+wKXr9EvQuXSgwrgd0NHtBClwm+Im4jYPiOoIJggni644ALZsGGDElMDBw5UZp7nnHOOnHTSSbHdUkJISPTBItD4DR31Qdhf/yDiTFMPdHXqemow/Svyr4fwzfMKBUZVmEbsblN+Vgt4YZbkZHi3lZ1+sceMWOr9nEgNADqVhw7XYt+wY6cIrb5Nf0eQSsNBHp/HpZu8liAm38WRoBptiKBAwQrTDsWNOMPr0wLNbY2YnsYQzhw/p4jWyB4t0zUZt4Lqueeeky+//FI++eQTVUf10ksvycsvvyyLFi2Sjz/+WD799FNrxh8hJD4iVLpWBgdPs8Yx0BlwTV29FaLXxbEFusvPpQ+VfVvc1udsNLoR4eieaJGTRME8EGenJ55wtWqjstMt25AShxpC/TnUaXEc0Ef6Pvf2EwnsE1ywzPDu3hOJlmRwlwI1Tgav9ZdtlSG/28FQnb2p3s5e7BNEJgd3KQxrXmi4XX725fVvSbIRsaCCgLrhhhvkiCOOaHTfr3/9a/nrX/8qL7zwQrTbRwiJQFAhHQcx5PYsVp8B2w8sP2wsVwdXzPHq0z7X70wT6Tc3qX0dGdAuy7q4PKzISQIe6BMFszMsNyPxhKtZbK5tQxwjVL7ldONGsM+9ToUN6pxvRe1aEgigYd2K/CJnAb8vISJUqrO3a4OAGta90HWjRzQRqnxjee3anmxELKgWL14sRx99dMD7jznmGBWtIoQ0HxA+OpLulPZr8KnJdPS6Wb6l0u9xDSMiiqS1b8X6hxEFpm4Ejo4WdGvj7Sp0e8BuEH/ZkuM70LOGKvaYwjVbp/wSqCjdFFQ6+uQUobVHqMzI7Nw1/qk07bsWT8aTus5JfyeDzfELva4GQRNOt505LzTSCFVGGBGxRCNi6b1jxw7p0KFDwPtxX2mpu4GOhJDYgBRFYba3UwtdTe1t3T4N0YgGcQNQe9K7fa6s2Fop5z8/Rwkz8JNvaLJZR4LUAwqAa+s9yosq1Bm8jha0y81QP6YYfYEaHZ2ecRM5aaihanpBhQPr3R8sU47wp4zoGvW67nz/J+nbIU9+Nzy6dTUV5oFYe4sF288vzV4rm8p2y1VHHRC1Nc5/Zq1Rqacrxx8QcBmksh78eLmce3Avx/EvpWaEyhd9sp9MIFqrmyh0FMueSjv7mdmSluJ9PQvWuZtt15yEcncPNsfPju7s1X+HA/YzPjPhdPkBvXw4EbF9RlDV1dVJamrgh6ekpEhtLc8mCWlu2udlqAPEmpKqRp00wQpXD+vTTgkqTIK3o2efAd3pBysDHKQ6FbiPIEAYVdfuDRnZQjeifpyKnFg1VE0fOfll2y557PNf1Jyyk4d3iUo0LNtSIU98sVIdxONVUFm1N0VZsnZHVdCidAjEW/+3VPmDnTS8a1RO1+j+xLrgG3Xy8K5+EUyTV+ask1fnrleNDfeeOjRw5MmIUNknBWiBhSCrPlkAOLCP27+tsiT4esV2v8dA/MdThApCBCcyG3fukfWlVdLV5yNnfx+d5vjZwVB0fIdb+dYbDvu1y1G/Iz2Knd+vQHT3vb+HHdDwW5JsRCyo8MVCN19GRkMbpUl1tbP9PyGkaRnWvUhFlpAamDiwo+uz2GsmHCAHdi+UatsoDhzoBnT2F2b5WV5vKDdeVKaggjDasSt06s7ejWh1nzVDyq/MF1FD9xfMRUNF0oKxvcK7rrLdzu7dLQ1EzcayPUYNVfCUH6KL2mx13Y6qqAQVomEQU3pdgQTVutIqa5lgEVCI1kDDwbUHFQSXTl1r7j5liPJl0s7hGkTDonnvYw0aBgZ1KZCF68pU84gpqNBJiyHOblN+eJ/f+NPByrcr3Bqx+yYNlV+27lLbEg5/PHw/GdKtQMbtn5wF6VEJqsmTJ4dc5g9/+EOkqyeERAgKPpGWsXfswWtnc3nDwdMOflhPGNrZ1XOE40VlCip9wA41K85M90GE5PiK0sOZAxgp5mvCdkRzUNV+SLreLB4KnE2QboOoUWaQBZmSHaIo3Rw3ZHdXDxezzsk++sbpJEA7/Aeq0SsKJqiMZexgply8Rg/tIAUJQYW030nDujbaR2j80J2aoUCKPxLa52WqS7hkpqXIEX3bSzIT8bd72rRpsd0SQkhM0DURSzaWqwOjPohvqahWB3bUiSAtGA15GQ3T48PxCbJSdyFqoewdS/pA79ZlPRrM1wSzxHDPxE3MWh4ItXgTVOt9QqZDfqakpbQO2U1pF5vRUOpWUPnu21S2R31+7f5F4USo9P2JCgrIn/xypaPNg5sOP9K0JGdlGCH7MIjq4GKOeTAjCp0KshqlPcJFm4KWhxGhwgHPbXG56UEFcqwIVXMIqtiJBvPAHs4w6ebC7l2k359AKVkzxWsOro46QhVgXfgMb97pjaqiCWJrhfdvR8Ge3SCoym22IaZXVSKDETG6zg/RxXA9qEjTQkFFSBJiOSsbZ7LhdAGFMz0+kghVpeuUn7dOpKGGqjlSfrFLa+nIiH298YI9EohC/GCCt7kjVBBQEFL27fUfa+Tdr+jw87MNMfa9JagMD6pEBB28/Trmqb/nGin9QN27pHmhoCIkCTHHS0TiUxMK7UVVEWmEKlRRepgH+lhiOsBHG4Xxj1DFX9ez/TMRyocqloLKjFAFEq72/W9/TkT9dDF5YXaaZRti3/fJEqEK+N0O4C9HmhcKKkKSOEKFAtbq2jq/4amxqLPQRemhuvxgf6C7whChynFZXG4WpYPmNJw0I0kxTfm56IhsbuxRS8spPYBwNdOWSMXZO+MijVCh0xDRpkbbZ9v/9ut6/+ZlpEpGqnfbneqozMaIRKdhrt+ORrVwbiwTSNNBQUVIErJ/uxwVEUKb+/frd/r71MQk5eeuy0+nvFAIj4NeqC6yQN2IoQ70sSSWURjzoO4mmtfc2IuZrVl+ASNUDYIKqbgtvvcp2n2DTkOzJkijTwKs7bVdN9PJGh2FSnZBtXRjufV+NNRQMeXXklBQEZKEwGpApwb0GI1YdgLBh8pNoXWpUTCMbcq1usgCiwunbkR9oG8OHyrzNeFAjChbpOzYVRPXRekbbCI7x7efIXCc5i3aB2JHU2Nm1peZURan9evPQaAIlSmoinLSGkXAkklQoSMTppoI6M1bUyp7aupkW4VXjLLLr2WhoCIkSdGzv9BiDVPJhjqLWNRQuYtQldgOZG5Sd3o7zW7EnGYcjmx/TZFGqbwF0/EboYLA09vUUEPljQQGEr32wvpoInh2awMncabXr6My9mXM+jxNm5yMRjVaySSogHWytGqHbPJ1QWKEjh5ATloGCipCkhQ9+2ve6lLZXrnXqmXqVJDZbF1+5pw1t8XlG8qqGnUjZjfjcORYiQaIFbPGKN66/LQ4QTG3bhaAF5Wes+ZU52YXhfaUXDhokaNHmDg1AOjb9GcZ180hxqYDuqaNLUKF5bWwTRZBZdZRmXVw8ebEv69BQUVIkoLRGXAmx0iKT3/aom5rl5ehHItj1eVnTwG5jVAFE0Z6FIoZSbMc1vfW+R1QmwL9mrCvoun00y7p9vXGC4FsNCzR6/Ae6bSltW9iEKHSxqn2damoqu+2ET3aWCIPY1bsDuh6KLJThAqf/5o6TyPhlegGn2DRup2yanul+ruLbbYfaX4oqAhJUtBCjiGo4I0FG2KW7vMvSg8vQqWLy4Ol7py6EbV/FSI+KLRvSvRr0n4/kdYJmek+c73xgr2TUmPVqzm8R9r6Idp9g7of/RkYrAWVTbhCOOllMJC3rU80mVGxoBEq3336M5iVliJZvs9RooOoHurKUOv27veb1G1daJnQ4lBQEZLE6NSA9qyJRYefKahw0EVXnusIlYvicnuhtPm4UI+NFrwWLSQQ4TO3J1z0jLl4raEKVFPXMCDZqYaq1n/fRBi909EpNB709Ykz+37WwglCClFVLfzM5YLWUPn2v/0zmDRNJ7bvNl3SWx4KKkKSGF28qrNkseoC0jVUodJ3jWuo3BSlVzXaVkTbEGFo6sJ087XoKEykokFHSFAsHI9dfpZwtX0mgllbaC8ta9+U+dc0ucUcF9PN9/yBPKe0UNCfBzMqVhrENsEeoUomQWWm/WL93SZJKqg2bNggZ511lhQXF0tWVpYMHjxY5s6da91/yy23SL9+/SQnJ0eKiopk/Pjx8t133/mtY8eOHXLmmWdKfn6+FBYWyvnnny+Vld6cs2bx4sVy6KGHSmZmpnTr1k3uvvvuRtsyffp09VxYBtvx3nvvNeErJyQ2DOlaYBUZx/IsFuvUQiFY5MXeXZUTwk8KB2enGio3j40F+rXgtfUozokuQqWLrtvkxGeEKkDKLydIR6VOW+qoEpYp841+CQfzc6HfZ+wfU3Ra3ko+odC5IKuRwHWKPunxMngOfJ6SMUJlRp819KBqeeJWUJWWlsrBBx8saWlpMmPGDFm6dKncd999SjhpDjjgAHnkkUfk+++/l6+//lp69uwpEyZMkG3btlnLQEz98MMP8tFHH8k777wjX375pVx00UXW/eXl5eoxPXr0kHnz5sk999yjhNqTTz5pLfPtt9/K6aefrsTYggUL5MQTT1SXJUuWNOMeISR8kCo5sGuhdT1WNVR+helBIi+6xkVHDXJCFJdjLlugbsSGdGHTRah0wTMicFpowGQ0WFozEKW2LrZESfk1zFusbWQDoW9rl5shbXMjL0w3u+7wvup2f1MsWdvnE1JaWJnP5xR90p81FKJje5M1QtW3Q55lXwI4dqbliVtBddddd6lo0bRp02T06NHSq1cvJXz2339/a5kzzjhDRaX2228/GThwoNx///1KICHiBH788Ud5//335emnn5YxY8bIIYccIg8//LC8/PLLsnHjRrXMCy+8IHv37pVnn31WreO0006TK664Qq1L89BDD8nRRx8t1157rfTv319uu+02GT58uBJzhCTSmWws6ywaxs+4SPn5ogahisv1QdSpG7FhQHLTR6hwoELRL2p8sK0wG404QuUTVDi4RzOqJZZgHNHWAGaQDR2V/vsZkUG9+TB21UXQkQgqXd+kU3WWWDIFlS1CZa+hQmG7rnczxRIKz3V6GFGqZPOg0sCjTaf0kRLvmE9B1dLEraB6++23ZeTIkXLqqadK+/btZdiwYfLUU08FXB6iCFGlgoICGTp0qLpt5syZKs2H9WggwFq3bm2lBrHMYYcdJunpDV+2iRMnyrJly1SUTC+Dx5lgGdweiOrqaiXuzAshLYEuXo11nUUoLyrT2FJHDUIVlzt5UGnCHZC8evsuOfrBL+X1+evFLfq14LXhgAVz0UB1VO8u3iQTH/hSVm3fFSJC5U35gcooolQQEL999Bv5x/9+kGjBHD6d2jQLuv1rqOocxSZEZkZqa0cR5Bb9udDPrd/vjTt3u66h0utIbd3KL1Jjn+eXrILK/G5DTKWmxO3hfJ8hbt+BlStXymOPPSZ9+vSRDz74QC655BIVOXr++ef9lkMaLzc3V9U2PfDAAyq117ZtW3Xf5s2blRgzSU1NlTZt2qj79DIdOnTwW0ZfD7WMvt+JO+64Q4k7fUG0jZCWYFTPIuneJlsO7FYoBb6RMbGgYfxMbcD0mY5o6EhEqOJyHTVxOtsOd0Dyq3PXyU+bK+Spr1a5e0HGa9GvrSEq4hV6Jk9++Yss21Ih7/na1u3oAzleCwRItIXpP2wsl0XryuTfM9dEHaXTc/MQCbSbQeYE6MTU245ULx7j1HUXbvRO2x3otKMpzuw1VPr5YFILcWmOnbG/hn1FUB09sKM60TjsgHYtvSkE+kLilPr6ehVZmjp1qrqOCBVqlh5//HGZPHmytdwRRxwhCxculO3bt6sI1qRJk1T0yS6kmpvrr79err76aus6IlQUVaQlQFTo46sPV2fysSSUF5U+aGI5OHCbxeWok3IqLtepIJ0idIpQuS1Kx1gO8NPmciXu3IjJhghVqt+BXhfKayA2lmwsD+oWbnkk5aSriFd1ZXVUgkpHvDCUeMHaMjmkj/fEMRJ0mlbXwTmmVvc6R6j0vrGiSpHUUGm7A9/7rNel5/lBMEE4mffh/cNnANsFEWcJJQezziJTUDl4VSULPdvmyPybjpJ0Rqfigrh9Fzp16iQDBgzwuw31S2vXrvW7DR1+vXv3loMOOkieeeYZFYHC/6Bjx46ydetWv+Vra2tV5x/u08ts2eJ1kdbo66GW0fc7kZGRoToLzQshLQW68vRcvFgRap6fPa2jCeZFZU8ROtdQhY5Q4YC8eP1O9Tdq3+et8Yor90Ij1S86YhdN89eWWvVQgSI0lot3TrrkZ7mbfeh29p0eeB0pZrTJTnaA1KqZDvWLKsUgQqWtG3SESq8TAkoLYRUVM5YLFnnSnzl8nuziLdnISE3hyJk4IW4FFTr8UMdksnz5ctWNFyqyhfolMHbsWCkrK1Pde5pPP/1ULYMidb0MOv9qahrOHJE27Nu3r9VRiGU++eQTv+fBMridkH2VhvEzNa4Kj90Io2At7uHUUC1cV6ZcpDXa/DAUWjTo19Y1gGjQ0a9AERqzYFpHqILtKzfoSIv3+UsiXo/aDlu0yc37Y4lNnziMqobKZsjZEAn0CSrDLd8UC2ZULJig0kINnye7eCNknxNUV111lcyaNUul/FasWCEvvviiKjq/9NJL1f27du2SG264QS2zZs0aJZrOO+885V2FQnYd0UJ33oUXXiizZ8+Wb775Ri677DLVyde5c2erUxAF6bBEgL3CK6+8orr6zHTdlClTVLcgbBt++uknZasAPyysi5B9lbwII1TBhFGwFnc3cwA1c3yCR0db9PVQ2NNaDbU9VQEFlX1gr1PBdKhoXrgRKqT89kYxgscebXLTTWk9JkOLTW/3IgTL7jDNVs36J1MooYYOHYhW/ZStOcGMigX7rOho1Lbyamuf2z+HhOwzgmrUqFHyxhtvyEsvvSSDBg1SVgUPPvig8pUCKSkpStycfPLJyo/q+OOPl5KSEvnqq6+U/YEGtggw5DzyyCPlN7/5jbJOMD2mUDD+4YcfyqpVq2TEiBFyzTXXyE033eTnVTVu3DhL0KGD8LXXXpM333xTbRch+yq6cLuiuiakG7azMGp8EA4WdbDa+V2k/HRK7Mwx3dX/SP+5OeiX24RGQ2fZHks04YC/YF2Z9RjUg8E/K1B0DhEWHfGKZp6fKahgOfH9hoZtiNgewhdtciN47VEtPFa/J2Z3XijM7k8tcvB+a6PYTWV7rIig3SPLjIpZkacgEapftnlNnJHtjmVDBiEJVZQOjjvuOHVxAl19r7/+esh1oKMPYigYQ4YMUUIsGIh66cgXISS0D5Uljmy1Kw0DkmvDElTZLovSYcI5b43X8uSkYV3lncWbZNPOPbJgXamM279tWEJDm4tq0YTt+n79ThUdKvaJJXTMIaJibrNdMFj7KgYRKogDlG/NXlUqI3r4u2W7RacenSJUgYYjW3VXRk0TzCSXb6lUAmf/drmuntvs/iz0CR/dNfjLtl1KTJkpPxOzeF0PS3aKPOn3YsXWSktgxbqGkJCEiVARQuIbnfoJFHUJ1IUVyPEcESDTQdtOTpCRKHZ7ASyDNBtmzmnzQzNNFwh7WgvmorAWAPogr+uxYJjaNUDRuj06Z0XzYhCh0kat0dRRmQamdqwRP41Sfo3rriKxTtC1YFiPORbJTOcFGotj1lAFqtEzPz9mHRshTQ0FFSGkSXyoAkWbAqWUKqpr1bgQp8eYhpOhaqjm+NJ9I3u28bpJ+wSIvj0cHyr/A32V33og1AIJCnt0Li8jdjVUEwd6u4vnrmnoNAwXe/G9mxoqHdUyHxNJYXqgz4XZ6RdocLN+PhiTai+tYBGqQNcJaQooqAghTeJDFejAGai4XBcZI7VnHzvjPwcwuCgxI0hgjO//+WvKpCbETD67D5V/p98eJWDmrS611h9IUNijcw0pv+h9qA7p3VbVLkGcwWMr5l1+RiTQLLZ3jlBlh+1FZXmN2T4XWpyu21FlObnbB/62z8tUhf7w4lpdUhWwe6+RoGKHH2kGKKgIIRERqi4okKAKVFweqr09Jz10UToKnucaESTQu12uFGanqTqoJRu83lSB0PVgfqLBEE0/bipXkTREnPp3yg9obml/7Q0pv8giVCiEx/MCpCCH9ygKq3sxYITKoVBbRwJrbfMWnToD9UBebcjphkBeY3o/o+Afzw3hpNOtGjjtd/I9p47OOflLoQDdLJlyMoolJNZQUBFCIkIfjFGgDd8l1xGqAMXloQwY9eOCpfxWbKtUxePoGBvcpaDRENlgdVR4Ddq7yi/l5ytMR8pPP35EzyJ1cA+Z8rOK0qPzoSrzdRHiOZFy01G3SA0+naJNmhxj3qJZr9aQDjWidzFM+XX2zU3UsxEhnPB67ejlNBDLdvA4XfCunosRKtIMUFARQiIiNz1VtOeiPfICiwJEhJxrqJxrdEJFqKzIVpCidC14hnUr8it4Ht0zdB2Vfg14TXhtmi5FOq21x69+KphbeKMIVZQ+VFYBdnaaTSCWNvLAirbLD2JEWxiY75FT3ZVOyW0u36O6K6MRVIE6+uyYyyFSCKdwJ8z1s4aKNAcUVISQiMCBXYscex2V7uRKS2lYJtSsOLt7dqBUFCJbgUSEFjy6fkqjr89ZXarSgk7o+iZsr9lib7Xql1ZZ69cRIn1wh0gwa7sCRqgiFFQ6TabF5pCuBWp+GwqzdUTHLRA+et87dfmBHMs6wRRUjef/tc/LUO8x0m96sHWkggpDpM2AlL1+yl7TFqp7z4xKUVCR5oCCihAS/fgZm1AwXaztc8a0MLIXl9vds+3k+A7y0FI6+mUCkaUjVHZBNbBzvkoZwgNp+dYKx/U7CQZTNCGViIG9iHwN7lpg1eroDj5zgLLd/iHaonT7SB4U7R/YrdB196KJmTJ1ilA5jZ9BMb+ODJppQgjPjlZK1F3aL9D7nJrSWokqTRdfrVSwCFUwocQIFWluKKgIITHv9AuWvssJUFwezNQTZBmdf05zAOEFBQNPFDMP6+4VG+bBekSIQu6GNJh/1AZRHDPKNqxboV+ayZ728zqB1zgWpaPeDAXm4eI0ZmVUr6Kw5hTahSPSemZaNNiA5EpDMOfa9o9VR+ayjmpHkEikKZbsKUCN6Z4eTCiZgo2CijQHFFSEkBgMSHZfYB6ouDyUoEI0JNgcQB2dGtSlwDIPNdF1R4EESKAIlXbx1tijX3brBEShdAeaFpSmIIukjsppaPToXsURRajs43XcRKj0NuO9S0vxP2zo1Fy0ESrvurJCpvzMZYIJJVOwUVCR5oCCihDSrBGqQMXluu4q2MFPe1g5Rajs9U12GuqodjjWYDl5UDlFSxoJKpt1gn7tSAXqCBAKvfXrjqTTzylCNbx7oao5WrcDkTn3XXZO1hB2rE5Mn+gtd7FvwhVUjFCRZCOuZ/kRQhJFUAWIUAVxPNfF5brGKlSECiBCtS3APD8dodKRKDuoOUIB9Zbyalm7o0p6FOf43R9MNGi/JQij4d2LbPf5CwpL/Niic0gdIioXSYTKad8gwjSwc4F8v2GnvLNokxzSp2FOIYSsrm0KxyW9sei1C6rGj9G1Tiu2VCqfLk1Gamvp1TbHr4bO7P50ilCZYknPUbSD+jHM8UM9m5sIVSCjWEJiDQUVISQG42fsEarqgAfNHFtxuU7PuRJUAcailFRWy8rtu5TlQSBBhYPq0K6FamQLuv3sgqphMLKTaPCmnwZ1zre2IVDKL1B0TomRnXtiJqgAXisE1e3v/djoMa9cdJCM2c+bFnTrQRVoQLKOajl1Bep9A0+sYx7yHzL/5wkHyGW/7uPY/amL+f3X5d2XbXMzgoogLKcEVRB/Kf3ZC2TDQUisYcqPEBLzCNXi9V5H8v3b5ToWl+ughU7doVhbryPYQTInwIDk1SW7LNPHAgejRw3czcGq7ZVhCY3jh3ZSEa5LftW70X12c89A0TltiBlJp18gQTVpVFfZr12OEiD6gsgQgNByotxFhMo+INnJJV2DYv+RPYr8tgHdj+D9Hzb7Lav3DUSOvftTC8RRPYtk8tgeQfaGyJljeijj1kMPaIjKBdqusw4Kvi5CYgUjVISQGNgmNIgE/K1TP/Z6I11cnp2WoqIf3pRShpT5IheoCdIHYycCDUhGh1+wuhs3w3ydhv9quhZly5uXHuy4Tu0Wrs0trQiVTfxoMRJo9mEwdGTHHm3p1zFfPr3mV3633fL2D/Lct6ut7bDTEIlLDbso3Sl6l5WeIq9dMs7vNtR0jb3jU1m6sVy9Xv3anYrr7c87/WL/dTkxaVQ3dQkG0pb27SKkKWGEihASMQ0jVRoEzrw1pYImtx7F2dLB8BUKNiDZTJOZppp2cizLBX9BpaNDpumjE4FGxajXYEWoAgs6J9rlNphbbqmoDhihChTNCwXqzEKN5THRz6sfYydYtElj76YMVl/mRKeCLOnWJkt9DvB5CFZcT0iyQEFFCIlpl5/2edLjXpywd/q5PdBaqShbyk932JlFzU7o+00TTk24okEDAQgBoSNfgWwBGiwmwotQQehhWLDbeiD9vIEiVMHqoew1VFrwuqm7sjO6Z2Nbh1ARKkISGQoqQkgMitJrG3fbBbAvcGrLD5Qma/w456L0DS5Tfvb0nEmwtFYoGiJfVQHtHxrc0sOLUGmBluOyWy1khKo6dITKLnjddAbaGe0zHp2zihEqsm9AQUUIiVmEak9NnVWQHsgPCuTYisv1qJZAc/ysx1lja/wjVDqFF2igrlN6DqLKrQ9VKMzIl1VAbosmBeqIDEWokTx29HL6cRH5UNmK0t1Etexo49GF68rU5wIwQkWSGQoqQkjE6AOsju7g4Lm3rl4Nze3extnp2qm4vKTSnWhwsk1AjZHbCJU9PedclB6+oNLPi+J4S1DlxqaGKpgRphN6OR0ps+Mm2pRjE7xuolp2ehZnq44/fB60yGaEiiQzFFSEkIjRB2UcpCFsrPqpXm0c2+LtwkgXl7uOUDnYJiB6omuqYJsQCsvZ3HAXx7ZrcRdOWkuji+ERKQsUoYq0y680wghVWVVNo7Sm9/nDd0p30xloB++/jlLOXlXi2muMkESFgooQEjFaJKBmGqIG5o6B7BKcusi0EAo2qibUHMD1ZVWWGEMLfyicrBOwHb6677C7/Mx1rtxWaYk9J6d0p7mHoQg3TVaYlWb5fJU5FMCHNctPd/lZg6PD2zfwlAKzV3vrqKz6MpptkiSEgooQEjGZaa1VTZKOpOgW+VCCyl5c7tYWwIpsGaNn3Kb7glknaMGA14LXFGkNlfbDcnICtyJUvvSZW0rDFCGpKa0tLy+nOqryMHyoquw+VGEKKl1HNW/1DhUtC5QOJSQZoKAihEQM0jpaKMxaWaKiMziYH9A+L+jj7F1kVuF1CNFgN5w0LRNCFaQ3FlR7HNJgaUFTlYGwz51zcgK315u5RdeXhSNCdDTLLqhQHA5Xerc+VHreYqSWEn075qnHIAL4w8Zyy8CVESqSjFBQEUKiQh9kP/lxq5XmCWbO6dRF5ra2xjzQaza49KBqnPLzpgpBpIJBAzuDdnkZ1nWn16G7/CCoIFKaKkJlLmsXVFrMQes5zdKzC1ekQVGLVVPniWj/YJi0nq340dItVlrVbT0YIYkEBRUhJCp0GujLn7ep/wMNJzbJMYrLlRN4AO+mwKnCurAtE5xSflrYROKzZMcUdE6vQ4sRWDbYbR+CEYnVQKAIlX6duempQUUv5i1qtL0EFs/x7f9w0J8HPdcP+yEthYceknzwU00IiQotFLRICFU/ZS8ur6iutSIgoURDbgxqqDoVetNze2rqpbSqJmIncDvm2BunCAxESqpPxIST9iuNqaBy9zrVvEXfe7R55x5r34eKPDqhPw8rtlaGZf9ASKJBQUUIiQrz4AzRMKhLQcjHmMJoh69GKNuFE7hOFUK81fvyR7oWym2EKiO1IT2nxVhDF1vkgsoUdE6iwVtvpt3S3Rem74ihoNLP68YNXqf9NvkEVSTdj2BwlwK/Qn+m+0iyQkFFCIkKM002vEehq3SOHo6M1F2gUS1O5Bgpp6qaOlVkvb2yOixBZR8V49f5FkXKz3z+QMX14XpRVdfWWRYRzRmhMuvVdMovkpE8ID21tRzYrdC6zggVSVYoqAghUWFGLtzUT9mLy3WEyo1gQKRDZ51gCqo7/BDdKsxOi8jZ3N7l11Q1VKZVgVsvqtJdNVZxdzhiTz+/rk3ThOMnpevVNvsMUKOJ3mn7BLcDnglJRCioCCFRYR5oR7sVVBFGqJA2y9GF6Xvr/ArSw7E76GrM3vNPhUWR8nMhqPIywpvn12AnkRZW/ZJOq2nLBU2Dn5SLCJUvvapTftFE78zPBT2oSLJCQUUIiQqdCkLB9bDuXmfsUORYXX61AUe1uLFcsDyoXBak26NJOuUXiwiVuQ0BBVWYXlSRjmopDhChahgA7b6GaotO+UURoUIqWBfk04OKJCsUVISQqGiT4z04D+la4Gr0i724vMRXA+VWNOQYLuu6qNytB1Ugt/RYFKVDcGiHcgyHdlwmK8wIlU8QhZsm08vDcsH0vHLjkq7JSbcXpUe+b5A+1M0Kpl8XIclE5N8QQggRkfH9O8h5B/eSY4d0cv0Y3eVn1jG57f5qGD9TJ+vD9KCyR5N0yi8WPlRIOd5+0iBlD9C7fW5sIlQ+sRlqJI8dvTxc0bGf9D5zM8dPo20TGgYjR75vwI3HDZB3Fm+UowZ0iGo9hMQrFFSEkKjAwfmm4weE9ZiMVG9xOZwPtKBy2/1leljplF/XCFN+SKkh7RhObVEwjhvSOej9+WF2+e3w+WSFG6GCfQX2cXWtd36eFlRhdfk1mkUY3b4Z0aNIXQhJVpjyI4Q0O2Zx+TrfCJjwI1S1Ybuka5Ca06NXIMrCidxEg+VD5bLLb8cuX4QqzBoq7N9iB+sEndp0E4nTRemapt43hCQ6FFSEkBZB11FhVlwkESpEWzZpU88wI1TmYxAha0hrNW3QPtwIlbZNiMQMs8hBUIUTodK2CZpo0qGE7AtQUBFCWgR7SsmtaND1V6u275Laeo/qHmuf5x0nEw46qrVuR5U1NqepozBasLmtoSrxRajC7fIzH+MnqKrdR+LMOjfvY1ghQkgwKKgIIS1Cji0C4j5C5X3cz1u8s+E6FmQq48tw0XVUP22uaDbRoIVMeZgRqlgJKp1qLHARidORQE20RemEJDtxLag2bNggZ511lhQXF0tWVpYMHjxY5s6dq+6rqamR6667Tt2Wk5MjnTt3lj/84Q+yceNG6/Gff/65qiVwusyZM0cts3r1asf7Z82a5bct06dPl379+klmZqZ6zvfee6+Z9wYhyYV5wA7HCVzX9izfWhFR/ZQ95ffjpnKrkNvN2JxoCLfLD7YHUQsqn/UC7BP0GJtwfKg0jFARkqCCqrS0VA4++GBJS0uTGTNmyNKlS+W+++6ToiJvl0hVVZXMnz9fbrzxRvX/66+/LsuWLZMTTjjBWse4ceNk06ZNfpcLLrhAevXqJSNHjvR7vo8//thvuREjRlj3ffvtt3L66afL+eefLwsWLJATTzxRXZYsWdKMe4SQ5MJMKYXjBK4P9Lr2KpL6KVOILfNFqJpDMGjRqIvDgwEBpI05IxJUvs5APdoHac0630BpN+LVHqGioCIkOHH7DbnrrrukW7duMm3aNOs2CCFNQUGBfPTRR36PeeSRR2T06NGydu1a6d69u6Snp0vHjh2t+xHVeuutt+Tyyy9vNKYCUTBzWZOHHnpIjj76aLn22mvV9dtuu009N57v8ccfj9lrJmRfQg9IDtcWQM8B1EQbocIIm+ZKaWlRsssnboKlKpGe0wIokvl3esSLjlDpNCNqzjATMdwaKhalE5KgEaq3335bRZFOPfVUad++vQwbNkyeeuqpoI/ZuXOnEkqFhYUB11lSUiLnnntuo/sQ2cLzHHLIIWo5k5kzZ8r48eP9bps4caK6PRDV1dVSXl7udyGEOAujcCIw9u6ziAWV7XHNEYExU22VIdJ+WghhP2WmuXOgd4xQ+dKGZoefm7mH5n5OT2kd0TYQsi8Rt4Jq5cqV8thjj0mfPn3kgw8+kEsuuUSuuOIKef755x2X37Nnj6qpQmouPz/fcZlnnnlGCaGuXbtat+Xm5qpUImqk3n33XSWokM4zRdXmzZulQwd/d19cx+2BuOOOO1QUTV8QbSOEOB+wwxFUdn+kSFN+7XIzlFBoTp+l9FQIk9auCtO1B1Wkw4R112SpT1BZHlQuI3Hmfma6j5DQxO23pL6+XkWopk6dqq4jQoWaJaTYJk+e7LcsUnmTJk1SNQcQYU6sX79eCbNXX33V7/a2bdvK1VdfbV0fNWqUKmy/5557/OqxwuX666/3Wy8iVBRVhDSQaxywwxNUsYlQoWarU2GmrCmpiolLulsg3PbUVLsQVDVRDRPWXZO6sD0cDyr7fmaHHyEJHKHq1KmTDBjgP86if//+qj7KSUytWbNG1TUFik6hFgt1Um5E0pgxY2TFihXWddRWbdmyxW8ZXA9UcwUyMjLUtpgXQohzDVU0Kb9wByMHEmPN5QSe77LTz4pQRVCQbkaodu6ukdq6+gY3+AyXESpjPzNCRUgCCyp0+KFrz2T58uXSo0ePRmLq559/Vl16EExOIHIFQQVbBXQNhmLhwoVK0GnGjh0rn3zyid8yEG+4nRDSvDVUZiqqbW56VLU9phhrapf0Rl5Uu91FqCJxSQeFWWmiS6VKq2qkPEw3eKQmdc08C9IJCU3cnnZcddVVyvYAKT+IptmzZ8uTTz6pLlpMnXLKKcoy4Z133pG6ujqrpqlNmzaqw0/z6aefyqpVq5Rlgh3UZGFZpBQB7BeeffZZefrpp61lpkyZIocffriqtTr22GPl5ZdfVn5YelsIIeFjppTCElRG5CTSdJ/T45tLNOj0mdsIVbhz/DSpKa3VzELYS8B+QY+7cRuJ0/MWK6prGaEixAVx+y1BLdMbb7yhapFuvfVWZZnw4IMPyplnnmmZfurC8QMPPNDvsZ999pn86le/8itGhziDMacTsEFAyjA1NVUt88orryixpsFjX3zxRfn73/8uN9xwgyqUf/PNN2XQoEFN9OoJSX4iL0pPjbog3enxzSUaGsw9mzZCpfcrBBU6/ax5hWEIR8xbpKAixB1x/S057rjj1MWJnj17qlSeGyCGAoECd3uRuxOwb8CFEBIbzNRdOD5LpuFk54IEjFD5xIlOwTVVhEoXtK+UXUpQ6RRjOOIoR4neaqb8CEnkGipCyL4ToSoOwxogI7W1MqeMSYTKryi9ec4vtTgJGaHyOcFHYurpNM8v3C4/HaHyPoaCipBQUFARQuJg9Ix70YDaHh2liraGCrYJmuYSDVrQ6EHFISNUEfpQNRZU4flQgRyf6GXKj5DQUFARQlqEdnkZavRKh/yMsDv1OvlSfb3b50a1DRmpKdKrbY5vnQ3iqilBoTjQc/qcQDlDiW8GX5ucjJgIKqvLLwxxpPdJtJFAQvYFeNpBCGkRcLD/z/ljpDA7/MjQo2cOl/WlVbJfu+gEFXjqDyNkY9ke6dYmW5oDLQY37twdcBkUkmOYcbRCzylCFU4k7m/HDpCJAzvKkf3aR7wNhOwrUFARQlqMsfs7e8eFApGpaKNTDevKU5fmQkd7IOICsaHMK7ba5oYfvXMSVIiG6RRjOAXmiCIeM7jBk48QEhim/AghpBnRggpRo6q9znVU60u9gqqLUeMVCdpyAenDhggVz6MJaQooqAghpBlBhEiLmo2+SJQdfXu0tUvacmFbZbXs8qUQOZePkKaBgooQQpoZ3Z2oI1GBUn7RdjHq7sltFd6OQcAIFSFNAwUVIYQ0M1ooBaqj2mCl/KKMUNksFzCfLy2FP/uENAX8ZhFCSDOjU3kbyqqCRqjM4c2RkJWWooxQNXQ8J6TpoKAihJBmRkeedCSqqWqoYIJqjq5huo+QpoOCihBCmhkdedKRKJPde+ukZJfX1LNrYfTeWOZwZY6QIaTpoKAihJBmJpgXlRZZGM2TnxV9REl7UQF2+BHSdFBQEUJIM9PVF6HaXL5HauvqA9RPZaqUXSwFFVN+hDQdFFSEENLMwAE9PaW11NV7lKhyrJ+KsiDdMULFlB8hTQYFFSGENDOtW7eSTj4XdHvaz7JMiNFA4jY+L6pwByMTQsKDgooQQlqy089mndBg6hmbYc1tDC8qpvwIaTooqAghJI6sE/R11FDFPELFonRCmgwKKkIIiSPrBH29a6xSfixKJ6RZoKAihJAWdUtvqKFCx58uUo9Zys8UVBmMUBHSVFBQEUJIC1onbChtqKHaUlGtOv/SUlpJ+7yMmDwPfagIaR4oqAghpEUjVLvF4/H41U91LMhUnYCxoDA7XbSdFVN+hDQdFFSEENICQDSBPTX1ssM3aibWHlQgpXUrGdg5X/IyUmNWl0UIaQxPVwghpAXISE1Rab2tFdXKi6o4NyPmlgma1y4eJ3tq6jjLj5AmhBEqQghp8bSft45qfYxNPTWZaSkq9UcIaTooqAghpIXQqT0tpBoiVLHxoCKENB8UVIQQ0sKCSo+f2dhEKT9CSNNDQUUIIXGQ8kOnX6zn+BFCmg8KKkIIafF5frultKpGdtfUqeudfB2AhJDEgYKKEEJaevxM6W4rOtU2N0MVkRNCEgsKKkIIaSF0ag/RqRXbKvxuI4QkFhRUhBDSQuRnplnu5XNWl/qNpCGEJBYUVIQQEgd1VLNX7fBeZ4SKkISEgooQQuJAUK3YWqn+78yCdEISEgoqQghpQewRqS5F9KAiJBGhoCKEkBbEPgg5loORCSHNBwUVIYTEVYSKgoqQRISCihBC4sCLCuRmpEq+r+uPEJJYxLWg2rBhg5x11llSXFwsWVlZMnjwYJk7d666r6amRq677jp1W05OjnTu3Fn+8Ic/yMaNG/3W0bNnT2nVqpXf5c477/RbZvHixXLooYdKZmamdOvWTe6+++5G2zJ9+nTp16+fWgbP+d577zXxqyeE7AuYNglI9+E3ihCSeMStoCotLZWDDz5Y0tLSZMaMGbJ06VK57777pKioSN1fVVUl8+fPlxtvvFH9//rrr8uyZcvkhBNOaLSuW2+9VTZt2mRdLr/8cuu+8vJymTBhgvTo0UPmzZsn99xzj9xyyy3y5JNPWst8++23cvrpp8v5558vCxYskBNPPFFdlixZ0kx7gxCSrMAZPT3F+1PMdB8hiUvcxpbvuusuFS2aNm2adVuvXr2svwsKCuSjjz7ye8wjjzwio0ePlrVr10r37t2t2/Py8qRjx46Oz/PCCy/I3r175dlnn5X09HQZOHCgLFy4UO6//3656KKL1DIPPfSQHH300XLttdeq67fddpt6bjzf448/HvPXTgjZd2jdupV0LsyU1SVVLEgnJIGJ2wjV22+/LSNHjpRTTz1V2rdvL8OGDZOnnnoq6GN27typwuWFhYV+tyPFh7Qh1oEIVG1trXXfzJkz5bDDDlNiSjNx4kQV7UKUTC8zfvx4v3ViGdweiOrqahX9Mi+EEBKsjsqspyKEJBZxK6hWrlwpjz32mPTp00c++OADueSSS+SKK66Q559/3nH5PXv2qJoqpOby8/Ot2/GYl19+WT777DP54x//KFOnTpW//OUv1v2bN2+WDh06+K1LX8d9wZbR9ztxxx13qCiaviDaRgghTpw4rIt0b5Mtv+7XvqU3hRCSbCm/+vp6FaGCAAKILqFmCSm2yZMn+y2LAvVJkyaJx+NRIszk6quvtv4eMmSIikRBWEHwZGRkNNn2X3/99X7PjQgVRRUhxIlJI7upCyEkcYnbCFWnTp1kwIABfrf1799f1Uc5iak1a9aouiYzOuXEmDFjVMpv9erV6jpqq7Zs2eK3jL6u664CLROoLgtArGFbzAshhBBCkpO4FVTo8EMdk8ny5ctVN55dTP3888/y8ccfqzqpUKDgvHXr1qouC4wdO1a+/PJLtS4NhFnfvn2tjkIs88knn/itB8vgdkIIIYSQuBVUV111lcyaNUul/FasWCEvvviisjK49NJL1f0QQKeccorypUKnXl1dnappwgVdewBF4w8++KAsWrRI1WRhOawX3lZaLJ1xxhkqDQhLhB9++EFeeeUV1dVnpuumTJki77//vrJt+Omnn5StAp73sssua6G9QwghhJC4whPH/O9///MMGjTIk5GR4enXr5/nySeftO5btWqVB5vvdPnss8/UMvPmzfOMGTPGU1BQ4MnMzPT079/fM3XqVM+ePXv8nmfRokWeQw45RD1Ply5dPHfeeWejbXn11Vc9BxxwgCc9Pd0zcOBAz7vvvhvWa9m5c6faNvxPCCGEkMTA7fG7Ff5paVG3L4CidHT7wdqB9VSEEEJIch2/4zblRwghhBCSKFBQEUIIIYRECQUVIYQQQkiUUFARQgghhEQJBRUhhBBCSJRQUBFCCCGERAkFFSGEEEJIlFBQEUIIIYRECQUVIYQQQkiUpEa7AuIObUgPx1VCCCGEJAb6uB1qsAwFVTNRUVGh/u/WrVtLbwohhBBCIjiOYwRNIDjLr5mor6+XjRs3Sl5enrRq1Sqmyhkibd26dZwR2Mxw37cc3PctB/d9y8F93zJAJkFMde7cWVq3DlwpxQhVM4E3oWvXrk22fny5+AVrGbjvWw7u+5aD+77l4L5vfoJFpjQsSieEEEIIiRIKKkIIIYSQKKGgSnAyMjLk5ptvVv+T5oX7vuXgvm85uO9bDu77+IZF6YQQQgghUcIIFSGEEEJIlFBQEUIIIYRECQUVIYQQQkiUUFARQgghhEQJBVWC8+ijj0rPnj0lMzNTxowZI7Nnz27pTUoq7rjjDhk1apRyuG/fvr2ceOKJsmzZMr9l9uzZI5deeqkUFxdLbm6unHzyybJly5YW2+Zk5c4771RTBq688krrNu77pmPDhg1y1llnqX2blZUlgwcPlrlz51r3o5/ppptukk6dOqn7x48fLz///HOLbnMyUFdXJzfeeKP06tVL7df9999fbrvtNr85ctz38QkFVQLzyiuvyNVXX63aaOfPny9Dhw6ViRMnytatW1t605KGL774Qh2wZ82aJR999JHU1NTIhAkTZNeuXdYyV111lfzvf/+T6dOnq+UxYuh3v/tdi253sjFnzhx54oknZMiQIX63c983DaWlpXLwwQdLWlqazJgxQ5YuXSr33XefFBUVWcvcfffd8s9//lMef/xx+e677yQnJ0f9/kDkksi566675LHHHpNHHnlEfvzxR3Ud+/rhhx+2luG+j1Ngm0ASk9GjR3suvfRS63pdXZ2nc+fOnjvuuKNFtyuZ2bp1K04TPV988YW6XlZW5klLS/NMnz7dWubHH39Uy8ycObMFtzR5qKio8PTp08fz0UcfeQ4//HDPlClT1O3c903Hdddd5znkkEMC3l9fX+/p2LGj55577rFuw/uRkZHheemll5ppK5OTY4891nPeeef53fa73/3Oc+aZZ6q/ue/jF0aoEpS9e/fKvHnzVKjXnBeI6zNnzmzRbUtmdu7cqf5v06aN+h/vAaJW5vvQr18/6d69O9+HGIEI4bHHHuu3jwH3fdPx9ttvy8iRI+XUU09Vqe5hw4bJU089Zd2/atUq2bx5s9++x6wzlB1w30fHuHHj5JNPPpHly5er64sWLZKvv/5ajjnmGHWd+z5+4XDkBGX79u0q196hQwe/23H9p59+arHtSmbq6+tV/Q5SIYMGDVK34YctPT1dCgsLG70PuI9Ex8svv6zS2Uj52eG+bzpWrlyp0k4oKbjhhhvU/r/iiivU/p48ebK1f51+f7jvo+Ovf/2rlJeXq5ODlJQU9Tt/++23y5lnnqnu576PXyioCAkjUrJkyRJ1tkiannXr1smUKVNU7RqaLkjznjwgQjV16lR1HREqfPZRswNBRZqOV199VV544QV58cUXZeDAgbJw4UJ1Ite5c2fu+ziHKb8EpW3btursxd7RhOsdO3Zsse1KVi677DJ555135LPPPpOuXbtat2NfI/1aVlbmtzzfh+hBSg8NFsOHD5fU1FR1QeE5inHxN87Iue+bBnSPDRgwwO+2/v37y9q1a9Xfev/y9yf2XHvttSpKddppp6nOyrPPPls1X6DjGHDfxy8UVAkKQu8jRoxQuXbzrBLXx44d26LblkygPRli6o033pBPP/1UtTKb4D1AJ5T5PsBWAQcevg/RceSRR8r333+vztD1BVETpD7039z3TQPS2nZ7ENT09OjRQ/2N7wEO3ua+R5oKHWfc99FRVVWl6mFNcPKM33fAfR/HtHRVPImcl19+WXV2PPfcc56lS5d6LrroIk9hYaFn8+bNLb1pScMll1ziKSgo8Hz++eeeTZs2WZeqqiprmYsvvtjTvXt3z6effuqZO3euZ+zYsepCYo/Z5Qe475uG2bNne1JTUz2333675+eff/a88MILnuzsbM9//vMfa5k777xT/d689dZbnsWLF3t++9vfenr16uXZvXt3i257ojN58mRPly5dPO+8845n1apVntdff93Ttm1bz1/+8hdrGe77+ISCKsF5+OGH1QElPT1d2SjMmjWrpTcpqcA5h9Nl2rRp1jL4EfvTn/7kKSoqUgedk046SYku0vSCivu+6fjf//7nGTRokDpp69evn+fJJ5/0ux/t+zfeeKOnQ4cOapkjjzzSs2zZshbb3mShvLxcfcbxu56ZmenZb7/9PH/729881dXV1jLc9/FJK/zT0lEyQgghhJBEhjVUhBBCCCFRQkFFCCGEEBIlFFSEEEIIIVFCQUUIIYQQEiUUVIQQQgghUUJBRQghhBASJRRUhBBCCCFRQkFFCCGEEBIlFFSEEOKCc845R0488cSW3gxCSJyS2tIbQAghLU2rVq2C3n/zzTfLQw89pIZlE0KIExRUhJB9nk2bNll/v/LKK3LTTTfJsmXLrNtyc3PVhRBCAsGUHyFkn6djx47WpaCgQEWszNsgpuwpv1/96ldy+eWXy5VXXilFRUXSoUMHeeqpp2TXrl1y7rnnSl5envTu3VtmzJjh91xLliyRY445Rq0Tjzn77LNl+/btLfCqCSGxhIKKEEIi5Pnnn5e2bdvK7Nmzlbi65JJL5NRTT5Vx48bJ/PnzZcKECUowVVVVqeXLysrk17/+tQwbNkzmzp0r77//vmzZskUmTZrU0i+FEBIlFFSEEBIhQ4cOlb///e/Sp08fuf766yUzM1MJrAsvvFDdhtRhSUmJLF68WC3/yCOPKDE1depU6devn/r72Weflc8++0yWL1/e0i+HEBIFrKEihJAIGTJkiPV3SkqKFBcXy+DBg63bkNIDW7duVf8vWrRIiSeneqxffvlFDjjggGbZbkJI7KGgIoSQCElLS/O7jtor8zbdPVhfX6/+r6yslOOPP17uuuuuRuvq1KlTk28vIaTpoKAihJBmYvjw4fLf//5XevbsKamp/PklJJlgDRUhhDQTl156qezYsUNOP/10mTNnjkrzffDBB6orsK6urqU3jxASBRRUhBDSTHTu3Fm++eYbJZ7QAYh6K9guFBYWSuvW/DkmJJFp5aH1LyGEEEJIVPCUiBBCCCEkSiioCCGEEEKihIKKEEIIISRKKKgIIYQQQqKEgooQQgghJEooqAghhBBCooSCihBCCCEkSiioCCGEEEKihIKKEEIIISRKKKgIIYQQQqKEgooQQgghRKLj/wMIg2ZyTKHe1gAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1872,14 +1957,16 @@
     }
    ],
    "source": [
-    "# Use the requests method to obtain 1 day of instantneous gage height data\n",
-    "water_level_USGS_data = river.io.usgs.request_usgs_data(\n",
-    "    station=\"15515500\",\n",
-    "    parameter=\"00065\",\n",
-    "    start_date=\"2010-08-10\",\n",
-    "    end_date=\"2010-08-10\",\n",
-    "    data_type=\"Instantaneous\",\n",
-    ")\n",
+    "# Use stored data file of 1 day of instantneous gage height data\n",
+    "water_level_USGS_data = pd.read_csv(\"data/river/usgs_gage_height_TRTS_20100810_instantaneous.csv\")\n",
+    "# This file was created with the following mhkit call:\n",
+    "# water_level_USGS_data = river.io.usgs.request_usgs_data(\n",
+    "#     station=\"15515500\",\n",
+    "#     parameter=\"00065\",\n",
+    "#     start_date=\"2010-08-10\",\n",
+    "#     end_date=\"2010-08-10\",\n",
+    "#     options={\"data_type\":\"Instantaneous\"},\n",
+    "# )\n",
     "\n",
     "# Plot data\n",
     "water_level_USGS_data.plot()\n",
@@ -1888,14 +1975,16 @@
     "plt.xlabel(\"Time\")\n",
     "plt.ylabel(\"Gage Height (feet)\")\n",
     "\n",
-    "# Use the requests method to obtain 1 day of instantneous discharge data\n",
-    "discharge_USGS_data = river.io.usgs.request_usgs_data(\n",
-    "    station=\"15515500\",\n",
-    "    parameter=\"00060\",\n",
-    "    start_date=\"2010-08-10\",\n",
-    "    end_date=\"2010-08-10\",\n",
-    "    data_type=\"Instantaneous\",\n",
-    ")\n",
+    "# Use stored data file of 1 day of instantneous discharge data\n",
+    "discharge_USGS_data = pd.read_csv(\"data/river/usgs_discharge_TRTS_20100810_instantaneous.csv\")\n",
+    "# This file was created with the following mhkit call:\n",
+    "# discharge_USGS_data = river.io.usgs.request_usgs_data(\n",
+    "#     station=\"15515500\",\n",
+    "#     parameter=\"00060\",\n",
+    "#     start_date=\"2010-08-10\",\n",
+    "#     end_date=\"2010-08-10\",\n",
+    "#     options={\"data_type\":\"Instantaneous\"},\n",
+    "# )\n",
     "\n",
     "# Print data\n",
     "discharge_USGS_data.plot()\n",
@@ -1915,7 +2004,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 21,
    "id": "745835ce-0224-4121-9d1b-a135c8ee3e0c",
    "metadata": {},
    "outputs": [],
@@ -1940,7 +2029,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": null,
    "id": "64e7f82b-3632-41f4-a5be-9421a817b3d4",
    "metadata": {},
    "outputs": [
@@ -1973,40 +2062,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": null,
    "id": "69a3a77d-2a2a-4944-8923-05159d52ad8d",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([400960., 400980., 401000., 401020., 401040., 401060., 401080.,\n",
-       "        401100.]),\n",
-       " [Text(400960.0, 0, '400,960'),\n",
-       "  Text(400980.0, 0, '400,980'),\n",
-       "  Text(401000.0, 0, '401,000'),\n",
-       "  Text(401020.0, 0, '401,020'),\n",
-       "  Text(401040.0, 0, '401,040'),\n",
-       "  Text(401060.0, 0, '401,060'),\n",
-       "  Text(401080.0, 0, '401,080'),\n",
-       "  Text(401100.0, 0, '401,100')])"
-      ]
-     },
-     "execution_count": 24,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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v0FQMGq9i18NM2PWgbGDIUE7toPEqTrciU6xcuRKffPKJfPubb77B5MmTNayILM0mPv179uwJIUSGGzkId/1/m4VIrnqDAEIqcNP/txH9PyExbCghJf+3WVJiLsMAQpSVBQsWGISN0NBQuwgbekiKN0dkE4GDHJiFQ0ZGUoMHw4fKGDwcWmrIYNAwnhYhIyOpwYPhgzJz9OhRg8Xht27dQpUqVTSsSD2pU6qUbMaYOnUqatWqhVy5ciEgIADt27fHlStXsnzOqlWrIEmSwebh4WHKt2sym5hSRQ5G44CRFa71MAOu9XAoDBjKWEPAyArXetCr4uPjUa9ePfn27du3UaRIEQ0rUpfy63AY95wDBw7gq6++Qq1atZCcnIxRo0ahWbNmuHjxInLkyJHp87y9vQ2CiSRp+8uXgYOshxUHjVdxrYeZcK2HXWLIUM7ag8aruNaDUnl6espf//XXX3YVNgBALyTolZwW18jn7Nq1y+D2qlWrEBAQgFOnTqFBgwaZPk+SJKs6sRI/1UlbGqzNUBunW5kB13rYBU6ZUkartRlq43QrxySEwJdffinffvfdd9GkSRMNK7IvEREpV+rMkydPlo+Ljo5GkSJFEBQUhHbt2uHChQuWKC9T7HCQNmw4YGSGXQ8zYdfDpjBgKGfrASMz7Ho4jkOHDuHzzz/HpUuX5H0bNmzQsCLz0SucUpV6HY5Xr6aenYtV6/V6DBw4EPXr10fFihUzfVyZMmWwYsUKVK5cGREREZg5cybq1auHCxcupDvbq6XwE5wsxw66GdnFrocZsOth1djNUMZeuhnZxa6H/Ro+fDgaNGhgEDaePXumYUXmpRdOijcACAoKgo+Pj7xNnTr1te/51Vdf4fz581i3bl2Wj6tbty66d++OqlWromHDhti0aRP8/f2xbNkyVb53JdjhIPNzgICRGXY9zIRdD6vAgKGcowSMzLDrYV8uXbqEGTNmGOwLDQ197bQfW6aDBJ2CU9ymPufevXvw9vaW97+uu9G3b19s374dBw8eNLpL4erqimrVquH69etG16sWflqTeThQNyO72PUwA3Y9NMFuhjKO1s3ILnY9bF/Dhg3lrwsVKoRTp07ZzelvzcXb29tgyyxwCCHQt29fbN68Gfv27UOxYsWMfi+dTodz584hf/78ppatGDscpC4GjNdi18NM2PUwO4YMZRgwsoen1rVN69atQ3h4OICUM1P9888/mh7YWkra6VHGPs8YX331FdauXYutW7ciV65cePz4MQDAx8dHPhNY9+7dUbBgQXla1sSJE1GnTh2ULFkSL1++xIwZM3Dnzh18+umnRterFgYOMh1DhmK8rocZ8LoeqmLIUIYhQzlOt7ItXbt2lb9+/vy55heYsxQdoHBKlXGWLFkCAGjUqJHB/pUrV6Jnz54AgLt378LJ6b/PuxcvXqB37954/PgxfH19UaNGDfz9998oX7680fWqhYGDlGPQUA27HmbCrodiDBrKMGioi10P67Zy5Ur5619//dVhwgZguQ6HEOK1jwkJCTG4PWfOHMyZM8eo9zE3Bg4yDkOG2bHrYQbsemQLQ4YyDBnmx66Hdfrkk0/krzt16qRhJWTtGDgoexg0LI5dDzNh1yMdBg1lGDS0wa6HdZg/f7789datWzWsRBs64QSdgg6HkufYAwYOyhxDhtVg+DADB+96MGQow5BhPdj10I4QAgMGDJBvt23bVsNqtCEgQa9gDYdQ8Bx7wMBB6TFoWDVOuTIDB+p6MGgow6Bh3dj1sKxRo0bJXx84cEDDSrTDDodxGDgoBUOGzWHXwwzstOvBkKEMQ4btYdfD/BISEjBt2jT5doMGDTSshmwFA4ejY9CwC+x6mIEddD0YNJRh0LAP7HqYR+3ateWvr1y5omEl2tILCXoFv2SVPMceMHA4KgYNu8SuhxnYWPBw0M8yVTBo2Cd2PdRz6tQpnD17FgBQunRplC5dWuOKtKODE3RQMKVKwXPsAQOHI2HIcCjseqjMyqdbMWgow5DhWNj1UC4iIgI1a9aUb4eGhmpXjBVgh8M4DByOgEHDobHrYQZW1PVw0M8ukzFoODYGD+MkJCQgd+7c8u1NmzbB09NTu4LI5jBw2CuGDMoAux4q06jrwZChDEMGvYrTrV4vLi4OXl5e8u3GjRujQ4cOGlZkHfRwgl7B9Cglz7EHjvld2zN3PcMGvZbkqpc3Uomb3jCAmIGQGDaUkJIZNuj1EnMZBhACzpw5YxA2KlasiF27dmlYkfXQCUnx5ogYOOxBashg0CAFGDxUlho8VAofqSHDQT+jFEsNGQwaZKzU4OHo4WPevHmoXr26fPuTTz7BuXPn4OrqqmFV1iN1DYeSzRFxSpUtY8AgFXGthxmYsNbDQT+TTMaAQWpy1LUeb731Fg4fPizf3r59O1q3bq1hRWTrGDhsDUMGWQDXeqgsm2s9GDKUYcggc3OUtR5RUVHw9vY22Pfw4UPkz59fo4qslxBO0Cu4arhw0CuNO+Z3bYtyJDNskMVxrYcZZDDdilOmlOGUKdPp3bSuwPbY43Sr5ORkzJw50yBsuLq6Ijk5mWEjEzpIijdHxA6HLXFJOUiRJKFxIbZLcv5v7PTJzNvGkNx1KV/o//tlKZId8xenGvRu/HdsquQcHEMlnJIM/92enzFIo0pIS0IIhIaGYvr06Vi3bp3BfV988QUWL16sUWW2QS+UXVND76C/thg4bJBI8wPO8KGc0/8HOAYP5SSXlJ8/Bg8i6/dq0CDH9OLFC4wePTrTQHHkyBHUq1fPwlWRvWPgsHEMH6ZLDR4Aw4dSqcEDYPggsiYMGZTq2bNnKF++PMLCwjK8f+HChfjiiy/g5MTPwezQK1zDoeQ59oCBw46khg8GD+XY9TAdwweR9hg0KFViYiKCg4Px999/p7tv0qRJ6NmzJwoVKqRBZbZNDwl6BesxlDzHHjBw2CF2PUzHroc6OOWKyHIYMigtIQSGDBmCOXPmGOyvW7cuNm3ahMDAQI0qsw9KL+LnqBf+Y+Cwc+x6mI7hw3TsehCZD4MGvSo8PBwBAQEG+woVKoQLFy6kO+0tkSXw6MlBCCHJGynn5KI3CCBkPMlFGAQQIjKeU5Ikb0RpHT58OF3YuHHjBu7du8ewoaLUNRxKNkfkmN+1g2PwMF1q8GD4UC41eDB8EGUfQwZl5cKFC3jrrbfk26NGjYIQAsWLF9ewKvukhwS9ULA56BoOmwkcbdu2ReHCheHh4YH8+fPjo48+wsOHD7Uuy6ax66EOBg/TMXgQZY7dDMqOp0+fomLFivLtXbt24dtvv9WwIvsm/n/RuLGbYOCwbsHBwfj1119x5coVbNy4ETdu3MB7772ndVl2g8HDdOx6mI5dD6L/MGRQdiUnJ8Pf31++vWXLFjRv3lzDiogM2cyi8UGD/rsSapEiRTBixAi0b98eSUlJcHV11bAy+8IzXKmDp9c1HReakyNiwCBjJSUlwc3NTb49depUtGvXTsOKHEPqFCklz3NENhM40nr+/Dl++eUX1KtXj2HDjHiGK9PxDFfq4Ol1yd4xaJASL168QJ48eeTbbdq0wYgRIzSsyHHwwn/Gsanv+uuvv0aOHDmQN29e3L17F1u3bs3y8QkJCYiMjDTYyHhc66EOTrcyHadckT3h2gwyxc6dOw3CRnBwMLZt26ZhRY5F0YJxhV0Re6Bp4BgxYgQkScpyu3z5svz4YcOG4cyZM/jzzz/h7OyM7t27Q4jMDzymTp0KHx8feQsKCrLEt2XXGDxMx7Ue6mDwIFvEkEGmSkhIQP78+dGqVSt5X69evbB3714NqyLKmqZTqoYMGYKePXtm+Zi0p3Lz8/ODn58fSpcujXLlyiEoKAjHjh1D3bp1M3zuyJEjMXjwYPl2ZGQkQ4dKuNZDHVzrYTqu9SBbwIBBavjf//6HDz74wGDfoUOH8Oabb2pUkeNKPeuUkuc5Ik0Dh7+/v8FZFYyh16ccqCUkJGT6GHd3d7i7uyt6fco+rvUwHdd6qINrPciaMGSQWl5dqwEAb775Jg4cOAAnJ35maIGLxo1jE4vGjx8/jhMnTuDNN9+Er68vbty4gTFjxqBEiRKZdjfI8tj1UAe7HqZj14O0xKBBann58iU6d+6MPXv2GOw/efIkatSooVFVBDBwGMsmjmi8vLywadMmNGnSBGXKlEGvXr1QuXJlHDhwgB0MK8W1HqbjWg91cK0HWQLXZpBahBBYuXIlJEmCr6+vQdjo1KkT9Ho9wwbZHJvocFSqVAn79u3TugxSgF0PdbDrYTp2PcgcGDBILTqdDtOnT8eoUaPS3efh4YGbN28if/78GlRGGWGHwzg8eiGLYdfDdOx6qIOn1yVTsJtBaoqOjkbv3r3h4uKSLmy8//77ePr0KeLi4hg2rAxPi2scm+hwkH1h10MdXGiuDi40p+xiwCC17du3D02aNEm3//fff8c777yjQUWUXQLKzjjlqEc9DBykKZ7hSh2ccmU6TrmijDBkkDncvXsXRYsWTXctMZ7i1nZwSpVxeHRCVoFXM1cHp1ypg9OtiFOmSG3JycmYMGECJElCkSJFDMLGqlWroNfrGTbIbrHDQVaHU67Uwa6H6dj1cCwMGKS2W7duYdKkSVi5cmWG97dr1w6bNm3itTRsEDscxmHgIKvGKVem41oPdXCth/1i0CA1Xb58Gb1798bhw4czfczEiRMxatQoODs7W7AyUhMDh3EYOMgmsOuhDnY9TMeuh31gyCBzePz4McqVK5fhfV999RWmTZuGnDlzWrgqMgcGDuMwcJDNYdfDdOx6qIPhw/YwaJC5xMfHpzt1La8ITpSCRxpks7jQXB1cZK4OLjS3XrxuBpmbEAK5cuWSb3/wwQe8IridS3sMYuzmiNjhILvArofp2PVQB7se1oMBgyylf//+SE5OBgC88cYb+OWXXzSuiMxND0nRdTiUPMceMHCQXeFaD3VwrYc6uNDc8hgyyNIWLFiAhQsXyrcPHjyoYTVkKVzDYRwGDrJb7HqYjl0PdbDrYX4MGmRpQgh07twZGzZskPf9888/cHd317AqIuvEIwiye44+b1ItXOuhDq71UA/XZpBW/vrrLzg5OaULG7Vq1dKwKrIkS63hmDp1KmrVqoVcuXIhICAA7du3x5UrV177vN9++w1ly5aFh4cHKlWqhD/++EPpt6oKdjjIoTjzgFmRNBfEhZ4XqFKHM0OHqXSe//17dkpg6CDze/jwIQoWLJhuf0REBLy9vTWoiLRiqSlVBw4cwFdffYVatWohOTkZo0aNQrNmzXDx4kXkyJEjw+f8/fff6Nq1K6ZOnYp33nkHa9euRfv27XH69GlUrFjR6JrVwMBhI5yceaBsDBc3XYb72eUwjuAxsfmkdjk4xUoVeveU8WTwIHO4f/8+KleujBcvXhjs37x5M9q3b69NUaQppTMnjH3Orl27DG6vWrUKAQEBOHXqFBo0aJDhc+bNm4cWLVpg2LBhAIBJkyZhz549WLhwIZYuXWp0zWrgnyrJrri46TING5Q9Qvy3kQW4iP82MpneXcgbkalu3LgBSZIQFBRkEDbef/996PV6hg1SLDIy0mBLSEjI1vMiIiIAAHny5Mn0MUePHsXbb79tsK958+Y4evSo8oJNZHSH49atWzh06BDu3LmD2NhY+Pv7o1q1aqhbty48PDzMUSNRlhgw1MGAYQXY9VAVux6k1Pnz51GpUqV0+5s0aYKtW7dmOpWFHIdQOKUqtcMRFBRksH/cuHEYP358ls/V6/UYOHAg6tevn+XUqMePHyNfvnwG+/Lly4fHjx8bXa9ash04fvnlF8ybNw8nT55Evnz5UKBAAXh6euL58+e4ceMGPDw80K1bN3z99dcoUqSIOWsmAsCgoQaGDCuVttvB8GGytN0Ohg/KyrFjx1C3bt10+9977z388ssvcHNz06AqskYCyj5DU59y7949g3U/2Tm72VdffYXz58/j8OHDxr+xxrIVOKpVqwY3Nzf07NkTGzduTJfKEhIScPToUaxbtw41a9bE4sWL0alTJ7MUTI6NIcN0DBk2hl0PVbHrQRk5ePAgGjZsmG7/Z599hiVLlsCJJ8ugV+ghQTLhwn/e3t5GnWigb9++2L59Ow4ePIhChQpl+djAwEA8efLEYN+TJ08QGBhodL1qyda/oGnTpuH48eP48ssv04UNICWVNWrUCEuXLsXly5dRvHhx1Qslx8a1Gabjugwbx7UequJaDwKAM2fOQJKkdGFj+PDh0Ov1WLZsGcMGaUoIgb59+2Lz5s3Yt28fihUr9trn1K1bF3v37jXYt2fPngy7d5aSrQ5H8+bNs/2CefPmRd68eRUXRJSKAcN0DBh2il0PVbHr4Xju3r2b4fTvKVOmYMSIEZAk/ixQ1ix1lqqvvvoKa9euxdatW5ErVy55HYaPjw88PT0BAN27d0fBggUxdepUAMCAAQPQsGFDzJo1C61bt8a6detw8uRJLF++3Oh61aL4tLhhYWEICwuDXm94utbKlSubXBQ5NgYN0zFoOAiu9VAV13rYv5cvX6JixYp48OCBwf5x48Zh3LhxDBqUbXohQbLAdTiWLFkCAGjUqJHB/pUrV6Jnz54AUgJ02k5cvXr1sHbtWowePRqjRo1CqVKlsGXLFs2uwQEoCBynTp1Cjx49cOnSJYj/P6qRJAlCCEiSBJ2OB4tkPIYM0zFkODh2PVTF8GFfEhMT0bx5c4SEhBjs79GjB1auXMmgQUZTOk3Z2OeIbDzh1Z9rAOjUqZNVrac2OnB88sknKF26NH788Ufky5eP/0jJJAwapmPQIAPseqiOU65sl16vx+eff44ffvjBYH/9+vWxd+/ebJ0ZiIhMZ3TguHnzJjZu3IiSJUuaox5yAAwZpmPIoGxh10NV7HrYDiEEZs+ejaFDhxrsz5cvHy5dugRfX1+NKiN7Yak1HPbC6MDRpEkTnD17loGDjMagYToGDVKEXQ/VsethvSIiIpA7d+50+2/evJmtM/wQZQcDh3GMDhw//PADevTogfPnz6NixYpwdXU1uL9t27aqFUe2jyHDdAwZpCqGD1Wx62FdkpKS0oWNf/75B7Vq1dKmILJbllo0bi+MDhxHjx7FkSNHsHPnznT3cdE4pWLQMB2DBpkdp1ypil0Pben1eoMrgQcHB2PPnj1wdnbWsCoi21C9enWjHi9JErZt24aCBQtm6/FGB45+/frhww8/xJgxY5AvXz5jn052jCHDdAwZpAl2PVTFroc20l6YuG3btti6dauG1ZC9s9RZqiwlNDQUQ4YMQc6cOV/7WCEEpk2bhoSEhGy/vtGB49mzZxg0aBDDBskYNExnrb+AyAGx66Eqdj0sY9CgQXj48CEAoGTJkgwbZHYpgUPJGg4zFKOSYcOGISAgIFuPnTVrllGv7fT6hxjq2LEj9u/fb+zTyM64uOnkjZRJ/euINf/yIQfmIv7byGR6dyFvpK6FCxdi7ty58u2rV69qVww5jNRF40o2a3Tr1i34+/tn+/EXL15EkSJFsv14ozscpUuXxsiRI3H48GFUqlQp3aLx/v37G/uSZEMYMEzHgEE2h10PVbHroZ5Zs2YZnPo2IiKC1wcjUsCY8AAYTmHMDkVnqcqZMycOHDiAAwcOGNwnSRIDhx1iyDAdQwbZBa71UBXXeij37NkzBAUFIS4uTt539epVeHt7a1gVORLx/5uS51m7Xbt2IWfOnHjzzTcBAIsWLcL333+P8uXLY9GiRYquY2P0lKpbt25lut28edPoAsg6ccqU6Thliuwap1upitOtskcIgT59+sDPz88gbJw5cwalSpXSsDJyNPY2pSqtYcOGITIyEgBw7tw5DBkyBK1atcKtW7cwePBgRa9pdIeD7BsDhukYMMihsOuhKnY9Mnf//v100ziCg4Px119/wcnJ6L+fEpnGjlsct27dQvny5QEAGzduxDvvvIMpU6bg9OnTaNWqlaLXzNa/0GnTphn8JSErx48fx44dOxQVkx0JCQmoWrUqJElCaGio2d7HkbCbYTp2M4jArofK2PX4T2hoaLqw8eTJE+zbt49hg0hlbm5uiI2NBQD89ddfaNasGQAgT548cufDWNn6V3rx4kUULlwYX375JXbu3Inw8HD5vuTkZPz7779YvHgx6tWrhy5duiBXrlyKismO4cOHo0CBAmZ7fUfCkGE6hgyiDPAMV6py9DNcbdmyBdWqVZNvDx06FHq9Ptun7yQyC6XTqWxgStWbb76JwYMHY9KkSfjnn3/QunVrACnrpAoVKqToNbMVONasWYO//voLSUlJ+OCDDxAYGAg3NzfkypUL7u7uqFatGlasWIHu3bvj8uXLaNCggaJiXmfnzp34888/MXPmTLO8viNgN8N07GYQGYHBQ1WOFjwmTZqEDh06yLd//fVXzJgxg2eiIs2lPRYwdrN2CxcuhIuLCzZs2IAlS5bIVxPfuXMnWrRooeg1s72Go0qVKvj++++xbNky/Pvvv7hz5w7i4uLg5+eHqlWrws/PT1EB2fXkyRP07t0bW7ZsgZeXV7aek5CQYHAVRKVtIHvAgGE6W/glQWS1uNZDVY6w1qNFixbYvXu3fPvff/9FpUqVNKyI6D9KF4Bb86Lxffv2oWHDhihcuDC2b9+e7v45c+Yofm2jF407OTmhatWqqFq1quI3NZYQAj179kSfPn1Qs2ZN3L59O1vPmzp1KiZMmGDe4qwYQ4bpGDKIzIDX9VCVvV3XIykpCV5eXkhOTpb3PXv2DHny5NGwKiL79+mnn+Lly5do0aIF2rdvj5YtW6q2TELTlVYjRoyAJElZbpcvX8aCBQsQFRWFkSNHGvX6I0eOREREhLzdu3fPTN+JdeGUKdPZStuTyKZxrYeq7GGtx/bt2+Hm5mYQNpKSkhg2yPqkrsdQslmpmzdvIiQkBOXLl8fMmTMREBCApk2bYsGCBbh7965Jr63paXGHDBmCnj17ZvmY4sWLY9++fTh69Cjc3d0N7qtZsya6deuG1atXZ/hcd3f3dM+xVwwYpmPAINIQp1ypypa6HomJifj6668xd+5cg/1VqlTBmTNnuF6DrJLSP0xa+7FG5cqVUblyZYwePRoPHz7Etm3bsG3bNgwfPhxlypRB27Zt0bZtW9SsWdOo19U0cPj7+8Pf3/+1j5s/fz4mT54s33748CGaN2+O9evXo3bt2uYs0eoxaJjO2v/xEzkcTrlSjbWv9fj555/x0Ucfpdv/22+/4b333tOgIqJssuPrcKQqUKAA+vTpgz59+iAmJgY7d+7Etm3b0KJFCwwePBijRo3K9mvZxIX/ChcubHA7Z86cAIASJUooPj2XLWPIMB1DBpENYNdDVdYUPnQ6HXx9fREVFWWwf9u2bXjnnXfY1SCyMjly5MB7772H9957DzqdDs+fPzfq+TYROCgFg4bpGDSIbBS7HqrScspVXFxcurNNHjt2zOFnLJBtscezVKV14sQJ7N+/H2FhYdDr9fJ+SZIwa9asbM1QSsvowBETE4Np06Zh79696YoAUhacmFvRokUhHOzI0dlVbzM/pNbGxSVNUJN4RVoim5am6yGcHetzwBx0XpYdw+joaIOz3lStWhWnTp3i1cLJNtnpr6ApU6Zg9OjRKFOmDPLly2fQcVTafTQ6cHz66ac4cOAAPvroI+TPn59tT7JaBkHj/zk7pQRknZ4fbko5u+pf/yAykNHfR/Sp+3T8WSTt3P5yqMXe68mTJwgMDJRvf/bZZ1i2bJnF3p9ITfbc4Zg3bx5WrFjx2hM7GcPowLFz507s2LED9evXV60IIrVkFDIykho8AIYPMo9sN2Gd0wQ4hg+yAEuGjFQhISEIDg6Wbw8bNgzTp0+3eB1EqrHjReNOTk6qH+cb/enm6+vL82GT1XFx0WU7bLzK2UlvEECITGHSNVyc9YYBhEhFt78cavGwkZycjPr16xuEjZkzZzJsEFmxQYMGYdGiRaq+ptEdjkmTJmHs2LFYvXp1ukVfRJakNGBkhtOtSCnVl5Slhg52PMhEWnQzAECv16Nv375YsmSJwf5du3ahefPmmtREpC7p/zclz7NuQ4cORevWrVGiRAmUL18erq6uBvdv2rTJ6NfMVuCoVq2awVqN69evI1++fChatGi6Ik6fPm10EUTGUDtovIrTrSi7zH7uCk63IoW0CBo6nQ6HDh3C8OHDceLECYP7/P39cf/+fbi5uVm8LiKzsOMpVf3798f+/fsRHByMvHnzqrJeO1uBo3379ia/EZEpzB0yMsOuB71KsxPksetBr6FVN+Ovv/5C06ZNM73//PnzqFChggUrIrIAOw4cq1evxsaNG9G6dWvVXjNbgWPcuHGqvSGRMbQKGq9i14Os5kzc7HpQGlqFjFQ9e/bE6tWr0+338fHBsWPHULZsWQ2qIiJT5MmTByVKlFD1NY3+tCpevDiePXuWbv/Lly9RvHhxVYoix5a6ANxawsaruMjccaQuALeasPEqLjJ3WFosAE8rKSkJkiQZhI1evXrh4cOHEELg5cuXDBtk34SkfLNy48ePx7hx4xAbG6vaaxq9aPz27dvQ6dIfCCYkJOD+/fuqFEWOyVoDRmbY9bBfVhswMsOuh0PQupuRKjIyEj4+Pgb7nj59irx582pUEZHlKf1jlC18vsyfPx83btxQdb12tgPHtm3b5K93795t8MtGp9Nh7969KFasmNEFkGOztZCRGa71sH228CGQLVzrYXesJWgAwL1791C4cGH5drt27bBlyxbtCiLSih2v4TDH2u1sB47UN5ckCT169DC4z9XVFUWLFsWsWbNULY7sl70EjVex62F77CZovIpdD5tmTSEDAGJiYjB79myMHTtW3te3b18sWLBAw6qIyBzMsXY724FDr0/58CpWrBhOnDgBPz8/1Ysh+2avISMz7HpYL7sNGZlh18NmWFvQ0Ol0WL16NUaPHo1Hjx7J+3v16sWwQY5N6XoMG1jDYQ5Gr+G4deuWOeogO+ZoQeNV7HpYD4cLGq9i18MqWVvISLVmzZp0MxoAoEePHvj+++81qIjIekgiZVPyPGuUJ08eXL16NdsNhcKFC+PQoUMoUqRIth5vdOAAgL1792LOnDm4dOkSAKBcuXIYOHAg3n77bSUvR3bI0UNGZtj1sDyHDxmZYddDc9YYNJKSkvD1119jzpw56e5zdnZGjx49sGTJEqMuBKbT6ZCUlKRmmWTjXF1d4ezsrHUZprGzNRwvX77Ezp07050QIjPPnj3L8CRSmTE6cCxevBgDBgzAe++9hwEDBgAAjh07hlatWmHOnDn46quvjH1JsiMMGtnD4GF+DBrZxOBhUdYYMtL69NNPsWbNGoN9n332Gd5//33Uq1cP7u7u2X4tIQQeP36Mly9fqlwl2YPcuXMjMDBQlatYkzoy6miqxejAMWXKFMyZMwd9+/aV9/Xv3x/169fHlClTGDgcEEOGcpxupS6GDBNwupVZWXvQAIAffvjBIGzs3bsXjRs3Vvx6qWEjICAAXl5ePLAkAClBNDY2FmFhYQCA/Pnza1yRQna2hiN1rba5GB04Xr58iRYtWqTb36xZM3z99deqFEW2gUFDXex6KMegoTJ2PVRhCyEDSDkAXL16NXr37i3vu3LlCkqXLq34NXU6nRw2eH0OepWnpycAICwsDAEBAbY5vcrOplSZm9GfJm3btsXmzZvT7d+6dSveeecdVYoi62XtVwG3B6lXMufVzLNm9VcBtwepVzLn1cyNovVVwI0REhKC2rVr4+OPP5b3Xbp0yaSwAUBes+Hl5WXS65D9Sv3ZsNn1PcKEzQEZ3eEoX748vv32W4SEhKBu3boAUtZwHDlyBEOGDMH8+fPlx/bv31+9SklTDBjaYNcjPQYMjbDrkSVbCRipdDodevTogV9++cVg/99//42yZcuq9j6cRkWZ4c+GYzE6cPz444/w9fXFxYsXcfHiRXl/7ty58eOPP8q3JUmy2sBx8OBBdmOygSHDejj6Wg+GDCvCtR4GbC1oAMDTp0/h7+9vsM/FxQXbt2+X/5BIRK/BKVVGccjrcHTt2hVRUVFal2G1GDSsmyN1PRg0rJyDdj1sMWSkWr58OT7//HODfWvXrkWnTp3g4qLoTPkOQZIkbN68Ge3bt7fo+4aEhCA4OBgvXrxA7ty5Lfre9Bp2tmjc3BR/SiQmJuLKlStITk5Wsx6LiI6ONvtqfFvDtRm2x17XenBthg1ykLUetrQ241Xbtm2DJEkGYWPo0KHQ6/Xo2rUrw8ZrPHr0CC1bttS6DMWKFi2KuXPnGuxbtWoVQ4wJUi/8p2Szdg0bNsSaNWsQFxen2msaHThiY2PRq1cveHl5oUKFCrh79y4AoF+/fpg2bZpqhZnbq/NWHRVDhn2wh+DBkGEn7Cx4pIYMWwwaFy9eROfOnSFJEtq1a2dwX2hoKGbMmMF59K+RmJgIAAgMDDTqGiTG0ul0/EMoZergwYNo06YNChQoAEmSsGXLliwfHxISAkmS0m2PHz/O1vtVq1YNQ4cORWBgIHr37o1jx46Z/D0YHThGjhyJs2fPIiQkBB4eHvL+t99+G+vXrze5IEvp3r271iVoikHDPtli14NBw07ZeNfDVkPGy5cvUbVqVUiShAoVKuC3334zuP/777+HEAJVqlTRqELr1qhRI/Tt2xcDBw6En58fmjdvDgAGB3n16tVLdxmA8PBwuLq64uDBgwCAhIQEDB06FAULFkSOHDlQu3ZthISEyI9P7S5s27YN5cuXh7u7u/wH3IwcOXIElStXhoeHB+rUqYPz588b3L9x40ZUqFAB7u7uKFq0KGbNmmXwPd25cweDBg2SDzxDQkLw8ccfIyIiQt43fvx4AMCLFy/QvXt3+Pr6wsvLCy1btsS1a9fS1b59+3aUKVMGXl5eeO+99xAbG4vVq1ejaNGi8PX1Rf/+/Y26ErXNseBZqmJiYlClShUsWrTIqOdduXIFjx49kreAgIBsPW/u3Ll4+PAhVq5cibCwMDRo0ADly5fHzJkz8eTJE+O/AShYw7FlyxasX78ederUMfjLSIUKFXDjxg1FRWglLi5OPhe0I2DAcCzWvNaDAcPB2MhaD1sMGKmEEOjTpw+WL1+e4f3r169H586dLVyVodQLvlmasRcdXL16Nb744gscOXIkw/u7deuG6dOnY9q0afLrrl+/HgUKFMBbb70FAOjbty8uXryIdevWoUCBAti8eTNatGiBc+fOoVSpUgBSZox89913+OGHH5A3b94sDwaHDRuGefPmITAwEKNGjUKbNm1w9epVuLq64tSpU+jcuTPGjx+PLl264O+//8aXX36JvHnzomfPnti0aROqVKmCzz77TL7WSp48eTB37lyMHTsWV65cAQDkzJkTANCzZ09cu3YN27Ztg7e3N77++mu0atUKFy9ehKurq1z7/PnzsW7dOkRFRaFjx47o0KEDcufOjT/++AM3b97Eu+++i/r166NLly7ZHnvKWMuWLRVN6QsICFA8bc7FxQUdO3ZEx44dERYWhuXLl2PMmDEYNWoUWrVqhf79+xt1YVCjA0d4eHiG/yhiYmJsrjU7d+5cjBw5UusyzI5Bw7FZU/Bg0HBwVho8bDloJCUlYdCgQen+8pk/f35s3boVtWrV0qiy9GJjY+WDWkuKjo5Gjhw5sv34UqVKYfr06Zne37lzZwwcOBCHDx+WA8batWvRtWtXSJKEu3fvYuXKlbh79y4KFCgAIGW9zK5du7By5UpMmTIFQMr/u8WLF2er2zRu3Dg0bdoUQEogKlSoEDZv3ozOnTtj9uzZaNKkCcaMGQMAKF26NC5evIgZM2agZ8+eyJMnD5ydnZErVy4EBgbKr+nj4wNJkgz2pQaNI0eOoF69egBSpqAHBQVhy5Yt6NSpk1z7kiVLUKJECQDAe++9h59++glPnjxBzpw5Ub58eQQHB2P//v12GzgkKFuPYckj5apVqyIhIQEVK1bE+PHjUb9+faNf459//sHKlSuxbt06BAQEoGfPnnjw4AHeeecdfPnll5g5c2a2XsfowFGzZk3s2LED/fr1A/DfeZR/+OEHmzud3qhRo+w2cDBk0Ku0OrUuQwalYwWn1rXlkAEAly9fRu3atREZGZnuvps3b6JYsWIaVGUfatSokeX9/v7+aNasGX755Re89dZbuHXrFo4ePYply5YBAM6dOwedTpfu4okJCQkGV113c3ND5cqVs1VT2uOrPHnyoEyZMrh06RKAlAs1vrpGp379+pg7dy50Op1RV/G+dOkSXFxcULt2bXlf3rx5Dd4PSOkapYYNAMiXLx+KFi1qECjz5cuHsLCwbL+3o3n13667u7tq64Ty58+PpUuXombNmkhISMAPP/yARo0a4fjx46hevfprnx8WFoaffvoJK1euxLVr19CmTRv873//Q/PmzeXj/p49e6JFixbmCxxTpkxBy5YtcfHiRSQnJ2PevHm4ePEi/v77bxw4cMDYl9PEsGHDMGPGDADAw4cP5b9A2AMGDcoOS3Q9GDQoWyzY9bD1kAH8d5rUjOzbty/T+6yBl5cXoqOjNXlfY2SnG9KtWzf0798fCxYswNq1a1GpUiVUqlQJQEpHxdnZGadOnUp3sJ/2gNzT09PmZoakSp1alUqSpAz32fVCeBNPixsUFGSwe9y4cfI6GlOVKVMGZcqUkW/Xq1cPN27cwJw5c/DTTz+99vmFChVCiRIl8Mknn6Bnz57prtsDAJUrVzaqg2p04HjzzTcRGhqKadOmoVKlSvjzzz9RvXp1HD16VP7HZu2++OILOXAULFgQwsaPjBgySCm1ux42/k+JtGTGroc9BI0VK1agV69e6fY3bNgQv/76a7YXg2pJkiSjpjZZs3bt2uGzzz7Drl27sHbtWoMT0VSrVg06nQ5hYWHylCtTHTt2DIULFwaQsqj76tWrKFeuHACgXLly6dabHDlyBKVLl5YDj5ubW7oF3BntK1euHJKTk3H8+HF5StWzZ89w5coVlC9fXpXvxW6YeOG/e/fuwdvbW95tzrOgAcAbb7yBw4cPZ+uxe/fufe3Prre3N/bv35/t91d04u0SJUrg+++/V/JUq5C2pWnLGDRITaZ0PRg0SFUqdD3sIWQAwNatWzO82Nzw4cMxZcoUo6bLkHpy5MiB9u3bY8yYMbh06RK6du0q31e6dGl069YN3bt3x6xZs1CtWjWEh4dj7969qFy5Mlq3bm30+02cOBF58+ZFvnz58M0338DPz0/+uRgyZAhq1aqFSZMmoUuXLjh69CgWLlyIxYsXy88vWrQoDh48iPfffx/u7u7w8/ND0aJFER0djb1796JKlSrw8vJCqVKl0K5dO/Tu3RvLli1Drly5MGLECBQsWDDdtC2HZ2Lg8Pb2Nggc5hYaGor8+fNn67Hjxo3Dpk2b0i04j4yMRPv27bFv3z6j39/owBEREYE9e/bg9u3bkCQJxYsXR5MmTSw6aGqoWbMmTp48CQD47bff5IVQ1o4hg8wtu10PhgwyOwVdD3sJGteuXUu3BgBI6XR8/PHHGlREr+rWrRtatWqFBg0ayN2HVCtXrsTkyZMxZMgQPHjwAH5+fqhTpw7eeecdRe81bdo0DBgwANeuXUPVqlXx+++/w83NDQBQvXp1/Prrrxg7diwmTZqE/PnzY+LEiejZs6f8/IkTJ+Lzzz9HiRIlkJCQACEE6tWrhz59+qBLly549uyZPKVn5cqVGDBgAN555x0kJiaiQYMG+OOPP9JNmSLLiY6OxvXr1+Xbt27dQmhoKPLkyYPChQtj5MiRePDgAdasWQMg5aRIxYoVQ4UKFRAfH48ffvgB+/btw59//pmt9ztw4IB8DZq04uPjcejQIUXfgySMmE/0888/o2/fvukWuvj4+GDp0qVWfyaCyMhI+Pj4ICIiAjly5DC4sqq1T6sqt3mC1iXYBScn6/7/bO2EAOLj3LQuwy7ok6zrTE02I23wiEv52l5CBpByxsfKlSvj5s2bBvvXrVtn9Z+xacXHx+PWrVsoVqyYwTW7iFJl9TOS9njN2v6gnVpb0W+/hZOCn219fDxuf/ONUd9bZmu3evTogVWrVqFnz564ffu2fK2X6dOnY/ny5Xjw4AG8vLxQuXJljB079rVrvP79918AKWe32rdvH/LkySPfp9PpsGvXLixbtgy3b9/O3jebRrY7HKdPn8bHH3+Mbt26YdCgQShbtiyEELh48SLmzp2Ljz76CGXLlrWZiwm92oZOTk42CCDWRihZmEQZYOBQIm0elxjaVMFxVMjpvy7vrY+Ha1iIuoQQ+Pjjj7F69WqD/SNGjMDUqVM1qoqIMmXilCpjNGrUKMs/jK9atcrg9vDhwzF8uPG/H1MvGipJUobX2PD09MSCBQuMfl3AiMCxYMECtG/fPt03Vb16daxZswaxsbGYN28eVqxYoagQLezfv19Oe2XKlLG5CxdS9rn+/1Q0PYNbtmX2u835/6e56KzsWgrkOG59aF+nM1++fDk+//xzg301a9bEkSNH5GkzRGRlLBg4LOXWrVsQQqB48eL4559/DM5O5ebmhoCAAMXrxrIdOI4cOWKwAOlVffr0wZdffqmoiOwoWrQo7ty5Y7Bv6tSpGDFihOLXbNSokfz1q+1rsn2uXO+iSHZnFzqnmV/P8EHmZm8hQ6/XY8GCBRg4cGC6+x48eGBXp2snIttQpEgRADDL6YyzHTgePnyY4QK2VKVLl8aDBw9UKSozEydORO/eveXbuXLlMvk18+bNi2fPngFIWaRXqlQpk1+TtMWgYTxTlzCx60HmYutB4+XLlzh69CgOHTqEQ4cOZXlaysOHDyu6EjARWZ4kFF5p3Eo7HNu2bUPLli3h6uqKbdu2ZfnYtm3bGv362Q4csbGxWS78cnd3R3x8vNEFGCNXrlwIDAxU9TXDwsLk9lD16tURFRWl6uuTZTBkGM8c50lg14PUYKshIywsDPny5TP6eT///DO6detmhoq0Z+0nZCHt2PzPhokX/rM27du3x+PHjxEQEJDhqbhTSZKU7vot2WHUKundu3fDx8cnw/tevnxp9Jsba9q0aZg0aRIKFy6MDz74AIMGDcpyoXdCQgISEhLk26+eXQsAnJyc0LRpU+zZswfR0dF48eIFfH19zVI/qY9Bw3iW+h3PrgcZy1aDRmansM1M/vz50a1bN4wcOdLgLDD2JPUUqrGxsfD09NS4GrJGsbGxANJftdxm2NkajrTTqDSdUgWknH4rK5JkvtTWv39/VK9eHXny5MHff/+NkSNH4tGjR5g9e3amz5k6dSomTHj96WQ3btwon5qsTZs22b4SI2mDIcN4Wv4hiV0Pyoqthgwg5S+0LVq0yPDc9l26dEHTpk3x1ltvoWTJknBycqyffWdnZ+TOnRthYWEAAC8vL7MeI5DtEEIgNjYWYWFhyJ07Ny9e6SCMug6H2kaMGIHvvvsuy8dcunQJZcuWTbd/xYoV+PzzzxEdHZ3p5eAz6nAEBQVleO7j/Pnz4/HjxwCAqKgo5MyZ09hvx6zKbpqodQmaUitkONpZqszxrzs5WZ0PB0cPH45+HQ5bDhoAcOzYMdStW9dg31dffYV58+bxAOr/CSHw+PFji8yAINuTO3duBAYGZhhEbeE6HMXHTVF8HY6bE0ZZ5feWqn///ihZsiT69+9vsH/hwoW4fv065s6da/RranrhiSFDhhhcCTMjxYsXz3B/7dq1kZycjNu3b6NMmTIZPsbd3T3TMPKqAwcOyK8zePBgLF++PFvPI/NiN8N4tjItllOuHI+thwwg5WJlb7/9No4cOSLv8/LywtOnTzl16BWSJCF//vwICAhAUlKS1uWQFXF1dbX9YG5nU6rS2rhxY4YLx+vVq4dp06bZXuDw9/c3OMevMUJDQ+Hk5ISAgABVakk7//b777/H3Llz4eXlpcprk3EYMpSxlaDxKk65sn/2EDQAYPv27WjTpo3BvvXr16Nz584aVWQbnJ2dbf/gkuhVCs9SZQuB49mzZxmu2fb29sbTp08Vvab1Xlo7jaNHj+L48eMIDg5Grly5cPToUQwaNAgffvihqgu89+7diyZNmgAA+vXrhx9//FG116bXY9Awnq2GjMyw62E/7CVkACnXaSpRooTBPj8/P9y+fRs5cuTQqCoiIvMoWbIkdu3ahb59+xrs37lzZ6Yzj17HJgKHu7s71q1bh/HjxyMhIQHFihXDoEGDMHjwYFXfJ+1l3FesWIHFixdne0oWKcOQoYy9BY1Xsethu+wlaAghsGDBAgwYMCDdfWvXrkXXrl01qIqIrIYdT6kaPHgw+vbti/DwcPnYeO/evZg1a5ai6VSAkYFDp9PhyJEjqFy5MnLnzq3oDZWoXr06jh07ZpH3unLliryWo127dti1a5dF3tfRMGgYz95DRmbY9bB+9hIyAOD8+fNo1qwZHj16lO6+du3a4fvvv1c8FZiI7IgdB45PPvkECQkJ+PbbbzFp0iQAQNGiRbFkyRJ0795d0WsaFTicnZ3RrFkzXLp0yaKBw5JKly4tnxlh9+7diIqKUuWK5sSQoZSjBo1XsethXewpZERFRaFfv35YvXp1hvePHTsWY8eO5ToEIpLZ25XGX/XFF1/giy++QHh4ODw9PU0+e6vRU6oqVqyImzdvolixYia9sTW7fv26/BesmjVr4sqVKxpXZLsYMpRhyMgaw4d27CVonDhxAiNHjsTevXszvL969erYvHkzChcubOHKiIisQ3h4uHwMXLZsWfj5+Sl+LaMDx+TJkzF06FBMmjQJNWrUSLdgzlrPKWwMPz8/VK1aFaGhobh69Sru37+PQoUKaV2WTWHQUIZBw3iccmV+th4yIiMjERoaih9//BFr1qzJ8rHr1q1Dly5dLFQZEZH1iYmJQb9+/bBmzRr5quPOzs7o3r07FixYoOgsrkYHjlatWgEA2rZta3CxFiEEJEmCTmcfB5qHDh2Sp1IFBQVBw+sj2gyGDGX4o6UOdj3UZ8tB4+bNm+jTpw/27Nnz2sfOmTMHffv2hYuLTZxHhYisgR2v4Rg8eDAOHDiA33//HfXr1wcAHD58GP3798eQIUOwZMkSo1/T6N+u+/fvN/pNbFHOnDnRrVs3/PLLLwCAbdu2oW3bthpXZZ0YNJRh0DAfdj2Us9WQERUVhTVr1mDhwoW4fPlylo/t2rUrJk6ciJIlS1qoOiKyN/a8hmPjxo3YsGEDGjVqJO9r1aoVPD090blzZ8sEjoYNGxr9JrZq9erVcuBo164d9Hq9QVfHkTFkKMOQYVnsemSfLQUNvV6Pv/76CzNnzsxWB2P69Ono0qUL12MQkbrs9DM9NjYW+fLlS7c/ICAAsbGxil5T0SfwoUOH8OGHH6JevXp48OABAOCnn37C4cOHFRVhrZydnfHHH3/It3ne9ZSgwbBhPCEYNrTm7Kw3CCCUEjJSN1vw119/QZIkODs7o3nz5pmGDUmSsGjRIkRHR0MIgWHDhjFsEBFlU926dTFu3DjEx8fL++Li4jBhwgTUrVtX0Wsa3eHYuHEjPvroI3Tr1g2nT59GQkICACAiIgJTpkwxOEC3By1btpS/Xr9+PWbOnOlwC8gZMJRhwLBO7HrYVjcDSPmgK1SoEJ4/f57h/e3atUPfvn3RuHFjODk55v9TIrIwO17DMW/ePDRv3hyFChVClSpVAABnz56Fh4cHdu/ereg1FZ2launSpejevTvWrVsn769fvz4mT56sqAhr9/z5c+TJkweAYy0gZ9BQxkF+POyCI631sLWQkWrr1q1o3769wb4ePXpg5syZJp2ikYjIFPa8hqNixYq4du0afvnlF3lNXNeuXdGtWzd4enoqek2jA8eVK1fQoEGDdPt9fHzw8uVLRUVYO19fX4wePVoOVLNmzcKQIUM0rso8GDKUYciwbfbc9bDVoBEXF4eSJUvi4cOH8r6GDRvizz//hJubm4aVERHBrjscAODl5YXevXur9npGB47AwEBcv34dRYsWNdh/+PBhFC9eXK26rM6kSZPkwDF06FB8/vnnJl910VowZCjHoGF/7CF82GrIAFJOsT5ixAhMnz7dYP8///yDWrVqaVQVEZF927ZtW7Yfq+SsrUYHjt69e2PAgAFYsWIFJEnCw4cPcfToUQwdOhRjxowxugBbcu3aNZQqVQoAkDdvXnn9iq1i0FCGIcNx2NqUK1sOGgDwww8/pPuLWp06dXDkyBGuzSAiq2JvU6penbqaGaXX3DM6cIwYMQJ6vR5NmjRBbGwsGjRoAHd3dwwdOhT9+vUzugBbUrJkSbRt2xbbtm1DYmIitm7dinbt2mldllEYMpRhyHBs1tz1sPWQAQAvXryQ18ml9e+//6JSpUoaVERE9Bp2NqUq9Yri5mJ04JAkCd988w2GDRuG69evIzo6GuXLl7eb6UWvs2XLFvkvbe3bt0dycjKcnZ01rur1GDSUYdCgV1lL18MeggYAHD16FPXq1TPYt3fvXjRu3FijioiIssHOAkdm4uPj4eHhYfLrGP2J+cknnyAqKgpubm4oX7483njjDeTMmRMxMTH45JNPTC7I2kmShAMHDsi3lZ6P2BJSr5nBsGGc1GtmMGxQVlKv62HJa3vY2nUzXmfFihUGYaNPnz4QQjBsEBFpSKfTYdKkSShYsCBy5syJmzdvAgDGjBmDH3/8UdFrGh04Vq9ejbi4uHT74+LisGbNGkVF2JoGDRrIF5E6ceIEzpw5o3FFhhgylGHIIKXMGTzsLWSkmjNnDnr16iXf3rdvH5YsWaJhRURE2Ze6hkPJZu2+/fZbrFq1CtOnTzc4K2DFihXxww8/KHrNbE+pioyMhBACQghERUUZtFd0Oh3++OMPBAQEKCrCFl27dg3u7u4AgOrVq2t+bQ4GDGUYMEhNaq71sLeAkdbcuXMxePBg+fadO3d4JXAisi12PKVqzZo1WL58OZo0aYI+ffrI+6tUqSJfl8NY2Q4cuXPnhiRJkCQJpUuXTne/JEmYMGGCoiJskZubG1atWoWePXsCAD777DMsX77cojUwZCjHoEHmpiR82HPISDV37lwMGjRIvh0eHs4L+BGR7bHjwPHgwQOULFky3X69Xo+kpCRFr5ntwLF//355bu3GjRsNziji5uaGIkWKoECBAoqKsFU9evSQA8f333+P8ePHW2QMGDSUY9AgLbxuobkjBA0AGDRoEObOnSvfDgsLY9ggIrIy5cuXx6FDh1CkSBGD/Rs2bEC1atUUvWa2A0fDhg0BALdu3UJQUBDPif7/nj59Kn9gFixY0GxTq9zdlCVKMqTTW/8Zxch+pe163OjyjYaVWFZcXBy8vLwM9j158gT+/v4aVUREZBp7uw5HWmPHjkWPHj3w4MED6PV6bNq0CVeuXMGaNWuwfft2Ra9pdGooUqQInJycEBsbi8uXL+Pff/812BxN3rx58fXXX8u30/71Tk3JOmck63iwbCo3V126zdlJcDNic/r/jeh1hBAYOXJkurDx8uVLh1rzR0R2SJiwWbl27drh999/x19//YUcOXJg7NixuHTpEn7//Xc0bdpU0WtKwsg/yYeHh+Pjjz/Gzp07M7xfydUHLSUyMhI+Pj6IiIiAt7e3qq8tSZL8dXR0NHLkyKHq61fYOj7dPhdn6x1ra5Xmf1OGtL62gi1ISk4ffPX61wwspXP1vTFal2A2er0effr0wffff2+wv1mzZti9e7dGVRGRrTDn8ZqpUmsr13cKnN2Nvz6FLiEelxaOssrvzZyMProaOHAgXr58iePHj8PT0xO7du3C6tWrUapUKWzbts0cNdqEK1euyF8XLFjQIu/Jrof6LH1dBXvBrgcBKX+Q+vbbb+Hs7JwubJw5c4Zhg4jIBnz66acICQlR9TWNDhz79u3D7NmzUbNmTTg5OaFIkSL48MMPMX36dEydOlXV4mxJ6dKl5TZTREQErl+/brH3Tg0eDB/q0eKibvbAiVOuHI4QAvv378f777+PgIAAjB492uD+8+fPQwiBqlWralMgEZE52PGUqvDwcLRo0QJBQUEYNmwYQkNDTX5NowNHTEyMPPfW19cX4eHhAIBKlSrh9OnTJhdky9JOMytVqpQmNTB4qI/hQxkGD/sWHx+P2bNno0yZMmjcuDHWr19vcP/atWshhECFChU0qpCIyIzsOHBs3boVjx49wpgxY3DixAnUqFEDFSpUwJQpU3D79m1Fr2l04ChTpow8fahKlSpYtmwZHjx4gKVLlyJ//vyKirAXzs7OBlfOPXv2rGa1sOthHgwexmPXw/48evQIpUqVwpAhQ3Dt2jWD++rVq4c7d+6ga9euGlVHRGR+kgmbLfD19cVnn32GkJAQ3LlzBz179sRPP/2U4fU5ssPowDFgwAA8evQIADBu3Djs3LkThQsXxvz58zFlyhRFRdiTpUuXyl9byxQChg/1seuhDIOH7Zs/fz4KFCiA+/fvG+zv3r079u3bh0OHDvGq4UREdiIpKQknT57E8ePHcfv2beTLl0/R62T7Ohy3bt1CsWLF8OGHH8r7atSogTt37uDy5csoXLgwL+AEwMXFBf3798f8+fMBACEhIWjUqJG2RaWRGjp4hiv1vO6ibpRe2tDBM1zZhoSEBOTLlw8RERHyvgIFCmDy5Ml47733kCtXLg2rIyKyMDu+0jiQcsHvtWvXYuPGjdDr9ejYsSO2b9+Oxo0bK3q9bAeOEiVKoEiRIggODkbjxo3RqFEjFCpUCF5eXqhevbqiN7dX06dPlwNHcHCw2S4GaIq03Q6GD3Wk7XYwfGRfavhg8LBeGzZsQKdOnQz2nTlzxmq6uERElmbPF/4rWLAgnj9/jhYtWmD58uVo06YN3N3dTXrNbAeOffv2ISQkBCEhIfjf//6HxMREFC9eHI0bN0ZwcDCCg4MVt1nsjbu7O9555x3FV2O0NHY91Meuh/HY9bA+ERERyJ07t8G+4OBg/PXXX3By4s82ETkwO+5wjB8/Hp06dUr3+98U2f7EaNSoEcaPH4+QkBC8ePECe/bsQdeuXXHp0iX07NkTBQoU4NlI0ki7lsNWzt7FtR7q41oPZbjWQ3uTJ09O92Fz/Phx7Nu3j2GDiMiO9e7dW9WwARjR4UjLw8MDjRs3xptvvong4GDs3LkTy5Ytw+XLl1Utzpalvfjf3LlzsWbNGg2rMR67Hupj18N47HpY3o0bN9KdhaRz585Yt24dJIn/D4iIZPy7WLYZdeSTmJiIgwcPYsKECQgODkbu3LnRp08fvHjxAgsXLsStW7fMVadN++mnn7QuQTF2PdTHrocy7HqYlxAC77zzTrqwcevWLaxfv55hg4gojdQ1HEo2R5TtDkfjxo1x/PhxFCtWDA0bNsTnn3+OtWvXOvy1N7LSoEEDHDx4UOsyVMOF5upj18N47Hqob8+ePWjWrJnBvqlTp2LEiBEaVUREZOXseA2HOWT7KOfQoUPImzcvGjdujCZNmqBp06YWDxs7duxA7dq14enpCV9fX7Rv396i72+sgQMHyl/HxcVpV4gZsOuhLnY9lGHXwzRRUVHw8fExCBuenp6Iiopi2CAishIHDx5EmzZtUKBAAUiShC1btrz2OSEhIahevTrc3d1RsmRJrFq1yux1ZiXbgePly5dYvnw5vLy88N1336FAgQKoVKkS+vbtiw0bNiA8PNycdWLjxo346KOP8PHHH+Ps2bM4cuQIPvjgA7O+p6natm0rf7127VoNKzEfTrlSH4OH8Xg1c+MkJiaiZcuW8Pb2RmRkpLz/jz/+QGxsLHLmzKlhdURE1s+SU6piYmJQpUoVLFq0KFuPv3XrFlq3bo3g4GCEhoZi4MCB+PTTT7F7927j31wlklB4kYioqCgcPnwY+/fvR0hICM6ePYtSpUrh/PnzateI5ORkFC1aFBMmTECvXr0Uv05kZCR8fHwQEREBb29vFSvMXOq854oVK+LcuXOKX6fC1vEqVWR+1jzdylanoVvTlKukZNsIl9Y+3erqe2Ms/p7h4eFo1aoVTp48abC/adOm2LVrF88+RURWQYvjtexKra1SrylwdvMw+vm6xHic+3GU4u9NkiRs3rw5y1k+X3/9NXbs2GFwTP7+++/j5cuX2LVrl9HvqQbFny45cuRAnjx5kCdPHvj6+sLFxQWXLl1SszbZ6dOn8eDBAzg5OaFatWrInz8/WrZs+dpwk5CQgMjISINNK+YIYtaKXQ/1sethPHY9/rNlyxZIkoSAgACDsJEzZ048ePAAf/75J8MGEZERTO1wvHp8mpCQoFptR48exdtvv22wr3nz5jh69Khq72GsbH/C6PV6/PPPP5g+fTpatmyJ3Llzo169eli8eDECAwOxaNEi3Lx50yxFpr7u+PHjMXr0aGzfvh2+vr5o1KgRnj9/nunzpk6dCh8fH3kLCgoyS31ZGTBggMXf05oweKiLaz2UccTwkZycjAkTJkCSJHTo0MHgvkKFCuH+/fuIiopCgQIFNKqQiMhxBQUFGRyjTp06VbXXfvz4cbqLcefLlw+RkZGarSnOduDInTs36tati3nz5iFv3ryYM2cOrl69irt372L16tXo2bMnihQpYtSbjxgxApIkZbldvnwZen3KwdU333yDd999FzVq1MDKlSshSRJ+++23TF9/5MiRiIiIkLd79+4ZVZ8a+vfvL3999epVi7+/tWDXQ30MH8o4QvDYsGEDXF1dMX78eIP9gwYNQlJSEu7du2dwrSAiIjKSMGEDcO/ePYNj1JEjR1r6O7CobJ8Wd8aMGQgODkbp0qVVe/MhQ4agZ8+eWT6mePHiePToEQCgfPny8n53d3cUL14cd+/ezfS57u7ucHd3V6VWpYoXLy5/PW/evGwv+LFnvKig+nh6XePZ4+l1r169ijJlyqTbv2vXLjRv3lyDioiI7JSJp8X19vY22/qUwMBAPHnyxGDfkydP4O3tDU9PT7O85+tkO3B8/vnnqr+5v78//P39X/u4GjVqwN3dHVeuXMGbb74JAEhKSsLt27eN7qpoafHixQwcafC6HupL2+1g+Mi+1PBhq8FDp9OhWbNm2Ldvn8H+P/74Ay1bttSoKiIi+6X0jFOWuPBf3bp18ccffxjs27NnD+rWrWv+N8+ETRyReHt7o0+fPhg3bhz+/PNPXLlyBV988QUAoFOnThpX93rVqlXTugSrxylX6uN0K+PZ4lqPmzdvwsXFxSBsDB8+HEIIhg0iIjsQHR2N0NBQhIaGAkg57W1oaKg8y2fkyJHo3r27/Pg+ffrg5s2bGD58OC5fvozFixfj119/xaBBg7QoH4ARHQ6tzZgxAy4uLvjoo48QFxeH2rVrY9++ffD19dW6tNcaOHAgevToASClM+Pq6qpxRdaNU67Uxa6HMrbQ9Zg/f77BiSk8PT3x4MEDm/i9SERk0yx4pfGTJ08iODhYvj148GAAQI8ePbBq1So8evTIYIlBsWLFsGPHDgwaNAjz5s1DoUKF8MMPP2g6tVbxdThskVbndU5ISICHR8q5mtevX4/OnTsb/Rq2dB0Oc1ArfNjqdTjUZkrwsJXrcJiDmuHDlOtwCCFQunRpXL9+Xd43f/589OvXT43SiIg0ZQvX4aj60beKr8MR+tM3Vvm9mZPNdDhsWdqF63PnzlUUOBwdux7qYtdDGWvoejx48ACFChUy2Hfv3r10+4iIyIws2OGwBzzSsDAtL7piD7jWQ31c62E8rdZ6/PTTTwbBokSJEtDr9QwbREQWZuqF/xwNA4eF9OrVS+sS7A6Dh7p4XQ9lLBE8hBCoVauWwaLAuXPn4vr165A4T5CIiKwcA4eFpF3YqcUFCO0Zux7qY/Awnrm6Hvfv34eTkxNOnjwp77ty5YrB7xQiIrIwEy/852gYOCykUqVK8te8Fof5MHyoi10PZdQKHlOmTEFQUJB828/PD8nJyapegJWIiIzHKVXGYeDQwNy5c7UuwSEweKiLwcN4Sroe0dHRGDx4MCRJwjfffCPvnzFjBsLDw+HszJ9pIiLNscNhFJ6lyoJKliyJ69evIyEhwejnursmm6Eix6LTS3Cck0Cbj+Sof54xkbNz5uMWFRWFAQMGYOXKlRnez7NQERGRLWOHw4IGDhwof63XG/eX4mSeutRkzk4CLvwLPWnsUodxuNRhHADg559/hiRJ8Pb2zjBsLF26lGehIiKyQpxSZRwexVpQ6tXGAWD37t1GPz9Z5yRvpJyLs57BgywubdBYsGABJEnCRx99lO5xM2bMQGJiIoQQ+Pzzz3kWKiIia8QpVUbhlCoLypkzp/z1nDlz0LJlS8WvlRo6eOCsXNqxY4gjc0gNGKmuXr2KMmXKpHvciBEjMHHiRLi6ulqqNCIiMpGjdiuUYODQyJ49e1R5nbQHygwfyqWOHYMHqeHVoCGEQKdOnbBx40aD/bt370azZs0sWRoREZHFMXBYWKdOnfDbb7+Z5bXZ9TAdux6k1KshI1VSUhLc3NwM9s2ePRuDBg2yRFlERGQOQkDRmWgc9Ow1DBwWNmjQIDlwPH36FH5+fqq/B7se6mDXg7Ijs6CR6tWwER0djRw5cpizJCIiMjOlC8AddRoWA4eF1alTR/562bJlBufZNwd2PUzHrge96nUhI1WRIkXkrytXrozQ0FAuAicisgdKF4A7aODg0ZOFpT3YmDNnjsXel2e4UgfPcOXY0p5p6nUGDx6Mu3fvAgA8PT1x9uxZhg0iInJI7HBoIF++fHjy5AmePXumyfuz62E6dj0cR3YDRlqHDh0y+INCdHS0miUREZHGJH3KpuR5johHShpIewFAoeHiIXY91MGuh30yppuRVmRkJBo0aCDffvr0KZyc+G+MiMiu8DocRuGnoAZ69+4tf33kyBENK/kPg4fpUoMHw4ftSg0ZSoJGKh8fH/nrvXv3Im/evGqURkREVoRXGjcOp1RpIO0ByNy5c/Hmm29qWI0hnuFKHZxyZVtMCRhptWrVSv66b9++aNy4sSqvS0REZMsYODT26oXArAnDhzp4el3rpFbISLV+/Xrs3LlTvr1gwQJVX5+IiKwIr8NhFAYOjbRo0QK7du3Suoxs40Jz07HrYR3UDhoA8OTJE7z//vvy7cTERNXfg4iIrAevw2EcBg6NDBo0SA4ckZGR8Pb21rii7GHXQx3seliWOUJGKiEEAgMD5dsXL16Eq6ur2d6PiIisAK/DYRQe7WikadOm8terVq3SrhATcKG56bjQ3LxMXQCeHc2bN5e/njZtGsqVK2fW9yMiIrI17HBoJO0FwObOnYv+/ftrWI1p2PVQB7se6jB3wEjr2LFj2LNnDwDAw8MDX3/9tcXem4iItMMpVcZh4NCQl5cXYmNjcevWLa1LUQ3XepiOaz2UsWTQAFKmUtWtW1e+rdWFPImISANcNG4UBg4NDRw4EFOmTNG6DLNg10Md7HpkzdIhI63Zs2fLXy9evBheXl6a1UJERJbFDodxeBSjoS+//FL++uTJkxpWYl5c62E6rvUwZIm1GVlJTk7G0KFD5dtffPGFZrUQERFZO3Y4NFSwYEH56/79++Pvv//WsBrzY9dDHY7a9dAyYLwq7QX97PmPBURElAmepcooDBwac3FxQXJyMo4ePap1KRbFtR6mc4S1HtYUMlKFhITg0KFDAABfX1/UqFFD44qIiMjSOKXKOPZ5lGJDli1bJn99/fp1DSvRRup0K3s9YLYUe5tupfWUqcw8fPgQwcHB8u0HDx5oWA0REWlGL5RvDohHeRrr1KmT/PXUqVM1rER7DB6ms+W1HqkhwxqDBpByQb+00yCXLl0KT09PDSsiIiKyDTy601iuXLnkr1esWAHhoKdLS4tdD3XYSvCw1pARGxuLn376CXXr1oUkSahQoYJ8X5cuXdC7d28NqyMiIk0JEzYHxDUcVqB///6YP38+AODUqVOoWbOmxhVZD671MJ01rvWwxoDx/PlzDBo0CGvWrMnycYsWLTI4wxwRETkeCQrXcKheiW2wjqMPB9euXTv5619++UXDSqwXux7q0LrrYY3djNWrV0OSJOTNmzfLsDF69GgkJiYybBAR0X8X/lOyOSCbOHoLCQmBJEkZbidOnNC6PJOlvVrx3LlzodPpNKzG+jF4mM6Saz2scW2GEALjxo2DJEno2bNnuvs7d+6MPXv2QKfTQQgBIQQmTZoEV1dXyxdLRERk42xiSlW9evXw6NEjg31jxozB3r177WL6kaenJ+rXr48jR44AAPbt24emTZtqXJX143U91GGu63pYU8BI686dOyhatGi6/d988w3GjRvHUEFERK/F0+IaxyYCh5ubGwIDA+XbSUlJ2Lp1K/r16wdJso/ZcC1atJADxy+//MLAYSSu9TCdGms9rDVkpNqyZQs6dOhgsO/AgQNo0KCBRhUREZFN4oX/jGKT81K2bduGZ8+e4eOPP87ycQkJCYiMjDTYrFXaKxdv2LABcXFxGlZju7jWQx3GTreytilTGZkxY4ZB2JgyZQqEEAwbRERkNEkIxZsjsokOx6t+/PFHNG/eHIUKFcrycVOnTsWECRMsVJVpatWqBS8vL8TGxiImJga///47OnfurHVZNu3VroezxO6Hsdyc068nSkxK+bVh7QEjrZUrV2L48OHy7fPnzxuc5paIiIjMRxIaXvhhxIgR+O6777J8zKVLl1C2bFn59v3791GkSBH8+uuvePfdd7N8bkJCAhISEuTbkZGRCAoKQkREBLy9vU0r3gw++ugj/PzzzwCAtm3bYuvWrRpXZF9q7RyldQl2QSeccLrVZK3LyLZr166hdOnS8u0bN26gePHiGlZERERZiYyMhI+Pj1Uer6XW9laDcXBx8TD6+cnJ8Th0cIJVfm/mpGmHY8iQIRmeISatVw8MVq5cibx586Jt27avfX13d3e4u7ubUqJFTZo0SQ4c27Ztw/Pnz5EnTx6NqyJKCRm2SAiBzz//XL49cuRIhg0iIjKZ0ulRnFKlAX9/f/j7+2f78UIIrFy5Et27d7fLM8kULVoUX3/9tdz1Wbt2Lfr27atxVeTIbDVopPrtt9+wf/9++faIESM0rIaIiOwGF40bxaaOJvbt24dbt27h008/1boUsxk16r9pP/369dOwEnJUOuEkb7YsMjISn3zyiXx70aJFDtW+JiIishY2dUTx448/ol69egZrOuyNt7c3Ro8eLd8ODQ3VrhhyKPYQMtIaP348YmJiAABFihRB7969Na6IiIjsBq80bhSbOrpYu3atfK0KezZu3H9n/6lWrZqGlZC9s5duxqvOnj2LOXPmyLeXLFlil9MwiYhIG6kX/lOyOSL7OsqwEy4uLhg0aJB8e9euXRpWQ/bIHkNGKr1ejyZNmsi3hw0bhpYtW2pYERER2R0LdzgWLVqEokWLwsPDA7Vr18Y///yT6WNXrVoFSZIMNg8P48+opSb7POKwA7Nnz5a/5sESqcFeuxmvWrhwIZ49ewYAKFGiBKZMmaJxRUREZG8kvfLNWOvXr8fgwYMxbtw4nD59GlWqVEHz5s0RFhaW6XO8vb3x6NEjebtz544J363p7PvIw8atX79e/nrixIkaVkK2zBFCRqqnT59iwIAB8u1Dhw7BxcUmr29KREQEIOWP0L1798bHH3+M8uXLY+nSpfDy8sKKFSsyfY4kSQgMDJS3fPnyWbDi9BzjKMRGpb3S+Lhx45CcnKxhNWRLHKWb8aq0p9neu3cv8ufPr2E1RERktyw0pSoxMRGnTp3C22+/Le9zcnLC22+/jaNHj2b6vOjoaBQpUgRBQUFo164dLly4oPhbVYNjHY3YoHPnzslfN2jQQMNKyNo5ashINXXqVPnr9u3bo3HjxhpWQ0REdk2YsCHl1O1pt4SEhAzf5unTp9DpdOk6FPny5cPjx48zfE6ZMmWwYsUKbN26FT///DP0ej3q1auH+/fvm/hNK+eYRyY2pGLFiggICAAAHD16FJs3b9a4IrI2jhwyUl26dMngGjabNm3SsBoiIrJ3qVcaV7IBQFBQEHx8fOQt7R/NTFW3bl10794dVatWRcOGDbFp0yb4+/tj2bJlqr2HsTi52Qbcu3cP7u7uAICOHTvi0aNHCAwM1Lgq0pKjB4y0dDodypcvL99+9OgRJEnSsCIiIqKs3bt3z+BitKnHea/y8/ODs7Mznjx5YrD/yZMn2T4WdHV1RbVq1XD9+nXlBZuIRy02wM3NzeACgPnz5+d6DgfFbkZ6hQoVkr9eu3YtwzgREZmfiWs4vL29DbbMAoebmxtq1KiBvXv3yvv0ej327t2LunXrZqtUnU6Hc+fOabqukUcuNqJKlSqYMGGCfNvV1RWVK1fGggULEBsbq2FlZG6OvjYjK4sXL5bnsNaoUQNdu3bVuCIiInIIAoBewabgMhyDBw/G999/j9WrV+PSpUv44osvEBMTg48//hgA0L17d4wcOVJ+/MSJE/Hnn3/i5s2bOH36ND788EPcuXMHn376qfLv10Q8grEhY8eOhZ+fn3z73Llz6N+/P3LkyIFq1aohMTHR4PFxcXGIiorCkSNHcPHiRUuXSyZiyMjavXv38NVXX8m3T5w4oWE1RETkSExdw2GMLl26YObMmRg7diyqVq2K0NBQ7Nq1S15IfvfuXTx69Eh+/IsXL9C7d2+UK1cOrVq1QmRkJP7++2+D6ceWJgmh8JKHNigyMhI+Pj6IiIgwmDdnS5KTkzF8+HDMmTMnw/uDg4Nx7969DOfpFShQABcuXEDu3LnNXKV1qrVz1OsfpDFbCRinW03W9P2FEHBy+m+sbt26haJFi2pXEBERqcaaj9dSa2tcbQRcnI2/eneyLh77zkyzyu/NnGzj6IZkLi4umD17NoQQEEIgMjISJUuWlO/fv39/pouCHj58CF9fX7Rr1w4xMTGWKpmygd0M46Q9RfTs2bMZNoiIyLIEFK7h0LpwbfAIx8blypUL165dy3DK1LBhw7B7926sXbsWlSpVkvdv27YNOXPmhCRJmD17NuLi4ixZMv0/rs1QZvPmzTh8+DAAwMfHB4MGDdK4IiIicjgWuvCfveBpce1EuXLlIIRAWFgYcuXKBU9PT4P7u3btisePH6NQoULQ6XTy/iFDhmDIkCF4//33sWzZModq72mFAUO5ly9fomPHjvLtp0+falgNERE5LD0AJWdg16tdiG3gkY+dCQgISBc2UgUGBiI5ORlxcXHo16+fwX3r1q2Dj48PKlasaLDwiNTBboY6fH195a/PnDkDFxf+zYSIiMja8ejHAXl4eGD+/PkQQuDgwYMG9124cAEFChSAJElYsWKFQTeEjMeQoZ53331X/nrQoEGoWrWqdsUQEZFDs+RZquwBj4Qc3FtvvQUhBO7cuYPSpUsb3NerVy+4uLhAkiSUK1cOV69e1ahK28Juhvo2bNiATZs2ybdnz56tYTVEROTwuIbDKDwiIgBA4cKFceXKFSQmJmLgwIHp7r98+TLKlCkDSZLw999/W75AG8CQYR4XLlxAp06d5Nvx8fEaVkNERAQGDiPx6IgMuLq6Ys6cORBCICYmBvPnz0eePHkMHlO/fn1IkgQ3NzcMHz4cY8eOxccff4wmTZqgdOnS8PT0hCRJKFGiBE6fPq3Rd2IZ7GaYV3h4OCpWrCjfPnv2LNzd3TWsiIiIiIzFFZeUKS8vL/Tr1w/9+vWDTqfDsGHDDC44mJSUhBkzZmT6/Js3b6JGjRoAUubcz5w50+BibbaMAcP8wsPDERAQIN/esmULKleurGFFRERE/09pt4IdDqLMOTs7Y/bs2dDr9di6dSsqVKhg1PPnzJkDZ2dnNGvWDLGxsWaq0rzYzbCcCxcuGISNWbNmoV27dhpWRERElIbehM0B8ciJjCJJEtq2bYvz58/LVzvPbNPr9fj1118Nnr9nzx7kyJEDkiRh27ZtEDaQ9BkyLGvZsmUG06hmzZqFwYMHa1gRERGRIZ6lyjg8iiKzkSQJnTp1ghACN27cSHdRwXbt2sHJyQkBAQHYs2ePVYUPdjMs7+HDh5AkCX369JH3bdq0iWGDiIjIxvFoiiyiePHiiIiIQEREBNq2bWtwX3h4OJo1awYnJyf4+flputCcIcPyEhMT8cYbb6BgwYIG+y9evIgOHTpoVBUREVEWeJYqo/DIiizK29sbW7duhRAC//77b7pFwM+ePUONGjUgSRLGjx9vkZrYzdDO8OHD4e7ujhMnTsj7evXqBb1ej3LlymlYGRERURb0QvnmgHiERZqpVKkSzp49CyEEzp07J5/RKtWECRMgSRLOnj1rlvdnyNDOxo0bIUmSwVnOihUrhujoaPzwww+QJEnD6oiIiF6DHQ6j8GiLrELFihVx8uRJCCEMrigNAFWrVkXVqlWRkJBg0nuk7WQwaCh3utVkxc89ceIEJEnCe++9Z7D/2rVruHnzJnLkyGFqeURERGRleB0OsjodOnSAEAI//vgjPv30UwApF3zz8PBAx44dsX79eri4GP+ja8qBMpnm5MmTqFWrVrr9O3bsQKtWrTSoiIiIyBRKuxXscBBZlV69eiEuLg4lS5aU923atAmurq4YN26chpVRdv3zzz+QJCld2Pjuu+8ghGDYICIi28QpVUZh4CCr5uHhgWvXruHu3btwdnaW90+cOBGSJOHZs2caVkeZ2bFjByRJQu3atQ32jxgxAnq9HsOHD9eoMiIiIhVw0bhRGDjIJgQFBSE5ORmXL1822O/n54fNmzdrVBWlJYTAmDFjIEkS3nnnHYP7vvnmG+j1ekydOpULwomIiBwMAwfZlDJlykAIgUGDBsn7OnbsiC+++ELDqhzTnDlz4O3tDUmSIEkSnJycMHmy4TqZb7/9Fnq9HpMnT2bQICIi+yH0yjcHxMBBNmn27Nk4d+6cfHvp0qVyGCHzSr0i+ODBgxEVFZXhY7Zt2wYhBEaNGsWgQURE9odrOIzCwEE2q2LFioiJiZFvX716FU5OTgwdZnTs2LF0VwRP1bBhQ9y4cQNCCLRp08bClREREVkQ13AYxWYCx9WrV9GuXTv4+fnB29sbb775Jvbv3691WaQxLy8v6PV6eHl5yfsYOtRx584d1KlTR54yJUkS6tatK9/fu3dvvHjxAsnJyRBCICQkBMWLF9ewYiIiIrJGNhM43nnnHSQnJ2Pfvn04deoUqlSpgnfeeQePHz/WujTSmCRJiImJQbFixeR9hQoV0rAi23bhwgVIkoSiRYvi+PHjGT5m8eLFWL58OXLnzm1w9jAiIiKHwClVRrGJwPH06VNcu3YNI0aMQOXKlVGqVClMmzYNsbGxOH/+vNblkZW4efMmvL29AaSsMwgODta4ItsSFhaGXLlyoWLFiunu8/PzQ5EiRTBo0CDExMRwkT4RETk2AYWBQ+vCtWETgSNv3rwoU6YM1qxZg5iYGCQnJ2PZsmUICAhAjRo1Mn1eQkICIiMjDTayby9fvpS/DgkJQd++fbUrxkb89ddf8PLyQr58+RAdHS3vHzJkiDxdKjw8HLdv38bs2bMNpq8RERE5JHY4jOKidQHZIUkS/vrrL7Rv3x65cuWCk5MTAgICsGvXLvj6+mb6vKlTp2LChAkWrJS0JkkSkpOT4eKS8qO9aNEi+Pv7O+yVyfV6Pe7cuYPIyEhERUUhMjIS169fx65du7Bz584Mn9OiRQv8/vvv8hgSERERmULTI4oRI0bgu+++y/Ixly5dQpkyZfDVV18hICAAhw4dgqenJ3744Qe0adMGJ06cQP78+TN87siRIzF48GD5dmRkJIKCglT9Hsj6ODs7IyYmBjly5AAAjB8/Hi4uLvjmm280rsyyzp07h/fffx8XL17M1uODg4Px66+/ws/Pz8yVERER2Ti9HoCCa2roHfM6HJLQ8HQ+4eHhePbsWZaPKV68OA4dOoRmzZrhxYsX8hx9AChVqhR69eqFESNGZOv9IiMj4ePjg4iICIPXIfv04sUL5MmTR77dsWNHbNy4UcOKLEOv1+ONN97AqVOnXvvYJk2aYNGiRShTpowFKiMiIno9az5eS63tbf9ecHFyM/r5yfpE/BX+o1V+b+akaYfD398f/v7+r31cbGwsgJTTnabl5OQEvYMmRXo9X19fPH78GIGBgQCATZs2yWe0std1CJcuXUL58uXT7W/cuDHq16+P+vXro06dOvDx8dGgOiIiIjuhdD0G13BYr7p168LX1xc9evTA2LFj4enpie+//x63bt1C69attS6PrFi+fPmQkJAAd3d3eV+OHDnwyy+/4IMPPtCwMvWNHDkS06ZNk2/7+fnhjz/+QNWqVeHq6qphZUREROTIbOIsVX5+fti1axeio6PRuHFj1KxZE4cPH8bWrVtRpUoVrcsjK+fm5gYhBLp37y7v69atG3LkyIE7d+5oWJk6YmJiIEmSQdj46aefEB4ejlq1ajFsEBERqY1XGjeKTQQOAKhZsyZ2796NZ8+eITIyEkePHkXLli21LotsyOrVq3Hs2DH5dmxsLIoWLYoRI0YgJiZGw8qU27VrF3LmzGmw7+nTp/jwww81qoiIiMj+CaFXvDkimwkcRGqoXbs24uPj0ahRI3nfd999h5w5c6Js2bKYN2+evGbImiQkJGD//v0YPXo0evfujcqVK0OSJIPQ3b17dwghkDdvXg0rJSIicgBCYXfDQddwaHqWKkuz5rMekOVt3rwZHTt2zPC+cuXK4ciRI1le58WchBDYuHEjhg4dmq1pX0ePHkWdOnUsUBkREZF5WfPxWmptTXw+gouk4CxVIhF7I36yyu/NnNjhIIfVoUMH3LlzB+PHj09336VLl5AnTx4sWbLEYvVERERgzZo1kCQJTk5O6NSp02vDRteuXZGUlMSwQURERFaLgYMcWuHChTFu3DgIISCEwN27d1GsWDH5/i+//BKSJGHPnj1mrePp06fInTs3evToke6+wMBArFy5Eg8ePEB0dDT0er1c79q1a3lFcCIiIkvT65VvDoiBgyiNoKAg3Lx5EydOnDDY36xZM0iShA4dOuD58+eqvqcQIt31aLp3747nz59DCIFHjx6hZ8+eKFCgAHLkyAFJklR9fyIiIjJS6nU4lGwOiIGDKAM1a9aEEALfffedwf4tW7Ygb968kCQJkyZNgk6nM+l9YmNjDS5omTt3bty5cwerV6/WbP0IERERZU3o9Yo3R8TAQZSF4cOHQwiBHTt2pLtv7NixcHFxQeHChREVFWX0a1+9ehU5cuSQb/v7++Pp06coXLiwSTUTERERWRMGDqJsaNWqFYQQSE5OxsSJEw3uu3fvHry9vTF//vxsv96vv/6KMmXKyLe9vLzw5MkTODs7q1YzERERmQmnVBmFgYPICM7OzhgzZgyEEHjy5InBAvMBAwZg586dWT4/Pj4ehQoVQpcuXeR9Tk5OuH37NtdmEBER2QpeadwoDBxECgUEBODmzZv47LPP5H3t2rVDdHR0uscmJyejb9++8PT0xIMHD+T9AwcOxMOHD9MtGiciIiIrJgQg9Ao2Bg4iUmDevHkoVaoUACApKQm9e/eW77t8+TLy588PV1dXLFq0SN5frVo1hIeHY86cOciXL5/FayYiIiKyFAYOIhN5eHhg+/bt8u1169ZBkiRIkoRy5crh8ePHBo8PDQ3F6dOn4efnZ+lSiYiISAVCLxRvjoiBg0gFpUuXxp9//pnp/TVr1sSDBw8ghECVKlUsWBkRERGpTtF0qv/fFFi0aBGKFi0KDw8P1K5dG//880+Wj//tt99QtmxZeHh4oFKlSvjjjz8Uva9aGDiIVNK0aVM8fvzY4LoaEyZMgE6nw4kTJ1CgQAENqyMiIiK1WLLDsX79egwePBjjxo3D6dOnUaVKFTRv3hxhYWEZPv7vv/9G165d0atXL5w5cwbt27dH+/btcf78eVO/bcUkIRxn9UpkZCR8fHwQEREBb29vrcshO3X9+nXcu3cPDRs2NAgfRERE9HrWfLyWWlsjqQNcJFejn58skhAiNhv1vdWuXRu1atXCwoULAQB6vR5BQUHo168fRowYke7xXbp0QUxMjMF07zp16qBq1apYunSp0TWrwUWTd9VIaraKjIzUuBKyZwEBAQgICMjwbFVERESUtdTjNGv+m3iySFA0PSoZSQDSH4u6u7vD3d093eMTExNx6tQpjBw5Ut7n5OSEt99+G0ePHs3wPY4ePYrBgwcb7GvevDm2bNlidL1qcajA8ezZMwBAUFCQxpUQERERUVaioqLg4+OjdRkG3NzcEBgYiMOPla+JyJkzZ7pj0XHjxmH8+PHpHvv06VPodLp0Z7TMly8fLl++nOHrP378OMPHv3oSG0tyqMCRJ08eAMDdu3et7gfY3kVGRiIoKEi+KjdZFsdfOxx77XDstcXx146tj70QAlFRUVa59tHDwwO3bt1CYmKi4tcQQqS72G9G3Q174lCBI3U+vY+Pj03+A7QH3t7eHHsNcfy1w7HXDsdeWxx/7djy2FvzH4Y9PDzg4eFhkffy8/ODs7Mznjx5YrD/yZMnCAwMzPA5gYGBRj3eEriilYiIiIjICrm5uaFGjRrYu3evvE+v12Pv3r2oW7duhs+pW7euweMBYM+ePZk+3hIcqsNBRERERGRLBg8ejB49eqBmzZp44403MHfuXMTExODjjz8GAHTv3h0FCxbE1KlTAQADBgxAw4YNMWvWLLRu3Rrr1q3DyZMnsXz5cs2+B4cKHO7u7hg3bpzdz5OzRhx7bXH8tcOx1w7HXlscf+1w7O1Lly5dEB4ejrFjx+Lx48eoWrUqdu3aJS8Mv3v3rsFp+OvVq4e1a9di9OjRGDVqFEqVKoUtW7agYsWKWn0LjnUdDiIiIiIisiyu4SAiIiIiIrNh4CAiIiIiIrNh4CAiIiIiIrNh4CAiIiIiIrNh4CAiIiIiIrNh4DACT+ilHY49EZHj4O98bXH8SW0MHNmQ+g9PkiSNK3E8HHsiIsfB3/na4viTuTjUhf+UiI6OxoQJE3Dv3j0UK1YMTZs2RePGjbUuyyFw7LUTHR2NBQsW4M6dO6hRowaqV6+OGjVqaF2Ww+D4a4djrx3+ztcWx5/MiRf+y0JMTAyqVKmCUqVKIWfOnIiMjERISAimTp2KwYMHa12eXePYayc6OhpVq1ZFUFAQdDod4uLi8PDhQ8yYMQMffPCB1uXZPY6/djj22uHvfG1x/Mnc2OHIwtKlS+Hr64stW7bA3d0dz549w88//4whQ4YgIiICEyZM0LpEu8Wx18706dORP39+/PHHH/D09MTFixexatUqfPjhh4iNjcWnn36qdYl2jeOvHY69dvg7X1scfzI3Bo4shIWFwdvbG+7u7gCAvHnzYsCAAfD29kavXr1QsGBBfPbZZxpXaZ849tq5f/8+8ufPD09PTwBA+fLl8c0338DLywt9+vRBnjx50LFjR42rtF8cf+1w7LXD3/na4viT2QnK1Jo1a0SuXLnE+fPn0903YcIEkT9/fnHx4kUNKrN/HHvtzJw5U5QoUULcuXPHYH94eLjo06ePeOONN8T9+/c1qs7+cfy1w7HXDn/na4vjT+bGs1SlERcXhyNHjsi369atizp16uC7777DzZs3Afx3Bod3330Xrq6u8n5SF8deOzVq1ICvry9++OEHhIeHy/v9/Pzw3nvv4fbt23j06JGGFdo3jr/l6PV6g/++8cYbHHsL4eetdeH4k7kxcPy/yMhINGzYECtWrMCNGzcAACVLlkSnTp1w8eJFzJw5E1evXpVPFVeyZEnkzp0bUVFRWpZtF6KjozF48GB07doVX331FRITE1GyZEm89957HHszi42NxZo1a7BmzRps374dANCoUSO0a9cOP//8M1auXImHDx/Kj69WrRpy586Nly9falSxfYmPj8eePXuwdetWHD9+HEDK+Ldq1Qq//PILx9+MoqKi8Oabb2Lnzp1wckr5KHzrrbfQpk0bjr2Z8fNWW/zMJU1o22CxDjExMaJ8+fKiffv2IiIiQuj1eoP7586dK9544w3RsmVLcfjwYXHv3j2xfPly4evrK06fPq1R1fYhMjJSlClTRjRp0kR89tlnws/PT3z00Ufy/TNnzhS1a9fm2JtBZGSkKFWqlKhevboICgoSefLkEW3atBGPHj0SQggxYsQIUbp0afHll1+Kc+fOicjISLFs2TLh7+/P1roKIiIiRPXq1UXt2rWFt7e3qFGjhpg/f758/9ChQ0WZMmU4/mYyY8YMIUmSyJEjh/j9998N7hsyZAh/9s2En7fa4mcuaYWBQwjxv//9T7z99tvyL741a9aImTNniokTJ4pnz54JIYTYsmWL6Nixo5AkSZQrV04EBgaKdevWaVm2zUtOThYdOnQQ7777rrxvxYoVonPnzgYfQps3bxYdOnTg2KtIp9OJ999/X7Rq1UokJiaKBw8eiMOHD4uiRYuKatWqiatXrwohhJg9e7YIDg4Wzs7Oonr16sLX15djr4KYmBhRvXp10alTJxEWFibOnDkjBg4cKNq2bSueP38uP2727NmicePGHH8z2Lx5s+jSpYsYM2aMcHFxEdu2bTO4f8mSJfzZNwN+3mqHn7mkJZ6lCsCdO3cghIAkSejatSvOnTsHf39//Pvvv9iwYQPmzJmDdu3aoVWrVjh37hwkSUKOHDlQunRpXpXTBPHx8Xj69KnBmS+ePHmCK1euoHXr1vD09MSgQYPQvn17tGnTBmfPnuXYq8TJyQkRERF488034erqigIFCqBAgQI4efIk6tatiw8//BAhISEYNGgQunTpgosXLwIA8uXLh0qVKsn/XkiZpUuXInfu3Fi8eDH8/Pzg7++Pp0+fonXr1njy5Al8fX0BAIMGDUK3bt3w77//AuD4q6lo0aK4efMmZsyYgcePH+Pdd9/FkSNHcPHiRTg7O6NPnz7o0KEDzp07B4BjrxZ+3mqHn7mkJQYOAO7u7nBycsL58+dx9+5dbN++HYUKFQIANG7cGMOGDcOxY8fg6uqK6tWrGzyX//CUc3Jywp07d7Bnzx40bNgQISEhGD16ND777DNUqFABa9aswRdffIFdu3ahYMGCHHsVCSHw9OlTXLlyRd6XlJSEvHnz4sCBA6hRowb69u2LH3/8UQ4jpA6dTgdfX1+8/fbbyJ07t7y/Vq1aKFCgAOLj4+XHOTs7IyAgAG+//bZG1dqvPHnyQK/XI2fOnJg7dy7y5MmDunXrQq/Xy+tpOPbq4+etdviZS5rSqrViTW7duiW8vLxEvXr1RIsWLURsbKzQ6XRCiJT5jp6enmLDhg0aV2mfdu7cKXLkyCEaNWokfHx8xKRJkwzu9/LyErNnz9aoOvu2fv16ERAQIFauXCnvS0hIEEKkTHMoXbq0uHHjRro51mS627dvi7i4OCGEkMc3OTlZlC5dWhw/flx+3IULFzSpz54lJyfLX7/99tvyeI8cOVK4ubkJZ2dnsXv3biGE4M++GfDzVlv8zCWtONxZqhISEnD16lWEhoYiOTkZQEprfcyYMbh27RqePXsGNzc3ODk5Qa/XIz4+HuXKlYO/v7/Gldu+tGOflJQEAGjRogXu3buH9evXo3LlyvJfE+Pi4hAZGYkqVaogX758WpZtF+Lj43Hq1CkcOHBA/gt648aN0aZNGyxZsgTr168HALi5uQEA/P39ER0dDRcXF/5VSwWp4x8SEoL4+HgUKVIEHh4e0Ov1kCQJQghERETgxYsX8r+Nn3/+GVWrVsWlS5fkqQxkvFd/9p2dnQ3uv3//PhYuXIi5c+di5cqV6N+/P1q0aIHt27fzZ99EGf3e4eet5WQ0/vzMJa041JSqyMhItGjRArGxsYiKioKLiwtmzZqFJk2a4JNPPkFYWBjmzp2LL7/8EtOmTYOTkxP27t2Lp0+fynOqSZnMxr5OnTrw8/NDfHw8Hj9+jGPHjqFOnTrw9PTE1q1bcfv2bZQuXVrr8m1a6ikoY2Ji8Pz5c3h6emLSpEno1KkThg8fjtGjR2P69Ol4+vQpvvrqK+h0Ojx48AA+Pj484FLBq+Pv5eWFiRMnonnz5sifPz+AlCluLi4ucHZ2hq+vLzZu3IgePXrgp59+Qrly5TT+DmxXZmP/9ttvo1ChQmjZsiUGDBiAly9fYt26dWjbti1atWqVYTAh42Q29u3atUPv3r35eWtmr/vZ52cuWZzGHRaLiYuLEzVr1hQffvihOH/+vLh+/bp47733RK5cucTkyZPFy5cvxbNnz8TcuXNFrly5RJEiRUSVKlVE3rx5xfr167Uu36ZlNvY+Pj5i8uTJ4t69e0IIIcaPHy8CAgJEkyZNRLdu3YSvr6/49ddfNa7etiUmJoomTZqITp06iStXrojbt2+Lzz//XBQvXlwMHTpUvHjxQly/fl0MHjxYeHl5iUqVKolGjRqJHDly8OdeBZmNf4kSJcTQoUPF7du35cfqdDpRp04d8emnnwonJyfx888/CyFSpvVwao/xXjf2T548EUeOHBHVqlUTf/zxh8Fzk5KShBCcUqXU637vhIWFiZcvX/Lz1kxe97N//fp1IUTKZ66/vz8/c8kiHCZwHD58WLzxxhvywa0QQpw4cULkyJFDFC1aVMyZM0fef+/ePfH999+L3377TZw8eVIIwQ8eU2Q19sWLF5fH/vbt22LNmjWiRYsWYvDgweKvv/4SQnDsTREWFibKlSsnNm/ebLB/2rRponz58mLUqFEiPj5exMbGijNnzohRo0aJ+fPni4MHDwohOPamymr8K1asKL755hv5VKBhYWHC09NTSJIkfvvtNyEEw4Ypshr7ChUqiFGjRgkhhMFpiEkd2Rn7qKgoIQQ/b80hO793YmNjxcOHD/mZSxbjMFOq7t+/j0uXLhnMDfXx8UGbNm0ghMDYsWPRqVMnFCxYEIUKFcKnn35q8HxOLVEuu2NfpEgRfPTRR+jWrRskSZLntpNySUlJ0Ov1iI6OBpAyp9fDwwNff/01kpKSsHjxYjRq1AhNmzZF1apVUbVqVW0LtjOvG/+lS5eiYcOGaNq0KTw8PPDZZ5+hdevWaNq0KU9BaSJjfvb1er18tXEy3evGfsmSJfLY8/NWfdkZ/9TfO/zMJUuRhIP8dD1+/BiNGjVCw4YNMXXqVMTHx6Nx48Z466238P3336NatWpo27YtJkyYoHWpdic7Y9+mTRtMnDiR57g3g3fffReXLl3ChQsXIEkSEhIS4O7uDgDo0KEDwsLCcOTIEY2rtF/GjH90dDRy5szJsKES/uxrh2OvrdeN/5MnT/D3339rXCU5Eof5k46vry8GDx6MAwcOoGTJkqhWrRpKly6N77//HgCQN29ehIeHa1ylfcrO2D99+hQAD7DUpNfrAQBz585FUlISmjZtCiDlPPipZ0Jq2rQp4uPjkZiYqFmd9sqY8U9ISAAA5MyZEwDkvzaSMvzZ1w7HXlvZHf+EhASOP1mUwwQOd3d39OzZE/v27cOKFSvw888/Y9u2bQBSLrDl5+eHEiVKAABbiirj2GsjdYpIwYIFMX/+fFy/fh3BwcGIioqCq6srAODp06fIkSMHEhMTOfYqM2b8k5KSOP4q4s++djj22uL4k7WyyylVqdNysjM9JykpCT/99BMGDx6MHTt2oH79+haq0j5x7K1TUlISDh06hH79+iEqKgr16tVDrly5sHr1aqxfvx4dOnTQukS7xvHXDsdeOxx7bXH8yZrYVeDQ6XTyXEVPT8/XPv7atWtYuHAhfv75ZyxevBhdunSxQJX2iWOvPZ1O99prByQmJmLixIm4e/cuXF1d0blzZzRv3pxrZ1TA8dcOx147HHttcfzJVthN4IiKisJnn32Ge/fuIUeOHGjevDkGDx782uetXLkSxYsXR8OGDblQUyGOvXZiYmKwadMmfPTRRwCQ5dl2Xv1wSduNAjj2SnD8tcOx1w7HXlscf7JFdrGGIzY2FrVq1UJ8fDyaN2+O8uXL45tvvsF7772HW7duGTw2dUFV6uKpjz/+GA0bNpTv5z8+43DstRMTE4P69eujR48e+PbbbwGkzN9NHee09Ho9JElCXFycvC/tBw7H3ngcf+1w7LXDsdcWx59slV0Ejp07d8LJyQkrVqzAmDFjMGfOHBw5cgQHDhzAF198gQcPHgBI+Yfm5OSEY8eOYcmSJQBg8I+U//iMx7HXRlJSEgYOHIhcuXKhX79++OWXXzBx4kQAGX/4ODk54ejRoxgzZowc+HjdAeU4/trh2GuHY68tjj/ZMrv4yYuMjERCQgJ8fX0BpPyjrF69Oo4ePYpTp05h1KhRAP47qN27dy8GDhyIS5cu8R+fiTj22oiLi4NOp8PHH3+MYcOG4f3338f//vc/+ToyGX34XL58GbNnz8aJEye0KNmucPy1w7HXDsdeWxx/smkqXrVcM2fPnhUuLi7ip59+kvclJiYKIYTYt2+f8PT0FBs3bpTv0+l04qOPPhKrV6+2eK32hmNveTqdTgghxLNnz0RMTIwQQoj79++LcePGibJly4px48bJj01OThZ6vV6+3a9fPzFhwgSDfWQcjr92OPba4dhri+NPts5F68CjhrJly+KLL77AnDlz4O/vj+bNm8PV1RVCCNSqVQvly5fHlStX5Mc7OTmhefPmaNy4sYZV2weOveWldoby5MkDIOUsJQULFsTnn38OAFi/fj0kScK4ceMwd+5cPHv2DJMnT4aTkxM6duyIcuXKcQqbCTj+2uHYa4djry2OP9k6mwsc0dHRGDt2LKKjo+Hk5IQhQ4agVKlS6N27N27fvo0pU6ZAp9OhVatWkCQJOXPmhL+/P5KTkwH8dwq5bt26afyd2B6OvXYyGvvChQvD3d1dPuNI/vz55Q+fDRs24ODBg9i/fz9WrVolf1g1atRIw+/CdnH8tcOx1w7HXlscf7IrGnZXjBYdHS2KFSsmGjVqJLp16yYqVKggChcuLObMmSPi4+PFP//8Izp27CiKFy8uZs2aJUJCQsSCBQuEp6enOHDggNbl2zSOvXYyGvuiRYuK2bNni7CwMCHEf+12IYR4+PChaNSokZAkSWzZskUIIdhKNwHHXzsce+1w7LXF8Sd7Y1OBY9KkSaJBgwYG+7766itRvnx5MX78eJGQkCCuX78uJk+eLHx9fUXp0qVF2bJlxa+//qpRxfaDY6+dzMa+YsWKYuzYseLp06cG9y1btkxIkiQ2bdokhEj50OEHj3Icf+1w7LXDsdcWx5/sjU2dJig+Ph56vR7x8fHQ6XQAgIULF6JNmzb43//+h3Xr1qFEiRL45ptvcOPGDRw8eBD79u1Dp06dIFLClcbfge3i2Gsns7Fv3bo1NmzYgO3btwNIOfVwbGws9uzZg/Xr16NDhw68uJMKOP7a4dhrh2OvLY4/2R0tUo4x0ib0yZMnixIlSojo6GghhBDx8fHyfR9++KEoXry4iI2NtXiN9opjrx1jxr5EiRIGY596P//CpRzHXzsce+1w7LXF8Sd7ZtWBIyoqSowYMUIcO3ZMCCFEXFycKFSokOjQoYP8mLi4OCFEynzHPHnyiJ9//lmTWu0Nx147pow9P2xMx/HXDsdeOxx7bXH8yd5Z7ZQqIQQGDRqE6dOn48cff8Tx48fh4eGB5cuX48CBA/KZjjw8PAAAMTExyJ8/v3wBOlKOY68dU8dekiS20U3A8dcOx147HHttcfzJEVht4JAkCV5eXihVqhTOnz+PhQsX4syZM2jZsiUWLVqEgwcPIjg4GOfPn8f169exe/duPHr0CIGBgVqXbvM49trh2GuL468djr12OPba4viTI5CEsL7VvEIISJKE//3vfwgPD0fZsmUxYsQIVKhQAfPmzcOOHTtQokQJ9O3bF2FhYXBxcUFSUhJmzZqF999/X+vybRrHXjsce21x/LXDsdcOx15bHH9yFFZ54b/U1qC3tzeWLVuGkJAQPHv2DAsWLMDbb7+N0NBQXL58GadPn8b+/fvh7u4OX19flCtXjmdnMBHHXjsce21x/LXDsdcOx15bHH9yFFYZOICU1F+kSBEkJCQAALp27Ypt27bht99+Q6NGjRATEwMACA4ONnge/+GZjmOvHY69tjj+2uHYa4djry2OPzkCq17DUbZsWbi6uuLly5eYPHkytm3bhsGDByM+Ph5TpkzBqVOntC7TLnHstcOx1xbHXzsce+1w7LXF8SdHYLUdDgBITk6Gi4sLWrVqhX///Rfr1q1DmzZtsHbtWsyYMQMuLlZdvk3j2GuHY68tjr92OPba4dhri+NPds8S5941xcKFC0XRokXFli1bDPY/fPhQo4ocB8deOxx7bXH8tcOx1w7HXlscf7JnVnmWqrRiYmIQHh6OokWLAvjvjA6p/yXz4dhrh2OvLY6/djj22uHYa4vjT/bM6gMHERERERHZLqtdNE5ERERERLaPgYOIiIiIiMyGgYOIiIiIiMyGgYOIiIiIiMyGgYOIiIiIiMyGgYOIiIiIiMyGgYOIiIiIiMyGgYOIiIiIiMyGgYOIiIiIiMyGgYOIyM49e/YMAQEBuH37tkmv8/7772PWrFnqFEVERA6DgYOISEVCCOTOnRsLFixId9+XX36JN954A7t374YkSVluf/75Jxo2bAhJkjBlypR071G7dm1IkoSJEye+tqZvv/0W7dq1Q9GiRU363kaPHo1vv/0WERERJr0OERE5FgYOIiIV3bhxAxEREahZs2a6+06dOoUaNWqgQYMGePTokbzlzZsXY8aMMdjXpEkTnDlzBkWKFMG5c+cMXmf16tV4+PAhAKB69epZ1hMbG4sff/wRvXr1Mvl7q1ixIkqUKIGff/7Z5NciIiLHwcBBRKSiU6dOwcXFBVWrVjXYn5SUhH///Rc1atSAp6cnAgMDERgYCJ1Oh2fPnuGtt96S9wUGBuLGjRuIiopCjx49DAJHVFQURo4ciZ49ewIAatSokWU9f/zxB9zd3VGnTh2D/bdv34YkSdi4cSMaNGgAT09P1KpVC3fv3sWhQ4dQp04deHl5oUmTJnj58qX8vDZt2mDdunUmjRERETkWBg4iIhWdOnUK5cuXh6enp8H+CxcuID4+Pl1AOHPmDID0nYpTp07By8sLXbt2xZUrV5CYmAgAmDRpEmrWrAl/f38EBgYif/78WdZz6NChDEPJ2bNnAQBLlizBlClT8Pfff+PJkyf48MMPMW3aNCxcuBD79+/H2bNnsXLlSvl5b7zxBv755x8kJCRkc0SIiMjRuWhdABGRPTl16lSm06nc3d1RsWJFg/2nT59GUFAQ8ubNm25/5cqVUaZMGXh4eODy5cvw9PTEkiVLcPr0aXz77bevnU4FAHfu3EGBAgXS7Q8NDUWePHmwfv16+b0bNmyIw4cP48KFC/Dy8gIA1KpVC48fP5afV6BAASQmJuLx48coUqTI6weEiIgcHjscREQqOn36dKaBo1KlSnB1dU33+IyCQ+p+SZJQuXJlnDt3DoMGDcIXX3yBUqVKyetBXicuLg4eHh7p9p89exYdOnQwCDp3795Fly5d5LCRuq9YsWLy7dTOTWxs7Gvfm4iICGDgICJSzb179/Dy5UtUqFAh3X1//fUX6tWrl27/6wIHAFStWhVz587FyZMnMWbMGMTHx+Py5csGz6tfvz6OHz8OAOjVqxfmzJkDAPDz88OLFy/SvX5oaChq165tsO/s2bMGaz3i4+Nx5coVVKlSRd73/PlzAIC/v3/mA0FERJQGAwcRkUqSkpIAANHR0Qb79+zZg2vXrqFLly4G+58+fYp79+6lCxw3b97Ey5cv5f3VqlXDyZMnMXXqVOTKlQtnz55FcnKyQYdjzJgxmDZtGmbPng0nJycMGjRIfu7FixcNXj8yMhK3b99GtWrV5H23bt1CRESEwb5z585BCIFKlSrJ+86fP49ChQrBz8/P6PEhIiLHxMBBRKSSYsWKoWzZshg5ciT27t2Ls2fPYvny5fjggw/Qs2fPdB2O06dPA8h4wbibm5u83qNHjx4IDw+Xz0x1+vRp+Pv7IygoSH5OixYtcPfuXezYsQOLFy+W9zdv3hwXLlww6HKcPXsWzs7OButJUtd0pF2XERoaihIlSiBnzpzyvkOHDqFZs2ZKh4iIiBwQAwcRkUokScKOHTtQokQJdOnSBQ0aNMCyZcswZcoU/PDDD+kef+bMGeTLly/dou7Tp0+jYsWK8noPV1dX+Pn5QZIk+f60nQgAOHHiBJ4/fw4fHx+DdSKVKlVC9erV8euvv8r7zp49Ky9GT7vv1dc8e/aswXSq+Ph4bNmyBb179zZ2aIiIyIFJQgihdRFERKTcgwcP0KpVK2zZsgXvvvsu1qxZY9C92LFjB4YNG4bz58/DyUn535mWLFmCzZs3488//1SjbCIichDscBAR2bC4uDh06tQJCxYsQLFixTBy5EhMmjTJ4DGtW7fGZ599hgcPHpj0Xq6urliwYIFJr0FERI6HHQ4iIiIiIjIbdjiIiIiIiMhsGDiIiIiIiMhsGDiIiIiIiMhsGDiIiIiIiMhsGDiIiIiIiMhsGDiIiIiIiMhsGDiIiIiIiMhsGDiIiIiIiMhsGDiIiIiIiMhsGDiIiIiIiMhsGDiIiIiIiMhsGDiIiIiIiMhs/g9j+d/uEFSNJwAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 1000x440 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Plot Delft3D interpolated Data\n",
     "fig, ax = plt.subplots(figsize=(10, 4.4))\n",
@@ -2054,7 +2113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": null,
    "id": "d01e3d76-daab-4597-8b5d-5b588f6c81c2",
    "metadata": {},
    "outputs": [],
@@ -2076,7 +2135,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
+   "execution_count": null,
    "id": "147f4bf3-7ad3-4cb0-8c08-fd946557e2f8",
    "metadata": {},
    "outputs": [],
@@ -2106,21 +2165,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": null,
    "id": "bf78c6e0-c4cd-421e-b9c0-3465d05c6dd3",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(0.33569684683914963)"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Calculate and priont the Mean Absolute Error\n",
     "MAE = np.sum(L1_Magnitude * error_filter) / len(L1_Magnitude[L1_Magnitude < 1000])\n",
@@ -2129,40 +2177,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": null,
    "id": "0acdaf51-d08e-4d9e-8070-4d2e5477b456",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(array([400960., 400980., 401000., 401020., 401040., 401060., 401080.,\n",
-       "        401100.]),\n",
-       " [Text(400960.0, 0, '400,960'),\n",
-       "  Text(400980.0, 0, '400,980'),\n",
-       "  Text(401000.0, 0, '401,000'),\n",
-       "  Text(401020.0, 0, '401,020'),\n",
-       "  Text(401040.0, 0, '401,040'),\n",
-       "  Text(401060.0, 0, '401,060'),\n",
-       "  Text(401080.0, 0, '401,080'),\n",
-       "  Text(401100.0, 0, '401,100')])"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x440 with 2 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Set the min and max error values\n",
     "max_plot_error = 1\n",
@@ -2210,7 +2228,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": null,
    "id": "1d10a637-2105-40de-8f22-e8b46b5c2d16",
    "metadata": {},
    "outputs": [],
@@ -2227,7 +2245,7 @@
    "id": "39753c37-c501-462f-b45f-2030f16632c4",
    "metadata": {},
    "source": [
-    "The mean squared error (MSE) is the average of $L_2$ error over the transect area. $N$ is equation is equle to the number of points in the river cross-sectional area. \n",
+    "The mean squared error (MSE) is the average of the $L_2$ error over the transect area. $N$ is the number of points in the river cross-sectional area. \n",
     "\n",
     "\n",
     "$MSE= \\frac{\\sum{L_2}}{N}$"
@@ -2235,21 +2253,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": null,
    "id": "e71c17c4-93f7-4ad2-8b66-bff1b7da0519",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(0.15431169343691561)"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "MSE = np.sum(L2_Magnitude * error_filter) / np.size(L2_Magnitude[L2_Magnitude < 1000])\n",
     "MSE"
@@ -2257,48 +2264,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": null,
    "id": "3921f31e-1f0e-45a6-9394-c7e42e1ebb2d",
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "C:\\Users\\sterl\\AppData\\Local\\Temp\\ipykernel_4716\\3877048929.py:20: UserWarning: Adding colorbar to a different Figure <Figure size 1000x440 with 3 Axes> than <Figure size 1000x440 with 1 Axes> which fig.colorbar is called on.\n",
-      "  cbar = plt.colorbar(contour_plot_L1)\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "(array([400960., 400980., 401000., 401020., 401040., 401060., 401080.,\n",
-       "        401100.]),\n",
-       " [Text(400960.0, 0, '400,960'),\n",
-       "  Text(400980.0, 0, '400,980'),\n",
-       "  Text(401000.0, 0, '401,000'),\n",
-       "  Text(401020.0, 0, '401,020'),\n",
-       "  Text(401040.0, 0, '401,040'),\n",
-       "  Text(401060.0, 0, '401,060'),\n",
-       "  Text(401080.0, 0, '401,080'),\n",
-       "  Text(401100.0, 0, '401,100')])"
-      ]
-     },
-     "execution_count": 31,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 1000x440 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Create a contour plot of the error\n",
     "# Plotting\n",
@@ -2343,21 +2312,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": null,
    "id": "de8f93fc-8254-4181-817c-dae48b39456b",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "np.float64(0.888520413692259)"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
+   "outputs": [],
    "source": [
     "# L inf\n",
     "L_inf = np.nanmax(L1_Magnitude * error_filter)\n",
@@ -2401,7 +2359,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.7"
+   "version": "3.12.11"
   }
  },
  "nbformat": 4,
diff --git a/examples/PacWave_resource_characterization_example.ipynb b/examples/PacWave_resource_characterization_example.ipynb
index f0484365d..e702d4d19 100644
--- a/examples/PacWave_resource_characterization_example.ipynb
+++ b/examples/PacWave_resource_characterization_example.ipynb
@@ -6,7 +6,7 @@
    "source": [
     "# PacWave Resource Assessment\n",
     "\n",
-    "This example notebook provides an example using MHKiT to perform a resource assessment similar to Dunkel et. al at the PACWAVE site following the IEC 62600-101 where applicable. PacWave is an open ocean, grid-connected, full-scale test facility consisting of two sites (PacWave-North & PacWave-South) for wave energy conversion technology testing located just a few miles from the deep-water port of Newport, Oregon. This example notebook performs a resource analysis using omni-directional wave data from a nearby NDBC buoy and replicates plots created by Dunkel et. al and prescribed by IEC TS 62600-101 using these data.\n",
+    "This example notebook provides an example using MHKiT to perform a resource assessment similar to Dunkel et. al at the PACWAVE site following the IEC 62600-101 Ed. 2.0 en 2024 where applicable. PacWave is an open ocean, grid-connected, full-scale test facility consisting of two sites (PacWave-North & PacWave-South) for wave energy conversion technology testing located just a few miles from the deep-water port of Newport, Oregon. This example notebook performs a resource analysis using omni-directional wave data from a nearby NDBC buoy and replicates plots created by Dunkel et. al and prescribed by IEC TS 62600-101 Ed. 2.0 en 2024 using these data.\n",
     "\n",
     "Note: this example notebook requires the Python package folium which is not a requirement of MHKiT and may need to be pip installed seperately.\n",
     "\n",
@@ -1295,7 +1295,7 @@
    "source": [
     "## Monthly Cumulative Distribution\n",
     "\n",
-    "A cumulative distribution of the energy flux, as described in the IEC TS 62600-101 is created using MHKiT as shown below. The summer months have a lower maximum energy flux and are found left of the black data line representing the cumulative distribution of all collected data. April and October most closely follow the overall energy flux distribution while the winter months show less variation than the summer months in their distribution.\n"
+    "A cumulative distribution of the energy flux, as described in the IEC TS 62600-101 Ed. 2.0 en 2024 is created using MHKiT as shown below. The summer months have a lower maximum energy flux and are found left of the black data line representing the cumulative distribution of all collected data. April and October most closely follow the overall energy flux distribution while the winter months show less variation than the summer months in their distribution.\n"
    ]
   },
   {
diff --git a/examples/acoustics_example.ipynb b/examples/acoustics_example.ipynb
index fe73e44c7..0b53d6670 100644
--- a/examples/acoustics_example.ipynb
+++ b/examples/acoustics_example.ipynb
@@ -6,12 +6,12 @@
    "source": [
     "# Analyzing Passive Acoustic Data with MHKiT\n",
     "\n",
-    "The following example illustrates how to read and analyze some basic parameters for passive acoustics data. Functionality to analyze .wav files recorded using hydrophones has been integrated into MHKiT to support analysis based on the IEC-TS 62600-40 standard.\n",
+    "The following example illustrates how to read and analyze passive acoustics data collected by a hydrophone. This functionality has been primarily integrated into MHKiT to support analysis based on the IEC-TS 62600-40 technical standard for marine energy devices.\n",
     "\n",
     "The standard workflow for passive acoustics analysis is as follows:\n",
     "\n",
-    "1. Import .wav file\n",
-    "2. Calibrate data\n",
+    "1. Import a .wav file\n",
+    "2. Calibrate pressure sensitivity\n",
     "3. Calculate spectral density\n",
     "4. Calculate other parameters\n",
     "5. Create plots\n",
@@ -36,9 +36,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Read in Hydrophone Measurements\n",
+    "### Read Hydrophone Data\n",
     "\n",
-    "All hydrophone .wav files can be read in MHKiT using a base function called `read_hydrophone` from the acoustics.io submodule. Because the sampling frequency is so fast, measurements are stored in the lowest memory format possible and need to be scaled and transformed to return the measurements in units of voltage or pressure.\n",
+    "Hydrophones typically output a .wav file, which can be read in MHKiT using a base function called `read_hydrophone` from the acoustics.io submodule. Because a hydrophone's sampling frequency is so fast, measurements are stored in the lowest memory format possible and need to be scaled and transformed to return the measurements in physical units of voltage or pressure.\n",
     "\n",
     "The `read_hydrophone` function scales and transforms raw measurements given a few input parameters. Most parameters needed to convert the raw data are stored in the native .wav format header blocks, but two, the peak voltage (\"peak_voltage\") of the sensor's analog-to-digital converter (ADC) and file \"start_time\" (usually stored in the filename) are required. \n",
     "\n",
@@ -87,11 +87,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "\"Smart\" hydrophones are those where the hydrophone element, pre-amplifier board, ADC, motherboard and memory card are sold in a single package. Companies that sell these often store metadata in the .wav file header, and MHKiT has a couple of wrapper functions for these hydrophones.\n",
+    "\"Smart\" hydrophones are those where the hydrophone element, pre-amplifier board, analog-to-digital converter (ADC), motherboard and memory card are sold in a single package. Companies that sell these often store metadata in the .wav file header.\n",
     "\n",
-    "OceanSonics icListen and OceanInstruments Soundtrap are two common smart hydrophone models, with examples as follows.\n",
+    "MHKiT has wrapper functions for OceanSonics icListen and OceanInstruments Soundtrap hydrophones, with examples as follows.\n",
     "\n",
-    "For icListen datafiles, only the filename is necessary to provide to return file contents in units of pressure. The stored sensitivity calibration value can be overridden by setting the \"sensitivity\" input, and to return measurements in units of voltage, set `sensitivity` to None and `use_metadata` to False."
+    "For icListen datafiles, only the filename is necessary to provide to return file contents in units of pressure. The stored sensitivity calibration value can be overridden by setting the \"sensitivity\" input to a predetermined value. If sensitivity calibration data is on hand, return measurements in units of voltage by setting `sensitivity` to None and `use_metadata` to False."
    ]
   },
   {
@@ -100,7 +100,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Pressure output\n",
     "P = acoustics.io.read_iclisten(\"data/acoustics/RBW_6661_20240601_053114.wav\")\n",
+    "# Voltage output\n",
     "V = acoustics.io.read_iclisten(\n",
     "    \"data/acoustics/RBW_6661_20240601_053114.wav\", \n",
     "    sensitivity=None, \n",
@@ -112,7 +114,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "For Ocean Instruments Soundtrap datafiles, the filename and sensitivity should be provided to return the measurements in units of pressure. If the hydrophone has been calibrated, set the sensitivity to None to return the measurements in units of voltage."
+    "For Ocean Instruments Soundtrap datafiles, the filename and sensitivity should be provided to return the measurements in units of pressure. Again, if the hydrophone has been calibrated, set the sensitivity to None to return the measurements in units of voltage."
    ]
   },
   {
@@ -131,7 +133,7 @@
    "source": [
     "### Mean Square Sound Pressure Spectral Density\n",
     "\n",
-    "After the .wav file is read in, either in units of pressure or voltage, we calculate the mean square sound pressure spectral density (SPSD) of the timeseries using `sound_pressure_spectral_density`. This splits the timeseries into windows and uses fast Fourier transforms to convert the raw measurements into the frequency domain, with units of $Pa^2/Hz$ or $V^2/Hz$, depending on the input. The function takes the original datafile, the hydrophone's sampling rate (\"fs\"), which is stored as an attribute of the measurement timeseries, and a window size (\"bin_length\") in seconds as input.\n",
+    "After the .wav file is read, either in units of pressure or voltage, we calculate the mean square sound pressure spectral density (SPSD) of the time-series using `sound_pressure_spectral_density`. This splits the timeseries into windows and uses fast Fourier transforms to convert the raw measurements into the frequency domain, with units of $Pa^2/Hz$ or $V^2/Hz$, depending on the input. The function takes the original datafile, the hydrophone's sampling rate (\"fs\"), which is stored as an attribute of the measurement timeseries, and a window size (\"bin_length\") in seconds as input.\n",
     "\n",
     "The IEC-40 considers an acoustic sample to have a length of 1 second, so we'll set the bin length as such here."
    ]
@@ -143,7 +145,7 @@
    "outputs": [],
    "source": [
     "# Create mean square spectral densities using 1 s bins.\n",
-    "spsd = acoustics.sound_pressure_spectral_density(V, V.fs, bin_length=1)"
+    "spsd = acoustics.sound_pressure_spectral_density(V, fs=V.fs, bin_length=1)"
    ]
   },
   {
@@ -154,7 +156,7 @@
     "\n",
     "For conducting scientific-grade analysis, it is critical to use calibration curves to correct the SPSD calculations. Hydrophones should be calibrated (i.e., a sensitivity calibration curve should be generated for a hydrophone) every few years. The IEC-40 asks that a hydrophone be calibrated both before and after the test deployment.\n",
     "\n",
-    "A calibration curve consists of the hydrophone's sensitivity (in units of $dB$ rel $1$ $V^2/uPa^2$) vs frequency and should be applied to the spectral density we just calculated.\n",
+    "A calibration curve consists of the hydrophone's sensitivity (in units of $dB$ $rel$ $1$ $V^2/uPa^2$) vs frequency and should be applied to the spectral density we just calculated.\n",
     "\n",
     "The easiest way to apply a sensitivity calibration curve in MHKiT is to first copy the calibration data into a CSV file, where the left column contains the calibrated frequencies and the right column contains the sensitivity values. Here we use the function in the following codeblock to read in a CSV file created with the column headers \"Frequency\" and \"Analog Sensitivity\"."
    ]
@@ -217,9 +219,9 @@
    "source": [
     "### Mean Square Sound Pressure Spectral Density Level\n",
     "\n",
-    "We can use the function `sound_pressure_spectral_density_level` to calculate the mean square sound pressure spectral density levels (SPSDLs) from the calibrated SPSD. This function converts absolute pressure into relative pressure in log-space, the traditional means with which we measure sound, in units of decibels relative to 1 uPa ($dB$ rel $1$ $uPa$), the standard for underwater sound. \n",
+    "We can use the function `sound_pressure_spectral_density_level` to calculate the mean square sound pressure spectral density levels (SPSDLs) from the calibrated SPSD. This function converts absolute pressure into relative pressure in log-space, the traditional means with which we measure sound, in units of decibels relative to 1 uPa [dB rel 1 uPa], the standard for underwater sound. \n",
     "    \n",
-    "Sidenote: Sound in air is measured in decibels relative to 20 uPa, the minimum sound pressure humans can hear. To convert between \"$dB$ rel $1$ $uPa$\" and \"$dB$ rel $20$ $uPa$\", one simply needs to subtract 26 dB from the \"$dB$ rel $1$ $uPa$\" value."
+    "Sidenote: Sound in air is measured in decibels relative to 20 uPa, the minimum sound pressure humans can hear. To convert between [dB rel 1 uPa] and [dB rel 20 uPa], one simply needs to subtract 26 dB from the [dB rel 1 uPa] value."
    ]
   },
   {
@@ -235,7 +237,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Now that the SPSDL is calculated, we can create spectrograms, or waterfall plots, using the `plot_spectrogram` function in the graphics submodule. While spectrograms aren't required by the IEC-40, they are useful to do quality control so we can avoid using contaminated soundbytes in further analysis (like the boat noise shown in this one).\n",
+    "Now that the SPSDL is calculated, we can create spectrograms, or waterfall plots, using the `plot_spectrogram` function in the graphics submodule. While spectrograms aren't required by the IEC-40, they are useful to do quality control so we can avoid using contaminated soundbytes in further analysis (i.e., we'd remove the boat noise shown here from further analysis of a marine energy device).\n",
     "\n",
     "To do this, we'll give the function the minimum and maximum frequencies to plot, as well as keyword arguments supplied to the matplotlib `pcolormesh` function. For these measurements, we're setting fmin = 10 Hz, the minimum specified by the IEC-40, and fmax = 48,000 Hz, the Nyquist frequency for these data. \n",
     "\n",
@@ -268,7 +270,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x500 with 2 Axes>"
       ]
@@ -297,7 +299,7 @@
    "source": [
     "If you see something interesting in the spectrogram, the next step you should do is listen to the .wav file. This can tell you a lot about what you're looking at. If you listen to this file, you'll hear the boat cruising by around 3 minutes in.\n",
     "\n",
-    "Some audio players aren't able to play some hydrophone recodings (i.e. icListens), so be sure to try multiple if you can't hear anything in one particular player. Higher-end hydrophones tend to user higher ADC peak voltages, which will translate to quieter audio tracks. You can use the `export_audio` file in the io submodule to rescale these audio tracks and increase the gain if need be."
+    "Some audio players aren't able to play some hydrophone recodings (i.e., icListens), so be sure to try different players if you can't hear anything in one particular player. Higher-end hydrophones tend to user higher ADC peak voltages, which will translate to quieter audio tracks. You can use the `export_audio` file in the io submodule to rescale these audio tracks and increase the gain if need be."
    ]
   },
   {
@@ -319,7 +321,7 @@
     "\n",
     "The IEC-40 requires a few aggregate statistics for characterizing the sound of marine energy devices. For the first, the IEC-40 asks for plots showing the 25%, 50%, and 75% quantiles of the SPSDL during specific marine energy device states. For current energy devices, the IEC-40 requires 10 SPSDL samples at a series of turbine states (braked, freewheel, 25% power, 50% power, 75% power, 100% power). For wave energy devices, the spec requires 30 SPSDL samples in each wave height and period bin observed.\n",
     "\n",
-    "For this example notebook we'll keep it simple and use a random set of 30 samples and collate them together. Otherwise one can pick and choose which to use. Then we can find the median and quantiles of those 30 samples."
+    "For this example notebook we'll keep it simple and use a random set of 30 samples and collate them together. Typically, one will pick and choose which 1 second samples to collate together and analyze. Then we can find the median and quantiles of those 30 samples."
    ]
   },
   {
@@ -330,7 +332,8 @@
     {
      "data": {
       "text/plain": [
-       "Text(0.5, 1.0, 'Median and Quantile Sound Pressure Spectral Density Level')"
+       "[(20.0, 80.0),\n",
+       " Text(0, 0.5, 'Sound Pressure Spectral Density Level\\n[dB rel 1 uPa$^2$/Hz]')]"
       ]
      },
      "execution_count": 12,
@@ -339,7 +342,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x500 with 1 Axes>"
       ]
@@ -358,17 +361,20 @@
     "spsdl_q75 = spsdl_clip.quantile(0.75, \"time\")\n",
     "\n",
     "# Plot medians and quantiles\n",
-    "fig, ax = acoustics.graphics.plot_spectra(spsdl_median, fmin, fmax)\n",
+    "fig, ax = acoustics.graphics.plot_spectra(spsdl_median, fmin, fmax, label=\"Median\")\n",
     "ax.fill_between(\n",
     "    spsdl_clip[\"freq\"],\n",
     "    spsdl_q25,\n",
     "    spsdl_q75,\n",
     "    alpha=0.5,\n",
     "    facecolor=\"C0\",\n",
-    "    edgecolor=None\n",
+    "    edgecolor=None,\n",
+    "    label=\"Quantiles\"\n",
     ")\n",
-    "ax.set(ylabel=\"dB rel 1 uPa^2/Hz^2\", ylim=(20, 80))\n",
-    "ax.set_title(\"Median and Quantile Sound Pressure Spectral Density Level\")"
+    "ax.legend(loc=\"upper right\")\n",
+    "ax.set_axisbelow(True)\n",
+    "ax.grid()\n",
+    "ax.set(ylim=(20, 80), ylabel=\"Sound Pressure Spectral Density Level\\n[dB rel 1 uPa$^2$/Hz]\")"
    ]
   },
   {
@@ -377,7 +383,7 @@
    "source": [
     "### Window Aggregating\n",
     "\n",
-    "If desired, one can also group a series of measurements into blocks of time. In the following block, we'll take our 5 minutes of measurements, `time_aggregate` them into 30 second intervals, and find the median, 25% and 75% quantiles of each interval. We then plot the stats of the first time block (block #0)."
+    "If desired, one can group a series of measurements into blocks of time, though this isn't required by the IEC-40. In the following block, we'll take our 5 minutes of measurements, `time_aggregate` them into 30 second intervals, and find the median, 25% and 75% quantiles of each interval. We then plot the stats of the median parameters."
    ]
   },
   {
@@ -386,11 +392,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# Time average into 30 s windows\n",
+    "# Time average into 30 s windows and take the median parameter value\n",
     "window = 30\n",
-    "spsdl_50 = acoustics.time_aggregate(spsdl, window, method=\"median\")\n",
-    "spsdl_25 = acoustics.time_aggregate(spsdl, window, method={\"quantile\":0.25})\n",
-    "spsdl_75 = acoustics.time_aggregate(spsdl, window, method={\"quantile\":0.75})"
+    "spsdl_time = acoustics.time_aggregate(spsdl, window, method=\"median\")"
    ]
   },
   {
@@ -408,7 +412,8 @@
     {
      "data": {
       "text/plain": [
-       "Text(0.5, 1.0, 'Median and Quantile Sound Pressure Spectral Density Level')"
+       "[(20.0, 80.0),\n",
+       " Text(0, 0.5, 'Sound Pressure Spectral Density Level\\n[dB rel 1 uPa$^2$/Hz]')]"
       ]
      },
      "execution_count": 14,
@@ -417,7 +422,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 600x500 with 1 Axes>"
       ]
@@ -428,28 +433,31 @@
    ],
    "source": [
     "# Plot medians and quantiles\n",
-    "fig, ax = acoustics.graphics.plot_spectra(spsdl_50[0], fmin, fmax)\n",
+    "fig, ax = acoustics.graphics.plot_spectra(spsdl_time.median(\"time_bins\"), fmin, fmax, label=\"Median\")\n",
     "ax.fill_between(\n",
-    "    spsdl_50[\"freq\"],\n",
-    "    spsdl_25[0],\n",
-    "    spsdl_75[0],\n",
+    "    spsdl_time[\"freq\"],\n",
+    "    spsdl_time.quantile(0.25, \"time_bins\"),\n",
+    "    spsdl_time.quantile(0.75, \"time_bins\"),\n",
     "    alpha=0.5,\n",
     "    facecolor=\"C0\",\n",
-    "    edgecolor=None\n",
+    "    edgecolor=None,\n",
+    "    label=\"Quantiles\"\n",
     ")\n",
-    "ax.set_ylim(20, 80)\n",
-    "ax.set_title(\"Median and Quantile Sound Pressure Spectral Density Level\")"
+    "ax.legend(loc=\"upper right\")\n",
+    "ax.set_axisbelow(True)\n",
+    "ax.grid()\n",
+    "ax.set(ylim=(20, 80), ylabel=\"Sound Pressure Spectral Density Level\\n[dB rel 1 uPa$^2$/Hz]\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Band Averaging\n",
+    "### Frequency Band Analysis\n",
     "\n",
-    "Analysis can also be completed by grouping the data into specific frequency bands, called \"band aggregating\" here. In other words, instead of aggregating by the time dimension, we aggregate by the frequency dimension. The `band_aggregate` function operates by taking the SPSDL and grouping it based on a specified octave.\n",
+    "Frequency band analysis can be completed by grouping the data into specific frequency bands, called \"band aggregating\" here. In other words, instead of aggregating by the time dimension, we aggregate by the frequency dimension. The `band_aggregate` function operates by taking the SPSDL and grouping it based on a specified octave and octave base.\n",
     "\n",
-    "If one wants to do more analysis on the grouped frequency bands than the simple statistical methods that xarray offers, it is possible to use the \"map\" function to apply a custom function to a file. In the following block of code, we find the empirical quantile function (the empirical version of the cumulative distribution function, CDF) to each decidecade (10th octave) frequency band and plot the 160 Hz band. "
+    "In the following codeblock, we use the same plotting function as above, but do so by creating the decidecade frequency bands (10th octave, octave base 10 => $10^{1/10}$). We'll calculate the median and quantiles of each sample, then take the median of each of those parameters. For the IEC-40, one will calculate these parameters for each device operating state (current energy converters) or wave state matrix (wave energy converters)."
    ]
   },
   {
@@ -457,20 +465,10 @@
    "execution_count": 15,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\users\\mcve343\\mhkit-python\\mhkit\\acoustics\\analysis.py:83: UserWarning: `fmax` = 100000 is greater than the Nyquist frequency. Settingfmax = 48000.0\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "[Text(0.5, 1.0, '160.0 Hz'),\n",
-       " Text(0, 0.5, 'Exceedance Probability'),\n",
-       " Text(0.5, 0, 'Decidecade SPSDL [dB re 1 uPa^2/Hz]')]"
+       "[(20.0, 80.0), Text(0, 0.5, 'Decidecade SPSDL [dB rel 1 uPa$^2$/Hz]')]"
       ]
      },
      "execution_count": 15,
@@ -479,9 +477,9 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 400x400 with 1 Axes>"
+       "<Figure size 600x500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -489,39 +487,33 @@
     }
    ],
    "source": [
-    "def quantile_function(x):\n",
-    "    # Empirical CDF/Quantile Function/Exceedance Probability\n",
-    "    # Use the median of the coordinate we're grouped in\n",
-    "    x = x.median(\"freq\")\n",
-    "    # Squeeze to remove frequency dimension\n",
-    "    shape = np.shape(x)\n",
-    "    x_sorted = np.sort(np.squeeze(x))\n",
-    "    # calculate the proportional values of samples\n",
-    "    p = 1.0 - np.arange(len(x)) / (len(x) + 1)\n",
-    "    # recreate dataarray\n",
-    "    x = x.assign_coords({\"time\": p}).rename({\"time\": \"probability\"})\n",
-    "    x.values = np.reshape(x_sorted, shape)\n",
-    "    return x\n",
-    "\n",
+    "octave = [10, 10]  # [octave, octave base]\n",
+    "spsdl10 = acoustics.band_aggregate(spsdl, octave, fmin, fmax, method=\"median\")\n",
     "\n",
-    "cdfs = acoustics.band_aggregate(spsdl, octave=10, method={\"map\": quantile_function})\n",
-    "# Plot\n",
-    "fig, ax = plt.subplots(figsize=(4, 4))\n",
-    "ax.plot(cdfs[40].values, cdfs[\"probability\"].values)\n",
-    "ax.set(\n",
-    "    title=f\"{np.round(cdfs['freq_bins'][40].values, 2)} Hz\",\n",
-    "    ylabel=\"Exceedance Probability\",\n",
-    "    xlabel=\"Decidecade SPSDL [dB re 1 uPa^2/Hz]\",\n",
-    ")"
+    "# Plot medians and quantiles\n",
+    "fig, ax = acoustics.graphics.plot_spectra(spsdl10.median(\"time\"), fmin, fmax, label=\"Median\")\n",
+    "ax.fill_between(\n",
+    "    spsdl10[\"freq_bins\"],\n",
+    "    spsdl10.quantile(0.25, \"time\"),\n",
+    "    spsdl10.quantile(0.75, \"time\"),\n",
+    "    alpha=0.5,\n",
+    "    facecolor=\"C0\",\n",
+    "    edgecolor=None,\n",
+    "    label=\"Quantiles\"\n",
+    ")\n",
+    "ax.legend(loc=\"upper right\")\n",
+    "ax.set_axisbelow(True)\n",
+    "ax.grid()\n",
+    "ax.set(ylim=(20, 80), ylabel=\"Decidecade SPSDL [dB rel 1 uPa$^2$/Hz]\")"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Another plot that is useful for IEC-40 compliance are decidecade boxplots of the SPSDL. We can also use the aggregate methods to apply plotting functions, like matplotlib's native boxplot. In this case, we supply the \"map\" function and an iterable of the custom function inputs, in this case the figure axes we want to use to plot.\n",
+    "The plot above shows significant spread in sound measurements at higher frequency due to the vessel noise, but less so at lower frequency. This mirrors what we can see in the sound pressure density level figure we first plotted.\n",
     "\n",
-    "This plot shows significant spread in sound measurements due to the vessel noise, with whiskers stretching to the 1st and 99th quantiles. Generally any significant spread in a frequency band is caused by sound generated by an external source, and not the ambient soundscape."
+    "Boxplots for each frequency band can also be created instead of simple line plots. We can use the aggregate methods to apply plotting functions, like matplotlib's native boxplot. In this case, we'll supply the \"map\" function and an iterable of the custom function inputs (the figure axes we want to use to plot)."
    ]
   },
   {
@@ -529,33 +521,25 @@
    "execution_count": 16,
    "metadata": {},
    "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "c:\\users\\mcve343\\mhkit-python\\mhkit\\acoustics\\analysis.py:83: UserWarning: `fmax` = 100000 is greater than the Nyquist frequency. Settingfmax = 48000.0\n",
-      "  warnings.warn(\n"
-     ]
-    },
     {
      "data": {
       "text/plain": [
-       "[[<matplotlib.axis.XTick at 0x20703772990>,\n",
-       "  <matplotlib.axis.XTick at 0x207037a05d0>,\n",
-       "  <matplotlib.axis.XTick at 0x2070694f490>,\n",
-       "  <matplotlib.axis.XTick at 0x20704e02690>,\n",
-       "  <matplotlib.axis.XTick at 0x20704de5750>,\n",
-       "  <matplotlib.axis.XTick at 0x20704e1db90>],\n",
+       "[[<matplotlib.axis.XTick at 0x24648eb2b50>,\n",
+       "  <matplotlib.axis.XTick at 0x246000ac350>,\n",
+       "  <matplotlib.axis.XTick at 0x246000752d0>,\n",
+       "  <matplotlib.axis.XTick at 0x24601842f90>,\n",
+       "  <matplotlib.axis.XTick at 0x24601ce0e10>,\n",
+       "  <matplotlib.axis.XTick at 0x24606034f90>],\n",
        " [Text(0.0, 0, '0'),\n",
        "  Text(1.0, 0, '1'),\n",
        "  Text(2.0, 0, '2'),\n",
        "  Text(3.0, 0, '3'),\n",
        "  Text(4.0, 0, '4'),\n",
        "  Text(5.0, 0, '5')],\n",
-       " (1.68, 4.7),\n",
+       " (1.0, 4.8),\n",
        " (20.0, 100.0),\n",
        " Text(0.5, 0, 'log(Frequency) [Hz]'),\n",
-       " Text(0, 0.5, 'Decidecade SPSDL [dB re 1 uPa^2/Hz]')]"
+       " Text(0, 0.5, 'Decidecade SPSDL [dB re 1 uPa$^2$/Hz]')]"
       ]
      },
      "execution_count": 16,
@@ -564,9 +548,9 @@
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 900x500 with 1 Axes>"
+       "<Figure size 700x500 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -584,23 +568,86 @@
     "        whis=(1, 99),\n",
     "        showfliers=False,\n",
     "        positions=[np.log10(freq.values)],\n",
-    "        widths=0.015,\n",
+    "        widths=0.04,\n",
     "        flierprops={\"marker\": \".\", \"markersize\": 2},\n",
     "    )\n",
     "    return x\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(9, 5))\n",
+    "fig, ax = plt.subplots(figsize=(7, 5))\n",
     "acoustics.band_aggregate(\n",
-    "    spsdl, octave=10, method={\"map\": (boxplot, [ax])}\n",
+    "    spsdl, octave, fmin, fmax, method={\"map\": (boxplot, [ax])}\n",
     ")\n",
     "xticks = np.linspace(0, 5, 6)\n",
     "ax.set(\n",
     "    xticks=xticks,\n",
     "    xticklabels=xticks.astype(int),\n",
-    "    xlim=(1.68, 4.7),\n",
+    "    xlim=(1, 4.8),\n",
     "    ylim=(20, 100),\n",
     "    xlabel=\"log(Frequency) [Hz]\",\n",
-    "    ylabel=\"Decidecade SPSDL [dB re 1 uPa^2/Hz]\",\n",
+    "    ylabel=\"Decidecade SPSDL [dB re 1 uPa$^2$/Hz]\",\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The \"map\" function will also allow one to conduct further frequency band analysis than the simple statistical methods that xarray offers. In the following block of code, we find the empirical quantile function (the empirical version of the cumulative distribution function, CDF) of each decidecade frequency band and plot the decidecade band centered nearest to 160 Hz. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0.5, 1.0, '158.49 Hz'),\n",
+       " Text(0, 0.5, 'Exceedance Probability'),\n",
+       " Text(0.5, 0, 'SPSDL [dB re 1 uPa$^2$/Hz]')]"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 400x400 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def quantile_function(x):\n",
+    "    # Empirical CDF/Quantile Function/Exceedance Probability\n",
+    "    # Use the median of the coordinate we're grouped in\n",
+    "    x = x.median(\"freq\")\n",
+    "    # Squeeze to remove frequency dimension\n",
+    "    shape = np.shape(x)\n",
+    "    x_sorted = np.sort(np.squeeze(x))\n",
+    "    # calculate the proportional values of samples\n",
+    "    p = 1.0 - np.arange(len(x)) / (len(x) + 1)\n",
+    "    # recreate dataarray\n",
+    "    x = x.assign_coords({\"time\": p}).rename({\"time\": \"probability\"})\n",
+    "    x.values = np.reshape(x_sorted, shape)\n",
+    "    return x\n",
+    "\n",
+    "\n",
+    "octave = [10, 10]  # 1/10th octave at base 10\n",
+    "cdfs = acoustics.band_aggregate(spsdl, octave, fmin, fmax, method={\"map\": quantile_function})\n",
+    "# Plot\n",
+    "fig, ax = plt.subplots(figsize=(4, 4))\n",
+    "ax.plot(cdfs.sel(freq_bins=160, method=\"nearest\").values, cdfs[\"probability\"].values)\n",
+    "ax.set(\n",
+    "    title=f\"{np.round(cdfs['freq_bins'].sel(freq_bins=160, method='nearest').values, 2)} Hz\",\n",
+    "    ylabel=\"Exceedance Probability\",\n",
+    "    xlabel=\"SPSDL [dB re 1 uPa$^2$/Hz]\",\n",
     ")"
    ]
   },
@@ -610,17 +657,17 @@
    "source": [
     "### Sound Pressure Level\n",
     "\n",
-    "The IEC-40 has two requirements considering calculations of sound pressure level (SPL). We'll first calculate the SPL over the full frequency range of the turbine and/or hydrophone. The IEC-40 asks that the range be set from 10 to 100,000 Hz, though the lower limit can be increased due to flow noise or low frequency signal loss due to shallow water. \n",
+    "The IEC-40 has two requirements considering calculations of sound pressure level (SPL). We'll first calculate the SPL over the full frequency range of the turbine and/or hydrophone using the function `sound_pressure_level`. First, however, note that the IEC-40 asks that the range be set from 10 to 100,000 Hz. The lower limit can be increased due to flow noise or low frequency signal loss due to shallow water. \n",
     "\n",
     "#### Shallow water cutoff frequency\n",
-    "Low frequency sound is absorbed into the seabed in shallow water depths. We can use the function `minimum_frequency` to get an approximation of what our minimum frequency should be. This approximation uses the water depth, estimates of the in-water sound speed and sea/riverbed sound speed to determine what the cutoff frequency will be. The difficult part with this approximation is figuring out the speed of sound in the bed material, which generally ranges from 1450-1800 m/s. \n",
+    "Low frequency sound is absorbed into the seabed in shallow water depths. We can use the function `minimum_frequency` to get an approximation of what our minimum frequency should be. This approximation uses the water depth, estimates of the in-water sound speed, and sea/riverbed sound speed to determine what the cutoff frequency will be. The difficult part with this approximation is figuring out the speed of sound in the bed material, which generally ranges from 1450-1800 m/s. \n",
     "\n",
     "This function should only be used as a rough approximation and sanity check if significant attenuation is seen at various low frequencies and harmonics."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -660,23 +707,25 @@
     "#### Flow Noise\n",
     "Flow noise, or psuedo-sound, is the other reason to increase the minimum frequency of our SPL measurements. Flow noise is caused by one of three things: turbulence advected past the hydrophone element, turbulence caused by the hydrophone element, and the sensitivity of the hydrophone element to temperature inhomogeneities in the advected flow. Flow noise is most noticeably apparent when flow speeds increase above 0.5 m/s, seen in spectrograms as a logarithmic increase in pressure with decreasing frequency.\n",
     "\n",
+    "The particular data shown here was measured in around 8-10 m of water, and a mix of mild flow noise below 20 Hz and low frequency attenutation below ~50 Hz can be seen in the spectrogram. We'll again use the Nyquist frequency of 48,000 Hz.\n",
+    "\n",
     "#### Cumulative SPL\n",
     "\n",
-    "The particular data shown here was measured in around 8-10 m of water, and a mix of mild flow noise below 20 Hz and low frequency attenutation below ~50 Hz can be seen in the spectrogram. We'll again use the Nyquist frequency of 48,000 Hz."
+    "Running the code block below, we can see our cumulative SPL start out at 86 dB and then peak at 125 dB as the boat drives by. If you haven't listened to the audio track, this peak SPL of 125 dB rel 1 uPa (underwater) is equivalent to 99 dB rel 20 uPa (air). For reference, the OSHA time limit for workers experiencing 100 dB rel 20 uPa of sound is 2 hours. Vessel traffic can be quite loud and is one of the largest contributors to noise in the marine environment."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x20706250fd0>]"
+       "[<matplotlib.lines.Line2D at 0x24601c66a90>]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -700,61 +749,54 @@
     "spl.plot()"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "So we can see our cumulative SPL start out at 86 dB and then peak at 120 dB as the boat drives by. If you haven't listened to the audio track, this peak SPL of 125 dB rel 1 uPa (underwater) is equivalent to 99 dB rel 20 uPa (air). For reference, the OSHA time limit for workers experiencing 100 dB rel 20 uPa of sound is 2 hours. Vessel traffic quite loud and is the largest contributor to noise in the marine environment."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
     "### Decidecade Sound Pressure Levels\n",
     "\n",
-    "The last stat that IEC-40 requests are the decidecade SPLs. Note that the IEC-40 incorrectly labels these as synonymous with the third-octave SPLs, following the relevant (and also incorrect) ANSI specifications. \n",
+    "The last stat that IEC-40 requests are the decidecade SPLs, where the SPL is calculated for each decidecade frequency band. Note that the IEC-40 labels these as synonymous the third-octave SPLs; while for some applications this may be true, mathematically they have different definitions. \n",
     "\n",
-    "To explain, an octave is a frequency band where the upper frequency is double (2^1) that of the lower frequency. The one-third octave is a frequency band where the upper frequency is 2^(1/3) times the lower frequency. The decidecade is a frequency band with a bandwidth of 2^(1/10), which means it's the tenth octave, not the third. Wherever the IEC-40 says third octave they actually mean the decidecade band.\n",
+    "To explain, a true octave is a frequency band where the upper frequency is double (i.e., base 2) that of the lower frequency. Third octaves are often measured because mammals to have evolved to interpret sound at this bandwidth. The true one-third octave is a frequency band where the upper frequency is 2^(1/3) = 1.25992 times the lower frequency. The decidecade band referenced by the IEC-40 refers to the one-tenth octave of base 10, where the upper frequency is 10 times that of the lower frequency. Mathematically this means the decidecade band has a bandwidth of 10^(1/10) = 1.25892. So, when reporting frequency analysis, it is important to note both the octave and its bandwidth.\n",
     "\n",
     "We can calculate the SPL in each decidecade band using the function `decidecade_sound_pressure_level`. This function uses the same calculation as `sound_pressure_level` above and runs it on each tenth octave band. It returns 1 SPL in each frequency band every timestamp, so our boxplots show 5 minutes worth of SPL measurements in each decidecade band. You'll notice a similar spread as in the SPSDL boxplots, especially in the upper quantile. Boats are loud."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[[<matplotlib.axis.XTick at 0x20709d51dd0>,\n",
-       "  <matplotlib.axis.XTick at 0x20709da66d0>,\n",
-       "  <matplotlib.axis.XTick at 0x2070aef2b90>,\n",
-       "  <matplotlib.axis.XTick at 0x2070af30b50>,\n",
-       "  <matplotlib.axis.XTick at 0x20704d97690>,\n",
-       "  <matplotlib.axis.XTick at 0x20709e04a10>],\n",
+       "[[<matplotlib.axis.XTick at 0x24600155790>,\n",
+       "  <matplotlib.axis.XTick at 0x246001a3c90>,\n",
+       "  <matplotlib.axis.XTick at 0x24603434350>,\n",
+       "  <matplotlib.axis.XTick at 0x246001d7290>,\n",
+       "  <matplotlib.axis.XTick at 0x24602e809d0>,\n",
+       "  <matplotlib.axis.XTick at 0x24602e81b10>],\n",
        " [Text(0.0, 0, '0'),\n",
        "  Text(1.0, 0, '1'),\n",
        "  Text(2.0, 0, '2'),\n",
        "  Text(3.0, 0, '3'),\n",
        "  Text(4.0, 0, '4'),\n",
        "  Text(5.0, 0, '5')],\n",
-       " (1.68, 4.75),\n",
-       " (40.0, 120.0),\n",
+       " (1.6532125137753437, 4.7160033436347994),\n",
+       " (50.0, 120.0),\n",
        " Text(0.5, 0, 'log(Frequency) [Hz]'),\n",
        " Text(0, 0.5, 'Decidecade SPL [dB re 1 uPa]')]"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 900x500 with 1 Axes>"
+       "<Figure size 1000x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -766,21 +808,37 @@
     "spl10 = acoustics.decidecade_sound_pressure_level(spsd, fmin, fmax)\n",
     "\n",
     "# Plot the decidecade sound pressure level\n",
-    "fig, ax = plt.subplots(figsize=(9, 5))\n",
-    "ax.boxplot(\n",
+    "fig, ax = plt.subplots(1,2, figsize=(10, 4), constrained_layout=True)\n",
+    "fig, ax[0] = acoustics.graphics.plot_spectra(spl10.median(\"time\"), fmin, fmax, fig=fig, ax=ax[0], label=\"Median\")\n",
+    "ax[0].fill_between(\n",
+    "    spl10[\"freq_bins\"],\n",
+    "    spl10.quantile(0.25, \"time\"),\n",
+    "    spl10.quantile(0.75, \"time\"),\n",
+    "    alpha=0.5,\n",
+    "    facecolor=\"C0\",\n",
+    "    edgecolor=None,\n",
+    "    label=\"Quantiles\"\n",
+    ")\n",
+    "ax[0].legend(loc=\"upper right\")\n",
+    "ax[0].set_axisbelow(True)\n",
+    "ax[0].grid()\n",
+    "ax[0].set(ylim=(50, 120), ylabel=\"Decidecade SPL [dB rel 1 uPa]\", xscale=\"log\")\n",
+    "\n",
+    "# Boxplots\n",
+    "ax[1].boxplot(\n",
     "    spl10.values,\n",
     "    whis=(1, 99),\n",
     "    showfliers=True,\n",
     "    positions=np.log10(spl10[\"freq_bins\"].values),\n",
-    "    widths=0.015,\n",
+    "    widths=0.04,\n",
     "    flierprops={\"marker\": \".\", \"markersize\": 1.5},\n",
     ")\n",
     "xticks = np.linspace(0, 5, 6)\n",
-    "ax.set(\n",
+    "ax[1].set(\n",
     "    xticks=xticks,\n",
     "    xticklabels=xticks.astype(int),\n",
-    "    xlim=(1.68, 4.75),\n",
-    "    ylim=(40, 120),\n",
+    "    xlim=(np.log10(45), np.log10(52000)),\n",
+    "    ylim=(50, 120),\n",
     "    xlabel=\"log(Frequency) [Hz]\",\n",
     "    ylabel=\"Decidecade SPL [dB re 1 uPa]\",\n",
     ")"
@@ -792,44 +850,44 @@
    "source": [
     "### Third Octave Sound Pressure Level\n",
     "\n",
-    "Since you're now curious, you can also calculate the 1/3 octave SPLs using `third_octave_sound_pressure_level`. Third octaves are often measured because the human ear appears to have evolved to filter sound at this bandwidth, and these plots may also be used to reduce the \"busy-ness\" of a figure. "
+    "One can also calculate the true 1/3 octave SPLs using `third_octave_sound_pressure_level` if desired. Note the results are quite similar to `decidecade_sound_pressure_level`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[[<matplotlib.axis.XTick at 0x2070b2aa190>,\n",
-       "  <matplotlib.axis.XTick at 0x2070b04c650>,\n",
-       "  <matplotlib.axis.XTick at 0x2070bb42e10>,\n",
-       "  <matplotlib.axis.XTick at 0x20709a48150>,\n",
-       "  <matplotlib.axis.XTick at 0x2070bb4e350>,\n",
-       "  <matplotlib.axis.XTick at 0x2070b1743d0>],\n",
+       "[[<matplotlib.axis.XTick at 0x246064476d0>,\n",
+       "  <matplotlib.axis.XTick at 0x24606434350>,\n",
+       "  <matplotlib.axis.XTick at 0x24606421e50>,\n",
+       "  <matplotlib.axis.XTick at 0x246061d4350>,\n",
+       "  <matplotlib.axis.XTick at 0x24606460e10>,\n",
+       "  <matplotlib.axis.XTick at 0x24606462ed0>],\n",
        " [Text(0.0, 0, '0'),\n",
        "  Text(1.0, 0, '1'),\n",
        "  Text(2.0, 0, '2'),\n",
        "  Text(3.0, 0, '3'),\n",
        "  Text(4.0, 0, '4'),\n",
        "  Text(5.0, 0, '5')],\n",
-       " (1.68, 4.75),\n",
-       " (50.0, 130.0),\n",
-       " Text(0.5, 0, 'log(Frequency) [Hz]'),\n",
-       " Text(0, 0.5, 'Decidecade SPL [dB re 1 uPa]')]"
+       " (1.6532125137753437, 4.7160033436347994),\n",
+       " (50.0, 120.0),\n",
+       " Text(0.5, 0, 'log(Frequency) [kHz]'),\n",
+       " Text(0, 0.5, 'Third Octave SPL [dB re 1 uPa]')]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 800x400 with 1 Axes>"
+       "<Figure size 1000x400 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -840,8 +898,25 @@
     "# Median third octave sound pressure level\n",
     "spl3 = acoustics.third_octave_sound_pressure_level(spsd, fmin, fmax)\n",
     "\n",
-    "fig, ax = plt.subplots(figsize=(8, 4))\n",
-    "ax.boxplot(\n",
+    "# Plot the decidecade sound pressure level\n",
+    "fig, ax = plt.subplots(1,2, figsize=(10, 4), constrained_layout=True)\n",
+    "fig, ax[0] = acoustics.graphics.plot_spectra(spl3.median(\"time\"), fmin, fmax, fig=fig, ax=ax[0], label=\"Median\")\n",
+    "ax[0].fill_between(\n",
+    "    spl3[\"freq_bins\"],\n",
+    "    spl3.quantile(0.25, \"time\"),\n",
+    "    spl3.quantile(0.75, \"time\"),\n",
+    "    alpha=0.5,\n",
+    "    facecolor=\"C0\",\n",
+    "    edgecolor=None,\n",
+    "    label=\"Quantiles\"\n",
+    ")\n",
+    "ax[0].legend(loc=\"upper right\")\n",
+    "ax[0].set_axisbelow(True)\n",
+    "ax[0].grid()\n",
+    "ax[0].set(ylim=(50, 120), ylabel=\"Third Octave SPL [dB rel 1 uPa]\", xscale=\"log\")\n",
+    "\n",
+    "# Boxplots\n",
+    "ax[1].boxplot(\n",
     "    spl3.values,\n",
     "    whis=(1, 99),\n",
     "    showfliers=True,\n",
@@ -850,16 +925,160 @@
     "    flierprops={\"marker\": \".\", \"markersize\": 1.5},\n",
     ")\n",
     "xticks = np.linspace(0, 5, 6)\n",
-    "ax.set(\n",
+    "ax[1].set(\n",
     "    xticks=xticks,\n",
     "    xticklabels=xticks.astype(int),\n",
-    "    xlim=(1.68, 4.75),\n",
-    "    ylim=(50, 130),\n",
-    "    xlabel=\"log(Frequency) [Hz]\",\n",
-    "    ylabel=\"Decidecade SPL [dB re 1 uPa]\",\n",
+    "    xlim=(np.log10(45), np.log10(52000)),\n",
+    "    ylim=(50, 120),\n",
+    "    xlabel=\"log(Frequency) [kHz]\",\n",
+    "    ylabel=\"Third Octave SPL [dB re 1 uPa]\",\n",
     ")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Sound Exposure Level and Marine Mammal Weightings\n",
+    "\n",
+    "While the IEC-40 does not have recommendations for sound exposure level (SEL), it is a better parameter for quantifying auditory stress/injury to organisms. Sound exposure level is a metric that quantifies sound pressure level relative to exposure duration, based on the idea that that the effects on an organism's hearing are the same for equivalent totals sound energy received - even if that energy was received at different rates. Numerically, an SPL of 190 dB re 1 uPa over a 1 second interval and an SPL 160 dB re 1 uPa for 1000 seconds both have a SEL of 190 dB re 1 uPa^2 s.\n",
+    "\n",
+    "The National Marine Fisheries Service (NMFS) publishes auditory weighting functions for five groups of marine mammals. These weighting functions are designed to try to emulate the auditory sensitivity of each group to better predict auditory injury thresholds. They are mathematically equivalent to band-pass filters. A link to the latest recommendations from NMFS is [here](https://www.fisheries.noaa.gov/national/marine-mammal-protection/marine-mammal-acoustic-technical-guidance-other-acoustic-tools).\n",
+    "\n",
+    "The SEL function `sound_exposure_level` has a few inputs. One is the pressure spectral density (PSD) in Pa^2/Hz, found from `sound_pressure_spectral_density`. The 'n_bin' input to that function should be the same length of time as you want to calculate SEL for. If you would like to calculate the 24 hr SEL, you need 24 hours worth of data - the best way to do this and save computer RAM would be to calculate the PSD from each 1-5 minute individual file and then concatenate the PSD from each file together.\n",
+    "Set \"group=None\" to calculate the standard SEL. Set \"group\" to \"LF\" for 'low frequency' cetaceans; \"HF\" for 'high frequency' cetaceans; \"VHF\" for 'very high frequency' cetaceans; \"PW\" for phocid pinnepeds; and \"OW\" for otariid pinnepeds.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray 'sel' (time: 1)> Size: 4B\n",
+      "array([136.9158], dtype=float32)\n",
+      "Coordinates:\n",
+      "  * time     (time) datetime64[ns] 8B 2023-02-04T15:07:37.996493339\n",
+      "Attributes:\n",
+      "    units:             dB re 1 uPa^2 s\n",
+      "    long_name:         Sound Exposure Level\n",
+      "    weighting_group:   None\n",
+      "    integration_time:  300 s\n",
+      "    freq_band_min:     50\n",
+      "    freq_band_max:     48000\n",
+      "\n",
+      "\n",
+      "Data variables:\n",
+      "    sel      (time) float32 4B 136.9\n",
+      "    sel_lf   (time) float32 4B 136.1\n",
+      "    sel_hf   (time) float32 4B 133.6\n",
+      "    sel_vhf  (time) float32 4B 128.5\n",
+      "    sel_pw   (time) float32 4B 135.1\n",
+      "    sel_ow   (time) float32 4B 132.7\n"
+     ]
+    }
+   ],
+   "source": [
+    "import xarray as xr\n",
+    "\n",
+    "# Five minute SEL\n",
+    "spsd_300s = acoustics.sound_pressure_spectral_density(V, V.fs, bin_length=300)\n",
+    "# Calibrate PSD\n",
+    "fill_Sf = sensitivity_curve[-1].values\n",
+    "spsd_300s = acoustics.apply_calibration(spsd_300s, sensitivity_curve, fill_value=fill_Sf)\n",
+    "\n",
+    "ds_sel = xr.Dataset()\n",
+    "ds_sel[\"sel\"] = acoustics.sound_exposure_level(spsd_300s, None, fmin, fmax)\n",
+    "for group in [\"LF\", \"HF\", \"VHF\", \"PW\", \"OW\"]:\n",
+    "    ds_sel[\"sel_\" + group.lower()] = acoustics.sound_exposure_level(\n",
+    "        spsd_300s, group, fmin, fmax\n",
+    "    )\n",
+    "\n",
+    "print(ds_sel[\"sel\"])\n",
+    "print(\"\\n\")\n",
+    "print(ds_sel.data_vars)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Compare this to the 5 minute SPL:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<xarray.DataArray (time: 1)> Size: 4B\n",
+      "array([112.14459], dtype=float32)\n",
+      "Coordinates:\n",
+      "  * time     (time) datetime64[ns] 8B 2023-02-04T15:07:37.996493339\n",
+      "Attributes:\n",
+      "    units:          dB re 1 uPa\n",
+      "    long_name:      Sound Pressure Level\n",
+      "    freq_band_min:  50\n",
+      "    freq_band_max:  48000\n"
+     ]
+    }
+   ],
+   "source": [
+    "spl_300s = acoustics.sound_pressure_level(spsd_300s, fmin, fmax)\n",
+    "print(spl_300s)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can look at the specific marine mammal auditory weighting and noise exposure functions as well using `nmfs_auditory_weighting`, given a frequency vector and one of the mammal groups. It outputs the weighting function and the exposure function (the inverse of the former) in units of dB. To convert back to a unitless magnitude, use `10 ** (<func> / 10)`. The exposure function shows the SEL in dB at and above which temporary or permanent hearing damage can occur to an individual in the specified group. The minimum value in the exposure function is the known or estimated injury level for a given group."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x400 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Calculate weighting and exposure functions\n",
+    "weight_func, exp_func = acoustics.nmfs_auditory_weighting(spsd_300s[\"freq\"], group=\"LF\")\n",
+    "\n",
+    "fig, ax = plt.subplots(1,2, figsize=(7,4), subplot_kw={\"xscale\": \"log\"}, constrained_layout=True)\n",
+    "ax[0].plot(spsd_300s[\"freq\"], weight_func, label=\"Weighting Function\")\n",
+    "ax[0].axhline(y=0, color='k', linestyle='--', label=\"Highest Sensitivity\")\n",
+    "ax[0].set(ylabel=\"Transmission [dB]\", ylim=(-50, 20))\n",
+    "ax[0].legend(loc=\"upper right\")\n",
+    "\n",
+    "ax[1].plot(spsd_300s[\"freq\"], exp_func, label=\"Exposure Function\")\n",
+    "ax[1].axhline(y=exp_func.min(), color='k', linestyle='--', label=\"Highest Sensitivity\")\n",
+    "ax[1].fill_between(spsd_300s[\"freq\"], exp_func, np.ones_like(exp_func)*300, color='red', alpha=0.2, label=\"Injury Region\")\n",
+    "ax[1].set(ylabel=\"Exposure Level [dB]\", ylim=(exp_func.min()-20, exp_func.min()+50))\n",
+    "ax[1].legend(loc=\"lower right\")\n",
+    "\n",
+    "for a in ax:\n",
+    "    a.grid()\n",
+    "    a.set(xlabel=\"Frequency [Hz]\", xlim=(10,48000))"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
diff --git a/examples/adcp_discharge_example.ipynb b/examples/adcp_discharge_example.ipynb
new file mode 100644
index 000000000..f8d1e96c4
--- /dev/null
+++ b/examples/adcp_discharge_example.ipynb
@@ -0,0 +1,1773 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# ADCP Discharge Example\n",
+    "\n",
+    "This example notebook overviews how to calculate discharge from an ADCP transect. The example data used in this notebook was taken by a Teledyne RDI RiverPro ADCP, an instrument purpose-built for calculating discharge from a survey transect, but the following workflow can be used with any ADCP's data, given that there is GPS information and/or bottom track measurements stored in the ADCP file.\n",
+    "\n",
+    "The basic steps that the notebook conducts are to\n",
+    "1. Read in the binary ADCP file\n",
+    "2. Rotate the dataset into the Earth coordinate system (\"East\", \"North\", \"Up\")\n",
+    "3. Correct vessel motion using stored bottom track (You can also use the GPS's velocity measurement, found from the VTG sentence and converted from speed and direction to velocity-east and velocity-north)\n",
+    "4. Rotate the dataset into the water current's principal coordinate system (\"streamwise\", \"cross-stream\", \"vertical\")\n",
+    "5. Calculate the distance from the ADCP to the riverbed/seabed. (We use the bottom track ping here, but can also use the ADCP's altimeter, if available)\n",
+    "6. Finally, calculate the water discharge. Additional parameters that are not availble within the ADCP's dataset are required here.\n",
+    "\n",
+    "We'll start by immediately jumping through steps 1 and 2. Note if that quality control is required, that should be done here.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Reading file c:\\users\\mcve343\\mhkit-python\\examples\\data\\dolfyn/RiverPro_test01.PD0 ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "from mhkit import dolfyn\n",
+    "\n",
+    "ds = dolfyn.read_example(\"RiverPro_test01.PD0\")\n",
+    "ds.velds.set_declination(18)  # Set declination to 18 degrees East\n",
+    "ds.velds.rotate2(\"earth\")\n",
+    "\n",
+    "# # Note, if the range coordinate has not been adjusted given the depth of the ADCP \n",
+    "# # below the waterline, do so using the following line.\n",
+    "# ds = dolfyn.adp.clean.set_range_offset(ds, x.x)  # Set range offset to x.x m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "We can plot the dataset below so we know what we are looking at"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0, 0.5, 'Depth [m]')]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots(1,2, figsize=(10,3), constrained_layout=True)\n",
+    "e = ax[0].pcolormesh(ds[\"time\"].values, -ds[\"range\"].values, ds[\"vel\"][0], cmap=\"coolwarm\", vmin=-2, vmax=2)\n",
+    "n = ax[1].pcolormesh(ds[\"time\"].values, -ds[\"range\"].values, ds[\"vel\"][1], cmap=\"coolwarm\", vmin=-2, vmax=2)\n",
+    "fig.colorbar(e, ax=ax[0], label=\"East Velocity (m/s)\")\n",
+    "fig.colorbar(n, ax=ax[1], label=\"North Velocity (m/s)\")\n",
+    "for a in ax:\n",
+    "    a.set(xlabel=\"Time (UTC)\", ylim=(-10, 0))\n",
+    "ax[0].set(ylabel=\"Depth [m]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The next step we need to do is to make sure we correct the ADCP measurement for the vessel motion. This is not done natively in the raw ADCP file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Correct velocity\n",
+    "ds[\"vel_bt\"] = ds[\"vel_bt\"].where((ds[\"vel_bt\"] < 5) & (ds[\"vel_bt\"] > -5))\n",
+    "ds[\"vel\"] -= ds[\"vel_bt\"]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Then rotate into principal coordinates."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Rotate to principal reference frame\n",
+    "ds.attrs[\"principal_heading\"] = dolfyn.calc_principal_heading(ds[\"vel\"].mean(\"range\"))\n",
+    "ds.velds.rotate2(\"principal\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And replot...\n",
+    "\n",
+    "The sign associated with the streamwise velocity is a byproduct of the principal direction calculation; it should be associated with ebb or flood tide based on visual observation. The sign associated with the cross-stream (transverse) velocity is related to the streamwise velocity by the right-hand-rule."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0, 0.5, 'Depth [m]')]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,2, figsize=(10,3), constrained_layout=True)\n",
+    "s = ax[0].pcolormesh(ds[\"time\"].values, -ds[\"range\"].values, ds[\"vel\"][0], cmap=\"coolwarm\", vmin=-2, vmax=2)\n",
+    "t = ax[1].pcolormesh(ds[\"time\"].values, -ds[\"range\"].values, ds[\"vel\"][1], cmap=\"coolwarm\", vmin=-2, vmax=2)\n",
+    "fig.colorbar(s, ax=ax[0], label=\"Streamwise Velocity (m/s)\")\n",
+    "fig.colorbar(t, ax=ax[1], label=\"Transverse Velocity (m/s)\")\n",
+    "for a in ax:\n",
+    "    a.set(xlabel=\"Time (UTC)\", ylim=(-10, 0))\n",
+    "ax[0].set(ylabel=\"Depth [m]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Next step is to calculate the water depth from one of the ADCP's measurements. This can come from the bottom track ping, an altimeter ping, or an external depth sounder. You may need to do some quality control on this measurement, and make sure to add the `range_offset`, the depth of the ADCP below the waterline, to this array."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Find the water depth based on the average bottom track pings\n",
+    "water_depth = ds.attrs[\"range_offset\"] + ds[\"dist_bt\"].mean(\"beam\").values"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "And we can superimpose that on our plot."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[Text(0, 0.5, 'Depth [m]')]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x300 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(1,2, figsize=(10,3), constrained_layout=True)\n",
+    "s = ax[0].pcolormesh(ds[\"time\"].values, -ds[\"range\"].values, ds[\"vel\"][0], cmap=\"coolwarm\", vmin=-2, vmax=2)\n",
+    "t = ax[1].pcolormesh(ds[\"time\"].values, -ds[\"range\"].values, ds[\"vel\"][1], cmap=\"coolwarm\", vmin=-2, vmax=2)\n",
+    "ax[0].plot(ds[\"time\"].values, -water_depth, color=\"k\", label=\"Water Depth\")\n",
+    "ax[1].plot(ds[\"time\"].values, -water_depth, color=\"k\", label=\"Water Depth\")\n",
+    "fig.colorbar(s, ax=ax[0], label=\"Streamwise Velocity (m/s)\")\n",
+    "fig.colorbar(t, ax=ax[1], label=\"Transverse Velocity (m/s)\")\n",
+    "for a in ax:\n",
+    "    a.set(xlabel=\"Time (UTC)\", ylim=(-10, 0))\n",
+    "ax[0].set(ylabel=\"Depth [m]\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now we can use the `discharge` function to calculate discharge, among other values (including power [W], power density [W/m^2], and the channel's Reynolds Number). This function does quite a number of things internally:\n",
+    "1. Linearly extrapolates velocity to the riverbed/seafloor (assumes velocity at the seafloor, specified by the \"water_depth\" input, is 0 m/s)\n",
+    "2. Constant extrapolation of velocity to the water surface (water velocity at the uppermost bin is the same speed as that at the water surface)\n",
+    "3. Remaps the velocity transect from \"time\" onto \"distance\" based on the GPS-measured location (`latitude_gps` and `longitude_gps` variables). It does this by converting the lat/lon to UTM and interpolating the UTM location onto the timegrid.\n",
+    "4. Velocity data is then integrated over the cross-sectional area to find discharge [m^3/s], power [W], power density [W/m^2], and hydraulic depth [m]. The last is used to find the Reynolds Number. \n",
+    "5. Values are saved into the returned dataset\n",
+    "\n",
+    "The inputs are as follows:\n",
+    "1. `ds` - ADCP dataset\n",
+    "2. `water_depth` - as calculated above\n",
+    "3. `rho` - water density for the water current in question\n",
+    "4. `mu` - kinematic viscosity, based on the water temperature and salinity. Can be found from a look-up table online.\n",
+    "5. `surface_offset` - Location of water surface on a vertical datum. Typically will be 0 for a river. In a tidal channel, this will depend on the height of the tide relative to MLLW (or MSL or desired level) at the time of the transect measurement. If the water level during the transect is above MLLW, this value will be negative. If above MLLW, this value will be positive.\n",
+    "6. `utm_zone` - UTM zone at the survey location. Can easily be found for a specific location by searching here: https://www.usgs.gov/media/images/mapping-utm-grid-conterminous-48-united-states"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
+       "<defs>\n",
+       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
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+       "</symbol>\n",
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+       "</symbol>\n",
+       "</defs>\n",
+       "</svg>\n",
+       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
+       " *\n",
+       " */\n",
+       "\n",
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+       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
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+       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
+       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
+       "  --xr-background-color: var(--jp-layout-color0, white);\n",
+       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
+       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
+       "}\n",
+       "\n",
+       "html[theme=dark],\n",
+       "html[data-theme=dark],\n",
+       "body[data-theme=dark],\n",
+       "body.vscode-dark {\n",
+       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
+       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
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+       "  --xr-border-color: #1F1F1F;\n",
+       "  --xr-disabled-color: #515151;\n",
+       "  --xr-background-color: #111111;\n",
+       "  --xr-background-color-row-even: #111111;\n",
+       "  --xr-background-color-row-odd: #313131;\n",
+       "}\n",
+       "\n",
+       ".xr-wrap {\n",
+       "  display: block !important;\n",
+       "  min-width: 300px;\n",
+       "  max-width: 700px;\n",
+       "}\n",
+       "\n",
+       ".xr-text-repr-fallback {\n",
+       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-header {\n",
+       "  padding-top: 6px;\n",
+       "  padding-bottom: 6px;\n",
+       "  margin-bottom: 4px;\n",
+       "  border-bottom: solid 1px var(--xr-border-color);\n",
+       "}\n",
+       "\n",
+       ".xr-header > div,\n",
+       ".xr-header > ul {\n",
+       "  display: inline;\n",
+       "  margin-top: 0;\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type,\n",
+       ".xr-array-name {\n",
+       "  margin-left: 2px;\n",
+       "  margin-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-obj-type {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-sections {\n",
+       "  padding-left: 0 !important;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input + label {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label {\n",
+       "  cursor: pointer;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-item input:enabled + label:hover {\n",
+       "  color: var(--xr-font-color0);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary {\n",
+       "  grid-column: 1;\n",
+       "  color: var(--xr-font-color2);\n",
+       "  font-weight: 500;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary > span {\n",
+       "  display: inline-block;\n",
+       "  padding-left: 0.5em;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label {\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: '►';\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: '▼';\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: '(';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: ')';\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: ',';\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  margin-bottom: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  background-color: var(--xr-background-color) !important;\n",
+       "  padding-bottom: 5px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 236kB\n",
+       "Dimensions:              (time: 262, time_gps: 262, range: 24, range_sl: 5,\n",
+       "                          beam: 4, dir: 4, x1: 4, x2: 4, earth: 3, inst: 3)\n",
+       "Coordinates:\n",
+       "  * time                 (time) datetime64[ns] 2kB 2022-08-19T20:14:21.930000...\n",
+       "  * time_gps             (time_gps) datetime64[ns] 2kB 2022-08-19T20:14:23.59...\n",
+       "  * range                (range) float32 96B 0.95 1.43 1.91 ... 11.51 11.99\n",
+       "  * range_sl             (range_sl) float64 40B 0.54 0.66 0.78 0.9 1.02\n",
+       "  * beam                 (beam) int32 16B 1 2 3 4\n",
+       "  * dir                  (dir) &lt;U10 160B &#x27;streamwise&#x27; &#x27;x-stream&#x27; &#x27;vert&#x27; &#x27;err&#x27;\n",
+       "  * x1                   (x1) int32 16B 1 2 3 4\n",
+       "  * x2                   (x2) int32 16B 1 2 3 4\n",
+       "  * earth                (earth) &lt;U1 12B &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
+       "  * inst                 (inst) &lt;U1 12B &#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;\n",
+       "Data variables: (12/47)\n",
+       "    number               (time) uint32 1kB 398 399 400 401 ... 656 657 658 659\n",
+       "    builtin_test_fail    (time) bool 262B False False False ... False False\n",
+       "    c_sound              (time) float32 1kB 1.458e+03 1.458e+03 ... 1.459e+03\n",
+       "    depth                (time) float32 1kB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
+       "    pitch                (time) float32 1kB -1.21 -1.01 -1.46 ... -0.5 -0.57\n",
+       "    roll                 (time) float32 1kB 1.97 0.49 1.07 ... -0.66 -0.53 0.72\n",
+       "    ...                   ...\n",
+       "    beam2inst_orientmat  (x1, x2) float32 64B 1.462 -1.462 0.0 ... -1.034 -1.034\n",
+       "    orientmat            (earth, inst, time) float32 9kB -0.8992 ... 0.9999\n",
+       "    discharge            float32 4B -1.136e+03\n",
+       "    power                float32 4B -1.211e+06\n",
+       "    power_density        float32 4B -1.307e+03\n",
+       "    reynolds_number      float32 4B 6.2e+06\n",
+       "Attributes: (12/46)\n",
+       "    prog_ver:                  56.1\n",
+       "    inst_make:                 TRDI\n",
+       "    inst_type:                 ADCP\n",
+       "    rotate_vars:               [&#x27;vel&#x27;, &#x27;vel_sl&#x27;, &#x27;vel_bt&#x27;]\n",
+       "    has_imu:                   0\n",
+       "    inst_model:                RiverPro\n",
+       "    ...                        ...\n",
+       "    range_offset:              0.37\n",
+       "    blank_dist_sl:             0.05\n",
+       "    fs:                        1.47\n",
+       "    declination:               18\n",
+       "    declination_in_orientmat:  1\n",
+       "    principal_heading:         173.522</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-edc30d19-1df3-4c73-950c-50611da517d9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-edc30d19-1df3-4c73-950c-50611da517d9' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 262</li><li><span class='xr-has-index'>time_gps</span>: 262</li><li><span class='xr-has-index'>range</span>: 24</li><li><span class='xr-has-index'>range_sl</span>: 5</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-15a59d4d-7c1a-43aa-a351-240223de5f7c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-15a59d4d-7c1a-43aa-a351-240223de5f7c' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-08-19T20:14:21.930000066 .....</div><input id='attrs-57e364a7-e2bc-4ce9-8616-2d6e7eede585' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57e364a7-e2bc-4ce9-8616-2d6e7eede585' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1455e039-cd59-4396-add3-1ff76a068f14' class='xr-var-data-in' type='checkbox'><label for='data-1455e039-cd59-4396-add3-1ff76a068f14' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-08-19T20:14:21.930000066&#x27;, &#x27;2022-08-19T20:14:22.500000000&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:23.220000028&#x27;, ..., &#x27;2022-08-19T20:17:18.329999923&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:18.910000085&#x27;, &#x27;2022-08-19T20:17:19.450000047&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-08-19T20:14:23.599999904 .....</div><input id='attrs-74481117-7050-4b99-9d94-cd5a737d30bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74481117-7050-4b99-9d94-cd5a737d30bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a124b621-ae78-48ea-9787-24c808f7ec91' class='xr-var-data-in' type='checkbox'><label for='data-a124b621-ae78-48ea-9787-24c808f7ec91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-08-19T20:14:23.599999904&#x27;, &#x27;2022-08-19T20:14:24.200000047&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:25.000000000&#x27;, ..., &#x27;2022-08-19T20:17:20.000000000&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:20.400000095&#x27;, &#x27;2022-08-19T20:17:21.000000000&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.95 1.43 1.91 ... 11.51 11.99</div><input id='attrs-eca2edbc-d894-41dd-a978-8849c6606ce9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eca2edbc-d894-41dd-a978-8849c6606ce9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fb3f937d-a402-45d8-81df-d3fb5ae8387f' class='xr-var-data-in' type='checkbox'><label for='data-fb3f937d-a402-45d8-81df-d3fb5ae8387f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.95,  1.43,  1.91,  2.39,  2.87,  3.35,  3.83,  4.31,  4.79,  5.27,\n",
+       "        5.75,  6.23,  6.71,  7.19,  7.67,  8.15,  8.63,  9.11,  9.59, 10.07,\n",
+       "       10.55, 11.03, 11.51, 11.99], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_sl</span></div><div class='xr-var-dims'>(range_sl)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.54 0.66 0.78 0.9 1.02</div><input id='attrs-7644bf78-5eed-4bd7-8fcb-cd9fa9334ea9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7644bf78-5eed-4bd7-8fcb-cd9fa9334ea9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cfd9fa4-3554-41d9-be21-b5457d382629' class='xr-var-data-in' type='checkbox'><label for='data-7cfd9fa4-3554-41d9-be21-b5457d382629' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([0.54, 0.66, 0.78, 0.9 , 1.02])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-8254643f-2a6e-4b12-8b3e-8437153c2542' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8254643f-2a6e-4b12-8b3e-8437153c2542' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bb2ab1e-bbe9-44f8-98de-0e1e134a43c8' class='xr-var-data-in' type='checkbox'><label for='data-5bb2ab1e-bbe9-44f8-98de-0e1e134a43c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; ... &#x27;err&#x27;</div><input id='attrs-16b5fe43-5aac-4714-b709-23656dbbcdc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16b5fe43-5aac-4714-b709-23656dbbcdc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7e52a6f-1ff6-41af-8145-9ab0fee2c1b0' class='xr-var-data-in' type='checkbox'><label for='data-e7e52a6f-1ff6-41af-8145-9ab0fee2c1b0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>principal</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;, &#x27;err&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-32fed5e1-0f0c-4e0b-a33a-1033efdb7b7c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-32fed5e1-0f0c-4e0b-a33a-1033efdb7b7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf00c755-a990-4093-bd60-e796987ce774' class='xr-var-data-in' type='checkbox'><label for='data-bf00c755-a990-4093-bd60-e796987ce774' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-523ff7c5-65b8-4ddf-a8e5-230c738c200b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-523ff7c5-65b8-4ddf-a8e5-230c738c200b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67ca9b62-c099-4c46-9a78-ad69ee442a55' class='xr-var-data-in' type='checkbox'><label for='data-67ca9b62-c099-4c46-9a78-ad69ee442a55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-5f3bc6ba-def4-4673-8287-61a29fa408f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f3bc6ba-def4-4673-8287-61a29fa408f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9766e962-08a5-4198-b79d-5dac66b3d606' class='xr-var-data-in' type='checkbox'><label for='data-9766e962-08a5-4198-b79d-5dac66b3d606' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-48cf72de-87f8-4f52-b12a-2bd752a02a61' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48cf72de-87f8-4f52-b12a-2bd752a02a61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3accfdfb-81b7-48ac-9894-b9e71159e91d' class='xr-var-data-in' type='checkbox'><label for='data-3accfdfb-81b7-48ac-9894-b9e71159e91d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-739420bc-1597-42d7-a7ac-b9ebda745079' class='xr-section-summary-in' type='checkbox'  ><label for='section-739420bc-1597-42d7-a7ac-b9ebda745079' class='xr-section-summary' >Data variables: <span>(47)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>398 399 400 401 ... 656 657 658 659</div><input id='attrs-e68ad34e-bf91-439c-a0fb-5ba5825fa1d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e68ad34e-bf91-439c-a0fb-5ba5825fa1d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2790d20c-27a4-48e8-8a93-456bce71df3b' class='xr-var-data-in' type='checkbox'><label for='data-2790d20c-27a4-48e8-8a93-456bce71df3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410,\n",
+       "       411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423,\n",
+       "       424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436,\n",
+       "       437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449,\n",
+       "       450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462,\n",
+       "       463, 464, 465, 466, 467, 468, 469, 470, 471, 472, 473, 474, 475,\n",
+       "       476, 477, 478, 479, 480, 481, 482, 483, 484, 485, 486, 487, 488,\n",
+       "       489, 490, 491, 492, 493, 494, 495, 496, 497, 498, 499, 500, 501,\n",
+       "       502, 503, 504, 505, 506, 507, 508, 509, 510, 511, 512, 513, 514,\n",
+       "       515, 516, 517, 518, 519, 520, 521, 522, 523, 524, 525, 526, 527,\n",
+       "       528, 529, 530, 531, 532, 533, 534, 535, 536, 537, 538, 539, 540,\n",
+       "       541, 542, 543, 544, 545, 546, 547, 548, 549, 550, 551, 552, 553,\n",
+       "       554, 555, 556, 557, 558, 559, 560, 561, 562, 563, 564, 565, 566,\n",
+       "       567, 568, 569, 570, 571, 572, 573, 574, 575, 576, 577, 578, 579,\n",
+       "       580, 581, 582, 583, 584, 585, 586, 587, 588, 589, 590, 591, 592,\n",
+       "       593, 594, 595, 596, 597, 598, 599, 600, 601, 602, 603, 604, 605,\n",
+       "       606, 607, 608, 609, 610, 611, 612, 613, 614, 615, 616, 617, 618,\n",
+       "       619, 620, 621, 622, 623, 624, 625, 626, 627, 628, 629, 630, 631,\n",
+       "       632, 633, 634, 635, 636, 637, 638, 639, 640, 641, 642, 643, 644,\n",
+       "       645, 646, 647, 648, 649, 650, 651, 652, 653, 654, 655, 656, 657,\n",
+       "       658, 659], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>builtin_test_fail</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-21fa84c4-0eb2-4553-ac3c-b31321e50662' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21fa84c4-0eb2-4553-ac3c-b31321e50662' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec15f26a-d2a9-4940-9ca3-ba74c2fd9d44' class='xr-var-data-in' type='checkbox'><label for='data-ec15f26a-d2a9-4940-9ca3-ba74c2fd9d44' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Built-In Test Failures</dd></dl></div><div class='xr-var-data'><pre>array([False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False, False, False, False, False, False, False, False, False,\n",
+       "       False])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.458e+03 1.458e+03 ... 1.459e+03</div><input id='attrs-f22b08e9-d6bb-43e7-93e3-87b9745589fb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f22b08e9-d6bb-43e7-93e3-87b9745589fb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3beda6d6-4ecb-40e1-a60f-205da4cce821' class='xr-var-data-in' type='checkbox'><label for='data-3beda6d6-4ecb-40e1-a60f-205da4cce821' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1459., 1459., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1459., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458., 1458.,\n",
+       "       1458., 1458., 1458., 1458., 1458., 1459., 1459., 1458., 1458.,\n",
+       "       1459., 1459., 1459., 1459., 1459., 1458., 1458., 1458., 1458.,\n",
+       "       1459., 1459., 1458., 1458., 1459., 1459., 1459., 1459., 1459.,\n",
+       "       1459., 1459., 1459., 1459., 1459., 1459., 1459., 1459., 1459.,\n",
+       "       1459.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-2d412c7d-ab87-48b4-8c7d-2dda6053c70e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2d412c7d-ab87-48b4-8c7d-2dda6053c70e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cf0e520-aacc-43b5-884c-b6474e549b82' class='xr-var-data-in' type='checkbox'><label for='data-3cf0e520-aacc-43b5-884c-b6474e549b82' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.21 -1.01 -1.46 ... -0.5 -0.57</div><input id='attrs-ce76c58a-5f51-44c1-9480-ec77485d5808' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce76c58a-5f51-44c1-9480-ec77485d5808' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f136827-92b5-454a-a64b-a812335d6f71' class='xr-var-data-in' type='checkbox'><label for='data-6f136827-92b5-454a-a64b-a812335d6f71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-1.21, -1.01, -1.46, -1.61, -0.25, -0.25, -0.25, -0.58, -1.57,\n",
+       "       -1.61, -2.01, -0.44,  0.07, -0.82, -0.69, -1.4 , -1.49, -1.01,\n",
+       "       -1.23, -1.38, -0.82, -0.18, -0.7 , -0.83, -0.71, -1.47, -0.36,\n",
+       "       -1.58, -2.57, -2.37, -0.7 , -0.37, -1.31, -0.64,  0.33, -1.54,\n",
+       "        0.13, -0.37, -0.81,  0.26, -1.96, -0.44, -2.18, -0.12, -0.89,\n",
+       "       -1.16, -0.12,  0.14,  0.97, -0.69,  0.01, -1.99, -0.69, -2.12,\n",
+       "        0.52,  0.33, -0.32,  0.46,  0.33,  0.33,  0.33,  0.33,  0.52,\n",
+       "        0.39,  0.14, -1.09, -2.62, -0.37, -2.54, -0.78, -0.94, -1.14,\n",
+       "       -0.99, -0.99, -0.56, -0.56, -0.56, -1.89, -0.82, -2.95,  0.08,\n",
+       "       -1.82, -3.25, -1.65, -2.7 , -0.56, -0.84,  0.52, -0.45, -1.99,\n",
+       "       -0.43, -1.03,  0.46, -0.95, -0.58, -0.89, -0.7 ,  0.  ,  0.78,\n",
+       "       -1.93, -1.41, -1.83, -0.5 ,  0.78, -0.87,  0.53,  1.04,  0.2 ,\n",
+       "       -0.19, -0.46, -2.8 , -0.31, -1.86,  0.07,  1.1 , -1.01,  0.14,\n",
+       "        1.23,  0.2 , -1.14,  0.26, -0.95,  1.68, -4.  , -0.98, -0.57,\n",
+       "       -1.78, -3.35,  0.21,  0.39, -0.69, -0.88, -1.93,  0.33, -0.51,\n",
+       "       -3.12, -1.93, -0.13, -0.44,  0.84, -0.44,  0.39,  0.71,  0.4 ,\n",
+       "        1.09,  0.65,  1.99, -0.37,  1.93,  1.03,  2.13,  2.13,  2.13,\n",
+       "       -0.7 , -0.05, -0.05, -0.05,  0.72, -1.19,  0.2 ,  0.46, -2.  ,\n",
+       "       -0.37, -2.08,  1.29,  0.59, -1.08,  0.14, -1.01, -2.67, -0.58,\n",
+       "        0.08,  0.4 ,  1.93,  0.14,  1.86, -1.73, -3.07,  0.39,  0.39,\n",
+       "       -1.19, -0.31, -2.44, -0.63, -0.57, -1.16, -1.32, -1.87, -2.41,\n",
+       "       -0.57, -2.4 , -1.44, -1.74, -1.2 ,  0.91, -1.29, -1.74,  0.65,\n",
+       "        1.36, -1.96,  0.01, -0.31, -1.79, -0.37,  0.2 , -1.98,  0.14,\n",
+       "        0.08,  0.39, -1.51,  1.17, -0.26, -0.18, -0.18, -0.18, -0.12,\n",
+       "       -0.19,  0.14, -1.08, -0.06, -2.2 , -2.2 , -2.2 ,  0.52, -1.17,\n",
+       "       -0.62, -0.44, -0.76, -1.78, -0.87, -1.26, -1.01, -0.44, -0.77,\n",
+       "       -0.44, -0.44, -1.23, -0.31,  0.21, -0.19, -0.88, -0.64, -0.32,\n",
+       "       -1.2 , -1.77, -0.88,  0.27,  0.71, -0.25, -0.3 , -0.5 , -0.13,\n",
+       "       -1.13, -0.37, -0.12,  0.07, -0.96, -1.41, -1.46, -0.19, -0.5 ,\n",
+       "       -0.57], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.97 0.49 1.07 ... -0.66 -0.53 0.72</div><input id='attrs-83914d03-45c5-4ce9-a42a-4d804101db30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83914d03-45c5-4ce9-a42a-4d804101db30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5d702ee-7a45-43f6-a589-089ae59ad724' class='xr-var-data-in' type='checkbox'><label for='data-c5d702ee-7a45-43f6-a589-089ae59ad724' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([ 1.97,  0.49,  1.07,  1.05,  0.01,  0.01,  0.01, -0.47,  2.04,\n",
+       "       -0.46,  0.97, -0.21,  0.18, -0.43, -0.43,  1.62,  1.74,  0.24,\n",
+       "        0.6 ,  1.44, -0.15,  0.88,  0.66,  0.31, -1.3 ,  2.  ,  1.14,\n",
+       "        1.61,  1.65,  1.66,  1.01,  1.35,  1.53,  0.3 ,  0.11,  1.44,\n",
+       "        0.63,  0.42,  1.37,  0.37,  0.24, -0.05, -0.49,  0.62, -0.5 ,\n",
+       "        0.28,  1.61,  0.01,  0.8 ,  1.32,  1.67,  1.98,  1.67,  0.05,\n",
+       "       -0.31,  0.59, -1.75, -0.47, -0.05, -0.05, -0.05,  2.34,  1.18,\n",
+       "        0.14,  1.73,  0.95, -0.65, -0.63,  0.32,  0.41,  1.32,  0.72,\n",
+       "        0.13,  0.52,  1.79,  1.79,  1.79,  1.06,  0.98,  1.7 ,  1.87,\n",
+       "        0.19,  0.39,  0.69, -1.  ,  2.83,  2.61,  1.54,  1.96,  2.17,\n",
+       "        0.77,  1.8 ,  0.58,  0.33,  0.6 ,  0.85,  0.37,  0.11,  1.35,\n",
+       "        0.76,  1.08,  0.58,  1.14, -0.86,  0.93,  2.13,  1.92,  2.16,\n",
+       "        1.66, -1.76,  2.93, -0.08, -0.43, -0.53, -0.83,  2.42,  0.69,\n",
+       "        2.91,  0.99,  1.33, -0.88,  0.27,  3.6 ,  1.87, -1.18,  0.47,\n",
+       "        0.95,  0.96,  2.87,  2.13,  2.13,  0.87,  0.6 , -1.4 , -2.45,\n",
+       "        0.23,  0.02, -0.24,  1.62,  1.12,  1.16, -0.79,  0.79,  4.73,\n",
+       "       -0.76, -0.98,  0.63,  1.22, -0.41,  0.07, -2.18, -2.18, -2.18,\n",
+       "       -0.34,  0.94,  0.94,  0.94,  1.06,  1.05, -0.37,  0.32,  1.44,\n",
+       "        1.31,  0.36,  1.08,  1.06,  1.96, -0.44, -0.18,  1.65,  0.57,\n",
+       "        1.18,  1.12, -0.51, -0.41, -3.35, -1.45,  0.18, -0.57, -0.76,\n",
+       "        1.86,  1.27, -0.62,  0.39,  1.01,  1.7 ,  0.55, -0.46,  0.66,\n",
+       "       -1.07,  1.1 ,  0.29,  2.38,  0.36, -0.34, -0.11,  1.09,  1.17,\n",
+       "        1.58,  0.22, -0.69,  0.17,  1.63, -0.28,  0.2 ,  1.65,  1.9 ,\n",
+       "        2.5 , -0.66,  0.2 , -0.25, -0.43,  0.2 ,  0.2 ,  0.2 , -2.13,\n",
+       "       -0.02, -1.2 , -0.37, -0.21,  0.87,  0.87,  0.87,  0.59,  0.9 ,\n",
+       "        0.7 ,  0.01,  2.11,  1.01, -0.09,  0.92,  0.97,  1.42,  1.56,\n",
+       "        0.4 ,  0.37, -0.53, -0.28,  0.57,  1.08,  0.55,  0.43,  0.53,\n",
+       "        0.2 ,  0.43,  1.64,  0.23, -0.41,  0.56,  0.84,  0.72, -0.27,\n",
+       "        1.38,  0.72, -0.24, -0.18,  1.21,  0.76,  1.3 , -0.66, -0.53,\n",
+       "        0.72], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-154.2 -146.9 ... 159.4 155.4</div><input id='attrs-43be276b-b839-4a49-b08f-fb687d768b0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43be276b-b839-4a49-b08f-fb687d768b0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3aafdb77-1324-4291-8eda-7285e5d6f19c' class='xr-var-data-in' type='checkbox'><label for='data-3aafdb77-1324-4291-8eda-7285e5d6f19c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([-154.16, -146.94, -149.73, -149.08, -142.38, -142.38, -142.38,\n",
+       "       -138.54, -150.82, -140.2 , -145.69, -138.74, -138.54, -137.62,\n",
+       "       -137.32, -146.94, -147.46, -141.54, -145.16, -150.39, -147.23,\n",
+       "       -152.72, -155.97, -157.79, -153.27, -170.08, -169.21, -172.79,\n",
+       "       -173.52, -174.86, -174.83, -178.64, -177.88, -174.07, -174.06,\n",
+       "       -178.08, -177.42, -175.4 , -179.11, -176.44, -172.87, -174.05,\n",
+       "       -170.72, -178.3 , -172.07, -175.75,  176.65, -176.27,  178.85,\n",
+       "        178.7 ,  175.97,  178.48,  177.12, -175.92, -177.85,  177.34,\n",
+       "       -171.18, -178.37,  179.31,  179.31,  179.31,  167.96,  172.52,\n",
+       "        177.7 ,  170.57,  175.68, -176.88,  178.71,  178.44,  174.43,\n",
+       "        171.22,  173.2 ,  175.78,  174.36,  169.04,  169.04,  169.04,\n",
+       "        174.78,  173.5 ,  174.49,  167.44,  175.53,  175.54,  170.25,\n",
+       "        177.2 ,  157.25,  157.76,  157.99,  158.78,  161.56,  161.43,\n",
+       "        158.78,  159.33,  163.81,  163.14,  163.47,  166.03,  166.89,\n",
+       "        160.38,  169.02,  166.81,  170.04,  165.43,  169.3 ,  166.31,\n",
+       "        159.1 ,  158.09,  159.45,  162.64,  176.71,  164.01,  168.79,\n",
+       "        173.7 ,  170.36,  169.52,  162.49,  165.44,  154.91,  164.57,\n",
+       "        166.59,  171.5 ,  169.89,  152.04,  171.17, -179.85,  171.93,\n",
+       "        170.65,  173.46,  159.55,  161.73,  163.4 ,  167.76,  171.29,\n",
+       "        174.45, -178.8 ,  174.32,  173.31,  171.61,  165.1 ,  164.11,\n",
+       "        166.82,  173.13,  166.24,  154.54,  173.9 ,  176.31,  165.78,\n",
+       "        168.37,  170.27,  169.04,  176.61,  176.61,  176.61,  173.82,\n",
+       "        166.56,  166.56,  166.56,  164.95,  168.67,  171.44,  166.63,\n",
+       "        167.48,  163.93,  170.73,  159.4 ,  161.15,  161.24,  165.7 ,\n",
+       "        165.39,  165.08,  163.16,  159.8 ,  159.5 ,  161.17,  166.81,\n",
+       "        176.11,  173.99,  170.09,  163.81,  163.81,  158.22,  156.79,\n",
+       "        167.65,  158.87,  156.31,  155.09,  158.19,  162.2 ,  159.09,\n",
+       "        159.77,  158.65,  159.81,  154.  ,  158.14,  154.61,  160.49,\n",
+       "        158.01,  151.21,  147.92,  162.34,  160.3 ,  157.92,  157.12,\n",
+       "        160.22,  156.76,  157.91,  151.12,  149.55,  158.79,  161.03,\n",
+       "        154.09,  160.07,  158.06,  158.06,  158.06,  166.28,  158.86,\n",
+       "        162.37,  162.58,  159.29,  160.53,  160.53,  160.53,  151.05,\n",
+       "        156.2 ,  155.22,  156.83,  150.5 ,  156.62,  157.63,  154.88,\n",
+       "        154.28,  151.08,  150.79,  153.06,  153.4 ,  157.91,  154.43,\n",
+       "        150.07,  149.9 ,  153.38,  152.86,  152.34,  156.03,  157.2 ,\n",
+       "        151.1 ,  152.18,  152.47,  153.35,  152.24,  153.47,  156.04,\n",
+       "        154.19,  154.04,  156.93,  156.44,  154.83,  157.14,  155.93,\n",
+       "        158.46,  159.43,  155.37], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>13.13 13.13 13.13 ... 13.19 13.25</div><input id='attrs-1c6e3e97-0f7f-4d4e-b624-5898ce3c2da9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c6e3e97-0f7f-4d4e-b624-5898ce3c2da9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c44b9712-ab2f-439c-88af-9ed68c398c85' class='xr-var-data-in' type='checkbox'><label for='data-c44b9712-ab2f-439c-88af-9ed68c398c85' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.06, 13.06, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.19, 13.19, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.19, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.13, 13.13, 13.13, 13.13, 13.13, 13.19, 13.19, 13.13, 13.13,\n",
+       "       13.19, 13.19, 13.19, 13.19, 13.19, 13.13, 13.13, 13.13, 13.13,\n",
+       "       13.19, 13.19, 13.13, 13.13, 13.19, 13.19, 13.19, 13.19, 13.19,\n",
+       "       13.19, 13.19, 13.19, 13.19, 13.19, 13.19, 13.19, 13.19, 13.19,\n",
+       "       13.25], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>salinity</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-6512a3b6-ba8e-4b50-8966-078461c566a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6512a3b6-ba8e-4b50-8966-078461c566a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69e7ea10-5bbb-453c-a3fd-0ffb40c40993' class='xr-var-data-in' type='checkbox'><label for='data-69e7ea10-5bbb-453c-a3fd-0ffb40c40993' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>psu</dd><dt><span>long_name :</span></dt><dd>Salinity</dd><dt><span>standard_name :</span></dt><dd>sea_water_salinity</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>min_preping_wait</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>420.8 420.4 420.1 ... 600.7 600.3</div><input id='attrs-7cfd4a04-5369-4acc-8f2e-2e3da99c239e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cfd4a04-5369-4acc-8f2e-2e3da99c239e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4fb16855-49be-4413-8bd1-b9ab14211e09' class='xr-var-data-in' type='checkbox'><label for='data-4fb16855-49be-4413-8bd1-b9ab14211e09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Minimum Pre-Ping Wait Time Between Measurements</dd></dl></div><div class='xr-var-data'><pre>array([420.78, 420.39, 420.07, 420.79, 420.46, 420.37, 420.05, 420.71,\n",
+       "       420.43, 420.1 , 420.81, 420.49, 420.22, 420.91, 420.53, 420.1 ,\n",
+       "       420.71, 420.37, 420.09, 420.81, 420.61, 420.3 , 420.1 , 420.9 ,\n",
+       "       420.56, 420.3 , 420.97, 420.66, 420.32, 420.03, 420.72, 420.46,\n",
+       "       420.08, 420.76, 420.42, 420.13, 420.81, 420.55, 420.26, 420.01,\n",
+       "       420.78, 420.57, 420.38, 420.22, 420.06, 420.66, 420.3 , 420.9 ,\n",
+       "       420.54, 420.14, 420.8 , 420.41, 420.09, 420.73, 420.42, 420.06,\n",
+       "       420.76, 420.44, 420.15, 420.02, 420.71, 420.38, 420.08, 420.75,\n",
+       "       420.46, 420.13, 420.85, 420.54, 420.27, 420.95, 420.68, 420.36,\n",
+       "       420.08, 420.77, 420.5 , 420.36, 480.05, 480.72, 480.44, 480.11,\n",
+       "       480.84, 480.51, 480.22, 480.88, 480.6 , 480.27, 480.98, 480.64,\n",
+       "       480.36, 480.02, 480.72, 480.39, 480.1 , 480.77, 480.48, 480.13,\n",
+       "       480.83, 480.49, 480.19, 480.85, 480.55, 480.21, 480.91, 480.57,\n",
+       "       480.28, 480.94, 480.64, 480.31, 480.  , 480.67, 480.38, 480.04,\n",
+       "       480.75, 480.42, 480.12, 480.8 , 480.5 , 480.14, 480.83, 480.47,\n",
+       "       480.15, 480.79, 480.48, 480.11, 480.76, 480.39, 480.05, 480.68,\n",
+       "       480.34, 480.99, 480.67, 480.32, 480.01, 480.65, 480.34, 480.98,\n",
+       "       480.66, 480.31, 480.99, 480.63, 480.29, 480.9 , 480.56, 480.17,\n",
+       "       480.81, 480.41, 480.05, 480.66, 480.31, 480.91, 480.56, 480.36,\n",
+       "       480.99, 480.6 , 480.26, 480.06, 480.69, 480.31, 480.94, 480.53,\n",
+       "       480.16, 480.75, 480.39, 480.96, 480.78, 480.58, 540.39, 540.19,\n",
+       "       540.98, 540.76, 540.48, 540.24, 540.95, 540.69, 540.39, 540.12,\n",
+       "       540.8 , 540.54, 540.22, 540.96, 540.63, 540.36, 540.03, 540.73,\n",
+       "       540.39, 540.09, 540.74, 540.43, 540.08, 540.78, 540.44, 540.14,\n",
+       "       540.8 , 540.49, 540.13, 540.81, 540.45, 540.12, 540.74, 540.41,\n",
+       "       540.02, 540.69, 540.3 , 540.94, 540.54, 540.28, 540.96, 540.69,\n",
+       "       540.39, 540.13, 540.73, 540.46, 540.13, 540.02, 540.7 , 540.35,\n",
+       "       540.02, 540.63, 540.29, 540.9 , 540.58, 540.4 , 540.04, 540.67,\n",
+       "       540.34, 540.95, 540.61, 540.23, 540.95, 540.57, 540.36, 540.08,\n",
+       "       540.84, 540.62, 540.4 , 540.17, 540.88, 540.64, 540.34, 540.09,\n",
+       "       540.77, 540.43, 540.04, 540.7 , 540.32, 540.97, 540.57, 540.2 ,\n",
+       "       540.78, 540.41, 540.98, 540.59, 540.14, 540.75, 600.32, 600.92,\n",
+       "       600.47, 600.07, 600.62, 600.21, 600.74, 600.33], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-092b1426-c7e2-42b7-b483-5c1b30f215cf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-092b1426-c7e2-42b7-b483-5c1b30f215cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9fbc7f2-e477-462e-a668-562159993e9c' class='xr-var-data-in' type='checkbox'><label for='data-b9fbc7f2-e477-462e-a668-562159993e9c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-0a8e2fe7-658b-48e5-aba0-bc226099dbe0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a8e2fe7-658b-48e5-aba0-bc226099dbe0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d48ed86-61f5-425f-9d93-30251f873931' class='xr-var-data-in' type='checkbox'><label for='data-9d48ed86-61f5-425f-9d93-30251f873931' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-120969eb-f708-4532-a80b-a2b68be59b9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-120969eb-f708-4532-a80b-a2b68be59b9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16e8d9a0-802e-4761-a040-72490bc2c885' class='xr-var-data-in' type='checkbox'><label for='data-16e8d9a0-802e-4761-a040-72490bc2c885' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-5c17f3e5-e347-4d95-bdb8-5a7c0bcf5cbe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5c17f3e5-e347-4d95-bdb8-5a7c0bcf5cbe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9dafba6f-628d-40fc-b1d0-bec5f82cbf17' class='xr-var-data-in' type='checkbox'><label for='data-9dafba6f-628d-40fc-b1d0-bec5f82cbf17' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure_std</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-7d56cc68-5fb4-4388-a167-c1b2bf5d7771' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d56cc68-5fb4-4388-a167-c1b2bf5d7771' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7eeabdff-751d-43ba-87a0-c865489455d6' class='xr-var-data-in' type='checkbox'><label for='data-7eeabdff-751d-43ba-87a0-c865489455d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+       "       0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>nan -1.378 -1.597 ... nan nan nan</div><input id='attrs-96ab13ea-ad02-4bb4-87c0-cb9045c89ab5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-96ab13ea-ad02-4bb4-87c0-cb9045c89ab5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-15484545-0b2a-4442-8477-b2167bb10e30' class='xr-var-data-in' type='checkbox'><label for='data-15484545-0b2a-4442-8477-b2167bb10e30' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[           nan, -1.3780606e+00, -1.5968075e+00, ...,\n",
+       "         -2.6226619e-01, -2.5654507e-01, -2.2308642e-01],\n",
+       "        [           nan, -2.5692660e-01, -2.6959109e-01, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        ...,\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan]],\n",
+       "\n",
+       "       [[           nan,  2.6142585e-01, -4.9651265e-03, ...,\n",
+       "          3.5546001e-02,  3.3569042e-02, -1.2026111e-02],\n",
+       "        [           nan,  4.8740420e-02, -8.3826878e-04, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "...\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan]],\n",
+       "\n",
+       "       [[           nan, -3.6425743e-01, -7.1455896e-02, ...,\n",
+       "          5.0781541e-02, -3.7472174e-03,  1.1241753e-02],\n",
+       "        [           nan, -6.7912415e-02, -1.2063992e-02, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        ...,\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>145 149 147 147 149 ... 0 0 0 0 0</div><input id='attrs-a01e6990-2294-4050-a493-124de4bea17a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a01e6990-2294-4050-a493-124de4bea17a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b265b6a-83a7-4698-bbe0-3e7fb37fa2e5' class='xr-var-data-in' type='checkbox'><label for='data-8b265b6a-83a7-4698-bbe0-3e7fb37fa2e5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[145, 149, 147, ..., 133, 131, 134],\n",
+       "        [138, 147, 148, ...,  85,  87,  86],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        ...,\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[145, 153, 151, ..., 137, 138, 136],\n",
+       "        [155, 164, 170, ...,  93,  90,  85],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        ...,\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[142, 145, 143, ..., 128, 128, 130],\n",
+       "        [154, 157, 155, ...,  85,  86,  87],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        ...,\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[141, 147, 147, ..., 135, 132, 132],\n",
+       "        [141, 139, 138, ...,  85,  88,  83],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        ...,\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>135 113 114 107 117 ... 0 0 0 0 0</div><input id='attrs-a0fdb2be-e418-4afc-bdee-b07a28b4691b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0fdb2be-e418-4afc-bdee-b07a28b4691b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-946e07ec-ee76-4912-b942-decc794f612b' class='xr-var-data-in' type='checkbox'><label for='data-946e07ec-ee76-4912-b942-decc794f612b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[135, 113, 114, ..., 241, 245, 246],\n",
+       "        [110, 111, 115, ..., 136, 149, 145],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        ...,\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[173, 118, 110, ..., 240, 245, 247],\n",
+       "        [133, 110, 124, ..., 141, 143, 145],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        ...,\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[128, 114, 112, ..., 245, 244, 246],\n",
+       "        [134, 110, 109, ..., 138, 145, 148],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        ...,\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[151, 117, 118, ..., 241, 244, 246],\n",
+       "        [108, 101, 112, ..., 141, 150, 142],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        ...,\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dist_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.3 1.18 1.16 ... 1.04 0.96 0.96</div><input id='attrs-74bfbac9-9a38-4797-a9f1-4f2e852d1de8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-74bfbac9-9a38-4797-a9f1-4f2e852d1de8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a57dfd9-09ae-4a2b-b7f9-a160f59f0d22' class='xr-var-data-in' type='checkbox'><label for='data-3a57dfd9-09ae-4a2b-b7f9-a160f59f0d22' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Bottom Track Measured Depth</dd></dl></div><div class='xr-var-data'><pre>array([[1.3 , 1.18, 1.16, ..., 0.98, 0.96, 0.96],\n",
+       "       [1.11, 1.13, 1.09, ..., 0.97, 0.95, 0.97],\n",
+       "       [1.17, 1.12, 1.12, ..., 0.95, 0.97, 0.92],\n",
+       "       [1.24, 1.25, 1.26, ..., 1.04, 0.96, 0.96]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_bt</span></div><div class='xr-var-dims'>(dir, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.03986 0.02038 ... 0.007236</div><input id='attrs-7c96ceed-47ac-4ceb-b6c0-89ff3a31c8a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c96ceed-47ac-4ceb-b6c0-89ff3a31c8a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d08fa1b7-d508-4789-b104-906f4a32c718' class='xr-var-data-in' type='checkbox'><label for='data-d08fa1b7-d508-4789-b104-906f4a32c718' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.03985795,  0.02037775,  0.03288013, ..., -0.01411782,\n",
+       "        -0.01381912,  0.04580029],\n",
+       "       [-0.07396869, -0.08312948, -0.07563345, ..., -0.6168041 ,\n",
+       "        -0.6167893 , -0.5712449 ],\n",
+       "       [ 0.00555705, -0.00484052, -0.00232305, ...,  0.01484554,\n",
+       "         0.0183713 ,  0.00577757],\n",
+       "       [ 0.00413489,  0.00206744, -0.00103372, ...,  0.00206745,\n",
+       "        -0.00516861,  0.00723603]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>251 251 253 249 ... 222 254 248 223</div><input id='attrs-c96e8494-21c7-4b76-8546-d25184170aa1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c96e8494-21c7-4b76-8546-d25184170aa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c632b10d-0163-4bba-94ef-d113a6472841' class='xr-var-data-in' type='checkbox'><label for='data-c632b10d-0163-4bba-94ef-d113a6472841' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[251, 251, 253, ..., 234, 236, 231],\n",
+       "       [252, 251, 250, ..., 229, 252, 232],\n",
+       "       [249, 250, 243, ..., 224, 239, 228],\n",
+       "       [249, 252, 243, ..., 254, 248, 223]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>35 31 29 33 39 ... 46 40 60 48 52</div><input id='attrs-4d27f779-dff1-450c-9053-8b6ec40b9cf6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4d27f779-dff1-450c-9053-8b6ec40b9cf6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71873448-50d1-4a42-918c-0baa0b4c6b18' class='xr-var-data-in' type='checkbox'><label for='data-71873448-50d1-4a42-918c-0baa0b4c6b18' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[35, 31, 29, ..., 61, 74, 58],\n",
+       "       [36, 51, 54, ..., 47, 56, 52],\n",
+       "       [43, 56, 52, ..., 64, 51, 62],\n",
+       "       [42, 53, 48, ..., 60, 48, 52]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>prcnt_gd_bt</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 1 1 1 1 1 1 1</div><input id='attrs-d06de2db-bf71-420c-8751-192b629abecb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d06de2db-bf71-420c-8751-192b629abecb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02d877ef-d6ed-4721-9f19-41debc04eb75' class='xr-var-data-in' type='checkbox'><label for='data-02d877ef-d6ed-4721-9f19-41debc04eb75' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Percent Good</dd><dt><span>standard_name :</span></dt><dd>proportion_of_acceptable_signal_returns_from_acoustic_instrument_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [0, 0, 0, ..., 0, 0, 0],\n",
+       "       [1, 1, 1, ..., 1, 1, 1]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt_dist</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.1 1.08 1.07 ... 1.02 0.98 0.97</div><input id='attrs-52453374-61ee-421f-954e-531aa8a454d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-52453374-61ee-421f-954e-531aa8a454d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5455f622-78a1-46d3-9e7e-dbf0e98aa3e6' class='xr-var-data-in' type='checkbox'><label for='data-5455f622-78a1-46d3-9e7e-dbf0e98aa3e6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Altimeter Range</dd><dt><span>standard_name :</span></dt><dd>altimeter_range</dd></dl></div><div class='xr-var-data'><pre>array([1.1  , 1.08 , 1.07 , 1.06 , 1.06 , 1.31 , 1.31 , 1.29 , 1.26 ,\n",
+       "       1.271, 1.22 , 1.21 , 1.21 , 1.13 , 1.22 , 1.21 , 1.23 , 1.37 ,\n",
+       "       1.45 , 1.46 , 1.4  , 1.64 , 2.03 , 2.217, 2.379, 2.52 , 2.75 ,\n",
+       "       2.99 , 3.54 , 3.58 , 3.83 , 4.179, 4.453, 4.52 , 4.729, 5.79 ,\n",
+       "       5.914, 5.966, 0.   , 0.   , 7.651, 7.394, 7.53 , 7.56 , 7.65 ,\n",
+       "       7.793, 7.893, 8.042, 8.172, 8.31 , 8.66 , 8.67 , 8.75 , 8.82 ,\n",
+       "       8.88 , 8.96 , 8.97 , 9.01 , 9.05 , 9.09 , 9.13 , 9.16 , 9.2  ,\n",
+       "       9.27 , 9.27 , 8.96 , 9.04 , 9.18 , 8.93 , 9.09 , 9.05 , 9.01 ,\n",
+       "       8.97 , 8.97 , 8.94 , 8.88 , 8.86 , 8.86 , 8.84 , 8.88 , 8.86 ,\n",
+       "       8.77 , 8.78 , 8.78 , 8.78 , 8.59 , 8.77 , 8.73 , 8.69 , 8.71 ,\n",
+       "       8.77 , 8.78 , 8.698, 8.698, 8.73 , 8.67 , 8.61 , 8.63 , 8.67 ,\n",
+       "       8.69 , 8.65 , 8.641, 8.61 , 8.584, 8.617, 8.698, 8.811, 8.887,\n",
+       "       8.96 , 8.99 , 8.925, 9.01 , 8.754, 8.78 , 8.071, 8.635, 8.565,\n",
+       "       8.4  , 8.35 , 8.27 , 7.58 , 8.142, 8.15 , 7.46 , 8.07 , 8.136,\n",
+       "       8.243, 8.261, 7.905, 8.408, 7.922, 7.959, 7.926, 8.408, 8.35 ,\n",
+       "       7.709, 8.275, 8.173, 8.17 , 8.047, 7.905, 7.86 , 7.295, 7.668,\n",
+       "       7.544, 7.444, 7.314, 6.99 , 7.391, 7.252, 7.236, 7.375, 7.576,\n",
+       "       7.644, 7.577, 7.444, 6.774, 6.566, 6.509, 6.737, 6.86 , 6.75 ,\n",
+       "       6.71 , 6.61 , 6.63 , 6.73 , 6.124, 6.084, 5.772, 5.444, 5.294,\n",
+       "       5.294, 5.163, 4.956, 4.78 , 4.7  , 4.66 , 5.127, 5.213, 4.831,\n",
+       "       4.593, 4.465, 4.425, 4.054, 4.026, 4.216, 4.26 , 4.26 , 4.292,\n",
+       "       4.292, 4.273, 4.168, 4.06 , 3.95 , 3.89 , 3.81 , 3.797, 3.996,\n",
+       "       3.614, 3.54 , 3.52 , 3.49 , 3.41 , 3.24 , 3.11 , 3.06 , 3.14 ,\n",
+       "       3.22 , 3.22 , 3.15 , 3.027, 2.9  , 2.82 , 2.7  , 2.58 , 2.49 ,\n",
+       "       2.43 , 2.41 , 2.39 , 2.38 , 2.37 , 2.4  , 2.4  , 2.38 , 2.36 ,\n",
+       "       2.31 , 2.3  , 2.29 , 2.29 , 2.29 , 2.28 , 2.25 , 2.21 , 2.19 ,\n",
+       "       2.13 , 2.08 , 2.01 , 1.95 , 1.86 , 1.76 , 1.69 , 1.6  , 1.53 ,\n",
+       "       1.46 , 1.41 , 1.38 , 1.33 , 1.3  , 1.26 , 1.23 , 1.18 , 1.16 ,\n",
+       "       1.14 , 1.12 , 1.11 , 1.09 , 1.06 , 1.   , 0.96 , 1.02 , 0.98 ,\n",
+       "       0.97 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt_rssi</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>154 143 145 140 ... 154 132 136 136</div><input id='attrs-7c7749a4-9f3c-48fc-a6be-c43a91fd5af1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7c7749a4-9f3c-48fc-a6be-c43a91fd5af1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc7bbe47-5f13-451d-81c9-71f59b3a0e64' class='xr-var-data-in' type='checkbox'><label for='data-dc7bbe47-5f13-451d-81c9-71f59b3a0e64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Altimeter Recieved Signal Strength Indicator</dd></dl></div><div class='xr-var-data'><pre>array([154, 143, 145, 140, 122, 140, 150, 147, 119, 142, 141, 135, 139,\n",
+       "       120, 134, 139, 142, 143, 139, 126,  98, 121, 136, 111, 127, 121,\n",
+       "       130, 105, 105, 118, 109, 104, 174, 175, 163, 131, 156, 154, 154,\n",
+       "       147, 139, 151, 153, 149, 161, 156, 154, 155, 165, 154, 159, 155,\n",
+       "       160, 155, 157, 158, 156, 151, 157, 155, 160, 164, 159, 154, 157,\n",
+       "       156, 164, 149, 160, 161, 153, 160, 162, 150, 162, 163, 162, 169,\n",
+       "       154, 170, 164, 165, 158, 147, 163, 171, 174, 162, 161, 158, 161,\n",
+       "       161, 159, 163, 161, 158, 154, 156, 143, 150, 160, 147, 153, 153,\n",
+       "       152, 150, 140, 150, 151, 160, 160, 136, 152, 155, 157, 152, 154,\n",
+       "       163, 151, 142, 154, 154, 154, 154, 149, 149, 142, 137, 154, 153,\n",
+       "       146, 146, 144, 147, 151, 156, 146, 156, 131, 149, 142, 146, 139,\n",
+       "       141, 142, 140, 138, 145, 143, 161, 147, 146, 151, 160, 151, 157,\n",
+       "       157, 147, 145, 159, 150, 168, 163, 168, 174, 166, 154, 155, 147,\n",
+       "       150, 151, 145, 141, 152, 175, 175, 158, 153, 170, 150, 155, 148,\n",
+       "       153, 162, 156, 159, 168, 160, 147, 159, 153, 150, 162, 196, 121,\n",
+       "       116, 106, 101,  91, 123,  92,  86, 104,  98, 101, 124, 114, 116,\n",
+       "       117, 111,  98, 110, 127,  95, 103, 115, 125, 133, 104, 116, 108,\n",
+       "       127, 128, 124, 129, 117, 133, 132, 132, 129, 107, 122, 131, 122,\n",
+       "       122, 122, 129, 124, 129, 125, 120, 146, 128, 124, 143, 127, 133,\n",
+       "       135, 126, 133, 111, 102, 115, 148, 146, 148, 139, 121, 154, 132,\n",
+       "       136, 136], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt_eval</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>60 60 54 46 58 ... 73 100 70 77 72</div><input id='attrs-6eb6b6fa-1702-440b-8260-8e7873cf3db7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6eb6b6fa-1702-440b-8260-8e7873cf3db7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c52bc71-0f80-4b28-9f07-6efaa6597bec' class='xr-var-data-in' type='checkbox'><label for='data-1c52bc71-0f80-4b28-9f07-6efaa6597bec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Altimeter Evaluation Amplitude</dd></dl></div><div class='xr-var-data'><pre>array([ 60,  60,  54,  46,  58,  36,  41,  40,  42,  46,  47,  50,  51,\n",
+       "        56,  62,  59,  53,  49,  51,  43,  41,  32,  45,  46,  59,  57,\n",
+       "        52,  38,  40,  51,  42,  44,  53,  60,  40,  39,  47,  45,  28,\n",
+       "        27,  34,  45,  49,  53,  55,  54,  53,  56,  59,  58,  58,  61,\n",
+       "        61,  58,  60,  60,  64,  60,  63,  66,  60,  63,  61,  61,  53,\n",
+       "        60,  60,  55,  61,  61,  60,  62,  66,  58,  59,  60,  61,  62,\n",
+       "        55,  68,  65,  65,  61,  62,  63,  66,  62,  61,  60,  57,  57,\n",
+       "        53,  51,  50,  53,  50,  51,  52,  53,  55,  52,  53,  47,  56,\n",
+       "        52,  53,  52,  53,  53,  58,  49,  48,  44,  48,  46,  46,  42,\n",
+       "        54,  50,  56,  48,  55,  59,  52,  51,  50,  46,  39,  38,  41,\n",
+       "        39,  40,  41,  43,  49,  48,  48,  51,  49,  51,  49,  49,  46,\n",
+       "        49,  52,  50,  45,  42,  46,  51,  53,  49,  48,  50,  50,  45,\n",
+       "        44,  41,  42,  53,  54,  52,  59,  59,  60,  57,  51,  47,  47,\n",
+       "        47,  34,  45,  47,  54,  63,  60,  53,  43,  47,  45,  44,  45,\n",
+       "        53,  49,  44,  51,  59,  57,  52,  54,  53,  56,  47,  83,  57,\n",
+       "        66,  45,  45,  45,  62,  49,  47,  47,  49,  63,  59,  48,  50,\n",
+       "        61,  48,  45,  56,  57,  42,  54,  59,  62,  67,  56,  58,  56,\n",
+       "        55,  59,  58,  57,  63,  61,  61,  62,  59,  53,  60,  62,  63,\n",
+       "        65,  64,  67,  65,  64,  68,  71,  75,  67,  71,  71,  60,  72,\n",
+       "        68,  69,  76,  63,  57,  59,  71,  77,  82,  70,  73, 100,  70,\n",
+       "        77,  72], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>alt_status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>1 1 1 2 2 1 1 1 ... 1 1 1 1 2 1 1 1</div><input id='attrs-fc4390a0-a07d-43db-889e-b566ffb3e364' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc4390a0-a07d-43db-889e-b566ffb3e364' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-283e5b21-dff4-4bc3-8d0c-6fe1361fbbc3' class='xr-var-data-in' type='checkbox'><label for='data-283e5b21-dff4-4bc3-8d0c-6fe1361fbbc3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>bit</dd><dt><span>long_name :</span></dt><dd>Altimeter Status</dd></dl></div><div class='xr-var-data'><pre>array([1, 1, 1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 2, 2,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 5, 5, 5, 5, 5, 5, 4, 4, 5, 5, 5, 5,\n",
+       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6,\n",
+       "       6, 5, 6, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 5, 5,\n",
+       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
+       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
+       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
+       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,\n",
+       "       5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
+       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1],\n",
+       "      dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>64.56 64.56 64.56 ... 64.56 64.56</div><input id='attrs-8b6a71b6-9ba8-4a2b-9b4a-057dc6553541' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b6a71b6-9ba8-4a2b-9b4a-057dc6553541' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-619b07f2-9bed-45b4-a6a7-3ce607400ba9' class='xr-var-data-in' type='checkbox'><label for='data-619b07f2-9bed-45b4-a6a7-3ce607400ba9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_north</dd><dt><span>long_name :</span></dt><dd>Latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([64.56195 , 64.56195 , 64.56195 , 64.56195 , 64.56195 , 64.56195 ,\n",
+       "       64.56195 , 64.56195 , 64.56195 , 64.56195 , 64.56195 , 64.56195 ,\n",
+       "       64.56195 , 64.56195 , 64.56195 , 64.56195 , 64.56195 , 64.56195 ,\n",
+       "       64.56195 , 64.56195 , 64.56196 , 64.56196 , 64.56196 , 64.56196 ,\n",
+       "       64.56196 , 64.56196 , 64.561966, 64.561966, 64.561966, 64.561966,\n",
+       "       64.561966, 64.561966, 64.561966, 64.561966, 64.56197 , 64.56197 ,\n",
+       "       64.56197 , 64.56197 , 64.56197 , 64.56198 , 64.56198 , 64.56198 ,\n",
+       "       64.56199 , 64.56199 , 64.56199 , 64.56199 , 64.562   , 64.562   ,\n",
+       "       64.562   , 64.562   , 64.562   , 64.562   , 64.562   , 64.562   ,\n",
+       "       64.562   , 64.562   , 64.562   , 64.562004, 64.562004, 64.562004,\n",
+       "       64.562004, 64.562004, 64.56201 , 64.56201 , 64.56201 , 64.56201 ,\n",
+       "       64.56201 , 64.56201 , 64.56202 , 64.56202 , 64.56202 , 64.56202 ,\n",
+       "       64.56202 , 64.56203 , 64.56203 , 64.56203 , 64.56203 , 64.562035,\n",
+       "       64.562035, 64.56204 , 64.56204 , 64.56204 , 64.56205 , 64.56205 ,\n",
+       "       64.56206 , 64.56206 , 64.56206 , 64.562065, 64.562065, 64.56207 ,\n",
+       "       64.56208 , 64.56208 , 64.56209 , 64.56209 , 64.562096, 64.562096,\n",
+       "       64.5621  , 64.5621  , 64.56211 , 64.56211 , 64.56212 , 64.56212 ,\n",
+       "       64.562126, 64.562126, 64.56213 , 64.56213 , 64.56214 , 64.56214 ,\n",
+       "       64.56214 , 64.56215 , 64.56215 , 64.56216 , 64.56216 , 64.562164,\n",
+       "       64.562164, 64.56217 , 64.56217 , 64.56218 , 64.56218 , 64.56218 ,\n",
+       "...\n",
+       "       64.56225 , 64.562256, 64.562256, 64.562256, 64.562256, 64.562256,\n",
+       "       64.562256, 64.562256, 64.562256, 64.562256, 64.562256, 64.56226 ,\n",
+       "       64.56226 , 64.56226 , 64.56226 , 64.56226 , 64.56226 , 64.56226 ,\n",
+       "       64.56227 , 64.56227 , 64.56227 , 64.56227 , 64.56227 , 64.56228 ,\n",
+       "       64.56228 , 64.56228 , 64.56228 , 64.56229 , 64.56229 , 64.56229 ,\n",
+       "       64.56229 , 64.56229 , 64.56229 , 64.562294, 64.562294, 64.562294,\n",
+       "       64.562294, 64.5623  , 64.5623  , 64.5623  , 64.5623  , 64.56231 ,\n",
+       "       64.56231 , 64.56231 , 64.56232 , 64.56232 , 64.56232 , 64.562325,\n",
+       "       64.562325, 64.562325, 64.56233 , 64.56233 , 64.56233 , 64.56233 ,\n",
+       "       64.56234 , 64.56234 , 64.56234 , 64.56235 , 64.56235 , 64.56235 ,\n",
+       "       64.56235 , 64.562355, 64.562355, 64.562355, 64.56236 , 64.56236 ,\n",
+       "       64.56236 , 64.56237 , 64.56237 , 64.56237 , 64.56237 , 64.56237 ,\n",
+       "       64.56238 , 64.56238 , 64.56238 , 64.56238 , 64.562386, 64.562386,\n",
+       "       64.562386, 64.562386, 64.56239 , 64.56239 , 64.56239 , 64.56239 ,\n",
+       "       64.56239 , 64.5624  , 64.5624  , 64.5624  , 64.56241 , 64.56241 ,\n",
+       "       64.56241 , 64.56241 , 64.562416, 64.562416, 64.562416, 64.562416,\n",
+       "       64.56242 , 64.56242 , 64.56242 , 64.56242 , 64.56243 , 64.56243 ,\n",
+       "       64.56243 , 64.56243 , 64.56243 , 64.56243 , 64.56244 , 64.56244 ,\n",
+       "       64.56244 , 64.56244 , 64.56244 , 64.56244 , 64.56244 , 64.56244 ,\n",
+       "       64.56244 , 64.56244 , 64.56245 , 64.56245 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-149.1 -149.1 ... -149.1 -149.1</div><input id='attrs-bcfec285-3699-4cc0-a990-84442f8ac73d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bcfec285-3699-4cc0-a990-84442f8ac73d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd470c68-02a5-4c59-9932-62930d02f3a0' class='xr-var-data-in' type='checkbox'><label for='data-cd470c68-02a5-4c59-9932-62930d02f3a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_east</dd><dt><span>long_name :</span></dt><dd>Longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([-149.06721, -149.06721, -149.06721, -149.06721, -149.06721,\n",
+       "       -149.06721, -149.06721, -149.06721, -149.06721, -149.06721,\n",
+       "       -149.0672 , -149.0672 , -149.0672 , -149.0672 , -149.0672 ,\n",
+       "       -149.0672 , -149.0672 , -149.0672 , -149.0672 , -149.0672 ,\n",
+       "       -149.06718, -149.06718, -149.06718, -149.06717, -149.06717,\n",
+       "       -149.06715, -149.06715, -149.06714, -149.06714, -149.06712,\n",
+       "       -149.06712, -149.06712, -149.06711, -149.0671 , -149.0671 ,\n",
+       "       -149.06708, -149.06708, -149.06706, -149.06706, -149.06705,\n",
+       "       -149.06705, -149.06703, -149.06703, -149.06702, -149.06702,\n",
+       "       -149.06702, -149.067  , -149.067  , -149.06699, -149.06699,\n",
+       "       -149.06697, -149.06697, -149.06696, -149.06696, -149.06694,\n",
+       "       -149.06693, -149.06693, -149.06691, -149.06691, -149.0669 ,\n",
+       "       -149.0669 , -149.06688, -149.06686, -149.06686, -149.06685,\n",
+       "       -149.06683, -149.06682, -149.06682, -149.0668 , -149.06679,\n",
+       "       -149.06677, -149.06677, -149.06676, -149.06674, -149.06673,\n",
+       "       -149.06671, -149.06671, -149.0667 , -149.06668, -149.06667,\n",
+       "       -149.06667, -149.06665, -149.06664, -149.06662, -149.0666 ,\n",
+       "       -149.06659, -149.06659, -149.06656, -149.06656, -149.06654,\n",
+       "       -149.06653, -149.06651, -149.0665 , -149.06648, -149.06647,\n",
+       "       -149.06645, -149.06645, -149.06642, -149.06642, -149.0664 ,\n",
+       "...\n",
+       "       -149.06529, -149.06526, -149.06523, -149.06522, -149.06519,\n",
+       "       -149.06517, -149.06514, -149.06512, -149.0651 , -149.06508,\n",
+       "       -149.06506, -149.06503, -149.06502, -149.065  , -149.06499,\n",
+       "       -149.06496, -149.06494, -149.06493, -149.06491, -149.0649 ,\n",
+       "       -149.06487, -149.06485, -149.06483, -149.06482, -149.0648 ,\n",
+       "       -149.06479, -149.06477, -149.06476, -149.06474, -149.06473,\n",
+       "       -149.0647 , -149.06468, -149.06467, -149.06465, -149.06464,\n",
+       "       -149.06462, -149.0646 , -149.06459, -149.06456, -149.06454,\n",
+       "       -149.06453, -149.06451, -149.06448, -149.06447, -149.06445,\n",
+       "       -149.06444, -149.0644 , -149.06439, -149.06438, -149.06435,\n",
+       "       -149.06433, -149.06432, -149.0643 , -149.06429, -149.06427,\n",
+       "       -149.06425, -149.06424, -149.06422, -149.06421, -149.0642 ,\n",
+       "       -149.06418, -149.06416, -149.06415, -149.06413, -149.0641 ,\n",
+       "       -149.06409, -149.06407, -149.06406, -149.06404, -149.06403,\n",
+       "       -149.06401, -149.06398, -149.06396, -149.06395, -149.06393,\n",
+       "       -149.06393, -149.06392, -149.0639 , -149.06389, -149.06387,\n",
+       "       -149.06386, -149.06386, -149.06384, -149.06383, -149.06383,\n",
+       "       -149.06381, -149.0638 , -149.0638 , -149.06378, -149.06378,\n",
+       "       -149.06377, -149.06377, -149.06375, -149.06374, -149.06374,\n",
+       "       -149.06374, -149.06372], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fix_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>2 2 2 2 2 2 2 2 ... 2 2 2 2 2 2 2 2</div><input id='attrs-2cb855c7-2028-4e6d-9b65-7a05eedb25f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cb855c7-2028-4e6d-9b65-7a05eedb25f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f61daf69-febf-4f5c-836c-fabac000b2ac' class='xr-var-data-in' type='checkbox'><label for='data-f61daf69-febf-4f5c-836c-fabac000b2ac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>GPS Fix</dd></dl></div><div class='xr-var-data'><pre>array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
+       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2],\n",
+       "      dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>n_sat_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>31 31 31 31 31 ... 31 31 31 31 31</div><input id='attrs-a1345f9b-3f90-4ccf-92bb-e459cca5f313' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1345f9b-3f90-4ccf-92bb-e459cca5f313' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-84f59b02-cfee-4548-b8a8-6640df058dec' class='xr-var-data-in' type='checkbox'><label for='data-84f59b02-cfee-4548-b8a8-6640df058dec' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>count</dd><dt><span>long_name :</span></dt><dd>Number of Satellites</dd></dl></div><div class='xr-var-data'><pre>array([31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
+       "       31, 31, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 30, 31, 31,\n",
+       "       30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
+       "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
+       "       30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,\n",
+       "       30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 30, 30,\n",
+       "       30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,\n",
+       "       30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,\n",
+       "       30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30,\n",
+       "       30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31,\n",
+       "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
+       "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
+       "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
+       "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
+       "       31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31, 31,\n",
+       "       31, 31, 31, 31, 31, 31, 31], dtype=int8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdop_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.4 0.4 0.4 0.4 ... 0.4 0.4 0.4 0.4</div><input id='attrs-3a74b34e-0e0c-4227-869e-3c83667a35a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a74b34e-0e0c-4227-869e-3c83667a35a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-49cf059b-2d65-435a-b2a5-25a7ea20fda2' class='xr-var-data-in' type='checkbox'><label for='data-49cf059b-2d65-435a-b2a5-25a7ea20fda2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Horizontal Dilution of Precision</dd></dl></div><div class='xr-var-data'><pre>array([0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
+       "       0.5, 0.4, 0.4, 0.4, 0.4, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0.5,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4, 0.4,\n",
+       "       0.4, 0.4], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>elevation_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>114.9 114.9 114.9 ... 115.1 115.1</div><input id='attrs-e7fd5cf8-1953-40dd-8d0b-edee85f79337' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7fd5cf8-1953-40dd-8d0b-edee85f79337' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d99ceb1-6b50-4475-afb9-3f8427b605ab' class='xr-var-data-in' type='checkbox'><label for='data-8d99ceb1-6b50-4475-afb9-3f8427b605ab' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Elevation above MLLW</dd></dl></div><div class='xr-var-data'><pre>array([114.878, 114.878, 114.881, 114.884, 114.88 , 114.888, 114.884,\n",
+       "       114.883, 114.893, 114.893, 114.895, 114.895, 114.9  , 114.912,\n",
+       "       114.914, 114.922, 114.924, 114.919, 114.925, 114.933, 114.935,\n",
+       "       114.938, 114.928, 114.933, 114.94 , 114.945, 114.947, 114.941,\n",
+       "       114.942, 114.944, 114.95 , 114.952, 114.948, 114.939, 114.941,\n",
+       "       114.944, 114.945, 114.95 , 114.945, 114.95 , 114.952, 114.955,\n",
+       "       114.953, 114.955, 114.961, 114.966, 114.973, 114.973, 114.979,\n",
+       "       114.983, 114.98 , 114.981, 114.977, 114.971, 114.984, 114.997,\n",
+       "       115.004, 115.   , 115.007, 115.01 , 115.023, 115.02 , 115.021,\n",
+       "       115.012, 115.006, 115.008, 115.011, 115.015, 115.028, 115.028,\n",
+       "       115.019, 115.028, 115.029, 115.033, 115.029, 115.015, 115.016,\n",
+       "       115.029, 115.024, 115.034, 115.031, 115.019, 115.018, 114.993,\n",
+       "       114.989, 114.982, 114.986, 114.977, 114.961, 114.95 , 114.949,\n",
+       "       114.946, 114.93 , 114.935, 114.927, 114.937, 114.927, 114.911,\n",
+       "       114.898, 114.905, 114.916, 114.918, 114.909, 114.901, 114.907,\n",
+       "       114.902, 114.895, 114.894, 114.884, 114.888, 114.892, 114.893,\n",
+       "       114.896, 114.897, 114.892, 114.891, 114.917, 114.923, 114.923,\n",
+       "       114.913, 114.916, 114.93 , 114.931, 114.907, 114.893, 114.899,\n",
+       "       114.908, 114.924, 114.922, 114.91 , 114.905, 114.902, 114.896,\n",
+       "       114.9  , 114.897, 114.896, 114.907, 114.899, 114.893, 114.9  ,\n",
+       "       114.902, 114.914, 114.939, 114.948, 114.951, 114.952, 114.943,\n",
+       "       114.94 , 114.944, 114.948, 114.947, 114.928, 114.927, 114.938,\n",
+       "       114.947, 114.959, 114.956, 114.954, 114.945, 114.943, 114.945,\n",
+       "       114.948, 114.944, 114.939, 114.933, 114.925, 114.915, 114.91 ,\n",
+       "       114.925, 114.925, 114.915, 114.918, 114.922, 114.918, 114.906,\n",
+       "       114.906, 114.906, 114.906, 114.914, 114.916, 114.911, 114.904,\n",
+       "       114.913, 114.922, 114.926, 114.929, 114.919, 114.925, 114.943,\n",
+       "       114.95 , 114.953, 114.95 , 114.954, 114.959, 114.961, 114.962,\n",
+       "       114.973, 114.984, 114.985, 114.977, 114.987, 114.994, 115.002,\n",
+       "       115.011, 115.013, 115.015, 115.021, 115.025, 115.02 , 115.045,\n",
+       "       115.05 , 115.046, 115.043, 115.041, 115.044, 115.048, 115.049,\n",
+       "       115.053, 115.06 , 115.04 , 115.039, 115.053, 115.066, 115.062,\n",
+       "       115.061, 115.052, 115.049, 115.055, 115.06 , 115.058, 115.073,\n",
+       "       115.076, 115.063, 115.067, 115.065, 115.057, 115.055, 115.06 ,\n",
+       "       115.057, 115.049, 115.052, 115.049, 115.053, 115.057, 115.055,\n",
+       "       115.054, 115.06 , 115.057, 115.052, 115.055, 115.057, 115.056,\n",
+       "       115.06 , 115.063, 115.061, 115.062, 115.063, 115.057, 115.06 ,\n",
+       "       115.061, 115.063, 115.062], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rtk_age_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.6 6.2 7.0 6.6 ... 7.4 7.0 7.4 7.0</div><input id='attrs-38b7ceab-8b44-4c23-866a-ec34f8604759' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38b7ceab-8b44-4c23-866a-ec34f8604759' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fd92539c-6ef1-4d85-8108-85d22402cfbc' class='xr-var-data-in' type='checkbox'><label for='data-fd92539c-6ef1-4d85-8108-85d22402cfbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s</dd><dt><span>long_name :</span></dt><dd>Age of Received Real Time Kinetic Signal</dd></dl></div><div class='xr-var-data'><pre>array([5.6, 6.2, 7. , 6.6, 7.4, 7.2, 3.8, 4.6, 5.2, 6. , 6.6, 7.4, 7. ,\n",
+       "       6.6, 7.2, 3.8, 4.6, 5.2, 6. , 6.8, 7.4, 7.2, 7. , 3.8, 4.4, 5.2,\n",
+       "       5.8, 6.6, 7.2, 7. , 6.6, 7.2, 4. , 4.6, 5.2, 6. , 6.8, 7.4, 7.2,\n",
+       "       7. , 3.8, 4.6, 5.4, 6.2, 6.8, 7.4, 7. , 6.6, 7.4, 4. , 4.6, 5.2,\n",
+       "       6. , 6.6, 7.2, 6.8, 6.6, 7.2, 4. , 3.8, 4.6, 5.2, 6. , 6.6, 7.4,\n",
+       "       7. , 3.8, 4.4, 5.2, 5.8, 6.6, 7.2, 7. , 6.6, 7.4, 4.2, 4.8, 5.6,\n",
+       "       6.2, 7. , 6.8, 7.4, 7. , 3.8, 4.4, 5.2, 5.8, 6.6, 7.2, 6.8, 6.6,\n",
+       "       7.2, 4. , 4.6, 5.4, 6. , 6.6, 7.4, 7. , 6.8, 7.4, 4. , 4.8, 5.4,\n",
+       "       6.2, 6.8, 6.6, 7.2, 6.8, 3.6, 4.2, 4.8, 5.6, 6.2, 7. , 6.6, 7.4,\n",
+       "       7. , 3.6, 4.4, 5. , 5.6, 6.2, 7. , 6.6, 7.2, 6.8, 7.4, 4.2, 4.8,\n",
+       "       5.6, 6.2, 6.8, 6.6, 7.2, 6.8, 3.6, 4.2, 4.8, 5.4, 6. , 6.8, 7.4,\n",
+       "       7. , 6.6, 7.2, 3.8, 4.4, 5. , 5.8, 6.4, 7.2, 6.8, 7.4, 7. , 3.8,\n",
+       "       4.6, 5. , 5.8, 6.4, 7. , 6.6, 7.2, 7. , 3.8, 4.6, 5.4, 6.2, 7. ,\n",
+       "       6.6, 7.4, 7.2, 3.8, 4.6, 5.2, 6. , 6.8, 7.4, 7.2, 6.8, 3.6, 4.2,\n",
+       "       4.8, 5.6, 6.2, 7. , 6.6, 7.2, 7. , 3.6, 4.2, 5. , 5.6, 6.4, 7. ,\n",
+       "       6.6, 7.2, 7. , 3.6, 4.2, 4.8, 5.4, 6. , 6.8, 7.4, 7.2, 6.8, 3.6,\n",
+       "       4.2, 5. , 5.6, 6.4, 7. , 6.8, 6.6, 7.2, 3.8, 4.4, 5. , 5.8, 6.4,\n",
+       "       7.2, 6.8, 7.4, 7.2, 3.8, 4.4, 5. , 5.8, 6.6, 7.2, 7. , 6.8, 3.6,\n",
+       "       4.4, 5. , 5.8, 6.6, 7.2, 7. , 6.6, 7.2, 3.8, 4.6, 5.2, 5.8, 6.4,\n",
+       "       7. , 6.6, 7.2, 6.8, 7.4, 4. , 4.6, 5. , 5.6, 6.2, 6.8, 7.4, 7. ,\n",
+       "       7.4, 7. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>speed_over_grnd_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.09774 0.09774 ... 0.6019 0.5761</div><input id='attrs-2669b692-2a8a-493c-9ae5-1e9ff6596e8c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2669b692-2a8a-493c-9ae5-1e9ff6596e8c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-663dc6cf-8486-42ab-a163-8eb6ba0821bb' class='xr-var-data-in' type='checkbox'><label for='data-663dc6cf-8486-42ab-a163-8eb6ba0821bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Platform Speed over Ground</dd><dt><span>standard_name :</span></dt><dd>platform_speed_wrt_ground</dd></dl></div><div class='xr-var-data'><pre>array([0.09773663, 0.09773663, 0.07201646, 0.0925926 , 0.0771605 ,\n",
+       "       0.0308642 , 0.08744856, 0.06687243, 0.03600823, 0.05144033,\n",
+       "       0.08230452, 0.0617284 , 0.05144033, 0.05658436, 0.0617284 ,\n",
+       "       0.08744856, 0.16460904, 0.13374485, 0.25720164, 0.28806585,\n",
+       "       0.38065845, 0.39609054, 0.42695472, 0.49382716, 0.47325104,\n",
+       "       0.4835391 , 0.47325104, 0.52469134, 0.5504115 , 0.6069959 ,\n",
+       "       0.5606996 , 0.5606996 , 0.56584364, 0.5761317 , 0.62757206,\n",
+       "       0.5555556 , 0.5812757 , 0.5298354 , 0.5401234 , 0.5761317 ,\n",
+       "       0.54526746, 0.5761317 , 0.58641976, 0.5555556 , 0.52469134,\n",
+       "       0.5092593 , 0.4989712 , 0.56584364, 0.63271606, 0.622428  ,\n",
+       "       0.6532922 , 0.70987654, 0.70987654, 0.7253086 , 0.6893004 ,\n",
+       "       0.63271606, 0.5967078 , 0.6018518 , 0.63271606, 0.58641976,\n",
+       "       0.66872424, 0.66358024, 0.75617284, 0.7613169 , 0.8076132 ,\n",
+       "       0.8281893 , 0.7973251 , 0.8436214 , 0.7921811 , 0.9259259 ,\n",
+       "       0.8847737 , 0.9053498 , 0.89506173, 0.9002058 , 0.81790125,\n",
+       "       0.7973251 , 0.77160496, 0.81790125, 0.8384774 , 0.936214  ,\n",
+       "       0.9722222 , 1.0288066 , 1.0853909 , 1.0905349 , 1.0545267 ,\n",
+       "       1.0339506 , 1.100823  , 1.1522634 , 1.1985596 , 1.1831275 ,\n",
+       "       1.2191358 , 1.1882715 , 1.1728395 , 1.1728395 , 1.1779835 ,\n",
+       "       1.244856  , 1.1676955 , 1.1471194 , 1.1162552 , 1.1625514 ,\n",
+       "...\n",
+       "       1.5895061 , 1.5226338 , 1.5020577 , 1.5020577 , 1.5020577 ,\n",
+       "       1.5072017 , 1.5020577 , 1.4866256 , 1.4866256 , 1.4351852 ,\n",
+       "       1.4146091 , 1.394033  , 1.3734568 , 1.3683128 , 1.3271605 ,\n",
+       "       1.3065844 , 1.3220165 , 1.3117284 , 1.3014404 , 1.3271605 ,\n",
+       "       1.3065844 , 1.244856  , 1.1934156 , 1.1985596 , 1.1779835 ,\n",
+       "       1.2139918 , 1.2551441 , 1.2860082 , 1.2962962 , 1.3477366 ,\n",
+       "       1.3323045 , 1.3220165 , 1.3734568 , 1.3837448 , 1.3837448 ,\n",
+       "       1.3374485 , 1.3734568 , 1.3734568 , 1.388889  , 1.3631687 ,\n",
+       "       1.3786008 , 1.3786008 , 1.3528807 , 1.3168724 , 1.3271605 ,\n",
+       "       1.399177  , 1.3837448 , 1.3837448 , 1.3683128 , 1.3168724 ,\n",
+       "       1.2860082 , 1.2139918 , 1.2191358 , 1.234568  , 1.1779835 ,\n",
+       "       1.1265433 , 1.1471194 , 1.2139918 , 1.2294239 , 1.244856  ,\n",
+       "       1.1779835 , 1.1985596 , 1.2088476 , 1.1574074 , 1.1162552 ,\n",
+       "       1.1471194 , 1.1419753 , 1.1625514 , 1.1676955 , 1.1471194 ,\n",
+       "       1.1625514 , 1.1111112 , 1.1162552 , 1.1265433 , 1.0493827 ,\n",
+       "       1.0339506 , 1.0185186 , 0.9876543 , 0.9516461 , 0.9259259 ,\n",
+       "       0.9104938 , 0.9104938 , 0.8436214 , 0.84876543, 0.8333333 ,\n",
+       "       0.7973251 , 0.77160496, 0.80246913, 0.781893  , 0.73045266,\n",
+       "       0.7458848 , 0.73559666, 0.6841564 , 0.6430041 , 0.63271606,\n",
+       "       0.6018518 , 0.5761317 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dir_over_grnd_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.72 44.35 43.78 ... 73.83 80.23</div><input id='attrs-225e4f84-75c0-41be-bf8b-fc10b966ef94' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-225e4f84-75c0-41be-bf8b-fc10b966ef94' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97818d1d-3850-42ae-ba6d-a95f41d49298' class='xr-var-data-in' type='checkbox'><label for='data-97818d1d-3850-42ae-ba6d-a95f41d49298' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Platform Direction over Ground</dd><dt><span>standard_name :</span></dt><dd>platform_course</dd></dl></div><div class='xr-var-data'><pre>array([ 72.72,  44.35,  43.78,  49.87,  46.01,  97.11, 104.02,  60.97,\n",
+       "        32.95,  93.34,  49.23,  50.77,  40.85,   6.39,  20.07,  45.72,\n",
+       "        50.2 ,  50.69,  58.9 ,  57.84,  62.46,  59.65,  63.37,  62.99,\n",
+       "        67.92,  69.65,  72.65,  72.21,  74.13,  72.15,  73.66,  75.01,\n",
+       "        74.6 ,  69.57,  70.5 ,  67.84,  61.23,  67.05,  60.61,  58.55,\n",
+       "        55.72,  52.02,  50.42,  50.28,  50.22,  57.5 ,  60.96,  66.68,\n",
+       "        79.81,  84.9 ,  87.21,  91.27,  88.96,  80.19,  76.88,  72.07,\n",
+       "        70.25,  67.17,  61.13,  67.11,  74.79,  79.83,  82.53,  85.06,\n",
+       "        80.79,  73.74,  71.15,  68.83,  72.07,  68.26,  72.83,  71.99,\n",
+       "        73.01,  74.41,  70.12,  62.63,  60.16,  56.99,  56.71,  60.35,\n",
+       "        59.81,  63.25,  63.99,  60.47,  58.38,  56.72,  56.57,  59.46,\n",
+       "        57.19,  58.53,  54.36,  53.07,  51.32,  51.63,  54.26,  56.79,\n",
+       "        59.69,  61.14,  58.39,  57.56,  59.36,  58.53,  59.86,  60.52,\n",
+       "        64.01,  64.71,  64.53,  65.43,  66.07,  64.94,  64.24,  62.6 ,\n",
+       "        59.83,  59.2 ,  56.48,  55.96,  61.52,  62.71,  64.38,  66.06,\n",
+       "        71.77,  78.84,  81.8 ,  75.95,  72.45,  69.59,  68.75,  71.24,\n",
+       "        70.67,  70.58,  67.8 ,  64.3 ,  64.67,  61.71,  59.79,  57.44,\n",
+       "        59.57,  57.96,  56.76,  55.99,  57.98,  61.39,  71.72,  78.45,\n",
+       "        80.64,  83.65,  83.97,  84.37,  88.13,  89.6 ,  87.59,  83.46,\n",
+       "        80.34,  83.03,  82.44,  82.74,  82.57,  81.85,  82.39,  82.42,\n",
+       "        83.09,  83.16,  82.29,  84.85,  83.7 ,  78.19,  79.66,  79.22,\n",
+       "        79.29,  80.13,  79.34,  81.14,  84.08,  83.23,  81.84,  80.06,\n",
+       "        78.68,  78.3 ,  79.28,  78.33,  79.25,  78.43,  77.09,  76.28,\n",
+       "        76.99,  72.75,  69.48,  67.48,  67.77,  71.86,  70.99,  69.07,\n",
+       "        70.22,  70.32,  72.33,  72.61,  71.88,  73.91,  74.14,  72.15,\n",
+       "        73.1 ,  75.5 ,  74.3 ,  74.19,  73.77,  73.66,  74.78,  73.75,\n",
+       "        70.26,  72.  ,  75.1 ,  77.64,  78.61,  79.36,  79.65,  79.38,\n",
+       "        77.87,  75.41,  75.99,  72.76,  69.83,  72.26,  75.56,  79.4 ,\n",
+       "        78.22,  77.92,  78.05,  76.88,  74.82,  72.37,  71.78,  73.08,\n",
+       "        73.98,  74.32,  76.55,  74.07,  72.79,  74.25,  73.88,  74.37,\n",
+       "        73.39,  72.57,  71.65,  72.7 ,  71.16,  72.53,  74.24,  74.79,\n",
+       "        72.56,  74.71,  74.95,  75.83,  77.18,  78.42,  79.11,  79.43,\n",
+       "        80.85,  77.13,  79.9 ,  78.86,  73.83,  80.23], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dist_nmea</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.3 1.3 1.3 1.3 ... 1.2 1.1 1.1 1.1</div><input id='attrs-5e25944f-ec8f-414a-af10-b191ce3ff482' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5e25944f-ec8f-414a-af10-b191ce3ff482' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28aa2c6c-e94f-4bdc-97b5-86c0605e3b3b' class='xr-var-data-in' type='checkbox'><label for='data-28aa2c6c-e94f-4bdc-97b5-86c0605e3b3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Depth Sounder Range</dd></dl></div><div class='xr-var-data'><pre>array([1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 1.4, 0. , 1.4,\n",
+       "       1.4, 1.4, 1.4, 1.5, 1.5, 1.7, 1.7, 1.7, 0. , 1.8, 2. , 2.6, 2.6,\n",
+       "       2.8, 2.7, 2.7, 3.8, 3.8, 3.8, 4.2, 4.2, 4.2, 4.5, 4.8, 6.5, 6.5,\n",
+       "       6.4, 6.4, 6.8, 6.9, 0. , 7.7, 7.9, 7.9, 7.9, 7.9, 8.1, 8.2, 8.2,\n",
+       "       8.6, 8.9, 8.9, 9. , 9.1, 9.1, 9.2, 9.2, 9.3, 9.3, 9.3, 9.4, 9.4,\n",
+       "       9.5, 9.5, 9.5, 9.5, 9.3, 9.3, 9.3, 9.3, 9.2, 9.2, 9.2, 9.2, 9.1,\n",
+       "       9.1, 9.1, 9.1, 9.1, 9.1, 9. , 9. , 9. , 9. , 9. , 9. , 8.9, 8.9,\n",
+       "       8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9, 8.9,\n",
+       "       8.9, 8.8, 8.8, 8.8, 9. , 9.1, 9.1, 9.2, 9.2, 9.2, 9.1, 9.1, 9.1,\n",
+       "       8.9, 8.8, 8.8, 8.6, 8.4, 8.4, 8.3, 8.3, 8.3, 8.3, 8.4, 8.4, 8.4,\n",
+       "       8.5, 8.5, 8.6, 8.7, 8.7, 8.6, 8.6, 8.6, 8.5, 8.4, 8.4, 8.3, 8.3,\n",
+       "       7.5, 7.7, 7.7, 7.7, 7.7, 7.7, 7.7, 7.7, 7.6, 7.5, 7.5, 7.5, 7.7,\n",
+       "       7.7, 7.7, 7.7, 7.7, 7.1, 7.1, 7.1, 7. , 7. , 7. , 7. , 7. , 7. ,\n",
+       "       6.6, 6.6, 6. , 5.7, 5.6, 5.6, 5.2, 5. , 4.9, 4.9, 5.1, 5.1, 5.1,\n",
+       "       4.9, 4.7, 4.7, 4.6, 4.6, 4.6, 4.4, 4.4, 4.4, 4.4, 4.4, 4.4, 4.3,\n",
+       "       4.1, 4.1, 3.9, 3.9, 3.9, 3.8, 3.8, 3.8, 3.3, 3.3, 3.3, 3.3, 3.3,\n",
+       "       3.3, 3.4, 3.4, 3.4, 3.1, 3.1, 3.1, 3.1, 2.8, 2.8, 2.7, 2.5, 2.5,\n",
+       "       2.5, 2.6, 2.6, 2.6, 2.6, 2.6, 2.5, 2.5, 2.5, 2.5, 2.5, 2.4, 2.4,\n",
+       "       2.4, 2.4, 2.3, 2.2, 2.2, 2.1, 2.1, 2.1, 1.8, 1.8, 1.8, 1.7, 1.7,\n",
+       "       1.6, 1.5, 1.5, 1.5, 1.5, 1.3, 1.3, 1.3, 1.3, 1.3, 1.2, 1.2, 1.1,\n",
+       "       1.1, 1.1], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_sl</span></div><div class='xr-var-dims'>(dir, range_sl, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.338 -1.327 -1.275 ... nan nan</div><input id='attrs-86fccae0-3ebf-452c-9773-3e363f23b875' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86fccae0-3ebf-452c-9773-3e363f23b875' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32f84030-2848-4592-98ff-96c59a1b2e3d' class='xr-var-data-in' type='checkbox'><label for='data-32f84030-2848-4592-98ff-96c59a1b2e3d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[-1.3384625e+00, -1.3271410e+00, -1.2751875e+00, ...,\n",
+       "         -2.3546405e-01, -2.2787344e-01, -1.6161118e-01],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan]],\n",
+       "\n",
+       "       [[ 1.0810345e-03,  8.8868886e-03,  2.0653188e-01, ...,\n",
+       "         -7.0343661e-01, -4.8634797e-01, -5.6815118e-01],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "...\n",
+       "         -3.2227486e-02, -2.9505478e-02, -1.6433744e-02],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan]],\n",
+       "\n",
+       "       [[ 4.7551185e-02,  7.8045934e-02, -7.5461626e-02, ...,\n",
+       "          6.4090714e-02,  1.5505850e-03,  8.5798822e-02],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan],\n",
+       "        [           nan,            nan,            nan, ...,\n",
+       "                    nan,            nan,            nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_sl</span></div><div class='xr-var-dims'>(beam, range_sl, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>144 173 184 165 186 ... 0 0 0 0 0</div><input id='attrs-fb995f4f-3611-4628-8bcf-9ccbcd7fe939' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb995f4f-3611-4628-8bcf-9ccbcd7fe939' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efb58985-7a37-4c09-a663-5f30f159f6f2' class='xr-var-data-in' type='checkbox'><label for='data-efb58985-7a37-4c09-a663-5f30f159f6f2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[144, 173, 184, ..., 145, 151, 161],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[151, 174, 156, ..., 118, 159, 179],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[160, 166, 188, ..., 146, 133, 170],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[169, 160, 138, ..., 172, 212, 125],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_sl</span></div><div class='xr-var-dims'>(beam, range_sl, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>137 143 142 143 144 ... 0 0 0 0 0</div><input id='attrs-e6986ed3-25e2-44b1-9ca9-21315afece08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e6986ed3-25e2-44b1-9ca9-21315afece08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d399ae10-fab9-459c-b555-ab3adb2fd865' class='xr-var-data-in' type='checkbox'><label for='data-d399ae10-fab9-459c-b555-ab3adb2fd865' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[137, 143, 142, ..., 128, 127, 130],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[140, 148, 144, ..., 134, 130, 136],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[133, 139, 140, ..., 125, 122, 129],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]],\n",
+       "\n",
+       "       [[135, 142, 139, ..., 129, 135, 125],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0],\n",
+       "        [  0,   0,   0, ...,   0,   0,   0]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hdwtime_gps</span></div><div class='xr-var-dims'>(time_gps)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2022-08-19T20:14:21.930000066 .....</div><input id='attrs-1b03ff73-41c6-4d3d-9dc4-aa510b73149f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1b03ff73-41c6-4d3d-9dc4-aa510b73149f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24b9dee8-45f6-4f1d-aeb1-e214f7c8fb2e' class='xr-var-data-in' type='checkbox'><label for='data-24b9dee8-45f6-4f1d-aeb1-e214f7c8fb2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2022-08-19T20:14:21.930000066&#x27;, &#x27;2022-08-19T20:14:22.500000000&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:23.220000028&#x27;, &#x27;2022-08-19T20:14:23.900000095&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:24.609999895&#x27;, &#x27;2022-08-19T20:14:25.279999971&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:26.180000066&#x27;, &#x27;2022-08-19T20:14:26.859999895&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:27.539999961&#x27;, &#x27;2022-08-19T20:14:28.250000000&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:28.920000076&#x27;, &#x27;2022-08-19T20:14:29.640000104&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:30.329999923&#x27;, &#x27;2022-08-19T20:14:31.059999942&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:31.640000104&#x27;, &#x27;2022-08-19T20:14:32.250000000&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:32.819999933&#x27;, &#x27;2022-08-19T20:14:33.519999980&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:34.200000047&#x27;, &#x27;2022-08-19T20:14:34.970000028&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:35.720000028&#x27;, &#x27;2022-08-19T20:14:36.450000047&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:37.210000038&#x27;, &#x27;2022-08-19T20:14:38.009999990&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:38.710000038&#x27;, &#x27;2022-08-19T20:14:39.420000076&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:40.119999885&#x27;, &#x27;2022-08-19T20:14:40.769999980&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:41.470000028&#x27;, &#x27;2022-08-19T20:14:42.140000104&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:42.869999885&#x27;, &#x27;2022-08-19T20:14:43.569999933&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:44.230000019&#x27;, &#x27;2022-08-19T20:14:44.869999885&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:45.569999933&#x27;, &#x27;2022-08-19T20:14:46.240000009&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:46.960000038&#x27;, &#x27;2022-08-19T20:14:47.670000076&#x27;,\n",
+       "       &#x27;2022-08-19T20:14:48.410000085&#x27;, &#x27;2022-08-19T20:14:49.130000114&#x27;,\n",
+       "...\n",
+       "       &#x27;2022-08-19T20:16:55.450000047&#x27;, &#x27;2022-08-19T20:16:56.099999904&#x27;,\n",
+       "       &#x27;2022-08-19T20:16:56.720000028&#x27;, &#x27;2022-08-19T20:16:57.380000114&#x27;,\n",
+       "       &#x27;2022-08-19T20:16:58.059999942&#x27;, &#x27;2022-08-19T20:16:58.720000028&#x27;,\n",
+       "       &#x27;2022-08-19T20:16:59.480000019&#x27;, &#x27;2022-08-19T20:17:00.250000000&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:00.970000028&#x27;, &#x27;2022-08-19T20:17:01.750000000&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:02.529999971&#x27;, &#x27;2022-08-19T20:17:03.299999952&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:04.039999961&#x27;, &#x27;2022-08-19T20:17:04.759999990&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:05.509999990&#x27;, &#x27;2022-08-19T20:17:06.210000038&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:06.930000066&#x27;, &#x27;2022-08-19T20:17:07.559999942&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:08.200000047&#x27;, &#x27;2022-08-19T20:17:08.819999933&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:09.480000019&#x27;, &#x27;2022-08-19T20:17:10.089999914&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:10.730000019&#x27;, &#x27;2022-08-19T20:17:11.329999923&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:11.950000047&#x27;, &#x27;2022-08-19T20:17:12.539999961&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:13.140000104&#x27;, &#x27;2022-08-19T20:17:13.710000038&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:14.299999952&#x27;, &#x27;2022-08-19T20:17:14.869999885&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:15.480000019&#x27;, &#x27;2022-08-19T20:17:16.039999961&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:16.630000114&#x27;, &#x27;2022-08-19T20:17:17.190000057&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:17.779999971&#x27;, &#x27;2022-08-19T20:17:18.329999923&#x27;,\n",
+       "       &#x27;2022-08-19T20:17:18.910000085&#x27;, &#x27;2022-08-19T20:17:19.450000047&#x27;],\n",
+       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.462 -1.462 0.0 ... -1.034 -1.034</div><input id='attrs-00e74a63-3d77-44ea-8f49-b95f2f1fdbd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00e74a63-3d77-44ea-8f49-b95f2f1fdbd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fc973d5-423f-4b82-849b-428f67c68ac1' class='xr-var-data-in' type='checkbox'><label for='data-7fc973d5-423f-4b82-849b-428f67c68ac1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.4619021 , -1.4619021 ,  0.        ,  0.        ],\n",
+       "       [ 0.        ,  0.        , -1.4619021 ,  1.4619021 ],\n",
+       "       [ 0.26604444,  0.26604444,  0.26604444,  0.26604444],\n",
+       "       [ 1.033721  ,  1.033721  , -1.033721  , -1.033721  ]],\n",
+       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.8992 -0.838 ... 0.9999 0.9999</div><input id='attrs-71d27077-dd47-4576-8328-f69328bab1e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71d27077-dd47-4576-8328-f69328bab1e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a61d61a6-6896-46aa-82ad-413c3784a397' class='xr-var-data-in' type='checkbox'><label for='data-a61d61a6-6896-46aa-82ad-413c3784a397' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.8991666 , -0.8379869 , -0.86326927, ..., -0.9300857 ,\n",
+       "         -0.93617517, -0.9089982 ],\n",
+       "        [ 0.43625483,  0.5456233 ,  0.5043985 , ..., -0.36716184,\n",
+       "         -0.35141215, -0.41661045],\n",
+       "        [-0.03436856, -0.00855068, -0.01866788, ...,  0.01151885,\n",
+       "          0.00924976, -0.01256542]],\n",
+       "\n",
+       "       [[-0.4357624 , -0.5454322 , -0.5039119 , ...,  0.36714867,\n",
+       "          0.35133827,  0.41673636],\n",
+       "        [-0.89981425, -0.8379696 , -0.8633793 , ..., -0.9301563 ,\n",
+       "         -0.9362079 , -0.908973  ],\n",
+       "        [-0.02110444, -0.01762627, -0.02547461, ..., -0.0033159 ,\n",
+       "         -0.00872616, -0.00994743]],\n",
+       "\n",
+       "       [[-0.04013224, -0.01678251, -0.02896681, ...,  0.01193181,\n",
+       "          0.01172618, -0.00727742],\n",
+       "        [-0.00399988, -0.01010676, -0.01258448, ...,  0.00114506,\n",
+       "         -0.00491942, -0.01427866],\n",
+       "        [ 0.99918634,  0.9998081 ,  0.99950117, ...,  0.9999282 ,\n",
+       "          0.9999191 ,  0.99987155]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>discharge</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.136e+03</div><input id='attrs-5bb89513-6938-4519-8163-5c115c412004' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bb89513-6938-4519-8163-5c115c412004' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6eeb58e0-210f-4db9-969a-13d85dea4ed2' class='xr-var-data-in' type='checkbox'><label for='data-6eeb58e0-210f-4db9-969a-13d85dea4ed2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m3 s-1</dd><dt><span>long_name :</span></dt><dd>Discharge</dd></dl></div><div class='xr-var-data'><pre>array(-1136.2273, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>power</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.211e+06</div><input id='attrs-5eefdae6-e3ce-4f2d-921f-005eaa0c866c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5eefdae6-e3ce-4f2d-921f-005eaa0c866c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d8e85d43-72c7-4325-88f7-b8a95555300b' class='xr-var-data-in' type='checkbox'><label for='data-d8e85d43-72c7-4325-88f7-b8a95555300b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W</dd><dt><span>long_name :</span></dt><dd>Power</dd></dl></div><div class='xr-var-data'><pre>array(-1210604.4, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>power_density</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.307e+03</div><input id='attrs-b025b8d7-e39c-466c-8def-4276cb77675c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b025b8d7-e39c-466c-8def-4276cb77675c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-43fb84d8-465d-44c9-8e12-cde076511f19' class='xr-var-data-in' type='checkbox'><label for='data-43fb84d8-465d-44c9-8e12-cde076511f19' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>W m-2</dd><dt><span>long_name :</span></dt><dd>Power Density</dd></dl></div><div class='xr-var-data'><pre>array(-1306.6486, dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>reynolds_number</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>6.2e+06</div><input id='attrs-90042812-fee4-4e70-b18a-d53ae43eb033' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90042812-fee4-4e70-b18a-d53ae43eb033' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67609ab3-f559-452a-9658-1e985d8989f0' class='xr-var-data-in' type='checkbox'><label for='data-67609ab3-f559-452a-9658-1e985d8989f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reynolds Number</dd></dl></div><div class='xr-var-data'><pre>array(6199513.5, dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4d459e0d-cff0-4086-8e66-7015c8815624' class='xr-section-summary-in' type='checkbox'  ><label for='section-4d459e0d-cff0-4086-8e66-7015c8815624' class='xr-section-summary' >Indexes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ebb3b6c0-1725-4d0a-8fb1-625eef1a82b2' class='xr-index-data-in' type='checkbox'/><label for='index-ebb3b6c0-1725-4d0a-8fb1-625eef1a82b2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-08-19 20:14:21.930000066&#x27;,\n",
+       "                  &#x27;2022-08-19 20:14:22.500000&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:23.220000028&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:23.900000095&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:24.609999895&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:25.279999971&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:26.180000066&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:26.859999895&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:27.539999961&#x27;,\n",
+       "                  &#x27;2022-08-19 20:14:28.250000&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2022-08-19 20:17:14.299999952&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:14.869999885&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:15.480000019&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:16.039999961&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:16.630000114&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:17.190000057&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:17.779999971&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:18.329999923&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:18.910000085&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:19.450000047&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=262, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_gps</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fd9b0b0d-edb4-468e-a219-7589a34805d7' class='xr-index-data-in' type='checkbox'/><label for='index-fd9b0b0d-edb4-468e-a219-7589a34805d7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2022-08-19 20:14:23.599999904&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:24.200000047&#x27;,\n",
+       "                         &#x27;2022-08-19 20:14:25&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:25.599999904&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:26.400000095&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:27.200000047&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:27.799999952&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:28.599999904&#x27;,\n",
+       "               &#x27;2022-08-19 20:14:29.200000047&#x27;,\n",
+       "                         &#x27;2022-08-19 20:14:30&#x27;,\n",
+       "               ...\n",
+       "                         &#x27;2022-08-19 20:17:16&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:16.599999904&#x27;,\n",
+       "                         &#x27;2022-08-19 20:17:17&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:17.599999904&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:18.200000047&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:18.799999952&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:19.400000095&#x27;,\n",
+       "                         &#x27;2022-08-19 20:17:20&#x27;,\n",
+       "               &#x27;2022-08-19 20:17:20.400000095&#x27;,\n",
+       "                         &#x27;2022-08-19 20:17:21&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_gps&#x27;, length=262, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-64b1e991-51d9-43b7-a8c4-041513b52806' class='xr-index-data-in' type='checkbox'/><label for='index-64b1e991-51d9-43b7-a8c4-041513b52806' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.949999988079071, 1.4299999475479126,  1.909999966621399,\n",
+       "       2.3899998664855957,  2.869999885559082, 3.3499999046325684,\n",
+       "       3.8299999237060547,  4.309999942779541,  4.789999961853027,\n",
+       "        5.269999980926514,               5.75,  6.230000019073486,\n",
+       "        6.710000038146973,  7.190000057220459,  7.670000076293945,\n",
+       "        8.149999618530273,  8.630000114440918,  9.109999656677246,\n",
+       "         9.59000015258789, 10.069999694824219, 10.550000190734863,\n",
+       "       11.029999732971191, 11.510000228881836, 11.989999771118164],\n",
+       "      dtype=&#x27;float32&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_sl</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e24555c3-c469-41da-b7f7-c6968a9469ef' class='xr-index-data-in' type='checkbox'/><label for='index-e24555c3-c469-41da-b7f7-c6968a9469ef' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([             0.54, 0.659999997317791, 0.779999994635582,\n",
+       "       0.899999991953373, 1.019999989271164],\n",
+       "      dtype=&#x27;float64&#x27;, name=&#x27;range_sl&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7fb297f7-f44f-480f-9338-6fe1aa7f694d' class='xr-index-data-in' type='checkbox'/><label for='index-7fb297f7-f44f-480f-9338-6fe1aa7f694d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-10561123-efb5-4e06-8f1e-15e11a08adda' class='xr-index-data-in' type='checkbox'/><label for='index-10561123-efb5-4e06-8f1e-15e11a08adda' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;, &#x27;err&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-61de4b44-42d1-4ee2-867a-5a781f64d477' class='xr-index-data-in' type='checkbox'/><label for='index-61de4b44-42d1-4ee2-867a-5a781f64d477' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e5aa512a-169c-4d92-a774-7144a769a242' class='xr-index-data-in' type='checkbox'/><label for='index-e5aa512a-169c-4d92-a774-7144a769a242' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4d4fc557-3b54-459d-994f-787d959b7cf1' class='xr-index-data-in' type='checkbox'/><label for='index-4d4fc557-3b54-459d-994f-787d959b7cf1' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dcba92da-4702-4545-8ab7-08b0dc28da3e' class='xr-index-data-in' type='checkbox'/><label for='index-dcba92da-4702-4545-8ab7-08b0dc28da3e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ace2899d-5862-448b-badb-3234f6b40582' class='xr-section-summary-in' type='checkbox'  ><label for='section-ace2899d-5862-448b-badb-3234f6b40582' class='xr-section-summary' >Attributes: <span>(46)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>prog_ver :</span></dt><dd>56.1</dd><dt><span>inst_make :</span></dt><dd>TRDI</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;vel_sl&#x27;, &#x27;vel_bt&#x27;]</dd><dt><span>has_imu :</span></dt><dd>0</dd><dt><span>inst_model :</span></dt><dd>RiverPro</dd><dt><span>beam_angle :</span></dt><dd>20</dd><dt><span>freq :</span></dt><dd>1200</dd><dt><span>beam_pattern :</span></dt><dd>convex</dd><dt><span>orientation :</span></dt><dd>down</dd><dt><span>n_beams :</span></dt><dd>5</dd><dt><span>n_cells :</span></dt><dd>24</dd><dt><span>pings_per_ensemble :</span></dt><dd>1</dd><dt><span>cell_size :</span></dt><dd>0.48</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>profiling_mode :</span></dt><dd>3</dd><dt><span>min_corr_threshold :</span></dt><dd>64</dd><dt><span>n_code_reps :</span></dt><dd>2</dd><dt><span>min_prcnt_gd :</span></dt><dd>1</dd><dt><span>max_error_vel :</span></dt><dd>9.999</dd><dt><span>sec_between_ping_groups :</span></dt><dd>0.0</dd><dt><span>coord_sys :</span></dt><dd>principal</dd><dt><span>use_pitchroll :</span></dt><dd>no</dd><dt><span>use_3beam :</span></dt><dd>no</dd><dt><span>bin_mapping :</span></dt><dd>no</dd><dt><span>heading_misalign_deg :</span></dt><dd>0.0</dd><dt><span>magnetic_var_deg :</span></dt><dd>0.0</dd><dt><span>sensors_src :</span></dt><dd>01011101</dd><dt><span>sensors_avail :</span></dt><dd>00011101</dd><dt><span>transmit_pulse_m :</span></dt><dd>0.03</dd><dt><span>water_ref_cells :</span></dt><dd>[1, 1]</dd><dt><span>false_target_threshold :</span></dt><dd>255</dd><dt><span>transmit_lag_m :</span></dt><dd>0.96</dd><dt><span>bandwidth :</span></dt><dd>0</dd><dt><span>power_level :</span></dt><dd>0</dd><dt><span>serialnum :</span></dt><dd>2888</dd><dt><span>surface_layer :</span></dt><dd>1</dd><dt><span>n_cells_sl :</span></dt><dd>5</dd><dt><span>cell_size_sl :</span></dt><dd>0.12</dd><dt><span>sourceprog :</span></dt><dd>WinRiver2</dd><dt><span>range_offset :</span></dt><dd>0.37</dd><dt><span>blank_dist_sl :</span></dt><dd>0.05</dd><dt><span>fs :</span></dt><dd>1.47</dd><dt><span>declination :</span></dt><dd>18</dd><dt><span>declination_in_orientmat :</span></dt><dd>1</dd><dt><span>principal_heading :</span></dt><dd>173.522</dd></dl></div></li></ul></div></div>"
+      ],
+      "text/plain": [
+       "<xarray.Dataset> Size: 236kB\n",
+       "Dimensions:              (time: 262, time_gps: 262, range: 24, range_sl: 5,\n",
+       "                          beam: 4, dir: 4, x1: 4, x2: 4, earth: 3, inst: 3)\n",
+       "Coordinates:\n",
+       "  * time                 (time) datetime64[ns] 2kB 2022-08-19T20:14:21.930000...\n",
+       "  * time_gps             (time_gps) datetime64[ns] 2kB 2022-08-19T20:14:23.59...\n",
+       "  * range                (range) float32 96B 0.95 1.43 1.91 ... 11.51 11.99\n",
+       "  * range_sl             (range_sl) float64 40B 0.54 0.66 0.78 0.9 1.02\n",
+       "  * beam                 (beam) int32 16B 1 2 3 4\n",
+       "  * dir                  (dir) <U10 160B 'streamwise' 'x-stream' 'vert' 'err'\n",
+       "  * x1                   (x1) int32 16B 1 2 3 4\n",
+       "  * x2                   (x2) int32 16B 1 2 3 4\n",
+       "  * earth                (earth) <U1 12B 'E' 'N' 'U'\n",
+       "  * inst                 (inst) <U1 12B 'X' 'Y' 'Z'\n",
+       "Data variables: (12/47)\n",
+       "    number               (time) uint32 1kB 398 399 400 401 ... 656 657 658 659\n",
+       "    builtin_test_fail    (time) bool 262B False False False ... False False\n",
+       "    c_sound              (time) float32 1kB 1.458e+03 1.458e+03 ... 1.459e+03\n",
+       "    depth                (time) float32 1kB 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0\n",
+       "    pitch                (time) float32 1kB -1.21 -1.01 -1.46 ... -0.5 -0.57\n",
+       "    roll                 (time) float32 1kB 1.97 0.49 1.07 ... -0.66 -0.53 0.72\n",
+       "    ...                   ...\n",
+       "    beam2inst_orientmat  (x1, x2) float32 64B 1.462 -1.462 0.0 ... -1.034 -1.034\n",
+       "    orientmat            (earth, inst, time) float32 9kB -0.8992 ... 0.9999\n",
+       "    discharge            float32 4B -1.136e+03\n",
+       "    power                float32 4B -1.211e+06\n",
+       "    power_density        float32 4B -1.307e+03\n",
+       "    reynolds_number      float32 4B 6.2e+06\n",
+       "Attributes: (12/46)\n",
+       "    prog_ver:                  56.1\n",
+       "    inst_make:                 TRDI\n",
+       "    inst_type:                 ADCP\n",
+       "    rotate_vars:               ['vel', 'vel_sl', 'vel_bt']\n",
+       "    has_imu:                   0\n",
+       "    inst_model:                RiverPro\n",
+       "    ...                        ...\n",
+       "    range_offset:              0.37\n",
+       "    blank_dist_sl:             0.05\n",
+       "    fs:                        1.47\n",
+       "    declination:               18\n",
+       "    declination_in_orientmat:  1\n",
+       "    principal_heading:         173.522"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate discharge, power, power density, and the Reynolds number of the channel\n",
+    "dolfyn.adp.api.discharge(ds, water_depth, rho=1020, mu=0.0012, surface_offset=0, utm_zone=5)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "work",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/examples/adcp_example.ipynb b/examples/adcp_example.ipynb
index f83d007f8..8df4aa02b 100644
--- a/examples/adcp_example.ipynb
+++ b/examples/adcp_example.ipynb
@@ -44,7 +44,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 1,
+      "execution_count": 33,
       "metadata": {
         "scrolled": true
       },
@@ -73,7 +73,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 2,
+      "execution_count": 34,
       "metadata": {
         "scrolled": true
       },
@@ -100,7 +100,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 35,
       "metadata": {},
       "outputs": [
         {
@@ -136,14 +136,14 @@
               "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
               "}\n",
               "\n",
-              "html[theme=dark],\n",
-              "html[data-theme=dark],\n",
-              "body[data-theme=dark],\n",
+              "html[theme=\"dark\"],\n",
+              "html[data-theme=\"dark\"],\n",
+              "body[data-theme=\"dark\"],\n",
               "body.vscode-dark {\n",
               "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
               "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
               "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-              "  --xr-border-color: #1F1F1F;\n",
+              "  --xr-border-color: #1f1f1f;\n",
               "  --xr-disabled-color: #515151;\n",
               "  --xr-background-color: #111111;\n",
               "  --xr-background-color-row-even: #111111;\n",
@@ -188,7 +188,7 @@
               ".xr-sections {\n",
               "  padding-left: 0 !important;\n",
               "  display: grid;\n",
-              "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+              "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
               "}\n",
               "\n",
               ".xr-section-item {\n",
@@ -196,7 +196,9 @@
               "}\n",
               "\n",
               ".xr-section-item input {\n",
-              "  display: none;\n",
+              "  display: inline-block;\n",
+              "  opacity: 0;\n",
+              "  height: 0;\n",
               "}\n",
               "\n",
               ".xr-section-item input + label {\n",
@@ -208,6 +210,10 @@
               "  color: var(--xr-font-color2);\n",
               "}\n",
               "\n",
+              ".xr-section-item input:focus + label {\n",
+              "  border: 2px solid var(--xr-font-color0);\n",
+              "}\n",
+              "\n",
               ".xr-section-item input:enabled + label:hover {\n",
               "  color: var(--xr-font-color0);\n",
               "}\n",
@@ -229,7 +235,7 @@
               "\n",
               ".xr-section-summary-in + label:before {\n",
               "  display: inline-block;\n",
-              "  content: '►';\n",
+              "  content: \"►\";\n",
               "  font-size: 11px;\n",
               "  width: 15px;\n",
               "  text-align: center;\n",
@@ -240,7 +246,7 @@
               "}\n",
               "\n",
               ".xr-section-summary-in:checked + label:before {\n",
-              "  content: '▼';\n",
+              "  content: \"▼\";\n",
               "}\n",
               "\n",
               ".xr-section-summary-in:checked + label > span {\n",
@@ -312,15 +318,15 @@
               "}\n",
               "\n",
               ".xr-dim-list:before {\n",
-              "  content: '(';\n",
+              "  content: \"(\";\n",
               "}\n",
               "\n",
               ".xr-dim-list:after {\n",
-              "  content: ')';\n",
+              "  content: \")\";\n",
               "}\n",
               "\n",
               ".xr-dim-list li:not(:last-child):after {\n",
-              "  content: ',';\n",
+              "  content: \",\";\n",
               "  padding-right: 5px;\n",
               "}\n",
               "\n",
@@ -514,26 +520,33 @@
               "    coord_sys:             earth\n",
               "    fs:                    1\n",
               "    has_imu:               1\n",
-              "    beam_angle:            25</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-98124a36-96bc-49eb-b5f9-6eafb43abe7c' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-98124a36-96bc-49eb-b5f9-6eafb43abe7c' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 55000</li><li><span class='xr-has-index'>time_b5</span>: 55000</li><li><span class='xr-has-index'>range</span>: 28</li><li><span class='xr-has-index'>range_b5</span>: 28</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li><li><span class='xr-has-index'>q</span>: 4</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-de6aea9e-79dd-486c-b771-84722fcd110b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-de6aea9e-79dd-486c-b771-84722fcd110b' class='xr-section-summary' >Coordinates: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:20:00.500999927 .....</div><input id='attrs-3742ab4e-df1d-4a84-a12d-79afb49b4db0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3742ab4e-df1d-4a84-a12d-79afb49b4db0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bdded0e0-3e9f-48fe-88f6-1614cd5121de' class='xr-var-data-in' type='checkbox'><label for='data-bdded0e0-3e9f-48fe-88f6-1614cd5121de' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:20:00.500999927&#x27;, &#x27;2020-08-15T00:20:01.501100063&#x27;,\n",
+              "    beam_angle:            25</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-3fd009a2-dae4-4635-8613-be7515d4baf3' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-3fd009a2-dae4-4635-8613-be7515d4baf3' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 55000</li><li><span class='xr-has-index'>time_b5</span>: 55000</li><li><span class='xr-has-index'>range</span>: 28</li><li><span class='xr-has-index'>range_b5</span>: 28</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li><li><span class='xr-has-index'>q</span>: 4</li><li><span class='xr-has-index'>x1</span>: 4</li><li><span class='xr-has-index'>x2</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-921732fd-d14b-455c-bd19-6708ed058630' class='xr-section-summary-in' type='checkbox'  checked><label for='section-921732fd-d14b-455c-bd19-6708ed058630' class='xr-section-summary' >Coordinates: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:20:00.500999927 .....</div><input id='attrs-24a35bc3-cfeb-4aef-9902-2fffc9e8a9d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24a35bc3-cfeb-4aef-9902-2fffc9e8a9d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc72a277-6552-4bf1-a7e4-a046a5dcdf2b' class='xr-var-data-in' type='checkbox'><label for='data-fc72a277-6552-4bf1-a7e4-a046a5dcdf2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:20:00.500999927&#x27;, &#x27;2020-08-15T00:20:01.501100063&#x27;,\n",
               "       &#x27;2020-08-15T00:20:02.500999927&#x27;, ..., &#x27;2020-08-15T15:36:37.500999927&#x27;,\n",
               "       &#x27;2020-08-15T15:36:38.500999927&#x27;, &#x27;2020-08-15T15:36:39.500999927&#x27;],\n",
-              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:20:00.438499927 .....</div><input id='attrs-86df1a4f-7b85-4d79-a471-bf8a985a6ca4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86df1a4f-7b85-4d79-a471-bf8a985a6ca4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba55dae5-9d35-4165-a756-455253700f33' class='xr-var-data-in' type='checkbox'><label for='data-ba55dae5-9d35-4165-a756-455253700f33' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:20:00.438499927&#x27;, &#x27;2020-08-15T00:20:01.438499927&#x27;,\n",
+              "      shape=(55000,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:20:00.438499927 .....</div><input id='attrs-36f73ccd-88a8-4e60-a351-8d1090fe2f24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36f73ccd-88a8-4e60-a351-8d1090fe2f24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5527781-2deb-4052-8b75-2b0148f43ec5' class='xr-var-data-in' type='checkbox'><label for='data-d5527781-2deb-4052-8b75-2b0148f43ec5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:20:00.438499927&#x27;, &#x27;2020-08-15T00:20:01.438499927&#x27;,\n",
               "       &#x27;2020-08-15T00:20:02.438499927&#x27;, ..., &#x27;2020-08-15T15:36:37.438499927&#x27;,\n",
               "       &#x27;2020-08-15T15:36:38.438499927&#x27;, &#x27;2020-08-15T15:36:39.438499927&#x27;],\n",
-              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1</div><input id='attrs-495f2410-37a2-4e3a-bf93-865045fbd173' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-495f2410-37a2-4e3a-bf93-865045fbd173' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aae4e1f6-e69e-4c68-9500-c51e2a8f7f80' class='xr-var-data-in' type='checkbox'><label for='data-aae4e1f6-e69e-4c68-9500-c51e2a8f7f80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
+              "      shape=(55000,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1</div><input id='attrs-ffb35e59-f730-42b0-a96f-916b97fb181b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffb35e59-f730-42b0-a96f-916b97fb181b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-423e0438-a1ec-4677-b2b3-55547eb12915' class='xr-var-data-in' type='checkbox'><label for='data-423e0438-a1ec-4677-b2b3-55547eb12915' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
               "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
-              "       12.6, 13.1, 13.6, 14.1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_b5</span></div><div class='xr-var-dims'>(range_b5)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1</div><input id='attrs-9948b1fb-e82c-401e-b753-ebc0a8d2ea7e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9948b1fb-e82c-401e-b753-ebc0a8d2ea7e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aef11db6-49e4-451d-995f-d506a9d0d77d' class='xr-var-data-in' type='checkbox'><label for='data-aef11db6-49e4-451d-995f-d506a9d0d77d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
+              "       12.6, 13.1, 13.6, 14.1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_b5</span></div><div class='xr-var-dims'>(range_b5)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.6 1.1 1.6 2.1 ... 13.1 13.6 14.1</div><input id='attrs-d67f323c-d7dd-4fb7-9273-81c2d697b95c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d67f323c-d7dd-4fb7-9273-81c2d697b95c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d92cd158-bd33-431a-8d70-0aa1b8faeb6a' class='xr-var-data-in' type='checkbox'><label for='data-d92cd158-bd33-431a-8d70-0aa1b8faeb6a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
               "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
-              "       12.6, 13.1, 13.6, 14.1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-d4e49922-b4ee-4253-9a11-988b809916e6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d4e49922-b4ee-4253-9a11-988b809916e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-32dc3787-5b6f-46f9-a052-28e997531bfd' class='xr-var-data-in' type='checkbox'><label for='data-32dc3787-5b6f-46f9-a052-28e997531bfd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U1&#x27; &#x27;U2&#x27;</div><input id='attrs-d0b1a393-cea4-4da4-bc18-0934b4583c44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0b1a393-cea4-4da4-bc18-0934b4583c44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7dfd4045-9a39-4264-8fad-760841773634' class='xr-var-data-in' type='checkbox'><label for='data-7dfd4045-9a39-4264-8fad-760841773634' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U1&#x27;, &#x27;U2&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dirIMU</span></div><div class='xr-var-dims'>(dirIMU)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-2e1e9dd1-99d7-4e79-b038-73162236ab86' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e1e9dd1-99d7-4e79-b038-73162236ab86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-981a444f-3270-40b4-a7f6-4c2b8c43e979' class='xr-var-data-in' type='checkbox'><label for='data-981a444f-3270-40b4-a7f6-4c2b8c43e979' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-2e2f7940-a432-4382-b313-7bae9e851c67' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2e2f7940-a432-4382-b313-7bae9e851c67' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b626ec3-7382-4e40-b6a1-1679d08ce051' class='xr-var-data-in' type='checkbox'><label for='data-4b626ec3-7382-4e40-b6a1-1679d08ce051' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-ffd700b9-c786-4b9b-9dbd-11d939d1f8d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ffd700b9-c786-4b9b-9dbd-11d939d1f8d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8fa9132a-fae0-48c8-b67f-fbb7ccb954c2' class='xr-var-data-in' type='checkbox'><label for='data-8fa9132a-fae0-48c8-b67f-fbb7ccb954c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-eae7666f-2493-43a2-a2fa-ed3121637043' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eae7666f-2493-43a2-a2fa-ed3121637043' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-788be9be-fd0b-4d0d-9e4f-46eb70f8992b' class='xr-var-data-in' type='checkbox'><label for='data-788be9be-fd0b-4d0d-9e4f-46eb70f8992b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Quaternion Vector Components</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-44e6bf41-b2c7-4b87-8b92-f4970d2fc050' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-44e6bf41-b2c7-4b87-8b92-f4970d2fc050' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-83fa7a02-f569-4ad1-861d-08c4c620ef76' class='xr-var-data-in' type='checkbox'><label for='data-83fa7a02-f569-4ad1-861d-08c4c620ef76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-9afbc5be-8650-4b4d-8c3b-b25619ba2c64' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9afbc5be-8650-4b4d-8c3b-b25619ba2c64' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b125bae-0d71-417d-a158-2c7b2de44f31' class='xr-var-data-in' type='checkbox'><label for='data-8b125bae-0d71-417d-a158-2c7b2de44f31' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8ef48832-a7b3-4149-ada9-67a760318115' class='xr-section-summary-in' type='checkbox'  ><label for='section-8ef48832-a7b3-4149-ada9-67a760318115' class='xr-section-summary' >Data variables: <span>(38)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.502e+03 1.502e+03 ... 1.498e+03</div><input id='attrs-3a447ad8-2c4a-48e5-9f4f-2d0fd6d1a423' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a447ad8-2c4a-48e5-9f4f-2d0fd6d1a423' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d97a8d45-9d44-4719-8298-12415b532d0d' class='xr-var-data-in' type='checkbox'><label for='data-d97a8d45-9d44-4719-8298-12415b532d0d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1501.9, 1501.9, 1501.9, ..., 1498.4, 1498.3, 1498.3], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.55 14.55 14.55 ... 13.47 13.47</div><input id='attrs-5f776757-1d3e-46bc-84f7-c2fde35622f9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f776757-1d3e-46bc-84f7-c2fde35622f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b820e742-2deb-473f-b626-548a6c748fd2' class='xr-var-data-in' type='checkbox'><label for='data-b820e742-2deb-473f-b626-548a6c748fd2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([14.55, 14.55, 14.55, ..., 13.47, 13.47, 13.47], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.713 9.718 9.718 ... 9.594 9.596</div><input id='attrs-5ef86819-35e0-424c-81da-2a96dffe7422' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ef86819-35e0-424c-81da-2a96dffe7422' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65af2c06-b899-48ff-ad68-bf47154c0ebb' class='xr-var-data-in' type='checkbox'><label for='data-65af2c06-b899-48ff-ad68-bf47154c0ebb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([9.713, 9.718, 9.718, ..., 9.596, 9.594, 9.596], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.5 72.7 72.6 ... -197.2 -195.7</div><input id='attrs-688d7e50-37ae-4bbf-9d57-8a731dc90429' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-688d7e50-37ae-4bbf-9d57-8a731dc90429' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3cc0e053-b41e-4820-b61d-8e99cad9565a' class='xr-var-data-in' type='checkbox'><label for='data-3cc0e053-b41e-4820-b61d-8e99cad9565a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.5,   72.7,   72.6, ...,   72.3,   72.8,   72.4],\n",
+              "       12.6, 13.1, 13.6, 14.1])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-d337e183-6bf7-4e58-8ac9-62bd7673ccd6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d337e183-6bf7-4e58-8ac9-62bd7673ccd6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e34b5df1-f8f0-4a52-a78f-dd64a8c6064d' class='xr-var-data-in' type='checkbox'><label for='data-e34b5df1-f8f0-4a52-a78f-dd64a8c6064d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U2</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U1&#x27; &#x27;U2&#x27;</div><input id='attrs-0bd4fdac-1f06-49bd-a7bd-b1dfe7a0c17c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bd4fdac-1f06-49bd-a7bd-b1dfe7a0c17c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-209efe02-47d0-4c33-b6e5-928fc97d8ba2' class='xr-var-data-in' type='checkbox'><label for='data-209efe02-47d0-4c33-b6e5-928fc97d8ba2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U1&#x27;, &#x27;U2&#x27;], dtype=&#x27;&lt;U2&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dirIMU</span></div><div class='xr-var-dims'>(dirIMU)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-39599054-9ed5-45da-b9c2-2319c4299e3a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39599054-9ed5-45da-b9c2-2319c4299e3a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6fc3ca8-a600-4562-969f-5c77b4104e89' class='xr-var-data-in' type='checkbox'><label for='data-a6fc3ca8-a600-4562-969f-5c77b4104e89' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>earth</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-9ca1843a-3745-40cd-9a13-339c4a662068' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ca1843a-3745-40cd-9a13-339c4a662068' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ad95dab-9fe5-4bcf-8b8f-7cafa3d6cb62' class='xr-var-data-in' type='checkbox'><label for='data-1ad95dab-9fe5-4bcf-8b8f-7cafa3d6cb62' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-b983bce2-445c-4f3d-b8b3-215e7d526756' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b983bce2-445c-4f3d-b8b3-215e7d526756' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-59be2b5c-034e-4b53-b036-f1b55d7f596b' class='xr-var-data-in' type='checkbox'><label for='data-59be2b5c-034e-4b53-b036-f1b55d7f596b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-e8503a65-310a-419a-80ea-9e4ff73ffdaa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e8503a65-310a-419a-80ea-9e4ff73ffdaa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-58ac4d63-9aa7-4f3f-8532-f30fbcee76f9' class='xr-var-data-in' type='checkbox'><label for='data-58ac4d63-9aa7-4f3f-8532-f30fbcee76f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Quaternion Vector Components</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-71113dee-e577-4768-a4ed-6e5b3d960fd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-71113dee-e577-4768-a4ed-6e5b3d960fd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7795347a-aece-4c82-8f03-8c78f1e2373c' class='xr-var-data-in' type='checkbox'><label for='data-7795347a-aece-4c82-8f03-8c78f1e2373c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-e0a53bfb-e170-4057-9210-59d223ea2ce5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0a53bfb-e170-4057-9210-59d223ea2ce5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-69ac815c-6eb9-4a62-bfac-61cb466fc32f' class='xr-var-data-in' type='checkbox'><label for='data-69ac815c-6eb9-4a62-bfac-61cb466fc32f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-caafa3b9-e18d-4be9-9e0b-bf7328f40652' class='xr-section-summary-in' type='checkbox'  ><label for='section-caafa3b9-e18d-4be9-9e0b-bf7328f40652' class='xr-section-summary' >Data variables: <span>(38)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.502e+03 1.502e+03 ... 1.498e+03</div><input id='attrs-1ff41122-0a03-4e45-9e58-c6de91bf0a07' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1ff41122-0a03-4e45-9e58-c6de91bf0a07' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f03ef4b5-20c4-4a04-aa33-304a03d349c3' class='xr-var-data-in' type='checkbox'><label for='data-f03ef4b5-20c4-4a04-aa33-304a03d349c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1501.9, 1501.9, 1501.9, ..., 1498.4, 1498.3, 1498.3],\n",
+              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.55 14.55 14.55 ... 13.47 13.47</div><input id='attrs-ef92540a-208d-459e-b712-fcff908224a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef92540a-208d-459e-b712-fcff908224a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-64c0628a-32f0-42e7-8c57-f5c973f25a80' class='xr-var-data-in' type='checkbox'><label for='data-64c0628a-32f0-42e7-8c57-f5c973f25a80' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([14.55, 14.55, 14.55, ..., 13.47, 13.47, 13.47],\n",
+              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.713 9.718 9.718 ... 9.594 9.596</div><input id='attrs-7399a93b-fc47-45fc-9873-1d6e168f20af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7399a93b-fc47-45fc-9873-1d6e168f20af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-816ce894-63f6-4355-8b1c-d87c7ad56e48' class='xr-var-data-in' type='checkbox'><label for='data-816ce894-63f6-4355-8b1c-d87c7ad56e48' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([9.713, 9.718, 9.718, ..., 9.596, 9.594, 9.596],\n",
+              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.5 72.7 72.6 ... -197.2 -195.7</div><input id='attrs-46f7f794-acba-4a89-9ce8-f756fc524b25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46f7f794-acba-4a89-9ce8-f756fc524b25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a519738-2ec4-483c-adf7-ae38fd4ca1cc' class='xr-var-data-in' type='checkbox'><label for='data-0a519738-2ec4-483c-adf7-ae38fd4ca1cc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.5,   72.7,   72.6, ...,   72.3,   72.8,   72.4],\n",
               "       [   9.3,    9.4,    8.9, ...,    5.9,    6.3,    6.4],\n",
               "       [-198. , -196.3, -198.1, ..., -197.4, -197.2, -195.7]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.00479 -0.01437 ... 9.685 9.729</div><input id='attrs-c609f139-bba5-4107-afdc-04f5663cad7b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c609f139-bba5-4107-afdc-04f5663cad7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-22070743-46da-4e68-9348-fc63861ec623' class='xr-var-data-in' type='checkbox'><label for='data-22070743-46da-4e68-9348-fc63861ec623' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-4.7900393e-03, -1.4370117e-02, -2.3950195e-02, ...,\n",
+              "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.00479 -0.01437 ... 9.685 9.729</div><input id='attrs-183da95b-9157-46e7-83e9-b9f00a9b8f43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-183da95b-9157-46e7-83e9-b9f00a9b8f43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a927da27-ceac-46bd-9ccb-96a0ad212717' class='xr-var-data-in' type='checkbox'><label for='data-a927da27-ceac-46bd-9ccb-96a0ad212717' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-4.7900393e-03, -1.4370117e-02, -2.3950195e-02, ...,\n",
               "        -1.4370117e-02, -4.7900393e-03, -2.3950195e-02],\n",
               "       [-1.2933105e+00, -1.2549902e+00, -1.2645704e+00, ...,\n",
               "        -1.1017090e+00, -1.0873389e+00, -1.0969189e+00],\n",
               "       [ 9.7285690e+00,  9.7141991e+00,  9.7237797e+00, ...,\n",
-              "         9.6710892e+00,  9.6854591e+00,  9.7285690e+00]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.6 16.6 16.6 ... 16.4 16.4 15.2</div><input id='attrs-23cd90d3-5c07-4e9d-bed3-65d380c13155' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23cd90d3-5c07-4e9d-bed3-65d380c13155' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a7be5322-fbe0-4464-99e1-2e336f4d419e' class='xr-var-data-in' type='checkbox'><label for='data-a7be5322-fbe0-4464-99e1-2e336f4d419e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>standard_name :</span></dt><dd>battery_voltage</dd></dl></div><div class='xr-var-data'><pre>array([16.6, 16.6, 16.6, ..., 16.4, 16.4, 15.2], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_clock</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 20.0 20.0 ... 19.5 19.5 19.5</div><input id='attrs-b0c154a6-3e11-4c66-bf0e-4f519f1e8526' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0c154a6-3e11-4c66-bf0e-4f519f1e8526' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-11ca357a-3b04-453a-b3de-3a35ff41cefc' class='xr-var-data-in' type='checkbox'><label for='data-11ca357a-3b04-453a-b3de-3a35ff41cefc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([20. , 20. , 20. , ..., 19.5, 19.5, 19.5], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-d90a3447-3a27-45c5-9e4e-d5dc840476ae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d90a3447-3a27-45c5-9e4e-d5dc840476ae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec8696ce-f7ac-467a-b5bb-68ee49b5294c' class='xr-var-data-in' type='checkbox'><label for='data-ec8696ce-f7ac-467a-b5bb-68ee49b5294c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-99888522-e5ac-4f59-a224-5de6c1182612' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99888522-e5ac-4f59-a224-5de6c1182612' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72a5eb8d-2f4b-4e4d-b258-b492076d4ee3' class='xr-var-data-in' type='checkbox'><label for='data-72a5eb8d-2f4b-4e4d-b258-b492076d4ee3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
-              "       1053557762], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-20941c67-f062-441b-87b6-4af785a8a441' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20941c67-f062-441b-87b6-4af785a8a441' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-432e8d54-f9ec-4254-9e73-f36a2ec03a0f' class='xr-var-data-in' type='checkbox'><label for='data-432e8d54-f9ec-4254-9e73-f36a2ec03a0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.015 -0.048 ... -0.015 -0.012</div><input id='attrs-e3aae32a-8fc0-45f2-9287-8047a4605fc6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3aae32a-8fc0-45f2-9287-8047a4605fc6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e67527c-67a1-4eed-82f3-fce3f6f0b52b' class='xr-var-data-in' type='checkbox'><label for='data-4e67527c-67a1-4eed-82f3-fce3f6f0b52b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.015, -0.048,  0.008, ..., -0.176, -0.071, -0.027],\n",
+              "         9.6710892e+00,  9.6854591e+00,  9.7285690e+00]],\n",
+              "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.6 16.6 16.6 ... 16.4 16.4 15.2</div><input id='attrs-7cda3505-8b84-4725-9fef-f07bfbbc5ef0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7cda3505-8b84-4725-9fef-f07bfbbc5ef0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-473323e7-3b54-429d-886d-ea84227ffbd6' class='xr-var-data-in' type='checkbox'><label for='data-473323e7-3b54-429d-886d-ea84227ffbd6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>standard_name :</span></dt><dd>battery_voltage</dd></dl></div><div class='xr-var-data'><pre>array([16.6, 16.6, 16.6, ..., 16.4, 16.4, 15.2],\n",
+              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_clock</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 20.0 20.0 ... 19.5 19.5 19.5</div><input id='attrs-674e4a82-f528-4c4b-86b9-b0b3f39d9e8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-674e4a82-f528-4c4b-86b9-b0b3f39d9e8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb878b99-2eaa-4918-84db-0162d2d02d9e' class='xr-var-data-in' type='checkbox'><label for='data-bb878b99-2eaa-4918-84db-0162d2d02d9e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([20. , 20. , 20. , ..., 19.5, 19.5, 19.5],\n",
+              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-e78211e3-0ff7-4d94-991b-6dd58686c1f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e78211e3-0ff7-4d94-991b-6dd58686c1f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f38dc2c8-8599-4407-80d2-5f1c6a73fe00' class='xr-var-data-in' type='checkbox'><label for='data-f38dc2c8-8599-4407-80d2-5f1c6a73fe00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-dcd7994a-e914-4f22-ac0f-2a1335906909' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcd7994a-e914-4f22-ac0f-2a1335906909' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-174005cc-1f9d-4de7-a72b-7ded11d548f0' class='xr-var-data-in' type='checkbox'><label for='data-174005cc-1f9d-4de7-a72b-7ded11d548f0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
+              "       1053557762], shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-4fcc2627-b4a9-4eb9-8c15-86a869465daa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fcc2627-b4a9-4eb9-8c15-86a869465daa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3d88ea9-e65d-4a58-9933-a7a6961d6087' class='xr-var-data-in' type='checkbox'><label for='data-c3d88ea9-e65d-4a58-9933-a7a6961d6087' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000],\n",
+              "      shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.015 -0.048 ... -0.015 -0.012</div><input id='attrs-a54ee94f-e180-44f6-8b7a-f83765c77547' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a54ee94f-e180-44f6-8b7a-f83765c77547' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5da06787-6cf3-4c82-9495-184ccfaf6cbc' class='xr-var-data-in' type='checkbox'><label for='data-5da06787-6cf3-4c82-9495-184ccfaf6cbc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[[-0.015, -0.048,  0.008, ..., -0.176, -0.071, -0.027],\n",
               "        [-0.093, -0.124, -0.031, ..., -0.175, -0.16 , -0.03 ],\n",
               "        [-0.163, -0.135, -0.105, ..., -0.129, -0.089, -0.116],\n",
               "        ...,\n",
@@ -564,7 +577,7 @@
               "        [-0.044,  0.015, -0.04 , ..., -0.015,  0.006,  0.012],\n",
               "        [-0.013,  0.014, -0.032, ...,  0.01 ,  0.   , -0.015],\n",
               "        [ 0.007,  0.016, -0.027, ..., -0.002, -0.015, -0.012]]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>81.0 80.0 80.5 ... 43.5 42.5 42.0</div><input id='attrs-d3ca6285-a912-48ab-9b38-05dfd5e9bb71' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3ca6285-a912-48ab-9b38-05dfd5e9bb71' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da00b08e-f833-4de8-b2d4-a133fc5f9f12' class='xr-var-data-in' type='checkbox'><label for='data-da00b08e-f833-4de8-b2d4-a133fc5f9f12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[81. , 80. , 80.5, ..., 81.5, 81.5, 81.5],\n",
+              "      shape=(4, 28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>81.0 80.0 80.5 ... 43.5 42.5 42.0</div><input id='attrs-277205bc-7d7e-494c-85b5-bd09c658f6d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-277205bc-7d7e-494c-85b5-bd09c658f6d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf0096df-72b6-46db-a3d7-03c26c74f57c' class='xr-var-data-in' type='checkbox'><label for='data-cf0096df-72b6-46db-a3d7-03c26c74f57c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[81. , 80. , 80.5, ..., 81.5, 81.5, 81.5],\n",
               "        [75. , 74.5, 75. , ..., 79. , 77. , 77.5],\n",
               "        [73.5, 72. , 72. , ..., 75. , 74.5, 75. ],\n",
               "        ...,\n",
@@ -594,7 +607,8 @@
               "        ...,\n",
               "        [51.5, 51. , 51. , ..., 46. , 46. , 45. ],\n",
               "        [51. , 50. , 51. , ..., 45. , 44. , 43.5],\n",
-              "        [50.5, 49.5, 50. , ..., 43.5, 42.5, 42. ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>100 91 91 97 99 ... 96 98 98 98 96</div><input id='attrs-7bebf0c8-8422-4e62-81e2-e5a1c52a1c0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7bebf0c8-8422-4e62-81e2-e5a1c52a1c0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92ccfbc2-0500-461a-ae25-f42a4181a274' class='xr-var-data-in' type='checkbox'><label for='data-92ccfbc2-0500-461a-ae25-f42a4181a274' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[100,  91,  91, ...,  89,  90,  96],\n",
+              "        [50.5, 49.5, 50. , ..., 43.5, 42.5, 42. ]]],\n",
+              "      shape=(4, 28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>100 91 91 97 99 ... 96 98 98 98 96</div><input id='attrs-98cf1824-4169-40fc-a1dc-56e164553766' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98cf1824-4169-40fc-a1dc-56e164553766' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39ca0db9-9fa6-4a96-bd45-3754d11d1eb1' class='xr-var-data-in' type='checkbox'><label for='data-39ca0db9-9fa6-4a96-bd45-3754d11d1eb1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[[100,  91,  91, ...,  89,  90,  96],\n",
               "        [ 74,  92,  96, ..., 100,  97, 100],\n",
               "        [100, 100,  99, ...,  99, 100, 100],\n",
               "        ...,\n",
@@ -624,7 +638,8 @@
               "        ...,\n",
               "        [100,  99,  96, ...,  98, 100, 100],\n",
               "        [100, 100, 100, ...,  94,  93,  95],\n",
-              "        [100,  98,  99, ...,  98,  98,  96]]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2164 ... 0.9937 0.9937</div><input id='attrs-1d3092f7-8c4b-49ca-a47b-f4fc5bbc03c9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d3092f7-8c4b-49ca-a47b-f4fc5bbc03c9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6bfbe30c-ea74-4860-992c-8d05b8722d3a' class='xr-var-data-in' type='checkbox'><label for='data-6bfbe30c-ea74-4860-992c-8d05b8722d3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16688156e-01, -2.16397047e-01, -2.16593146e-01, ...,\n",
+              "        [100,  98,  99, ...,  98,  98,  96]]],\n",
+              "      shape=(4, 28, 55000), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2164 ... 0.9937 0.9937</div><input id='attrs-e1503753-dd4d-4bb8-af92-ef879b7242fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e1503753-dd4d-4bb8-af92-ef879b7242fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecbe7724-d44e-4b94-b891-4ea51bf7fd55' class='xr-var-data-in' type='checkbox'><label for='data-ecbe7724-d44e-4b94-b891-4ea51bf7fd55' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16688156e-01, -2.16397047e-01, -2.16593146e-01, ...,\n",
               "         -2.16118217e-01, -2.16699123e-01, -2.16318965e-01],\n",
               "        [ 9.76196468e-01,  9.76219118e-01,  9.76202846e-01, ...,\n",
               "          9.76315439e-01,  9.76210594e-01,  9.76245522e-01],\n",
@@ -644,47 +659,52 @@
               "         -2.31788009e-02, -2.33813375e-02, -2.34001204e-02],\n",
               "        [ 9.91607308e-01,  9.91618454e-01,  9.91612315e-01, ...,\n",
               "          9.93657291e-01,  9.93655324e-01,  9.93659675e-01]]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.00293</div><input id='attrs-e2c45cf6-b614-498d-874d-62466fb2d72b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2c45cf6-b614-498d-874d-62466fb2d72b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3cbeb78-8eec-4f01-a0ae-3d388a5366be' class='xr-var-data-in' type='checkbox'><label for='data-d3cbeb78-8eec-4f01-a0ae-3d388a5366be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00390625,\n",
+              "      shape=(3, 3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.00293</div><input id='attrs-bb6c5dd6-b6f9-45e2-a972-7bc38b3deb3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bb6c5dd6-b6f9-45e2-a972-7bc38b3deb3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b06f35a-d6ba-4fbf-b789-cd8d63f2ead9' class='xr-var-data-in' type='checkbox'><label for='data-1b06f35a-d6ba-4fbf-b789-cd8d63f2ead9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00390625,\n",
               "        -0.00292969, -0.00195312],\n",
               "       [ 0.00097656,  0.        ,  0.        , ...,  0.        ,\n",
               "         0.00097656,  0.00097656],\n",
               "       [-0.00292969, -0.00292969, -0.00292969, ..., -0.00292969,\n",
-              "        -0.00195312, -0.00292969]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions</span></div><div class='xr-var-dims'>(q, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-9af830f4-6dab-4e91-88bf-8e0f80050b5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9af830f4-6dab-4e91-88bf-8e0f80050b5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53f9e55d-55e3-4bbe-8e86-0a4732edbc86' class='xr-var-data-in' type='checkbox'><label for='data-53f9e55d-55e3-4bbe-8e86-0a4732edbc86' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6244812 , ...,  0.625     ,\n",
+              "        -0.00195312, -0.00292969]], shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions</span></div><div class='xr-var-dims'>(q, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-d611b9eb-b7af-44da-82a3-454958c924f8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d611b9eb-b7af-44da-82a3-454958c924f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0fa9809-826b-47ff-991f-1439c5b8b446' class='xr-var-data-in' type='checkbox'><label for='data-c0fa9809-826b-47ff-991f-1439c5b8b446' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6244812 , ...,  0.625     ,\n",
               "         0.6247864 ,  0.62490845],\n",
               "       [-0.04101562, -0.04098511, -0.04098511, ..., -0.03570557,\n",
               "        -0.03564453, -0.03561401],\n",
               "       [-0.05014038, -0.05010986, -0.05014038, ..., -0.04354858,\n",
               "        -0.04360962, -0.04360962],\n",
               "       [ 0.77835083,  0.7782593 ,  0.7783203 , ...,  0.77856445,\n",
-              "         0.77874756,  0.7786255 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_press</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.8 12.8 12.8 ... 12.0 12.0 12.0</div><input id='attrs-78a79775-2c89-4e04-9134-784be901d9f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78a79775-2c89-4e04-9134-784be901d9f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39f93605-2afa-42d1-9bc5-390b36384524' class='xr-var-data-in' type='checkbox'><label for='data-39f93605-2afa-42d1-9bc5-390b36384524' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.8, 12.8, 12.8, ..., 12. , 12. , 12. ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>243 243 243 244 ... 246 246 246 246</div><input id='attrs-7350c419-3426-49a9-b576-2c67bb5742fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7350c419-3426-49a9-b576-2c67bb5742fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-07b23fcd-ed6a-41ab-82d7-eca594259ce9' class='xr-var-data-in' type='checkbox'><label for='data-07b23fcd-ed6a-41ab-82d7-eca594259ce9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([243, 243, 243, ..., 246, 246, 246], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.9 72.8 72.4 ... -198.0 -195.7</div><input id='attrs-682a5618-e5cb-447b-bd4b-73313a3cdc63' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-682a5618-e5cb-447b-bd4b-73313a3cdc63' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf4a4bf9-1734-48c7-b86e-69dce8a68a94' class='xr-var-data-in' type='checkbox'><label for='data-cf4a4bf9-1734-48c7-b86e-69dce8a68a94' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>uT</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.9,   72.8,   72.4, ...,   73. ,   73. ,   72.7],\n",
+              "         0.77874756,  0.7786255 ]], shape=(4, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_press</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.8 12.8 12.8 ... 12.0 12.0 12.0</div><input id='attrs-6f4513ae-72ec-46ee-892f-94d1cc698516' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f4513ae-72ec-46ee-892f-94d1cc698516' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40c5de0a-222f-4935-b1b9-db2e3745c456' class='xr-var-data-in' type='checkbox'><label for='data-40c5de0a-222f-4935-b1b9-db2e3745c456' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.8, 12.8, 12.8, ..., 12. , 12. , 12. ],\n",
+              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>243 243 243 244 ... 246 246 246 246</div><input id='attrs-828857f0-560a-4847-8fae-97a9870c53e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-828857f0-560a-4847-8fae-97a9870c53e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c3c11e8-de5e-4893-8301-92a783497502' class='xr-var-data-in' type='checkbox'><label for='data-5c3c11e8-de5e-4893-8301-92a783497502' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([243, 243, 243, ..., 246, 246, 246], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>72.9 72.8 72.4 ... -198.0 -195.7</div><input id='attrs-bf3c7d14-e904-4b67-ab3c-802a522946c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf3c7d14-e904-4b67-ab3c-802a522946c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7963343c-65d0-4f3f-a362-91c25d6ba372' class='xr-var-data-in' type='checkbox'><label for='data-7963343c-65d0-4f3f-a362-91c25d6ba372' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>uT</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[  72.9,   72.8,   72.4, ...,   73. ,   73. ,   72.7],\n",
               "       [   9.5,    9.5,    9.8, ...,    6. ,    6.4,    5.9],\n",
               "       [-197.1, -197.2, -197.7, ..., -197.4, -198. , -195.7]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.01437 0.0 ... 9.709 9.724</div><input id='attrs-1748d001-0e34-40ad-8462-d2dc941a2c93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1748d001-0e34-40ad-8462-d2dc941a2c93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-745609ac-0e29-4f47-9aea-611e0e1d297e' class='xr-var-data-in' type='checkbox'><label for='data-745609ac-0e29-4f47-9aea-611e0e1d297e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.4370117e-02,  0.0000000e+00, -1.4370117e-02, ...,\n",
+              "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.01437 0.0 ... 9.709 9.724</div><input id='attrs-946bb2f4-847b-49e2-9f8a-692d51c7f1dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-946bb2f4-847b-49e2-9f8a-692d51c7f1dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a84be801-4ea5-4cea-b873-dd7e8a46d899' class='xr-var-data-in' type='checkbox'><label for='data-a84be801-4ea5-4cea-b873-dd7e8a46d899' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.4370117e-02,  0.0000000e+00, -1.4370117e-02, ...,\n",
               "        -4.7900393e-03, -1.9160157e-02, -1.4370117e-02],\n",
               "       [-1.2693603e+00, -1.2885205e+00, -1.2837305e+00, ...,\n",
               "        -1.0825489e+00, -1.1017090e+00, -1.1064991e+00],\n",
               "       [ 9.7189894e+00,  9.7046194e+00,  9.7094088e+00, ...,\n",
-              "         9.7141991e+00,  9.7094088e+00,  9.7237797e+00]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-a9624189-e8db-4899-a831-c2626ee1dece' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9624189-e8db-4899-a831-c2626ee1dece' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99e9252f-98ac-49f0-9993-c41e5679e209' class='xr-var-data-in' type='checkbox'><label for='data-99e9252f-98ac-49f0-9993-c41e5679e209' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-56e84028-31b5-4b8d-828e-03542c01b6d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-56e84028-31b5-4b8d-828e-03542c01b6d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f155c59-34ad-4a10-8f47-9fde95ab88c1' class='xr-var-data-in' type='checkbox'><label for='data-6f155c59-34ad-4a10-8f47-9fde95ab88c1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
-              "       1053557762], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-20a7ed27-dd1c-4a2c-99b5-ddd4d3ff1faa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20a7ed27-dd1c-4a2c-99b5-ddd4d3ff1faa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e848770f-72e7-4ed8-ae45-8336994c6bc9' class='xr-var-data-in' type='checkbox'><label for='data-e848770f-72e7-4ed8-ae45-8336994c6bc9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000], dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.003 0.021 0.018 ... -0.048 0.09</div><input id='attrs-887f00f4-e9e3-4eb5-86bd-21fc7bb8454d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-887f00f4-e9e3-4eb5-86bd-21fc7bb8454d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1661645e-905d-4714-8339-0cb23a0d253a' class='xr-var-data-in' type='checkbox'><label for='data-1661645e-905d-4714-8339-0cb23a0d253a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.003,  0.021,  0.018, ...,  0.   , -0.044,  0.009],\n",
+              "         9.7141991e+00,  9.7094088e+00,  9.7237797e+00]],\n",
+              "      shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-3c3930ab-6b97-4863-bf09-bb7f81c310fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c3930ab-6b97-4863-bf09-bb7f81c310fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca6930c4-b057-4d3d-a37b-5fa727c66eb6' class='xr-var-data-in' type='checkbox'><label for='data-ca6930c4-b057-4d3d-a37b-5fa727c66eb6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1053556738 ... 1053557762</div><input id='attrs-1542f06f-2867-4ef6-aad7-c2c37f6f60a5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1542f06f-2867-4ef6-aad7-c2c37f6f60a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1de580ae-abe2-49c1-b53b-d5bc0ae85126' class='xr-var-data-in' type='checkbox'><label for='data-1de580ae-abe2-49c1-b53b-d5bc0ae85126' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1053556738, 1053556738, 1053556738, ..., 1053556738, 1053556738,\n",
+              "       1053557762], shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ensemble_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint32</div><div class='xr-var-preview xr-preview'>1 2 3 4 ... 54997 54998 54999 55000</div><input id='attrs-a9377d49-d4c3-4cbe-8a89-afce2dda7132' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9377d49-d4c3-4cbe-8a89-afce2dda7132' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-06b9d344-4506-400f-aa06-f747acffc709' class='xr-var-data-in' type='checkbox'><label for='data-06b9d344-4506-400f-aa06-f747acffc709' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>#</dd><dt><span>long_name :</span></dt><dd>Ensemble Number</dd><dt><span>standard_name :</span></dt><dd>number_of_observations</dd></dl></div><div class='xr-var-data'><pre>array([    1,     2,     3, ..., 54998, 54999, 55000],\n",
+              "      shape=(55000,), dtype=uint32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.003 0.021 0.018 ... -0.048 0.09</div><input id='attrs-5ab42a7d-2601-4861-b109-c16adb8d0917' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5ab42a7d-2601-4861-b109-c16adb8d0917' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17f3ee0f-9dba-4c53-adee-d93c2d890eda' class='xr-var-data-in' type='checkbox'><label for='data-17f3ee0f-9dba-4c53-adee-d93c2d890eda' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.003,  0.021,  0.018, ...,  0.   , -0.044,  0.009],\n",
               "       [ 0.057,  0.034,  0.017, ...,  0.01 ,  0.001, -0.002],\n",
               "       [ 0.039,  0.022,  0.02 , ...,  0.039, -0.008,  0.018],\n",
               "       ...,\n",
               "       [-0.036, -0.013,  0.058, ...,  0.058, -0.013,  0.028],\n",
               "       [-0.043, -0.009,  0.028, ...,  0.092, -0.021,  0.019],\n",
               "       [-0.073,  0.003,  0.   , ...,  0.049, -0.048,  0.09 ]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>80.5 80.5 80.0 ... 46.5 46.0 47.5</div><input id='attrs-919eed19-6960-4b0d-b18b-965fbfa3af0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-919eed19-6960-4b0d-b18b-965fbfa3af0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7703fa50-4ca7-40dc-a0be-3af7322d11c3' class='xr-var-data-in' type='checkbox'><label for='data-7703fa50-4ca7-40dc-a0be-3af7322d11c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[80.5, 80.5, 80. , ..., 82. , 81.5, 81. ],\n",
+              "      shape=(28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>80.5 80.5 80.0 ... 46.5 46.0 47.5</div><input id='attrs-acd08ed7-1663-4619-b563-42807a80e457' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-acd08ed7-1663-4619-b563-42807a80e457' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e316ab20-5cb2-4df9-bfd0-ec434344eca4' class='xr-var-data-in' type='checkbox'><label for='data-e316ab20-5cb2-4df9-bfd0-ec434344eca4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[80.5, 80.5, 80. , ..., 82. , 81.5, 81. ],\n",
               "       [75.5, 76.5, 76. , ..., 78.5, 78. , 77.5],\n",
               "       [72.5, 73.5, 73.5, ..., 75. , 76.5, 75.5],\n",
               "       ...,\n",
               "       [57.5, 57.5, 57. , ..., 49. , 49. , 48.5],\n",
               "       [56.5, 57. , 56. , ..., 47.5, 48. , 48. ],\n",
-              "       [55.5, 55.5, 55. , ..., 46.5, 46. , 47.5]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>98 100 92 93 98 ... 100 99 94 100</div><input id='attrs-e2e77dea-5206-4488-90bd-ed575627b035' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2e77dea-5206-4488-90bd-ed575627b035' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1c8b8f3-3e55-449b-80be-4cedeb4fac39' class='xr-var-data-in' type='checkbox'><label for='data-a1c8b8f3-3e55-449b-80be-4cedeb4fac39' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[ 98, 100,  92, ...,  88,  90,  91],\n",
+              "       [55.5, 55.5, 55. , ..., 46.5, 46. , 47.5]],\n",
+              "      shape=(28, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>98 100 92 93 98 ... 100 99 94 100</div><input id='attrs-68eb1d8f-4197-4f91-8f66-f237585a132a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-68eb1d8f-4197-4f91-8f66-f237585a132a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-160185be-6942-4cbe-a6b9-5ff02d66e55b' class='xr-var-data-in' type='checkbox'><label for='data-160185be-6942-4cbe-a6b9-5ff02d66e55b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[ 98, 100,  92, ...,  88,  90,  91],\n",
               "       [ 97,  94,  98, ...,  97,  95,  97],\n",
               "       [100, 100, 100, ...,  93, 100,  96],\n",
               "       ...,\n",
               "       [100, 100, 100, ..., 100, 100,  98],\n",
               "       [100, 100, 100, ...,  99, 100,  98],\n",
-              "       [100, 100, 100, ...,  99,  94, 100]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat_b5</span></div><div class='xr-var-dims'>(earth, inst, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2165 ... 0.9937 0.9937</div><input id='attrs-f4ae3766-82c0-4173-980e-4a3c165caa49' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4ae3766-82c0-4173-980e-4a3c165caa49' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1740f1e-7555-4599-9a75-17247eb6fc15' class='xr-var-data-in' type='checkbox'><label for='data-b1740f1e-7555-4599-9a75-17247eb6fc15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16694236e-01, -2.16498137e-01, -2.16593146e-01, ...,\n",
+              "       [100, 100, 100, ...,  99,  94, 100]],\n",
+              "      shape=(28, 55000), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat_b5</span></div><div class='xr-var-dims'>(earth, inst, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2167 -0.2165 ... 0.9937 0.9937</div><input id='attrs-38f987e4-4356-46e7-9bff-8731db8e8f2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-38f987e4-4356-46e7-9bff-8731db8e8f2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d50afe89-53f3-42b7-af50-55aabb08d039' class='xr-var-data-in' type='checkbox'><label for='data-d50afe89-53f3-42b7-af50-55aabb08d039' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16694236e-01, -2.16498137e-01, -2.16593146e-01, ...,\n",
               "         -2.16118217e-01, -2.16699123e-01, -2.16318965e-01],\n",
               "        [-9.67967808e-01, -9.68039632e-01, -9.68030274e-01, ...,\n",
               "         -9.70095694e-01, -9.69992876e-01, -9.70033050e-01],\n",
@@ -704,24 +724,25 @@
               "         -1.12442717e-01, -1.12462208e-01, -1.12422116e-01],\n",
               "        [ 9.91601229e-01,  9.91612315e-01,  9.91612315e-01, ...,\n",
               "          9.93657291e-01,  9.93655324e-01,  9.93659675e-01]]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.001953</div><input id='attrs-ef2586eb-d0af-45cd-bc4a-ae67663dea99' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef2586eb-d0af-45cd-bc4a-ae67663dea99' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e4dbd6cf-56b8-40b1-8496-e29616db3be2' class='xr-var-data-in' type='checkbox'><label for='data-e4dbd6cf-56b8-40b1-8496-e29616db3be2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00097656,\n",
+              "      shape=(3, 3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.001953 -0.001953 ... -0.001953</div><input id='attrs-ae315607-cef3-4aaf-84d1-0876de12946a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae315607-cef3-4aaf-84d1-0876de12946a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdf5b386-e221-4f52-9e01-47befdc4dee1' class='xr-var-data-in' type='checkbox'><label for='data-fdf5b386-e221-4f52-9e01-47befdc4dee1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-0.00195312, -0.00195312, -0.00195312, ..., -0.00097656,\n",
               "        -0.00195312, -0.00390625],\n",
               "       [ 0.        ,  0.        ,  0.00097656, ...,  0.00097656,\n",
               "         0.        ,  0.00097656],\n",
               "       [-0.00292969, -0.00292969, -0.00292969, ..., -0.00292969,\n",
-              "        -0.00292969, -0.00195312]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions_b5</span></div><div class='xr-var-dims'>(q, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-8faa1681-736e-4dfe-bf03-28ce586d6d0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8faa1681-736e-4dfe-bf03-28ce586d6d0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ecb0b20-9f03-480a-8871-97341c754bb8' class='xr-var-data-in' type='checkbox'><label for='data-8ecb0b20-9f03-480a-8871-97341c754bb8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6245117 , ...,  0.625     ,\n",
+              "        -0.00292969, -0.00195312]], shape=(3, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions_b5</span></div><div class='xr-var-dims'>(q, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6245 ... 0.7787 0.7786</div><input id='attrs-875ee1e5-47c6-4b02-b472-4175b7f4be87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-875ee1e5-47c6-4b02-b472-4175b7f4be87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3aed19f4-7103-4aa3-898c-26ba6cba838c' class='xr-var-data-in' type='checkbox'><label for='data-3aed19f4-7103-4aa3-898c-26ba6cba838c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6244507 ,  0.62454224,  0.6245117 , ...,  0.625     ,\n",
               "         0.6247864 ,  0.62490845],\n",
               "       [-0.04101562, -0.04098511, -0.04098511, ..., -0.03570557,\n",
               "        -0.03564453, -0.03561401],\n",
               "       [-0.0501709 , -0.05014038, -0.05014038, ..., -0.04354858,\n",
               "        -0.04360962, -0.04360962],\n",
               "       [ 0.77835083,  0.7782898 ,  0.7783203 , ...,  0.77856445,\n",
-              "         0.77874756,  0.7786255 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>65 65 65 65 65 ... 65 65 65 65 65</div><input id='attrs-4a48fe97-fbbc-4fe5-9d5a-a5f01cf32ac0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4a48fe97-fbbc-4fe5-9d5a-a5f01cf32ac0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e662358-d488-45c4-8ca4-8f07a161a8e0' class='xr-var-data-in' type='checkbox'><label for='data-5e662358-d488-45c4-8ca4-8f07a161a8e0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([65, 65, 65, ..., 65, 65, 65], dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-a219cfb7-9c63-4868-8ae3-58ab74036117' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a219cfb7-9c63-4868-8ae3-58ab74036117' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e432d20-1e34-4f3f-9d14-e56bd16e7cd4' class='xr-var-data-in' type='checkbox'><label for='data-7e432d20-1e34-4f3f-9d14-e56bd16e7cd4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>active_config</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-c025dd2a-d990-4384-a7e0-494aa0c4733c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c025dd2a-d990-4384-a7e0-494aa0c4733c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96a2da1e-6fca-4962-a587-ae91a21973da' class='xr-var-data-in' type='checkbox'><label for='data-96a2da1e-6fca-4962-a587-ae91a21973da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>telemetry_data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-d167b02b-873d-49df-979f-19efaa89ec4f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d167b02b-873d-49df-979f-19efaa89ec4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-932400e3-16d4-4eba-9ea0-b3e0a333717d' class='xr-var-data-in' type='checkbox'><label for='data-932400e3-16d4-4eba-9ea0-b3e0a333717d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boost_running</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 0 0 0 0 0 0 1</div><input id='attrs-8e7287ce-d793-467e-aaf4-b0ff192db21e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e7287ce-d793-467e-aaf4-b0ff192db21e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16e4bb0e-2b23-47ed-a6aa-a40d77d21afc' class='xr-var-data-in' type='checkbox'><label for='data-16e4bb0e-2b23-47ed-a6aa-a40d77d21afc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 1], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-12.52 -12.51 ... -12.52 -12.5</div><input id='attrs-782e7551-b59d-4161-af48-663e61a67864' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-782e7551-b59d-4161-af48-663e61a67864' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76ed6d42-361b-42c0-8abb-8faf3c1f6b8c' class='xr-var-data-in' type='checkbox'><label for='data-76ed6d42-361b-42c0-8abb-8faf3c1f6b8c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([-12.52    , -12.505001, -12.51    , ..., -12.490002, -12.52    ,\n",
-              "       -12.500001], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.065 -0.06 -0.06 ... -0.05 -0.05</div><input id='attrs-f09fac58-3ba1-4bc9-823a-cdf9a712a066' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f09fac58-3ba1-4bc9-823a-cdf9a712a066' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e870d55d-da25-4215-80fe-68abd2b3dbb4' class='xr-var-data-in' type='checkbox'><label for='data-e870d55d-da25-4215-80fe-68abd2b3dbb4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.065, -0.06 , -0.06 , ..., -0.06 , -0.05 , -0.05 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.425 -7.42 -7.42 ... -6.45 -6.45</div><input id='attrs-0e952fbb-a22e-4afc-9048-e0ce1d9ae381' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0e952fbb-a22e-4afc-9048-e0ce1d9ae381' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73aee514-0620-4dd9-94cd-aea1240b5ccb' class='xr-var-data-in' type='checkbox'><label for='data-73aee514-0620-4dd9-94cd-aea1240b5ccb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([-7.4250007, -7.42     , -7.42     , ..., -6.45     , -6.45     ,\n",
-              "       -6.45     ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.183 0.0 -1.183 ... 0.0 0.5518</div><input id='attrs-bd9d01e9-2ab8-4054-9199-ad3a1f94860d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd9d01e9-2ab8-4054-9199-ad3a1f94860d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d82851d0-2b27-4c1c-802f-8ded8cb5bf34' class='xr-var-data-in' type='checkbox'><label for='data-d82851d0-2b27-4c1c-802f-8ded8cb5bf34' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.1831,  0.    , -1.1831,  0.    ],\n",
+              "         0.77874756,  0.7786255 ]], shape=(4, 55000), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>65 65 65 65 65 ... 65 65 65 65 65</div><input id='attrs-3f9d4ba3-c1b4-47a4-a0fb-631e35298959' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3f9d4ba3-c1b4-47a4-a0fb-631e35298959' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a46ee7f-0227-409b-8d72-e36ac1a5a64f' class='xr-var-data-in' type='checkbox'><label for='data-9a46ee7f-0227-409b-8d72-e36ac1a5a64f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([65, 65, 65, ..., 65, 65, 65], shape=(55000,), dtype=uint16)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-7659606b-e0c0-4a3e-bc18-468c50df6b2c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7659606b-e0c0-4a3e-bc18-468c50df6b2c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38f98260-caf7-448d-a712-54ece9cd4846' class='xr-var-data-in' type='checkbox'><label for='data-38f98260-caf7-448d-a712-54ece9cd4846' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>active_config</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-f6fe8972-1dc7-470f-bc3b-360c6a6c6fe5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f6fe8972-1dc7-470f-bc3b-360c6a6c6fe5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eaedf31d-e0b9-440e-9786-84509474dbe6' class='xr-var-data-in' type='checkbox'><label for='data-eaedf31d-e0b9-440e-9786-84509474dbe6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>telemetry_data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-3a7ba2f6-caf9-4d89-bcee-d82a175fafea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a7ba2f6-caf9-4d89-bcee-d82a175fafea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ece9b1b-7a0a-49d6-baa6-4c2edea2fc78' class='xr-var-data-in' type='checkbox'><label for='data-6ece9b1b-7a0a-49d6-baa6-4c2edea2fc78' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boost_running</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 1 0 0 0 0 0 0 1</div><input id='attrs-6ba859bf-b58e-4a25-b954-8078fba21d2a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6ba859bf-b58e-4a25-b954-8078fba21d2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b91f3bf1-aad4-4288-8142-4946e7271f35' class='xr-var-data-in' type='checkbox'><label for='data-b91f3bf1-aad4-4288-8142-4946e7271f35' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 1], shape=(55000,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-12.52 -12.51 ... -12.52 -12.5</div><input id='attrs-ee281545-54d1-482f-997d-a2bf800023d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ee281545-54d1-482f-997d-a2bf800023d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad1aca52-6c9b-464a-86b0-6bc7f1cd6f41' class='xr-var-data-in' type='checkbox'><label for='data-ad1aca52-6c9b-464a-86b0-6bc7f1cd6f41' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([-12.52    , -12.505001, -12.51    , ..., -12.490002, -12.52    ,\n",
+              "       -12.500001], shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.065 -0.06 -0.06 ... -0.05 -0.05</div><input id='attrs-afd497d7-75be-45e6-969d-63d51ae43d6b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-afd497d7-75be-45e6-969d-63d51ae43d6b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cad3d08f-a2eb-43f0-b323-1542d3e231ea' class='xr-var-data-in' type='checkbox'><label for='data-cad3d08f-a2eb-43f0-b323-1542d3e231ea' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.065, -0.06 , -0.06 , ..., -0.06 , -0.05 , -0.05 ],\n",
+              "      shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.425 -7.42 -7.42 ... -6.45 -6.45</div><input id='attrs-b40b0456-4d12-49be-bf18-9990c6ba5287' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b40b0456-4d12-49be-bf18-9990c6ba5287' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-479207f2-58d9-44da-80ec-60d985c7461d' class='xr-var-data-in' type='checkbox'><label for='data-479207f2-58d9-44da-80ec-60d985c7461d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([-7.4250007, -7.42     , -7.42     , ..., -6.45     , -6.45     ,\n",
+              "       -6.45     ], shape=(55000,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.183 0.0 -1.183 ... 0.0 0.5518</div><input id='attrs-4af26767-d155-4c08-ba9d-b9cdb39f72fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4af26767-d155-4c08-ba9d-b9cdb39f72fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4c83551-5532-4483-9d8e-6dafb7c04249' class='xr-var-data-in' type='checkbox'><label for='data-a4c83551-5532-4483-9d8e-6dafb7c04249' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.1831,  0.    , -1.1831,  0.    ],\n",
               "       [ 0.    , -1.1831,  0.    ,  1.1831],\n",
               "       [ 0.5518,  0.    ,  0.5518,  0.    ],\n",
-              "       [ 0.    ,  0.5518,  0.    ,  0.5518]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a6a922f3-03ee-4871-9470-c14496ee8155' class='xr-section-summary-in' type='checkbox'  ><label for='section-a6a922f3-03ee-4871-9470-c14496ee8155' class='xr-section-summary' >Indexes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bd5e74da-de49-43c4-988e-48f7d4cf2e86' class='xr-index-data-in' type='checkbox'/><label for='index-bd5e74da-de49-43c4-988e-48f7d4cf2e86' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:20:00.500999927&#x27;,\n",
+              "       [ 0.    ,  0.5518,  0.    ,  0.5518]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fe7b8cee-76fb-424c-bac6-1240e8c25e87' class='xr-section-summary-in' type='checkbox'  ><label for='section-fe7b8cee-76fb-424c-bac6-1240e8c25e87' class='xr-section-summary' >Indexes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f7b5f078-ca86-466e-90f1-ba08e1090bb7' class='xr-index-data-in' type='checkbox'/><label for='index-f7b5f078-ca86-466e-90f1-ba08e1090bb7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:20:00.500999927&#x27;,\n",
               "               &#x27;2020-08-15 00:20:01.501100063&#x27;,\n",
               "               &#x27;2020-08-15 00:20:02.500999927&#x27;,\n",
               "               &#x27;2020-08-15 00:20:03.501100063&#x27;,\n",
@@ -742,7 +763,7 @@
               "               &#x27;2020-08-15 15:36:37.500999927&#x27;,\n",
               "               &#x27;2020-08-15 15:36:38.500999927&#x27;,\n",
               "               &#x27;2020-08-15 15:36:39.500999927&#x27;],\n",
-              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=55000, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_b5</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a2b8a1d9-5018-46ab-a0c5-51edf40fb80c' class='xr-index-data-in' type='checkbox'/><label for='index-a2b8a1d9-5018-46ab-a0c5-51edf40fb80c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:20:00.438499927&#x27;,\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=55000, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_b5</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d29f11f7-0b8f-4d2e-b8e2-2847d3aa18e5' class='xr-index-data-in' type='checkbox'/><label for='index-d29f11f7-0b8f-4d2e-b8e2-2847d3aa18e5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:20:00.438499927&#x27;,\n",
               "               &#x27;2020-08-15 00:20:01.438499927&#x27;,\n",
               "               &#x27;2020-08-15 00:20:02.438499927&#x27;,\n",
               "               &#x27;2020-08-15 00:20:03.438499927&#x27;,\n",
@@ -763,13 +784,13 @@
               "               &#x27;2020-08-15 15:36:37.438499927&#x27;,\n",
               "               &#x27;2020-08-15 15:36:38.438499927&#x27;,\n",
               "               &#x27;2020-08-15 15:36:39.438499927&#x27;],\n",
-              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_b5&#x27;, length=55000, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-6519ccac-ea74-4ca2-8a30-65b9044ed625' class='xr-index-data-in' type='checkbox'/><label for='index-6519ccac-ea74-4ca2-8a30-65b9044ed625' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_b5&#x27;, length=55000, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e6ed0073-a583-471c-963f-071dbff6aeab' class='xr-index-data-in' type='checkbox'/><label for='index-e6ed0073-a583-471c-963f-071dbff6aeab' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
               "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
               "       12.6, 13.1, 13.6, 14.1],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_b5</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-bcddd0ef-e105-48db-887b-c827dbd31bfd' class='xr-index-data-in' type='checkbox'/><label for='index-bcddd0ef-e105-48db-887b-c827dbd31bfd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
+              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_b5</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-275e03c2-59ed-4d76-ba28-abc3149c9aec' class='xr-index-data-in' type='checkbox'/><label for='index-275e03c2-59ed-4d76-ba28-abc3149c9aec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([ 0.6,  1.1,  1.6,  2.1,  2.6,  3.1,  3.6,  4.1,  4.6,  5.1,  5.6,  6.1,\n",
               "        6.6,  7.1,  7.6,  8.1,  8.6,  9.1,  9.6, 10.1, 10.6, 11.1, 11.6, 12.1,\n",
               "       12.6, 13.1, 13.6, 14.1],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range_b5&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-af85eef0-8306-4681-826e-f7f164a7fc23' class='xr-index-data-in' type='checkbox'/><label for='index-af85eef0-8306-4681-826e-f7f164a7fc23' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-60161a5a-845d-41cd-b889-ffb9a48cbbef' class='xr-index-data-in' type='checkbox'/><label for='index-60161a5a-845d-41cd-b889-ffb9a48cbbef' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U1&#x27;, &#x27;U2&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dirIMU</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4e8ed12b-2907-4929-8cd8-5b9c7be25150' class='xr-index-data-in' type='checkbox'/><label for='index-4e8ed12b-2907-4929-8cd8-5b9c7be25150' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dirIMU&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-83bf96f1-f265-4cba-ab4c-2794512a16aa' class='xr-index-data-in' type='checkbox'/><label for='index-83bf96f1-f265-4cba-ab4c-2794512a16aa' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9bc1d9b4-fe4e-453f-96bb-7ba90cbaa124' class='xr-index-data-in' type='checkbox'/><label for='index-9bc1d9b4-fe4e-453f-96bb-7ba90cbaa124' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>q</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d262abda-7774-4ebc-a43d-3f360af5cd7c' class='xr-index-data-in' type='checkbox'/><label for='index-d262abda-7774-4ebc-a43d-3f360af5cd7c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;q&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3d298e25-23a7-4b96-b66a-d638fdfb1c6d' class='xr-index-data-in' type='checkbox'/><label for='index-3d298e25-23a7-4b96-b66a-d638fdfb1c6d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0bd70f8e-9dc1-4b58-a243-04f8ec9c4ab2' class='xr-index-data-in' type='checkbox'/><label for='index-0bd70f8e-9dc1-4b58-a243-04f8ec9c4ab2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x2&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-708b4009-6901-44a2-944d-2b4fa9f30ca1' class='xr-section-summary-in' type='checkbox'  ><label for='section-708b4009-6901-44a2-944d-2b4fa9f30ca1' class='xr-section-summary' >Attributes: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature1000\\&quot;,SN=101663&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2212, &quot;FPGA&quot;: 169, &quot;DIGITAL&quot;: &quot;\\&quot;I-3\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-2\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;G-1\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;D-1(AHRS)\\&quot;&quot;, &quot;BOOT&quot;: 21, &quot;FWMINOR&quot;: 11}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 1940.43, &quot;BURST&quot;: 1937.69, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1940.43}, &quot;MEM&quot;: {&quot;PLAN&quot;: 2.733, &quot;BURST&quot;: 2.733, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 2.733}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: -9.99, &quot;BURSTHORZ&quot;: 2.6, &quot;BEAM5&quot;: 1.62, &quot;AVGBEAM&quot;: -9.99, &quot;BURSTBEAM&quot;: 1.55}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 0, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 32.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S101663A007_sea_spider.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 1000, &quot;NSTT&quot;: 0}, &quot;BURST&quot;: {&quot;NC&quot;: 28, &quot;NB&quot;: 5, &quot;CS&quot;: 0.5, &quot;BD&quot;: 0.1, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: 0.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1, &quot;VR&quot;: 2.5, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 8, &quot;CH&quot;: 0, &quot;ALTI&quot;: 0, &quot;VR5&quot;: 2.5, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 60, &quot;ALTISTART&quot;: 0.1, &quot;ALTIEND&quot;: 30.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.1831, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.1831, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.1831, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.1831, &quot;M31&quot;: 0.5518, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.5518, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.5518, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.5518}, &quot;USER&quot;: {&quot;POFF&quot;: 10.2, &quot;DECL&quot;: 0.0, &quot;HX&quot;: -48, &quot;HY&quot;: 48, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;OFF\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AHRS3D\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 155, &quot;DY&quot;: 778, &quot;DZ&quot;: -486, &quot;M11&quot;: 31481, &quot;M12&quot;: 1726, &quot;M13&quot;: -1109, &quot;M21&quot;: -1955, &quot;M22&quot;: 32767, &quot;M23&quot;: 379, &quot;M31&quot;: 750, &quot;M32&quot;: -1851, &quot;M33&quot;: 31699}, &quot;READAHRS&quot;: &quot;STR=\\&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM\\&quot;&quot;, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;C390Y1LU32C1B\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;6F2UB3HTH2C1B\\&quot;,DESC=\\&quot;Burst Five Beams\\&quot;,TYPE=17&quot;, &quot;KEY=\\&quot;R0X9JSZPH2C1B\\&quot;,DESC=\\&quot;128GB Recorder\\&quot;,TYPE=14&quot;], &quot;CALCOMPGET&quot;: &quot;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99906&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=1.791816E-02,B1X=-1.795970E-02,B2X=6.661898E-04,B3X=0.000000E+00,A1X=1.146936E-05,A2X=7.326843E-06,A3X=0.000000E+00&quot;, &quot;AY=1.000000E+00,B0Y=9.597497E-03,B1Y=-8.430577E-03,B2Y=9.310668E-04,B3Y=0.000000E+00,A1Y=7.215003E-05,A2Y=1.361528E-06,A3Y=0.000000E+00&quot;, &quot;AZ=1.000000E+00,B0Z=1.290458E-02,B1Z=2.959575E-02,B2Z=2.331257E-03,B3Z=0.000000E+00,A1Z=1.164268E-04,A2Z=-2.166612E-06,A3Z=0.000000E+00&quot;], &quot;CALGYROGET&quot;: [&quot;AX=1.094973E+00,B0X=-2.672331E+00,B1X=-1.493178E-03,B2X=9.496510E-05,B3X=0.000000E+00,A1X=2.702698E-04,A2X=8.100271E-05,A3X=0.000000E+00&quot;, &quot;AY=1.080807E+00,B0Y=3.432283E-01,B1Y=7.219538E-04,B2Y=1.416728E-04,B3Y=0.000000E+00,A1Y=9.466633E-04,A2Y=-4.216283E-05,A3Y=0.000000E+00&quot;, &quot;AZ=1.085556E+00,B0Z=-9.442614E-01,B1Z=6.433576E-04,B2Z=-1.306087E-05,B3Z=0.000000E+00,A1Z=1.430541E-03,A2Z=-7.494539E-05,A3Z=0.000000E+00&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5264760000e+02,RB0=7.7074756250e-01,RB1=-7.5541806250e-02,RB2=4.9886362500e-04,RB3=-1.4193179690e-07,T0=-2.4234620000e+03,T1=1.8815180000e+00,T2=-5.0111803130e-04,T3=4.7281500000e-08,ID=\\&quot;K244314\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=6.3850020000e+00,A1=-5.6274000000e-03,A2=1.6346411250e-06,A3=-1.6029240630e-10,B0=-2.6056009380e-01,B1=3.1699690630e-04,B2=-9.0774206250e-08,B3=8.5142393750e-12&quot;, &quot;C0=-1.2762860000e-03,C1=1.1561740000e-06,C2=-3.5109818750e-10,C3=3.5575718750e-14,D0=3.6002040630e-06,D1=-3.3359181250e-09,D2=1.0241850000e-12,D3=-1.0446648750e-16&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.99144,M12=0.00991,M13=-0.03081,M21=-0.00921,M22=0.99564,M23=0.01131,M31=0.03451,M32=-0.01242,M33=0.98426&quot;, &quot;CALROTGYROGET&quot;: &quot;M11=1.00000,M12=0.00803,M13=-0.02685,M21=-0.00933,M22=1.00000,M23=0.01646,M31=0.02587,M32=-0.01599,M33=1.00000&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.69,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature1000</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>28</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>5.066</dd><dt><span>SerialNum :</span></dt><dd>101663</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_b5 :</span></dt><dd>28</dd><dt><span>coord_sys_axes_b5 :</span></dt><dd>beam</dd><dt><span>n_beams_b5 :</span></dt><dd>1</dd><dt><span>ambig_vel_b5 :</span></dt><dd>5.066</dd><dt><span>SerialNum_b5 :</span></dt><dd>101663</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</dd><dt><span>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>power_level_dB_b5 :</span></dt><dd>0.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>AHRS</dd><dt><span>orient_status :</span></dt><dd>AHRS-3D</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, &#x27;mag&#x27;, &#x27;mag_b5&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>earth</dd><dt><span>fs :</span></dt><dd>1</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>beam_angle :</span></dt><dd>25</dd></dl></div></li></ul></div></div>"
+              "      dtype=&#x27;float64&#x27;, name=&#x27;range_b5&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-aae3364f-a7bb-473c-8cb8-47ab99fe09f6' class='xr-index-data-in' type='checkbox'/><label for='index-aae3364f-a7bb-473c-8cb8-47ab99fe09f6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9b13b25f-9026-4ada-8a3a-2896c6cbfeb5' class='xr-index-data-in' type='checkbox'/><label for='index-9b13b25f-9026-4ada-8a3a-2896c6cbfeb5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U1&#x27;, &#x27;U2&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dirIMU</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-cebacc15-1a43-4374-91de-1163db703022' class='xr-index-data-in' type='checkbox'/><label for='index-cebacc15-1a43-4374-91de-1163db703022' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dirIMU&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-78c8fda9-c04e-40c7-a227-76cb5d12b040' class='xr-index-data-in' type='checkbox'/><label for='index-78c8fda9-c04e-40c7-a227-76cb5d12b040' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-143e1025-bf20-4509-92d2-3f2b545d8180' class='xr-index-data-in' type='checkbox'/><label for='index-143e1025-bf20-4509-92d2-3f2b545d8180' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>q</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3fc3581a-d096-4092-8e6c-bcc7c4c76a1f' class='xr-index-data-in' type='checkbox'/><label for='index-3fc3581a-d096-4092-8e6c-bcc7c4c76a1f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;q&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-41c20b41-c58a-4d32-b392-0b85c7cc9965' class='xr-index-data-in' type='checkbox'/><label for='index-41c20b41-c58a-4d32-b392-0b85c7cc9965' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x2</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e341b0e7-18cb-4935-ba0f-c8336922d729' class='xr-index-data-in' type='checkbox'/><label for='index-e341b0e7-18cb-4935-ba0f-c8336922d729' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;x2&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cd03c791-b422-4adb-a0d5-8fa0645392f3' class='xr-section-summary-in' type='checkbox'  ><label for='section-cd03c791-b422-4adb-a0d5-8fa0645392f3' class='xr-section-summary' >Attributes: <span>(34)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature1000\\&quot;,SN=101663&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2212, &quot;FPGA&quot;: 169, &quot;DIGITAL&quot;: &quot;\\&quot;I-3\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-2\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;G-1\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;D-1(AHRS)\\&quot;&quot;, &quot;BOOT&quot;: 21, &quot;FWMINOR&quot;: 11}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 1940.43, &quot;BURST&quot;: 1937.69, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1940.43}, &quot;MEM&quot;: {&quot;PLAN&quot;: 2.733, &quot;BURST&quot;: 2.733, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 2.733}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: -9.99, &quot;BURSTHORZ&quot;: 2.6, &quot;BEAM5&quot;: 1.62, &quot;AVGBEAM&quot;: -9.99, &quot;BURSTBEAM&quot;: 1.55}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 0, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 32.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S101663A007_sea_spider.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 1000, &quot;NSTT&quot;: 0}, &quot;BURST&quot;: {&quot;NC&quot;: 28, &quot;NB&quot;: 5, &quot;CS&quot;: 0.5, &quot;BD&quot;: 0.1, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: 0.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1, &quot;VR&quot;: 2.5, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 8, &quot;CH&quot;: 0, &quot;ALTI&quot;: 0, &quot;VR5&quot;: 2.5, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 60, &quot;ALTISTART&quot;: 0.1, &quot;ALTIEND&quot;: 30.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.1831, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.1831, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.1831, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.1831, &quot;M31&quot;: 0.5518, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.5518, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.5518, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.5518}, &quot;USER&quot;: {&quot;POFF&quot;: 10.2, &quot;DECL&quot;: 0.0, &quot;HX&quot;: -48, &quot;HY&quot;: 48, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;OFF\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AHRS3D\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 155, &quot;DY&quot;: 778, &quot;DZ&quot;: -486, &quot;M11&quot;: 31481, &quot;M12&quot;: 1726, &quot;M13&quot;: -1109, &quot;M21&quot;: -1955, &quot;M22&quot;: 32767, &quot;M23&quot;: 379, &quot;M31&quot;: 750, &quot;M32&quot;: -1851, &quot;M33&quot;: 31699}, &quot;READAHRS&quot;: &quot;STR=\\&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM\\&quot;&quot;, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;C390Y1LU32C1B\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;6F2UB3HTH2C1B\\&quot;,DESC=\\&quot;Burst Five Beams\\&quot;,TYPE=17&quot;, &quot;KEY=\\&quot;R0X9JSZPH2C1B\\&quot;,DESC=\\&quot;128GB Recorder\\&quot;,TYPE=14&quot;], &quot;CALCOMPGET&quot;: &quot;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99906&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=1.791816E-02,B1X=-1.795970E-02,B2X=6.661898E-04,B3X=0.000000E+00,A1X=1.146936E-05,A2X=7.326843E-06,A3X=0.000000E+00&quot;, &quot;AY=1.000000E+00,B0Y=9.597497E-03,B1Y=-8.430577E-03,B2Y=9.310668E-04,B3Y=0.000000E+00,A1Y=7.215003E-05,A2Y=1.361528E-06,A3Y=0.000000E+00&quot;, &quot;AZ=1.000000E+00,B0Z=1.290458E-02,B1Z=2.959575E-02,B2Z=2.331257E-03,B3Z=0.000000E+00,A1Z=1.164268E-04,A2Z=-2.166612E-06,A3Z=0.000000E+00&quot;], &quot;CALGYROGET&quot;: [&quot;AX=1.094973E+00,B0X=-2.672331E+00,B1X=-1.493178E-03,B2X=9.496510E-05,B3X=0.000000E+00,A1X=2.702698E-04,A2X=8.100271E-05,A3X=0.000000E+00&quot;, &quot;AY=1.080807E+00,B0Y=3.432283E-01,B1Y=7.219538E-04,B2Y=1.416728E-04,B3Y=0.000000E+00,A1Y=9.466633E-04,A2Y=-4.216283E-05,A3Y=0.000000E+00&quot;, &quot;AZ=1.085556E+00,B0Z=-9.442614E-01,B1Z=6.433576E-04,B2Z=-1.306087E-05,B3Z=0.000000E+00,A1Z=1.430541E-03,A2Z=-7.494539E-05,A3Z=0.000000E+00&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5264760000e+02,RB0=7.7074756250e-01,RB1=-7.5541806250e-02,RB2=4.9886362500e-04,RB3=-1.4193179690e-07,T0=-2.4234620000e+03,T1=1.8815180000e+00,T2=-5.0111803130e-04,T3=4.7281500000e-08,ID=\\&quot;K244314\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=6.3850020000e+00,A1=-5.6274000000e-03,A2=1.6346411250e-06,A3=-1.6029240630e-10,B0=-2.6056009380e-01,B1=3.1699690630e-04,B2=-9.0774206250e-08,B3=8.5142393750e-12&quot;, &quot;C0=-1.2762860000e-03,C1=1.1561740000e-06,C2=-3.5109818750e-10,C3=3.5575718750e-14,D0=3.6002040630e-06,D1=-3.3359181250e-09,D2=1.0241850000e-12,D3=-1.0446648750e-16&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.99144,M12=0.00991,M13=-0.03081,M21=-0.00921,M22=0.99564,M23=0.01131,M31=0.03451,M32=-0.01242,M33=0.98426&quot;, &quot;CALROTGYROGET&quot;: &quot;M11=1.00000,M12=0.00803,M13=-0.02685,M21=-0.00933,M22=1.00000,M23=0.01646,M31=0.02587,M32=-0.01599,M33=1.00000&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.69,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature1000</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>28</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>5.066</dd><dt><span>SerialNum :</span></dt><dd>101663</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_b5 :</span></dt><dd>28</dd><dt><span>coord_sys_axes_b5 :</span></dt><dd>beam</dd><dt><span>n_beams_b5 :</span></dt><dd>1</dd><dt><span>ambig_vel_b5 :</span></dt><dd>5.066</dd><dt><span>SerialNum_b5 :</span></dt><dd>101663</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</dd><dt><span>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>power_level_dB_b5 :</span></dt><dd>0.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>AHRS</dd><dt><span>orient_status :</span></dt><dd>AHRS-3D</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, &#x27;mag&#x27;, &#x27;mag_b5&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>earth</dd><dt><span>fs :</span></dt><dd>1</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>beam_angle :</span></dt><dd>25</dd></dl></div></li></ul></div></div>"
             ],
             "text/plain": [
               "<xarray.Dataset> Size: 83MB\n",
@@ -819,7 +840,7 @@
               "    beam_angle:            25"
             ]
           },
-          "execution_count": 3,
+          "execution_count": 35,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -839,7 +860,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 36,
       "metadata": {},
       "outputs": [
         {
@@ -871,7 +892,7 @@
               "  ... and others (see `<obj>.variables`)"
             ]
           },
-          "execution_count": 4,
+          "execution_count": 36,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -904,22 +925,22 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 37,
       "metadata": {},
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x1c8b6554950>"
+              "<matplotlib.collections.QuadMesh at 0x112d98e1190>"
             ]
           },
-          "execution_count": 5,
+          "execution_count": 37,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -934,7 +955,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 6,
+      "execution_count": 38,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -952,7 +973,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 7,
+      "execution_count": 39,
       "metadata": {},
       "outputs": [
         {
@@ -988,14 +1009,14 @@
               "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
               "}\n",
               "\n",
-              "html[theme=dark],\n",
-              "html[data-theme=dark],\n",
-              "body[data-theme=dark],\n",
+              "html[theme=\"dark\"],\n",
+              "html[data-theme=\"dark\"],\n",
+              "body[data-theme=\"dark\"],\n",
               "body.vscode-dark {\n",
               "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
               "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
               "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-              "  --xr-border-color: #1F1F1F;\n",
+              "  --xr-border-color: #1f1f1f;\n",
               "  --xr-disabled-color: #515151;\n",
               "  --xr-background-color: #111111;\n",
               "  --xr-background-color-row-even: #111111;\n",
@@ -1040,7 +1061,7 @@
               ".xr-sections {\n",
               "  padding-left: 0 !important;\n",
               "  display: grid;\n",
-              "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+              "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
               "}\n",
               "\n",
               ".xr-section-item {\n",
@@ -1048,7 +1069,9 @@
               "}\n",
               "\n",
               ".xr-section-item input {\n",
-              "  display: none;\n",
+              "  display: inline-block;\n",
+              "  opacity: 0;\n",
+              "  height: 0;\n",
               "}\n",
               "\n",
               ".xr-section-item input + label {\n",
@@ -1060,6 +1083,10 @@
               "  color: var(--xr-font-color2);\n",
               "}\n",
               "\n",
+              ".xr-section-item input:focus + label {\n",
+              "  border: 2px solid var(--xr-font-color0);\n",
+              "}\n",
+              "\n",
               ".xr-section-item input:enabled + label:hover {\n",
               "  color: var(--xr-font-color0);\n",
               "}\n",
@@ -1081,7 +1108,7 @@
               "\n",
               ".xr-section-summary-in + label:before {\n",
               "  display: inline-block;\n",
-              "  content: '►';\n",
+              "  content: \"►\";\n",
               "  font-size: 11px;\n",
               "  width: 15px;\n",
               "  text-align: center;\n",
@@ -1092,7 +1119,7 @@
               "}\n",
               "\n",
               ".xr-section-summary-in:checked + label:before {\n",
-              "  content: '▼';\n",
+              "  content: \"▼\";\n",
               "}\n",
               "\n",
               ".xr-section-summary-in:checked + label > span {\n",
@@ -1164,15 +1191,15 @@
               "}\n",
               "\n",
               ".xr-dim-list:before {\n",
-              "  content: '(';\n",
+              "  content: \"(\";\n",
               "}\n",
               "\n",
               ".xr-dim-list:after {\n",
-              "  content: ')';\n",
+              "  content: \")\";\n",
               "}\n",
               "\n",
               ".xr-dim-list li:not(:last-child):after {\n",
-              "  content: ',';\n",
+              "  content: \",\";\n",
               "  padding-right: 5px;\n",
               "}\n",
               "\n",
@@ -1332,11 +1359,11 @@
               "    coverage_content_type:  coordinate\n",
               "    units:                  m\n",
               "    long_name:              Profile Range\n",
-              "    description:            Distance to the center of each depth bin</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'range'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>range</span>: 28</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-eff961cb-d2af-4d36-8756-bef4c22b3dcf' class='xr-array-in' type='checkbox' checked><label for='section-eff961cb-d2af-4d36-8756-bef4c22b3dcf' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.2 1.7 2.2 2.7 3.2 3.7 4.2 4.7 ... 11.7 12.2 12.7 13.2 13.7 14.2 14.7</span></div><div class='xr-array-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+              "    description:            Distance to the center of each depth bin</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'range'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>range</span>: 28</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-659c59d0-8c33-46d3-b3e2-c2de7a41df9b' class='xr-array-in' type='checkbox' checked><label for='section-659c59d0-8c33-46d3-b3e2-c2de7a41df9b' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>1.2 1.7 2.2 2.7 3.2 3.7 4.2 4.7 ... 11.7 12.2 12.7 13.2 13.7 14.2 14.7</span></div><div class='xr-array-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
               "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])</pre></div></div></li><li class='xr-section-item'><input id='section-164a5c6c-46f0-4dd6-9456-3ce42b7afbec' class='xr-section-summary-in' type='checkbox'  checked><label for='section-164a5c6c-46f0-4dd6-9456-3ce42b7afbec' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-7df415af-0f6f-4f1b-9636-8673695c0f0e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7df415af-0f6f-4f1b-9636-8673695c0f0e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3e12bdc-62d6-40fd-aad8-507e04b70d1b' class='xr-var-data-in' type='checkbox'><label for='data-d3e12bdc-62d6-40fd-aad8-507e04b70d1b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+              "       13.2, 13.7, 14.2, 14.7])</pre></div></div></li><li class='xr-section-item'><input id='section-df55617f-6d69-4336-9190-b5d4fced1165' class='xr-section-summary-in' type='checkbox'  checked><label for='section-df55617f-6d69-4336-9190-b5d4fced1165' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-a0492840-a7e8-45bf-9adc-2921795ad182' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0492840-a7e8-45bf-9adc-2921795ad182' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5296d03-5d36-4a6a-9850-8fdd6389762b' class='xr-var-data-in' type='checkbox'><label for='data-a5296d03-5d36-4a6a-9850-8fdd6389762b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
               "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bee506ed-ad82-49d3-83a3-fa7a84d3a41f' class='xr-section-summary-in' type='checkbox'  ><label for='section-bee506ed-ad82-49d3-83a3-fa7a84d3a41f' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a3d92044-d48c-4d4d-aeeb-b7b19517cc92' class='xr-index-data-in' type='checkbox'/><label for='index-a3d92044-d48c-4d4d-aeeb-b7b19517cc92' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
+              "       13.2, 13.7, 14.2, 14.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b1d0b5c0-8ae8-4f1f-8fa0-4fa9b227fd1a' class='xr-section-summary-in' type='checkbox'  ><label for='section-b1d0b5c0-8ae8-4f1f-8fa0-4fa9b227fd1a' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c6b563ca-ca92-4e50-b099-0c5743f93b02' class='xr-index-data-in' type='checkbox'/><label for='index-c6b563ca-ca92-4e50-b099-0c5743f93b02' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
               "                      2.7,                3.2,                3.7,\n",
               "                      4.2,  4.699999999999999,  5.199999999999999,\n",
               "        5.699999999999999,  6.199999999999999,  6.699999999999999,\n",
@@ -1346,7 +1373,7 @@
               "                     11.7,               12.2,               12.7,\n",
               "                     13.2,               13.7,               14.2,\n",
               "                     14.7],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3caee7ec-c705-4442-9438-ffe44198dda6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3caee7ec-c705-4442-9438-ffe44198dda6' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div></li></ul></div></div>"
+              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-23141fce-2350-4970-af07-1aa47e3ab02e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-23141fce-2350-4970-af07-1aa47e3ab02e' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Profile Range</dd><dt><span>description :</span></dt><dd>Distance to the center of each depth bin</dd></dl></div></li></ul></div></div>"
             ],
             "text/plain": [
               "<xarray.DataArray 'range' (range: 28)> Size: 224B\n",
@@ -1362,7 +1389,7 @@
               "    description:            Distance to the center of each depth bin"
             ]
           },
-          "execution_count": 7,
+          "execution_count": 39,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1387,7 +1414,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 8,
+      "execution_count": 40,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -1397,22 +1424,22 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 9,
+      "execution_count": 41,
       "metadata": {},
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x1c8b67bab90>"
+              "<matplotlib.collections.QuadMesh at 0x112d90785d0>"
             ]
           },
-          "execution_count": 9,
+          "execution_count": 41,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -1441,22 +1468,22 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 10,
+      "execution_count": 42,
       "metadata": {},
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x1c8b6648ad0>"
+              "<matplotlib.collections.QuadMesh at 0x112d975e410>"
             ]
           },
-          "execution_count": 10,
+          "execution_count": 42,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -1482,7 +1509,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 11,
+      "execution_count": 43,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -1491,22 +1518,22 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 12,
+      "execution_count": 44,
       "metadata": {},
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x1c8c1be4ed0>"
+              "<matplotlib.collections.QuadMesh at 0x112d83afe10>"
             ]
           },
-          "execution_count": 12,
+          "execution_count": 44,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -1537,7 +1564,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 13,
+      "execution_count": 45,
       "metadata": {},
       "outputs": [
         {
@@ -1563,7 +1590,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 14,
+      "execution_count": 46,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -1587,7 +1614,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 47,
       "metadata": {
         "scrolled": true
       },
@@ -1599,7 +1626,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": 48,
       "metadata": {},
       "outputs": [
         {
@@ -1635,14 +1662,14 @@
               "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
               "}\n",
               "\n",
-              "html[theme=dark],\n",
-              "html[data-theme=dark],\n",
-              "body[data-theme=dark],\n",
+              "html[theme=\"dark\"],\n",
+              "html[data-theme=\"dark\"],\n",
+              "body[data-theme=\"dark\"],\n",
               "body.vscode-dark {\n",
               "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
               "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
               "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
-              "  --xr-border-color: #1F1F1F;\n",
+              "  --xr-border-color: #1f1f1f;\n",
               "  --xr-disabled-color: #515151;\n",
               "  --xr-background-color: #111111;\n",
               "  --xr-background-color-row-even: #111111;\n",
@@ -1687,7 +1714,7 @@
               ".xr-sections {\n",
               "  padding-left: 0 !important;\n",
               "  display: grid;\n",
-              "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
+              "  grid-template-columns: 150px auto auto 1fr 0 20px 0 20px;\n",
               "}\n",
               "\n",
               ".xr-section-item {\n",
@@ -1695,7 +1722,9 @@
               "}\n",
               "\n",
               ".xr-section-item input {\n",
-              "  display: none;\n",
+              "  display: inline-block;\n",
+              "  opacity: 0;\n",
+              "  height: 0;\n",
               "}\n",
               "\n",
               ".xr-section-item input + label {\n",
@@ -1707,6 +1736,10 @@
               "  color: var(--xr-font-color2);\n",
               "}\n",
               "\n",
+              ".xr-section-item input:focus + label {\n",
+              "  border: 2px solid var(--xr-font-color0);\n",
+              "}\n",
+              "\n",
               ".xr-section-item input:enabled + label:hover {\n",
               "  color: var(--xr-font-color0);\n",
               "}\n",
@@ -1728,7 +1761,7 @@
               "\n",
               ".xr-section-summary-in + label:before {\n",
               "  display: inline-block;\n",
-              "  content: '►';\n",
+              "  content: \"►\";\n",
               "  font-size: 11px;\n",
               "  width: 15px;\n",
               "  text-align: center;\n",
@@ -1739,7 +1772,7 @@
               "}\n",
               "\n",
               ".xr-section-summary-in:checked + label:before {\n",
-              "  content: '▼';\n",
+              "  content: \"▼\";\n",
               "}\n",
               "\n",
               ".xr-section-summary-in:checked + label > span {\n",
@@ -1811,15 +1844,15 @@
               "}\n",
               "\n",
               ".xr-dim-list:before {\n",
-              "  content: '(';\n",
+              "  content: \"(\";\n",
               "}\n",
               "\n",
               ".xr-dim-list:after {\n",
-              "  content: ')';\n",
+              "  content: \")\";\n",
               "}\n",
               "\n",
               ".xr-dim-list li:not(:last-child):after {\n",
-              "  content: ',';\n",
+              "  content: \",\";\n",
               "  padding-right: 5px;\n",
               "}\n",
               "\n",
@@ -2010,7 +2043,7 @@
               "    range_offset:              0.6\n",
               "    declination:               15.8\n",
               "    declination_in_orientmat:  1\n",
-              "    principal_heading:         11.1898</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-76934faf-a19c-4510-bcf9-b5e1ce8a9353' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-76934faf-a19c-4510-bcf9-b5e1ce8a9353' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 183</li><li><span class='xr-has-index'>range</span>: 28</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li><li><span class='xr-has-index'>q</span>: 4</li><li><span class='xr-has-index'>time_b5</span>: 183</li><li><span class='xr-has-index'>range_b5</span>: 28</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2f1b57a8-9e0b-4644-80d8-9f7611817e8b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2f1b57a8-9e0b-4644-80d8-9f7611817e8b' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:22:30.001030683 .....</div><input id='attrs-4086a38f-dbaf-4867-a078-3747a09e6c7b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4086a38f-dbaf-4867-a078-3747a09e6c7b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a95f9125-ce3e-4d16-b4eb-9cf417d6a836' class='xr-var-data-in' type='checkbox'><label for='data-a95f9125-ce3e-4d16-b4eb-9cf417d6a836' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:22:30.001030683&#x27;, &#x27;2020-08-15T00:27:30.001019239&#x27;,\n",
+              "    principal_heading:         11.1898</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a24dc7b0-bc1d-4c64-b526-53e9bb42a57d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a24dc7b0-bc1d-4c64-b526-53e9bb42a57d' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 183</li><li><span class='xr-has-index'>range</span>: 28</li><li><span class='xr-has-index'>dirIMU</span>: 3</li><li><span class='xr-has-index'>dir</span>: 4</li><li><span class='xr-has-index'>beam</span>: 4</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li><li><span class='xr-has-index'>q</span>: 4</li><li><span class='xr-has-index'>time_b5</span>: 183</li><li><span class='xr-has-index'>range_b5</span>: 28</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-409759b6-3e17-452e-b728-d80cbe8faf36' class='xr-section-summary-in' type='checkbox'  checked><label for='section-409759b6-3e17-452e-b728-d80cbe8faf36' class='xr-section-summary' >Coordinates: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:22:30.001030683 .....</div><input id='attrs-4ceed203-e8ac-40ac-ae60-0d3c55fbd71e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ceed203-e8ac-40ac-ae60-0d3c55fbd71e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-812c8dc0-8d51-469a-b1cd-918c067293ad' class='xr-var-data-in' type='checkbox'><label for='data-812c8dc0-8d51-469a-b1cd-918c067293ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:22:30.001030683&#x27;, &#x27;2020-08-15T00:27:30.001019239&#x27;,\n",
               "       &#x27;2020-08-15T00:32:30.001010656&#x27;, &#x27;2020-08-15T00:37:30.001015901&#x27;,\n",
               "       &#x27;2020-08-15T00:42:30.001019954&#x27;, &#x27;2020-08-15T00:47:30.001018524&#x27;,\n",
               "       &#x27;2020-08-15T00:52:30.001013994&#x27;, &#x27;2020-08-15T00:57:30.001026630&#x27;,\n",
@@ -2101,9 +2134,9 @@
               "       &#x27;2020-08-15T15:02:30.001021146&#x27;, &#x27;2020-08-15T15:07:30.001027345&#x27;,\n",
               "       &#x27;2020-08-15T15:12:30.001029014&#x27;, &#x27;2020-08-15T15:17:30.001026630&#x27;,\n",
               "       &#x27;2020-08-15T15:22:30.001026153&#x27;, &#x27;2020-08-15T15:27:30.001022577&#x27;,\n",
-              "       &#x27;2020-08-15T15:32:30.001030683&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dirIMU</span></div><div class='xr-var-dims'>(dirIMU)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; &#x27;vert&#x27;</div><input id='attrs-991ec212-f5e6-4db6-87d4-b92333f5e4a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-991ec212-f5e6-4db6-87d4-b92333f5e4a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-296539d8-9fb1-4794-8075-a203a158dcbe' class='xr-var-data-in' type='checkbox'><label for='data-296539d8-9fb1-4794-8075-a203a158dcbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-acd69270-7935-45b9-b364-a0b2408e5e5c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-acd69270-7935-45b9-b364-a0b2408e5e5c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9e58693-caa5-4c1b-82be-bb98b6f11889' class='xr-var-data-in' type='checkbox'><label for='data-d9e58693-caa5-4c1b-82be-bb98b6f11889' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+              "       &#x27;2020-08-15T15:32:30.001030683&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dirIMU</span></div><div class='xr-var-dims'>(dirIMU)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; &#x27;vert&#x27;</div><input id='attrs-df549f4c-2cd8-4198-b092-ace098b447c8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-df549f4c-2cd8-4198-b092-ace098b447c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b100f99a-5eaa-45d5-ac2b-0b1df66acc49' class='xr-var-data-in' type='checkbox'><label for='data-b100f99a-5eaa-45d5-ac2b-0b1df66acc49' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-2a3dd00e-9b6b-425e-8de8-f7ab20eec33b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2a3dd00e-9b6b-425e-8de8-f7ab20eec33b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eca80e66-5d00-4f66-970f-146ea6673f68' class='xr-var-data-in' type='checkbox'><label for='data-eca80e66-5d00-4f66-970f-146ea6673f68' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
               "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; ... &#x27;vert2&#x27;</div><input id='attrs-4bb38e42-213c-4ac9-90a8-cd1092e962b6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4bb38e42-213c-4ac9-90a8-cd1092e962b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ea6576f-8880-461a-b253-db56b8ba9924' class='xr-var-data-in' type='checkbox'><label for='data-5ea6576f-8880-461a-b253-db56b8ba9924' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert1&#x27;, &#x27;vert2&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-946125ac-83cf-4627-b6c2-d35dd903c2a1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-946125ac-83cf-4627-b6c2-d35dd903c2a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f5eb9f70-959f-4101-a26a-7843f2455950' class='xr-var-data-in' type='checkbox'><label for='data-f5eb9f70-959f-4101-a26a-7843f2455950' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-110198f1-fcb2-4b80-b51f-c7c44aa1e8f0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-110198f1-fcb2-4b80-b51f-c7c44aa1e8f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ff5e431-351a-48fa-9c4f-b0fc39be916e' class='xr-var-data-in' type='checkbox'><label for='data-5ff5e431-351a-48fa-9c4f-b0fc39be916e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-a1c541d6-2c6e-4dca-bca3-ec27c7746caf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a1c541d6-2c6e-4dca-bca3-ec27c7746caf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2490e8c2-a9c5-4561-b1f8-1ab670081160' class='xr-var-data-in' type='checkbox'><label for='data-2490e8c2-a9c5-4561-b1f8-1ab670081160' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-3a231bb8-f1f9-4cba-ac38-2fb1defd614f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a231bb8-f1f9-4cba-ac38-2fb1defd614f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a2bf1dc3-cadc-431a-8106-d21d33b2b76a' class='xr-var-data-in' type='checkbox'><label for='data-a2bf1dc3-cadc-431a-8106-d21d33b2b76a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:22:29.938495159 .....</div><input id='attrs-76029295-006e-4132-a00f-72d760476a9a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76029295-006e-4132-a00f-72d760476a9a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f1b49ff4-8259-4cf4-8cd4-93704914d844' class='xr-var-data-in' type='checkbox'><label for='data-f1b49ff4-8259-4cf4-8cd4-93704914d844' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:22:29.938495159&#x27;, &#x27;2020-08-15T00:27:29.938490867&#x27;,\n",
+              "       13.2, 13.7, 14.2, 14.7])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U10</div><div class='xr-var-preview xr-preview'>&#x27;streamwise&#x27; &#x27;x-stream&#x27; ... &#x27;vert2&#x27;</div><input id='attrs-d6c84da7-01ea-43e4-bb44-365b7cf933d6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d6c84da7-01ea-43e4-bb44-365b7cf933d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05879cee-ce15-4012-8f63-564c7678ae25' class='xr-var-data-in' type='checkbox'><label for='data-05879cee-ce15-4012-8f63-564c7678ae25' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert1&#x27;, &#x27;vert2&#x27;], dtype=&#x27;&lt;U10&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3 4</div><input id='attrs-dbe17493-21e6-4f1e-b70a-07915b34d71e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dbe17493-21e6-4f1e-b70a-07915b34d71e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-709f73c5-2649-4256-a034-a1e0689265c5' class='xr-var-data-in' type='checkbox'><label for='data-709f73c5-2649-4256-a034-a1e0689265c5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1, 2, 3, 4], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-f292a0ea-d970-4f2a-8ea4-dce6816c8450' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f292a0ea-d970-4f2a-8ea4-dce6816c8450' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ab082ca-188f-4065-944c-81669cade959' class='xr-var-data-in' type='checkbox'><label for='data-6ab082ca-188f-4065-944c-81669cade959' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-2d57035c-a6e8-4b45-a4ee-ed2d85f3a7f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d57035c-a6e8-4b45-a4ee-ed2d85f3a7f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-89a72885-cd5d-4826-9261-e9f848d45b8d' class='xr-var-data-in' type='checkbox'><label for='data-89a72885-cd5d-4826-9261-e9f848d45b8d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>q</span></div><div class='xr-var-dims'>(q)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;w&#x27; &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-d8cca98d-6cb0-4889-aaa7-3a1baa97e10f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d8cca98d-6cb0-4889-aaa7-3a1baa97e10f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fa56711-ac84-4ef3-9aeb-edd05d2af165' class='xr-var-data-in' type='checkbox'><label for='data-1fa56711-ac84-4ef3-9aeb-edd05d2af165' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-08-15T00:22:29.938495159 .....</div><input id='attrs-8a1153d5-ea1b-449d-83dd-3908186caaa7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8a1153d5-ea1b-449d-83dd-3908186caaa7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfcb84d2-b340-473a-bd32-d56006d5eca3' class='xr-var-data-in' type='checkbox'><label for='data-dfcb84d2-b340-473a-bd32-d56006d5eca3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-08-15T00:22:29.938495159&#x27;, &#x27;2020-08-15T00:27:29.938490867&#x27;,\n",
               "       &#x27;2020-08-15T00:32:29.938487529&#x27;, &#x27;2020-08-15T00:37:29.938496828&#x27;,\n",
               "       &#x27;2020-08-15T00:42:29.938495397&#x27;, &#x27;2020-08-15T00:47:29.938496589&#x27;,\n",
               "       &#x27;2020-08-15T00:52:29.938497781&#x27;, &#x27;2020-08-15T00:57:29.938495397&#x27;,\n",
@@ -2194,9 +2227,9 @@
               "       &#x27;2020-08-15T15:02:29.938493013&#x27;, &#x27;2020-08-15T15:07:29.938493967&#x27;,\n",
               "       &#x27;2020-08-15T15:12:29.938494920&#x27;, &#x27;2020-08-15T15:17:29.938493251&#x27;,\n",
               "       &#x27;2020-08-15T15:22:29.938486099&#x27;, &#x27;2020-08-15T15:27:29.938484191&#x27;,\n",
-              "       &#x27;2020-08-15T15:32:29.938490390&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_b5</span></div><div class='xr-var-dims'>(range_b5)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-50c387a1-c443-4298-a551-077e360722e6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-50c387a1-c443-4298-a551-077e360722e6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-623fe903-5597-48e8-84d9-b98e66ad2acf' class='xr-var-data-in' type='checkbox'><label for='data-623fe903-5597-48e8-84d9-b98e66ad2acf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
+              "       &#x27;2020-08-15T15:32:29.938490390&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range_b5</span></div><div class='xr-var-dims'>(range_b5)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.2 1.7 2.2 2.7 ... 13.7 14.2 14.7</div><input id='attrs-d4e41c20-ea47-4e65-9079-0602edb1e99d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4e41c20-ea47-4e65-9079-0602edb1e99d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31247f07-f109-4151-a19a-3aff3b713fa2' class='xr-var-data-in' type='checkbox'><label for='data-31247f07-f109-4151-a19a-3aff3b713fa2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([ 1.2,  1.7,  2.2,  2.7,  3.2,  3.7,  4.2,  4.7,  5.2,  5.7,  6.2,  6.7,\n",
               "        7.2,  7.7,  8.2,  8.7,  9.2,  9.7, 10.2, 10.7, 11.2, 11.7, 12.2, 12.7,\n",
-              "       13.2, 13.7, 14.2, 14.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a44bbb16-b448-4b41-8935-9f77e2d80cce' class='xr-section-summary-in' type='checkbox'  ><label for='section-a44bbb16-b448-4b41-8935-9f77e2d80cce' class='xr-section-summary' >Data variables: <span>(38)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.502e+03 1.502e+03 ... 1.498e+03</div><input id='attrs-b6d104f0-8675-45fc-9287-4a9869cdce93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b6d104f0-8675-45fc-9287-4a9869cdce93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e37fb044-9056-4b0b-be54-0c20e958429e' class='xr-var-data-in' type='checkbox'><label for='data-e37fb044-9056-4b0b-be54-0c20e958429e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1501.6843, 1502.032 , 1501.8505, 1501.5714, 1501.8607, 1502.4133,\n",
+              "       13.2, 13.7, 14.2, 14.7])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0e80683a-8f7f-4804-93ff-ac0907983776' class='xr-section-summary-in' type='checkbox'  ><label for='section-0e80683a-8f7f-4804-93ff-ac0907983776' class='xr-section-summary' >Data variables: <span>(38)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.502e+03 1.502e+03 ... 1.498e+03</div><input id='attrs-4246abc6-57bf-4b4f-9d91-f92c077b36fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4246abc6-57bf-4b4f-9d91-f92c077b36fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9145f922-7811-4f7a-b406-a1eb5a60daf4' class='xr-var-data-in' type='checkbox'><label for='data-9145f922-7811-4f7a-b406-a1eb5a60daf4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1501.6843, 1502.032 , 1501.8505, 1501.5714, 1501.8607, 1502.4133,\n",
               "       1502.8121, 1502.7344, 1502.8704, 1502.9467, 1502.883 , 1502.7606,\n",
               "       1502.6915, 1502.8214, 1502.8864, 1502.7937, 1502.6268, 1502.6473,\n",
               "       1502.4198, 1502.3577, 1501.9543, 1501.729 , 1501.32  , 1500.9684,\n",
@@ -2226,7 +2259,7 @@
               "       1499.5286, 1499.5846, 1499.6293, 1499.6262, 1499.6666, 1499.6971,\n",
               "       1499.6998, 1499.7269, 1499.7843, 1499.6895, 1499.7037, 1499.6376,\n",
               "       1499.4894, 1499.0636, 1499.0073, 1498.8967, 1498.8627, 1498.961 ,\n",
-              "       1498.8287, 1498.6285, 1498.472 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U_std</span></div><div class='xr-var-dims'>(range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.04232 0.04293 0.04402 ... nan nan</div><input id='attrs-cd23a532-102e-450b-85fd-85a97b83b56a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cd23a532-102e-450b-85fd-85a97b83b56a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9467183-9cde-4308-b811-50f916a9ce2e' class='xr-var-data-in' type='checkbox'><label for='data-c9467183-9cde-4308-b811-50f916a9ce2e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([[0.04231985, 0.04293143, 0.04401674, ..., 0.04380286, 0.04201568,\n",
+              "       1498.8287, 1498.6285, 1498.472 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>U_std</span></div><div class='xr-var-dims'>(range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.04232 0.04293 0.04402 ... nan nan</div><input id='attrs-ed8cb1b7-2535-4f89-9fd1-7486c4e0d36e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed8cb1b7-2535-4f89-9fd1-7486c4e0d36e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bdbac96-d7ec-4ab6-9c9c-db1032f098a0' class='xr-var-data-in' type='checkbox'><label for='data-3bdbac96-d7ec-4ab6-9c9c-db1032f098a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity Standard Deviation</dd></dl></div><div class='xr-var-data'><pre>array([[0.04231985, 0.04293143, 0.04401674, ..., 0.04380286, 0.04201568,\n",
               "        0.04634431],\n",
               "       [0.04002878, 0.04084466, 0.03949426, ..., 0.04000184, 0.04028782,\n",
               "        0.03873245],\n",
@@ -2238,7 +2271,7 @@
               "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
               "               nan],\n",
               "       [       nan,        nan,        nan, ...,        nan,        nan,\n",
-              "               nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.49 14.59 14.54 ... 13.56 13.5</div><input id='attrs-89b799c9-be53-4f7a-afe9-e6247765b059' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89b799c9-be53-4f7a-afe9-e6247765b059' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-594a4b5b-a7ec-439d-9067-064ee9678900' class='xr-var-data-in' type='checkbox'><label for='data-594a4b5b-a7ec-439d-9067-064ee9678900' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([14.4871   , 14.592732 , 14.5369   , 14.450967 , 14.540566 ,\n",
+              "               nan]], shape=(28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>14.49 14.59 14.54 ... 13.56 13.5</div><input id='attrs-08415c2b-8d95-425b-8279-724b2a346b95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08415c2b-8d95-425b-8279-724b2a346b95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c91a5655-acb7-4fb1-ae61-b2260d83c1b7' class='xr-var-data-in' type='checkbox'><label for='data-c91a5655-acb7-4fb1-ae61-b2260d83c1b7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([14.4871   , 14.592732 , 14.5369   , 14.450967 , 14.540566 ,\n",
               "       14.712167 , 14.838034 , 14.812099 , 14.855833 , 14.879432 ,\n",
               "       14.860366 , 14.821934 , 14.799266 , 14.840433 , 14.8621   ,\n",
               "       14.833766 , 14.782434 , 14.788966 , 14.716966 , 14.695799 ,\n",
@@ -2274,7 +2307,7 @@
               "       13.860667 , 13.870465 , 13.876833 , 13.878232 , 13.884268 ,\n",
               "       13.907367 , 13.875599 , 13.875534 , 13.861733 , 13.8139   ,\n",
               "       13.689401 , 13.665034 , 13.632333 , 13.6227665, 13.651935 ,\n",
-              "       13.6164665, 13.5551   , 13.503766 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.712 9.699 9.685 ... 9.584 9.591</div><input id='attrs-1c947889-6802-48d6-8b8a-124794d8cbcc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1c947889-6802-48d6-8b8a-124794d8cbcc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-693a9a8f-88ea-4e64-abad-3e4f79a52135' class='xr-var-data-in' type='checkbox'><label for='data-693a9a8f-88ea-4e64-abad-3e4f79a52135' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([9.712007 , 9.699074 , 9.685317 , 9.670219 , 9.65324  , 9.640334 ,\n",
+              "       13.6164665, 13.5551   , 13.503766 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.712 9.699 9.685 ... 9.584 9.591</div><input id='attrs-309660b2-41be-4ffa-a839-573acf7f3d62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-309660b2-41be-4ffa-a839-573acf7f3d62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-16c8db5f-08fc-40c3-abe9-04fda660a3fb' class='xr-var-data-in' type='checkbox'><label for='data-16c8db5f-08fc-40c3-abe9-04fda660a3fb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([9.712007 , 9.699074 , 9.685317 , 9.670219 , 9.65324  , 9.640334 ,\n",
               "       9.62429  , 9.608537 , 9.594397 , 9.576526 , 9.56048  , 9.541069 ,\n",
               "       9.524289 , 9.505187 , 9.486143 , 9.464264 , 9.44223  , 9.416344 ,\n",
               "       9.393453 , 9.367396 , 9.339387 , 9.305754 , 9.278394 , 9.245257 ,\n",
@@ -2304,7 +2337,7 @@
               "       9.35049  , 9.37106  , 9.393917 , 9.40948  , 9.428807 , 9.441616 ,\n",
               "       9.459889 , 9.474596 , 9.488944 , 9.495657 , 9.510249 , 9.520061 ,\n",
               "       9.530423 , 9.537613 , 9.546494 , 9.554764 , 9.561024 , 9.57158  ,\n",
-              "       9.579874 , 9.583782 , 9.591404 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.534 3.565 3.547 ... -197.1 -197.1</div><input id='attrs-fd590823-10f2-4c33-b708-ece4d51f2fab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd590823-10f2-4c33-b708-ece4d51f2fab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fe2b112-2736-4c6b-86cb-17644db61fc8' class='xr-var-data-in' type='checkbox'><label for='data-2fe2b112-2736-4c6b-86cb-17644db61fc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[ 3.53383756e+00,  3.56493616e+00,  3.54652500e+00,\n",
+              "       9.579874 , 9.583782 , 9.591404 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.534 3.565 3.547 ... -197.1 -197.1</div><input id='attrs-e2c0d628-a4b0-4cb7-988b-1de10e3bc149' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2c0d628-a4b0-4cb7-988b-1de10e3bc149' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e6c5d00-5de3-427f-b948-0c75d3da0ad2' class='xr-var-data-in' type='checkbox'><label for='data-6e6c5d00-5de3-427f-b948-0c75d3da0ad2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>uT</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[ 3.53383756e+00,  3.56493616e+00,  3.54652500e+00,\n",
               "         3.60746217e+00,  3.56795740e+00,  3.58543777e+00,\n",
               "         3.56357908e+00,  3.57667398e+00,  3.60632110e+00,\n",
               "         3.56949067e+00,  3.57479167e+00,  3.59876704e+00,\n",
@@ -2344,7 +2377,7 @@
               "        -1.97044174e+02, -1.97063873e+02, -1.97090515e+02,\n",
               "        -1.97129501e+02, -1.97007523e+02, -1.97017838e+02,\n",
               "        -1.97019165e+02, -1.97129166e+02, -1.97051849e+02,\n",
-              "        -1.97113190e+02, -1.97104507e+02, -1.97053848e+02]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.261 -1.263 -1.26 ... 9.714 9.712</div><input id='attrs-645a3f3f-e131-4435-b62e-75ff0f1ce4ba' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-645a3f3f-e131-4435-b62e-75ff0f1ce4ba' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-faeedbbc-3bf4-4333-a184-2b1c56f01c02' class='xr-var-data-in' type='checkbox'><label for='data-faeedbbc-3bf4-4333-a184-2b1c56f01c02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.2608225 , -1.2632061 , -1.2602278 , -1.2620431 , -1.2610018 ,\n",
+              "        -1.97113190e+02, -1.97104507e+02, -1.97053848e+02]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.261 -1.263 -1.26 ... 9.714 9.712</div><input id='attrs-bfa2fa40-388a-458f-8a67-8948ade22edd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfa2fa40-388a-458f-8a67-8948ade22edd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4515de1f-2a05-4616-868e-5c8ad284f0dc' class='xr-var-data-in' type='checkbox'><label for='data-4515de1f-2a05-4616-868e-5c8ad284f0dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.2608225 , -1.2632061 , -1.2602278 , -1.2620431 , -1.2610018 ,\n",
               "        -1.2613868 , -1.2610568 , -1.2621608 , -1.2631991 , -1.2639122 ,\n",
               "        -1.2618904 , -1.258823  , -1.2596216 , -1.258702  , -1.2583325 ,\n",
               "        -1.2578007 , -1.2583394 , -1.2576339 , -1.2579147 , -1.2534361 ,\n",
@@ -2384,7 +2417,7 @@
               "         9.714537  ,  9.714841  ,  9.714376  ,  9.71848   ,  9.7165    ,\n",
               "         9.715717  ,  9.711741  ,  9.710465  ,  9.712621  ,  9.71337   ,\n",
               "         9.715622  ,  9.715989  ,  9.711598  ,  9.71219   ,  9.71021   ,\n",
-              "         9.712366  ,  9.7141695 ,  9.711616  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.48 16.48 16.48 ... 16.26 16.26</div><input id='attrs-515dc633-b2f5-47b7-a6c1-397ab3130ce4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-515dc633-b2f5-47b7-a6c1-397ab3130ce4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05d4d539-58cf-4146-a3fe-69228727033c' class='xr-var-data-in' type='checkbox'><label for='data-05d4d539-58cf-4146-a3fe-69228727033c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>standard_name :</span></dt><dd>battery_voltage</dd></dl></div><div class='xr-var-data'><pre>array([16.481167, 16.478   , 16.478   , 16.477167, 16.481   , 16.484167,\n",
+              "         9.712366  ,  9.7141695 ,  9.711616  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>16.48 16.48 16.48 ... 16.26 16.26</div><input id='attrs-9c7282f7-38ba-4b1a-89ef-39c0abd8ca9f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c7282f7-38ba-4b1a-89ef-39c0abd8ca9f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-17cc7d9d-2b53-4d12-a30e-3fd4bb52e6fd' class='xr-var-data-in' type='checkbox'><label for='data-17cc7d9d-2b53-4d12-a30e-3fd4bb52e6fd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd><dt><span>standard_name :</span></dt><dd>battery_voltage</dd></dl></div><div class='xr-var-data'><pre>array([16.481167, 16.478   , 16.478   , 16.477167, 16.481   , 16.484167,\n",
               "       16.477167, 16.479834, 16.479166, 16.482666, 16.478167, 16.481668,\n",
               "       16.479668, 16.475334, 16.479668, 16.479   , 16.474   , 16.475334,\n",
               "       16.477667, 16.473167, 16.472168, 16.468334, 16.472334, 16.471169,\n",
@@ -2414,7 +2447,7 @@
               "       16.258333, 16.262335, 16.262169, 16.2575  , 16.261   , 16.260332,\n",
               "       16.25633 , 16.257998, 16.258333, 16.253334, 16.257998, 16.257332,\n",
               "       16.253166, 16.256998, 16.256998, 16.252666, 16.256998, 16.256998,\n",
-              "       16.252666, 16.256998, 16.256998], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_clock</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 20.0 20.0 ... 19.5 19.5 19.5</div><input id='attrs-beafc30b-b7fa-4682-bddb-f6abc027a023' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-beafc30b-b7fa-4682-bddb-f6abc027a023' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f49b50ad-427e-4a60-9f8c-526f64876ba5' class='xr-var-data-in' type='checkbox'><label for='data-f49b50ad-427e-4a60-9f8c-526f64876ba5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([20.      , 20.      , 20.      , 20.043333, 20.25    , 20.25    ,\n",
+              "       16.252666, 16.256998, 16.256998], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_clock</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>20.0 20.0 20.0 ... 19.5 19.5 19.5</div><input id='attrs-c9e1c8f9-b359-4e6e-9e24-a8872069a962' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9e1c8f9-b359-4e6e-9e24-a8872069a962' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcb3ef16-1bf9-4232-a6fa-1afbf26a9a76' class='xr-var-data-in' type='checkbox'><label for='data-fcb3ef16-1bf9-4232-a6fa-1afbf26a9a76' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Internal Clock Temperature</dd></dl></div><div class='xr-var-data'><pre>array([20.      , 20.      , 20.      , 20.043333, 20.25    , 20.25    ,\n",
               "       20.25    , 20.25    , 20.263334, 20.446667, 20.5     , 20.5     ,\n",
               "       20.5     , 20.5     , 20.5     , 20.5     , 20.5     , 20.5     ,\n",
               "       20.5     , 20.5     , 20.5     , 20.5     , 20.5     , 20.31    ,\n",
@@ -2444,7 +2477,7 @@
               "       19.5     , 19.5     , 19.5     , 19.5     , 19.5     , 19.53    ,\n",
               "       19.696667, 19.75    , 19.75    , 19.75    , 19.75    , 19.75    ,\n",
               "       19.75    , 19.75    , 19.71    , 19.5     , 19.5     , 19.5     ,\n",
-              "       19.5     , 19.5     , 19.5     ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-136cd155-908f-4417-a00a-5f726d101d9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-136cd155-908f-4417-a00a-5f726d101d9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5e1d23be-b1aa-41a2-bea9-566ef876a6d7' class='xr-var-data-in' type='checkbox'><label for='data-5e1d23be-b1aa-41a2-bea9-566ef876a6d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+              "       19.5     , 19.5     , 19.5     ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-ab03e4c4-2c85-4a8d-a516-121216653cbd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab03e4c4-2c85-4a8d-a516-121216653cbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-546fe20c-d8bd-4566-bc66-9b1b01b181eb' class='xr-var-data-in' type='checkbox'><label for='data-546fe20c-d8bd-4566-bc66-9b1b01b181eb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
@@ -2454,7 +2487,7 @@
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.054e+09 1.054e+09 ... 1.054e+09</div><input id='attrs-ef94cf8f-9370-4f1b-a8ae-d726be31b4c4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef94cf8f-9370-4f1b-a8ae-d726be31b4c4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1017b663-7d54-4771-a202-0218d1b545ca' class='xr-var-data-in' type='checkbox'><label for='data-1017b663-7d54-4771-a202-0218d1b545ca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.054e+09 1.054e+09 ... 1.054e+09</div><input id='attrs-01af1ccd-c3a4-419e-aed4-56b23e926757' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-01af1ccd-c3a4-419e-aed4-56b23e926757' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0e5413a-be8e-496f-af35-fb662daba530' class='xr-var-data-in' type='checkbox'><label for='data-d0e5413a-be8e-496f-af35-fb662daba530' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
@@ -2494,7 +2527,7 @@
               "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
               "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
               "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09, 1.05355699e+09,\n",
-              "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.2358 0.266 0.3007 ... nan nan nan</div><input id='attrs-e3b4292a-425b-4950-b2fc-a8d9efbfb23c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3b4292a-425b-4950-b2fc-a8d9efbfb23c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48dc5038-7f2a-40d1-a642-b55dbece095e' class='xr-var-data-in' type='checkbox'><label for='data-48dc5038-7f2a-40d1-a642-b55dbece095e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.23582192,  0.2660148 ,  0.30069652, ..., -0.179669  ,\n",
+              "       1.05355699e+09, 1.05355699e+09, 1.05355699e+09], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.2358 0.266 0.3007 ... nan nan nan</div><input id='attrs-665ebe14-ba24-40f5-9226-a118f6761f9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-665ebe14-ba24-40f5-9226-a118f6761f9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3a5d9f8-a1cb-42be-ab63-663907c9dd0b' class='xr-var-data-in' type='checkbox'><label for='data-f3a5d9f8-a1cb-42be-ab63-663907c9dd0b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.23582192,  0.2660148 ,  0.30069652, ..., -0.179669  ,\n",
               "         -0.18383324, -0.17606331],\n",
               "        [ 0.25633767,  0.28601688,  0.3117814 , ..., -0.19580415,\n",
               "         -0.19696543, -0.20011853],\n",
@@ -2534,7 +2567,7 @@
               "        [        nan,         nan,         nan, ...,         nan,\n",
               "                 nan,         nan],\n",
               "        [        nan,         nan,         nan, ...,         nan,\n",
-              "                 nan,         nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>79.95 80.28 80.69 ... nan nan nan</div><input id='attrs-09e41d60-52b6-42c5-b3fc-3426bf48169b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09e41d60-52b6-42c5-b3fc-3426bf48169b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce44551e-20df-42c2-9f7b-8b534197f409' class='xr-var-data-in' type='checkbox'><label for='data-ce44551e-20df-42c2-9f7b-8b534197f409' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[79.94666 , 80.28167 , 80.68667 , ..., 81.721664, 81.80334 ,\n",
+              "                 nan,         nan]]], shape=(4, 28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>79.95 80.28 80.69 ... nan nan nan</div><input id='attrs-607b4830-7e21-4da6-8d8b-1f65eaf5f39c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-607b4830-7e21-4da6-8d8b-1f65eaf5f39c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5adc8999-e82a-423c-88e9-4af1054160d1' class='xr-var-data-in' type='checkbox'><label for='data-5adc8999-e82a-423c-88e9-4af1054160d1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[79.94666 , 80.28167 , 80.68667 , ..., 81.721664, 81.80334 ,\n",
               "         81.68667 ],\n",
               "        [75.535   , 76.043335, 76.66167 , ..., 77.62666 , 77.73    ,\n",
               "         77.528336],\n",
@@ -2574,7 +2607,7 @@
               "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
               "               nan],\n",
               "        [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "               nan]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>92.59 92.61 92.47 ... 0.0 0.0 0.0</div><input id='attrs-e7ca759a-3efb-4800-b7f8-5157a85f734a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7ca759a-3efb-4800-b7f8-5157a85f734a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fa206765-4a5c-493d-b3a2-5e9c8b3e4445' class='xr-var-data-in' type='checkbox'><label for='data-fa206765-4a5c-493d-b3a2-5e9c8b3e4445' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[92.58667 , 92.613335, 92.47    , ..., 91.933334, 91.96667 ,\n",
+              "               nan]]], shape=(4, 28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, range, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>92.59 92.61 92.47 ... 0.0 0.0 0.0</div><input id='attrs-99e9ecb5-0096-4877-b309-9fa45c5b5f25' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99e9ecb5-0096-4877-b309-9fa45c5b5f25' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e29c093-c2c3-40d5-aab8-ff4fcf2f60f9' class='xr-var-data-in' type='checkbox'><label for='data-2e29c093-c2c3-40d5-aab8-ff4fcf2f60f9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[[92.58667 , 92.613335, 92.47    , ..., 91.933334, 91.96667 ,\n",
               "         92.06333 ],\n",
               "        [96.236664, 96.526665, 96.206665, ..., 96.00667 , 96.3     ,\n",
               "         96.03333 ],\n",
@@ -2614,7 +2647,7 @@
               "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
               "          0.      ],\n",
               "        [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "          0.      ]]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.05743 0.05697 ... 0.9937 0.9937</div><input id='attrs-47bfe4a9-8140-46d9-9d38-82f9abaf17d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47bfe4a9-8140-46d9-9d38-82f9abaf17d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00a9f918-28da-40c9-9a9e-8e4094c38401' class='xr-var-data-in' type='checkbox'><label for='data-00a9f918-28da-40c9-9a9e-8e4094c38401' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[ 5.74317947e-02,  5.69723397e-02,  5.82992062e-02, ...,\n",
+              "          0.      ]]], shape=(4, 28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.05743 0.05697 ... 0.9937 0.9937</div><input id='attrs-241d0ed9-b027-414e-83dc-a606034fb4af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-241d0ed9-b027-414e-83dc-a606034fb4af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-21ad0c23-83d5-48f5-9708-af9142aa6a71' class='xr-var-data-in' type='checkbox'><label for='data-21ad0c23-83d5-48f5-9708-af9142aa6a71' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[ 5.74317947e-02,  5.69723397e-02,  5.82992062e-02, ...,\n",
               "          5.81927113e-02,  5.85613623e-02,  5.85512854e-02],\n",
               "        [ 9.98300374e-01,  9.98323679e-01,  9.98248577e-01, ...,\n",
               "          9.98254061e-01,  9.98234987e-01,  9.98234689e-01],\n",
@@ -2634,7 +2667,7 @@
               "          7.35414820e-03,  7.39390310e-03,  7.38611678e-03],\n",
               "        [ 9.91627276e-01,  9.91604209e-01,  9.91649806e-01, ...,\n",
               "          9.93667483e-01,  9.93692815e-01,  9.93687749e-01]]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.000807 0.0008203 ... -0.002598</div><input id='attrs-ac2fb82a-7974-4514-8ffe-2e2a48dfca60' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ac2fb82a-7974-4514-8ffe-2e2a48dfca60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7ab1296-2028-44e7-8867-e2508f5a87c8' class='xr-var-data-in' type='checkbox'><label for='data-b7ab1296-2028-44e7-8867-e2508f5a87c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00080699,  0.00082028,  0.00085015,  0.0008093 ,  0.00077764,\n",
+              "      shape=(3, 3, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dirIMU, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.000807 0.0008203 ... -0.002598</div><input id='attrs-12507aeb-982c-485c-b80b-ef6a2e6e4e1d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12507aeb-982c-485c-b80b-ef6a2e6e4e1d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-050e9f71-3258-44b4-8690-2f92378ece16' class='xr-var-data-in' type='checkbox'><label for='data-050e9f71-3258-44b4-8690-2f92378ece16' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00080699,  0.00082028,  0.00085015,  0.0008093 ,  0.00077764,\n",
               "         0.00076247,  0.00084005,  0.00080013,  0.00073725,  0.00069978,\n",
               "         0.00068869,  0.000708  ,  0.00070397,  0.00064101,  0.0007507 ,\n",
               "         0.00071757,  0.00060209,  0.00062077,  0.00062501,  0.0005675 ,\n",
@@ -2674,7 +2707,7 @@
               "        -0.00263345, -0.00258136, -0.00250975, -0.00261717, -0.00264321,\n",
               "        -0.00260741, -0.00261066, -0.00250649, -0.00248371, -0.0026074 ,\n",
               "        -0.00260415, -0.00260415, -0.00250324, -0.00259764, -0.002513  ,\n",
-              "        -0.00256509, -0.00253904, -0.00259764]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions</span></div><div class='xr-var-dims'>(q, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6243 ... 0.7784 0.7784</div><input id='attrs-26506b36-f604-4cff-9dbd-9526c8fef1d3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26506b36-f604-4cff-9dbd-9526c8fef1d3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-53c4959c-000e-4511-96f6-ce1c57443f03' class='xr-var-data-in' type='checkbox'><label for='data-53c4959c-000e-4511-96f6-ce1c57443f03' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6245074 ,  0.624316  ,  0.6248463 ,  0.6245108 ,  0.6247046 ,\n",
+              "        -0.00256509, -0.00253904, -0.00259764]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions</span></div><div class='xr-var-dims'>(q, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6243 ... 0.7784 0.7784</div><input id='attrs-16ed46ae-7d4d-4c4e-bcc1-329f5d24ac12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-16ed46ae-7d4d-4c4e-bcc1-329f5d24ac12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2b1d7b13-8be4-4d86-a16c-860579014f57' class='xr-var-data-in' type='checkbox'><label for='data-2b1d7b13-8be4-4d86-a16c-860579014f57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6245074 ,  0.624316  ,  0.6248463 ,  0.6245108 ,  0.6247046 ,\n",
               "         0.62467265,  0.62460417,  0.62476116,  0.6245991 ,  0.6246464 ,\n",
               "         0.624686  ,  0.6249721 ,  0.6249648 ,  0.6250649 ,  0.62494904,\n",
               "         0.62500936,  0.6250884 ,  0.6250855 ,  0.6253171 ,  0.6254035 ,\n",
@@ -2714,7 +2747,7 @@
               "         0.7785521 ,  0.77869254,  0.77849793,  0.7787016 ,  0.77872854,\n",
               "         0.77860415,  0.7784391 ,  0.7783881 ,  0.7785563 ,  0.77852774,\n",
               "         0.77856874,  0.77847373,  0.7784385 ,  0.7782882 ,  0.778279  ,\n",
-              "         0.778462  ,  0.77835196,  0.77835387]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_press</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.8 12.8 12.8 ... 12.0 12.0 12.0</div><input id='attrs-8308da19-cd0d-4b50-9436-88a6bd1ea6ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8308da19-cd0d-4b50-9436-88a6bd1ea6ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7bae97a-4fb6-4670-a9c3-0cd1153185a0' class='xr-var-data-in' type='checkbox'><label for='data-b7bae97a-4fb6-4670-a9c3-0cd1153185a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.800002 , 12.800002 , 12.800002 , 12.800002 , 12.800002 ,\n",
+              "         0.778462  ,  0.77835196,  0.77835387]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp_press</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.8 12.8 12.8 ... 12.0 12.0 12.0</div><input id='attrs-a09fcfb0-cde6-413d-ad34-9add349bacff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a09fcfb0-cde6-413d-ad34-9add349bacff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0a7e2d2c-f10e-4baa-85f6-0ace6279d138' class='xr-var-data-in' type='checkbox'><label for='data-0a7e2d2c-f10e-4baa-85f6-0ace6279d138' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Pressure Sensor Temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.800002 , 12.800002 , 12.800002 , 12.800002 , 12.800002 ,\n",
               "       12.966666 , 13.       , 13.050666 , 13.199998 , 13.199998 ,\n",
               "       13.199998 , 13.199998 , 13.199998 , 13.199998 , 13.199998 ,\n",
               "       13.199998 , 13.199998 , 13.199998 , 13.176665 , 13.       ,\n",
@@ -2750,7 +2783,7 @@
               "       12.199998 , 12.199998 , 12.199998 , 12.199998 , 12.199998 ,\n",
               "       12.199998 , 12.199998 , 12.199998 , 12.199998 , 12.199998 ,\n",
               "       12.1119995, 12.       , 12.       , 12.       , 12.       ,\n",
-              "       12.       , 12.       , 12.       ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>244.0 244.0 244.0 ... 246.0 246.0</div><input id='attrs-ec8aa61b-ef20-4f4a-84d3-ae6ca5a7f53f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ec8aa61b-ef20-4f4a-84d3-ae6ca5a7f53f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-26f81164-f4ae-4064-aebe-b077d17f6f23' class='xr-var-data-in' type='checkbox'><label for='data-26f81164-f4ae-4064-aebe-b077d17f6f23' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([244.04333, 243.96   , 244.04666, 244.12334, 244.30667, 244.32   ,\n",
+              "       12.       , 12.       , 12.       ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>244.0 244.0 244.0 ... 246.0 246.0</div><input id='attrs-75efebf3-f8be-4e5c-a22d-905b3bbad4fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-75efebf3-f8be-4e5c-a22d-905b3bbad4fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c0ebbd6-078d-4f32-b25a-55eeb5f93919' class='xr-var-data-in' type='checkbox'><label for='data-8c0ebbd6-078d-4f32-b25a-55eeb5f93919' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([244.04333, 243.96   , 244.04666, 244.12334, 244.30667, 244.32   ,\n",
               "       244.04   , 243.95667, 243.72   , 243.45334, 243.46   , 243.49333,\n",
               "       243.49667, 243.69667, 243.80333, 243.78334, 243.81667, 244.     ,\n",
               "       244.27333, 244.39667, 244.58333, 244.73334, 244.95334, 245.     ,\n",
@@ -2780,7 +2813,7 @@
               "       245.95667, 245.94   , 245.93   , 245.95667, 245.95   , 245.95334,\n",
               "       245.94667, 245.96   , 245.96333, 245.97333, 245.96666, 245.96   ,\n",
               "       245.97667, 245.99   , 245.98   , 245.98666, 245.98666, 245.98334,\n",
-              "       245.99   , 245.98334, 245.99   ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.589 3.576 3.611 ... -197.1 -197.1</div><input id='attrs-47d8bc58-c734-4d61-a704-91fb0af64746' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-47d8bc58-c734-4d61-a704-91fb0af64746' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-85c7a851-a71e-4311-a82b-71e45b8238a4' class='xr-var-data-in' type='checkbox'><label for='data-85c7a851-a71e-4311-a82b-71e45b8238a4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>uT</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[ 3.5890043e+00,  3.5764265e+00,  3.6108000e+00,  3.5779481e+00,\n",
+              "       245.99   , 245.98334, 245.99   ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mag_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.589 3.576 3.611 ... -197.1 -197.1</div><input id='attrs-fd489df3-8b21-4d7f-8332-54532a8591f0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd489df3-8b21-4d7f-8332-54532a8591f0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8aef5a18-22ef-4bad-8ed8-98dd5d4c9dac' class='xr-var-data-in' type='checkbox'><label for='data-8aef5a18-22ef-4bad-8ed8-98dd5d4c9dac' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>uT</dd><dt><span>long_name :</span></dt><dd>Compass</dd></dl></div><div class='xr-var-data'><pre>array([[ 3.5890043e+00,  3.5764265e+00,  3.6108000e+00,  3.5779481e+00,\n",
               "         3.6116719e+00,  3.5757926e+00,  3.5947008e+00,  3.6243417e+00,\n",
               "         3.6057498e+00,  3.5963907e+00,  3.5682683e+00,  3.5830538e+00,\n",
               "         3.6165168e+00,  3.5789921e+00,  3.5747981e+00,  3.5690453e+00,\n",
@@ -2820,7 +2853,7 @@
               "        -1.9710718e+02, -1.9718983e+02, -1.9715218e+02, -1.9712483e+02,\n",
               "        -1.9706255e+02, -1.9714519e+02, -1.9712418e+02, -1.9705786e+02,\n",
               "        -1.9708685e+02, -1.9711749e+02, -1.9707716e+02, -1.9707919e+02,\n",
-              "        -1.9719186e+02, -1.9713850e+02, -1.9714883e+02]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.262 -1.263 -1.26 ... 9.714 9.713</div><input id='attrs-c1d6d653-5b93-469f-a4c4-dd67053289bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c1d6d653-5b93-469f-a4c4-dd67053289bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2f95eee-74c9-4a93-ae4e-d670fd81dd64' class='xr-var-data-in' type='checkbox'><label for='data-b2f95eee-74c9-4a93-ae4e-d670fd81dd64' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.2623771 , -1.2625197 , -1.2600151 , -1.2631888 , -1.2615377 ,\n",
+              "        -1.9719186e+02, -1.9713850e+02, -1.9714883e+02]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.262 -1.263 -1.26 ... 9.714 9.713</div><input id='attrs-c72fb572-a5c8-4c91-8da0-efa35e435329' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c72fb572-a5c8-4c91-8da0-efa35e435329' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-88c02d03-b496-43f8-a086-59776e5b79b2' class='xr-var-data-in' type='checkbox'><label for='data-88c02d03-b496-43f8-a086-59776e5b79b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[-1.2623771 , -1.2625197 , -1.2600151 , -1.2631888 , -1.2615377 ,\n",
               "        -1.2616537 , -1.262574  , -1.2627327 , -1.2632962 , -1.2634871 ,\n",
               "        -1.2617764 , -1.2599101 , -1.2592376 , -1.2577692 , -1.259011  ,\n",
               "        -1.2584039 , -1.2585176 , -1.2577502 , -1.2569212 , -1.2552022 ,\n",
@@ -2860,7 +2893,7 @@
               "         9.714553  ,  9.715957  ,  9.7148075 ,  9.717874  ,  9.716309  ,\n",
               "         9.715032  ,  9.7090435 ,  9.711168  ,  9.7130995 ,  9.713355  ,\n",
               "         9.715256  ,  9.714936  ,  9.7148075 ,  9.711726  ,  9.711886  ,\n",
-              "         9.713675  ,  9.714042  ,  9.712781  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-2c6d8191-7791-49a1-a910-c8e51da81970' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2c6d8191-7791-49a1-a910-c8e51da81970' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05ff37e1-6593-4dc1-a257-fb203a586dd5' class='xr-var-data-in' type='checkbox'><label for='data-05ff37e1-6593-4dc1-a257-fb203a586dd5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+              "         9.713675  ,  9.714042  ,  9.712781  ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-173e0f05-5aad-4109-aef4-292f0d07423f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-173e0f05-5aad-4109-aef4-292f0d07423f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcca7d45-de13-4bfc-a608-bd7afacf7458' class='xr-var-data-in' type='checkbox'><label for='data-fcca7d45-de13-4bfc-a608-bd7afacf7458' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
@@ -2870,7 +2903,7 @@
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.054e+09 1.054e+09 ... 1.054e+09</div><input id='attrs-5f588f8e-fb6d-4463-a95a-1283770f8dda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5f588f8e-fb6d-4463-a95a-1283770f8dda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1351ed27-e678-435e-b2fc-4d153781ad3f' class='xr-var-data-in' type='checkbox'><label for='data-1351ed27-e678-435e-b2fc-4d153781ad3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
+              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.054e+09 1.054e+09 ... 1.054e+09</div><input id='attrs-cb79631d-b8cd-4f38-be3c-d31808c5ce3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb79631d-b8cd-4f38-be3c-d31808c5ce3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6622e78-2817-4caf-a5b2-5a12ee985f2b' class='xr-var-data-in' type='checkbox'><label for='data-c6622e78-2817-4caf-a5b2-5a12ee985f2b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
@@ -2910,7 +2943,7 @@
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
               "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09, 1.05355686e+09,\n",
-              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0006333 -0.004143 ... nan nan</div><input id='attrs-9c1e5567-1f67-4cad-a3c1-e8e1df96eef4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9c1e5567-1f67-4cad-a3c1-e8e1df96eef4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6d5596c-4a46-4d42-9e65-dbf21650e6d8' class='xr-var-data-in' type='checkbox'><label for='data-c6d5596c-4a46-4d42-9e65-dbf21650e6d8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00063333, -0.00414333, -0.00776667, ..., -0.01952   ,\n",
+              "       1.05355686e+09, 1.05355686e+09, 1.05355686e+09], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0006333 -0.004143 ... nan nan</div><input id='attrs-8cdda550-3fce-4d0b-91b9-172fdfaf973f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cdda550-3fce-4d0b-91b9-172fdfaf973f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfd612d6-b3a1-49b1-9e09-51eaa5d47905' class='xr-var-data-in' type='checkbox'><label for='data-bfd612d6-b3a1-49b1-9e09-51eaa5d47905' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00063333, -0.00414333, -0.00776667, ..., -0.01952   ,\n",
               "        -0.02035667, -0.01573333],\n",
               "       [ 0.02268667,  0.01564333,  0.01504   , ..., -0.00309667,\n",
               "        -0.00658667, -0.00238   ],\n",
@@ -2922,7 +2955,7 @@
               "       [        nan,         nan,         nan, ...,         nan,\n",
               "                nan,         nan],\n",
               "       [        nan,         nan,         nan, ...,         nan,\n",
-              "                nan,         nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>80.08 80.44 80.75 ... nan nan nan</div><input id='attrs-e31b9542-cb75-4807-8fb2-3cb8dcf2df32' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e31b9542-cb75-4807-8fb2-3cb8dcf2df32' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c145cf74-6074-4243-85c2-7a0413485b51' class='xr-var-data-in' type='checkbox'><label for='data-c145cf74-6074-4243-85c2-7a0413485b51' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[80.085   , 80.43833 , 80.75166 , ..., 81.761665, 81.86    ,\n",
+              "                nan,         nan]], shape=(28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>80.08 80.44 80.75 ... nan nan nan</div><input id='attrs-4674d53d-23f4-4ca3-a6c0-6716ceb13fad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4674d53d-23f4-4ca3-a6c0-6716ceb13fad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b30d7f25-c68f-4ad0-a6dc-3f864b9ddb81' class='xr-var-data-in' type='checkbox'><label for='data-b30d7f25-c68f-4ad0-a6dc-3f864b9ddb81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[80.085   , 80.43833 , 80.75166 , ..., 81.761665, 81.86    ,\n",
               "        81.745   ],\n",
               "       [76.05    , 76.60333 , 77.066666, ..., 78.23    , 78.21667 ,\n",
               "        78.045   ],\n",
@@ -2934,7 +2967,7 @@
               "       [      nan,       nan,       nan, ...,       nan,       nan,\n",
               "              nan],\n",
               "       [      nan,       nan,       nan, ...,       nan,       nan,\n",
-              "              nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>93.52 94.19 93.97 ... 0.0 0.0 0.0</div><input id='attrs-eee961d6-8d00-481a-80af-520b5b5d08a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eee961d6-8d00-481a-80af-520b5b5d08a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1425e06-5587-41d3-8c6f-8b939eec3ba9' class='xr-var-data-in' type='checkbox'><label for='data-a1425e06-5587-41d3-8c6f-8b939eec3ba9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[93.51667 , 94.18667 , 93.97    , ..., 92.276665, 92.09    ,\n",
+              "              nan]], shape=(28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr_b5</span></div><div class='xr-var-dims'>(range_b5, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>93.52 94.19 93.97 ... 0.0 0.0 0.0</div><input id='attrs-245f98b9-d7d1-48e6-b010-4aa5f61c9093' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-245f98b9-d7d1-48e6-b010-4aa5f61c9093' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f71b4578-897e-4a9c-97cd-d126fcc44f3f' class='xr-var-data-in' type='checkbox'><label for='data-f71b4578-897e-4a9c-97cd-d126fcc44f3f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd><dt><span>standard_name :</span></dt><dd>beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water</dd><dt><span>Comments :</span></dt><dd>Filtered of data with a correlation value below 50%</dd></dl></div><div class='xr-var-data'><pre>array([[93.51667 , 94.18667 , 93.97    , ..., 92.276665, 92.09    ,\n",
               "        92.61667 ],\n",
               "       [96.75    , 96.403336, 96.32    , ..., 96.433334, 96.89    ,\n",
               "        96.57667 ],\n",
@@ -2946,7 +2979,7 @@
               "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
               "         0.      ],\n",
               "       [ 0.      ,  0.      ,  0.      , ...,  0.      ,  0.      ,\n",
-              "         0.      ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat_b5</span></div><div class='xr-var-dims'>(earth, inst, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2166 -0.217 ... 0.9937 0.9937</div><input id='attrs-27e0e37b-b4e2-442e-b0f2-e2dba8d4f75d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-27e0e37b-b4e2-442e-b0f2-e2dba8d4f75d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0e9a21ba-68cb-44a3-9810-700cda0b98ed' class='xr-var-data-in' type='checkbox'><label for='data-0e9a21ba-68cb-44a3-9810-700cda0b98ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16565624e-01, -2.17020363e-01, -2.15714216e-01, ...,\n",
+              "         0.      ]], shape=(28, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat_b5</span></div><div class='xr-var-dims'>(earth, inst, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.2166 -0.217 ... 0.9937 0.9937</div><input id='attrs-40f80246-0761-48d4-bb1b-2d605298003b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-40f80246-0761-48d4-bb1b-2d605298003b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8b94896-a91d-47e1-a3cf-e2ce087e3fd7' class='xr-var-data-in' type='checkbox'><label for='data-c8b94896-a91d-47e1-a3cf-e2ce087e3fd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[-2.16565624e-01, -2.17020363e-01, -2.15714216e-01, ...,\n",
               "         -2.15817824e-01, -2.15457186e-01, -2.15469748e-01],\n",
               "        [-9.68014121e-01, -9.67880666e-01, -9.68221545e-01, ...,\n",
               "         -9.70180333e-01, -9.70283389e-01, -9.70276892e-01],\n",
@@ -2966,7 +2999,7 @@
               "         -1.12357967e-01, -1.12130813e-01, -1.12177916e-01],\n",
               "        [ 9.91627216e-01,  9.91603792e-01,  9.91649806e-01, ...,\n",
               "          9.93667185e-01,  9.93692756e-01,  9.93687451e-01]]],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0008123 0.0008472 ... -0.002637</div><input id='attrs-de1cf8b3-ccc0-405c-a9a2-285ff8c771c8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de1cf8b3-ccc0-405c-a9a2-285ff8c771c8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-daaafa6b-1361-4cbe-b5bd-941f17673f7f' class='xr-var-data-in' type='checkbox'><label for='data-daaafa6b-1361-4cbe-b5bd-941f17673f7f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00081228,  0.00084717,  0.00085335,  0.00075436,  0.00084513,\n",
+              "      shape=(3, 3, 183), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt_b5</span></div><div class='xr-var-dims'>(dirIMU, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0008123 0.0008472 ... -0.002637</div><input id='attrs-a88a404b-46d4-4219-ad86-282168e89591' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a88a404b-46d4-4219-ad86-282168e89591' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e6bf3922-5af0-410c-ba31-ebe6a624b783' class='xr-var-data-in' type='checkbox'><label for='data-e6bf3922-5af0-410c-ba31-ebe6a624b783' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00081228,  0.00084717,  0.00085335,  0.00075436,  0.00084513,\n",
               "         0.00085451,  0.00073719,  0.00067843,  0.00075248,  0.00074199,\n",
               "         0.00070569,  0.00065896,  0.0006914 ,  0.00069842,  0.00074138,\n",
               "         0.0006676 ,  0.00060215,  0.00059847,  0.0006675 ,  0.0006136 ,\n",
@@ -3006,7 +3039,7 @@
               "        -0.00265949, -0.00270831, -0.00258787, -0.0025423 , -0.00262368,\n",
               "        -0.00264647, -0.00268879, -0.00251626, -0.00256183, -0.00255207,\n",
               "        -0.00251626, -0.00251626, -0.00249998, -0.00248371, -0.0024479 ,\n",
-              "        -0.00254881, -0.00254881, -0.0026367 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions_b5</span></div><div class='xr-var-dims'>(q, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6243 ... 0.7784 0.7784</div><input id='attrs-dd6027fd-956e-45c7-9a97-3702dd1a52fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd6027fd-956e-45c7-9a97-3702dd1a52fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4cadd9b-bbfe-4b06-92a4-42395cd50159' class='xr-var-data-in' type='checkbox'><label for='data-d4cadd9b-bbfe-4b06-92a4-42395cd50159' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6245025 ,  0.62430865,  0.62484366,  0.62450564,  0.624701  ,\n",
+              "        -0.00254881, -0.00254881, -0.0026367 ]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>quaternions_b5</span></div><div class='xr-var-dims'>(q, time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.6245 0.6243 ... 0.7784 0.7784</div><input id='attrs-79342d36-54df-4ac7-b7a6-8ade131b8aa8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-79342d36-54df-4ac7-b7a6-8ade131b8aa8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb4aa0ce-a72a-4cb6-87b0-f837b7a85472' class='xr-var-data-in' type='checkbox'><label for='data-cb4aa0ce-a72a-4cb6-87b0-f837b7a85472' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Quaternions</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.6245025 ,  0.62430865,  0.62484366,  0.62450564,  0.624701  ,\n",
               "         0.62466866,  0.62460196,  0.6247584 ,  0.62459546,  0.62464297,\n",
               "         0.6246826 ,  0.62496835,  0.6249581 ,  0.6250585 ,  0.62494546,\n",
               "         0.6250052 ,  0.62508506,  0.62507963,  0.6253132 ,  0.6254015 ,\n",
@@ -3046,7 +3079,7 @@
               "         0.77855694,  0.778695  ,  0.77849764,  0.7787041 ,  0.77873075,\n",
               "         0.7786052 ,  0.7784392 ,  0.7783923 ,  0.77855927,  0.77852917,\n",
               "         0.7785722 ,  0.77847767,  0.77844226,  0.77829295,  0.7782816 ,\n",
-              "         0.77846414,  0.778354  ,  0.77835685]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>65.0 65.0 65.0 ... 65.0 65.0 65.0</div><input id='attrs-57ba0c05-0b71-4dba-ae61-9bf5d5743f31' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-57ba0c05-0b71-4dba-ae61-9bf5d5743f31' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eec2bdb9-0e7e-4f8b-9cb9-70faf8c5fc3a' class='xr-var-data-in' type='checkbox'><label for='data-eec2bdb9-0e7e-4f8b-9cb9-70faf8c5fc3a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
+              "         0.77846414,  0.778354  ,  0.77835685]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xmit_energy_b5</span></div><div class='xr-var-dims'>(time_b5)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>65.0 65.0 65.0 ... 65.0 65.0 65.0</div><input id='attrs-90a82423-bd66-438b-b1eb-ba055085ce88' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90a82423-bd66-438b-b1eb-ba055085ce88' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5030b7a-9072-4086-8bca-0d9385aea495' class='xr-var-data-in' type='checkbox'><label for='data-a5030b7a-9072-4086-8bca-0d9385aea495' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dB</dd><dt><span>long_name :</span></dt><dd>Sound Pressure Level of Acoustic Signal</dd></dl></div><div class='xr-var-data'><pre>array([65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
               "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
               "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
               "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
@@ -3060,7 +3093,7 @@
               "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
               "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
               "       65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65., 65.,\n",
-              "       65.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-0eda79e2-a2db-4fc7-a06b-6105d09e81d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0eda79e2-a2db-4fc7-a06b-6105d09e81d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5180daea-6030-4163-b44d-247bbbe17b36' class='xr-var-data-in' type='checkbox'><label for='data-5180daea-6030-4163-b44d-247bbbe17b36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+              "       65.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>low_volt_skip</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-46bd7f90-904c-4bb4-885d-5966426d4586' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-46bd7f90-904c-4bb4-885d-5966426d4586' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ea7fe455-de57-49f8-a767-2e3999b861ff' class='xr-var-data-in' type='checkbox'><label for='data-ea7fe455-de57-49f8-a767-2e3999b861ff' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
@@ -3070,7 +3103,7 @@
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>active_config</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-4c28ce24-b344-4bbd-8cbe-a64c644928cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4c28ce24-b344-4bbd-8cbe-a64c644928cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-45439abd-0d0b-40ad-ac5c-76786b9e0fbe' class='xr-var-data-in' type='checkbox'><label for='data-45439abd-0d0b-40ad-ac5c-76786b9e0fbe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>active_config</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-76e19f02-9be1-425f-98ce-a8b8b3158a17' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-76e19f02-9be1-425f-98ce-a8b8b3158a17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5171da35-a4b5-4683-b9c2-81947203191c' class='xr-var-data-in' type='checkbox'><label for='data-5171da35-a4b5-4683-b9c2-81947203191c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
@@ -3080,7 +3113,7 @@
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>telemetry_data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-3a8f49c3-438d-4f76-bad6-fffb9de626e2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3a8f49c3-438d-4f76-bad6-fffb9de626e2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4c1bf1ee-e389-48f3-9009-53f673d17d12' class='xr-var-data-in' type='checkbox'><label for='data-4c1bf1ee-e389-48f3-9009-53f673d17d12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
+              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>telemetry_data</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-462cefba-0bfb-4f65-a7d0-84ba279bddeb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-462cefba-0bfb-4f65-a7d0-84ba279bddeb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4aa0bf2-4dee-41c1-82fd-70af1445586e' class='xr-var-data-in' type='checkbox'><label for='data-b4aa0bf2-4dee-41c1-82fd-70af1445586e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
@@ -3090,7 +3123,7 @@
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
               "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
-              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boost_running</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1267 0.1333 0.13 ... 0.22 0.22</div><input id='attrs-e65b9c28-3705-4b21-9b10-e6c5e04d835c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e65b9c28-3705-4b21-9b10-e6c5e04d835c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42d8157f-8cb7-41a4-9f33-a1eea3d9cd81' class='xr-var-data-in' type='checkbox'><label for='data-42d8157f-8cb7-41a4-9f33-a1eea3d9cd81' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.12666667, 0.13333334, 0.13      , 0.13333334, 0.13      ,\n",
+              "       0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>boost_running</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1267 0.1333 0.13 ... 0.22 0.22</div><input id='attrs-0c5481a4-18a4-42ff-9ca0-26f6c143daf8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0c5481a4-18a4-42ff-9ca0-26f6c143daf8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f63b6680-7fcf-4cda-8783-43fce0c4ad63' class='xr-var-data-in' type='checkbox'><label for='data-f63b6680-7fcf-4cda-8783-43fce0c4ad63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.12666667, 0.13333334, 0.13      , 0.13333334, 0.13      ,\n",
               "       0.13      , 0.13      , 0.11666667, 0.12333333, 0.11666667,\n",
               "       0.11333334, 0.11      , 0.11      , 0.11333334, 0.11666667,\n",
               "       0.11      , 0.11333334, 0.11333334, 0.11333334, 0.12333333,\n",
@@ -3126,7 +3159,7 @@
               "       0.22666667, 0.22      , 0.22      , 0.22666667, 0.22      ,\n",
               "       0.22      , 0.22666667, 0.22      , 0.22      , 0.22666667,\n",
               "       0.22      , 0.22      , 0.22666667, 0.22      , 0.22      ,\n",
-              "       0.22666667, 0.22      , 0.22      ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.287 3.261 3.337 ... 3.352 3.352</div><input id='attrs-3bed8e10-6fb4-4199-a70e-8730edcba36e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bed8e10-6fb4-4199-a70e-8730edcba36e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c62cf3ec-2e64-48bc-9255-5218bf5e7841' class='xr-var-data-in' type='checkbox'><label for='data-c62cf3ec-2e64-48bc-9255-5218bf5e7841' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([3.287083 , 3.2605155, 3.3369133, 3.2892823, 3.3165808, 3.3119812,\n",
+              "       0.22666667, 0.22      , 0.22      ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>3.287 3.261 3.337 ... 3.352 3.352</div><input id='attrs-89c1f95c-d6a4-4603-a322-ce27aff150b3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-89c1f95c-d6a4-4603-a322-ce27aff150b3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec191042-2bc1-4fbe-baf9-a5bbfa9dc4cd' class='xr-var-data-in' type='checkbox'><label for='data-ec191042-2bc1-4fbe-baf9-a5bbfa9dc4cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([3.287083 , 3.2605155, 3.3369133, 3.2892823, 3.3165808, 3.3119812,\n",
               "       3.3027983, 3.3258302, 3.3029814, 3.3100154, 3.3152144, 3.3549793,\n",
               "       3.3538957, 3.3682969, 3.3522127, 3.3599124, 3.3716784, 3.370829 ,\n",
               "       3.404896 , 3.4170458, 3.4101129, 3.4259632, 3.3813803, 3.352863 ,\n",
@@ -3156,7 +3189,7 @@
               "       3.2660322, 3.2617817, 3.3116815, 3.314548 , 3.289132 , 3.3249977,\n",
               "       3.287216 , 3.282166 , 3.306248 , 3.3363798, 3.3452635, 3.314365 ,\n",
               "       3.3198478, 3.3122149, 3.3298814, 3.3361466, 3.3632295, 3.3652124,\n",
-              "       3.331114 , 3.352297 , 3.351696 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.05523 -0.07217 ... -0.0429</div><input id='attrs-6ce6cfa4-4d2f-4e6e-b3e4-0e71f7de28e4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ce6cfa4-4d2f-4e6e-b3e4-0e71f7de28e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fce35df6-b5db-48b6-8219-4946768943a0' class='xr-var-data-in' type='checkbox'><label for='data-fce35df6-b5db-48b6-8219-4946768943a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.05523333, -0.07216667, -0.05975001, -0.0719    , -0.06111666,\n",
+              "       3.331114 , 3.352297 , 3.351696 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.05523 -0.07217 ... -0.0429</div><input id='attrs-6ddbe7fd-0d4a-43de-a61e-a765e89b45b2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6ddbe7fd-0d4a-43de-a61e-a765e89b45b2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9d7de0b-9402-405f-b436-f1b1629cd297' class='xr-var-data-in' type='checkbox'><label for='data-f9d7de0b-9402-405f-b436-f1b1629cd297' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-0.05523333, -0.07216667, -0.05975001, -0.0719    , -0.06111666,\n",
               "       -0.05995   , -0.07213333, -0.07148333, -0.07696667, -0.08181667,\n",
               "       -0.0731    , -0.0579    , -0.05488333, -0.05605   , -0.05533333,\n",
               "       -0.05278333, -0.047     , -0.0521    , -0.05088333, -0.04305   ,\n",
@@ -3192,7 +3225,7 @@
               "       -0.04428333, -0.05206667, -0.04676667, -0.05188334, -0.0574    ,\n",
               "       -0.06316667, -0.0484    , -0.04308333, -0.04601667, -0.0499    ,\n",
               "       -0.05471666, -0.05536667, -0.04616667, -0.02825   , -0.03388334,\n",
-              "       -0.0423    , -0.04288333, -0.0429    ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.414 -7.424 ... -6.433 -6.436</div><input id='attrs-8db76249-ff61-4aa8-909b-9af199cafcf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8db76249-ff61-4aa8-909b-9af199cafcf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7efb92ed-0095-4d84-b650-4be2eebe0461' class='xr-var-data-in' type='checkbox'><label for='data-7efb92ed-0095-4d84-b650-4be2eebe0461' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([-7.414066 , -7.424116 , -7.4041324, -7.422417 , -7.4120665,\n",
+              "       -0.0423    , -0.04288333, -0.0429    ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-7.414 -7.424 ... -6.433 -6.436</div><input id='attrs-0d7d1c1f-4856-47c2-8ee3-cac17108f21c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d7d1c1f-4856-47c2-8ee3-cac17108f21c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dd7fe75f-ea11-41f2-8671-9eb4066199b5' class='xr-var-data-in' type='checkbox'><label for='data-dd7fe75f-ea11-41f2-8671-9eb4066199b5' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([-7.414066 , -7.424116 , -7.4041324, -7.422417 , -7.4120665,\n",
               "       -7.4167333, -7.4193   , -7.4148664, -7.4301662, -7.419133 ,\n",
               "       -7.4179006, -7.402567 , -7.4040666, -7.394583 , -7.403933 ,\n",
               "       -7.394567 , -7.39615  , -7.3910007, -7.386317 , -7.3802505,\n",
@@ -3228,7 +3261,7 @@
               "       -6.4438   , -6.450166 , -6.4428334, -6.45625  , -6.455867 ,\n",
               "       -6.44465  , -6.436783 , -6.430967 , -6.4454665, -6.441799 ,\n",
               "       -6.4480996, -6.4399   , -6.4372997, -6.4309335, -6.4305167,\n",
-              "       -6.4460497, -6.4332495, -6.4359174], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_density</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.023e+03 1.023e+03 ... 1.023e+03</div><input id='attrs-08c16091-8a9b-498b-b54d-d6c1bc07b97d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08c16091-8a9b-498b-b54d-d6c1bc07b97d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05a32057-2580-42ce-bb33-fe8146682073' class='xr-var-data-in' type='checkbox'><label for='data-05a32057-2580-42ce-bb33-fe8146682073' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Water Density</dd><dt><span>standard_name :</span></dt><dd>sea_water_density</dd><dt><span>description :</span></dt><dd>Water density from linear approximation of sea water equation of state</dd></dl></div><div class='xr-var-data'><pre>array([1023.2506 , 1023.2348 , 1023.2431 , 1023.2559 , 1023.2423 ,\n",
+              "       -6.4460497, -6.4332495, -6.4359174], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_density</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.023e+03 1.023e+03 ... 1.023e+03</div><input id='attrs-20ca3cf4-a5e7-421f-b8f7-6af5213aed7c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20ca3cf4-a5e7-421f-b8f7-6af5213aed7c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ff21534-9173-4595-b780-e6ba70ff82a6' class='xr-var-data-in' type='checkbox'><label for='data-8ff21534-9173-4595-b780-e6ba70ff82a6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kg m-3</dd><dt><span>long_name :</span></dt><dd>Water Density</dd><dt><span>standard_name :</span></dt><dd>sea_water_density</dd><dt><span>description :</span></dt><dd>Water density from linear approximation of sea water equation of state</dd></dl></div><div class='xr-var-data'><pre>array([1023.2506 , 1023.2348 , 1023.2431 , 1023.2559 , 1023.2423 ,\n",
               "       1023.21655, 1023.1977 , 1023.2015 , 1023.19476, 1023.19116,\n",
               "       1023.194  , 1023.1996 , 1023.20294, 1023.19666, 1023.19336,\n",
               "       1023.19763, 1023.205  , 1023.20404, 1023.2148 , 1023.2179 ,\n",
@@ -3264,7 +3297,7 @@
               "       1023.3432 , 1023.34186, 1023.34094, 1023.3408 , 1023.34   ,\n",
               "       1023.33655, 1023.34143, 1023.34155, 1023.34357, 1023.3508 ,\n",
               "       1023.3694 , 1023.37335, 1023.3781 , 1023.3796 , 1023.3752 ,\n",
-              "       1023.3806 , 1023.3899 , 1023.3975 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>10.28 10.26 10.25 ... 10.15 10.15</div><input id='attrs-e0ce5d26-b410-4f9b-a569-5da23b0269af' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e0ce5d26-b410-4f9b-a569-5da23b0269af' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8eb08c7-e3d7-452d-b09a-4e193313b655' class='xr-var-data-in' type='checkbox'><label for='data-b8eb08c7-e3d7-452d-b09a-4e193313b655' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Water Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([10.275155 , 10.262421 , 10.248638 , 10.233478 , 10.216689 ,\n",
+              "       1023.3806 , 1023.3899 , 1023.3975 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>depth</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>10.28 10.26 10.25 ... 10.15 10.15</div><input id='attrs-62dc49db-3c51-4954-b4cb-390df3c41304' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-62dc49db-3c51-4954-b4cb-390df3c41304' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0840afc8-a014-497a-9e16-c27c3a759f9f' class='xr-var-data-in' type='checkbox'><label for='data-0840afc8-a014-497a-9e16-c27c3a759f9f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Water Depth</dd><dt><span>standard_name :</span></dt><dd>depth</dd></dl></div><div class='xr-var-data'><pre>array([10.275155 , 10.262421 , 10.248638 , 10.233478 , 10.216689 ,\n",
               "       10.204074 , 10.188269 , 10.172538 , 10.158512 , 10.140744 ,\n",
               "       10.124731 , 10.10534  , 10.088593 , 10.069619 , 10.050677 ,\n",
               "       10.028841 , 10.00682  ,  9.981041 ,  9.958139 ,  9.932152 ,\n",
@@ -3300,7 +3333,7 @@
               "        9.972928 ,  9.992192 , 10.00496  , 10.023165 , 10.037822 ,\n",
               "       10.052144 , 10.058786 , 10.073322 , 10.083076 , 10.093331 ,\n",
               "       10.100321 , 10.109131 , 10.117323 , 10.123545 , 10.1341   ,\n",
-              "       10.142311 , 10.14612  , 10.153638 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8dc246c4-9ab6-44bd-a7c2-ee8705784f0f' class='xr-section-summary-in' type='checkbox'  ><label for='section-8dc246c4-9ab6-44bd-a7c2-ee8705784f0f' class='xr-section-summary' >Indexes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-100a5734-9280-4bc1-a604-7e228c512459' class='xr-index-data-in' type='checkbox'/><label for='index-100a5734-9280-4bc1-a604-7e228c512459' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:22:30.001030683&#x27;,\n",
+              "       10.142311 , 10.14612  , 10.153638 ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-34d41744-573a-4081-a409-9fee3bf6593e' class='xr-section-summary-in' type='checkbox'  ><label for='section-34d41744-573a-4081-a409-9fee3bf6593e' class='xr-section-summary' >Indexes: <span>(10)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-c7659f11-a202-45a0-a263-7fa50ecf74a6' class='xr-index-data-in' type='checkbox'/><label for='index-c7659f11-a202-45a0-a263-7fa50ecf74a6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:22:30.001030683&#x27;,\n",
               "               &#x27;2020-08-15 00:27:30.001019239&#x27;,\n",
               "               &#x27;2020-08-15 00:32:30.001010656&#x27;,\n",
               "               &#x27;2020-08-15 00:37:30.001015901&#x27;,\n",
@@ -3321,7 +3354,7 @@
               "               &#x27;2020-08-15 15:22:30.001026153&#x27;,\n",
               "               &#x27;2020-08-15 15:27:30.001022577&#x27;,\n",
               "               &#x27;2020-08-15 15:32:30.001030683&#x27;],\n",
-              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=183, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dirIMU</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3bc8ab76-75a3-4430-a447-2f16fe9419c7' class='xr-index-data-in' type='checkbox'/><label for='index-3bc8ab76-75a3-4430-a447-2f16fe9419c7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dirIMU&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-56a81a3c-206c-43c8-8faa-01b5acba7255' class='xr-index-data-in' type='checkbox'/><label for='index-56a81a3c-206c-43c8-8faa-01b5acba7255' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=183, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dirIMU</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-79d0022c-bb73-4025-b16c-0e69f1a07b55' class='xr-index-data-in' type='checkbox'/><label for='index-79d0022c-bb73-4025-b16c-0e69f1a07b55' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dirIMU&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-d1b99d26-7067-489a-b9b9-c458bcb33e66' class='xr-index-data-in' type='checkbox'/><label for='index-d1b99d26-7067-489a-b9b9-c458bcb33e66' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
               "                      2.7,                3.2,                3.7,\n",
               "                      4.2,  4.699999999999999,  5.199999999999999,\n",
               "        5.699999999999999,  6.199999999999999,  6.699999999999999,\n",
@@ -3331,7 +3364,7 @@
               "                     11.7,               12.2,               12.7,\n",
               "                     13.2,               13.7,               14.2,\n",
               "                     14.7],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-09c4822e-635a-4d43-867e-d55e2ea1d80f' class='xr-index-data-in' type='checkbox'/><label for='index-09c4822e-635a-4d43-867e-d55e2ea1d80f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert1&#x27;, &#x27;vert2&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a05d4196-a586-4fab-a7c7-3695847efce5' class='xr-index-data-in' type='checkbox'/><label for='index-a05d4196-a586-4fab-a7c7-3695847efce5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1960540d-de8b-4b04-a8fc-7f9a690e9700' class='xr-index-data-in' type='checkbox'/><label for='index-1960540d-de8b-4b04-a8fc-7f9a690e9700' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-383bc76d-0cda-4895-b5de-080c9f67c1fb' class='xr-index-data-in' type='checkbox'/><label for='index-383bc76d-0cda-4895-b5de-080c9f67c1fb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>q</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9c5995d6-7c6f-474e-b9e0-97a579d5ed66' class='xr-index-data-in' type='checkbox'/><label for='index-9c5995d6-7c6f-474e-b9e0-97a579d5ed66' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;q&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_b5</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f4cee843-5a81-4daf-98d4-f456387cf394' class='xr-index-data-in' type='checkbox'/><label for='index-f4cee843-5a81-4daf-98d4-f456387cf394' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:22:29.938495159&#x27;,\n",
+              "      dtype=&#x27;float64&#x27;, name=&#x27;range&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8becd749-8b7d-416c-9dc2-346d1af1c6b7' class='xr-index-data-in' type='checkbox'/><label for='index-8becd749-8b7d-416c-9dc2-346d1af1c6b7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;streamwise&#x27;, &#x27;x-stream&#x27;, &#x27;vert1&#x27;, &#x27;vert2&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-3289eea6-61af-495a-b255-47ab10d76a15' class='xr-index-data-in' type='checkbox'/><label for='index-3289eea6-61af-495a-b255-47ab10d76a15' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3, 4], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-8ec3b2b4-3c32-4d5c-82d6-af39928e5524' class='xr-index-data-in' type='checkbox'/><label for='index-8ec3b2b4-3c32-4d5c-82d6-af39928e5524' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-5c4e62f4-cadd-4802-9306-b513f788b78d' class='xr-index-data-in' type='checkbox'/><label for='index-5c4e62f4-cadd-4802-9306-b513f788b78d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>q</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-328767fe-c918-4658-9d10-ab092faff002' class='xr-index-data-in' type='checkbox'/><label for='index-328767fe-c918-4658-9d10-ab092faff002' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;w&#x27;, &#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;q&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time_b5</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-dec06c77-a6b4-4695-8d3a-28eac47621be' class='xr-index-data-in' type='checkbox'/><label for='index-dec06c77-a6b4-4695-8d3a-28eac47621be' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-08-15 00:22:29.938495159&#x27;,\n",
               "               &#x27;2020-08-15 00:27:29.938490867&#x27;,\n",
               "               &#x27;2020-08-15 00:32:29.938487529&#x27;,\n",
               "               &#x27;2020-08-15 00:37:29.938496828&#x27;,\n",
@@ -3352,7 +3385,7 @@
               "               &#x27;2020-08-15 15:22:29.938486099&#x27;,\n",
               "               &#x27;2020-08-15 15:27:29.938484191&#x27;,\n",
               "               &#x27;2020-08-15 15:32:29.938490390&#x27;],\n",
-              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_b5&#x27;, length=183, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_b5</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a6fc0f7c-1e76-4ec2-bc41-01ab387e3ba4' class='xr-index-data-in' type='checkbox'/><label for='index-a6fc0f7c-1e76-4ec2-bc41-01ab387e3ba4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
+              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time_b5&#x27;, length=183, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>range_b5</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-9f2781bd-a200-4094-ad9e-4a2c07c32bbb' class='xr-index-data-in' type='checkbox'/><label for='index-9f2781bd-a200-4094-ad9e-4a2c07c32bbb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([               1.2, 1.7000000000000002,                2.2,\n",
               "                      2.7,                3.2,                3.7,\n",
               "                      4.2,  4.699999999999999,  5.199999999999999,\n",
               "        5.699999999999999,  6.199999999999999,  6.699999999999999,\n",
@@ -3362,7 +3395,7 @@
               "                     11.7,               12.2,               12.7,\n",
               "                     13.2,               13.7,               14.2,\n",
               "                     14.7],\n",
-              "      dtype=&#x27;float64&#x27;, name=&#x27;range_b5&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56680903-578e-4a86-98df-4d65112bfa9c' class='xr-section-summary-in' type='checkbox'  ><label for='section-56680903-578e-4a86-98df-4d65112bfa9c' class='xr-section-summary' >Attributes: <span>(41)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>fs :</span></dt><dd>1</dd><dt><span>n_bin :</span></dt><dd>300</dd><dt><span>n_fft :</span></dt><dd>300</dd><dt><span>description :</span></dt><dd>Binned averages calculated from ensembles of size &quot;n_bin&quot;</dd><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature1000\\&quot;,SN=101663&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2212, &quot;FPGA&quot;: 169, &quot;DIGITAL&quot;: &quot;\\&quot;I-3\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-2\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;G-1\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;D-1(AHRS)\\&quot;&quot;, &quot;BOOT&quot;: 21, &quot;FWMINOR&quot;: 11}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 1940.43, &quot;BURST&quot;: 1937.69, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1940.43}, &quot;MEM&quot;: {&quot;PLAN&quot;: 2.733, &quot;BURST&quot;: 2.733, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 2.733}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: -9.99, &quot;BURSTHORZ&quot;: 2.6, &quot;BEAM5&quot;: 1.62, &quot;AVGBEAM&quot;: -9.99, &quot;BURSTBEAM&quot;: 1.55}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 0, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 32.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S101663A007_sea_spider.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 1000, &quot;NSTT&quot;: 0}, &quot;BURST&quot;: {&quot;NC&quot;: 28, &quot;NB&quot;: 5, &quot;CS&quot;: 0.5, &quot;BD&quot;: 0.1, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: 0.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1, &quot;VR&quot;: 2.5, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 8, &quot;CH&quot;: 0, &quot;ALTI&quot;: 0, &quot;VR5&quot;: 2.5, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 60, &quot;ALTISTART&quot;: 0.1, &quot;ALTIEND&quot;: 30.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.1831, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.1831, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.1831, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.1831, &quot;M31&quot;: 0.5518, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.5518, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.5518, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.5518}, &quot;USER&quot;: {&quot;POFF&quot;: 10.2, &quot;DECL&quot;: 0.0, &quot;HX&quot;: -48, &quot;HY&quot;: 48, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;OFF\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AHRS3D\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 155, &quot;DY&quot;: 778, &quot;DZ&quot;: -486, &quot;M11&quot;: 31481, &quot;M12&quot;: 1726, &quot;M13&quot;: -1109, &quot;M21&quot;: -1955, &quot;M22&quot;: 32767, &quot;M23&quot;: 379, &quot;M31&quot;: 750, &quot;M32&quot;: -1851, &quot;M33&quot;: 31699}, &quot;READAHRS&quot;: &quot;STR=\\&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM\\&quot;&quot;, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;C390Y1LU32C1B\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;6F2UB3HTH2C1B\\&quot;,DESC=\\&quot;Burst Five Beams\\&quot;,TYPE=17&quot;, &quot;KEY=\\&quot;R0X9JSZPH2C1B\\&quot;,DESC=\\&quot;128GB Recorder\\&quot;,TYPE=14&quot;], &quot;CALCOMPGET&quot;: &quot;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99906&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=1.791816E-02,B1X=-1.795970E-02,B2X=6.661898E-04,B3X=0.000000E+00,A1X=1.146936E-05,A2X=7.326843E-06,A3X=0.000000E+00&quot;, &quot;AY=1.000000E+00,B0Y=9.597497E-03,B1Y=-8.430577E-03,B2Y=9.310668E-04,B3Y=0.000000E+00,A1Y=7.215003E-05,A2Y=1.361528E-06,A3Y=0.000000E+00&quot;, &quot;AZ=1.000000E+00,B0Z=1.290458E-02,B1Z=2.959575E-02,B2Z=2.331257E-03,B3Z=0.000000E+00,A1Z=1.164268E-04,A2Z=-2.166612E-06,A3Z=0.000000E+00&quot;], &quot;CALGYROGET&quot;: [&quot;AX=1.094973E+00,B0X=-2.672331E+00,B1X=-1.493178E-03,B2X=9.496510E-05,B3X=0.000000E+00,A1X=2.702698E-04,A2X=8.100271E-05,A3X=0.000000E+00&quot;, &quot;AY=1.080807E+00,B0Y=3.432283E-01,B1Y=7.219538E-04,B2Y=1.416728E-04,B3Y=0.000000E+00,A1Y=9.466633E-04,A2Y=-4.216283E-05,A3Y=0.000000E+00&quot;, &quot;AZ=1.085556E+00,B0Z=-9.442614E-01,B1Z=6.433576E-04,B2Z=-1.306087E-05,B3Z=0.000000E+00,A1Z=1.430541E-03,A2Z=-7.494539E-05,A3Z=0.000000E+00&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5264760000e+02,RB0=7.7074756250e-01,RB1=-7.5541806250e-02,RB2=4.9886362500e-04,RB3=-1.4193179690e-07,T0=-2.4234620000e+03,T1=1.8815180000e+00,T2=-5.0111803130e-04,T3=4.7281500000e-08,ID=\\&quot;K244314\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=6.3850020000e+00,A1=-5.6274000000e-03,A2=1.6346411250e-06,A3=-1.6029240630e-10,B0=-2.6056009380e-01,B1=3.1699690630e-04,B2=-9.0774206250e-08,B3=8.5142393750e-12&quot;, &quot;C0=-1.2762860000e-03,C1=1.1561740000e-06,C2=-3.5109818750e-10,C3=3.5575718750e-14,D0=3.6002040630e-06,D1=-3.3359181250e-09,D2=1.0241850000e-12,D3=-1.0446648750e-16&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.99144,M12=0.00991,M13=-0.03081,M21=-0.00921,M22=0.99564,M23=0.01131,M31=0.03451,M32=-0.01242,M33=0.98426&quot;, &quot;CALROTGYROGET&quot;: &quot;M11=1.00000,M12=0.00803,M13=-0.02685,M21=-0.00933,M22=1.00000,M23=0.01646,M31=0.02587,M32=-0.01599,M33=1.00000&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.69,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature1000</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>28</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>5.066</dd><dt><span>SerialNum :</span></dt><dd>101663</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_b5 :</span></dt><dd>28</dd><dt><span>coord_sys_axes_b5 :</span></dt><dd>beam</dd><dt><span>n_beams_b5 :</span></dt><dd>1</dd><dt><span>ambig_vel_b5 :</span></dt><dd>5.066</dd><dt><span>SerialNum_b5 :</span></dt><dd>101663</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</dd><dt><span>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>power_level_dB_b5 :</span></dt><dd>0.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>AHRS</dd><dt><span>orient_status :</span></dt><dd>AHRS-3D</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, &#x27;mag&#x27;, &#x27;mag_b5&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>principal</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>beam_angle :</span></dt><dd>25</dd><dt><span>range_offset :</span></dt><dd>0.6</dd><dt><span>declination :</span></dt><dd>15.8</dd><dt><span>declination_in_orientmat :</span></dt><dd>1</dd><dt><span>principal_heading :</span></dt><dd>11.1898</dd></dl></div></li></ul></div></div>"
+              "      dtype=&#x27;float64&#x27;, name=&#x27;range_b5&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8405663f-d2d0-4c79-b5d8-3988c9cad12a' class='xr-section-summary-in' type='checkbox'  ><label for='section-8405663f-d2d0-4c79-b5d8-3988c9cad12a' class='xr-section-summary' >Attributes: <span>(41)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>fs :</span></dt><dd>1</dd><dt><span>n_bin :</span></dt><dd>300</dd><dt><span>n_fft :</span></dt><dd>300</dd><dt><span>description :</span></dt><dd>Binned averages calculated from ensembles of size &quot;n_bin&quot;</dd><dt><span>filehead_config :</span></dt><dd>{&quot;CLOCKSTR&quot;: {&quot;TIME&quot;: &quot;\\&quot;2020-08-13 13:56:21\\&quot;&quot;}, &quot;ID&quot;: &quot;STR=\\&quot;Signature1000\\&quot;,SN=101663&quot;, &quot;HW&quot;: {&quot;FW&quot;: 2212, &quot;FPGA&quot;: 169, &quot;DIGITAL&quot;: &quot;\\&quot;I-3\\&quot;&quot;, &quot;INTERFACE&quot;: &quot;\\&quot;H-2\\&quot;&quot;, &quot;ANALOG&quot;: &quot;\\&quot;G-1\\&quot;&quot;, &quot;SENSOR&quot;: &quot;\\&quot;D-1(AHRS)\\&quot;&quot;, &quot;BOOT&quot;: 21, &quot;FWMINOR&quot;: 11}, &quot;BOARDSENSGET&quot;: &quot;AV=23,NB=5,HF=1000,TTR=2.0000,TTRB5=2.0000&quot;, &quot;PWR&quot;: {&quot;PLAN&quot;: 1940.43, &quot;BURST&quot;: 1937.69, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 1940.43}, &quot;MEM&quot;: {&quot;PLAN&quot;: 2.733, &quot;BURST&quot;: 2.733, &quot;AVG&quot;: 0.0, &quot;PLAN1&quot;: 0.0, &quot;BURST1&quot;: 0.0, &quot;AVG1&quot;: 0.0, &quot;TOTAL&quot;: 2.733}, &quot;PRECISION&quot;: {&quot;AVGHORZ&quot;: -9.99, &quot;BURSTHORZ&quot;: 2.6, &quot;BEAM5&quot;: 1.62, &quot;AVGBEAM&quot;: -9.99, &quot;BURSTBEAM&quot;: 1.55}, &quot;PLAN&quot;: {&quot;MIAVG&quot;: 600, &quot;AVG&quot;: 0, &quot;DIAVG&quot;: 0, &quot;VD&quot;: 0, &quot;MV&quot;: 10, &quot;SA&quot;: 32.0, &quot;BURST&quot;: 1, &quot;MIBURST&quot;: 1, &quot;DIBURST&quot;: 0, &quot;SV&quot;: 0.0, &quot;FN&quot;: &quot;\\&quot;S101663A007_sea_spider.ad2cp\\&quot;&quot;, &quot;SO&quot;: 0, &quot;FREQ&quot;: 1000, &quot;NSTT&quot;: 0}, &quot;BURST&quot;: {&quot;NC&quot;: 28, &quot;NB&quot;: 5, &quot;CS&quot;: 0.5, &quot;BD&quot;: 0.1, &quot;CY&quot;: &quot;\\&quot;ENU\\&quot;&quot;, &quot;PL&quot;: 0.0, &quot;SR&quot;: 1, &quot;NS&quot;: 1, &quot;VR&quot;: 2.5, &quot;VP&quot;: 0.0, &quot;DF&quot;: 3, &quot;NPING&quot;: 8, &quot;CH&quot;: 0, &quot;ALTI&quot;: 0, &quot;VR5&quot;: 2.5, &quot;BT&quot;: 0, &quot;DISV&quot;: 0, &quot;ECHO&quot;: 0, &quot;RAWALTI&quot;: 60, &quot;ALTISTART&quot;: 0.1, &quot;ALTIEND&quot;: 30.0, &quot;HR&quot;: 0, &quot;HR5&quot;: 0}, &quot;XFBURST&quot;: {&quot;ROWS&quot;: 4, &quot;COLS&quot;: 4, &quot;M11&quot;: 1.1831, &quot;M12&quot;: 0.0, &quot;M13&quot;: -1.1831, &quot;M14&quot;: 0.0, &quot;M21&quot;: 0.0, &quot;M22&quot;: -1.1831, &quot;M23&quot;: 0.0, &quot;M24&quot;: 1.1831, &quot;M31&quot;: 0.5518, &quot;M32&quot;: 0.0, &quot;M33&quot;: 0.5518, &quot;M34&quot;: 0.0, &quot;M41&quot;: 0.0, &quot;M42&quot;: 0.5518, &quot;M43&quot;: 0.0, &quot;M44&quot;: 0.5518}, &quot;USER&quot;: {&quot;POFF&quot;: 10.2, &quot;DECL&quot;: 0.0, &quot;HX&quot;: -48, &quot;HY&quot;: 48, &quot;HZ&quot;: 0}, &quot;INST&quot;: {&quot;BR&quot;: 9600, &quot;RS&quot;: 232, &quot;LED&quot;: &quot;\\&quot;OFF\\&quot;&quot;, &quot;ORIENT&quot;: &quot;\\&quot;AHRS3D\\&quot;&quot;, &quot;CMTOUT&quot;: 300, &quot;DMTOUT&quot;: 60, &quot;CFMTOUT&quot;: 60}, &quot;COMPASSCAL&quot;: {&quot;DX&quot;: 155, &quot;DY&quot;: 778, &quot;DZ&quot;: -486, &quot;M11&quot;: 31481, &quot;M12&quot;: 1726, &quot;M13&quot;: -1109, &quot;M21&quot;: -1955, &quot;M22&quot;: 32767, &quot;M23&quot;: 379, &quot;M31&quot;: 750, &quot;M32&quot;: -1851, &quot;M33&quot;: 31699}, &quot;READAHRS&quot;: &quot;STR=\\&quot;OSv6_a2_V5101_0.6 Oct  3 2019, SerialNumber=60004222,type=OS3DM\\&quot;&quot;, &quot;RECSTAT&quot;: &quot;SS=512,CS=32768,FC=127813877760,TC=127848677376,VS=127848677376&quot;, &quot;BEAMCFGLIST&quot;: [&quot;BEAM=1,THETA=25.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=1,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=2,THETA=25.00,PHI=-90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=2,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=3,THETA=25.00,PHI=180.00,FREQ=1000,BW=25,BRD=1,HWBEAM=3,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=4,THETA=25.00,PHI=90.00,FREQ=1000,BW=25,BRD=1,HWBEAM=4,ZNOM=60.00,DIA=0.0&quot;, &quot;BEAM=5,THETA=0.00,PHI=0.00,FREQ=1000,BW=25,BRD=1,HWBEAM=5,ZNOM=60.00,DIA=0.0&quot;], &quot;BEAMIMPLIST&quot;: [&quot;BEAM=1,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=2,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=3,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=4,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;, &quot;BEAM=5,P0=1.00000e+02,P1=0.00000e+00,P2=0.00000e+00,P3=0.00000e+00,P4=0.00000e+00,T1=0.00000e+00&quot;], &quot;LISTLICENSE&quot;: [&quot;KEY=\\&quot;C390Y1LU32C1B\\&quot;,DESC=\\&quot;Averaging Mode\\&quot;,TYPE=1&quot;, &quot;KEY=\\&quot;6F2UB3HTH2C1B\\&quot;,DESC=\\&quot;Burst Five Beams\\&quot;,TYPE=17&quot;, &quot;KEY=\\&quot;R0X9JSZPH2C1B\\&quot;,DESC=\\&quot;128GB Recorder\\&quot;,TYPE=14&quot;], &quot;CALCOMPGET&quot;: &quot;DX=155,DY=778,DZ=-486,M11=31481,M12=1726,M13=-1109,M21=-1955,M22=32767,M23=379,M31=750,M32=-1851,M33=31699&quot;, &quot;CALTEMPGET&quot;: &quot;SC=0.99906&quot;, &quot;CALTILTGET&quot;: &quot;PO=0.00,RO=0.00,MAGG=1,HO=0.00&quot;, &quot;CALACCLGET&quot;: [&quot;AX=1.000000E+00,B0X=1.791816E-02,B1X=-1.795970E-02,B2X=6.661898E-04,B3X=0.000000E+00,A1X=1.146936E-05,A2X=7.326843E-06,A3X=0.000000E+00&quot;, &quot;AY=1.000000E+00,B0Y=9.597497E-03,B1Y=-8.430577E-03,B2Y=9.310668E-04,B3Y=0.000000E+00,A1Y=7.215003E-05,A2Y=1.361528E-06,A3Y=0.000000E+00&quot;, &quot;AZ=1.000000E+00,B0Z=1.290458E-02,B1Z=2.959575E-02,B2Z=2.331257E-03,B3Z=0.000000E+00,A1Z=1.164268E-04,A2Z=-2.166612E-06,A3Z=0.000000E+00&quot;], &quot;CALGYROGET&quot;: [&quot;AX=1.094973E+00,B0X=-2.672331E+00,B1X=-1.493178E-03,B2X=9.496510E-05,B3X=0.000000E+00,A1X=2.702698E-04,A2X=8.100271E-05,A3X=0.000000E+00&quot;, &quot;AY=1.080807E+00,B0Y=3.432283E-01,B1Y=7.219538E-04,B2Y=1.416728E-04,B3Y=0.000000E+00,A1Y=9.466633E-04,A2Y=-4.216283E-05,A3Y=0.000000E+00&quot;, &quot;AZ=1.085556E+00,B0Z=-9.442614E-01,B1Z=6.433576E-04,B2Z=-1.306087E-05,B3Z=0.000000E+00,A1Z=1.430541E-03,A2Z=-7.494539E-05,A3Z=0.000000E+00&quot;], &quot;CALPRESSGET&quot;: &quot;MT=1,RREF=4.5264760000e+02,RB0=7.7074756250e-01,RB1=-7.5541806250e-02,RB2=4.9886362500e-04,RB3=-1.4193179690e-07,T0=-2.4234620000e+03,T1=1.8815180000e+00,T2=-5.0111803130e-04,T3=4.7281500000e-08,ID=\\&quot;K244314\\&quot;&quot;, &quot;CALPRESSCOEFFGET&quot;: [&quot;A0=6.3850020000e+00,A1=-5.6274000000e-03,A2=1.6346411250e-06,A3=-1.6029240630e-10,B0=-2.6056009380e-01,B1=3.1699690630e-04,B2=-9.0774206250e-08,B3=8.5142393750e-12&quot;, &quot;C0=-1.2762860000e-03,C1=1.1561740000e-06,C2=-3.5109818750e-10,C3=3.5575718750e-14,D0=3.6002040630e-06,D1=-3.3359181250e-09,D2=1.0241850000e-12,D3=-1.0446648750e-16&quot;], &quot;CALROTACCLGET&quot;: &quot;M11=0.99144,M12=0.00991,M13=-0.03081,M21=-0.00921,M22=0.99564,M23=0.01131,M31=0.03451,M32=-0.01242,M33=0.98426&quot;, &quot;CALROTGYROGET&quot;: &quot;M11=1.00000,M12=0.00803,M13=-0.02685,M21=-0.00933,M22=1.00000,M23=0.01646,M31=0.02587,M32=-0.01599,M33=1.00000&quot;, &quot;CALECHOGET&quot;: &quot;CHA0=0.00,CHB0=-17.69,CHC0=0.00&quot;}</dd><dt><span>inst_model :</span></dt><dd>Signature1000</dd><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_type :</span></dt><dd>ADCP</dd><dt><span>burst_config :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells :</span></dt><dd>28</dd><dt><span>n_beams :</span></dt><dd>4</dd><dt><span>ambig_vel :</span></dt><dd>5.066</dd><dt><span>SerialNum :</span></dt><dd>101663</dd><dt><span>nominal_corr :</span></dt><dd>67</dd><dt><span>cell_size :</span></dt><dd>0.5</dd><dt><span>blank_dist :</span></dt><dd>0.1</dd><dt><span>power_level_dB :</span></dt><dd>0.0</dd><dt><span>burst_config_b5 :</span></dt><dd>{&quot;press_valid&quot;: true, &quot;temp_valid&quot;: true, &quot;compass_valid&quot;: true, &quot;tilt_valid&quot;: true, &quot;vel&quot;: true, &quot;amp&quot;: true, &quot;corr&quot;: true, &quot;le&quot;: false, &quot;altraw&quot;: false, &quot;ast&quot;: false, &quot;echo&quot;: false, &quot;ahrs&quot;: true, &quot;p_gd&quot;: false, &quot;std&quot;: false}</dd><dt><span>n_cells_b5 :</span></dt><dd>28</dd><dt><span>coord_sys_axes_b5 :</span></dt><dd>beam</dd><dt><span>n_beams_b5 :</span></dt><dd>1</dd><dt><span>ambig_vel_b5 :</span></dt><dd>5.066</dd><dt><span>SerialNum_b5 :</span></dt><dd>101663</dd><dt><span>nominal_corr_b5 :</span></dt><dd>65</dd><dt><span>cell_size_b5 :</span></dt><dd>0.5</dd><dt><span>blank_dist_b5 :</span></dt><dd>0.1</dd><dt><span>power_level_dB_b5 :</span></dt><dd>0.0</dd><dt><span>wakeup_state :</span></dt><dd>clock</dd><dt><span>orientation :</span></dt><dd>AHRS</dd><dt><span>orient_status :</span></dt><dd>AHRS-3D</dd><dt><span>proc_idle_less_3pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_6pct :</span></dt><dd>0</dd><dt><span>proc_idle_less_12pct :</span></dt><dd>0</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;accel_b5&#x27;, &#x27;angrt&#x27;, &#x27;angrt_b5&#x27;, &#x27;mag&#x27;, &#x27;mag_b5&#x27;]</dd><dt><span>coord_sys :</span></dt><dd>principal</dd><dt><span>has_imu :</span></dt><dd>1</dd><dt><span>beam_angle :</span></dt><dd>25</dd><dt><span>range_offset :</span></dt><dd>0.6</dd><dt><span>declination :</span></dt><dd>15.8</dd><dt><span>declination_in_orientmat :</span></dt><dd>1</dd><dt><span>principal_heading :</span></dt><dd>11.1898</dd></dl></div></li></ul></div></div>"
             ],
             "text/plain": [
               "<xarray.Dataset> Size: 379kB\n",
@@ -3409,7 +3442,7 @@
               "    principal_heading:         11.1898"
             ]
           },
-          "execution_count": 16,
+          "execution_count": 48,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -3432,7 +3465,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 17,
+      "execution_count": 49,
       "metadata": {
         "scrolled": true
       },
@@ -3460,22 +3493,22 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": 50,
       "metadata": {},
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "<matplotlib.colorbar.Colorbar at 0x1c8b919ac90>"
+              "<matplotlib.colorbar.Colorbar at 0x112d84c8410>"
             ]
           },
-          "execution_count": 18,
+          "execution_count": 50,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 1000x600 with 2 Axes>"
             ]
@@ -3507,14 +3540,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": 51,
       "metadata": {
         "scrolled": false
       },
       "outputs": [
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 1000x600 with 2 Axes>"
             ]
@@ -3552,7 +3585,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 20,
+      "execution_count": 52,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -3581,22 +3614,22 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 21,
+      "execution_count": 53,
       "metadata": {},
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x1c8b91f69d0>"
+              "<matplotlib.collections.QuadMesh at 0x112d5652650>"
             ]
           },
-          "execution_count": 21,
+          "execution_count": 53,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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",
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0kjcvLSa9gO1IRUZGAgAOHDjgtiwzMxMAPHayiIiI/IWRFw+r8cmYgL20d8MNNyAxMRG7d+92u4S3bds2tGzZskLcpEZERET+K2A7UpIkYcWKFQgNDcXEiROxYcMGAMC+ffvw448/YteuXTCbzR7j5uXlISMjQ/chIiKqaNQzUkY+ZEzAdqQAoGfPnvjwww8RHByMESNG4N///je2bduGL774AjVq1Cg03oIFCxATE6N9EhISyrHURERExaMOf2DkE2i++eabcs0voDtSAHDnnXfixRdfxGOPPYbXXnsNb7zxBrZs2VJknBkzZiA9PV37nD17tpxKS0REREb07t0bBw8eLLf8AvZmc9WLL76IxMRETJo0CZ07d8a//vUv3H///UhOTsakSZM8xjGbzR4v+0khoYBwFMwICpKnebn6qTlCnjqHVVktSmLy/wKE+tuSo48T6jRYqDrPYddPteXy0PgiN7ugrMGh8heTsortNnmar4ydZZLLLtT5TmVyy0cNIyn9blteQZwqsXI6F0/qwzhs+rgAEKLUyZ6vL7+arxbXpX4AEKKsj/w8fRzlt1TvRvc4RIEoS3kHmLZfKvuC8/HGdb9X9yn1mGVz2QedKccGjfM+7Gm58zxt35WUoinHOZPT/9m1V3m4nAlR50ui8HyU9AuOW66vBXFKM6qqPFXrbHc5rng6E6OVweUcg9K2UnSc/NPm1CaRVeSpeiyPjAKk8vvTypvN3TkcDnTt2hV33HEHHnjgAQwYMABBQR62p1IS0GekFi1ahK1bt+Kee+4BAAwdOhSbN2+GyWTCY489hn379vm4hERERN7jpT134eHh2LZtG3r06IG5c+ciPj4e06dPx4kTJ8okv4DtSP3999+YPXs2BgwYoJt/9913Y+HChRBCYO7cuT4qHREREZWFzZs3Y8CAAZg8eTJ+/PFHfPrpp8jJyUGXLl3QtWtXrFq1CtnZ2ddOqJgCtiO1Y8cO5OXloWbNmm7L/v3vf6NGjRrleg2ViIiotPGpPXeuJ1DatGmDpUuX4sKFC5g0aRLef/991KlTB+PGjcP+/fsN5xewHSn1PXrnzp1zWxYcHIwGDRogNDS0vItFRERUakySZPhTWQQHB0MIgeTkZGRlZWHVqlXo2bMnbrjhBrz00ku4fPmyV+kGbEfqzjvvRFBQED744APYbPqbJdPT03H8+HEMGjTIR6UjIiIyjmek3LkOf5Cbm4ulS5eiSZMm+Ne//oWff/4Z9evXxyuvvILU1FS8/fbb+O2339CsWTNMnjxZe/tJcQVsR6pJkyZ46aWXcPz4cYwZMwbp6ekAgIsXL+Lee+9F/fr1MX/+fB+XkoiIiErTwIEDkZWVhfPnz+Ppp59GQkIC/v3vf+P06dNo3bo1NmzYgD///BOPP/44IiMjtfumfvnlF+zduxfdu3dHampqsfML6OEPJk+ejGbNmmHx4sVo2LAhoqKiEBYWhsGDB2Pz5s2IiorydRGJiIi8JsHYk3eB+NLijIwMVK9eHfn58jAfQgj07dsX06ZNQ+/evQuNV7duXYwfPx4PPfQQnnjiCaxatapY+QV0RwoA7rjjDtxxxx2+LgYREVGpk0zuw16VKL7rUFwBwmq1Ijg4GMOGDcO0adPQqlWrYsX75JNPAAD/+c9/ip1XwHekiIiIqHLp2rUr3n77bTRr1qxE8bKysgAAN95Y/EGe2ZEiIiLyV0YH1QzAu82rV6+OTz/9FFWqVClx3I0bN2LPnj3o169fseME7M3mREREgY5P7bkbP358sTpR8+fPR05Ojm5ezZo1MWjQoBJ1wtiRIiIiooCxZcuWYoWbPHkynn76acP5sSNFRETkp+SzSkbetefrGviG1WrF/v37sX79esNp8R4pIiIiP2X08lwgdKT27NmDRx55BMeOHdPmBQUFFStuYmKi4fx5RoqIiIj8Vrdu3fDzzz/j2Wef1eYJIYr8hIWFoW3btnj33XcN588zUiUkcrK071JQiDxPyANxSHblVTR2eRAwBCnNa80rSMC1+6/EFVaLbrbkIQxMSg/bblfmO5T05bhSiNO7A9V8bEpZbFbdfKGWVU0DABxO3wFAfbVOcKg+jSDnzUZJ36GWUS25pI/jiTb4idDn77C710NtU7Ut1Dj5cvrif384lVsJG2J2yVDo0/D0XzHJ5X8xSvtISlrCmutetiC5faQa9dzTI/+WI78RAfnKPqztr07bgU1eJoW5DPCrhlW2Z8kcXrBM3fadt3WgYB8viut2qx4PTE7/L3a47N9CP1iQUPY1yTk/17BqGd2OWS7HCad5kloGh/61XM71FNrRTd0fHZ7zceJ6rC2I49KOTmlIYZHKMiWsegyBclxQj4HO60CNEyznB/WYquarho2rVhBHbcPIaHmamQ7YXNZrGTL6vrxAedeeJEmYNWsW6tSpgxdeeAEnTpwot7zZkSIiIvJTvLSn98ADDyAlJaVc8+SlPSIiIj9l7EZzg2NQVVDTpk0rdtiPP/7YcH7sSBEREVGlk5ubixEjRhhOh5f2iIiI/FRlvrT33HPPYfPmzZg+fTqGDx+uzR87dmzB/XSFyM/Px/fff4/MzEzD5WBHioiIyE9V5o7Uyy+/jLS0NLzxxhu6jtTJkyexZ8+ea3amAJTKpU12pIiIiMjvbNy4ER999BHGjRunm//QQw/h6NGjmDJlCmrVqoWQkBC3uFarFUlJSdi0aZPhcrAjRURE5KckkwTJ5P1ZFUn47ympvn37om/fvm7zBw0ahC+++AKzZs0qMv748ePx1VdfGS4HO1JERER+qjJf2itMSEgIFi5cWKywzqOhe4tP7REREVFAqVWrVqHLkpOTYVMGnK5WrVqh4YqLHSkiIiI/pY5sbuQTaD766CPdR3XgwAE0a9YMtWrVQlxcHF566aVSyY+X9oiIiPwUL+25u/vuuxEUFIRRo0ZpN6KfO3cOt99+OzIzM5GQkIB27dph4cKFaNSoEQYNGmQoP3akiIiIKKAsW7YMEyZM0H5PmzYNGRkZaNeuHb7++mtERETg2LFjGDt2LDtSRERElZXR17wE4itiIiMjdZ2o3377DVu2bEFQUBBWrlyJiIgIAECLFi2QnJxsOD92pIiIiPyUBIOX9kqtJBVH3bp1db9nz54NIQTuvfdetGrVSpuflZWFM2fOGM6PN5sTERH5Kb602F3z5s3x+uuvIycnBy+++CK2bdsGs9mMefPm6cLNmzcPdrvdcH48I0VEREQB4+WXX0bPnj3x+OOPa6+JeeWVV9CwYUMAwJ9//okXX3wRK1eu5Ctiypuw5UOqGlfw22qRvzjkHq0wOQAAkpCnCK8iT+357olJLicD83LlaahZngaHFiyzZMtTc7g8zc2SpyFhWrkAQDI7DYPvUMqgbiSmIDmsOh/KO4jUsgKAa888VE4fNqs8zbfq6wUADvVdRspUTU4daVeNAwDBZn36WrsoYYM8bI7qu5KUMT+0+qj1MJncy662ZUioPqyalrK+1DbR5a2WSV2mrlu1DZzbS/sqxxEX/9SXJVhZH3abc4WU/FyXubwTKshpXap5KutZq4daRnW5c/upYdV56m81H2ueUkb3Nhfpybr0xf6d8u/G18vTM38VBL54QQ6TkwMA+GXNHgDAjcM7AgCk2Fg5+1//0KJYTl9RiiSXO3b2Y3J2bXvLATJT5WlYZEE+SluK1ItKujXl+eoxMCtdnjpvm6ogpZ1C5P1HUrYZbb8JLmhroawPSW03bTvQb2+S87YTGqFEVjcISfkpbwfagdr5vV/CeZtwpoRRtyHng7z2XdKHdY0LOO2XLmHVdB1K/s710LYn9dhg1/9WjllqveRZ+m2w4PiilkMNW4I/Vq5pOJe/IGOXxerxzml7jpC3BaEeP5VtXlK3K+XYpK+PSVduKTxKDpOToc+vqtPYQ+pxUt2XbLaC41V5MPjUXiBe22vQoAGOHj2K7du348qVK+jVqxfatGmjLf/oo4/QrFkzDn9ARERU2fFmc8+ioqIwYsQIj8umTJmifU9PTzecF++RIiIiokqpWbNmhtPgGSkiIiI/JZncr3SWNH4gEkLg+++/x5kzZ5Cbm6vdK6XKz8/Hnj17cOXKFcN5sSNFRETkp3hpz92xY8cwaNAg/PHHH0WGE0LwZnMiIiIiZ+PGjcPvv/+O0NBQdO3aFXXq1EGwhwdrfv75Z/z888+G82NHioiIyF+ZpIKnpL2NH2COHj2K4OBgHDlyBE2bNi00nM1mQ40aNQznx44UERGRv+Jbi920a9cOp0+fLrITBQDBwcFYunSp4fwC9DYzIiKiwMeRzd0tXrwYqampOHfu3DXDnjx50nB+7EgRERFRwGjbti22bt2KJ5980u1pPWepqalYtGiR4fx4aY+IiMhf8R4pN3379oXNZsPZs2dx8803o2rVqm5hbDYbfv/9d+Qob2Uwgh0pIiIif8V7pNzUrl0b69evL1ZYDn9ARERE5GT27Nn44IMP8Oqrr6JBgwYICQlx6zBZLBbs27cPzz//vOH82JEiIiLyU5JJgmTg8pyRuBVV06ZNMWHCBIwZM8bj+FGqfv36YdOmTYbzY0eKiIjIX/HSnkcvvfRSkZ0o1YkTJwznxaf2iIiIKKDYbDbMnTsXLVu2RIMGDbT5X3/9NUaOHInPPvus1PLiGSkiIiI/JUkGL+0F4Bmpq1evomfPnvjll18ghED16tW1ZT179kSLFi1w66234rPPPsNrr71mOD92pEpIOOzOP+Spy4Yo7HIYda6w5RcsDFKa3C7PE5lX5d+hZjlOeJQ835JdEEd9PbfJpM83PFqeWi36+QCQpzzSaQ6Xp87lBgCHwz2OwybPys9TylJFHyc4BG5c20D9LSR9/k7pwxSkTJW2yFfKr9XPw7gfwq6Pa7PKU6WtEeIURy2L2u52m0sZlbB2p/ViilDKqKTn2j5qvnandtTWpU2frtYGwfo0gYJ1qc5T47ouh1PZtO1H0pc7OFSfnzMtTIg+TL7cbiI3U04xLLIgjlp+pQwiI1X+nZ0lz46tKc9PuVwQp1lzefr9AQBAaKgc15Esb9dBcXFyMa5vrEVxnJTjm5SDv1RVeUVDuvwWdpGj5Oe8Hajbomt7KW2ibbMhoQWL1HZT4yptru3D6j4SHAs3LtugGkdS2zGoYF8Qtjzli1Ifl/1UKNuSFGJ2ShC6Mmnbl5qvun1LThcN1MsUdnU9ufwBdN42hUsYbdtUt2+XtgAgqduztk8r24Hy2/MfXDVdl23f9XigiyLpwmhlUOM4XNcxIBwmfRhX6jHQHFFQH2XbFuqyoCCXsgnlp4c01e1MLVt2hjyNULYldX0B2j6FKjHKMoPDEZSUDy/tWa1WvPzyy1i9ejVsNhvi4+Mxb948dO/evdhp2O12LFu2DMuXL8dff/2FmjVr4v7778czzzwDs9l87QQ8mD17No4ePYoWLVqgZcuW2Lt3r255jRo1MH/+fNx9991o2rQpJk2a5FU+Kl7aIyIiohLJy8vD7bffjnfffRc7d+7EyZMnMWnSJPTp0wdbtmwpdjrjx4/HlClTkJmZCbvdjjNnzmDhwoUYPXq012Xbtm0bZs6ciaNHj+K9995DlSpV3MK0adMGQgi8+eabXuejYkeKiIjIX5lQcBbMq4932U6fPh1JSUlYvXo16tWrBwAYMmQIBg8ejNGjR+PUqVPXTGPTpk3IysrCuXPncObMGVy9ehUPPPAAAGDjxo04cuSIV2VzOByYO3dukWGuXJHPgPMVMURERJWYL961d/r0aSxbtgzNmzdH+/btdctGjBiBnJwczJgx45rpnDlzBhs3bkTt2rUBAJGRkVi+fDkaNWoEwPsn6uLj469ZrxUrVgAAatWq5VUeztiRIiIi8leGzkZ5dz/Xpk2bYLPZ0LlzZ7dlHTp0ACBfXktJSSkynWnTpsFk0ndDgoOD0bZtWwBAq1atSlw2ABg4cCCee+65Qper92RJkoRBgwZ5lYcz3mxORERExbZjxw4A0M4cOYuLi0PdunVx/vx57Nu3D3fddVeJ07948SLuv/9+NGvWzKvyPfHEE+jWrRu+++47DB48GDk5Ofjyyy9x4sQJbN68Gd9++y0AIDExEU8//bRXeThjR4qIiMhfldJTexkZGbrZZrO50KfmDh06BEC+hOZJbGwszp8/j8OHD5e4I/XTTz8hPz/f0E3g4eHh2LVrFx577DFMmDABdrsd/fv3BwAI5WnNu+66CytWrEBsbKzX+ajYkSIiIvJTkkk/SoY38QEgISFBN3/OnDl45pln3MJbLBZkZclDlBTWCYmJkYeCSE5OLlFZPv/8c4wZMwZ9+/ZFdnY2oqOjSxTftQxr167FCy+8gN27d+PMmTOw2WyoU6cOunXr5vFsmrdKpSP1zTff4IMPPkBKSgree+89AEBSUhIOHjyIkSNHok6dOqWRjSE2mw2bNm3CJ598guDgYMTHx2PChAlo2LChr4tGRETkU2fPntV1XAo7G+V831NERITHMOp9TxaLpVh5//rrr5g3bx4++OAD2Gw2rFu3Dl9++SW++uor3HDDDcWtgubjjz/GP/7xDwBA7dq1MXToUI/hNmzYgOHDh5c4fVeGbjYXQmDs2LG49dZb8cYbb+C///2vtqxXr17o0KEDunTpgg8++MBwQY346aef0KpVK3z44YdYuHAh3n33XSxYsICdKCIi8m/qpT0jHwDR0dG6T2EdqdDQgkFvhafBkyEP1AnI90sVR/PmzfH+++/j8uXLeO655xAWFoaLFy9i3LhxJWkJzdSpU4sVrl27dnj99de9ysOZoY7U4sWLsXr1akiShFq1aiEoKEi3vGfPnnjyySdx77336jpZ5ek///kPunTpgmHDhmHr1q2oX7++T8pBRERU2iSTZPhTEnFxcVpnKjs722OYtLQ0ANC9mqU4qlatilmzZmkDen777bc4c+ZMidIoiVq1auGtt94ynI6hS3tvv/02+vbti3Xr1qFmzZoeT8Hddttt2uBYffr0MZJdiX3zzTcYNmwYxo0bhzlz5pRr3kRERIEmKCgIzZs3x+HDh3HhwgWPYS5dugQAaN26tVd53HnnnWjfvj0OHjyICxcuaAN+FmbPnj14/vnntTNh58+fx6233lpkHIvFgt9++w0hIR5efVZChjpSFy9exHfffafdcOZpACy78v6nn376yUhWJXbp0iXcc889qFOnDhYtWlSueRMREZULH7xrr1+/fjh8+DCOHTvmtiw5ORnp6emIjIxEt27dvC5W165dcfDgwWLdY92tWze88847GDduHL744gtIkoSvv/76mvFCQkJ8f0aqQYMG13x0cNu2bQDkxxHL05NPPomrV69i/vz5CAsLK9e8iYiIyoXRlyR7EXfs2LF46aWXsHv3brdl+/fvBwAMGjTI65cOA0B6ejpuuummYt+OU7duXezYsQMTJkzAF198gfXr1xcaVpIkhIeHo2nTptoThkYY6kh16NABn3zyCe68806Py3/44QfMmzcPkiShd+/eRrIqkXPnzmHt2rUwm81ITEzEgw8+iOPHj+Ovv/5Cs2bNMHPmzCJP++Xl5SEvL0/77Tq+BhERUWWVmJiICRMm4K233sLhw4d1l/DWrl2L8PBw3e00SUlJePLJJzF8+HA89thj10w/NTUVn376Kd59990SlctkMmH58uW499570aNHjxLFNcLQzeZPP/00Jk2ahEWLFmkvKLRarTh69Chmz56NHj16ICsrC5GRkXj22WdLpcDF8cEHH0AIgdDQUBw4cADPP/88vvnmG6xYsQI//PADbrvtNm2YBk8WLFiAmJgY7eM6vgYREVFF4It37QHAokWL0LZtWzz00ENITU2FEAJLly7Fxx9/jHXr1unGaVq8eDEOHjyIWbNmafOSk5PRuHFjtGzZEqtXr9ZOXpw8eRJDhw7F4sWLvToBExQUpN2sXhzXerlxcRjqSMXHx2Pbtm1YsWIFmjRpguPHjyM8PBytW7fGggULkJubizp16uDTTz/1eqh3b3zzzTcAgAceeACzZs3Snhzo378/Fi1aBIfDgQkTJmhvf3Y1Y8YMpKena5+zZ8+WW9mJiIiKzQfv2gPkFwwnJSWhY8eOaNeuHRITE7Fr1y58//33GDx4sC7sfffdh6ioKIwaNUqbFxsbix49euB///sfxo0bh4SEBPTr1w9vvfUWVq9ejfvuu89QsxTHn3/+iYULFxpOx/CAnG3atMGvv/6Kbdu2ISkpSTd6aPfu3XHvvfeW+/1R586dAyBfM3U1fPhwPPHEE0hPT8fmzZvxyCOPuIUpdGh8IQBbfsFvkzzcgxQs3/UvrMrgY8pvWOUethRacI+WsNvkeepQEer/BkKUMMHKKnEeqjZffhJBipAHSxPqModDn4bzmB5K2eA6zofDpv/tXB+lvFrewqFPSymHR2pZ8pU0wiPd81fL6XBJ16QfNkPLFwCCQvRx7fn6sOrvvFynOGp6kktcpe5qnZ3zVeum5h3k8iSHOt+5Pmp62rpSltmsLuVwTke4lEUNq+Qn7GolCuI47Pp8XNtCLbu5YHA8YcmRg6rblclle1PDOa9TS7Y+H7XO6japliO2WkEcdRuvUgUAUKe2vN5NUS4D9Tk9GZNrketuy5fTjwmR9zWhrEORmaKvHwDJLB9DpMgYfdmkIF05nLd2db/Uyq2lp4TKk9sIEU6jJ6ttoM1Tt1k5DaEMNCg5b6NKGKmw7Vhd146COCLfoo+j7RtqVHWdF8SR7C5/8NTKKtuUcN63XfcxNbDrMUPNx2meULZJSVkvartpbetcD/WLXd2OQ/W/7Wo9nMvuOvaQpC+rw8O+ph3zXNal+ls9doU6/a1R01OPDWpYdTtT139IwbFecm0fNQ31GO5QyhRdtSAf5bgvVZG3TZGbVdAO5cLgzebwPm5UVBSWLFmCJUuWFBlu+PDhbgNfBgcHY9WqVV7nfS3r16/He++9hzNnziA3N9dtzKv8/HxcvHgRDoejkBSKr1RGNg8ODsaQIUMwZMiQ0kjOMPWeJk/Dy4eHh6NXr17Yvn07fvvtt/IuGhEREZWhOXPm4Lnnnit0wFBn3l7adGbo0l5JXkb48ccfY9iwYfjnP/+Jt99+WxsWoSzUqFEDQOE3iasvWiyNnigREZGv+OoeqYrsjTfeAACMGjUKu3fvxh9//IFTp065fbZs2aK9zsYIQ2ekjh8/DrvdjoMHD+L8+fMIDg7GTTfd5PbqlQ0bNmDkyJF4+OGHMXHiRLz33nvo1q0bPv3001J587Krdu3aYd++fR7HuACgDYfQtGnTUs+biIio3Phg+IOKLjg4GNWrV8fq1auLDFe/fn20bNnScH6GumLnz5/Hddddh65du2LYsGEYNGgQmjRpgt69e+PPP//Uwr388ssAgOnTp6N58+Z47rnn0L17d9xzzz3GSl+Ie++9FwCwc+dO2Gw2t+WnTp2CyWTC3XffXSb5ExERkW88+OCDyM/PL9alvaSkJMP5GepI5ebm4sqVKxBCoGrVqmjdujVuuukm/PDDD+jcubP2jpwTJ04AgG6E0smTJ+Obb77Bpk2bjBTBo44dO+Kee+7BhQsX3Ablunz5Mj7//HNMmDABDRo0KPW8iYiIygsv7bl76qmn0KlTpyIH5VS1aNHCcH6l8tTeokWL0KtXL21eTk4OpkyZgrlz5+Kdd95Bbq78lITzS41r1aqF2rVrY+3atRg2bJjRYrhZvXo1/vjjDzz22GNo3LgxunXrhtTUVIwcORJt2rTBK6+8Uup5EhERlSte2nOzb98+TJ48GfPnz0eVKlVQrVo1tzA2mw379u3D//73P8P5GepIBQUF4YsvvnB7w3NERASWLFmCNm3aQAgBIQSCg92zioqKKrN38MXExGDPnj2YNWsWhg4dCrPZjNjYWAwdOhRPPPGE9vZqIiIiChwPPPCANki4Oq5kWTLUkapdu7ZbJ0qVlZWF/Px87ck4T2NJZWRk4OrVq0aKUKTY2FgsW7YMy5YtK7M8iIiIfMYHLy2u6GbOnIlx48YhPj4eCQkJCAkJcbuEabFYcPz48VJ5BZyhjlT9+vWxZMkS/Pvf/9bN//PPPzF+/HjcdNNNyM+XB05zfTovOTkZly5dKrQjRkREREWTTBIkA5fnjMStqEaNGoW3335be4FyYVJTU1GvXj3D+RnqSE2ZMgWDBw/G8uXLtRu2/v77bxw+fBi33347vv76ayxYsEALn56err1ped26dQCAW265xUgRiIiIiDRBQUF49tlnYbPZPN5WpIqLi8Orr75qOD9DHal//vOfeO655zBnzhztyTwAGDJkCDZs2IBNmzZh7NixAIAxY8ZgyJAhuPXWWxEcHIynn34akiThgQceMFYDIiKiyqqSX9qbMmWKNsSSs9tuu61Y8Qsbb7IkDD+1N3PmTAwbNgyfffYZrFYrOnXqhE6dOgGQ36/TvXt3nDlzBl26dEF2djYmT56Md955B0IIDBkyBIMGDTJcCSIiokrJBINP7ZVaSXxi1apVHjtSxWGz2bB69Wqv46tK5V17jRs3xqRJkzwuS0hIQEJCAgD5bdErVqzAlClTcOrUKfTt27c0siciIqqUjI4F5e/jSGVkZGDs2LG45557EBERce0IitzcXGzfvt33N5uXxLhx47By5UoAwPXXX4/rr7++vLImIiKiALVmzRqsWbPGZ/mX+Uk9u92OpKQk7eZyIiIiKiXqgJxGPn5OHa/Sm09pMNSRstvteO6559C8eXNUqVIFQUFBbp/Q0FD06dMHdru9VApMRERECvVmcyMfP7dq1Srk5OTA4XAU+5OTk1NqJ3gMXdqbNGkSVqxYUaxenb9fhwUACAdgz9d+SuFV5Nl58itwpNAwZYGkzM+Rf5oLrttKJvk1OcKmpKPGCQqRp9Y8/W8AyLfK0zAlnWTld7hajij5i8Opsyoc+rjafGVdOWzucZRX+EghofqwktLfDlbL5LQuhV0/L8SspCv05QBQ0G9Xl0n6MMJDHLWcSrtp5VV/q1PnbVDttEsF60pHTcM5jjrPrDSqTVkPJmUXUdeXyen/HnalbJJLudWwalrO+ahNp+bn+h8MdT9x2s4K6qO2l5Keup7U9aJrN4d+nk3eDoQlW5+fzer+Xd321DLWkN+RKbKVewnCnAbXVcugDLgbFB6iL3P1mvI0tyDfiAi5TYOjlHSUdhLpyfLvy8orG+pFanFEVpqu2FKYskxtC7W9pIKyqXWVXLeV3Cx5ubJv6I9McnpCWf9aXLUd7co0JKwgSn6eElPo46jtp64L3TrNV4qv33akYJd9z1Hw0nWhpuNSJikoWFcOAIA6z27Vp6flr5TN6S+Atisr269w3Q5c4wIF+4O2fygJutZZcv4/u1J+q0UpQ6i+zGo9nOOo6SjHWm0bVY+1yrYpVYmFG+U4rB2nq9bS18u53dT9I1Q5jgmXNteOjU77Wo6yPalxHY6C+lOZq127NkaPHl3ieGFhYfjXv/6FadOmGS6DoTNS77//PgDg6aefxpkzZ7SRzF0/P/zwA0JCQq6RGhEREZVIJT8j9dZbbxmKv3z5csNlMHRGKjY2FhEREXjmmWeKDHfzzTejX79+RrIiIiIiN0Y7Q/7dkbrrrrt8Gh8weEZq6tSpyMrKgtVqvWZY5xHOiYiIiAKBoY7Uo48+ivvuuw9vvPHGNcP27t3bSFZERETkymQy/iFDDF3aW7duHbp06YK33noLDofD4wuIbTYbvv32W1y+fNlIVkREROSqkr8ipiIw1JF65ZVXcOTIEQDAd999V2g4IURgPLVHRERE5MRQR+qpp57C4MGD0alTJzRo0AAhISFuHSaLxYKffvoJf/75p6GCEhERkQuekfI5Qx2pf/7zn7jtttvwxRdfFBnOYrGgVq1aRrIiIiIiV+xI+Zzhu8zmz58Pm81W6PLDhw/DZDJh8+bNRrMiIiIiZ7zZ3KPc3FzMnTsXLVu2RIMGDbT5X3/9NUaOHInPPvus1PIy/NLitm3bFrk8KioKPXv2xPbt241mRURERFSkq1evomfPnvjll18ghNA9CNezZ0+0aNECt956Kz777DO89tprhvMz3JFKTU3Fvn37kJ6eDofLsPgOhwPJycn4+eefMWLEiGteAiQiIqIS4KU9N7Nnz8bRo0fRokULtGzZEnv37tUtr1GjBubPn4+7774bTZs2xaRJkwzlZ6gj9f3336Nfv35IT08vMpwQAj/99JORrIiIiMgVO1Jutm3bhpkzZ+K5554DADRv3twtTJs2bSCEwJtvvunbjtTcuXORlZWFHj16ID4+Hl999RVuu+02XZjPPvsMgwYNwogRIwwVlIiIiOhaHA4H5s6dW2SYK1euAABOnjxpOD9DHamDBw/iyy+/RM+ePQEAzz//PHr06IGuXbtqYVavXo1vvvkGHTt2NFRQIiIicsEzUm7i4+OvOXblihUrAKBURhQwdLt+lSpVtE4UADzwwANYuXKlLsyIESOwfft2LF261EhWRERE5IpP7bkZOHCgdlnPk2XLlmH58uWQJAmDBg0ynJ+hM1IxMTFITk7W7oivU6cOhBA4dOgQ2rRpAwAwmUwIDg7GokWL8OijjxousC9JYVWA7NSCGZGx8tSSDQAQIaHybyHkaWiY/FNZLv9w6MNIykacl6sLK4WaC+KYlJ61XR5mQtjy5DC5WfL88Eh5Guy0OpX0EBoiT4OUqdpLV3/bsgriKOXVqGEcNn3ZIQrCKGXSwmo7pUv9AMCWr4QNUoIoZclXXnptUud7SD9YSd9uV36b9emraQJAkNIOWvtEqYnp85E8HEDU/NQyBSlxtHXrFFYNo7abWj+HEsehBLY5vdQ7OFSXj1CWSZJSH7Xu9nwtilDr7EJS66zm67w3q2VQ2kCo9bLkKGE97PratqiEUdpJanS9nIbVIv82RxSULfOq/KVaDQBAZoocNyI2Vl7+6y9ynKgoLU5GulznatXkeVJ4FX1a2Zny7+yMgqKFKG2sbtdVa+rrrpQNVWIL6qMOy6K1qdLmaVd01RbC6SEZtd2s8j6mbWfqfHhYp9r+YXIOAagP3yjrUlsHTmXRwqj7jSlYn2Z+XkEcdR8Q+v1EK7+tYJvR9nOXB4DcypzvtFzdxtUa5Oe61ENpA3UdAIB6nFK3qxCzPo7VqfxaHOW4qKxfKUw5fqnHMbU+6joFCuqqbCOSGkbZZkWWMr9GfEEctd3VMmSkyb8zUuTf6joMKTjuiczz8pe42vp6aPu//FvdZgFA5Cp1V9sgQDsn/uSJJ55At27d8N1332Hw4MHIycnBl19+iRMnTmDz5s349ttvAQCJiYl4+umnDednqCPVv39/dOjQAd27d8fNN9+MRx99FDNmzMCAAQOwdOlS1KtXD4sXL0ZqaqrbE31ERERkEC/tuQkPD8euXbvw2GOPYcKECbDb7ejfvz8A+eE3ALjrrruwYsUKxCr/6TPCUEfqySefxI4dO7B27Vps3LgRDz/8MK6//no8/PDDuPPOO3XXKP/5z38aLiwRERE5kWCwI1VqJalQYmJisHbtWrzwwgvYvXs3zpw5A5vNhjp16qBbt25o1KhRqeVlqCOVm5uLBx98EFWqVEHLli0RpFxqmDp1KmJjY/HWW2/B4XCgX79+mD17dqkUmIiIiKg4ateujaFDh5ZpHoY6Un379sUvv/yC4cOHY+TIkbplY8eOxdixYw0VjoiIiIrAS3seXb58Ge+99x769OmDG2+8UZu/evVqXL58GePGjUO1atVKJS9Dd8Sp4zDcddddpVIYIiIiKj7JZDL8CTQXLlzAzTffjKlTp2Lq1Km6ZWPGjEGrVq3Qtm1bfPLJJ6WSn6EWXLJkCeLi4jBw4MBrhk1MTDSSFREREbmRCs5KefMJwJuknn76aVy4cAHh4eEeT/T0798f06dPx+DBg/HDDz8Yzs9QR2ro0KHYunUrHnnkEZw4ccJjGIvFgjfeeAN//fWXkayIiIiIrum///0vNm/ejIyMDDzyyCMew9x7772wWq2lcv+24XGksrLkcWreeecdw4UhIiKiEuA9Um6Cg4MxePDgIsOEhMjjsrm+0Ngbhs5IjR8/HkKIYn2IiIiolBm5rGe0E1ZBValSBdnZ2UWGWbduHQB5zCmjDHWk/v3vfyMuLg6///478vPz4XA43D52ux07d+685ntviIiIiIy6//77MWbMGOTk5Lgts9vtWL58OaZOnQpJkjBgwADD+Rm6tBcfH49HHnkETZo0KTSMJEno3bs3B+QkIiIqbUZfSROAT+09/vjj6N69Oxo1aoRhw4ahSZMmsNvtOHnyJLZv344LFy5ACIE6depg/vz5hvMz1JECgLlz5xYr3ObNm41mRURERM54j5Qbs9mML7/8EuPHj8fSpUt1V8TUW43at2+P9evX47rrrjOcn+GOFBEREVFFEhMTg82bN+PYsWP49NNPcfr0aTgcDsTHx6Nnz57o0qVLqeXFjhQREZG/4hmpIrVo0QItWrQodPkXX3yBfv36Gcoj8C6OEhERVRZ8as9rFosFw4YNM5wOz0gRERFRQDl16hS2bNmCM2fOIDc3120Ypvz8fPzwww/IzMw0nBc7UiVlcmqyoCB5Kikn9vJydUGl8CoAAN3qy1XGtghW0jEpaYSa5TgOmxI3SosiHEoKVos8zUxT5tvlsDXq6ssBAMF2ZZ7yv43gELVU+vrY8wu+i1ClXiH6MPlWpYxhcBOk1sOkn9qV/NX6AUCIpE/PNR+lPrA5lUmtk9oGWj1cxiZzjuPKXsiyIKeyqWUSDnmSnydnr7ZfbpaSf6h7umq7KeUXynYgRUQr4ewFcUxKGHU7UNNQ20mZCrU8TulqdTSH6/NRy6RuH85lsWTr01V+S1FV3eII5/gAYFEeHVbXU47SBk7bJjKuKmHlsqRnyOWO2n8MABBxs/xEr7DZtCiRkXJ6oTWjddmZaibIRb98Xp6RlV6wMFp+uajIyZDL75DXE+xyuupBUsq8WhBHWZfaT3WZuo6V35JzvdWDrbIfCpuyHtR1oObnfBzQtmeXfVqNqy3PK4ijpidsurjqPg1l+4PVKY66L7gOy6e2hXN9XbZnLT9lXYqcTCVJp33AFKPEcTneqGGUNLVtCoCkbL/q9iop7QObUm51udMfMUnd75QyaOtOna9s18Jpn5aU/V7kKn/0lH1AK5u6HeY7bc9qfHU95OYodVe2IXW7lgr2Na0sat3V/V9dDyHKvhbptO1e+Z+St5KO3a7f58san9pzs3XrVtx3332w2WzXHMeyNIZmYkeKiIjIX/EeKTf/93//h/z8fLRo0QLDhg1DnTp1EBzs3t358ccfsWzZMsP5sSNFRETkr9iRcpOcnIzIyEh89913iIiIKDTcqFGj8MEHHxjOL/DO6REREVGldeeddyImJqbITpRq9+7dhvNjR4qIiMhfqfdIGfkEmJdffhlmsxn79++/ZthJkyYZzi/wWpCIiKiykGBw+ANfV6D01axZE9988w1ef/11XL161WMYh8OBPXv24KuvvjKcH++RIiIiooARGxurDWuwcePGMs+PZ6SIiIj8FQfkdPPYY49BCFGsT2moVB2pTz75BJIkYc2aNb4uChERkXHsSLmZMmUK4uPj8fvvvyMvLw8Oh8Ptk5ubi//+978Ich5P0EuV5tJeSkoKxo8f7+tiEBERURmKjY3F9OnT0aBBA4/jRwGA2WzGrbfeihEjRhjOr9KckXrooYeQlZXl62IQERGVHsngE3tSYHYDHnnkEQQHByM5ORmHDx/W5icnJyM/v2DU/HfeecdwXoHZgi42bNiAS5cu4Z577vF1UYiIiEoPL+15dOzYMfTq1Qu1atVCv379tPmXL1/GsGHDMH36dF2HyoiA70idP38eM2fOxNq1a2EKwPEyiIiIqMCvv/6Krl274ptvvnG7qbx58+b44IMPcPz4cXTp0qVUXloc8D2LsWPHYs6cOWjYsKGvi0JERFS6eEbKzcyZM5Gbm4uHH34Yb731FqKionTLTSYTpk+fjh9++AFz5swxnF9Ad6TefPNNhIWF4YEHHihRvLy8PGRkZOg+REREFY5kMv4JMF9//TU2btyI119/HRMmTIDZbHYL06BBAwDA5s2bDecXsE/tnTx5Ei+99BIOHDhQ4rgLFizA3Llzy6BUREREpcgkyR8j8QNMXFwc7r777iLDHD9+HID8RL9RgdcVhTz0+8iRI/HKK6+gZs2aJY4/Y8YMpKena5+zZ8+WQSmJiIj8l9VqxcKFC9GsWTM0btwYPXr0KPFLgLOysvB///d/aNiwIUJDQxEfH4+HHnoI//vf/7wuV926dYu8kuRwOPD8888DAJo0aeJ1PqqAPCP14osv4vrrr8fAgQO9im82mz2eCgQAhIQWfM+3ylN1QC+7/ASAyMtVftvc47sO/qX+zrfL0yqx8tT5urUyDoawZMu/rWq+LvmERRbEsSlhTC6rWE3XlidPzeFO+Sh1Ew59WOc6A/pTwdpXJax6T59Wfqd6qGVS01PrHhqGQqlhHDaXdJXsrBalHAVlktTyuZ6yVuKqcaTgELd0pMhoeUauMlSGcpOisOW71gYwqetOqZeanvpbbUdn6jq0eQ4j1Pl2p6dJ1Hq4jsKrrvcgZftQtzsAIiNFqU+sPMORp4+rtptTmpKyXrQ2tduVtFLl36Fm97LFxMnTTPmgFRIil/V/p9MAANcF/QUACG9TcLCKSKgqJ5Mr1zXYYYdHeQVlFjkZ+nKr7ZSvby9hcUpLXWcx1eXfyjoVmcq7t9JT9WkBBdtIVrr8M1ips0NZT+r6UssBFKzTCOU+DHWbyc7QpS857fsiV4mjbLeSut/b1frI61Y45aNte+r+ou0TyvaR71QPdR0pYbWyaNuo3LbCVnCMksKr6OqmtrkUrtRLXU9K2wCAcD5+OIex5unL75SPWneRm6mrl1DbVt0mbQXbmXA9lqrrUG3TlMtyuAwPZxcylfKq67tqNTmse0it/EKZSiFKW0dU0ZfJ+diitnWIGibb875fVoxenvMybl5eHvr3749Lly5h586dqFevHrZs2YI+ffpgw4YNGDJkyDXTyMrKQvfu3XHo0CEEBQXB4XDg/PnzWL58Of7zn/9g9+7dSExMLHHZxo8fjxEjRuC9995DZGSkbll6ejoefPBBJCUlQZKkUhlfMuDOSB05cgRr1qzBkiVLfF0UIiKisuWjm82nT5+OpKQkrF69GvXq1QMADBkyBIMHD8bo0aNx6tSpa6Yxb948CCHw1VdfIScnBxkZGXjxxRcRHByMixcvYtSoUV6VbeTIkahevTqaNGmCUaNGITk5GTNmzMCgQYOQkJCALVu2AADuvvtuPProo17l4SzgOlKvvvoqTpw4gejoaEiSpPusXbsWADBmzBhIkoTRo0f7trBERER+5vTp01i2bBmaN2+O9u3b65aNGDECOTk5mDFjRpFp2O127N69G0lJSejVqxdCQ0NRpUoVTJs2TYu7f/9+/PXXX16V8Z133sH8+fPx448/Ijk5GS+88AK2bduGrKwsxMfH4+WXX8YHH3wAqRSeWgy4S3s1a9ZEs2bNPC773//+h4yMDNSuXRsxMTGoU6dOOZeOiIioFKkjlBuJX0KbNm2CzWZD586d3ZZ16NABALBt2zakpKSgWrVqHtO4ePEipk+fjtjYWLdlU6dOxbx58wAAV65cQaNGjUpcRkA+aTJmzBhcvnwZZ86cgc1mQ506dVC/fn2v0itMwHWkFixYgAULFnhcNnr0aKxduxYLFizg2SgiIvJ/RseC8iLujh07AMBjBycuLg5169bF+fPnsW/fPtx1110e06hbty7q1q3rcVlMTAxq1qyJy5cva5cNjahZs2ahD57t2rULvXv3NpR+wF3aIyIiopJxHTsxLy+v0LCHDh0CAMTHx3tcrp5lcn7HXUnYbDakpaWhffv2ZXrlyOFwYOjQoYbTCbgzUkRERJVGKT21l5CQoJs9Z84cPPPMM27BLRYLsrLkJ2A9XZYD5DNKgPyCYG/s2bMHVqsV06ZNKzLcs88+61X6AJCfn489e/YgLS3N6zRU7EgRERH5KwkGL+3Jk7NnzyI6OlqbXdgQQM4DWEZERHgMo77X1mKxeFx+LUuXLkWfPn0wePDgIsNt374dP//8s/ZbuA4RUwRJkiCE4M3mJbVmzRqsWbPG18UgIiKqUKKjo3UdqcKEhhaMK1hYx8WqjHUYFxdX4nJ8/fXX2Lt3r3b5sCiPPPIIJk+ejOHDh6N69eoIDi5+lyY/Px979+7Fnj17SlxGV5WqI0VERBRQyvmpvbi4OISGhsJqtSI7O9tjGPVyWfXq1UuU9tWrV/Hwww9j69athd6I7mz48OHYs2cP3nzzzRLloxJCePX2E1e82ZyIiMhflfOAnEFBQWjevDkA4MKFCx7DXLp0CQDQunXrYqdrt9sxcuRIzJs3D127di1WnLCwMMyZM6fYebiSJAk//vij1/FV7EgRERH5K/VmcyOfEurXrx8A4NixY27LkpOTkZ6ejsjISHTr1q3YaU6cOBEDBw7EoEGDSlSWhg0blii8s4ULF5bK8ArsSBEREVGxjR07FiaTyeMLivfv3w8AGDRoUOHvrHUxdepUJCYmYty4cW7LUlJSinwBsTdsNhuOHj2KJUuW4PLly4bTY0eKiIjIX0kSYDLw8eKptcTEREyYMAFHjx51Gytq7dq1CA8P111yS0pKQocOHfDaa6+5pTVt2jTExsZ6HOrg6NGjuOeeexDk9MLv4ggKCiryYzab0bp1a1y5cgUvv/xyidL2hB0pIiIif+WDS3sAsGjRIrRt2xYPPfQQUlNTIYTA0qVL8fHHH2PdunW6Uc8XL16MgwcPYtasWdo8IQQeeeQRLF68GK+++iqqV6+ufapVq4aIiAi0atUK9erVQ2RkZInKJoQo9sfbG9WdsSNFREREJRIZGYmkpCR07NgR7dq1Q2JiInbt2oXvv//ebfyn++67D1FRURg1apQ278knn8Qbb7wBIQRSUlJ0n9TUVOTm5gKQn8wrKZPJhL1798Jms8HhcLh90tLScM899+D06dNIT0831hDg8AdERET+ywfv2lNFRUVhyZIlWLJkSZHhhg8f7tYheuGFF/DCCy94nXdR+vXr5/GFyqro6Gj83//9H7p3745vv/0W1113naH82JEqAWHPB5yu1Yps+QY4qYo8HL4j86r8OzRMF08Kr1IQx6qM9KoOZGbL12diCtIvB4AQ5Ya9fJd3H+XIw/SLVPlRU6mm0xD/QcqqVQcoU3cW4dCnEaIvqxJYKYtywlIE6ec773iSssxh15dfzca5Hna7vj5aGkp6dpvy2+lEqcnl2rgaRp1vl9tPCvFw6tc1riur06i7+Vb9tLA0gkK0r8KSreStDFCnbhtqulZlfaltA0C4ru88+X9dwildAAVtBQAhSrrqulTTU6dhSt2d160lR55GROvLlKtsM0q76eqntLukzBNqvZTtTKufcx3UZUo9wsxyGZ8/Ij8W/UKdSH1cACG1q8pFOSGHCVXaEcFKflVryNOzfxXkk6uEsSnrX2lbka68gsKhbGd295GU1fWklVtNIzdHV3a5nEobq+2VnSZP1X1aTcN5dantrm6bsOjTtcnblAhyOtwq87Q6Z6frf9tctkMUHDskJT+hpC8p6184bc9ScIi+HspUbS9J3S6c81G2CZGRqquXcKTJv5X2EtYctzhavo5acpjcTGWGyS0fkafEV9ehGkaZL5mV0bKdtzMlvlbXHOXYGxSpS8t5XWrtkaGUP0XZVuIb6MM6nI5RVaLkdJX1IJT1L5mUY5RyLNfaCADSlfQjlON8Xh6Q577+ykwpvSImkKgvVS5Khw4dYLfb8eijj+LDDz80lF/gtSARERFREdLT05GamoqdO3caTotnpIiIiPyV+vSdkfgBZt26dUUuT01NxXvvvYfc3Fw0btzYcH7sSBEREfkrH94jVVGNHj36mi8jFkIgKCgIL774ouH82JEiIiKigNKsWTPccsstMLm8S1CSJISHh6N+/fq4++670bRpU8N5sSNFRETkr3izuZuIiAgcPHgQVapUuXbgUsCOFBERkb/iPVJunn322XLrRAHsSBEREfkvSTJ4RirwOlJTpkwBAOTk5OCjjz7CoUOHkJaWhtjYWLRp0wZ33HEHoqOjSy0/dqSIiIgooGzbtg0TJkxAaqo85pdQxjSUJAkxMTF4/vnnMXHixFLJix0pIiIif8Wn9tzs3LkTQ4YMgcMhDyrbtWtXXH/99ahZsyauXr2KH374AY888gguXLiAefPmGc6PHSkiIiJ/xZvN3cyePRsOhwNNmzbF+vXr0a5dO7cw33//PYYNG4bevXujZ8+ehvILvBYkIiKiSuvo0aMwmUz4z3/+47ETBQC33HILPvroIyxevNhwfjwjRURE5K/41J6bRo0aIScnB82aNSsy3I033oi///7bcH48I0VEROSv1Et7Rj4BZu7cubh48SIyMzOLDJeWloazZ88azi/wWpCIiIgqrUGDBmHx4sWYPn16keFmz57Nd+0RERFVapX4qb1mzZohPz+/0OWXL1/Gjh07EBQU5LZMCIEzZ85g/vz5hsvBjhQREZG/Mpnkj5H4fqpt27bYuHFjkWFycnIKXVajRo1SGUuKHamSyM0G4uIKfivXlkV6svJT6fXalB5ynrwChc2px6yGycmQl1ktclxzhDzfYXePk29RlskDiiEqRslf+Z+EcCjlyyqIUyVWyc+kKytMyiq35+uXO6enjL2h/VbDuM73FAbKb0kJKwqCIixSCevyPyDXa/Se/odkt8nTfKs8DVLqobQXQkILwubl6tNV20dte2VgNl0bK2HU9QG7XZ+/EkdrN6f0nAd6AwCh5q+mpWtjkz591zKq9RFODWfN05ff6nJgUOPkOs13aUOhllttvzBle7O4xxGuZVLrUSVGme3UNmq7K9N8mxynfpi8fjLS5fwiLZaCbKpVk6tjvqKUX6mr2m6ZV/VpA0BYuC6MsGTLUzWs2jZZmW5xRPIFfRiXdSxyneJo60fe3rRtRFuXShpqOwIF+6e6HYSYlbKlyL/V7V5djwBgs+rSEWpdJaUNgpXf6joAgOx0eVaVqvJvpe7a+nBa50I5vmi/1WNDdoY+bHBIQRg1vSx9m0pBIfr5zvuGOVxXHy0fdT05t5MqK10pg0lfJiUtYVP2deftTC1LhPLaD7UtQ5XtOFyZBhXUB1fP6uoq0tLkn+lKPdSyWXIL4ijrQajrVC2LWb/96da/um2GhillyAGC/Ldz4k8mTZqEH3/8EWvWrEHNmjURHFyyLk2VKlVKZYRzdqSIiIj8lsFLe/DfS3udO3fGPffcg06dOrktO3XqFBo2bFgu5WC3mYiIyF9V8qf2Fi5c6DZv8ODBaNKkCaZNm1YuZeAZKSIiIn9ViW82L8yuXbsAAHHOt+KUIf/uihIRERE5efDBBxETE4P/+7//u2bYsWPHGs6PHSkiIiJ/pT61Z+QTYBYuXIhp06bhmWeeKXJ4hEOHDmHdunWG8+OlPSIiIn/FS3tu+vbtC5vNhrNnz+Ldd99Fo0aN3MLk5OTgyJEjcDgcHlIoGXakiIiIKGBUrVoVH3zwgTY0zZkzZwoNK5VCR5IdKSIiIn8lScaevAvAM1JPPPEEtm3bhmXLlqFRo0Yex5dyOBz46quvOLI5ERFRpcZLe25uueUW3H333Rg/fnyR4Xr16oW3337bcH6Bd5cZERERVWrvvvvuNcOsXr0aFy9eNJwXO1JERET+qpIPyFkYs9lc5PKUlBTMmzcPWVlZRYYrDl7aIyIi8lcmyf39pSWNH2A8PaXnzGq1Ijk5Gfn5+Vi6dClmzJhhKD92pIiIiChgnD59uthhlyxZwo4UERFRpWX08lyAXtpbv349OnbsiKCgILdlaWlpmDp1KhYtWoSqVasazosdKSIiIn/Fp/bctGjRAvfff3+hy+vXr48nnngCY8aMwddff204P3akSkDkZgJBNQtmZGfI05BQeWpSer6uPWDhNHKqwy5PzeEAAG0TDlVujMvLVdJwWjXqvGA5Hym8ipyszSpPc5Ry2GxaFCmmhscySMEh8k+7h2HztR1KKa/2PxXlt1o/k1P9lAHPYFPSU9LX4pqc6m5X6u5QlqllEEWMLJtvVdINVoLKaWjt5hBuUYRaFrUN7XK7SGGR+oD2gvYq9H9l6vpS0xbu+Wl1V9pfUtelsn4QGuaUgEM3FVaLHEctq9p+ynwAELnKzZBqumkpcpxqteXlWelKWZ3qo5RFi6u2o0XZlpRtCLnZ7vWJUV70mXJZnkZGy1N1fan1AgBrni7dajUj5J9/yu30v8s5AIDrqlVzqo8c1hSp7Dfq/mJT2iZbKVN1p31N3XbUeqj7RKZSd3WcmIy0gjjqvByX9lPbuEGifjmgrSthkcugDtYnlPykKOV/rzan/UfZdrSw2en6sir7rW5bVe5LEVnyviupbRwUoq+f074mlPQkZZmaj7YvKMcUwOmYoMZXy5J2VV6utoGn44w6zZbbRUTHKvMt+rTkSsvT5EvytEZdOU7KRbfya7Iy5Wmkyzao7lvqSNPOry5R6qauFyjtJtSwVqVMzsc1tY4XzinlVo9Rwbo0kJFeEKeqsp2mp8rTPGX7jpG3Y5GdJv92OtYiQqmH+vfAbgfsxkfLLjaekXJz9OjRa4a5/fbbMWbMGEyZMgXvvPOOofwCrwWJiIiIipCXl4ecnBxs3brVcFo8I0VEROSnJEky9JqT0nhFSkWze/fuIpenpqZi9erVyMzMxHXXXWc4P3akiIiI/BUv7bnp2bPnNTuI6m0as2fPNpwfO1JEREQUUKpVq4YbbrgBJpO+oyhJEsLDw1G/fn0MHjwYt956q+G82JEiIiLyVzwj5SY0NBRHjhxB7dq1yyU/dqSIiIj8lWRwZHM/v0dq2bJlyMzMRGZmJhITEzF69GhMnDix3DpRADtSRERE5KceffRRtGvXDmvXrsUNN9wAAHjllVfKtQyBd07PiRACy5cvx0033YSwsDDExcVh4MCB+OGHH3xdNCIiIuMq+UuLJUnCli1btE6UL/h3C17Dgw8+iIceeghHjhyBzWbD1atX8dFHH6Fz586lMnYEERGRT6kjmxv5+LE6deqgfv36Xse/fPmy4TIEbEfqs88+w7Zt27B27VpkZGTAYrFg+/btqFGjBvLz8zFmzBgkJyf7uphERETkpYiICEPxO3ToYLgMAduRWrNmDXbu3ImRI0ciKioKwcHBGDhwIN5//30AQEZGBj766CMfl5KIiMgASTJ4ac+/z0gZceLECZw7d85wOgF7s3m3bt3QunVrt/m9e/dGmzZtcOjQIVy5cqX8C0ZERFRaKvlLi8+dO4fu3buXOF5OTg5+++03OBzG34sYsB2pSZMmFbosMTERhw4dMnRdlYiIyOcq+ThSFosFe/fu9Tp+abwiJ2A7UkVJTk6G2WzG7bff7uuiEBERkZeqVq2Kxx57rMTxsrKy8OWXX+KXX34xXIZK15HKycnB/v37MW7cOMTGxnoMk5eXh7y8PO13RkZGOZWOiIioBEwGB+Q0ErcCiIuLw5w5c7yKO3v27FIZuNO/z+l5YeXKlYiKisKzzz5baJgFCxYgJiZG+yQkJJRjCYmIiIqpko8jZURMTAy6detmOJ1KdUYqJSUFzz//PNauXYu4uLhCw82YMQNTpkzRfmdkZMidqXwrkJFSEDC8ijy12fQJ5FsBAFJUVQCAsFoKlpmVRzVzMuVllmw5bEiYkla+e4HsdnlqUqbmcDlOUJCchpKfsOZoUSQhh5WCzfKMoBBlgaTMD5XjOOzu+RWk4jEulLdmOwdBsBLGFKTUQy2z0yaWK9cVIaH6sHlWJR/lt92pPdUwahuo7WMtOGMIAMJSUHcodZJC5TYV+Xm6cmvz85ziqHmr7aH+L03NXyEFFdRHWLOUuMH69MIilQDKTYzO61+llF9S21bNNyhcH9c5vtpuShuI3Cx9fZzbRE0vWzmbqrapJVeeKnGRnV0QJ1beXnFV2cbDlW01WKmzup1nXC2Io7ZbrlNbArguVJ6vHaKdthkpKkpeZg7R1ydLSTdIiXXlYkGCalmUfER6sr6e6Zn6+gKA2awvt+v2q+xHAk7rJ09pn9RL8rK4Wrp6iky5jJK6jgGIbKXc6jpT01DWh1Dzj6iixZGUvLXyu27f6nHC7nQ8UPfzlAvy77RU+bcSV1LL6pS3Vmc1/Rxlvacp67RqtYI42Uq5tX3OZV9LU7YLdZsFCv4IK9uV2j5aPdKUMXrUbQsAspV1pa5TtX2ys/RljI4tiKPeEKzGddnetLZW2x4AUpWHiZTt15EpxwlS80tTyqpuJ0DBtq+mnypvZ6KKvM0iI02eOh8X1DzVtgiPKNiGqcylpaUZiv/ll18aLkOlWtvjx4/HtGnTrnlvlNlsRnR0tO5DRERU4VTyATlTUlKwa9cun5ah0nSk5s+fj3r16uGJJ57wdVGIiIhKBy/t4e6778bMmTPx3Xff+ST/SnFpb/369Thx4gTWrFnj66IQERFRKUlLS0NWVhYyMzNhMvmmUxjwHamtW7di+/bt2Lhxo9t4EXa7HRcuXODN5ERE5J8q+YCcFeH2G/8/p1eE7du3Y+3atdiwYQOCg/V9xosXL2L06NH466+/fFQ6IiIig3hpz+cC9ozUhg0bMHr0aFSpUgV169bVLbNarcjMzERCQgLWrVvnoxISERGRvwvIjtSOHTswYsQICCGKfDTyvvvuK5Xh4YmIiHzCZJI/RuKTIQHZkRowYECpvIiQiIioIpMkydAJAZ5MMC4gO1JERESVgiQZfGkxO1JG8ZweERERkZd4RoqIiMhfVfLhDyoCdqSIiIj8ltEhDHhhyii2IBEREZGX2JEiIiLyVz58abHVasXChQvRrFkzNG7cGD169MDu3bu9SstiseCNN95AgwYNcPr0aa/L5Au8tEdEROSvfDSOVF5eHvr3749Lly5h586dqFevHrZs2YI+ffpgw4YNGDJkSLHSycnJwZtvvolXX30VZ8+e9aosvsYzUkRERFQi06dPR1JSElavXo169eoBAIYMGYLBgwdj9OjROHXqVLHSsdvtGDlyJJKSknz20mGjeEaqJK5egTA7NVm+FQAghUUCAETK/+TfVWLk5aYgef6V81oUqab8gmSRmyX/Do2QF1iy5alQBhJVlgOAsCn5mMPlGXm58nxlqsVRygMAyEiVF0VXU8IIfRrq6VxPNylqy1zCqPnY8wvCBgXrl6njoNqUMM5bWJDSHlnpcrLBIUo9cpTlIe5F0ZK36+YLq0X+EhKqz8+5vOoyJaxQ21hLRBR8V9syqqo8zc/T56NyLoeanrKetfTUNLIz5Knzex5d2lKtl6SUVWsT5/USUUWe5ir51bhOnqYly9Mwl20IKFh36oHJFKrPX22bnILtDJHydoxwJT1rnlL+EF2ZkZpcEKeWUpaQEKXKcpw7GlWXi5iutKOloB2lqCgAgD1LnhesbKu4ekWepl+Vp0FO7aaWSd1+s5VyX7koT6sq23leXkEcdX9Q66qmG6Osn8vy/orrnF5aru53uco2qa7/0DB5qrSXug3ryqS0l9DSUNZHbHW4Emq7K9uMtn0rbSyp22y6U1ur9bDk6uuntk2+U93VAYnV7Vpd79Gx8tRm06cBAHalDOr6zlbK7xD65c7bpto+ajrpyrpMu6qrj1ZmAMhziaMui1KOm8pxQqsvULAtqmHV9a/ueylKO2VcLYhzXjm7ESG3j+Wvy/LPWkeUuEo9Ll4siGM2y1VU4ojLchwpNFRftqtO+aj7faT6dyAFIs+pXcuaD57aO336NJYtW4bmzZujffv2umUjRozA+++/jxkzZmDjxo3XTCsqKgpRUVGoUaMGqlevjstKm/sT/+z+ERERkU9eWrxp0ybYbDZ07tzZbVmHDh0AANu2bUNKSkqJ0g0LCytxWSoCdqSIiIio2Hbs2AEAaNSokduyuLg41K1bF1arFfv27StRuv76uhp2pIiIiPxVKT21l5GRofvkOV8md3Ho0CEAQHx8vMflsbGxAIDDhw+XalUrKnakiIiI/JZUCh8gISEBMTEx2mfBggUec7NYLMjKku9RUztMrmJi5PvdkpOTPS4PNLzZnIiIyF+V0s3mZ8+eRXR0tDbbrNx478r5vqcI5aZ8V+rTdxaLxePyQMOOFBERUSUXHR2t60gVJjS04GlK4fzksxOrVX5qMS4urnQKV8GxI0VEROSvynn4g7i4OISGhsJqtSI7O9tjmLS0NABA9eruQ38EIt4jRURE5LdK5x6p4goKCkLz5s0BABcuXPAY5tKlSwCA1q1blyhtf8WOFBERERVbv379AADHjh1zW5acnIz09HRERkaiW7du5V00n2BHioiIyF/54KXFY8eOhclk8viC4v379wMABg0aVOgN64GGHSkiIiJ/Vb5X9gAAiYmJmDBhAo4ePeo2VtTatWsRHh6OOXPmaPOSkpLQoUMHvPbaa0Wma1NeXWS324sMV9GwI0VEREQlsmjRIrRt2xYPPfQQUlNTIYTA0qVL8fHHH2PdunW6Uc8XL16MgwcPYtasWYWmd+rUKe09ewcOHCjz8pcmdqSIiIj8lg9OSQGIjIxEUlISOnbsiHbt2iExMRG7du3C999/j8GDB+vC3nfffYiKisKoUaM8plW/fn00bdoU+fnyy+f/9a9/4brrrvObkdE5/AEREZG/KufhD5xFRUVhyZIlWLJkSZHhhg8fjuHDhxe6/O+///a6DBUBz0gREREReYlnpIiIiPyVBINnpEqtJJWWJAob4500GRkZiImJQXp6erGG0CciosqrPP5mqHmk/X4I0VFR3qeTmYnYpm34980AnpEiIiLyVz68R4pkvEeKiIiIyEs8I0VEROS3vB/CoCA+GcGOFBERkb/ipT2f46U9IiIiIi/xjBQREZG/4hkpn2NHioiIyG/xHilf46U9IiIiIi/xjBQREZGfkiQJkoHLc0bikowdKSIiIn/Fe6R8jpf2iIiIiLzEM1JERER+izeb+xo7UkRERH7L4KU9dqQMY0eKiIjIX/EeKZ/jPVJEREREXuIZKSIiIr/Fe6R8jR0pIiIif8VLez7HS3tEREREXuIZKSIiIn/FK3s+x44UERGR32JPytd4aY+IiIjISzwjRURE5K94s7nPsSNFRETkr9iR8jle2iMiIiLyUsB3pKxWKxYuXIhmzZqhcePG6NGjB3bv3u3rYhEREZUCqRQ+ZERAX9rLy8tD//79cenSJezcuRP16tXDli1b0KdPH2zYsAFDhgzxdRGJiIi8J8Hgpb1SK0mlFdAdqenTpyMpKQnfffcd6tWrBwAYMmQItm3bhtGjR6Ndu3Zo2LChj0tJRETkJd4j5XMBe2nv9OnTWLZsGZo3b4727dvrlo0YMQI5OTmYMWOGj0pHREREgSBgO1KbNm2CzWZD586d3ZZ16NABALBt2zakpKSUd9GIiIhKCe+R8rWA7Ujt2LEDANCoUSO3ZXFxcahbty6sViv27dtX3kUjIiIqHeqlPSMfMiRg75E6dOgQACA+Pt7j8tjYWJw/fx6HDx/GXXfdpVuWl5eHvLw87Xd6ejoAICMjo4xKS0REgUL9WyGEKPu8MjN9Gp8CtCNlsViQlZUFQO4weRITEwMASE5Odlu2YMECzJ07121+QkJC6RWSiIgCWmZmpva3prSFhoaidu3aSGjawnBatWvXRmhoaCmUqnIKyI6U831PERERHsOYTPJVTYvF4rZsxowZmDJlivbb4XAgNTUV1apVg+QHp0EzMjKQkJCAs2fPIjo62tfFMYz1qdhYn4qN9Sl/QghkZmbiuuuuK7M8wsLCcOrUKVitVsNphYaGIiwsrBRKVTkFZEfKuWdd2KlVdeOLi4tzW2Y2m2E2m3XzCjuzVZFFR0dX2AONN1ifio31qdhYn/JVVmeinIWFhbEDVAEE5M3mcXFxWmcqOzvbY5i0tDQAQPXq1curWERERBRgArIjFRQUhObNmwMALly44DHMpUuXAACtW7cur2IRERFRgAnIjhQA9OvXDwBw7Ngxt2XJyclIT09HZGQkunXrVt5FK3Nmsxlz5sxxuzzpr1ifio31qdhYH6KyJYnyeD7TB/744w9cf/31aNGiBY4cOaJb9vHHH+Ouu+7CyJEjsXbtWh+VkIiIiPxdwJ6RSkxMxIQJE3D06FEcPnxYt2zt2rUIDw/HnDlzfFM4IiIiCggBe0YKkG8079GjB4KDg/Hpp5+iatWqeP311/HEE09gw4YNGDx4sK+LSERERH4sIIc/UEVGRiIpKQlPPfUU2rVrB5PJhBtvvBHff/89WrVq5eviERERkZ8L6DNSRERERGUpYO+RqsisVisWLlyIZs2aoXHjxujRowd2797tMezJkycxfPhw1KhRA2azGTfccAOef/553bsAyypv1U8//YQBAwagYcOGaNKkCaZPn47c3FxDaarsdjs6duyIBg0aBER9/vjjD8yYMQN33XUXHn/8caxatcqv6pOTk4OnnnoKzZo1Q3x8PGrXro0BAwbg22+/LVE9yrI+qh07dqBz585Ys2ZNqaTnD/Xxh+NBSerjqiIeD4iuSVC5slgsolevXqJ58+bi77//FkIIsXnzZhESEiI2b96sC/vLL7+IqlWrCgAiODhYANA+Xbt2FTk5OWWWt+qjjz4SZrNZLF68WAghRFpamujSpYvo1KmTyMrK8ipNZ/PmzRMARP369UtUl4pWH6vVKp588klRs2ZNsW7dOmG32/2uPrm5uaJ9+/aiWbNm4siRI0IIIfLy8sSUKVNEUFCQ2L59u8/rI4QQmzZtEu3bt9f2hdWrVxdahuKk5y/18YfjQUnq40lFOx4QFQc7UuXs8ccfFwDEd999p5t/3333iYiICPHXX39p89q3by8GDx4sjh07JhwOhzh37pwYMWKEdoCaMWNGmeUthBBnzpwRUVFRon///rr5x48fF5IkiYkTJ5Y4TWeHDh0ScXFxXh84K0p9srOzRe/evUXDhg2LrG9Fr8+iRYsEAPHtt9/qwtrtdtGkSRNRp06dEnUQy6I+Qghx8uRJYbFYRGJiYpF/qIubnr/Uxx+OByWpj6uKeDwgKg52pMrRqVOnRHBwsGjevLnbsk8//VQAEMOGDRNCCHH48GExdOhQ4XA4dOEcDofo3r27ACASEhLKJG/V2LFjBQCP/5tT/8dZ0jRVFotFtGzZUqxYscKrA2dFqY/NZhN33nmniI6OFn/++WeJ6lDR6nPHHXcIAB7PbAwePFgAEJcuXfJZfSRJEr/++qs2b+jQoUX+oS5pehW5Pv5wPCjp+nFWEY8HJdk+qHLjPVLlaNOmTbDZbOjcubPbsg4dOgAAtm3bhpSUFJw7dw4vvPACJEnShZMkCVOnTgUAXLlypUzyBoD8/Hxs2bIFADzG6dixIwCUKE1nTz31FHr06IHbbrut2HWoiPV56qmn8Mknn+CFF15A48aNvapLRalPZGQkAODAgQNu4dU32deoUcNn9RFCYOXKldq8ol7W6k16Fbk+/nA8KEl9XFXE40FJtg+q3NiRKkc7duwAADRq1MhtWVxcHOrWrQur1Yp9+/ZhwIABhd5wmZiYCACoX7++27JFixYhKioKL730ktd5A8CePXuQkZEBs9mMunXrusVp2bKl9r24aar27t2LHTt24MUXX/RYP3+pzx9//IHFixcjPj4eY8eOvWZdKnp97rrrLgDAxIkTdS/7TklJwZ49e/DSSy+5/SEv7/okJSVp81zL4syb9CpyffzleFDc+jiryMcD1+2DyBN2pMrRoUOHAADx8fEel8fGxgKA20jsrpKTkwEAAwcOdFu2du1aZGVlYd26dYbyVsN7Osg4hweA6667rlhpAvIgqePGjcOaNWsQHh7uMZ6zilyfF198EVarFXfffTdWrlyJgQMHIjExEddffz2mT5+OrKwsv6rP/fffj9tvvx0nTpzA7bffjqtXr8LhcODhhx/GsmXLcP/99/u8PkePHoXdbvcYxlP+JU2votanKBXpeFDS+lT040FprB8KfOxIlROLxaL9YXX+I+csJiYGQMGBsTD//e9/ERYWhkcffdRt2axZs9C2bVvMmjXLUN7qZYLCwjsf9EJCQoqVJgBMmTIF9913H2655RaPcVxV1PrYbDZs27YNAPDzzz/jxhtvxNatW/Hdd9+hTZs2ePHFF9GzZ0/k5OT4RX0AwGQy4cMPP0S/fv2wd+9edOnSBQ8++CBmz56N0aNHe0yjvOqjhrfZbEhPT/cYxpm36VXU+hSlIhwPvK1PRT0elOb6ocAX0CObVyTO99VERER4DGMyyf1ai8VSaDp5eXl4++23MXv2bI//+7r33ntx7733Gs5bjVNYeOezLUFBQcVK8/PPP8fhw4exbNkyj+E9qaj1OXbsmJbmRx99pB2Q4+LisGbNGhw8eBA//vgjnnnmGd0li4paH+f833//fcyaNQt79uzBypUrcerUKWzZsgVVq1Z1S6O86qOGdy1vYbxNr6LWpzAV5XjgTX0q8vGgtNYPVQ48I1VOQkNDte+ikMHkrVYrAPmPcWEWLlyIJk2a4MknnyzTvNU4hYV3nh8VFXXNNK9evYpHH30U69atQ3Cwsf57RajPuXPnAMgHYtf/1ZrNZu2eqeIMRlgR6qP6+++/8dhjj+HVV1/F3r170b17d+zatQtdu3bF5cuXr1mXsqqPGt61vNcqQ2mkVxHqU5iKcjwoaX0q+vGgtNYPVQ7sSJWTuLg4bed1vpHXWVpaGgCgevXqHpd///332LhxIz744INCzzKUVt61a9cuMrzzfQP5+fnXTHPixIl4/PHH0axZs2KXuzAVoT4ZGRkAgOjoaI9hBwwYAEC+hHCtS7UVoT6AfNmjZ8+eGD58OEJCQhATE4PPPvsM3bp1w6+//ooRI0YUWY+yrI8aPjIyslhPg5VmehWhPp5UpONBSetT0Y8HpbF+qPJgR6qcBAUFoXnz5gCACxcueAxz6dIlAEDr1q3dll28eBETJ07Ef/7zH9SsWbPM81Zf6lxYeOdHra+VZu3atbFp0yY8+uijkCRJ92nYsCEA+UyIOu/06dMVuj6tW7fWhgFQO1SunC+zOBwOj2FUFaE+ADB79mxcuHABffr00cJERERg+/btSEhIwJdfflmsV8WURX2K2jc8Kc30KkJ9XFW040FJ6nPu3LkKfzwwun6ocmFHqhz169cPAHDs2DG3ZcnJyUhPT0dkZCS6deumW5aZmYl7770XK1asQNOmTcsl7169eiE0NBSXL1/2eEblzz//1L4XJ81mzZp5/KiPKwcHB2vzCrs5uiLV5+abb0ZQUBBycnI8HujV/8VWrVq1WGMv+bo+ALB161bExcW5XWqJi4vTxio6ePDgNetSlvXp379/sfIv7fR8XR9nFfl4UJz6SJLkN8cDb9YPVULlN/Yn/f7778JkMomWLVu6Lfvoo48EADFy5Ejd/KysLNGvXz/xzTffeEyzuK8k8SZv9fUTH374oVucm2++WUiSVOI0XZ06dcqrkYwrQn369esnAIhly5a5hT969KgAIMaMGeM39YmJiREmk0nk5eW5hd+xY0ehdS2v+phMJvH7779r80aNGlXkyNklTa+i10eIin088KY+rirS8aAk2wdVbuxIlbOHHnpIABCHDh3SzR80aJAIDw8XJ0+e1OZlZWWJ22+/XXzxxRdu6TgcDrFjxw5xzz336OZv3LhRtGvXzuNrD0qStxBC/PnnnyIyMlIMHDhQN1/tJEyYMKHEabq61oGzItfnxx9/FMHBwaJp06bCYrHowk+dOtXj+70qcn3UP3zvvvuuW9meeuopERoaqr0M1lf1cTZ8+HABQKxcudItb2/Sq+j18YfjQUnq40lFOx4QFQc7UuUsKytLtG3bVnTo0EGkpKQIh8MhXnvtNREaGiq2bNmihbt69aro1KmTCA0NFdWqVdN9qlatKkJCQgQA8fbbb+vSb9GihQAgbrzxRq/zdrZ+/XoRHBys/XH9+++/xU033SS6dOkisrOzvUrT2bUOnBW9PitXrhQAxP333y+ys7OFEEJ8+OGHokqVKmLr1q1+VZ/Lly+LxMREUb16dfHFF18Ih8MhHA6H2Lhxo4iIiBBvvfWWz+ujysnJES1bthQAxLhx4zymVZL0Knp9/OV4UNz6FKaiHQ+IioMdKR/IyMgQjz/+uGjYsKFo3LixGDhwoPj55591YdSXzhb1MZvN4urVq7p48+fPF5GRkWLhwoVe5+3qyy+/FJ06dRINGzYULVq0EIsWLdJd/vEmTdW1Dpz+UJ9PPvlEdOzYUcTGxoobbrhB3HXXXeL777/3y/qkpKSIKVOmiIYNG4oaNWqIunXrittvv1189dVXFaY+w4YNExEREbp9IS4uTrz55ptepecP9fGn40FJ14+zing8ILoWSYhCBtIgIiIioiLxqT0iIiIiL7EjRUREROQldqSIiIiIvMSOFBEREZGX2JEiIiIi8hI7UkREREReYkeKiIiIyEvsSBERERF5iR0pIiIiIi+xI0VERETkJXakiIhKgRACy5cvx0033YSwsDDExcVh4MCB+OGHHwqN89NPP2HAgAFo2LAhmjRpgunTpyM3N7fU0i9pHt767rvvMHLkyFJNszxdunQJ06dPR6tWrYoVftu2bZAkye0zbNiwMi4pVUi+fdUfEVUUq1atElFRUWLVqlW+LopfGj9+vPaS3qCgIO17SEiI+PDDD93Cf/TRR8JsNovFixcLIYRIS0sTXbp0EZ06dRJZWVmG0/cmD28NGzZMmM1mcenSpVJLszycPXtWTJ48WYSHhxf5smRXt9xyi8cXRx84cKBsC0wVEjtSRCSEEOKOO+4QAMSAAQN8XRS/8+mnn4rq1auLtWvXioyMDJGfny+2b98uatSoIQCI6OhoceXKFS38mTNnRFRUlOjfv78unePHjwtJksTEiRMNpe9NHt46d+6cCA4OFgDEvHnzSiXN8nL+/HmRm5srVq1aVeyO1M6dO0XXrl3Fb7/9pvucOHGi7AtMFRI7UkSV0DfffOM274svvhC33HKL+OKLL3xQIv82dOhQcejQIbf5//3vf7WzFe+88442f+zYsQKA2Lx5s1uc9u3bC0mSxK+//up1+t7k4a0nn3xSREVFCQCibt26Ij8/33Ca5e3XX38tdkfq1ltvFZ999lnZF4r8Bu+RIqpkHA4HHnnkEbf5ffv2xcGDB9G3b18flMq/devWDa1bt3ab37t3b7Rp0wYAcOXKFQBAfn4+tmzZAgDo3LmzW5yOHTtCCIGVK1d6lb63eXgjNzcXK1euxAcffICQkBCcP38eW7duNZSmL4SFhRUr3MGDB/Htt9/i77//xvHjx8u4VOQv2JEiqmQWLFiAX375xdfFCCiTJk0qdFliYiIAoH79+gCAPXv2ICMjA2azGXXr1nUL37JlSwBAUlKSV+l7m4c33n33XbRt2xZ9+/bF4MGDAQBLly4tNPy///1vmM1m7ebsr7/+Wlt24403avMbNGjgMf7bb7+NNm3aID4+HiaTSQsfExODxMREPPjgg17VQ5KkYoWbP38+LBYLHnroIdxwww245ZZb8MUXX3iVJwUOdqSIKpFVq1bhqaee8nUxKpXk5GSYzWbcfvvtAIBDhw4BgMcODgDExsYCAI4ePQq73V7i9Msij8K89tprmDp1KgBg8uTJAIC9e/fi8OHDHsMvWbIEp06dQvXq1d2W/fLLL3j33XcLzWvChAmYMGECZs6ciXPnzuGPP/5AvXr1AAANGjTAH3/8geXLl3tdl2tJSUlBcnIymjVrhqCgIADADz/8gNtvvx2TJ0+GEKLM8qaKjR0pokrixRdfxIIFC7QDfpMmTdCkSRPMnDkT2dnZePvtt3HzzTfjmWee0cXLyMjAs88+q/3xTU1NxZgxYxAdHY369etjzZo1Wtivv/4aXbp0QUREBNq0aYP9+/d7LMuvv/6K++67DzfeeCOqVKmCVq1aYdWqVWVRbZ/KycnB/v37MW7cOK391Etw6m9XMTExAACbzYb09PQSp1/aeRRm586dMJlMuO222wAAt9xyi3YZsaizUtdddx1uuOEGj8s6derkcX5SUhLefvtt9OnTB0OGDAEANG7cGHPmzAEAHDlyBOfPn/eqHsVVrVo17N27F8ePH0dycjJWrVqFOnXqAJA7iGpZqBLy7S1aRFTeoNycrPr111/FfffdJ8LCwgQAMWfOHG3ZihUrRMOGDbU4KSkponnz5uK6667TwptMJrF//36xbds2YTabRUJCgvZ4frVq1URaWpou/88//1w0aNBA7NmzRwghP4LeunVrAUA888wz5dIG5eXVV18VNWvWFCkpKdq8cePGCQCia9euHuM430B+/vz5Eqdf2nkUZsCAAWL16tW6eZs3bxYARFhYmEhOTi40bo8ePQQAkZSUpJt/6tQpjzd9T5kyRQAQkydP1s3PyMjQ6vHdd995VY+i8r2W9PR00aVLF20Yir/++svrMpD/4hkpokruhhtuwHvvvYfhw4e7LRsxYgQ++eQT7fdTTz2FFStW4Pz587hy5Qo6deoEh8OBWbNm4f3338fvv/+OM2fO4PTp04iPj0dKSgo+++wzLX5KSgruv/9+LFy4EF27dgUAxMfHY8WKFQCAZ599Fn/++WcZ17h8pKSk4Pnnn8fatWsRFxenzQ8NDQWAQi8FWa1W7btzvOKmX5p5FOaPP/7AoUOHcP/99+vm//Of/0S9evVgsVgM38juzLm8zqKiolC1alUAQHBwcKnlV1zR0dH49NNPUb9+feTn5+PDDz8s9zKQ77EjRUQAgFq1arnNCwsLQ+PGjbXfCxcuRJcuXQAAVapUwcMPPwwASEtLw8aNG7V7VuLj4/GPf/wDAHDmzBkt/qpVq5CZmYmBAwfq8lFHlHY4HNi2bVsp1sp3xo8fj2nTpunuXQKA2rVrAwCys7M9xktLSwMAREZGFvk0WWHpe5tH9erVPX727dvnFv/VV1+F1WpFq1atcP3112ufFi1aaKOmv/HGG4buv3KmXvL7+++/3ZYJIRAaGorrr79emzdw4ECPdXnxxRdLpTzOoqOjMXv2bADAyZMnSz19qvjKvwtPRBVSSEiIx/lms1n7HhUVpVt23XXXafNdn3xSbyh2fh3JV199BSGEx0f5q1WrBgC4fPlyyQtfwcyfPx/16tXDE0884bZM7TReuHDBY9xLly4BgMc2Kk763uaRkpLiMWx+fr7ud3p6OjZu3IjffvsNNWrUcAt/+fJlJCQk4MyZM/joo49wzz33FFqP4hoyZAiWLVuGL7/8Emlpadq9X2fPnkVaWhpGjRqFiIgIXRk91ScnJ8dwWTzp06cPAPk/F1T5sCNFRF4zmQo/qa0uc768dObMGcTFxQX0GDzr16/HiRMndDfhO+vVqxdCQ0Nx+fJlJCcnuz3Bpl7a7N+/v1fpe5tHYZcBXa1cuRL9+/f32IkCgJo1a+Luu+/G5s2b8dprr3nsSBV3uAFVSEgIvvzyS3Tv3h0PPvggVq5cqY2H1qhRI7z00ku68M7DKpQH9abzjh07lmu+VDHw0h4RlRubzYYrV65ol5YCzdatW7F9+3a88847bp0Fu92Os2fPIjo6Wnu57e7du93S2L9/P0wmE4YOHepV+gAM5VEUu92O119/HRMnTiwy3IQJEwDIHZqjR4+6LQ8PDwcgPwHqTH2C0NM9UUuXLsX58+eRk5ODVq1aoWPHjqhXrx72799faKeuvPzyyy9o2LAh7rzzTp+Wg3yDHSkiKjd16tSBEAKbN2/2uFwIUe5nE0rL9u3bsXbtWmzYsMHtxueLFy9i9OjR+OuvvwAAc+bMQWRkJNatW6cL98svv+Cnn37CuHHjtIE2vUnf2zyu5b333oMQwuNo6c569eql3cS+YMECt+Xq4KGrV6+GxWIBAHz++eeYMmUKAHlsrOTkZO0s2YEDBzBz5kw8++yz2LJlC/744w8cO3YMr7/+OmrWrFmiOnhis9kAoMh7uhwOB65evepx2cKFC7Fq1SrdZXCqRHz1uCAR+QZchj9QzZkzx234g2vFSUpKEgBEjx49ipXeU089JQCI6tWri5MnT7rFWb16tdiwYUOJ6lMRrF+/XgQHB4vY2FhRrVo13Ud9D11CQoJwOBxucd59910hhBB///23uOmmm0SXLl1Edna24fRLmse1HD9+XNSqVUvUq1evyKENhBAiNTVVe6EyALdhEpyHX4iKihK1atUSrVq1Ej/++KM2v379+mLlypVCCCHeffddbb7zR5IkUaVKFdG5c2dD779T0w8NDfW4XQohxD/+8Q8RFBQkHn/8cW24icuXL4vJkyeLzz//3Ou8yf+xI0VUyYSHhwsAIjc3Vzd/xowZAoCYOXOmbr7VatX+cLm+kHbXrl2Fjlf09NNPu6V39uxZERkZKQCImjVrijfffFP89ddf4sSJE+KFF14QTZs2FZmZmaVY27L3ySefCEmSPP6hd/783//9n1vcL7/8UnTq1Ek0bNhQtGjRQixatEjk5eWVWvrFzeNann/+eV0ZQkNDxSOPPOIx7CuvvKKNMeb8qVq1qi7fZcuWiTp16ojo6GgxatQokZqaKk6dOiWqV68u5s2bpxt/zOFwiNGjR4uGDRuKOnXqiIiICGEymXTpBwUFiR9//LFE9Tpw4ICoU6eOLp3g4GCRmJjoFjYpKUnccsstIjIyUsTGxop//OMfYuHChW5jeFHlw44UUSXTrl07AUD897//FdnZ2eK5554TdrtddO3aVQAQ3bp1EzabTQuvdpYAiG+//VaXlnqGqXbt2uLKlSva/Pz8fNGnTx8BQPTq1UuX3qZNm0RwcLDbH1qz2Sx2795d9g1AfufChQuib9++wmKxuC2zWCzijz/+EP/85z/FE0884YPSUWXHjhRRJfPdd9+JxMREUaNGDTF58mSxZ88eERcX53b24IcffhBjxozRRilX/9d/++23i4yMDFGrVi23jtDTTz8tdu7cKWJjY3XLYmNjxa+//qqV4dtvvxX9+vUTUVFRIjIyUtx2222GRqamwNanT59rXvL95JNPxOOPP14+BSJyIgnBNy0SEVHF9Pnnn6N///7YtGlTkU8ZDh8+HCNHjkS/fv3KsXREfGqPiIgqMPVJxIcffhjvvPOO20uWT58+jQceeAC5ubnsRJFP8IwUERFVWFlZWejfvz/27t0LQB7Ms06dOoiIiEB6ejquXLmCIUOGYN26dUW+UoeorLAjRUREFZrdbsfq1auxfv16HD58GDk5OahWrRo6deqEsWPHYsCAAb4uIlVi7EgREREReYn3SBERERF5iR0pIiIiIi+xI0VERETkJXakiIiIiLzEjhQRERGRl9iRIiIiIvISO1JEREREXmJHioiIiMhL7EgREREReYkdKSIiIiIv/T/5LD8q+CPXwgAAAABJRU5ErkJggg==",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -3627,7 +3660,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 22,
+      "execution_count": 54,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -3654,7 +3687,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 23,
+      "execution_count": 55,
       "metadata": {},
       "outputs": [
         {
@@ -3666,7 +3699,7 @@
               " (0.0005, 0.1)]"
             ]
           },
-          "execution_count": 23,
+          "execution_count": 55,
           "metadata": {},
           "output_type": "execute_result"
         },
@@ -3773,7 +3806,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 24,
+      "execution_count": 56,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -3791,12 +3824,12 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": 57,
       "metadata": {},
       "outputs": [],
       "source": [
         "# Frequency range of isotropic turubulence cascade in same units as PSD frequency vector\n",
-        "f_rng = [0.2, 0.5]\n",
+        "f_rng = [0.1, 0.5]\n",
         "# Dissipation rate\n",
         "ds_avg[\"dissipation_rate_5m\"] = avg_tool.dissipation_rate_LT83(\n",
         "    ds_avg[\"auto_spectra_5m\"], U, freq_range=f_rng, noise=ds_avg['noise_5m']\n",
@@ -3812,7 +3845,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": 58,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -3855,7 +3888,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 27,
+      "execution_count": 59,
       "metadata": {},
       "outputs": [],
       "source": [
@@ -3865,7 +3898,7 @@
         ")\n",
         "\n",
         "# Check that percent difference from -5/3 is not greater than 20%\n",
-        "mask = abs((slope[0].values - (-5 / 3)) / (-5.3)) <= 0.20\n",
+        "mask = abs((slope[0].values - (-5 / 3)) / (-5 / 3)) <= 0.20\n",
         "\n",
         "# Keep good data\n",
         "ds_avg[\"dissipation_rate\"] = ds_avg[\"dissipation_rate\"].where(mask)"
@@ -3882,22 +3915,22 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 28,
+      "execution_count": 60,
       "metadata": {},
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "<matplotlib.collections.QuadMesh at 0x1c8c1dc3910>"
+              "<matplotlib.collections.QuadMesh at 0x112d7649190>"
             ]
           },
-          "execution_count": 28,
+          "execution_count": 60,
           "metadata": {},
           "output_type": "execute_result"
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -3923,7 +3956,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 29,
+      "execution_count": 61,
       "metadata": {},
       "outputs": [
         {
@@ -3932,7 +3965,7 @@
               "Text(0.5, 1.0, 'TI Difference')"
             ]
           },
-          "execution_count": 29,
+          "execution_count": 61,
           "metadata": {},
           "output_type": "execute_result"
         },
@@ -3970,7 +4003,7 @@
         "#### Quick 5-beam ADCP lesson before we dive in:\n",
         "\n",
         "There are a couple caveats to calculating Reynolds stress tensor components:\n",
-        "  1. Because this instrument only has 5 beams, we can only find 5 of the 6 components (6 unkowns, 5 knowns)\n",
+        "  1. Because this instrument only has 5 beams, we can only find 5 of the 6 components (6 unknowns, 5 knowns)\n",
         "  2. Because the ADCP's instrument (XYZ) axes weren't aligned with the flow during deployment, we don't know what direction these components are aligned to (i.e. the 'u' direction is not necessarily the streamwise direction)\n",
         "  3. It is possible to rotate the tensor, but we'd need to know all 6 components to do so properly (\"coupled ADCPs\")\n",
         "  4. Measurements close to the seafloor can be suspect due to increased vertical flow. ADCPs operate under the \"assumption of homogeneity\", which means that they can only accurate measure consistent horizontal currents with relatively little vertical motion.\n"
@@ -3985,16 +4018,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 30,
+      "execution_count": 62,
       "metadata": {},
       "outputs": [
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "c:\\users\\mcve343\\mhkit-python\\mhkit\\dolfyn\\adp\\turbulence.py:407: UserWarning:     The beam-variance algorithms assume the instrument's (XYZ) coordinate system is aligned with the principal flow directions.\n",
-            "  warnings.warn(\n",
-            "c:\\users\\mcve343\\mhkit-python\\mhkit\\dolfyn\\adp\\turbulence.py:417: UserWarning:     100.0 % of measurements have a tilt greater than 5 degrees.\n",
+            "c:\\Users\\mcve343\\anaconda3\\envs\\work\\Lib\\site-packages\\mhkit\\dolfyn\\adp\\turbulence.py:407: UserWarning:     The beam-variance algorithms assume the instrument's (XYZ) coordinate system is aligned with the principal flow directions.\n",
             "  warnings.warn(\n"
           ]
         }
@@ -4034,14 +4065,14 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 31,
+      "execution_count": 63,
       "metadata": {},
       "outputs": [
         {
           "name": "stderr",
           "output_type": "stream",
           "text": [
-            "c:\\users\\mcve343\\mhkit-python\\mhkit\\dolfyn\\rotate\\api.py:72: UserWarning: You are attempting to rotate into the 'principal' coordinate system, but the dataset is in the inst coordinate system. Be sure that 'principal_heading' is defined based on the earth coordinate system.\n",
+            "c:\\Users\\mcve343\\anaconda3\\envs\\work\\Lib\\site-packages\\mhkit\\dolfyn\\rotate\\api.py:72: UserWarning: You are attempting to rotate into the 'principal' coordinate system, but the dataset is in the inst coordinate system. Be sure that 'principal_heading' is defined based on the earth coordinate system.\n",
             "  warnings.warn(\n"
           ]
         }
@@ -4068,12 +4099,12 @@
       "metadata": {},
       "source": [
         "### 7.8 TKE Balance \n",
-        "We can plot the production rates and the ratio of production rates to dissipation rates to get an understanding of the TKE balance.We always expect production to be greater than 0, though negative values can give us an indication of uncertainty. In a well mixed coastal environment, we expect production and dissipation to be approximately equal. Our production estimates are possibly high because our stress components aren't aligned with the flow (4x10^-3 $m^2/s^3$ is quite large), but if this weren't the case, we would conclude that TKE is produced (kinetic energy is lost to turbulence) but not dissipated (turbulent energy is lost to entropy) here."
+        "We can plot the production rates and the ratio of production rates to dissipation rates to get an understanding of the TKE balance.We always expect production to be greater than 0, though negative values can give us an indication of uncertainty. In a well mixed coastal environment, we expect production and dissipation to be approximately equal. Our production estimates are possibly high because our stress components aren't aligned with the flow (1.3x10^-3 $m^2/s^3$ is quite large), but if this weren't the case, we would conclude that TKE is produced (kinetic energy is lost to turbulence) but not dissipated (turbulent energy is lost to entropy) here."
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 32,
+      "execution_count": 64,
       "metadata": {},
       "outputs": [
         {
@@ -4082,7 +4113,7 @@
               "Text(0.5, 1.0, 'TKE Balance')"
             ]
           },
-          "execution_count": 32,
+          "execution_count": 64,
           "metadata": {},
           "output_type": "execute_result"
         },
@@ -4098,7 +4129,7 @@
         },
         {
           "data": {
-            "image/png": 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",
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CRJSfMDEkeiEqKsroD7l2mTNnjl69R48eZVtv7dq1Uvn79+9nWx6AWL16tfj66691kqGcLNu3bzdpv/fu3StGjBghKlasmGV7dnZ2olChQiIwMFC0bdtWfPPNN+LZs2dG212yZImoVKmScHZ2FnXq1BGbNm0SQgiRmJgoGjRoILy8vMTo0aNFamqqEEKI0NBQo7G1yWFUVJQYMGCAKFasmPD09BQ9evQQt2/fFkIIsWfPHlGkSBFRuXJlsWPHDiGEED169DDYXpEiRYQQQpQuXdrg9sy33IuJiRFjxowR5cqVE46OjsLPz08MHz5cPHz4MMvXdvPmzeK1114THh4ewsXFRdSpU0csWLCASSER5VsKIcy4eImIiIiIChxeY0hEREREAJgYEhEREdELTAyJiIiICAATQyIiIiJ6gYkhEREREQFgYkhEREREL9hnX4TUajXCw8Ph7u4OhUKR190hIqJ8TAiBuLg4+Pr6QqnMveMvycnJ0m0i5XB0dISzs7MFekQFARNDE4SHh8Pf3z+vu0FERDbk/v378PPzy5W2k5OTUcSlEBKR/S0ys+Pj44Pbt28zOSQATAxN4u7uDkDzR+7h4ZHHvSEiovwsNjYW/v7+0m9HbkhNTUUiVOiDUnCUcVVYKtRY/fghUlNTmRgSACaGJtGePvbw8GBiSEREJrHGpUcuUMJRYX5iaMd7n1EmTAyJiIhslJ1CATsZCagdFJo7hBO9wMSQiIjIRikVgJ2MA5NKgIkh6eB0NUREREQEgEcMiYiIbJZFTiUTZcDEkIiIyEbZyTyVbGe5rlABwVPJRERERASARwyJiIhsFk8lk6UxMSQiIrJRPJVMlsZTyURERJRjqampmD59OipVqoRy5cqhRYsWOHDgQI7befz4MYYMGYKyZcsiMDAQPXr0wL1797Kss2nTJtSrVw9ly5ZF9erVsXjx4mzjJCcnY+7cuShTpgzu3LmTbXlzYhQEPGJIRERko/LqVHJKSgo6dOiAJ0+e4O+//0ZAQAA2btyINm3aYPXq1ejWrZtJ7dy+fRvNmjVD48aNcfHiRTg6OmL06NGoW7cuDh48iEqVKunVGTduHObMmYPt27ejRYsWuHLlCpo3b45z585h9uzZeuUTExMxb948/PTTT7h//75J/cppjIKERwyJiIhslAKaH3JzF3NTypCQEISFhWHp0qUICAgAAHTr1g1du3ZFv379cPv27WzbUKlU6NatG1JTU7F06VK4uLjAzs4OM2fOhLOzM7p37460tDSdOtu2bUNoaCgmTpyIFi1aAAAqV66Mb775BnPmzMGGDRsMxnn//fcRFhYGpTL7tMecGAUJE0MiIiIy2Z07d/DLL7+gatWqqF+/vs62vn37IjExEWPHjs22nbVr1+LUqVPo1q0b3NzcpPV2dnbo1asXzp07hyVLlkjr1Wo1vvzySygUCvTr10+nrd69e8POzg6jRo2CSqXS2ebu7o5ixYqhXLlyKFq0aJZ9MjdGQcLEkIiIyEZpTyXLWXJq/fr1SE9PR+PGjfW2NWjQAACwdetWREZGZtnO6tWrAcBgOw0bNgQALFq0SFp34sQJXL9+HeXKlUPx4sV1yhcqVAjVqlXDw4cPsXPnTqMxnZ2ds+yTJWLYOiaGRERENko7KlnOklM7duwAAJQtW1Zvm7e3N0qVKoXU1FQcOnTIaBuJiYnYv3+/0XaCg4MBAKdPn0Z0dHS2cTPWCQsLMxpXkU0ibIkYto6JIRERkY3SJHdyjhjmPObp06cBAH5+fga3e3l5AQDOnDljtI3Lly8jOTnZaDvaNoQQOHfunMXiZscaMfI7jkomIiJ6xcXGxuo8d3JygpOTk1655ORkxMfHA3iZJGXm6ekJAIiIiDAa79mzZ9JjQ+1o28jYjraOnLjZsUaM/I5HDImIiGyUpU4l+/v7w9PTU1pCQ0MNxst43aCrq6vBMtqRv9ojgua0k3H0sLYdbR05cbNjjRj5HY8YEhER2ShLzWN4//59eHh4SOsNHS0EAEdHR+mxEMJgmdTUVACa6w2Nya4dbRsZ29HWkRM3O9aIkd/xiCEREdErzsPDQ2cxlhh6e3tLyVNCQoLBMtrBIllNDePj4yM9NtSOto2M7WjryImbHWvEyO+YGBIREdkopczTyMocHmy0s7ND1apVAQDh4eEGyzx58gQAULNmTaPtBAUFSSOEDbWjbcPR0RFVqlQBAFSvXl123OxYI0Z+x8SQiIjIRuXFPIbt2rUDAFy8eFFvW0REBGJiYuDm5oZmzZoZbaNw4cLS5NiG2rlx4wYAoHnz5tLk11nFzVinQ4cOpu6KHmvEyO+YGBIREZHJBg4cCKVSiQMHDuhtO3LkCACgS5cuRk9Haw0ePBgAsmynd+/e0ro2bdogMDAQly9f1hnVDGhO8V6+fBmBgYHS5NjmsEaM/I6JIRERkY3KiwmuK1SogMGDB+P8+fN68/ktX74cLi4umDRpkrQuLCwMDRo0wOzZs3XK9u3bF8HBwdiwYYPOKN/U1FSsW7cOQUFBeO+996T19vb2CA0NhVqtlu6aorVq1Sqo1WpMnToVdnZ2Rvuenp4OAEZvaWeJGLbOZhPDHTt2oHHjxli2bFmW5f777z906tQJgYGBKF++PEJCQpCUlGSdThIREeWivEgMAWDmzJmoU6cOhg4diufPn0MIgTlz5uCPP/7AihUrdO4c8v333+P48eMYP368ThsODg5Ys2YN0tPTMWrUKKSnpyMxMREDBgyAWq3Gpk2b4ODgoFOnR48eGDJkCL755htp4uuDBw9i/PjxGDlyJHr16mW0z7dv38bTp08BAEePHjVaTk6MgsDmEsMNGzagQYMGeOONN6RDzcb88ccfaNy4MVq3bo3bt2/j1KlTOHToEFq3bm10xBERERFlzc3NDWFhYWjYsCHq1q2LChUqYO/evThx4gS6du2qU7ZXr15wd3fHBx98oNdOUFAQjhw5gidPnqBChQqoWbMmvLy8cPbsWVSqVMlg7Hnz5mHy5Mno2bMnypUrh5CQEKxYsQI//PCD0f6WLl0aFStWRFpaGgDgvffeg6+vr9E7mJgTo6BQCGOT9eRTt27dQqlSpRAcHIzr169j6dKl6Nevn165+/fvo1q1amjatKnOza6vXr2KKlWqYOjQoZg7d65JMWNjY+Hp6YmYmBideZ6IiIgys8ZvhjbG3MIV4KIw/7RmklBheNR1/r6RxOaOGJYtWxZOTk6oVatWluX+97//IS4uDv3799dZX6lSJdSrVw/z58/H5cuXc7OrREREucoOMk8l5/UOUL5jc4mhlrOzs9FtaWlp2LhxIwCgcePGetsbNmwIIQQWL16ca/0jIiLKbUqZU9UoZdw1hQomm00MFVl8mA8ePIjY2Fg4OTmhVKlSetuDg4MBaEZKEREREZFGgbxX8unTpwHAYFIIAF5eXgCA8+fPQ6VSFehh50REVHDJGVmsrU+UUYFMDLWTUmoTwMw8PT0BaOYziomJ0bsZdkpKClJSUqTnsbGxudNRIiIiGcy9e0nG+kQZ2eyp5KxERkYCAFxdXQ1uVypf7nbGSTW1QkND4enpKS3+/v6501EiIiKifKRAJoaOjo4AAGMz8aSmpkqPMx8tBICxY8ciJiZGWu7fv587HSUiIpIhrya4poKrQJ5K9vHxAQCjk1hHR0cD0EzQaWh0s5OTU7b3eCQiIsprPJVMllYgjxhWr14dABAeHm5w+5MnTwAANWvWtFaXiIiIiPK9ApkYtmrVCo6Ojnj69CkiIiL0tt+4cQMA0KFDB2t3jYiIyGKUL+YilLMQZVQgE0MPDw/06NEDAHDgwAG97UeOHIFSqUT37t2t3TUiIiKLUdgpZC9EGdlsYpieng4AUKlUBrdPmjQJbm5uWLFihc76Cxcu4L///sOgQYNQoUKFXO8nERERka2wycQwKSkJ586dAwAcPXrUYJly5cphwYIF2LFjB1atWgUAuHfvHt577z00adIEs2bNslp/iYiIcoPSTiF7IcrI5hLDnj17omjRojh//jwAYPHixShSpAjmz5+vV7ZPnz7YuXMn5s6di7Jly6Jjx47o27cv9u3bZ3SOQyIiIpthp4RCxgI7m0sDKJfZ3HQ169aty1H5119/Ha+//nou9YaIiCjvKJTyrhNUgEcMSRf/q0BEREREAGzwiCERERFpyL1OUMkjhpQJE0MiIiIbpVAqoVCaf/JPYeTWsfTq4qlkIiIiIgLAI4ZEREQ2i6eSydKYGBIREdkouXcv4ahkyoynkomIiIgIAI8YEhER2SzNEUMZg0+gtmBvqCBgYkhERGSjeI0hWRpPJRMRERERAB4xJCIislkKhQIKpYzBJ2oeMSRdTAyJiIhslNJOCaWMawyVgicOSRcTQyIiIhsle7oawSOGpIv/VSAiIiIiADxiSEREZLN4xJAsjYkhERGRjeI1hmRp/EQQEREREQAeMSQiIrJdMk8lg6eSKRMmhkRERDZKqVBAKWMeQ6WCiSHp4qlkIiIiIgLAI4ZEREQ2S2GnhELG4BOFmseHSBcTQyIiIhultFNAKeMaQyVviUeZ8L8KRERERASARwyJiIhsluwJrnnEkDJhYkhERGSjeI0hWRoTQyIiIhultIPMawwt2BkqEPhfBSIiIiICwCOGRERENkuhVEAhY4JrOXWpYGJiSEREZKOUSiWUMq4xVKp44pB08RNBRERERAB4xJCIiMhmyZ6uRkZdKpiYGBIREdko2dPVyKhLBRM/EUREREQEgEcMiYiIbJZCqYRCKeOIoYy6VDAxMSQiIrJRSjuZo5J5Kpky4SeCiIiIiADwiCEREZHtkjn4BDxiSJkwMSQiIrJRCqXMUcm8xpAyYWJIRERkozj4hCyNnwgiIiIiAsAjhkRERDZLM8G1nYz6Kgv2hgoCJoZEREQ2inc+IUvjJyKPJSUnIyk5Oa+7QURERjyNScDTmIS87gaRVfCIIRERkY1SKpVQyhhAIqcuFUxMDImIiGwUTyWTpfETQUREREQAeMSQiIjIZvGIIVkaE0MiIiIbpVDInOBawcSQdPETQUREREQAXoHE8N9//0XHjh3h4+MDf39/VKpUCZMmTUIyp4ghIiIbpz2VLGchyqhAfyI2btyIFi1aoEGDBrh//z7u37+PdevWYenSpWjdujVSU1PzuotERERmy8vEMDU1FdOnT0elSpVQrlw5tGjRAgcOHMhxO48fP8aQIUNQtmxZBAYGokePHrh3716WdTZt2oR69eqhbNmyqF69OhYvXmzxGI8fP4azszMUCoXO4ufnh7S0tBzvp60osIlhSkoKhg0bhjZt2mDSpElwcHAAANSqVQtTp07F4cOHsWTJkjzuJRERkfmUdkrZizlSUlLQvn17rFy5En///Tdu3ryJjz/+GG3atMHGjRtNbuf27duoW7cuoqKicPHiRdy4cQO+vr6oW7curl69arDOuHHj0L9/f8ycORO3bt3Chg0bMG7cOIwYMcJiMQDghx9+QEpKit76ESNGSDlFQVRgE8Nz584hMjISNWvW1NtWu3ZtAMD58+et3CsiIiLbFxISgrCwMCxduhQBAQEAgG7duqFr167o168fbt++nW0bKpUK3bp1Q2pqKpYuXQoXFxfY2dlh5syZcHZ2Rvfu3fWOzG3btg2hoaGYOHEiWrRoAQCoXLkyvvnmG8yZMwcbNmyQHQMAoqKisGbNGpw5cwaXL1/WWYwloAWFyaOSBwwYkCsdUCgUuXLkzs3NDQBw9OhRvW1xcXEAYDBpJCIishUKpULeqGSlIsd17ty5g19++QVVq1ZF/fr1dbb17dsXa9euxdixY7Fu3bos21m7di1OnTqF4cOHS7/ZAGBnZ4devXphxowZWLJkCYYOHQoAUKvV+PLLL6FQKNCvXz+dtnr37o3hw4dj1KhR6NKlC+zs7MyKofXzzz/jvffeQ40aNXL8+tg6kxPDZcuW5UoHcisxrFKlCipUqIADBw5gyZIlGDhwoLRt69atCA4OxgcffGDxuERERNaSF/MYrl+/Hunp6WjcuLHetgYNGgDQ/M5GRkaiSJEiRttZvXo1ABhsp2HDhgCARYsWSUnbiRMncP36dZQvXx7FixfXKV+oUCFUq1YN586dw86dO/Hmm2+aFQMAEhMTMXv2bPTv3x+HDh1Co0aNXqlbB5qcGPr6+kovsKUIIdC3b1+LtqmlUCiwcOFCtGvXDsOGDYOzszP69OmDQ4cO4dSpU9i7dy+cnJxyJTYREVFBtWPHDgBA2bJl9bZ5e3ujVKlSePjwIQ4dOoS33nrLYBuJiYnYv3+/0XaCg4MBAKdPn0Z0dDS8vLyyjKutc+7cOYSFheHNN980KwYALFy4EBEREfjuu+/w3Xffwd/fHyEhIRg6dKh0JLIgMzkxdHV1lc7nW5KLi4vF29Rq2bIlNm/ejO7du6Nv3744ceIE7O3tsWvXrizf3JSUFJ0LTmNjY3Otj0RERObKiyOGp0+fBgD4+fkZ3O7l5YWHDx/izJkzRhPDy5cvS9PGGWpHm6QJIXDu3Dk0b97cpLgAcObMGbNjAMDJkycRFBSEW7duITExEffv38fHH3+MTZs2YcuWLShcuLDB+AWFyZ8IIUSudCC32tV64403MGPGDIwYMQKzZ8/G3Llzsx0xFRoaCk9PT2nx9/fP1T4SERGZQ3vnE7OXF3c+iY2N1VkMjcYFgOTkZMTHxwN4mVhl5unpCQCIiIgw2u9nz55Jjw21o20jYzvaOqbGNScGAKxatQrnz59HVFQUdu/ejbp16wIA9u/fj3fffRdqtdrofhUEJieGtWrVypUO5Fa7WjNmzECpUqXw448/Yt26dUhPT0fv3r3x888/G60zduxYxMTESMv9+/dztY9ERER5yd/fX+eASGhoqMFykZGR0mNXV1eDZbTX42V1I4ns2sl4TZ+2HW0dU+OaEyMjR0dHvP766zh27Bg+/fRTAJrkcM2aNUb2qmAw+VRy5iHgOXX16lVUqlTJ4u1mZebMmdiyZYs0Mrl79+5wdHRE165dMWLECNSqVQtNmjTRq+fk5GS16w9dnJ0BAMlJSQAAZwOn1pNefGAVapWmjKubwe3atoiIcsvzuETpsbe74R9oUyQmab63XF1M/97Kq++64p5u2RfKIwo7OyhlXPemeFH3/v378PDwkNYb+w10dHSUHhs746e9eYS3t7fRuNm1k/EGFNp2tHVMjWtODEOUSiV+/PFH3Lt3D1u3bsW6devw3nvvGS1v66wyzEatVhscEZSb7t69iwkTJqBTp046699++21Mnz4dQgj873//s2qfiIiILMlSdz7x8PDQWYwlht7e3lLClZCQYLBMdHQ0AKBo0aJG++3j4yM9NtSOto2M7WjrmBrXnBhZmT59OhQKBW7evJltWVtm8hFDY+Li4vDnn3/i3r17SEpK0svK09LScOTIEZ03wBp27NiBlJQUvSHtAPDZZ59hxowZOH78uFX7REREZMvs7OxQtWpVnDlzBuHh4QbLPHnyBEDWcwUHBQVBoVBACIHw8HCd6/0ytuHo6IgqVaoAAKpXr459+/aZHNecGFmpWLEiAgICUKhQoWzL2jJZieHRo0fx1ltv6ZzHz5wYat8UhSLnk2jKoT1E/ODBA71t9vb2KFOmDO7cuWPVPhEREVlSXoxKbteuHc6cOYOLFy/qbYuIiEBMTAzc3NzQrFkzo20ULlwY9evXx7Fjx3Dx4kW9xOzGjRsAgObNm0sTU7dr1w4//vijwbgZ63To0MHsGNkpWbKkNBiloJKVGA4fPhwRERHw8/PDm2++iaJFi+pNAimEwKFDh7Bv3z5ZHc2pN954A6NHj8amTZswadIk2Nu/3NWYmBhcuXIFffr0sWqfiIiILEk7ulhO/ZwaOHAgvvvuOxw4cEBv25EjRwAAXbp0yfZa/cGDB+PYsWM4cOAAunbtarCd3r17S+vatGmDwMBAXL58Gc+ePUOxYsWkbdHR0bh8+TICAwOliavNiZGV9PR03Lx5M1duypGfKISM+WLc3NyQlpaGO3fuwNfX12i59PR0FC9eHM+fPzc3lFlmzZqFUaNG4b333sPPP/8MT09PPH78GP3798eDBw9w4MABk+Yjio2NhaenJ2JiYnQuzrUkDj4hIlvwKg4+ySlr/GZoYzyYPwYeLuYPloxNSoHf0Ok57uuwYcMwf/58nD59WueUcdeuXbFz505cuHBBmlQ6LCwMY8aMQZ8+fXTuM5yWloY6derg6dOnuHPnDpxfvK+pqakIDAyEt7c3/vvvPzg4OEh11q9fj549e2LWrFn47LPPpPU///wzPvnkE6xZswa9evWSFSMiIsLgNYezZs1CUlISxo0bZ/LrZItkDT6pWbMmfH19s0wKAc2p2+zmDswNI0eOxI4dOxAeHo7AwECULl0aLVq0QO3atXH48OECP0klERFRbpg5cybq1KmDoUOH4vnz5xBCYM6cOfjjjz+wYsUKnTuNfP/99zh+/DjGjx+v04aDgwPWrFmD9PR0jBo1Cunp6UhMTMSAAQOgVquxadMmnYQNAHr06IEhQ4bgm2++wblz5wAABw8exPjx4zFy5EidpNCcGD/88AOKFSuGDh064MqVKwA0N72YPXs20tPTC3xSCMg8lTx16lR06NABDx8+RKlSpbIs+++//6J169ZywpmlY8eO6Nixo9XjEhER5TaFUiHvGkOledf/u7m5ISwsDBMnTkTdunWhVCoRFBSEEydOoHr16jple/XqhQMHDuD999/XaycoKAhHjhzBmDFjUKFCBTg4OKBt27Y4e/aswcGjADBv3jwEBQWhZ8+eSElJQYkSJbBixQp07tzZYPmcxOjWrRv27duHQ4cOoVatWqhXrx4aN26Mfv36oXLlyma9VrZG1qlkANi2bRvWr1+PtWvXGi0TGRkJf39/JCYmGi2Tn/FUMhGRBk8lZ8+ap5LDF4+Hh6v5r0dsYjJ8B03N1b6SbZE9XY2vry+ePXuGevXqwd3dXW+7SqXCjRs3jN5eh4iIiIjyB1mJ4fLlyzFo0CCo1eps73ls7elqiIiICjqF0g4KpYw7n8ioSwWTrMRw0qRJUKlUaNSoEYYPHw5fX1+96WrUajXCwsIwdepUWR0lIiKiTJR2mkVOfaIMZCWGUVFRcHFxwd9//230ptYA0KpVKyxatEhOKCIiIiLKZbKmq3n33Xfh7e2dZVKodf36dTmhiIiIKDOlUv5ClIGsT8T3338PT09PafbwrPTo0UNOKCIiIspEYWcneyHKSFZi6O3tjYMHD2LNmjVGp6JRq9U4evQo/v77bzmhiIiIiCiXybrGsHDhwoiNjQUAzJ071yIdskXxiZo5CJUvRl5nNS+Xsbm7Ms9fqG0TAOyMTECakhAHAHBx058miIhIjsxzBmq/u3Iyd6Gh77uYBM13m6eb5jsv7sV3nV2mmSsMfY/m9/kL8wQHn5CFyTpiOHLkSAghTFqIiIjIwpTKl8mhWQuvMSRdshPDwMBAXLp0CcnJyVCr1XpLamoq9uzZAztex0BERGRRCqVS9kKUkaxPhLu7O8aOHYuyZcvC0dHRYBl7e3u89tpr6Nu3r5xQRERERJTLZN8Sb9CgQSaVW7JkidxQRERElJFC5jWGCp7NI10mHzFs2LBhrnQgt9olIiIq8GRdXygzqaQCyeTEMDIyMlc6kFvtEhEREVHOmHwqWaEwPGWKXLnVLhERUUEndwAJB59QZiYnhrGxsVi5cqXFp56Ji4uzaHtERESvDM5jSBZmcmL49OlT9OvXz6LBhRA8YkhERESUT5icGE6aNCk3+0FEREQ5pZ3gWk59ogyYGBIREdkohZ0dFDJuICGnLhVM/K8CEREREQGwwATXRERElEeUSnmng3kqmTJhYkhERGSrOCqZLIyJIRERkY1SKO2gkJHcyalLBROPIRMRERERAB4xzJH4xCR4eHjorS/k6mJyG64uzgbXp8RFax5o53VUOkrbXJwN13Fyc9d5HpeYJD12z0GfyHTR8YnSY69CrgCApzEJAIDinm550icic8UkvPzO8HRz0Vmnfa5l7LsL0Hw3AkCKSnMDhCLumr8NtYEbImjbTUxKBvDyuyopWfNcpTZ+EwVtnMzfudrvvlfye08h8xpDBY8PkS5+IoiIiGyU9lSynIVsw9OnT/Hjjz/iwoULOuuXLl2Kb7/9FpGRkRaJY5HEMC4uDps3b8Yvv/wirTt+/DiOHj1qieaJiIiIXlnh4eGoXbs2Pv/8c3z++ec62/r374/q1aujTp062L59u+xYshPDFStWwN/fH927d8f//vc/aX316tWxa9cuNGrUCDdv3pQbhoiIiDLT3vnE7IUnDm3BV199hfDwcLi4uOCtt97S296hQweEhISga9euOHnypKxYsj4R27dvR//+/REbGwuR6VoSZ2dnTJo0Cc2bN0f9+vVx48YNWR0lIiKiTLTzGMpZKN/bs2cPNmzYgNjYWHz00UcGy/Ts2ROpqamYMGGCrFiyPhHffPMNAgICsGHDBly+fBne3t56ZT744ANERUVh3LhxckIRERERvZLs7e3RtWtXKLNI5B0cHAAA//77r7xYcipfuHABJ06cQJUqVQDAYIe1o3j37NkjJxQRERFlwnslvxoKFSqEhIQEuLkZn/1ixYoVAAAXF3mj82UdMQwICJCSQmMOHjwIAEhLS5MTioiIiDKTdX2hzLumkNX07t0b/fv3R2Jiot42lUqFBQsW4PPPP4dCoUCnTp1kxZJ1xLB8+fK4evUqKlWqZHD78+fPMXHiRCgUCtSpU0dOKCIiIsqMt8R7JXz66ado3rw5ypYtix49eqB8+fJQqVS4efMmtm3bhvDwcAghULJkSUybNk1WLFmJ4dixY9GlSxcsW7YMdevW1dm2Z88efPLJJ7h16xYUCgXGjBkjq6NEREREryInJyfs3r0bH374IebMmQOF9mYYgDT4t379+li1ahV8fX1lxZKVGDZq1AijR49G06ZNUaJECURGRqJRo0a4ceMGnj9/DiEElEolvvvuO7Rv315WR4mIiEiXQqmEQsbIYjl1ybo8PT2xYcMGXLhwATt37sTdu3ehVqvh5+eHli1bokmTJhaJI/uWeP369UPDhg0xZ84chIWF4fz580hPT0dAQACaN2+OESNG8DQyERFRblDIPJWs4KlkWxMUFISgoKBca98i90quXLmyzl1PiIiIiMiy/vnnH2zatAnPnz/H6tWrAQBhYWE4fvw43n//fZQsWVJ2DFmJ4TfffGPyRIqPHj3Ctm3bkJaWhnbt2hkdsEJEREQmUigAhYzTwRmuVaP8SwiBQYMGYdmyZQCAIkWKSNtatWoFhUKBJk2aYMaMGejatausWLIuLli5cqX0OCIiAtHR0QbLnTx5EpUrV8batWthb2+PoUOH4osvvpATmoiIiBRK+Qvle99//z2WLl0KhUKBEiVKwC7T/JMtW7bEmDFj0LNnT9nzRsv6RERGRuLtt9+Gi4sLSpQogSJFiqB06dL4+uuvkZqaKpUbN24c4uPjsWDBAgwfPhx79uzB1atXMWrUKFmdJyIiIiroFi1ahLZt2yI8PBzh4eHw8vLSK/P6669DrVbjf//7n6xYshLD58+f448//kBKSgqEEBBC4P79+5g0aRJatGghTcR44sQJAEDZsmUBAHZ2dggNDcVPP/2EY8eOydoBIiKiV5VQKGUvlP89fvwY69atQ/HixQFAZ7oaLZVKBQD477//ZMWSPfjEw8MDI0aMQLdu3VC6dGkIIXD27Fl8+eWXmD59OqZMmYK4uDgAmnl4tKpVqwYvLy/88ssvaNCggdxuWEUhV8O3mUlKTgYAuDg7m7TeELWDs05Zp6wKZ5KclAQAcDfQP+02Z5m3yMnv0sOvSo/tfXPv+lWvQq5664p7Gr9FUWbxiZr3I/Nn6Vms5j9RxTw07UfHv5zdXqWZogpF3DXbYhI0bXi66baRnJggPXZ2Nb1PpC8xSfO36+qS/d+utRh73+MSjf/9Zy6jfvFZytxGRlltM0b7eU6MSdCJZ6/U/HhpP/cZy2Z+bU35njQmq30v8OSeDmZiaBPKlClj8ChhRlu3bgWQx7fEAzQTWf/vf/9DUFAQ3N3d4eHhgWbNmmHr1q347bffIISAWq02eB/lIkWK4MCBA3K7QERERFRgNWjQANu3bze6/eTJk/j666+hUCjQunVrWbFkHTH09fU1Okdh4cKFkZCQIM3InfFooVZKSgqePn0qpwtERESvLoVC3shijkq2CV999RWaNm2KK1euoEuXLgCA1NRUXL16FevXr8esWbOQlJSEQoUKYcqUKbJiyUoMPT09cfjwYTRu3FhnvVqtxvjx41GuXDmkp6cD0Jxyzig5ORnh4eFwddU/NUdEREQmUCo1i5z6lO/5+flh69at6NGjB0JCQiCE0DllrL1P8vr162VPBygrMezbty/atm2L9957T5qF++7du9i8eTPS09Ph7OyM/fv3A9AkkRlt374dKpUK1apVk9MFIiKiV5bcASQcfGI7atWqhUuXLmHr1q0ICwvDvXv3kJ6ejpIlS6J58+bo2bOn7OsLAZmJ4RdffIEDBw5g4cKF0ggZIQQqVqyI48ePY8KECdI9kgMCAtC9e3d8/vnnsLOzwxdffAGFQoFu3brJ3gkiIiKigurRo0coWbIk7O3t0a1bt1zNnWQlhnZ2dti+fTuWL1+OHTt2IDU1FY0aNcLw4cPh6emJ+fPno1WrVrhx4wa++OILXLt2DT179sTly5chhECVKlUwfPhwS+0LERHRq4Wjkl8JFSpUwMOHD/XOvuYG2dPVKJVK9O/fH/379ze4vUePHtLjoKAgnD59GkuWLMHt27cxcuRIg4NSckN6ejrWr1+P7du3w97eHn5+fhg8eDACAwOtEp+IiMjimBi+EhITE1GzZk2EhISgb9++cHPLvSnJZCeGplqyZAkGDhwIBwcHDB061FphAWgme3zvvfdQuXJlzJo1C6VLl7ZqfCIiIiJzOTk5YebMmTh58iRq1aqFtm3bYtiwYbkyTsMq/1WIjIzEp59+ao1Qen777Tc0adIEPXr0wJYtW5gUEhFRwcF7Jb8SpkyZgi5duiA0NBSXLl1CixYt8Omnn6Jly5ZYv369NAOMJcg+Yvjvv/9izZo1uHfvHpKSkqR5C7XS0tJw5coVJCUlGWkh9/zzzz/o0aMHBg0ahEmTJlk9PhERUW4SCoXMUcmcx9AWfPHFF9LjjANQrl27hgULFmDKlCl45513MHjwYAQEBMiKJSsxnDdvHj7++GMA0EsIMzN0X7/c9OTJE7zzzjsoWbIkZs6cadXYRERERLmtYsWK6Ny5M27cuIHQ0FB8++236NChA37//Xez25SVGE6dOhUA8Nprr+GDDz6QhlJndvLkSXz55ZdyQuXYmDFjEBUVhWnTpsFZxj04iYiI8i0OPnkl7Nu3D6+99pr0PCkpCatWrcIvv/yC8+fPQwgBJycn9OjRAx999JGsWLISw+TkZHh6emLXrl0G74Ws1aJFC/z0009yQuXIgwcPsHz5cjg5OaFChQoYMmQIrly5glu3bqFSpUoYN26czgtMRERkk3hLvFfCm2++iatXryIyMhLLly/HsmXLEBMTAyEESpcujaFDh2LQoEEoUqSI7FiyEsOePXti48aNWSaFWlevXpUTKkc2bdoEIQQcHR1x9OhRTJ06FUWLFsWff/6JXr164fXXX8fKlSvRu3dvg/VTUlKQkpIiPY+NjbVW14mIiIh0JCUlSYNnhRBQKBRo06YNPvroI7z55psWvVxP1jHkb7/9FgEBAdixY0e2ZevUqSMnVI78888/AIABAwZg/PjxKFq0KACgQ4cOmDlzJtRqNQYPHoxnz54ZrB8aGgpPT09p8ff3t1rfiYiITMZRya8MIQQ8PDwwYsQIXLlyBbt27cJbb71l8TEcsj4RkZGR+PXXXzF//nycPn0a9+7d01tu3bqFlStX4tq1a5bqc7YePHgAAChVqpTetj59+sDT0xMJCQnYsGGDwfpjx45FTEyMtNy/fz9X+0tERGQO7b2S5SxkG/r374/79+/jxx9/RIUKFXItjqxTyQ0aNMDTp08BADt37rRIhyxBe+rXw8NDb5uLiwtatWqFbdu24fLlywbrOzk55eiOLC4vBrckJyYAAJxdNTOSq9RZj9TOWMfFNeezmMcnaqYAKuSquWl2UnKyXp+cLXBD7fwkMUmzj64uugOK7H0r5UV3DHoelwgA8HZ3BQCkJMRJ21ye39GsU2umE3AqpLm9UTEPV5027JQv/weoUul+jjzdDL+nzgY+Q8kvpolKf9GEPdQAAKG00/QnDwZmGXsPzRGToNk/7Wui/XsCDL8e2cn8N2XJvhmS+TsjKw5Kw0cF3E3oq7EyhtrUfl6d3Nx11ht6bbTvpfaAhfbIhbZVhVoFAFAJpV4dO3WayXG0323G3pe4F3VMeS0KHIUSMOFyrizrU75XqlQpLFiwwOAAX1Np77ecHVmfiC+++AJCCDg4OMDHxwf+/v4ICAjQWYoXL271qWqKFSsGwPi1gX5+fgAAtVpttT4RERERmeP+/fuykkJAMxDYFLKiDBs2DEuWLMGJEyfg6upqtNz169cRHBwsJ1SO1K1bF4cOHcLFixcNbtdOX1OxYkWr9YmIiMjiOF0NmSi7+aa1ZH0iXFxc8NVXX8HR0dFomeTkZFSoUAFjxoyREypHevbsCQD4+++/Dd4m5vbt21AqlXj77bet1iciIiKL4+ATMpGpZ29lfyJ69OiR5eHNvXv34qOPPsJXX30lN5TJGjZsiHfeeQfh4eFYtWqVzranT5/ir7/+wuDBg1GmTBmr9YmIiIgov5N9r2QAiIuLQ0xMjN41e2q1GsWLF8e6deugVCoxZ84cS4QzydKlS3H9+nWMGDEC5cqVQ7NmzfD8+XO8//77qFWrFmbNmmW1vhAREeUKnkomC5OVGD558gRdu3bF4cOHsywnhMDKlSutmhh6enri4MGDGD9+PLp37w4nJyd4eXmhe/fuGD16dJanv4mIiGyBUChkTTkjeOcTykTWfxXGjRuHQ4cOwcHBAaVKlYKTk5PeqGQHBwdUr14dU6ZMsVSfTebl5YVffvkFjx49wp07d3DmzBmMGzeOSSEREZFMqampmD59OipVqoRy5cqhRYsWOHDgQI7befz4MYYMGYKyZcsiMDAQPXr0wL1797Kss2nTJtSrVw9ly5ZF9erVsXjx4jyPUVDISgx3796Nr7/+GnFxcbh37x4GDhyIv//+G7dv35aW0aNHY/DgwRgxYoSl+kxERERAng0+SUlJQfv27bFy5Ur8/fffuHnzJj7++GO0adMGGzduNLmd27dvo27duoiKisLFixdx48YN+Pr6om7dukZvpTtu3Dj0798fM2fOxK1bt7BhwwaMGzfOaJ5hjRgFiazEMD09HePHj4eDgwMAYNCgQVi0aJFOmdGjR+PLL79EWFiYnFBERESUmUIhfzFDSEgIwsLCsHTpUgQEaCbs79atG7p27Yp+/frh9u3b2bahUqnQrVs3pKamYunSpXBxcYGdnR1mzpwJZ2dndO/eHWlpaTp1tm3bhtDQUEycOFGal69y5cr45ptvMGfOHL07mlkjRkEjKzEsXry4znQwNWrUwKVLl6S7oQCa07leXl74/PPP5YQiIiKifODOnTv45ZdfULVqVdSvX19nW9++fZGYmIixY8dm287atWtx6tQpdOvWDW5uL+/+Y2dnh169euHcuXNYsmSJtF6tVuPLL7+EQqFAv379dNrq3bs37OzsMGrUKKhUKqvGKGhkJYbVq1dHjx49sHz5cpw6dQoA8Mknn6Bnz56Ijo4GACxZsgTh4eG4fv267M4SERFRBnlwKnn9+vVIT09H48aN9bY1aNAAALB161ZERkZm2c7q1asBwGA7DRs2BACds5AnTpzA9evXUa5cORQvXlynfKFChVCtWjU8fPhQ5xa91ohR0MhKDCdPnoy9e/diwIABaNKkCVQqFdq2bQtfX1+ULFkSRYsWxeDBgwG8/LAQERGRZQiFUvaSUzt27AAAlC1bVm+bt7c3SpUqhdTUVBw6dMhoG4mJidi/f7/RdrR3Szt9+rR0oCmruBnraC9ds0YMW2KVO5+UK1cOx48fx6effooffvgBdnZ2AIDFixeja9euiIqKghACDRs21Lv2kIiIiGTKgyOGp0+fBgD4+fkZ3O7l5QUAOHPmjNE2Ll++jOTkZKPtaNsQQuDcuXNmxbVGDFti6gE62RNcV6xYET/88IPOOmdnZ6xcuRJz584FALi7u+PGjRtyQxEREVEuiI2N1Xnu5OQEJycnvXLJycmIj48H8DJJyszT0xMAEBERYTTes2fPpMeG2tG2kbEdbR1T41ojRn6wZs0aHD58GH5+fvjwww9RpEgRg+Uy3wnOGIvc+cQYd3d3AEBaWhqCg4ORlJSUm+GIiIheKZoJrs2fpFpb19/fX2f9pEmTMHnyZL3yGa8bdHV1NdimUqk5Cqk9WmdIdu1o28jYjraOqXGtEcNaateuDQCwt7fHa6+9hunTpwMAevXqhQ0bNkiniefNm4cTJ07oXR+ZE7maGGrt3LkTqamp1ghFRET0yhBCs8ipDwD379+Hh4eHtN7Q0UIAOjeIMHbNmvb33tvb22jc7NrJmDNo29HWMTWuNWJYy5kzZ9CoUSOsWrUKgYGBAIAVK1Zg/fr1cHR0xMyZM9GyZUvMnTsXEyZMwMKFC82OleuJYUREBL744ovcDpMvOLu66Twv5OqSbR2h1FyXmZik+d+Hq4uzyfEyt+/irF83LlFzlNbdhL7Ygpy8PvmFk5v7yydumguX7XJS307zP/rUqMcAgOdKTXs+nm5G60THJwIAXFWaf9PtNP/7LVTIeB1jbXgVMvw/Z3OZ+h6mxrw8VePoWdRgGU83zec6NVozRZZSZLhf+4u/x5S4aACAk7tXtjG1f1NJL44GGPqbMiYmIUmnTw5Kzftm6G9Q+146F/YxuX37lBen+l68fvEv2nUQminDFClxUllHb1+duimxzwEATh6aH7PU5+EAANdM5YCXn1ft/jirUwAAhQy8fpnfy8xvbXJigqbNDJexKdI0nyuF6sWPcqZ4ji8+70kZjsgossl8VGoZmZEJzPl+tjUeHh46iaEx3t7ecHR0RGpqKhISEgyW0Q7kKFrU8N8tAPj4vPzsJyQk6JzWzdhGxnZ8fHxw6dIlk+NaI4a1KBQKrFu3Tjqym5aWhokTJ0KhUGDy5Mn4+OOPAQCzZ89GjRo1ZMXK8VWnV69exTfffIPPPvsMS5YsQUpKitGyly9fRpMmTXh9IRERUS5QCyF7yQk7OztUrVoVABAeHm6wzJMnTwAANWvWNNpOUFAQFC9OYxtqR9uGo6MjqlSpAkAzRV5O4lojhrWUKlVK53T/kiVLcP/+fQQEBOjME21vb4/Hjx/LipWjxHDhwoWoXr06Jk2ahDlz5mDw4MGoVq0arl27pld28eLFqFevnjR/4fDhw2V1lIiIiHQJCyw51a5dOwDAxYsX9bZFREQgJiYGbm5uaNasmdE2ChcuLE2Obagd7QGl5s2bSxNTZxU3Y50OHTpYLYa1FC5cWLp5yNOnTzF58mQoFApMmjRJuvscABw6dEjnSKg5TE4M//vvPwwfPhxpaWkQQkjLrVu30KNHD6lcTEwMunfvjiFDhiAxMRElS5bEn3/+iTlz5sjqKBEREeW9gQMHQqlU4sCBA3rbjhw5AgDo0qWL0esUtbTzHGfVTu/evaV1bdq0QWBgIC5fvqwz4hjQnOK9fPkyAgMDpYmrrRXDGoYOHYrXXnsNX375JRo2bIinT5+iYcOGOndnuXXrFgYMGCA7lsmJ4Zw5c6BWq9G6dWts374dly9fxu+//446derg3Llz2LNnDw4fPoyaNWti8+bNEEKgS5cuOH/+vJSBExERkeWohfwlpypUqIDBgwfj/PnzevP5LV++HC4uLpg0aZK0LiwsDA0aNMDs2bN1yvbt2xfBwcHYsGGDzijf1NRUrFu3DkFBQXjvvfek9fb29ggNDYVarZbuaKK1atUqqNVqTJ06VZpT2VoxrGHYsGH48MMPsWXLFkRERODNN9/E5s2bpe1DhgxBo0aNEB4ernPrP7MIE1WtWlW8++67euuTkpJE7dq1RdWqVYWDg4NQKBTC09NTLF++3NSm872YmBgBQMTExFi87cSkJJGYlCQSEjWLpcUmJIrYhESLt0vGRcYmiMjYBFltaN+32IREEfdiSXn+SKQ8fyQeRceLR9HxWdaPiksQUXEJIiX6mUiJfiY9zwlz6liStu8p0c+yLxv1RLO8eI1Snj+StiXHRonk2Kgcxdb+XeZEdHyiiI5/+bem/Zs29DeYuY+m0O6jlvZzkRwfK5LjY0VK5ENpySw5JlIkx0S+bMtIOUP7Y87rp5WUEK9ZEhOlRduXzK+BNp72tc+4aOsak9uf1Zx+P+fmb0bmGHcfPpL235zl7sNHZvU1Pj5e1KlTRzRo0EBERkYKtVotZs+eLRwdHcXGjRt1ynbq1EkAEIUKFdJr5/z586JIkSJi2LBhIi0tTSQkJIg+ffoIHx8fceXKFYOxhwwZIooUKSLOnj0rhBDiwIEDwsPDQ4wcOdJgeWvEKEhMHpX86NEjbNiwQW+9s7MzJk+ejM6dOwMAWrRogeXLlyMgIEBexkpERET5kpubG8LCwjBx4kTUrVsXSqUSQUFBOHHihDSAQ6tXr144cOAA3n//fb12goKCcOTIEYwZMwYVKlSAg4MD2rZti7Nnzxqdi2/evHkICgpCz549kZKSghIlSmDFihVSHpIXMQoShRCmDUlycXFBfHy8wcOn8fHx8Pb2RmhoKEaNGiWNAsqoe/fuBhNLWxAbGwtPT0/ExMSYNJw/J7RTMmjfBUtPh1DQpquxBc/jNNNxeLubP82L9n0DAO1fk2NKjKZ9M6arSXwxXU1Opp7JrelqTGXKdDVS2RfT1SDDdDWOL6aCycl0NVqWmK5GO8WJ6sUft6HpahxzMF2Ndh8dvTQ/ZJaYriZzOUP7o52uJievn5Z2upqMt11TpGna1U5Xo30NMk9Xk5F2uhpnF8PfY7n9Wc3pdDW5+ZuROcbtB49kxYiNjUWgX8lc7SvJl/nONNr3KiIiAuPGjZPufDJhwgQ0bdpUViyTrzFMTU1Fenq6wW2FChWShkwbSgpTUlLw119/md9LIiIiMsiaI5Ipb3h5eUmjrLW3G05KSkLLli2xZMkSXLp0CSdPnsTrr7+Os2fPyopl8qlkIQR++OEHlChRwuD2pKQkLFq0SG+28MTEROzcudPoZJFERERkHnMHkGSsT7Zh4MCBmDdvnnTmdtq0abh06RJKlCiB3bt3Izg4GGvWrMG4ceOwY8cOs+Pk6M4nEyZMyHL70KFDDa4XQhg8kkhEREREWbO3t8dPP/0kJYWRkZH48ccfoVAoMHfuXAQHa+6q1bt3b3z99dfyYuW0Y8WLF4e9venVEhMT9eYCIiIiIvnEizmF5dSn/C8gIAAuGa6x/fbbb5GQkIBGjRrhnXfekdar1Wrcu3dPViyTM7yaNWvi4MGDZs2Ps3fvXrRt2zbH9YiIiMg49YtFTn3K/0qUKIHjx4+jfv36OHLkCGbPng2FQoHQ0FCdcqtWrdKZr9EcJieGnTt3NnvSxNatW6NkyZJm1SUiIiJ6lX3zzTdo27YtKlasiDNnziA9PR2DBg1C8+bNpTJr1qzBRx99JDuWyYmh3NusbNmyRVZ9IiIi0iXEy+nOzK1P+V+rVq2wd+9ezJs3Dz4+PmjVqhVGjBghbR8+fDju37+Pli1byo5lcmLo7+8vK5D2RtZERERkGRyV/OqoU6cOFi9ebHCbdgobS8jR4BOyPFMm0E2J10xs7FTIM8ft2+XyaHD1rZMAAGXZurkaJztpT+9Ijx2Kl8mzfgDyJrbWik15eeVPqcIvLuF4MUGyw4sJtFMjHgAAhIvmc+Hk5i7V0U70m5Kg0jx30+2ToUm4M08SnFcTW2tlN6l1Roo049fUaCdmzsnfUU4mttbSTmytldVkyKZObK19jwFAoUrTbSNVM6G1dsJrZHj/055pLj53KKa5A5VdQqRmw4sJrrUTW2snus64Tuvl/rjo9MWxqJ9eP9PDrwIAVIWKAXg5kbZdrGYibwefci8LG5mkOvPrZ4h2om7thNkqe+cXPUwzVsUiLH3jASI5IiIi8ODBA9SsWVN67unpCQcHB4u0b/IE10RERJS/aEcly1nINly8eBGtWrVCiRIl0K5dO2n906dP0aNHD4SEhCAtTf5/kpgYEhER2Si1BRbK/y5duoSmTZvin3/+0Uvoq1atik2bNuHKlSto0qQJ4uLismgpe0wMiYiIiPKxcePGISkpCcOHD8f8+fPh7u6us12pVCIkJAQnT57EpEmTZMViYkhERGSjBF6OTDZryesdIJPs378f69atw88//4zBgwfDyclJr0yZMmUAABs2bJAVyyKJoRACp06dwu+//y6tu3btGh49emSJ5omIiMgAtRCyF8r/vL298fbbb2dZ5sqVKwA0t8uTQ3ZiuHfvXlSoUAH169fHoEGDpPXu7u6YMGECevTogefPn8sNQ0RERJkICyyU/5UqVQqxsbFGt6vVakydOhUAUL58eVmxZCWGhw8fRqdOnXDr1i29kU0lS5bEkiVL4Obmhjp16uDJkyeyOkpERET0Kvrwww/Rt29fJCQk6G2LiYlB7969ERYWBoVCgQ8//FBWLFmJ4cSJE+Hm5oYZM2bgzz//hKen/vxgI0aMwN27dzFu3Dg5oYiIiCgT7QTXchbK/95//30ULVoU5cuXxwcffICIiAiMHTsWXbp0gb+/PzZu3AgAePvtt/HJJ5/IiiVrguvjx49j165daNy4MQAYnFyxeHHN5Kt//PGHnFBERESUmcxb4vFcsu1YsmQJli5diu+//x4RERH49ttvpW3+/v4YOXIkPv30Uyhk3thCVmLo6+srJYXG/PfffwCA+Ph4OaGIiIiIXlkqlQr9+/dH//798fTpU9y7dw/p6ekoWbIkSpcubbE4sk4l+/v7ZznyODk5GV999RUUCgWCgoLkhCIiIqJM1BCyF8r/Pv30Uzg5OeG7774DoDkbW7duXTRs2NCiSSEgMzEcOXIkunbtiocPH+ptu379Otq2bYszZ84A0FxrSERERJYjaw5DuaehyWqWLVsGIYTsqWhMISsx7NSpE9q3b4+KFSuiRYsWePToEXr16oU6deqgSpUq+PfffwEAn3zyCd577z2LdJiIiIjoVdKlSxd4eHjgq6++yrbs5MmTZcWSPY/hxIkTsX37dri7uyM9PR3r16/H6dOnYWdnh2bNmmHLli348ccf5YYhIiKiTDgq+dWwYMECdO/eHcuXL8+y3N27dxEaGiorlqzBJ1qtWrVCq1atoFar8fz5c6Snp6NIkSIGRykTERGRZcg9HcxTybZh6NChEELgl19+wYoVK1ClShW9MomJidi/fz/S09NlxbJIYqilVCpRtGhRg9uWL1+ODz74wJLhiIiIiAq88PBw7N69W7qZyLFjx4yWzdPpakyVmJiIESNGMDE0IC4xCQDg7uoCAEhJiAMAOLm5vyyk0D3jn5ykqePs4qKzPjEpWXrs6uKs868lxCRo4nq6vYyrLFs3yzppj29Kjx18ygEAVHfPAgDsStewWN/ULl4Wa0uu5ETNzPTOrm45rqt9vUq9eK0yin/xWSni7goAiFFq/vid1SlG21MkxQAAUtM1ZRw9Nf9xSzVw/ihNrfvc0PudG7SfWzmfVYdiAQCA1OinRss4FdKfgN8SUp+HAwAcvX1118dE6DzXvvYAkB5+FQBg71spy7bt4l+2YVempm57XsWN1tO+HukPL2vilNI/ugAAysSol08y9T8zx6J+Rrdp9yPzD4qDgc9xZqr75wEAdv7BAICUWM0tVBXi5QdS+9o5eXjr1NX+rSlStXeDyP49lvP3mR/JHVnMUcm2YfTo0di9ezfGjh2LsmXLwt5eP31Tq9XYv38/Vq1aJSuWSYnhwIED9W55Z6q0tDQcP36c8xgSERFZGE8lvxpat26Ntm3bSvdDNqZ///7YsWOHrFgmJYY3b97EwYMHzU4OAfmHNomIiEiXWgioZfw2y6lL1vPHH3/g119/zbbc6tWrce7cOVmxTEoMhw8fjsuXL2PUqFEoWrSowUOYxqSlpeHAgQNYvXq12Z0kIiIielV9/vnnuHbtWrbl6tatiy1btuDjjz82O5ZJGV6XLl2wZ88ehISEmBVk0KBB2LNnj1l1iYiIyDCVWrPIqU8FR4kSJTB//vzcTwzt7Oyk27CY68GDB7LqExERkS6eSi6YDh48iKlTpyI1NRUA8PDhQ7z22mtZ1klOTsbly5dlTxVo8jlhT095I/p27NiBN998U1YbRERERAVds2bNsGTJEgwaNAi7du2CQqHA/v37s63n4OCA+fPny4ptlelqkpKS0LdvX0RHR1sjHBER0StBLQRUPGJYIJUqVQo7duzA4MGDsWvXriynoVEoFHBxcUHFihVlH8gzKTH85ptvsGHDBoSEhKBPnz7SelOmsUlLS8OJEycQFxcnq6OWsH37drz55ptYunQp+vXrl9fdISIikkVzWzs5iaEFO0MWp1QqsWDBAvTs2RMtWrSwSkyTEsMffvgB0dHRmDt3rk5imJNpbPJ6uprIyEh8+OGHedoHIiIiopyws7PD4sWLERsbK63z8PAAAERERGDcuHE4fPgw/Pz8MGHCBDRt2lRWPJMSw3Xr1mHo0KF4++23ddYPHToU58+fx6hRo1CiRAmDFzympqYiLCwM69evl9VRuYYOHcpJtomIqEDhqORXg7e35q4/FSpUQL9+/TBmzBgkJSWhZcuWuHz5MoQQePz4MV5//XUcPXoUNWqYf2cxkxLD2NhY3LlzBzNmzMAXX3whre/SpQt27dqF8ePHZ1n/ww8/xL59+8zupFyrV6/GkydP8M4772DlypV51g8iIiJL4qjkV4MQAoMGDcK8efNgZ2cHAJg2bRouXbqEEiVKYPfu3QgODsaaNWswbtw4WXc/UWZfBJg+fTrs7OwwZcoUnfUODg6YPn26SYEuXryY895ZwMOHDzFu3DgsX74cSqVJu0tERESUb9jb2+Onn36SksLIyEj8+OOPUCgUmDt3LoKDNfca7927N27duiUrlkmZ0pUrV/Dbb79h2LBhOuu1p5BNcerUqZz3zgIGDhyISZMmITAwME/iExER5RbVi1HJchbK/wICAuDi4iI9//bbb5GQkICGDRvinXfekdar1Wrcu3dPViyTEsOgoCB07NhRb/327dtNDjR8+HDTe2Uh8+bNg7OzMwYMGJCjeikpKYiNjdVZiIiI8hs1tCOTzVzyegfIJCVKlMDx48cBAEeOHMHs2bOhUCgQGhqqU27VqlVITk6WFcukxLBly5Y4fPiw2UGuXr0qO4PNqZs3b+K7777DwoULc1w3NDQUnp6e0uLv758LPSQiIpJHpRayF8r/vvnmG7Rt2xb169dHixYtkJqaioEDB6J58+ZSmTVr1uCjjz6SHcukwSchISFo0KABlEolihQpAkdHRygUCpNu0RIXF4eLFy9CpVLJ7qyp1Go13n//fcyaNQvFixfPcf2xY8di1KhR0vPY2Fgmh0RERJQnWrVqhb1792LevHnw8fFBq1atMGLECGn78OHDcf/+fbRs2VJ2LJMSw8KFC+Off/7ByJEjsW3bNunefQBMukULYN15DGfMmIHKlSujc+fOZtV3cnKCk5OThXtlmLuri85zJzd3/f5kWufs4qJXBgBcXZwt1zEASS8OR7s4a9r1dDMcNysOPuX01tmVNjyMXnVpv2Z71Zb62+6f12zzDzZY18ndK8d9y0r6A81gKXu/atmWTTu2DQDg0OBtAICzq5vZcQ29XlrO0S+OurtW0jxHOoCX+5725PbLdkporqlVqDVllNEPAQBJToWMtl/Mw1XnuTnvtzmMfW5TEl5Oiq/9G0iJi9Y8z/R+a/ddmZb4cqVXzv9TmFna45sAsn5fHL19AQCJSZq/F+3+2D3XvF92gbUBAOqbx19Wio8GACQd36lpP1DzObOv0VanbfWTO9JjuzI1s+zrs1kjpcf2zprvr8LDdAcHJq7XnHZy7TFWsyJD+3jxWVfd/k+n35k/3zlhyt9R5r9pJw/vHMdRpCbluI5WctLLusa+W/MzIXNUsinzEFP+UKdOHSxevNjgtrlz51osjsm3xCtZsiTWrVuHhIQE3Lp1C8+fP0efPn2wevVqo3WEEIiPj8fmzZuxYsUKi3Q4O+fOncOyZctw4sQJq8QjIiLKKyqhWeTUJ8oox/dKdnNzk4ZFu7i4mHSLlvbt22Pr1q05750ZfvrpJ1y9elWaFdyQ/v37o3///vjggw+wbNkyq/SLiIiIKL/LcWKY0eTJk00LYm+PNWvWyAllsuLFi6NSpUoGtz169AixsbHw8fGBp6cnSpYsaZU+ERER5QZOcE2WJisxzHjf5OwYmu4mN4SGhuoN39bq168fli9fjtDQUPTr188q/SEiIsotckcWc1QyZSYrMdQSQmDbtm3YsWMH7t27Bw8PDwQHB6N79+6oUqWKJUIQERERUS6TnRjeuXMHXbt2xenTpwG8HOG0ZcsWTJkyBX369MFPP/2EwoULyw1FREREGfBUMlmarMQwKioKLVq0wP379+Hk5ITGjRujSpUqKFy4MNLT03H//n3s2bMHHTt2RFhYGJydLTudChER0auMo5LJ0mQlhl9//TUeP36Mr776CiNHjoSnp6demZSUFAwYMACzZ8/Gl19+KSecbMuWLeMoZCIiIiIjTLolnjG//fYbli1bhsmTJxtMCgHNZNELFy7E+vXr5YQiIiKiTLSnkuUsRBnJOmKYkpKCXr16ZVvOzc0NMTExckIRERFRJmq1gFrGyGI5dalgkpUYFi1aFGq1Gkpl1gcew8LCEBcXl2UZIiIiyhm1zGsMmRdSZrJOJTdr1gwzZ840ul0IgQ0bNqBbt24WubEzEREREeUeWUcMQ0JCUKdOHfz111945513UKZMGSgUCjx8+BBXr17Fhg0b8OjRIzg5OWHSpEmW6jMRERGB09WQ5clKDP38/LBz50707NkTn376KRQKhc52IQS8vLywbt06VK1aVVZHiYiISJdKCKhkJHdy6lLBJOtUMgDUqVMH58+fx88//4zGjRvDy8sLjo6OqFKlCkJCQnDlyhW0bdvWEn0lIiIiolwk64hhUlISjh49iiZNmmD48OEYPny4pfpFRERE2eCoZLI0WYlh+/bt8e+//2LgwIFYuHChpfpU4MQkJEmPPd1c8rAnpkmNiQAAqBzcAABJyckAAGVqIgDAycP7Zdnn4QAAR29fAED6g4sAAHu/anrtZrUNAOyqtjTap3TPUpoymdYnbf9Fs75wMWnd4+1/AgA8AksCANybts22fSnO2d2aPtYwfJRbGw8AXN74CADg0OBtAEDqoQ0AAMcm3bONk53ncYnSY293VwCAMjFKp4yTmzsAICUuWvO8RKBeOw7Fy+g8t4t+CgDw8SoOwPBnMyVBM4OA2s4BAODy4o5FiUmaz4GrS/Z3MEpOTAAAOLu6ZVvWGEVqwssnL/bVyd1Lp0zas3sAAIcX+666/Z+0TXX3LABAOGpeP/uSFQzGSQ+/Kj1WO2viaD/PCqHOtp/pp3Zo6viU1awopbk/vIjV/B0lrg8FALj2GCvVSdk2CwDg8vZIAEDqwXUAgPBvhgEAfCfM0+zXi8+WKYqNnCU9vjJIU097I9KU/av1+gAA9nU66bWjCr8BALALrK3Zj/RUozG1fw/av4XMjP2tZ0V966SmH+7FpXUOxQIMl1VovhGcfcrpbTP2Gcz8XGS6BMrWqCDzzicW6wkVFLJOJV+8qPmhr1OnjkU6Q0RERER5R1Zi+NVXX8HDwwODBw/OtmybNm3khCIiIqJMeOcTsjRZieGIESMwd+5cfPLJJ4iMjDRabufOnQgLC5MTioiIiDLRjkqWsxBlJOsawypVqiAlJQUJCQlYsWIFihYtqlcmMTERz549kxOGiIiIiKxAVmLYtGlTLFmyRHoeHx9vtGzmOQ6JiIhIHrVaQMVRyWRBsk4lf/7553BxccGuXbtw/fp13L59W2+5efMmFi1aZKn+EhER0QuqF4mhnMWahBBYsGABgoODUa5cOdSrVw/btm0zq63Y2FiEhISgfPnyKFu2LDp27IgLFy5kWWffvn1o0aIFAgMDUblyZXz77bdQqYyPzTYnRnJyMnx8fKBQKHQWV1fXLC+7yy9kHTGsXLky3nvvPbz++utZlhs4cCCTQyIiIguTm9xZMzEUQqBv374ICwvDX3/9heDgYBw8eBDt2rXDN998g1GjRpnc1vPnz9GiRQt4e3vj+PHj8Pb2xg8//IAGDRpg165daNq0qV6d+fPnY8SIEVi1ahW6d++OR48eoWXLljh27Bg2btwIOzs72TEA4Ndff8WTJ0/01vfr1w9FihQxeR/ziqzEEAAWLFhgUrmjR4/KDUVEREQ26qeffsLq1auxfv16BAcHAwCaNWuGzz77DF988QUaN26Mhg0bmtTWgAEDcO3aNdy8eRPe3pq5dUeNGoVNmzahe/fuuHTpEry8vKTyp06dwscff4zBgweje3fNXLMlS5bEnDlz0K5dO3z//ff48ssvZcUAgPT0dPzwww/Yv38/SpQoobOtVKlSJr9WeSnHp5Lnzp2LoKAguLm5oXz58pg8eTJSUlJyo29ERESUBZVa7ulk6/QzLi4OU6ZMgaenJ9555x2dbX379oVarcZnn31mUluHDh3Cb7/9hjZt2sDPz0+vrUePHmH69Ok660NCQqBSqdC/f3+d9W3btkWJEiUwefJkREW9vImAOTEAYO3atahbty5atGiBypUr6yzu7u4m7V9ey1FiOGTIEHzyySe4fPkykpKScOvWLXz99dfo1KkT1GorfbqIiIgIgO1cY7hz505ERUWhXr16cHBw0NlWuXJleHl54dixYzh//ny2ba1atQoA0LhxY71t2iOOS5cula4dfPz4Mfbt2wdnZ2fUrl1br06DBg2QlJQktWtODEBzqvzbb7+Fq6sr9u7di7S0tGz3JT8yOTHcs2cPFi1aBCEEHB0dUadOHZQtq7kFVFhYGJYuXZprnSQiIiLbtWOH5taR2rwhI4VCgWrVNLdPNGXO4507dxptKygoCADw9OlT6e5sO3fuhBACAQEBetcRApBOa2eMndMYAPDbb7/h4sWLWLp0Kdq0aQNfX19MmTIFSUlJem3kZyYnhsuWLQMA9OzZEw8fPsSJEydw48YNHDhwACVKlMCGDRtyq49ERERkgK0cMTx9+jQA6J2W1dJeq3fmzJks24mMjMS9e/eMtuXg4AA3NzedtnIa25wYgOb0c/Xq1eHp6QkAiIiIwKRJk9CgQQOpPVtgcmJ4/PhxVKpUCStXrpQuwgSAJk2aYN68ebh27VqudJCIiIgMU8tMCrXzGMbGxuoslh47oL3RRebBGloZkylT2slJWzmNbU4MAPjuu+9w9uxZREZG4vDhw9KMLefPn0f79u2RkJCQ1a7lGyYnhs+ePcO7775r8DBsx44ds63PU81ERET5k7+/Pzw9PaUlNDTUYLkxY8bozc+X3dKvXz9p/j5XV1eD7SqVmnQkOTk5y35mnAfQ1LZyGtucGBnZ2dmhUaNG2L17N77//nsAwOXLlzFr1iwje5W/mJwYxsXFoXjx4ga3OTg4wN/fP8v648aNy1nPiIiIKEsqIfNU8ot7Jd+/fx8xMTHSMnbsWIPxpk+fDiFEjpZly5bB0dERgGaAhiGpqakAoHNG0hBtOzlpK6exzYlhzKhRo6TR1uvWrcuybH5hcmKoVqsRHh4OtVqtt0RGRsLf3x9CCL1tsbGxmDdvHp4+fZqb+0FERPTKsdQ1hh4eHjqLk5OTRfvp4+MDAEZPp0ZHRwMAihYtalI7OWkrp7HNiZGVr776Cu7u7rh582a2ZfODHE1wPXPmTMycOdPodlvJhq3N081Fepwao7kewdEz+w9TduISNSOd3F1d9LalxMcAAJwKeZrcXvKLkVMKheb/C4VetKttS+Wgf0jd0dtX57m9XzWj7We1zZj0s7sBAMoS5TQrPHT/d+byxkcAgIS130jrAkJ1L1tIPaQZGKW9COJy/7cAAFWW/q7fxxptAQA3PtZMgFr+Z91BVdp4Galu/wcAcGzSPeudyULies1pG9cemv+lu4mMpyc0r7s6IRaA/v/mnNy9sm0//cGLkXOFiumsz/jZ1BIv3n8XZ+ds2zXG2dXN7LpajoV99NalPdNcwO1QLEDnXy27wJdTUagu7QcA2JeuAQBIP/2X5nmt9jp17H0rvaxz5wwAINVOc8RAqU7Xjf9Y88Xu4FPuZf06nQz2P/XKKQAv39OMXN4eqenTKc1ITcdmPQEAvi/+NcXfVeoAAF6/rIlzsmNraVvJBuV1yjq17GNyu9rP8ZVBbwMAKi/eZrSsXRHd9yhl3woAwLN/DgIA/P6nueOVdj8B46+X+uZxAIDq+WMAgEPZutK29PCrmroZ3isAsEvX/J2kP9C8Lxm/YzJ/BlMS4gAATm66c8nJ+ZyT6apXr45bt24hPDzc4HbtnUJq1qyZZTv+/v4oXLgwoqKiEB4ejho1auhsj46Olo7maduqXr06AJgc25wYWSlcuDBq166tM4I5P8vRPIY5PXyccSEiIiLLspVRye3atQMAg8mREAK3bt0CALRv315ve2Zt27Y12taNGzcAAOXLl0f58uV1Yl++fNlgPqKt06FDB7NjZKdkyZIm39Ulr+XoiOHgwYPRuXNnoxdjGhIfH4+NGzdi5cqVOe4cERERGZeuFrCTkdylWykx7NmzJ0JCQnDkyBGkp6fD3v5l+nHhwgXExcWhefPmRqeUyWjw4MFYv349Dhw4gNGjR+tsO3LkCACgd+/e0rrKlSujWbNmOHjwIE6fPq0zybVarcbx48fh7u6OTp1eHs3OaYzsXLhwAT/88IPJ5fOSyYmhp6cn5s+fb1aQDh06YOvWrWbVJSIiIsPkHvWz1hFDLy8vjBs3DmPGjMH27dvx9ttvS9uWL18OhUKBadOm6dQ5f/48hg4diubNm+uMkn7ttdfQvn177N69G0+ePNG5J/GKFStQokQJjBw5Uqet6dOno1mzZlixYoVOYrhz5048e/YM06ZN05maxpwYz58/R+HChaFQKHTWb926FY0aNZKmr8nvTD6VPHz4cPODKJX4+OOPza5PREREtm306NHo2LEjRo8eLU34vHnzZsyZMwezZs1CkyZNdMovWLAAhw8fxvTp03WmkAGAJUuWwNfXF0OGDEFiYiLS09Px5Zdf4urVq9i0aZPe/IONGzfG1KlTMX/+fOzduxeA5jTxsGHD0LVrV4SEhOj1NycxtmzZgiJFiqBhw4Y4fvzF9bIqFVauXIkTJ05g7ty5cl8+qzH5iOHUqVNlBZJbn4iIiHSpZR4xVFvpiCGgmd9v27ZtCA0NRevWraFWqxEYGIi///4bzZs31yvfpUsXrF27Fk2bNtWbFsbX1xeHDx/G2LFjUa1aNdjb26Nhw4Y4e/YsAgMDDcYfM2YMypQpg9GjRyM2Nhaenp743//+h379+knzEpob47XXXkPv3r2xe/duNGvWDDVq1EDjxo3Ru3dv9O3bV8arZn05usaQiIiI8g+VeDkXobn1rcnBwQFfffUVvvrqq2zLtmrVSu9IYUY+Pj45vnlGz5490bOn6aP/TY3h5eWF1atX56gv+VWORiUTERERUcHFI4ZEREQ2ylYGn5DtYGJIRERko5gYkqXxVDIRERERAeARQyIiIpvFI4ZkaUwMiYiIbJRKqKFSq2XVJ8qIp5KJiIiICACPGBIREdksW5rgmmwDE0MiIiIbpVILKHmNIVkQE0Mrc/QsmuX25KQkAICzi0u2bbm7Gi/jVMgzy7rxiZo4qaqXXwre7q6adSlxBttKiYt+scY5275ppR7ZLD12bNTF5Hpa6tjnmro12mZZzq3XBONtJGj259bI3gCAKkt/BwDErZgslXF/f7JOnfI/b9B5rrpzRvPvo5vSuqiD+wEAxbp+kGXfbnzcXa/d26M1t0gKnLkSAODaY6xOHfuYhy+feGhuBWVv5DVIe6a556hDsQCjfVAma14De79qRsto319ndy8A+p9FVxfT3/fcIhw0fUh7rHkfHHzKGS1rV7UlAEB97RAAwL5We83zWyc1/7poPtf2JStIdbTrlHf+02x78Zon714CAHBuO9DkvmZ+Tw1RvXjNjX0RR80bIz0uPGw6ACBi9ucAgNcvn9Ipa+doJz0uNWmBSX289P6b0uOqK/4AAJzv1REAELx2JwAg9ZDmM+vYRPM5Ptu9g1Sn0qCuAICFhSsDAAZHXQEAPJu9CgDg96KcfZ1O2fZFpKUAAGKPHQQAFKn3lrRNkZpksI7Ti8+q6vldvW0pL747nF78/Ti5uWfbB1uUrgYUMpK7dF5iSJnwGkMiIiIiAsAjhkRERDaLp5LJ0pgYEhER2SgmhmRpPJVMRERERAB4xJCIiMhm8YghWRoTQyIiIhvFeQzJ0ngqmYiIiIgAFPDEUAiBBQsWoEaNGnB2doa3tzc6d+6MkydP5nXXiIiIZFO9OGIoZyHKqEAnhkOGDMHQoUNx7tw5pKenIyoqCr///jsaN26MLVu25HX3iIiIZBFCQKhlLIKJIekqsInhn3/+ia1bt2L58uWIjY1FcnIytm3bhmLFiiEtLQ39+/dHREREXneTiIiIKN8osInhsmXL8Pfff+P999+Hu7s77O3t0blzZ6xduxYAEBsbi99//z2Pe0lERGQ+tVrIXogyKrCJYbNmzVCzZk299a1bt0atWrUAAM+ePbNyr4iIiCxHCCF7IcqowCaGH3/8sdFtFSpUAACULl3aWt0hIiKyOFnXF75YiDIqsIlhViIiIuDk5IT27dvndVeIiIiI8o1XboLrxMREHDlyBIMGDYKXl5fBMikpKUhJSZGex8bGWql3REREppN7nSCvMaTMXrkjhosXL4a7uzumTJlitExoaCg8PT2lxd/f34o9JCIiMo1Qy1+IMlKIV+jK08jISFStWhXLly/P8jSyoSOG/v7+iImJgYeHR672MTkxAQDg7OqWq3FMkZiUDABwdXHO456Y507IBwCAMt8uN7j9fK+O0uPgtTt1tk111VyHOj7xutH299dtBABoefKIwe1/ldMMcgp+v760rtSkBdl122Tqm8c1/0brD6Kyr9MJAJD+SNN/+5IVdLanPbkNAHAoEWix/lhDbvQ7Pfyq9NjetxIAIGXPUs1zv/IAALvKzSwWDwAS1n4DAFDYOwIAlO5eAADn9oMtGiezAw0aAwCaHzsMALg1sre0reysNTplk7bNAgC4vD0y23aHKsoAAOaLOyb3RX3jKABAWb6hyXXSH1wEANj7VTO5Tl6IjY2Fp6dnrv5maGM0mPwH7J3N/71IT07AsclvWuX3jWzDK3Uq+cMPP8QXX3yR7bWFTk5OcHJyslKviIiIzCN3ZPErdGyITPTKJIbTpk1DQEAARo8endddISIisgheY0iW9kokhqtWrcLVq1exbNmyvO4KERERUb5V4BPDLVu2YNu2bVi3bh0UCoXONpVKhfDwcA4uISIimyR3LkLOY0iZFehRydu2bcPy5cuxevVq2Nvr5sCPHz9Gv379cOvWrTzqHRERkUxyJ7dmYkiZFNgjhqtXr0a/fv1QqFAhlCpVSmdbamoq4uLi4O/vjxUrVuRRD4mIiIjylwKZGO7YsQN9+/aFEALR0dFGy/Xq1Uvv9DIREZGtUAsBhYyRxWqOSqZMCmRi2KlTJ6jVnLWTiIgKNiFkXmPIxJAyKZCJIRER0auAg0/I0gr04BMiIiIiMh2PGBIREdkotRpQyJrg2oKdoQKBiSEREZGN4i3xyNJ4KpmIiIiIAPCIIRERkc0Sas0ipz5RRkwMiYiIbJRaLWReY8hTyaSLp5KJiIiICACPGFpdXGISAMDd1cXgdmdXN7PbTn0eLj129PY1u52UuGgAgKu7l9ltmEJ1YS8AwC6oda607+juqvM8Zf9qAIBTyz4AgOC1O43WHZ94Pdv2W548kuX29jdPZ9vGvur1AQCBbStp/p25Mts6WspymrpZ/e/OvmQFAID61kkAQPrDGwAAx2Y9TY6TnziUCMxye/LuJdJj57YDTWrT3reS3jqnNv11nqvunAEA2JWpqXn+4rMLmPf5des1AQCQsPYbAIBz+8E5bsMczY8d1u1HySJGy7q8PTLb9uJWTAYAzBd3ctwXZfmGAID0s7sBAPY12mZbx96vWo7jFHScx5AsjYkhERGRjWJiSJbGU8lEREREBIBHDImIiGyWWggoZMxFqOY8hpQJE0MiIiIbxVPJZGlMDImIiGyUEDITQx4xpEx4jSERERERAeARQyIiIpsl1ELWJNU8lUyZMTEkIiKyUUIIWaeDeSqZMuOpZCIiIiICwCOGRERENoujksnSmBgSERHZKLVaADKSOznXJ1LBxFPJRERERASARwyJiIhsllCrINQqWfWJMmJiSEREZKOYGJKl8VQyEREREQHgEUMiIiKbJdRqmUcM1RbsDRUECsHZLbMVGxsLT09PxMTEwMPDI6+7Q0RE+Zg1fjO0MXy7z4XS0cXsdtSpSQjfMJy/byThEUMiIiIbJYTMawwFrzEkXbzGkIiIiIgA8IghERGRzeKoZLI0JoZEREQ2iokhWRpPJRMREZFVCCGwYMECBAcHo1y5cqhXrx62bdtmVluxsbEICQlB+fLlUbZsWXTs2BEXLlzIss6+ffvQokULBAYGonLlyvj222+hUmWdHKtUKqxevRpBQUHYv39/tv0yJ0Z+wsSQiIjIRmmPGMpZrNZXIdC3b19MmTIFa9aswc2bN/HDDz+gd+/e+OGHH3LU1vPnz9GkSRMcPXoUx48fx61bt9CmTRs0aNAA//77r8E68+fPR/v27fHRRx/h9u3bCAsLw6+//opu3boZTNzS09OxaNEiBAcH47333sPFixez7VdOY+RHTAyJiIhslHYeQ/MX681j+NNPP2H16tWYNWsWgoODAQDNmjXDZ599hi+++AJHjx41ua0BAwbg2rVrWL16Nby9vQEAo0aNQo0aNdC9e3dER0frlD916hQ+/vhjDBo0CN27dwcAlCxZEnPmzMHWrVvx/fffG4zTrl07HDp0CF5eXtn2ydwY+Q0TQyIiIspVcXFxmDJlCjw9PfHOO+/obOvbty/UajU+++wzk9o6dOgQfvvtN7Rp0wZ+fn56bT169AjTp0/XWR8SEgKVSoX+/fvrrG/bti1KlCiByZMnIyoqSmebvb09AgICULhwYVSsWDHbfpkTIz9iYkhERGSj1GqV7MUadu7ciaioKNSrVw8ODg462ypXrgwvLy8cO3YM58+fz7atVatWAQAaN26st61hw4YAgKVLl0qnbh8/fox9+/bB2dkZtWvX1qvToEEDJCUlSe0a4uzsnGWfLBEjv2BiSEREZKNs5RrDHTt2AADKli2rt02hUKBatWoAgLCwsGzb2rlzp9G2goKCAABPnz6VrgncuXMnhBAICAiAnZ2dXh3tae2sYisUimz7JDdGfsHEkIiIiHLV6dOnAUDv1K+W9hq+M2fOZNlOZGQk7t27Z7QtBwcHuLm56bRlqdhZsUYMa+E8hkRERDbKUvMYxsbG6qx3cnKCk5OTrL5l9OzZMwAwOojD09MTABAREWFSO9m1lZCQILVlqdim9Cs3Y1gLjxgSERHZKpUKQsaCF9fh+fv7w9PTU1pCQ0MNhhszZgwUCkWOln79+iEyMhIA4OrqarBdpVKTjiQnJ2e5u9p2ctKWpWKb0q/cjGEtPGJIRERko4RQAXKOGApN3fv378PDw0Nab+xo4fTp0/VG/Jpi48aNSE9PhxDC4PbU1FQAkKaeMcbR0VF6bGpb2jpyY5vSr9yMYS1MDImIiF5xHh4eOomhpfn4+ODWrVtISEgwuF0772DRokWzbUfL1La0deTGNqVfuRnDWngqmYiIyEbZygTX1atXBwCEh4cb3P7kyRMAQM2aNbNsx9/fH4ULFzbaVnR0tHR0TtuWpWJnxRoxrIWJIRERkY2ylelq2rVrBwAGbysnhMCtW7cAAO3bt8+2rbZt2xpt68aNGwCA8uXLo3z58jqxL1++bPBUr7ZOhw4dso1tjDViWAsTQyIiIspVPXv2hIeHB44cOYL09HSdbRcuXEBcXByaN29udLqXjAYPHgwAOHDggN62I0eOAAB69+4tratcuTKaNWuGuLg4aVoZLbVajePHj8Pd3R2dOnXK8X5ZM4a1MDEkIiKyUZpTyfIWa/Dy8sK4cePw/PlzbN++XWfb8uXLoVAoMG3aNJ3158+fR5MmTTB27Fid9a+99hrat2+P3bt3S6dotVasWIESJUpg5MiROuunT58OpVKJFStW6KzfuXMnnj17hrFjx2Z5P2RtMqu9m4ohcmPkF0wMiYiIbJStnEoGgNGjR6Njx44YPXq0NEn15s2bMWfOHMyaNQtNmjTRKb9gwQIcPnwY06dP15mmBgCWLFkCX19fDBkyBImJiUhPT8eXX36Jq1evYtOmTXoJWOPGjTF16lTMnz8fe/fuBaA5FT1s2DB07doVISEhRvsdGRkpnQo+evSo0XJyYuQnHJVMREREuc7Ozg7btm1DaGgoWrduDbVajcDAQPz9999o3ry5XvkuXbpg7dq1aNq0qd40L76+vjh8+DDGjh2LatWqwd7eHg0bNsTZs2cRGBhoMP6YMWNQpkwZjB49GrGxsfD09MT//vc/9OvXT5pnMLOmTZvi5MmTSElJAQBMmDABs2fPxpIlS/DGG29YJEZ+oxDGJt0hifbNjYmJydXh/EREZPus8ZuhjeHW6GMo7M2/Q4lIT0HCkZ/5+0YSHjEkIiKyUWq1CgoL3BKPSMs2jmvKkJqaiunTp6NSpUooV64cWrRoYXAkExEREdGrrkAfMUxJSUGHDh3w5MkT/P333wgICMDGjRvRpk0brF69Gt26dcvrLhIREZlNqNSAQsYRQ5V1RiWT7SjQiWFISAjCwsJw7NgxBAQEAAC6deuGrVu3ol+/fqhbt67Ri1SJiIjyO0vdK5lIq8CeSr5z5w5++eUXVK1aFfXr19fZ1rdvXyQmJurNjURERGRLbGm6GrINBTYxXL9+PdLT09G4cWO9bQ0aNAAAbN26VW9uJCIiIqJXVYFNDHfs2AEAKFu2rN42b29vlCpVCqmpqTh06JC1u0ZERGQRPGJIllZgE0PtvQqN3XdROyv6mTNnrNQjIiIiy2JiSJZWIAefJCcnIz4+HgCM3pfQ09MTABAREaG3LSUlRZrlHABiYmIAaCYUJSIiyor2t8Iq949QpUFWFFWapXpCBUSBTAwzXjfo6upqsIz21jTJycl620JDQ/G///1Pb72/v7+FekhERAVdXFycdBDC0hwdHeHj44PHlzbIbsvHxweOjo4W6BUVBAUyMcz4ATf2P7bU1FQA0Lv/IgCMHTsWo0aNkp6r1Wo8f/4cRYoUgUKhsHBvLS82Nhb+/v64f/9+gbjFEfcnf+P+5G/cH+sTQiAuLg6+vr65FsPZ2Rm3b9+WfsvkcHR0hLOzswV6RQVBgUwMvb294ejoiNTUVCQkJBgsEx0dDQAoWrSo3jYnJyc4Oenee9LYKen8zMPDI99+cZqD+5O/cX/yN+6PdeXWkcKMnJ2dmdCRxRXIwSd2dnaoWrUqACA8PNxgmSdPngAAatasaa1uEREREeVrBTIxBIB27doBAC5evKi3LSIiAjExMXBzc0OzZs2s3TUiIiKifKnAJoYDBw6EUqnEgQMH9LYdOXIEANClSxe9U8YFgZOTEyZNmlRg9o37k79xf/I37g8R5YRCWGU8fd4YNmwY5s+fj9OnT+ucMu7atSt27tyJCxcuGJwAm4iIiOhVVKATw4SEBLRo0QL29vbYuXMnChcujJ9//hmjR4/G6tWr0bVr17zuIhEREVG+USBHJWu5ubkhLCwMEydORN26daFUKhEUFIQTJ06gevXqed09IiIionylQB8xJCIiIiLTFdjBJ0RERESUM0wM80BqaiqmT5+OSpUqoVy5cmjRooXB0dMAcPPmTfTp0wfFihWDk5MTqlSpgqlTp+rcyzm3Ymv9999/6NSpEwIDA1G+fHmEhIQgKSlJVptaKpUKDRs2RJkyZQrE/ly/fh1jx47FW2+9hU8//RS//vqrTe1PYmIiJk6ciEqVKsHPzw8+Pj7o1KkTDh8+nKP9yM390dqxYwcaN26MZcuWWaQ9W9gfW/g+yMn+ZJYfvw+IXjmCrCo5OVm0atVKVK1aVdy9e1cIIcSGDRuEg4OD2LBhg07ZCxcuiMKFCwsAwt7eXgCQlqZNm4rExMRci631+++/CycnJ/H9998LIYSIjo4WTZo0EY0aNRLx8fFmtZnR119/LQCI0qVL52hf8tv+pKamijFjxojixYuLFStWCJVKZXP7k5SUJOrXry8qVaokzp07J4QQIiUlRYwaNUrY2dmJbdu25fn+CCHE+vXrRf369aW/haVLlxrtgynt2cr+2ML3QU72x5D89n1A9CpiYmhln376qQAgjh07prO+V69ewtXVVdy6dUtaV79+fdG1a1dx8eJFoVarxYMHD0Tfvn2lL9yxY8fmWmwhhLh3755wd3cXHTp00Fl/5coVoVAoxLBhw3LcZkanT58W3t7eZv8Q5Jf9SUhIEK1btxaBgYFZ7m9+35+ZM2cKAOLw4cM6ZVUqlShfvrwoWbJkjhLe3NgfIYS4efOmSE5OFhUqVMgy8TC1PVvZH1v4PsjJ/mSWH78PiF5FTAyt6Pbt28Le3l5UrVpVb9vOnTsFANGjRw8hhBBnzpwR3bt3F2q1WqecWq0WzZs3FwCEv79/rsTWGjhwoABg8H/b2iMCOW1TKzk5WQQHB4uFCxea9UOQX/YnPT1dvPHGG8LDw0PcuHEjR/uQ3/anY8eOAoDBI09du3YVAMSTJ0/ybH8UCoW4dOmStK579+5ZJh45bS8/748tfB/k9P3JKD9+H+Tk80FUkPAaQytav3490tPT0bhxY71tDRo0AABs3boVkZGRePDgAb799lsoFAqdcgqFAp9//jkA4NmzZ7kSGwDS0tKwceNGADBYp2HDhgCQozYzmjhxIlq0aIHXX3/d5H3Ij/szceJEbN++Hd9++y3KlStn1r7kl/1xc3MDABw9elSvfFxcHHx9fVGsWLE82x8hBBYvXiytc3Z2NhrfnPby8/7YwvdBTvYns/z4fZCTzwdRQcLE0Ip27NgBAAbvtuLt7Y1SpUohNTUVhw4dQqdOnYxegF2hQgUAQOnSpfW2zZw5E+7u7vjuu+/Mjg0ABw8eRGxsLJycnFCqVCm9OsHBwdJjU9vU+vfff7Fjxw7MmDHD4P7Zyv5cv34d33//Pfz8/DBw4MBs9yW/789bb70FQHPHoISEBKlsZGQkDh48iO+++04vMbH2/oSFhUnrMvclI3Pay8/7YyvfB6buT0b5+fsg8+eD6FXAxNCKTp8+DQDw8/MzuN3LywsAcObMmSzbiYiIAAB07txZb9vy5csRHx+PFStWyIqtLW/oSzNjeQDw9fU1qU1AczeaQYMGYdmyZXBxcTFYL6P8vD8zZsxAamoq3n77bSxevBidO3dGhQoVULlyZYSEhCA+Pt6m9qd3795o3749rl69ivbt2yMqKgpqtRrDhw/HL7/8gt69e+f5/pw/fx4qlcpgGUPxc9peft2frOSn74Oc7k9+/z6wxPtDZGuYGFpJcnKylChk/NHOyNPTE8DLL3pj9uzZA2dnZ3zyySd628aPH486depg/PjxsmJrT0sZK5/xS9zBwcGkNgFg1KhR6NWrF+rVq2ewTmb5dX/S09OxdetWAMDZs2cRFBSELVu24NixY6hVqxZmzJiBli1bIjEx0Sb2BwCUSiU2b96Mdu3a4d9//0WTJk0wZMgQTJgwAf369TPYhrX2R1s+PT0dMTExBstkZG57+XV/spIfvg/M3Z/8+n1gyfeHyNYU6Fvi5ScZr0tzdXU1WEap1OTpycnJRttJSUnBokWLMGHCBIP/O+7Zsyd69uwpO7a2jrHyGY+G2dnZmdTmX3/9hTNnzuCXX34xWN6Q/Lo/Fy9elNr8/fffpR8Yb29vLFu2DMePH8epU6cwefJknVNk+XV/MsZfu3Ytxo8fj4MHD2Lx4sW4ffs2Nm7ciMKFC+u1Ya390ZbP3F9jzG0vv+6PMfnl+8Cc/cnP3weWen+IbBGPGFqJo6Oj9FgYuQthamoqAE1yYcz06dNRvnx5jBkzJldja+sYK59xvbu7e7ZtRkVF4ZNPPsGKFStgby/v/yP5YX8ePHgAQPPDkvmog5OTk3TNoSmT++aH/dG6e/cuRowYgZ9++gn//vsvmjdvjr1796Jp06Z4+vRptvuSW/ujLZ+5v9n1wRLt5Yf9MSa/fB/kdH/y+/eBpd4fIlvExNBKvL29pS+jjBf2ZxQdHQ0AKFq0qMHtJ06cwLp167Bp0yajR4EsFdvHxyfL8hmvu0lLS8u2zWHDhuHTTz9FpUqVTO63Mflhf2JjYwEAHh4eBst26tQJgOaUVXaXBuSH/QE0p9latmyJPn36wMHBAZ6envjzzz/RrFkzXLp0CX379s1yP3Jzf7Tl3dzcTBrtasn28sP+GJKfvg9yuj/5/fvAEu8Pka1iYmgldnZ2qFq1KgAgPDzcYJknT54AAGrWrKm37fHjxxg2bBh+++03FC9ePNdjV69ePcvyGafGyK5NHx8frF+/Hp988gkUCoXOEhgYCEBzpEq77s6dO/l6f2rWrClN26JNEDPLeFpPrVYbLKOVH/YHACZMmIDw8HC0adNGKuPq6opt27bB398fu3fvNunWeLmxP1n9bRhiyfbyw/5klt++D3KyPw8ePMj33wdy3x8iW8bE0IratWsHALh48aLetoiICMTExMDNzQ3NmjXT2RYXF4eePXti4cKFqFixolVit2rVCo6Ojnj69KnBI143btyQHpvSZqVKlQwu2ukl7O3tpXXGBkvkp/2pXbs27OzskJiYaPCHS3uUoXDhwibN/ZfX+wMAW7Zsgbe3t96pPW9vb2muvOPHj2e7L7m5Px06dDApvqXby+v9ySg/fx+Ysj8KhcJmvg/MeX+IbJ715tKma9euCaVSKYKDg/W2/f777wKAeP/993XWx8fHi3bt2ol//vnHYJum3oLNnNja221t3rxZr07t2rWFQqHIcZuZ3b5926w7HeSH/WnXrp0AIH755Re98ufPnxcARP/+/W1mfzw9PYVSqRQpKSl65Xfs2GF0X621P0qlUly7dk1a98EHH2R5Z42ctpff90eI/P19YM7+ZJafvg9y8vkgKkiYGFrZ0KFDBQBx+vRpnfVdunQRLi4u4ubNm9K6+Ph40b59e7Fr1y69dtRqtdixY4d45513dNavW7dO1K1b1+BtnnISWwghbty4Idzc3ETnzp111muTnsGDB+e4zcyy+yHIz/tz6tQpYW9vLypWrCiSk5N1yn/++ecG78+an/dH+0O+cuVKvb5NnDhRODo6irt37+bp/mTUp08fAUAsXrxYL7Y57eX3/bGF74Oc7I8h+e37gOhVxMTQyuLj40WdOnVEgwYNRGRkpFCr1WL27NnC0dFRbNy4USoXFRUlGjVqJBwdHUWRIkV0lsKFCwsHBwcBQCxatEin/WrVqgkAIigoyOzYGa1atUrY29tLycLdu3dFjRo1RJMmTURCQoJZbWaU3Q9Bft+fxYsXCwCid+/eIiEhQQghxObNm0WhQoXEli1bbGp/nj59KipUqCCKFi0qdu3aJdRqtVCr1WLdunXC1dVVzJ8/P8/3RysxMVEEBwcLAGLQoEEG28pJe/l9f2zl+8DU/TEmv30fEL2KmBjmgdjYWPHpp5+KwMBAUa5cOdG5c2dx9uxZnTL169cXALJcnJycRFRUlE69adOmCTc3NzF9+nSzY2e2e/du0ahRIxEYGCiqVasmZs6cqXO60Zw2tbL7IbCF/dm+fbto2LCh8PLyElWqVBFvvfWWOHHihE3uT2RkpBg1apQIDAwUxYoVE6VKlRLt27cX+/btyzf706NHD+Hq6qrzt+Dt7S3mzZtnVnu2sD+29H2Q0/cno/z4fUD0qlEIYWQiJyIiIiJ6pXBUMhEREREBYGJIRERERC8wMSQiIiIiAEwMiYiIiOgFJoZEREREBICJIRERERG9wMSQiIiIiAAwMSQiIiKiF5gYEhEREREAJoZERERE9AITQyICACxduhQeHh5YunRpXnfFJgkhsGDBAtSoUQPOzs7w9vZG586dcfLkSaN1/vvvP3Tq1AmBgYEoX748QkJCkJSUZLH2cxrDXMeOHcP7779v0Tat6cmTJwgJCUH16tVNKr9161YoFAq9pUePHrncUyIryNtbNRNRftGxY0cBQHTq1Cmvu2KTPvzwQwFAABB2dnbSYwcHB7F582a98r///rtwcnIS33//vRBCiOjoaNGkSRPRqFEjER8fL7t9c2KYq0ePHsLJyUk8efLEYm1aw/3798XIkSOFi4uLACBKly5tUr169epJr3/G5ejRo7nbYSIrYGJI9Ar6559/9Nbt2rVL1KtXT+zatSsPemTbdu7cKYoWLSqWL18uYmNjRVpamti2bZsoVqyYACA8PDzEs2fPpPL37t0T7u7uokOHDjrtXLlyRSgUCjFs2DBZ7ZsTw1wPHjwQ9vb2AoD4+uuvLdKmtTx8+FAkJSWJX3/91eTE8O+//xZNmzYVly9f1lmuXr2a+x0msgImhkSvGJVKJYKCgvK6GwVK9+7dxenTp/XW79mzRzqatGTJEmn9wIEDBQCxYcMGvTr169cXCoVCXLp0yez2zYlhrjFjxgh3d3cBQJQqVUqkpaXJbtPaLl26ZHJi+Nprr4k///wz9ztFlEd4jSHRKyY0NBQXLlzI624UKM2aNUPNmjX11rdu3Rq1atUCADx79gwAkJaWho0bNwIAGjdurFenYcOGEEJg8eLFZrVvbgxzJCUlYfHixdi0aRMcHBzw8OFDbNmyRVabecHZ2dmkcsePH8fhw4dx9+5dXLlyJZd7RZQ3mBgSvUJ+/fVXTJw4Ma+7UeB8/PHHRrdVqFABAFC6dGkAwMGDBxEbGwsnJyeUKlVKr3xwcDAAICwszKz2zY1hjpUrV6JOnTpo27YtunbtCgCYM2eO0fKfffYZnJycpMEa+/fvl7YFBQVJ68uUKWOw/qJFi1CrVi34+flBqVRK5T09PVGhQgUMGTLErP1QKBQmlZs2bRqSk5MxdOhQVKlSBfXq1cOuXbvMikmUXzExJHpFzJgxA6GhoRBCAADKly+P8uXLY9y4cUhISMCiRYtQu3ZtTJ48WadebGwspkyZAi8vLwDA8+fP0b9/f3h4eKB06dJYtmyZVHb//v1o0qQJXF1dUatWLRw5csRgXy5duoRevXohKCgIhQoVQvXq1fHrr7/mxm7nuYiICDg5OaF9+/YAgNOnTwOAwYQNgPQ6nz9/HiqVKsft50YMY2bPno3PP/8cADBy5EgAwL///oszZ84YLP/jjz/i9u3bKFq0qN62CxcuYOXKlUZjDR48GIMHD8a4cePw4MEDXL9+HQEBAQCAMmXK4Pr161iwYIHZ+5KdyMhIREREoFKlSrCzswMAnDx5Eu3bt8fIkSOlvysim5e3Z7KJyNrw4po0rUuXLolevXoJZ2dnAUBMmjRJ2rZw4UIRGBgo1YmMjBRVq1YVvr6+UnmlUimOHDkitm7dKpycnIS/v780arZIkSIiOjpaJ/5ff/0lypQpIw4ePCiE0IwMrVmzpgAgJk+ebJXXwFoSEhKEi4uL+Oijj6R1ISEhAoCoXbu2wTq7d+/Web1z2r6lYxize/duERwcrLOucePGAoAYMGBAlnWbNWsmAIiwsDCd9Tdu3DB4rd++ffsEANGmTRud9UuWLJH248GDB2bthxBC3L59O0ejkqOiosSvv/4qSpYsKcWfOHGi2fGJ8hMeMSR6xVWpUgVr1qxBnz599Lb17dsX27dvl55PnDgRCxcuxMOHD/Hs2TM0atQIarUa48ePx9q1a3Ht2jXcu3cPd+7cgZ+fHyIjI/Hnn39K9SMjI9G7d29Mnz4dTZs2BQD4+flh4cKFAIApU6bgxo0bubzH1rN48WK4u7tjypQp0rrIyEgAgKurq8E6SuXLr+Xk5OQct2/pGMb89NNPGDVqlM66zz77DACwZs0aqQ/Zxc9IeyQuM+1nUHsKXKtbt27S44cPH2bbZ0vx8vJC//79ceXKFTRp0gQAMH36dNy+fdtqfSDKLUwMiQgAUKJECb11zs7OKFeunPR8+vTp0g9hoUKFMHz4cABAdHQ01q1bJ53a8/Pzw5tvvgkAuHfvnlT/119/RVxcHDp37qwTRzuxsFqtxtatWy24V3knMjISU6dOxfLly+Ht7S2td3R0BACjpx5TU1Olxxnrmdq+JWMYc/36dZw+fRq9e/fWWf/uu+8iICAAycnJsge2ZJSxvxm5u7ujcOHCAAB7e3uLxTOVh4cHdu7cidKlSyMtLQ2bN2+2eh+ILI2JIREBABwcHAyud3Jykh67u7vrbPP19ZXWZ76AX3sdWca7bOzbtw9CCNSsWROVK1eWlho1aqBIkSIoUqQInj59apH9yWsffvghvvjiC51r/wDAx8cHAJCQkGCwXnR0NADAzc0ty9Gyxto3N0bRokUNLocOHdKr/9NPPyE1NRXVq1fXeR+rVasmvd9z586Vdf1iRo0aNQIA3L17V2+bEAKOjo6oXLmytK5z584G92XGjBkW6U9GHh4emDBhAgDg5s2bFm+fyNqs/18sIiowjJ0SzLgt41Gre/fuwdvbu8BP9TFt2jQEBARg9OjRetu0R0fDw8MN1n3y5AkAGJyexpT2zY1h7NRvWlqazvOYmBisW7cOly9fRrFixfTKP336FP7+/rh37x5+//13vPPOO0b3w1TdunX7f3v3F9LUG8YB/KuuhS5lZW7tImOB3gR2U5Bl0UCzUUIE1aVaUVQXIUSQkUF/yCgwSOlGG+oSKggvCsY0Cgv/QEaQRSJZMYpww1zmtNr2/C50B09nmak/nfb9wLk573ve9z1jcB7Oed/nRVVVFdxuNwYGBpTFMx6PBwMDAygsLFR9Nvf7/VHvJxAITHss0eTm5gIYfYtONN/xjSERzZpgMAiv16u8sVqInE4nuru7UVFREbXcZrNBr9ejr68PPp9PUx6ZY2m326fU/lT7kNENDzTH1q1bVddWV1fDbrdHDQoBwGQyYdeuXQBGVy1HM9n0MBGLFi2C2+1GZmYmDh8+jMHBQfj9fhw7dgyrV6/GlStXVPUfP34c9V5+XXE/UywWC4DR/JBE8x0DQyKaNRaLBSKCO3fuRC0XEVVuu/nm3r17aGxsRE1NjSb4CYVC8Hg8SElJwb59+wAALS0tmjba2toQHx+PvXv3Tql9ANPqYyKhUAiVlZU4cuTIhPUOHToEYDRAe/nypaY8MTERwGjqo/H8fj+A6HMKr1+/jo8fPyIQCCArKwsbNmxAeno62trafhukzpauri5YrVbs3LlzTsdBNBMYGBLRrNmyZQsA4PTp0+jt7dWU19bW/vbzZ6xrbGxEbW0tbt26pVkI8fnzZxQVFSn3fPbsWRgMBtTV1anqdXV14fnz5zh48KCSuHoq7U+1jz9paGiAiETdTWU8m82mLGq5dOmSpjySjNvhcCirol0ul7LK2efzwefzKdMQ2tvbUVpainPnzuHu3bvo6enBq1evUFlZCZPJ9Ff3EE0wGASACedEhsNhfPnyJWpZeXk5bt68qZqPSzRvzW52HCKaa4mJiQJAhoeHVedPnTolAKS0tFR1/sePH0qutl/3wX348KEAkJycHE0/ZWVlmvY8Ho8YDAYBICaTSW7cuCG9vb3S3d0tly9flszMTBkcHJzBu50dTqdTdDqdGI1GSU1NVR2RfYRXrlwp4XBYc019fb2IiHz48EHWrl0rmzZtkqGhoWm3/7d9/MmbN2/EbDZLenq6+Hy+Cev29/dLWlqa8r9xOByq8vF7PCcnJ4vZbJasrCzp7OxUzq9atUqqq6tFRKS+vl45P/6Ii4uTJUuWyMaNG6e1f3Gkfb1eL2/fvo1ap6CgQBISEuT48eNK7se+vj4pKSkRl8s15b6JYg0DQ6J/zLp16wSANDc3y9DQkFy4cEFCoZDk5OQIANm8ebMEg0GlfiT4AyCtra2qts6cOSMAZMWKFeL1epXzP3/+lNzcXAEgNptN1d7t27dFp9NpHvKLFy+WlpaW//8HmGH379+XuLi4qIHL+OPkyZOaa91ut2RnZ4vVapU1a9bI1atX5fv37zPW/mT7+JOLFy+qxqDX6zVJtSMqKiqU5Ofjj6VLl6r6raqqEovFIikpKVJYWCj9/f3y7t07Wb58uZw/f16VGD0cDktRUZFYrVaxWCySlJQk8fHxqvYTEhKks7Pzr+6rvb1dlaQagOh0OsnIyNDUffTokaxfv14MBoMYjUYpKCiQ8vLyKScIJ4pVDAyJ/jEdHR2SkZEhaWlpUlJSIk+ePJFly5ZpHuLPnj2T4uJiZReTyMN3+/bt8vXrVzGbzZrArqysTJqamsRoNKrKjEajvH79WhlDa2ur5OfnS3JyshgMBsnLy5OOjo45/FUoln369Em2bdsmIyMjmrKRkRHp6emR3bt3y4kTJ+ZgdEQLS5wIN3gkIqLYlZeXh+LiYk1C7fEePHiApqYmXLt2bfYGRrQAcfEJERHFLJfLhebm5j/ubNLQ0PDbFD9ENHkMDImIKGZFVlofPXoUNTU1SkqbiPfv32P//v0YHh5Gfn7+XAyRaEHhp2QiIopZ3759g91ux9OnTwGMJse2WCxISkqC3++H1+vFnj17UFdXN+EWgkQ0OQwMiYgopoVCITgcDjidTrx48QKBQACpqanIzs7GgQMHsGPHjrkeItGCwcCQiIiIiABwjiERERERjWFgSEREREQAGBgSERER0RgGhkREREQEgIEhEREREY1hYEhEREREABgYEhEREdEYBoZEREREBICBIRERERGNYWBIRERERACA/wBAQRZh46T4JAAAAABJRU5ErkJggg==",
             "text/plain": [
               "<Figure size 640x480 with 2 Axes>"
             ]
@@ -4131,11 +4162,8 @@
     }
   ],
   "metadata": {
-    "interpreter": {
-      "hash": "5cfd453a1a1cce2f32ea80f99ff7da863344217116d39185ac62b248c2577445"
-    },
     "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
+      "display_name": "work",
       "language": "python",
       "name": "python3"
     },
diff --git a/examples/adv_example.ipynb b/examples/adv_example.ipynb
index 7ea0582d5..b497a8635 100644
--- a/examples/adv_example.ipynb
+++ b/examples/adv_example.ipynb
@@ -1,943 +1,1325 @@
 {
-  "cells": [
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# Reading ADV Data with MHKiT\n",
-        "\n",
-        "This example presents a simplified workflow for analyzing Acoustic Doppler Velocimetry (ADV) data using MHKiT. MHKiT incorporates the DOLfYN codebase as a module to handle ADV and Acoustic Doppler Current Profiler (ADCP) data.\n",
-        "\n",
-        "A standard ADV data analysis workflow can be segmented into the following steps:\n",
-        "\n",
-        "1. **Raw Data Review**: Evaluate the original data by verifying timestamps and assessing the quality of velocity data, specifically looking for any data spikes.\n",
-        "\n",
-        "2. **Data Cleaning**: Identify and eliminate any spurious data points. If needed, bad data points can be replaced with interpolated values.\n",
-        "\n",
-        "3. **Data Rotation**: Transform the data into the principal flow coordinates, which are the streamwise, cross-stream, and vertical directions.\n",
-        "\n",
-        "4. **Data Averaging**: Aggregate the data into bins or ensembles, each of which spans a predefined time length, typically between 5 and 10 minutes.\n",
-        "\n",
-        "5. **Statistical Analysis**: Compute turbulence statistics such as turbulence intensity, Turbulent Kinetic Energy (TKE), and Reynolds stresses for the observed flow field.\n",
-        "\n",
-        "Start your analysis by importing the necessary tools:"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "from mhkit import dolfyn\n",
-        "from mhkit.dolfyn.adv import api"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Read Raw Instrument Data"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "DOLfYN currently only carries support for the Nortek Vector ADV. The example loaded here is a short clip of data from a test deployment to show DOLfYN's capabilities.\n",
-        "\n",
-        "Start by reading in the raw datafile downloaded from the instrument. The `dolfyn.read` function reads the raw file and dumps the information into an xarray Dataset, which contains three groups of variables:\n",
-        "\n",
-        "1. Velocity, amplitude, and correlation of the Doppler velocimetry\n",
-        "2. Measurements of the instrument's bearing and environment\n",
-        "3. Orientation matrices DOLfYN uses for rotating through coordinate frames."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {
-        "scrolled": true
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Reading file data/dolfyn/vector_data01.VEC ...\n"
-          ]
-        }
-      ],
-      "source": [
-        "ds = dolfyn.read(\"data/dolfyn/vector_data01.VEC\")"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "There are two ways to see what's in a Dataset. The first is to simply type the dataset's name to see the standard xarray output. To access a particular variable in a dataset, use dict-style (`ds['vel']`) or attribute-style syntax (`ds.vel`). See the [xarray docs](http://xarray.pydata.org/en/stable/getting-started-guide/quick-overview.html) for more details on how to use the xarray format."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {},
-      "outputs": [
-        {
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-              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 11MB\n",
-              "Dimensions:              (time: 122912, beam: 3, dir: 3, x1: 3, x2: 3,\n",
-              "                          earth: 3, inst: 3)\n",
-              "Coordinates:\n",
-              "  * time                 (time) datetime64[ns] 983kB 2012-06-12T12:00:02.9687...\n",
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-              "  * dir                  (dir) &lt;U1 12B &#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;\n",
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-              "Data variables: (12/15)\n",
-              "    beam2inst_orientmat  (x1, x2) float64 72B 2.709 -1.34 ... -0.3438 -0.3499\n",
-              "    batt                 (time) float32 492kB 13.2 13.2 13.2 ... nan nan nan\n",
-              "    c_sound              (time) float32 492kB 1.493e+03 1.493e+03 ... nan nan\n",
-              "    heading              (time) float32 492kB 5.6 10.5 10.51 ... nan nan nan\n",
-              "    pitch                (time) float32 492kB -31.5 -31.7 -31.69 ... nan nan nan\n",
-              "    roll                 (time) float32 492kB 0.4 4.2 4.253 ... nan nan nan\n",
-              "    ...                   ...\n",
-              "    orientation_down     (time) bool 123kB True True True ... True True True\n",
-              "    vel                  (dir, time) float32 1MB -1.002 -1.008 ... nan nan\n",
-              "    amp                  (beam, time) uint8 369kB 104 110 111 113 ... 0 0 0 0\n",
-              "    corr                 (beam, time) uint8 369kB 97 91 97 98 90 ... 0 0 0 0 0\n",
-              "    pressure             (time) float32 492kB 5.448 5.436 5.484 ... 0.0 0.0 0.0\n",
-              "    orientmat            (earth, inst, time) float32 4MB 0.0832 ... -0.7065\n",
-              "Attributes: (12/39)\n",
-              "    inst_make:                   Nortek\n",
-              "    inst_model:                  Vector\n",
-              "    inst_type:                   ADV\n",
-              "    rotate_vars:                 [&#x27;vel&#x27;]\n",
-              "    n_beams:                     3\n",
-              "    profile_mode:                continuous\n",
-              "    ...                          ...\n",
-              "    recorder_size_bytes:         4074766336\n",
-              "    vel_range:                   normal\n",
-              "    firmware_version:            3.34\n",
-              "    fs:                          32.0\n",
-              "    coord_sys:                   inst\n",
-              "    has_imu:                     0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-eab5be39-b11c-402d-98f9-85edeeca014b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-eab5be39-b11c-402d-98f9-85edeeca014b' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 122912</li><li><span class='xr-has-index'>beam</span>: 3</li><li><span class='xr-has-index'>dir</span>: 3</li><li><span class='xr-has-index'>x1</span>: 3</li><li><span class='xr-has-index'>x2</span>: 3</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3c338e09-4402-44a9-948a-edb04640e176' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3c338e09-4402-44a9-948a-edb04640e176' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2012-06-12T12:00:02.968749046 .....</div><input id='attrs-e5740cc0-aa1b-4c88-a1a5-183a1fac042a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5740cc0-aa1b-4c88-a1a5-183a1fac042a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb3b6c14-cf2e-4b9e-bd0c-456450a49d2d' class='xr-var-data-in' type='checkbox'><label for='data-bb3b6c14-cf2e-4b9e-bd0c-456450a49d2d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2012-06-12T12:00:02.968749046&#x27;, &#x27;2012-06-12T12:00:02.999999046&#x27;,\n",
-              "       &#x27;2012-06-12T12:00:03.031249046&#x27;, ..., &#x27;2012-06-12T13:04:03.875000715&#x27;,\n",
-              "       &#x27;2012-06-12T13:04:03.906250715&#x27;, &#x27;2012-06-12T13:04:03.937500715&#x27;],\n",
-              "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-6e3e4c02-14e8-46f5-a0e7-4625ae3d81ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6e3e4c02-14e8-46f5-a0e7-4625ae3d81ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddcd6ebc-f481-480c-ae90-0f6e3d5b0f53' class='xr-var-data-in' type='checkbox'><label for='data-ddcd6ebc-f481-480c-ae90-0f6e3d5b0f53' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-c484c381-844a-47c1-876c-080a4583d019' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c484c381-844a-47c1-876c-080a4583d019' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f1558f7-1f4a-47ed-a014-3e7a7e07aad9' class='xr-var-data-in' type='checkbox'><label for='data-6f1558f7-1f4a-47ed-a014-3e7a7e07aad9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>inst</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-d55c44b9-4471-471e-8769-42527d651a30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d55c44b9-4471-471e-8769-42527d651a30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb7ed908-e4fb-4487-9b11-660839de85d7' class='xr-var-data-in' type='checkbox'><label for='data-eb7ed908-e4fb-4487-9b11-660839de85d7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-2355ad01-54b0-40fe-84e7-14f4f51ca69e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2355ad01-54b0-40fe-84e7-14f4f51ca69e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-972b9d44-ccf5-4078-9f05-953b673f4007' class='xr-var-data-in' type='checkbox'><label for='data-972b9d44-ccf5-4078-9f05-953b673f4007' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-b0498787-7a47-467c-9cc0-0f082bc29d93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b0498787-7a47-467c-9cc0-0f082bc29d93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97a459ed-745a-4abd-843c-3eee7ea106c2' class='xr-var-data-in' type='checkbox'><label for='data-97a459ed-745a-4abd-843c-3eee7ea106c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-278fc780-84e5-484e-b0a2-f1a1040df86d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-278fc780-84e5-484e-b0a2-f1a1040df86d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9b9ac744-09c7-4d1c-a411-c874e1acbaa7' class='xr-var-data-in' type='checkbox'><label for='data-9b9ac744-09c7-4d1c-a411-c874e1acbaa7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1477b3ad-75c5-46a7-8e9b-5d8678f61252' class='xr-section-summary-in' type='checkbox'  ><label for='section-1477b3ad-75c5-46a7-8e9b-5d8678f61252' class='xr-section-summary' >Data variables: <span>(15)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.709 -1.34 ... -0.3438 -0.3499</div><input id='attrs-43160071-623c-4049-98f8-88e548d4944e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-43160071-623c-4049-98f8-88e548d4944e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cdfed6e-49ee-4ab7-bb83-62b145770984' class='xr-var-data-in' type='checkbox'><label for='data-2cdfed6e-49ee-4ab7-bb83-62b145770984' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 2.70874023, -1.34008789, -1.36376953],\n",
-              "       [ 0.0144043 , -2.38354492,  2.39550781],\n",
-              "       [-0.3371582 , -0.34375   , -0.34985352]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>13.2 13.2 13.2 13.2 ... nan nan nan</div><input id='attrs-609cb65c-fa8e-4caa-a66f-5c632a5a96bf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-609cb65c-fa8e-4caa-a66f-5c632a5a96bf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8dc42e6-6c31-48f8-bc3d-6d93bdc78beb' class='xr-var-data-in' type='checkbox'><label for='data-b8dc42e6-6c31-48f8-bc3d-6d93bdc78beb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd></dl></div><div class='xr-var-data'><pre>array([13.2, 13.2, 13.2, ...,  nan,  nan,  nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.493e+03 1.493e+03 ... nan nan</div><input id='attrs-420ab129-6c43-49e5-b16f-96b8e14d8344' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-420ab129-6c43-49e5-b16f-96b8e14d8344' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a725281-2a2e-452f-ab3b-e510bc3018cd' class='xr-var-data-in' type='checkbox'><label for='data-9a725281-2a2e-452f-ab3b-e510bc3018cd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1492.6, 1492.6, 1492.6, ...,    nan,    nan,    nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.6 10.5 10.51 ... nan nan nan</div><input id='attrs-23c81e49-e775-498c-aacc-9dc98264d9fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-23c81e49-e775-498c-aacc-9dc98264d9fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7b8e5ee-1597-40b1-87ae-2bca3e865da3' class='xr-var-data-in' type='checkbox'><label for='data-f7b8e5ee-1597-40b1-87ae-2bca3e865da3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([ 5.6   , 10.5   , 10.5125, ...,     nan,     nan,     nan],\n",
-              "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-31.5 -31.7 -31.69 ... nan nan nan</div><input id='attrs-49f308da-38f4-4435-a17f-d4eb81602884' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49f308da-38f4-4435-a17f-d4eb81602884' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dca7a208-b1fd-417c-880e-efd39c9a8ad6' class='xr-var-data-in' type='checkbox'><label for='data-dca7a208-b1fd-417c-880e-efd39c9a8ad6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([-31.5     , -31.7     , -31.690626, ...,        nan,        nan,\n",
-              "              nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.4 4.2 4.253 4.306 ... nan nan nan</div><input id='attrs-2423c861-534b-421e-a427-e8507920a721' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2423c861-534b-421e-a427-e8507920a721' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fdbc1afc-c62d-4af8-af61-fb764a4323b2' class='xr-var-data-in' type='checkbox'><label for='data-fdbc1afc-c62d-4af8-af61-fb764a4323b2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([0.4      , 4.2000003, 4.253125 , ...,       nan,       nan,\n",
-              "             nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>12.67 12.68 12.68 ... nan nan nan</div><input id='attrs-5565293a-c1be-4bd2-8d76-2c4556d203cb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5565293a-c1be-4bd2-8d76-2c4556d203cb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fb98ff6-cc1a-4d18-b9e0-378a0968d623' class='xr-var-data-in' type='checkbox'><label for='data-1fb98ff6-cc1a-4d18-b9e0-378a0968d623' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([12.67    , 12.679999, 12.679999, ...,       nan,       nan,\n",
-              "             nan], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-46360ea3-2776-48c2-bb68-20a77f700677' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-46360ea3-2776-48c2-bb68-20a77f700677' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-501c7abf-4619-4f95-ac55-f92b193a7147' class='xr-var-data-in' type='checkbox'><label for='data-501c7abf-4619-4f95-ac55-f92b193a7147' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>117 117 0 0 0 0 0 ... 0 0 0 0 0 0 0</div><input id='attrs-be44ffc1-0545-485d-b5b0-634cb69d8a7a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-be44ffc1-0545-485d-b5b0-634cb69d8a7a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-590f05ec-f19e-45bb-8a0c-4d04e9a7c8da' class='xr-var-data-in' type='checkbox'><label for='data-590f05ec-f19e-45bb-8a0c-4d04e9a7c8da' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([117, 117,   0, ...,   0,   0,   0], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientation_down</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>True True True ... True True True</div><input id='attrs-13df7dc7-f7b6-471d-af62-9e67ffce3606' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-13df7dc7-f7b6-471d-af62-9e67ffce3606' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8be4afef-65fd-40ba-b25b-0300a8ff3bed' class='xr-var-data-in' type='checkbox'><label for='data-8be4afef-65fd-40ba-b25b-0300a8ff3bed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Orientation of ADV Communication Cable</dd></dl></div><div class='xr-var-data'><pre>array([ True,  True,  True, ...,  True,  True,  True])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-1.002 -1.008 -0.944 ... nan nan</div><input id='attrs-4f059c40-0b3d-4513-a974-f5d0b8620e52' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f059c40-0b3d-4513-a974-f5d0b8620e52' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b753293-613d-43d5-bec9-61bc74be32ee' class='xr-var-data-in' type='checkbox'><label for='data-8b753293-613d-43d5-bec9-61bc74be32ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-1.0020001 , -1.008     , -0.94400007, ...,         nan,\n",
-              "                nan,         nan],\n",
-              "       [ 0.097     ,  0.068     ,  0.066     , ...,         nan,\n",
-              "                nan,         nan],\n",
-              "       [ 0.115     ,  0.12400001,  0.12200001, ...,         nan,\n",
-              "                nan,         nan]], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>104 110 111 113 108 ... 0 0 0 0 0</div><input id='attrs-d1f59176-7eed-47ad-b1cd-d3f150db00ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1f59176-7eed-47ad-b1cd-d3f150db00ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-36b2c0d4-a344-4804-ba04-112665ca2882' class='xr-var-data-in' type='checkbox'><label for='data-36b2c0d4-a344-4804-ba04-112665ca2882' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[104, 110, 111, ...,   0,   0,   0],\n",
-              "       [109, 111, 118, ...,   0,   0,   0],\n",
-              "       [111, 107, 103, ...,   0,   0,   0]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>97 91 97 98 90 95 ... 0 0 0 0 0 0</div><input id='attrs-af37f44e-91ce-4a62-b8e6-4c515901fded' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-af37f44e-91ce-4a62-b8e6-4c515901fded' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe1b6e83-5151-44b3-923c-9d79269aae2c' class='xr-var-data-in' type='checkbox'><label for='data-fe1b6e83-5151-44b3-923c-9d79269aae2c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd></dl></div><div class='xr-var-data'><pre>array([[97, 91, 97, ...,  0,  0,  0],\n",
-              "       [97, 97, 98, ...,  0,  0,  0],\n",
-              "       [96, 97, 97, ...,  0,  0,  0]], dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>5.448 5.436 5.484 ... 0.0 0.0 0.0</div><input id='attrs-2a5254e2-455d-4ad8-95a9-a59e5233f307' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a5254e2-455d-4ad8-95a9-a59e5233f307' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eaa4fe43-2603-4eaa-9468-d9b2b9b5ea52' class='xr-var-data-in' type='checkbox'><label for='data-eaa4fe43-2603-4eaa-9468-d9b2b9b5ea52' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([5.448, 5.436, 5.484, ..., 0.   , 0.   , 0.   ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0832 0.155 ... -0.7065 -0.7065</div><input id='attrs-feb05803-2eb7-40a3-96e9-eaeb9315026b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-feb05803-2eb7-40a3-96e9-eaeb9315026b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1553685-99bf-4502-8313-d225d89ba52d' class='xr-var-data-in' type='checkbox'><label for='data-e1553685-99bf-4502-8313-d225d89ba52d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.08320302,  0.155048  ,  0.15524618, ..., -0.8397447 ,\n",
-              "         -0.8397447 , -0.8397447 ],\n",
-              "        [ 0.8485708 ,  0.83656424,  0.8366149 , ...,  0.21873687,\n",
-              "          0.21873687,  0.21873687],\n",
-              "        [-0.52249855, -0.5254717 , -0.52533245, ..., -0.49697396,\n",
-              "         -0.49697396, -0.49697396]],\n",
-              "\n",
-              "       [[ 0.9948472 ,  0.97360104,  0.9733992 , ...,  0.48448867,\n",
-              "          0.48448867,  0.48448867],\n",
-              "        [-0.10121067, -0.21958625, -0.22025378, ...,  0.7150815 ,\n",
-              "          0.7150815 ,  0.7150815 ],\n",
-              "        [-0.00595242, -0.06231184, -0.06310495, ..., -0.5039139 ,\n",
-              "         -0.5039139 , -0.5039139 ]],\n",
-              "\n",
-              "       [[-0.05793348, -0.1675142 , -0.16850099, ...,  0.24515234,\n",
-              "          0.24515234,  0.24515234],\n",
-              "        [-0.51931095, -0.50193846, -0.5015614 , ..., -0.6639372 ,\n",
-              "         -0.6639372 , -0.6639372 ],\n",
-              "        [-0.85261935, -0.84852624, -0.84855384, ..., -0.7064614 ,\n",
-              "         -0.7064614 , -0.7064614 ]]], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f2797f3f-bdee-4b0f-9bc2-4a79385cc2de' class='xr-section-summary-in' type='checkbox'  ><label for='section-f2797f3f-bdee-4b0f-9bc2-4a79385cc2de' class='xr-section-summary' >Indexes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-67c4be08-6952-41fe-9864-775b690f7da2' class='xr-index-data-in' type='checkbox'/><label for='index-67c4be08-6952-41fe-9864-775b690f7da2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2012-06-12 12:00:02.968749046&#x27;,\n",
-              "               &#x27;2012-06-12 12:00:02.999999046&#x27;,\n",
-              "               &#x27;2012-06-12 12:00:03.031249046&#x27;,\n",
-              "               &#x27;2012-06-12 12:00:03.062499046&#x27;,\n",
-              "               &#x27;2012-06-12 12:00:03.093749046&#x27;,\n",
-              "               &#x27;2012-06-12 12:00:03.124999046&#x27;,\n",
-              "               &#x27;2012-06-12 12:00:03.156249046&#x27;,\n",
-              "               &#x27;2012-06-12 12:00:03.187499046&#x27;,\n",
-              "               &#x27;2012-06-12 12:00:03.218749046&#x27;,\n",
-              "               &#x27;2012-06-12 12:00:03.249999046&#x27;,\n",
-              "               ...\n",
-              "               &#x27;2012-06-12 13:04:03.656250715&#x27;,\n",
-              "               &#x27;2012-06-12 13:04:03.687500715&#x27;,\n",
-              "               &#x27;2012-06-12 13:04:03.718750715&#x27;,\n",
-              "               &#x27;2012-06-12 13:04:03.750000715&#x27;,\n",
-              "               &#x27;2012-06-12 13:04:03.781250715&#x27;,\n",
-              "               &#x27;2012-06-12 13:04:03.812500715&#x27;,\n",
-              "               &#x27;2012-06-12 13:04:03.843750715&#x27;,\n",
-              "               &#x27;2012-06-12 13:04:03.875000715&#x27;,\n",
-              "               &#x27;2012-06-12 13:04:03.906250715&#x27;,\n",
-              "               &#x27;2012-06-12 13:04:03.937500715&#x27;],\n",
-              "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=122912, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-dc931fca-b2ac-4b2c-9434-dd7f3594d7ba' class='xr-index-data-in' type='checkbox'/><label for='index-dc931fca-b2ac-4b2c-9434-dd7f3594d7ba' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f8f7c107-9da7-4765-a089-b357b8581426' class='xr-index-data-in' type='checkbox'/><label for='index-f8f7c107-9da7-4765-a089-b357b8581426' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-afc3fb0c-d794-4ba8-b90a-912ec41d5aec' class='xr-index-data-in' type='checkbox'/><label for='index-afc3fb0c-d794-4ba8-b90a-912ec41d5aec' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3], dtype=&#x27;int32&#x27;, name=&#x27;x1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x2</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1de6d776-d8b4-4bd6-bf09-1748f98dd797' class='xr-index-data-in' type='checkbox'/><label for='index-1de6d776-d8b4-4bd6-bf09-1748f98dd797' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3], dtype=&#x27;int32&#x27;, name=&#x27;x2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-357318bf-16c8-4ab9-b13d-4c2be440443b' class='xr-index-data-in' type='checkbox'/><label for='index-357318bf-16c8-4ab9-b13d-4c2be440443b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a5d47faa-4f98-405b-89ff-4731462419e7' class='xr-index-data-in' type='checkbox'/><label for='index-a5d47faa-4f98-405b-89ff-4731462419e7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-f6e45691-0a12-4448-81ab-4bc518d9f30f' class='xr-section-summary-in' type='checkbox'  ><label for='section-f6e45691-0a12-4448-81ab-4bc518d9f30f' class='xr-section-summary' >Attributes: <span>(39)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_model :</span></dt><dd>Vector</dd><dt><span>inst_type :</span></dt><dd>ADV</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;]</dd><dt><span>n_beams :</span></dt><dd>3</dd><dt><span>profile_mode :</span></dt><dd>continuous</dd><dt><span>burst_mode :</span></dt><dd>True</dd><dt><span>power_level :</span></dt><dd>1</dd><dt><span>sync_out_pos :</span></dt><dd>end</dd><dt><span>sample_on_sync :</span></dt><dd>False</dd><dt><span>start_on_sync :</span></dt><dd>False</dd><dt><span>compass_update_rate :</span></dt><dd>1</dd><dt><span>n_bins :</span></dt><dd>1</dd><dt><span>deployment_name :</span></dt><dd>APLUW_</dd><dt><span>wrap_mode :</span></dt><dd>False</dd><dt><span>analog_in :</span></dt><dd>0</dd><dt><span>software_version :</span></dt><dd>1.32.00</dd><dt><span>salinity :</span></dt><dd>30.0</dd><dt><span>comments :</span></dt><dd>APL-UW vector on Tidal Turbulence Mooring in Admiralty, times PDT</dd><dt><span>user_specified_sound_speed :</span></dt><dd>False</dd><dt><span>analog_output :</span></dt><dd>False</dd><dt><span>output_format :</span></dt><dd>Vector</dd><dt><span>serial_output :</span></dt><dd>False</dd><dt><span>power_output_analog :</span></dt><dd>False</dd><dt><span>n_pings_per_burst :</span></dt><dd>1</dd><dt><span>pressure_sensor :</span></dt><dd>yes</dd><dt><span>compass :</span></dt><dd>yes</dd><dt><span>tilt_sensor :</span></dt><dd>yes</dd><dt><span>carrier_freq_kHz :</span></dt><dd>6000</dd><dt><span>serial_number :</span></dt><dd>VEC 9062</dd><dt><span>ProLogFWver :</span></dt><dd>4.08</dd><dt><span>PIC_version :</span></dt><dd>0</dd><dt><span>hardware_rev :</span></dt><dd>4</dd><dt><span>recorder_size_bytes :</span></dt><dd>4074766336</dd><dt><span>vel_range :</span></dt><dd>normal</dd><dt><span>firmware_version :</span></dt><dd>3.34</dd><dt><span>fs :</span></dt><dd>32.0</dd><dt><span>coord_sys :</span></dt><dd>inst</dd><dt><span>has_imu :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
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-            "text/plain": [
-              "<xarray.Dataset> Size: 11MB\n",
-              "Dimensions:              (time: 122912, beam: 3, dir: 3, x1: 3, x2: 3,\n",
-              "                          earth: 3, inst: 3)\n",
-              "Coordinates:\n",
-              "  * time                 (time) datetime64[ns] 983kB 2012-06-12T12:00:02.9687...\n",
-              "  * beam                 (beam) int32 12B 1 2 3\n",
-              "  * dir                  (dir) <U1 12B 'X' 'Y' 'Z'\n",
-              "  * x1                   (x1) int32 12B 1 2 3\n",
-              "  * x2                   (x2) int32 12B 1 2 3\n",
-              "  * earth                (earth) <U1 12B 'E' 'N' 'U'\n",
-              "  * inst                 (inst) <U1 12B 'X' 'Y' 'Z'\n",
-              "Data variables: (12/15)\n",
-              "    beam2inst_orientmat  (x1, x2) float64 72B 2.709 -1.34 ... -0.3438 -0.3499\n",
-              "    batt                 (time) float32 492kB 13.2 13.2 13.2 ... nan nan nan\n",
-              "    c_sound              (time) float32 492kB 1.493e+03 1.493e+03 ... nan nan\n",
-              "    heading              (time) float32 492kB 5.6 10.5 10.51 ... nan nan nan\n",
-              "    pitch                (time) float32 492kB -31.5 -31.7 -31.69 ... nan nan nan\n",
-              "    roll                 (time) float32 492kB 0.4 4.2 4.253 ... nan nan nan\n",
-              "    ...                   ...\n",
-              "    orientation_down     (time) bool 123kB True True True ... True True True\n",
-              "    vel                  (dir, time) float32 1MB -1.002 -1.008 ... nan nan\n",
-              "    amp                  (beam, time) uint8 369kB 104 110 111 113 ... 0 0 0 0\n",
-              "    corr                 (beam, time) uint8 369kB 97 91 97 98 90 ... 0 0 0 0 0\n",
-              "    pressure             (time) float32 492kB 5.448 5.436 5.484 ... 0.0 0.0 0.0\n",
-              "    orientmat            (earth, inst, time) float32 4MB 0.0832 ... -0.7065\n",
-              "Attributes: (12/39)\n",
-              "    inst_make:                   Nortek\n",
-              "    inst_model:                  Vector\n",
-              "    inst_type:                   ADV\n",
-              "    rotate_vars:                 ['vel']\n",
-              "    n_beams:                     3\n",
-              "    profile_mode:                continuous\n",
-              "    ...                          ...\n",
-              "    recorder_size_bytes:         4074766336\n",
-              "    vel_range:                   normal\n",
-              "    firmware_version:            3.34\n",
-              "    fs:                          32.0\n",
-              "    coord_sys:                   inst\n",
-              "    has_imu:                     0"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# print the dataset\n",
-        "ds"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "A second way provided to look at data is through the *DOLfYN view*. This view has several convenience methods, shortcuts, and functions built-in. It includes an alternate – and somewhat more informative/compact – description of the data object when in interactive mode. This can be accessed using"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<ADV data object>:  Nortek Vector\n",
-              "  . 1.07 hours (started: Jun 12, 2012 12:00)\n",
-              "  . inst-frame\n",
-              "  . (122912 pings @ 32.0Hz)\n",
-              "  Variables:\n",
-              "  - time ('time',)\n",
-              "  - vel ('dir', 'time')\n",
-              "  - orientmat ('earth', 'inst', 'time')\n",
-              "  - heading ('time',)\n",
-              "  - pitch ('time',)\n",
-              "  - roll ('time',)\n",
-              "  - temp ('time',)\n",
-              "  - pressure ('time',)\n",
-              "  - amp ('beam', 'time')\n",
-              "  - corr ('beam', 'time')\n",
-              "  ... and others (see `<obj>.variables`)"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "ds_dolfyn = ds.velds\n",
-        "ds_dolfyn"
-      ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Quality Control"
-      ]
-    },
+ "cells": [
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Reading ADV Data with MHKiT\n",
+    "\n",
+    "This example presents a simplified workflow for analyzing Acoustic Doppler Velocimetry (ADV) data using MHKiT. MHKiT incorporates the DOLfYN codebase as a module to handle ADV and Acoustic Doppler Current Profiler (ADCP) data.\n",
+    "\n",
+    "This particular dataset is an excerpt from a cabled ADV mounted to a Sea Spider tripod, where the ADV was sampling on a duty cycle where 5 minutes of data was recorded every 20 minutes. All of the steps for a non-cabled ADV still apply, excepting the probe-inst rotation step.\n",
+    "\n",
+    "A standard ADV data analysis workflow can be segmented into the following steps:\n",
+    "\n",
+    "1. **Raw Data Review**: Evaluate the original data by verifying timestamps and assessing the quality of velocity data, specifically looking for any data spikes.\n",
+    "\n",
+    "2. **Data Cleaning**: Identify and eliminate any spurious data points. If needed, bad data points can be replaced with interpolated values.\n",
+    "\n",
+    "3. **Data Rotation**: Transform the data into the principal flow coordinates, which are the streamwise, cross-stream, and vertical directions.\n",
+    "\n",
+    "4. **Data Averaging**: Aggregate the data into bins or ensembles, each of which spans a predefined time length, typically between 5 and 10 minutes.\n",
+    "\n",
+    "5. **Statistical Analysis**: Compute turbulence statistics such as turbulence intensity, Turbulent Kinetic Energy (TKE), and Reynolds stresses for the observed flow field.\n",
+    "\n",
+    "Start your analysis by importing the necessary tools:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mhkit import dolfyn\n",
+    "from mhkit.dolfyn.adv import api"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Read Raw Instrument Data"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "DOLfYN currently only carries support for the Nortek Vector ADV. The example loaded here is a short clip of data from a test deployment to show DOLfYN's capabilities.\n",
+    "\n",
+    "Start by reading in the raw datafile downloaded from the instrument. The `dolfyn.read` function reads the raw file and dumps the information into an xarray Dataset, which contains three groups of variables:\n",
+    "\n",
+    "1. Velocity, amplitude, and correlation of the Doppler velocimetry\n",
+    "2. Measurements of the instrument's bearing and environment\n",
+    "3. Orientation matrices DOLfYN uses for rotating through coordinate frames."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "ADV velocity data tends to have spikes due to Doppler noise, and the common way to \"despike\" the data is by using the phase-space algorithm by Goring and Nikora (2002). DOLfYN integrates this function using a 2-step approach: create a logical mask where True corresponds to a spike detection, and then utilize an interpolation function to replace the spikes."
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Reading file data/dolfyn/vector_cabled_imu01.VEC ...\n"
+     ]
+    }
+   ],
+   "source": [
+    "ds = dolfyn.read(\"data/dolfyn/vector_cabled_imu01.VEC\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "There are two ways to see what's in a Dataset. The first is to simply type the dataset's name to see the standard xarray output. To access a particular variable in a dataset, use dict-style (`ds['vel']`) or attribute-style syntax (`ds.vel`). See the [xarray docs](http://xarray.pydata.org/en/stable/getting-started-guide/quick-overview.html) for more details on how to use the xarray format."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {
-        "scrolled": false
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Percent of data containing spikes: 0.73%\n"
-          ]
-        }
+     "data": {
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+       "\n",
+       ".xr-section-summary-in + label:before {\n",
+       "  display: inline-block;\n",
+       "  content: \"►\";\n",
+       "  font-size: 11px;\n",
+       "  width: 15px;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:disabled + label:before {\n",
+       "  color: var(--xr-disabled-color);\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label:before {\n",
+       "  content: \"▼\";\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked + label > span {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary,\n",
+       ".xr-section-inline-details {\n",
+       "  padding-top: 4px;\n",
+       "  padding-bottom: 4px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-inline-details {\n",
+       "  grid-column: 2 / -1;\n",
+       "}\n",
+       "\n",
+       ".xr-section-details {\n",
+       "  display: none;\n",
+       "  grid-column: 1 / -1;\n",
+       "  margin-bottom: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap {\n",
+       "  grid-column: 1 / -1;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 20px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-array-wrap > label {\n",
+       "  grid-column: 1;\n",
+       "  vertical-align: top;\n",
+       "}\n",
+       "\n",
+       ".xr-preview {\n",
+       "  color: var(--xr-font-color3);\n",
+       "}\n",
+       "\n",
+       ".xr-array-preview,\n",
+       ".xr-array-data {\n",
+       "  padding: 0 5px !important;\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-array-data,\n",
+       ".xr-array-in:checked ~ .xr-array-preview {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       ".xr-array-in:checked ~ .xr-array-data,\n",
+       ".xr-array-preview {\n",
+       "  display: inline-block;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list {\n",
+       "  display: inline-block !important;\n",
+       "  list-style: none;\n",
+       "  padding: 0 !important;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li {\n",
+       "  display: inline-block;\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:before {\n",
+       "  content: \"(\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list:after {\n",
+       "  content: \")\";\n",
+       "}\n",
+       "\n",
+       ".xr-dim-list li:not(:last-child):after {\n",
+       "  content: \",\";\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-has-index {\n",
+       "  font-weight: bold;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list,\n",
+       ".xr-var-item {\n",
+       "  display: contents;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > div,\n",
+       ".xr-var-item label,\n",
+       ".xr-var-item > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-even);\n",
+       "  border-color: var(--xr-background-color-row-odd);\n",
+       "  margin-bottom: 0;\n",
+       "  padding-top: 2px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-item > .xr-var-name:hover span {\n",
+       "  padding-right: 5px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-list > li:nth-child(odd) > div,\n",
+       ".xr-var-list > li:nth-child(odd) > label,\n",
+       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
+       "  background-color: var(--xr-background-color-row-odd);\n",
+       "  border-color: var(--xr-background-color-row-even);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name {\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dims {\n",
+       "  grid-column: 2;\n",
+       "}\n",
+       "\n",
+       ".xr-var-dtype {\n",
+       "  grid-column: 3;\n",
+       "  text-align: right;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-preview {\n",
+       "  grid-column: 4;\n",
+       "}\n",
+       "\n",
+       ".xr-index-preview {\n",
+       "  grid-column: 2 / 5;\n",
+       "  color: var(--xr-font-color2);\n",
+       "}\n",
+       "\n",
+       ".xr-var-name,\n",
+       ".xr-var-dims,\n",
+       ".xr-var-dtype,\n",
+       ".xr-preview,\n",
+       ".xr-attrs dt {\n",
+       "  white-space: nowrap;\n",
+       "  overflow: hidden;\n",
+       "  text-overflow: ellipsis;\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name:hover,\n",
+       ".xr-var-dims:hover,\n",
+       ".xr-var-dtype:hover,\n",
+       ".xr-attrs dt:hover {\n",
+       "  overflow: visible;\n",
+       "  width: auto;\n",
+       "  z-index: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  display: none;\n",
+       "  border-top: 2px dotted var(--xr-background-color);\n",
+       "  padding-bottom: 20px !important;\n",
+       "  padding-top: 10px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in + label,\n",
+       ".xr-var-data-in + label,\n",
+       ".xr-index-data-in + label {\n",
+       "  padding: 0 1px;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
+       ".xr-var-data-in:checked ~ .xr-var-data,\n",
+       ".xr-index-data-in:checked ~ .xr-index-data {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > table {\n",
+       "  float: right;\n",
+       "}\n",
+       "\n",
+       ".xr-var-data > pre,\n",
+       ".xr-index-data > pre,\n",
+       ".xr-var-data > table > tbody > tr {\n",
+       "  background-color: transparent !important;\n",
+       "}\n",
+       "\n",
+       ".xr-var-name span,\n",
+       ".xr-var-data,\n",
+       ".xr-index-name div,\n",
+       ".xr-index-data,\n",
+       ".xr-attrs {\n",
+       "  padding-left: 25px !important;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs,\n",
+       ".xr-var-attrs,\n",
+       ".xr-var-data,\n",
+       ".xr-index-data {\n",
+       "  grid-column: 1 / -1;\n",
+       "}\n",
+       "\n",
+       "dl.xr-attrs {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  display: grid;\n",
+       "  grid-template-columns: 125px auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt,\n",
+       ".xr-attrs dd {\n",
+       "  padding: 0;\n",
+       "  margin: 0;\n",
+       "  float: left;\n",
+       "  padding-right: 10px;\n",
+       "  width: auto;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt {\n",
+       "  font-weight: normal;\n",
+       "  grid-column: 1;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dt:hover span {\n",
+       "  display: inline-block;\n",
+       "  background: var(--xr-background-color);\n",
+       "  padding-right: 10px;\n",
+       "}\n",
+       "\n",
+       ".xr-attrs dd {\n",
+       "  grid-column: 2;\n",
+       "  white-space: pre-wrap;\n",
+       "  word-break: break-all;\n",
+       "}\n",
+       "\n",
+       ".xr-icon-database,\n",
+       ".xr-icon-file-text2,\n",
+       ".xr-no-icon {\n",
+       "  display: inline-block;\n",
+       "  vertical-align: middle;\n",
+       "  width: 1em;\n",
+       "  height: 1.5em !important;\n",
+       "  stroke-width: 0;\n",
+       "  stroke: currentColor;\n",
+       "  fill: currentColor;\n",
+       "}\n",
+       "\n",
+       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
+       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
+       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
+       "  color: var(--xr-font-color0);\n",
+       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
+       "  stroke-width: 0.8px;\n",
+       "}\n",
+       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 25MB\n",
+       "Dimensions:              (time: 216039, beam: 3, dir: 3, x1: 3, x2: 3,\n",
+       "                          earth: 3, inst: 3)\n",
+       "Coordinates:\n",
+       "  * time                 (time) datetime64[ns] 2MB 2025-02-25T10:00:03.587208...\n",
+       "  * beam                 (beam) int32 12B 1 2 3\n",
+       "  * dir                  (dir) &lt;U1 12B &#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;\n",
+       "  * x1                   (x1) int32 12B 1 2 3\n",
+       "  * x2                   (x2) int32 12B 1 2 3\n",
+       "  * earth                (earth) &lt;U1 12B &#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;\n",
+       "  * inst                 (inst) &lt;U1 12B &#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;\n",
+       "Data variables: (12/17)\n",
+       "    beam2inst_orientmat  (x1, x2) float64 72B 2.575 -1.305 ... 0.3582 0.3455\n",
+       "    batt                 (time) float32 864kB 13.4 13.4 13.4 ... nan nan nan\n",
+       "    c_sound              (time) float32 864kB 1.477e+03 1.477e+03 ... nan nan\n",
+       "    heading              (time) float32 864kB 0.0 0.0 0.0 0.0 ... nan nan nan\n",
+       "    pitch                (time) float32 864kB 0.0 0.0 0.0 0.0 ... nan nan nan\n",
+       "    roll                 (time) float32 864kB 0.0 0.0 0.0 0.0 ... nan nan nan\n",
+       "    ...                   ...\n",
+       "    amp                  (beam, time) uint8 648kB 84 93 104 103 ... 106 106 95\n",
+       "    corr                 (beam, time) uint8 648kB 94 98 98 97 99 ... 95 97 97 92\n",
+       "    accel                (dir, time) float32 3MB 9.53 9.536 ... -1.229 -1.219\n",
+       "    angrt                (dir, time) float32 3MB 0.000111 ... -0.0005012\n",
+       "    orientmat            (earth, inst, time) float32 8MB 0.1533 ... -0.1279\n",
+       "    pressure             (time) float32 864kB 8.865 8.869 8.86 ... 7.073 7.073\n",
+       "Attributes: (12/42)\n",
+       "    inst_make:                   Nortek\n",
+       "    inst_model:                  Vector\n",
+       "    inst_type:                   ADV\n",
+       "    rotate_vars:                 [&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;angrt&#x27;]\n",
+       "    n_beams:                     3\n",
+       "    profile_mode:                continuous\n",
+       "    ...                          ...\n",
+       "    duty_cycle_n_burst:          9600\n",
+       "    duty_cycle_interval:         1200\n",
+       "    duty_cycle_description:      300.0 second bursts collected at 32.0 Hz, wi...\n",
+       "    fs:                          32.0\n",
+       "    coord_sys:                   inst\n",
+       "    has_imu:                     1</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-9186d64b-b865-42cc-a908-89f87d0ed3ef' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-9186d64b-b865-42cc-a908-89f87d0ed3ef' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 216039</li><li><span class='xr-has-index'>beam</span>: 3</li><li><span class='xr-has-index'>dir</span>: 3</li><li><span class='xr-has-index'>x1</span>: 3</li><li><span class='xr-has-index'>x2</span>: 3</li><li><span class='xr-has-index'>earth</span>: 3</li><li><span class='xr-has-index'>inst</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-49402275-9697-459b-9c64-9097a1e809b9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-49402275-9697-459b-9c64-9097a1e809b9' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2025-02-25T10:00:03.587208032 .....</div><input id='attrs-eff3ac2f-0af3-46e6-9a1b-00994e1407c3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eff3ac2f-0af3-46e6-9a1b-00994e1407c3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b5504c7-f21e-44cf-9ac4-59343399fd1c' class='xr-var-data-in' type='checkbox'><label for='data-1b5504c7-f21e-44cf-9ac4-59343399fd1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>seconds since 1970-01-01 00:00:00</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2025-02-25T10:00:03.587208032&#x27;, &#x27;2025-02-25T10:00:03.618207931&#x27;,\n",
+       "       &#x27;2025-02-25T10:00:03.649207830&#x27;, ..., &#x27;2025-02-25T17:22:33.502970933&#x27;,\n",
+       "       &#x27;2025-02-25T17:22:33.533971071&#x27;, &#x27;2025-02-25T17:22:33.564970970&#x27;],\n",
+       "      shape=(216039,), dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>beam</span></div><div class='xr-var-dims'>(beam)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-d3769759-8b33-4440-b664-424be3f6a136' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3769759-8b33-4440-b664-424be3f6a136' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3aad9ef2-f664-46a9-9b09-9cfbe244cf29' class='xr-var-data-in' type='checkbox'><label for='data-3aad9ef2-f664-46a9-9b09-9cfbe244cf29' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Beam Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dir</span></div><div class='xr-var-dims'>(dir)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-c20bd223-457c-4725-a7e2-7907937bf51e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c20bd223-457c-4725-a7e2-7907937bf51e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-18a77b6f-a9fa-464c-a931-a3d42d9c4096' class='xr-var-data-in' type='checkbox'><label for='data-18a77b6f-a9fa-464c-a931-a3d42d9c4096' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd><dt><span>ref_frame :</span></dt><dd>inst</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x1</span></div><div class='xr-var-dims'>(x1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-9531ef32-2f78-4994-a414-b5145b8b8a70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9531ef32-2f78-4994-a414-b5145b8b8a70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-962d69db-4cd8-4348-b7ab-828e64130f02' class='xr-var-data-in' type='checkbox'><label for='data-962d69db-4cd8-4348-b7ab-828e64130f02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x2</span></div><div class='xr-var-dims'>(x2)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>1 2 3</div><input id='attrs-462cc399-321e-41a0-bcd1-3e434618547a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-462cc399-321e-41a0-bcd1-3e434618547a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50dbbc40-296a-45b6-9f34-9aa5280be69a' class='xr-var-data-in' type='checkbox'><label for='data-50dbbc40-296a-45b6-9f34-9aa5280be69a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([1, 2, 3], dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>earth</span></div><div class='xr-var-dims'>(earth)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;E&#x27; &#x27;N&#x27; &#x27;U&#x27;</div><input id='attrs-3ba1208a-637f-4084-ab52-a62e69715ec4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ba1208a-637f-4084-ab52-a62e69715ec4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0f634a5-5d0e-4a42-af6d-c0c6b18721e9' class='xr-var-data-in' type='checkbox'><label for='data-c0f634a5-5d0e-4a42-af6d-c0c6b18721e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Earth Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>inst</span></div><div class='xr-var-dims'>(inst)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;X&#x27; &#x27;Y&#x27; &#x27;Z&#x27;</div><input id='attrs-accda159-01c2-47bc-9b9a-961b6bd24148' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-accda159-01c2-47bc-9b9a-961b6bd24148' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8fd4399-07bf-41a2-a616-3ba9294c8d46' class='xr-var-data-in' type='checkbox'><label for='data-b8fd4399-07bf-41a2-a616-3ba9294c8d46' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Instrument Reference Frame</dd><dt><span>coverage_content_type :</span></dt><dd>coordinate</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8dff74e9-7f8c-4938-b311-3d75825c109e' class='xr-section-summary-in' type='checkbox'  ><label for='section-8dff74e9-7f8c-4938-b311-3d75825c109e' class='xr-section-summary' >Data variables: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>beam2inst_orientmat</span></div><div class='xr-var-dims'>(x1, x2)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.575 -1.305 ... 0.3582 0.3455</div><input id='attrs-703132a7-060e-40d7-80ea-9c1c194b616e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-703132a7-060e-40d7-80ea-9c1c194b616e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c98e53b5-781b-4e70-a978-374e6337b474' class='xr-var-data-in' type='checkbox'><label for='data-c98e53b5-781b-4e70-a978-374e6337b474' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Rotation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[ 2.57543945, -1.3046875 , -1.25878906],\n",
+       "       [-0.02807617,  2.33764648, -2.31494141],\n",
+       "       [ 0.32958984,  0.3581543 ,  0.34545898]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>batt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>13.4 13.4 13.4 13.4 ... nan nan nan</div><input id='attrs-9fbc3a68-a119-428a-b5e0-4884f07aed8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9fbc3a68-a119-428a-b5e0-4884f07aed8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-91211106-919d-4fb9-8526-aa0c9624b0c2' class='xr-var-data-in' type='checkbox'><label for='data-91211106-919d-4fb9-8526-aa0c9624b0c2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>V</dd><dt><span>long_name :</span></dt><dd>Battery Voltage</dd></dl></div><div class='xr-var-data'><pre>array([13.400001, 13.400001, 13.400001, ...,       nan,       nan,\n",
+       "             nan], shape=(216039,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>c_sound</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.477e+03 1.477e+03 ... nan nan</div><input id='attrs-08f5c789-30e6-4957-a092-0111aab90a02' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08f5c789-30e6-4957-a092-0111aab90a02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e40162e-fc57-4ab4-ba56-4741fe6b2a87' class='xr-var-data-in' type='checkbox'><label for='data-2e40162e-fc57-4ab4-ba56-4741fe6b2a87' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>long_name :</span></dt><dd>Speed of Sound</dd><dt><span>standard_name :</span></dt><dd>speed_of_sound_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([1476.8, 1476.8, 1476.8, ...,    nan,    nan,    nan],\n",
+       "      shape=(216039,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>heading</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... nan nan nan nan</div><input id='attrs-696a27c2-0b75-4e07-aad9-d514c4d988df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-696a27c2-0b75-4e07-aad9-d514c4d988df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f507ce9f-f2a9-4bf8-9e7d-a8f7f0e0d8ae' class='xr-var-data-in' type='checkbox'><label for='data-f507ce9f-f2a9-4bf8-9e7d-a8f7f0e0d8ae' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Heading</dd><dt><span>standard_name :</span></dt><dd>platform_orientation</dd></dl></div><div class='xr-var-data'><pre>array([ 0.,  0.,  0., ..., nan, nan, nan], shape=(216039,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pitch</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... nan nan nan nan</div><input id='attrs-6d793d89-3908-4e9c-93fb-b463fc26b963' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d793d89-3908-4e9c-93fb-b463fc26b963' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e1e564d3-b960-4bb9-ab01-df67b4f075fe' class='xr-var-data-in' type='checkbox'><label for='data-e1e564d3-b960-4bb9-ab01-df67b4f075fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Pitch</dd><dt><span>standard_name :</span></dt><dd>platform_pitch</dd></dl></div><div class='xr-var-data'><pre>array([ 0.,  0.,  0., ..., nan, nan, nan], shape=(216039,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>roll</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... nan nan nan nan</div><input id='attrs-2714a71d-7966-4693-bc13-3e7177d1606f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2714a71d-7966-4693-bc13-3e7177d1606f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f29a596-9898-4616-a54f-d16f41ffd7c4' class='xr-var-data-in' type='checkbox'><label for='data-7f29a596-9898-4616-a54f-d16f41ffd7c4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree</dd><dt><span>long_name :</span></dt><dd>Roll</dd><dt><span>standard_name :</span></dt><dd>platform_roll</dd></dl></div><div class='xr-var-data'><pre>array([ 0.,  0.,  0., ..., nan, nan, nan], shape=(216039,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>7.51 7.51 7.51 7.51 ... nan nan nan</div><input id='attrs-7f1c6a3b-3cbf-4764-9478-b56c84c25ca6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7f1c6a3b-3cbf-4764-9478-b56c84c25ca6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f96a993e-4d5f-4406-b138-052df6351aa9' class='xr-var-data-in' type='checkbox'><label for='data-f96a993e-4d5f-4406-b138-052df6351aa9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degree_C</dd><dt><span>long_name :</span></dt><dd>Temperature</dd><dt><span>standard_name :</span></dt><dd>sea_water_temperature</dd></dl></div><div class='xr-var-data'><pre>array([7.5099998, 7.5099998, 7.5099998, ...,       nan,       nan,\n",
+       "             nan], shape=(216039,), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0 0</div><input id='attrs-51161105-e083-4022-b1b1-b28cc6a38d38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-51161105-e083-4022-b1b1-b28cc6a38d38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbfc8b23-634a-4037-8159-796637a6920c' class='xr-var-data-in' type='checkbox'><label for='data-bbfc8b23-634a-4037-8159-796637a6920c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Error Code</dd></dl></div><div class='xr-var-data'><pre>array([0, 0, 0, ..., 0, 0, 0], shape=(216039,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>112 0 0 0 0 0 0 0 ... 0 0 0 0 0 0 0</div><input id='attrs-e3db266f-7865-4291-9bb5-7ffa423e1d35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e3db266f-7865-4291-9bb5-7ffa423e1d35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-262740b5-afdc-49da-baa8-481ebd33832d' class='xr-var-data-in' type='checkbox'><label for='data-262740b5-afdc-49da-baa8-481ebd33832d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Status Code</dd></dl></div><div class='xr-var-data'><pre>array([112,   0,   0, ...,   0,   0,   0], shape=(216039,), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientation_down</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>bool</div><div class='xr-var-preview xr-preview'>False False False ... False False</div><input id='attrs-db0c387e-c000-407c-969b-4f529c88dbd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-db0c387e-c000-407c-969b-4f529c88dbd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a813a6fa-6ab8-4084-8054-d73a7c7201c0' class='xr-var-data-in' type='checkbox'><label for='data-a813a6fa-6ab8-4084-8054-d73a7c7201c0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>long_name :</span></dt><dd>Orientation of ADV Communication Cable</dd></dl></div><div class='xr-var-data'><pre>array([False, False, False, ..., False, False, False], shape=(216039,))</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vel</span></div><div class='xr-var-dims'>(dir, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.034 -0.043 ... 0.075 0.082</div><input id='attrs-94a03591-2a9b-4716-8679-a01998138cc3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94a03591-2a9b-4716-8679-a01998138cc3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d33f4988-79d8-4631-a2c4-e058057fb41b' class='xr-var-data-in' type='checkbox'><label for='data-d33f4988-79d8-4631-a2c4-e058057fb41b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Water Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[-3.4000002e-02, -4.3000001e-02, -3.3000000e-02, ...,\n",
+       "         1.1380000e+00,  1.0910001e+00,  1.0620000e+00],\n",
+       "       [ 2.0000001e-03,  0.0000000e+00,  0.0000000e+00, ...,\n",
+       "         2.5100002e-01,  1.9200000e-01,  1.5800001e-01],\n",
+       "       [-1.0000000e-03, -6.0000001e-03, -7.0000002e-03, ...,\n",
+       "         8.4000006e-02,  7.5000003e-02,  8.2000002e-02]],\n",
+       "      shape=(3, 216039), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>amp</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>84 93 104 103 ... 106 106 106 95</div><input id='attrs-32e26ac8-431b-4585-95a0-032f9bbbc32d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-32e26ac8-431b-4585-95a0-032f9bbbc32d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0363d896-6753-43b4-ad17-793eae97a1ad' class='xr-var-data-in' type='checkbox'><label for='data-0363d896-6753-43b4-ad17-793eae97a1ad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Amplitude</dd><dt><span>standard_name :</span></dt><dd>signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water</dd></dl></div><div class='xr-var-data'><pre>array([[ 84,  93, 104, ..., 110,  99, 102],\n",
+       "       [ 89,  93,  93, ..., 101,  98,  92],\n",
+       "       [ 96, 101,  99, ..., 106, 106,  95]],\n",
+       "      shape=(3, 216039), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>corr</span></div><div class='xr-var-dims'>(beam, time)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>94 98 98 97 99 ... 95 95 97 97 92</div><input id='attrs-f4c10bea-8188-4239-9107-6ae1e469d080' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4c10bea-8188-4239-9107-6ae1e469d080' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-81c68f19-1008-4f72-83d1-feeb6f7b0714' class='xr-var-data-in' type='checkbox'><label for='data-81c68f19-1008-4f72-83d1-feeb6f7b0714' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>%</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acoustic Signal Correlation</dd></dl></div><div class='xr-var-data'><pre>array([[94, 98, 98, ..., 94, 98, 97],\n",
+       "       [99, 99, 99, ..., 98, 93, 96],\n",
+       "       [98, 99, 99, ..., 97, 97, 92]], shape=(3, 216039), dtype=uint8)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>accel</span></div><div class='xr-var-dims'>(dir, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>9.53 9.536 9.526 ... -1.229 -1.219</div><input id='attrs-8313f6d6-55fe-49c2-84ca-3da6a337db35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8313f6d6-55fe-49c2-84ca-3da6a337db35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-75f65cb0-8618-419f-8738-e63a7604c3e9' class='xr-var-data-in' type='checkbox'><label for='data-75f65cb0-8618-419f-8738-e63a7604c3e9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s-2</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Acceleration</dd></dl></div><div class='xr-var-data'><pre>array([[ 9.530013  ,  9.536455  ,  9.525976  , ...,  9.601015  ,\n",
+       "         9.597994  ,  9.593181  ],\n",
+       "       [ 1.0119547 ,  1.0139961 ,  1.0076023 , ...,  0.996303  ,\n",
+       "         0.9979168 ,  0.99355274],\n",
+       "       [-1.1934966 , -1.1835487 , -1.1902841 , ..., -1.2235923 ,\n",
+       "        -1.2292643 , -1.2193956 ]], shape=(3, 216039), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angrt</span></div><div class='xr-var-dims'>(dir, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.000111 0.0004271 ... -0.0005012</div><input id='attrs-eff92a37-a78c-4330-86d8-1c72202f9aa1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eff92a37-a78c-4330-86d8-1c72202f9aa1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-10fcd485-cfac-40d7-ad84-95d75ab86cdf' class='xr-var-data-in' type='checkbox'><label for='data-10fcd485-cfac-40d7-ad84-95d75ab86cdf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>rad s-1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Angular Velocity</dd></dl></div><div class='xr-var-data'><pre>array([[ 0.00011099,  0.00042707, -0.00056194, ..., -0.00380341,\n",
+       "        -0.00334683, -0.00371215],\n",
+       "       [-0.00019397, -0.00023042, -0.00117751, ..., -0.00171529,\n",
+       "        -0.00122215, -0.00037502],\n",
+       "       [-0.00022086, -0.00021133,  0.0006542 , ..., -0.00037107,\n",
+       "        -0.00060423, -0.00050117]], shape=(3, 216039), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>orientmat</span></div><div class='xr-var-dims'>(earth, inst, time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.1533 0.1533 ... -0.128 -0.1279</div><input id='attrs-81336e5e-38b8-4c77-b35f-05167ea062ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-81336e5e-38b8-4c77-b35f-05167ea062ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-968c5f71-43ce-4b01-9678-a579235367ee' class='xr-var-data-in' type='checkbox'><label for='data-968c5f71-43ce-4b01-9678-a579235367ee' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1</dd><dt><span>coverage_content_type :</span></dt><dd>physicalMeasurement</dd><dt><span>long_name :</span></dt><dd>Orientation Matrix</dd></dl></div><div class='xr-var-data'><pre>array([[[ 0.15330946,  0.15328561,  0.1532521 , ...,  0.16398108,\n",
+       "          0.164008  ,  0.16400433],\n",
+       "        [-0.06399706, -0.06394877, -0.06394862, ..., -0.0096143 ,\n",
+       "         -0.00960174, -0.00956409],\n",
+       "        [ 0.9861038 ,  0.98611057,  0.98611575, ...,  0.98641664,\n",
+       "          0.9864123 ,  0.98641324]],\n",
+       "\n",
+       "       [[-0.88366276, -0.8835737 , -0.88350034, ..., -0.66311234,\n",
+       "         -0.663116  , -0.66309273],\n",
+       "        [-0.45553848, -0.4557169 , -0.45587146, ..., -0.7413981 ,\n",
+       "         -0.74139094, -0.7414087 ],\n",
+       "        [ 0.10781901,  0.10779381,  0.10774183, ...,  0.10300907,\n",
+       "          0.10303773,  0.1030594 ]],\n",
+       "\n",
+       "       [[ 0.44230807,  0.44249398,  0.44265214, ...,  0.730337  ,\n",
+       "          0.7303278 ,  0.7303497 ],\n",
+       "        [-0.88791287, -0.8878246 , -0.88774526, ..., -0.6709966 ,\n",
+       "         -0.6710047 , -0.67098564],\n",
+       "        [-0.12639016, -0.12635832, -0.12636189, ..., -0.12795062,\n",
+       "         -0.12796111, -0.1279361 ]]], shape=(3, 3, 216039), dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pressure</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>8.865 8.869 8.86 ... 7.073 7.073</div><input id='attrs-0ed78f99-057c-4dc3-9d4c-526b9acce917' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ed78f99-057c-4dc3-9d4c-526b9acce917' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb3f29c5-3399-43e9-bb40-4b10c57fee12' class='xr-var-data-in' type='checkbox'><label for='data-eb3f29c5-3399-43e9-bb40-4b10c57fee12' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>dbar</dd><dt><span>long_name :</span></dt><dd>Pressure</dd><dt><span>standard_name :</span></dt><dd>sea_water_pressure</dd></dl></div><div class='xr-var-data'><pre>array([8.865, 8.869, 8.86 , ..., 7.068, 7.073, 7.073],\n",
+       "      shape=(216039,), dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1c5324d0-dedb-4b2d-8a50-30fa86a36c6a' class='xr-section-summary-in' type='checkbox'  ><label for='section-1c5324d0-dedb-4b2d-8a50-30fa86a36c6a' class='xr-section-summary' >Indexes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b4658096-4005-4418-9701-61d9d31fd850' class='xr-index-data-in' type='checkbox'/><label for='index-b4658096-4005-4418-9701-61d9d31fd850' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2025-02-25 10:00:03.587208032&#x27;,\n",
+       "               &#x27;2025-02-25 10:00:03.618207931&#x27;,\n",
+       "               &#x27;2025-02-25 10:00:03.649207830&#x27;,\n",
+       "               &#x27;2025-02-25 10:00:03.680207967&#x27;,\n",
+       "               &#x27;2025-02-25 10:00:03.711207866&#x27;,\n",
+       "               &#x27;2025-02-25 10:00:03.742207765&#x27;,\n",
+       "               &#x27;2025-02-25 10:00:03.773207664&#x27;,\n",
+       "               &#x27;2025-02-25 10:00:03.804207563&#x27;,\n",
+       "               &#x27;2025-02-25 10:00:03.835207462&#x27;,\n",
+       "               &#x27;2025-02-25 10:00:03.866207599&#x27;,\n",
+       "               ...\n",
+       "               &#x27;2025-02-25 17:22:33.285971164&#x27;,\n",
+       "               &#x27;2025-02-25 17:22:33.316971302&#x27;,\n",
+       "               &#x27;2025-02-25 17:22:33.347971200&#x27;,\n",
+       "               &#x27;2025-02-25 17:22:33.378971099&#x27;,\n",
+       "               &#x27;2025-02-25 17:22:33.409970998&#x27;,\n",
+       "               &#x27;2025-02-25 17:22:33.440971136&#x27;,\n",
+       "               &#x27;2025-02-25 17:22:33.471971035&#x27;,\n",
+       "               &#x27;2025-02-25 17:22:33.502970933&#x27;,\n",
+       "               &#x27;2025-02-25 17:22:33.533971071&#x27;,\n",
+       "               &#x27;2025-02-25 17:22:33.564970970&#x27;],\n",
+       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, length=216039, freq=None))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>beam</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-efd01a09-807d-4e7d-bbe0-727eef21bdc6' class='xr-index-data-in' type='checkbox'/><label for='index-efd01a09-807d-4e7d-bbe0-727eef21bdc6' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3], dtype=&#x27;int32&#x27;, name=&#x27;beam&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dir</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-51013bab-9732-4ef4-a4be-0535f1430381' class='xr-index-data-in' type='checkbox'/><label for='index-51013bab-9732-4ef4-a4be-0535f1430381' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;dir&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x1</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-e769141e-b98e-44fd-8b97-9174a4b58f02' class='xr-index-data-in' type='checkbox'/><label for='index-e769141e-b98e-44fd-8b97-9174a4b58f02' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3], dtype=&#x27;int32&#x27;, name=&#x27;x1&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>x2</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-b1f2b53a-f25b-4fb5-9424-fbc0addb8094' class='xr-index-data-in' type='checkbox'/><label for='index-b1f2b53a-f25b-4fb5-9424-fbc0addb8094' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2, 3], dtype=&#x27;int32&#x27;, name=&#x27;x2&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>earth</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-ddb448d1-2a7c-4fe0-8f27-a1c3a494b559' class='xr-index-data-in' type='checkbox'/><label for='index-ddb448d1-2a7c-4fe0-8f27-a1c3a494b559' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;E&#x27;, &#x27;N&#x27;, &#x27;U&#x27;], dtype=&#x27;object&#x27;, name=&#x27;earth&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>inst</div></div><div class='xr-index-preview'>PandasIndex</div><input type='checkbox' disabled/><label></label><input id='index-f1d010a6-4a8f-48f2-a0ac-8922cc64b60f' class='xr-index-data-in' type='checkbox'/><label for='index-f1d010a6-4a8f-48f2-a0ac-8922cc64b60f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;X&#x27;, &#x27;Y&#x27;, &#x27;Z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;inst&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-44785129-9e06-4e10-8633-4f6a2e5448af' class='xr-section-summary-in' type='checkbox'  ><label for='section-44785129-9e06-4e10-8633-4f6a2e5448af' class='xr-section-summary' >Attributes: <span>(42)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>inst_make :</span></dt><dd>Nortek</dd><dt><span>inst_model :</span></dt><dd>Vector</dd><dt><span>inst_type :</span></dt><dd>ADV</dd><dt><span>rotate_vars :</span></dt><dd>[&#x27;vel&#x27;, &#x27;accel&#x27;, &#x27;angrt&#x27;]</dd><dt><span>n_beams :</span></dt><dd>3</dd><dt><span>profile_mode :</span></dt><dd>continuous</dd><dt><span>burst_mode :</span></dt><dd>True</dd><dt><span>power_level :</span></dt><dd>1</dd><dt><span>sync_out_pos :</span></dt><dd>end</dd><dt><span>sample_on_sync :</span></dt><dd>False</dd><dt><span>start_on_sync :</span></dt><dd>False</dd><dt><span>compass_update_rate :</span></dt><dd>1</dd><dt><span>n_bins :</span></dt><dd>1</dd><dt><span>deployment_name :</span></dt><dd>VIV</dd><dt><span>wrap_mode :</span></dt><dd>False</dd><dt><span>analog_in :</span></dt><dd>0</dd><dt><span>software_version :</span></dt><dd>2.00.02</dd><dt><span>salinity :</span></dt><dd>32.0</dd><dt><span>comments :</span></dt><dd>VIV deployment Feb2025 Time in PST </dd><dt><span>user_specified_sound_speed :</span></dt><dd>False</dd><dt><span>analog_output :</span></dt><dd>False</dd><dt><span>output_format :</span></dt><dd>Vector</dd><dt><span>serial_output :</span></dt><dd>False</dd><dt><span>power_output_analog :</span></dt><dd>False</dd><dt><span>n_pings_per_burst :</span></dt><dd>1</dd><dt><span>pressure_sensor :</span></dt><dd>yes</dd><dt><span>compass :</span></dt><dd>no</dd><dt><span>tilt_sensor :</span></dt><dd>no</dd><dt><span>carrier_freq_kHz :</span></dt><dd>6000</dd><dt><span>serial_number :</span></dt><dd>VEC14837</dd><dt><span>ProLogFWver :</span></dt><dd>4.13</dd><dt><span>PIC_version :</span></dt><dd>0</dd><dt><span>hardware_rev :</span></dt><dd>4</dd><dt><span>recorder_size_bytes :</span></dt><dd>3037724672</dd><dt><span>vel_range :</span></dt><dd>normal</dd><dt><span>firmware_version :</span></dt><dd>3.43</dd><dt><span>duty_cycle_n_burst :</span></dt><dd>9600</dd><dt><span>duty_cycle_interval :</span></dt><dd>1200</dd><dt><span>duty_cycle_description :</span></dt><dd>300.0 second bursts collected at 32.0 Hz, with bursts taken every 20.0 minutes</dd><dt><span>fs :</span></dt><dd>32.0</dd><dt><span>coord_sys :</span></dt><dd>inst</dd><dt><span>has_imu :</span></dt><dd>1</dd></dl></div></li></ul></div></div>"
       ],
-      "source": [
-        "# Clean the file using the Goring+Nikora method:\n",
-        "mask = api.clean.GN2002(ds.vel, npt=5000)\n",
-        "# Replace bad datapoints via cubic spline interpolation\n",
-        "ds[\"vel\"] = api.clean.clean_fill(ds[\"vel\"], mask, npt=12, method=\"cubic\", maxgap=None)\n",
-        "\n",
-        "print(\"Percent of data containing spikes: {0:.2f}%\".format(100 * mask.mean()))\n",
-        "\n",
-        "# If interpolation isn't desired:\n",
-        "ds_nan = ds.copy(deep=True)\n",
-        "ds_nan.coords[\"mask\"] = ((\"dir\", \"time\"), ~mask)\n",
-        "ds_nan[\"vel\"] = ds_nan[\"vel\"].where(ds_nan[\"mask\"])"
+      "text/plain": [
+       "<xarray.Dataset> Size: 25MB\n",
+       "Dimensions:              (time: 216039, beam: 3, dir: 3, x1: 3, x2: 3,\n",
+       "                          earth: 3, inst: 3)\n",
+       "Coordinates:\n",
+       "  * time                 (time) datetime64[ns] 2MB 2025-02-25T10:00:03.587208...\n",
+       "  * beam                 (beam) int32 12B 1 2 3\n",
+       "  * dir                  (dir) <U1 12B 'X' 'Y' 'Z'\n",
+       "  * x1                   (x1) int32 12B 1 2 3\n",
+       "  * x2                   (x2) int32 12B 1 2 3\n",
+       "  * earth                (earth) <U1 12B 'E' 'N' 'U'\n",
+       "  * inst                 (inst) <U1 12B 'X' 'Y' 'Z'\n",
+       "Data variables: (12/17)\n",
+       "    beam2inst_orientmat  (x1, x2) float64 72B 2.575 -1.305 ... 0.3582 0.3455\n",
+       "    batt                 (time) float32 864kB 13.4 13.4 13.4 ... nan nan nan\n",
+       "    c_sound              (time) float32 864kB 1.477e+03 1.477e+03 ... nan nan\n",
+       "    heading              (time) float32 864kB 0.0 0.0 0.0 0.0 ... nan nan nan\n",
+       "    pitch                (time) float32 864kB 0.0 0.0 0.0 0.0 ... nan nan nan\n",
+       "    roll                 (time) float32 864kB 0.0 0.0 0.0 0.0 ... nan nan nan\n",
+       "    ...                   ...\n",
+       "    amp                  (beam, time) uint8 648kB 84 93 104 103 ... 106 106 95\n",
+       "    corr                 (beam, time) uint8 648kB 94 98 98 97 99 ... 95 97 97 92\n",
+       "    accel                (dir, time) float32 3MB 9.53 9.536 ... -1.229 -1.219\n",
+       "    angrt                (dir, time) float32 3MB 0.000111 ... -0.0005012\n",
+       "    orientmat            (earth, inst, time) float32 8MB 0.1533 ... -0.1279\n",
+       "    pressure             (time) float32 864kB 8.865 8.869 8.86 ... 7.073 7.073\n",
+       "Attributes: (12/42)\n",
+       "    inst_make:                   Nortek\n",
+       "    inst_model:                  Vector\n",
+       "    inst_type:                   ADV\n",
+       "    rotate_vars:                 ['vel', 'accel', 'angrt']\n",
+       "    n_beams:                     3\n",
+       "    profile_mode:                continuous\n",
+       "    ...                          ...\n",
+       "    duty_cycle_n_burst:          9600\n",
+       "    duty_cycle_interval:         1200\n",
+       "    duty_cycle_description:      300.0 second bursts collected at 32.0 Hz, wi...\n",
+       "    fs:                          32.0\n",
+       "    coord_sys:                   inst\n",
+       "    has_imu:                     1"
       ]
-    },
-    {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Coordinate Rotations"
-      ]
-    },
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print the dataset\n",
+    "ds"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A second way provided to look at data is through the *DOLfYN view*. This view has several convenience methods, shortcuts, and functions built-in. It includes an alternate – and somewhat more informative/compact – description of the data object when in interactive mode. This can be accessed using"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now that the data has been cleaned, the next step is to rotate the velocity data into true East, North, Up (ENU) coordinates.\n",
-        "\n",
-        "ADVs use an internal compass or magnetometer to determine magnetic ENU directions. The `set_declination` function takes the user supplied magnetic declination (which can be looked up online for specific coordinates) and adjusts the orientation matrix saved within the dataset.\n",
-        "\n",
-        "Instruments save vector data in the coordinate system specified in the deployment configuration file. To make the data useful, it must be rotated through coordinate systems (\"beam\"<->\"inst\"<->\"earth\"<->\"principal\"), done through the `rotate2` function. If the \"earth\" (ENU) coordinate system is specified, DOLfYN will automatically rotate the dataset through the necessary coordinate systems to get there. The `inplace` set as true will alter the input dataset \"in place\", a.k.a. it not create a new dataset."
+     "data": {
+      "text/plain": [
+       "<ADV data object>:  Nortek Vector\n",
+       "  . 7.37 hours (started: Feb 25, 2025 10:00)\n",
+       "  . inst-frame\n",
+       "  . (216039 pings @ 32.0Hz)\n",
+       "  Variables:\n",
+       "  - time ('time',)\n",
+       "  - vel ('dir', 'time')\n",
+       "  - orientmat ('earth', 'inst', 'time')\n",
+       "  - heading ('time',)\n",
+       "  - pitch ('time',)\n",
+       "  - roll ('time',)\n",
+       "  - temp ('time',)\n",
+       "  - pressure ('time',)\n",
+       "  - amp ('beam', 'time')\n",
+       "  - corr ('beam', 'time')\n",
+       "  - accel ('dir', 'time')\n",
+       "  - angrt ('dir', 'time')\n",
+       "  ... and others (see `<obj>.variables`)"
       ]
-    },
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ds_dolfyn = ds.velds\n",
+    "ds_dolfyn"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Set rotation for cabled ADV head\n",
+    "For cable-head ADVs, be sure to record the position and orientation of the ADV head relative to the ADV pressure case ‘inst’ coordinate system. This ADV was set up in the same orientation as the one shown in Figure 1 [here](https://dolfyn.readthedocs.io/en/stable/motion-correction.html); the rotation matrix for this setup is written below. Per the Nortek documentation, in this orientation, the probe \"X\" direction is the IMU's \"-Z\" direction.\n",
+    "\n",
+    "If utilizing a non-cabled ADV (the standard version), skip this step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This is the rotation matrix per Figure 1: https://dolfyn.readthedocs.io/en/stable/motion-correction.html\n",
+    "dolfyn.set_inst2head_rotmat(ds, [[0, 0, -1], [0, -1, 0], [-1, 0, 0]])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Quality Control"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "ADV velocity data tends to have spikes due to Doppler noise. There are multiple approaches to removing bad values, including trimming beyond a maximum range, removing values with low acoustic correlation values, and finally another is to \"despike\" the data is by using the phase-space algorithm by Goring and Nikora (2002). DOLfYN integrates QC functions using a 2-step approach: create a logical mask where True corresponds to a spike detection, and then remove and/or interpolate those values."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# First set the magnetic declination\n",
-        "dolfyn.set_declination(\n",
-        "    ds, declin=10, inplace=True\n",
-        ")  # declination points 10 degrees East\n",
-        "\n",
-        "# Rotate that data from the instrument to earth frame (ENU):\n",
-        "dolfyn.rotate2(ds, \"earth\", inplace=True)"
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Percent of data containing spikes: 0.12%\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Remove low correlation values and noisy beam measurements\n",
+    "ds.velds.rotate2(\"beam\")\n",
+    "# Start a mask by copying the velocity data-array\n",
+    "mask = ds[\"vel\"].copy() * 0\n",
+    "# Now we'll remove values with correlation < 80% and velocity outside of +/- 0.7 m/s\n",
+    "# We decide this +/- 0.7 m/s threshold by looking at plots of the along-beam velocity\n",
+    "mask = mask + (ds[\"corr\"].values < 80) + (ds[\"vel\"] > 0.7) + (ds[\"vel\"] < -0.7)\n",
+    "# Set the mask to boolean\n",
+    "mask = mask.astype(bool)\n",
+    "# Replace bad datapoints using the ensemble mean over a 5-minute window\n",
+    "ds[\"vel\"] = dolfyn.adv.clean.fill_nan_ensemble_mean(\n",
+    "    ds[\"vel\"], mask, int(ds.fs), window=300\n",
+    ")\n",
+    "print(\"Percent of data containing spikes: {0:.2f}%\".format(100 * mask.mean()))\n",
+    "\n",
+    "# How to use the Goring+Nikora method to clean spikes in ADV data:\n",
+    "ds_ex = ds.copy(deep=True)\n",
+    "# Clean the file using the Goring+Nikora method:\n",
+    "mask_ex = api.clean.GN2002(ds.vel, npt=5000)\n",
+    "# Replace bad datapoints via cubic spline interpolation\n",
+    "ds_ex[\"vel\"] = api.clean.clean_fill(ds_ex[\"vel\"], mask, npt=12, method=\"cubic\", maxgap=None)\n",
+    "\n",
+    "# If interpolation isn't desired:\n",
+    "ds_nan = ds.copy(deep=True)\n",
+    "ds_nan.coords[\"mask\"] = ((\"dir\", \"time\"), ~mask_ex)\n",
+    "ds_nan[\"vel\"] = ds_nan[\"vel\"].where(ds_nan[\"mask\"])"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Coordinate Rotations"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Now that the data has been cleaned, the next step is to rotate the velocity data into true East, North, Up (ENU) coordinates.\n",
+    "\n",
+    "ADVs use an internal compass or magnetometer to determine magnetic ENU directions. The `set_declination` function takes the user supplied magnetic declination (which can be looked up online for specific coordinates) and adjusts the orientation matrix saved within the dataset.\n",
+    "\n",
+    "Instruments save vector data in the coordinate system specified in the deployment configuration file. To make the data useful, it must be rotated through coordinate systems (\"beam\"<->\"inst\"<->\"earth\"<->\"principal\"), done through the `rotate2` function. If the \"earth\" (ENU) coordinate system is specified, DOLfYN will automatically rotate the dataset through the necessary coordinate systems to get there. The `inplace` set as true will alter the input dataset \"in place\", i.e., it not create a new dataset."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First set the magnetic declination\n",
+    "dolfyn.set_declination(\n",
+    "    ds, declin=15.3, inplace=True\n",
+    ")  # declination points 15.3 degrees East\n",
+    "\n",
+    "# Rotate that data from the instrument to earth frame (ENU):\n",
+    "dolfyn.rotate2(ds, \"earth\", inplace=True)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once in the true ENU frame of reference, we can calculate the principal flow direction for the velocity data and rotate it into the principal frame of reference (streamwise, cross-stream, vertical). Principal flow directions are aligned with and orthogonal to the flow streamlines at the measurement location. \n",
+    "\n",
+    "First, the principal flow direction must be calculated through `calc_principal_heading`. As a standard for DOLfYN functions, those that begin with \"calc_*\" require the velocity data for input. This function is different from others in DOLfYN in that it requires placing the output in an attribute called \"principal_heading\", as shown below.\n",
+    "\n",
+    "Again we use `rotate2` to change coordinate systems."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds.attrs[\"principal_heading\"] = dolfyn.calc_principal_heading(ds[\"vel\"])\n",
+    "dolfyn.rotate2(ds, \"principal\", inplace=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Once we've done quality control and coordinate rotations (not necessarily in that order), we can plot the velocity vector to see how it looks:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Once in the true ENU frame of reference, we can calculate the principal flow direction for the velocity data and rotate it into the principal frame of reference (streamwise, cross-stream, vertical). Principal flow directions are aligned with and orthogonal to the flow streamlines at the measurement location. \n",
-        "\n",
-        "First, the principal flow direction must be calculated through `calc_principal_heading`. As a standard for DOLfYN functions, those that begin with \"calc_*\" require the velocity data for input. This function is different from others in DOLfYN in that it requires place the output in an attribute called \"principal_heading\", as shown below.\n",
-        "\n",
-        "Again we use `rotate2` to change coordinate systems."
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, '')"
       ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "ds.attrs[\"principal_heading\"] = dolfyn.calc_principal_heading(ds[\"vel\"])\n",
-        "dolfyn.rotate2(ds, \"principal\", inplace=True)"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "plt.figure()\n",
+    "ds[\"vel\"][0].plot(label=\"streamwise\")\n",
+    "ds[\"vel\"][1].plot(label=\"cross-stream\")\n",
+    "ds[\"vel\"][2].plot(label=\"vertical\")\n",
+    "plt.legend()\n",
+    "plt.title(\"\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Averaging Data\n",
+    "The next step in ADV analysis is to average the velocity data into time bins (ensembles) and calculate turbulence statistics. These averaged values are then used to calculate turbulence statistics. There are two distinct methods for performing this operation, both of which utilize the same variable inputs and produce identical datasets.\n",
+    "\n",
+    "1. **Object-Oriented Approach** (standard): Define an 'averaging object', create a dataset binned in time, and calculate basic turbulence statistics. This is accomplished by initiating an object from the ADVBinner class and then feeding that object with our dataset.\n",
+    "\n",
+    "2. **Functional Approach** (simple): The same operations can be performed using the functional counterpart of ADVBinner, turbulence_statistics.\n",
+    "\n",
+    "Function inputs shown here are the dataset itself: \n",
+    " - `n_bin`: the number of elements in each bin; \n",
+    " - `fs`: the ADV's sampling frequency in Hz; \n",
+    " - `n_fft`: optional, the number of elements per FFT for spectral analysis; \n",
+    " - `freq_units`: optional, either in Hz or rad/s, of the calculated spectral frequency vector.\n",
+    "\n",
+    "All of the variables in the returned dataset have been bin-averaged, where each average is computed using the number of elements specified in `n_bins`. Additional variables in this dataset include the turbulent kinetic energy (TKE) vector (\"ds_binned.tke_vec\"), the Reynold's stresses (\"ds_binned.stress\"), and the power spectral densities (\"ds_binned.psd\"), calculated for each bin."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Option 1 (standard)\n",
+    "binner = api.ADVBinner(n_bin=ds.fs * 600, fs=ds.fs, n_fft=ds.fs * 600)\n",
+    "ds_binned = binner.bin_average(ds)\n",
+    "\n",
+    "# Option 2 (simple)\n",
+    "# ds_binned = api.calc_turbulence(ds, n_bin=ds.fs*600, fs=ds.fs, n_fft=ds.fs*600, freq_units=\"Hz\")"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The benefit to using `ADVBinner` is that one has access to all of the velocity and turbulence analysis functions that DOLfYN contains. If basic analysis will suffice, the `turbulence_statistics` function is the most convienent. Either option can still utilize DOLfYN's shortcuts.\n",
+    "\n",
+    "See the [DOLfYN API](https://dolfyn.readthedocs.io/en/latest/apidoc/dolfyn.binners.html) for the full list of functions and shortcuts. A few examples are shown below.\n",
+    "\n",
+    "Some things to know:\n",
+    "- All functions operate bin-by-bin.\n",
+    "- Some functions will fail if there are NaN's in the data stream (Notably the PSD functions)\n",
+    "- \"Shortcuts\", as referred to in DOLfYN, are functions accessible by the xarray accessor `velds`, as shown below. The list of \"shortcuts\" available through `velds` are listed [here](https://dolfyn.readthedocs.io/en/latest/apidoc/dolfyn.shortcuts.html). Some shortcut variables require the raw dataset, some an averaged dataset.\n",
+    "\n",
+    "For instance, \n",
+    "- `bin_variance` calculates the binned-variance of each variable in the raw dataset, the complementary to `bin_average`. Variables returned by this function contain a \"_var\" suffix to their name.\n",
+    "- `power_spectral_density` calculates the power spectral density (velocity spectra) of the velocity vector\n",
+    "- `cross_spectral_density` calculates the cross spectral density between each direction of the supplied DataArray. Note that inputs specified in creating the `ADVBinner` object can be overridden or additionally specified for a particular function call.\n",
+    "- `turbulence_intensity` is calculated from the ratio of the standard deviation of the horizontal velocity magnitude (equivalent to the RMS of turbulent velocity fluctuations) to the mean of the horizontal velocity magnitude\n",
+    "- `integral_length_scales` estimates the integral length scale in the streamwise, transverse, and vertical directions from the first crossing of the autocorrelation function\n",
+    "- `turbulent_kinetic_energy` calculates the TKE (Reynolds normal stress) components\n",
+    "- `reynolds_stress` calculates the Reynolds shear stress components\n",
+    "- `dissipation_rate_LT83` uses the Lumley and Terray 1983 algorithm to estimate the TKE dissipation rate from the isoropic turbulence cascade seen in the spectral. This requires the frequency range of the cascade as input.\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Calculate the variance of each variable in the dataset and add to the averaged dataset\n",
+    "ds_binned = binner.bin_variance(ds, out_ds=ds_binned)\n",
+    "\n",
+    "# Calculate the power spectral density\n",
+    "ds_binned[\"auto_spectra\"] = binner.power_spectral_density(ds[\"vel\"], freq_units=\"Hz\")\n",
+    "\n",
+    "# Calculate the cross power spectral densities\n",
+    "ds_binned[\"cross_spectra\"] = binner.cross_spectral_density(\n",
+    "    ds[\"vel\"], freq_units=\"Hz\", n_fft_coh=ds.fs * 200\n",
+    ")\n",
+    "\n",
+    "# Water speed and direction\n",
+    "ds_binned[\"U_mag\"] = ds_binned.velds.U_mag\n",
+    "ds_binned[\"U_dir\"] = ds_binned.velds.U_dir\n",
+    "\n",
+    "# Calculate the Doppler noise level from the white noise floor of the auto-spectra\n",
+    "ds_binned[\"noise\"] = binner.doppler_noise_level(ds_binned[\"auto_spectra\"], pct_fN=0.8)\n",
+    "\n",
+    "# Calculate the turbulence intensity and subtract the average horizontal velocity noise level\n",
+    "ds_binned[\"TI\"] = binner.turbulence_intensity(\n",
+    "    ds.velds.U_mag, noise=ds_binned[\"noise\"][:2].mean(\"S\")\n",
+    ")\n",
+    "\n",
+    "# Calculate the auto-covariance to find the integral length scales\n",
+    "autocov = binner.autocovariance(ds[\"vel\"])\n",
+    "ds_binned[\"length_scale\"] = binner.integral_length_scales(autocov, ds_binned[\"U_mag\"])\n",
+    "\n",
+    "# Calculate the TKE components and Reynolds shear stresses\n",
+    "ds_binned['tke_vec'] = binner.turbulent_kinetic_energy(ds[\"vel\"])\n",
+    "ds_binned['stress_vec'] = binner.reynolds_stress(ds[\"vel\"])\n",
+    "\n",
+    "# Calculate dissipation rate from isotropic turbulence cascade\n",
+    "ds_binned[\"dissipation_rate\"] = binner.dissipation_rate_LT83(\n",
+    "    ds_binned[\"auto_spectra\"], ds_binned[\"U_mag\"], noise=ds_binned[\"noise\"], freq_range=[0.8, 2]\n",
+    ")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Saving and Loading DOLfYN datasets\n",
+    "Datasets can be saved and reloaded using the `save` and `load` functions. Xarray is saved natively in netCDF format, hence the \".nc\" extension.\n",
+    "\n",
+    "Note: DOLfYN datasets cannot be saved using xarray's native `ds.to_netcdf`; however, DOLfYN datasets can be opened using `xarray.open_dataset`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Uncomment these lines to save and load to your current working directory\n",
+    "# dolfyn.save(ds, 'your_data.nc')\n",
+    "# ds_saved = dolfyn.load('your_data.nc')"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Visualization\n",
+    "Plotting can be performed using matplotlib. As an example, the mean spectrum in the streamwise direction is plotted here. This spectrum shows the mean energy density in the flow at a particular flow frequency."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Averaging Data\n",
-        "The next step in ADV analysis is to average the velocity data into time bins (ensembles) and calculate turbulence statistics. These averaged values are then used to calculate turbulence statistics. There are two distinct methods for performing this operation, both of which utilize the same variable inputs and produce identical datasets.\n",
-        "\n",
-        "1. **Object-Oriented Approach** (standard): Define an 'averaging object', create a dataset binned in time, and calculate basic turbulence statistics. This is accomplished by initiating an object from the ADVBinner class and then feeding that object with our dataset.\n",
-        "\n",
-        "2. **Functional Approach** (simple): The same operations can be performed using the functional counterpart of ADVBinner, turbulence_statistics.\n",
-        "\n",
-        "Function inputs shown here are the dataset itself: \n",
-        " - `n_bin`: the number of elements in each bin; \n",
-        " - `fs`: the ADV's sampling frequency in Hz; \n",
-        " - `n_fft`: optional, the number of elements per FFT for spectral analysis; \n",
-        " - `freq_units`: optional, either in Hz or rad/s, of the calculated spectral frequency vector.\n",
-        "\n",
-        "All of the variables in the returned dataset have been bin-averaged, where each average is computed using the number of elements specified in `n_bins`. Additional variables in this dataset include the turbulent kinetic energy (TKE) vector (\"ds_binned.tke_vec\"), the Reynold's stresses (\"ds_binned.stress\"), and the power spectral densities (\"ds_binned.psd\"), calculated for each bin."
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1b22a9eec90>"
       ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": 8,
-      "metadata": {
-        "scrolled": true
-      },
-      "outputs": [],
-      "source": [
-        "# Option 1 (standard)\n",
-        "binner = api.ADVBinner(n_bin=ds.fs * 600, fs=ds.fs, n_fft=1024)\n",
-        "ds_binned = binner.bin_average(ds)\n",
-        "\n",
-        "# Option 2 (simple)\n",
-        "# ds_binned = api.calc_turbulence(ds, n_bin=ds.fs*600, fs=ds.fs, n_fft=1024, freq_units=\"Hz\")"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(5, 4), constrained_layout=True)\n",
+    "for spec in ds_binned[\"S\"]:\n",
+    "    ax.loglog(\n",
+    "        ds_binned[\"freq\"],\n",
+    "        ds_binned[\"auto_spectra\"].sel(S=spec).mean(dim=\"time\"),\n",
+    "        label=spec.values,\n",
+    "    )\n",
+    "ax.plot(\n",
+    "    ds_binned[\"freq\"], 4e-5 * ds_binned[\"freq\"] ** (-5 / 3), \"k--\", label=\"f$^{-5/3}$ slope\"\n",
+    ")\n",
+    "ax.set(ylim=(1e-5, 1), xlabel=\"Frequency [Hz]\", ylabel=\"Spectra [m$^2$/s$^2$/Hz]\")\n",
+    "ax.grid()\n",
+    "ax.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The benefit to using `ADVBinner` is that one has access to all of the velocity and turbulence analysis functions that DOLfYN contains. If basic analysis will suffice, the `turbulence_statistics` function is the most convienent. Either option can still utilize DOLfYN's shortcuts.\n",
-        "\n",
-        "See the [DOLfYN API](https://dolfyn.readthedocs.io/en/latest/apidoc/dolfyn.binners.html) for the full list of functions and shortcuts. A few examples are shown below.\n",
-        "\n",
-        "Some things to know:\n",
-        "- All functions operate bin-by-bin.\n",
-        "- Some functions will fail if there are NaN's in the data stream (Notably the PSD functions)\n",
-        "- \"Shorcuts\", as referred to in DOLfYN, are functions accessible by the xarray accessor `velds`, as shown below. The list of \"shorcuts\" available through `velds` are listed [here](https://dolfyn.readthedocs.io/en/latest/apidoc/dolfyn.shortcuts.html). Some shorcut variables require the raw dataset, some an averaged dataset.\n",
-        "\n",
-        "For instance, \n",
-        "- `bin_variance` calculates the binned-variance of each variable in the raw dataset, the complementary to `bin_average`. Variables returned by this function contain a \"_var\" suffix to their name.\n",
-        "- `turbulence_intensity` is calculated from the ratio of the standard deviation of the horizontal velocity magnitude (equivalent to the RMS of turbulent velocity fluctuations) to the mean of the horizontal velocity magnitude\n",
-        "- `power_spectral_density` calculates the power spectral density (velocity spectra) of the velocity vector\n",
-        "- `cross_spectral_density` calculates the cross spectral density between each direction of the supplied DataArray. Note that inputs specified in creating the `ADVBinner` object can be overridden or additionally specified for a particular function call.\n",
-        "- `dissipation_rate_LT83` uses the Lumley and Terray 1983 algorithm to estimate the TKE dissipation rate from the isoropic turbulence cascade seen in the spectral. This requires the frequency range of the cascade as input.\n",
-        "- `turbulent_kinetic_energy` calculates the TKE (Reynolds normal stress) components\n",
-        "- `reynolds_stress` calculates the Reynolds shear stress components\n",
-        "\n"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Turbulence intensity\n",
+    "fig, ax = plt.subplots(figsize=(5, 4), constrained_layout=True)\n",
+    "ax.scatter(\n",
+    "    abs(ds_binned[\"U_mag\"]),\n",
+    "    ds_binned[\"TI\"] * 100,\n",
+    ")\n",
+    "ax.set(xlabel=\"Velocity [m/s]\", ylabel=\"Turbulence Intensity [%]\", xlim=[0, 2], ylim=[0, 35])\n",
+    "ax.set_axisbelow(True)  # set grid below data\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {
-        "scrolled": true
-      },
-      "outputs": [],
-      "source": [
-        "# Calculate the variance of each variable in the dataset and add to the averaged dataset\n",
-        "ds_binned = binner.bin_variance(ds, out_ds=ds_binned)\n",
-        "\n",
-        "# Calculate the turbulence intensity\n",
-        "ds_binned[\"TI\"] = binner.turbulence_intensity(ds.velds.U_mag)\n",
-        "\n",
-        "# Calculate the power spectral density\n",
-        "ds_binned[\"auto_spectra\"] = binner.power_spectral_density(ds[\"vel\"], freq_units=\"Hz\")\n",
-        "\n",
-        "# Calculate the cross power spectral density\n",
-        "ds_binned[\"cross_spectra\"] = binner.cross_spectral_density(\n",
-        "    ds[\"vel\"], freq_units=\"Hz\", n_fft_coh=512\n",
-        ")\n",
-        "\n",
-        "# Calculate dissipation rate from isotropic turbulence cascade\n",
-        "ds_binned[\"dissipation\"] = binner.dissipation_rate_LT83(\n",
-        "    ds_binned[\"auto_spectra\"], ds_binned.velds.U_mag, freq_range=[0.5, 1]\n",
-        ")\n",
-        "\n",
-        "# Calculate the Reynolds stresses\n",
-        "ds_binned['tke_vec'] = binner.turbulent_kinetic_energy(ds[\"vel\"])\n",
-        "ds_binned['stress_vec'] = binner.reynolds_stress(ds[\"vel\"])\n"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x400 with 1 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# TKE Dissipation rate\n",
+    "import numpy as np\n",
+    "\n",
+    "x_ticks = [0.1, 0.5, 1, 2]\n",
+    "y_ticks = [0.00001, 0.0001, 0.001, 0.01]\n",
+    "y_labels = [r\"10$^{-5}$\", r\"10$^{-4}$\", r\"10$^{-3}$\", r\"10$^{-2}$\"]\n",
+    "U = np.arange(0, 2, 0.1)\n",
+    "\n",
+    "fig, ax = plt.subplots(\n",
+    "    figsize=(5, 4),\n",
+    "    subplot_kw={\"xscale\": \"log\", \"yscale\": \"log\"},\n",
+    "    constrained_layout=True,\n",
+    ")\n",
+    "ax.scatter(\n",
+    "    abs(ds_binned[\"U_mag\"]),\n",
+    "    ds_binned[\"dissipation_rate\"][0]\n",
+    ")\n",
+    "ax.plot(U, U**3 * 10 ** (-3), \"--\", color=\"black\", label=\"U$^3$ slope\")\n",
+    "\n",
+    "# Set axis labels\n",
+    "ax.set(\n",
+    "    xlabel=\"Velocity [m/s]\",\n",
+    "    ylabel=\"Dissipation Rate [m$^2$/s$^3$]\",\n",
+    "    ylim=(y_ticks[0], y_ticks[-1]),\n",
+    "    yticks=y_ticks,\n",
+    "    yticklabels=y_labels,\n",
+    "    xlim=(x_ticks[0], x_ticks[-1]),\n",
+    "    xticks=x_ticks,\n",
+    "    xticklabels=x_ticks,\n",
+    ")\n",
+    "ax.set_axisbelow(True)  # set grid below data\n",
+    "ax.grid()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Plotting can be performed using matplotlib. As an example, the mean spectrum in the streamwise direction is plotted here. This spectrum shows the mean energy density in the flow at a particular flow frequency."
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1b2336a9af0>"
       ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "Text(0.5, 1.0, 'Streamwise Direction')"
-            ]
-          },
-          "execution_count": 10,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 1 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "import matplotlib.pyplot as plt\n",
-        "\n",
-        "%matplotlib inline\n",
-        "\n",
-        "plt.figure()\n",
-        "plt.loglog(ds_binned[\"freq\"], ds_binned[\"auto_spectra\"].sel(S=\"Sxx\").mean(dim=\"time\"))\n",
-        "plt.xlabel(\"Frequency [Hz]\")\n",
-        "plt.ylabel(\"Energy Density $\\mathrm{[m^2/s^s/Hz]}$\")\n",
-        "plt.title(\"Streamwise Direction\")"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Length scales\n",
+    "fig, ax = plt.subplots(\n",
+    "    figsize=(7, 3), subplot_kw={\"yscale\": \"log\"}, constrained_layout=True\n",
+    ")\n",
+    "ax.plot(ds_binned[\"time\"].values, ds_binned[\"length_scale\"][0], \"o\")\n",
+    "ax.plot(ds_binned[\"time\"].values, ds_binned[\"length_scale\"][1], \"D\")\n",
+    "ax.plot(ds_binned[\"time\"].values, ds_binned[\"length_scale\"][2], \"s\")\n",
+    "\n",
+    "ax.set(xlabel=\"Time\", ylabel=\"Length Scale [m]\", ylim=(0.05, 50))\n",
+    "ax.legend(\n",
+    "    (\"Streamwise\", \"Transverse\", \"Vertical\"),\n",
+    "    bbox_to_anchor=(-0.05, 1.02, 1, 0.102),\n",
+    "    loc=\"lower center\",\n",
+    "    ncol=3,\n",
+    "    handlelength=1,\n",
+    "    columnspacing=0.5,\n",
+    "    handletextpad=0.3,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
     {
-      "attachments": {},
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Saving and Loading DOLfYN datasets\n",
-        "Datasets can be saved and reloaded using the `save` and `load` functions. Xarray is saved natively in netCDF format, hence the \".nc\" extension.\n",
-        "\n",
-        "Note: DOLfYN datasets cannot be saved using xarray's native `ds.to_netcdf`; however, DOLfYN datasets can be opened using `xarray.open_dataset`."
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x1b234258c20>"
       ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Uncomment these lines to save and load to your current working directory\n",
-        "# dolfyn.save(ds, 'your_data.nc')\n",
-        "# ds_saved = dolfyn.load('your_data.nc')"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x300 with 1 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "Python 3.9.12 ('base')",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11.7"
-    },
-    "vscode": {
-      "interpreter": {
-        "hash": "357206ab7e4935423e95e994af80e27e7e6c0672abcebb9d86ab743298213348"
-      }
-    }
+   ],
+   "source": [
+    "# Reynolds stresses\n",
+    "fig, ax = plt.subplots(figsize=(7, 3), constrained_layout=True)\n",
+    "# u'w' bar\n",
+    "ax.plot(\n",
+    "    ds_binned[\"time\"].values,\n",
+    "    np.log10(abs(ds_binned[\"stress_vec\"][1])),\n",
+    "    \"o-\"\n",
+    ")\n",
+    "# v'w' bar\n",
+    "ax.plot(\n",
+    "    ds_binned[\"time\"].values,\n",
+    "    np.log10(abs(ds_binned[\"stress_vec\"][2])),\n",
+    "    \"s-\"\n",
+    ")\n",
+    "\n",
+    "y_labels = [r\"10$^{-5}$\", r\"10$^{-4}$\", r\"10$^{-3}$\", r\"10$^{-2}$\", r\"10$^{-1}$\"]\n",
+    "ax.set(\n",
+    "    xlabel=\"Time\",\n",
+    "    ylabel=r\"$\\overline{u_iu_j}$ \" + \"[m$^2$/s$^2$]\",\n",
+    "    ylim=(-5, -1),\n",
+    "    yticks=[-5, -4, -3, -2, -1],\n",
+    "    yticklabels=y_labels,\n",
+    ")\n",
+    "ax.legend(\n",
+    "    (r\"$\\overline{u'w'}$\", r\"$\\overline{v'w'}$\"),\n",
+    "    # (r\"$\\overline{u'v'}$\", r\"$\\overline{u'w'}$\", r\"$\\overline{v'w'}$\"),\n",
+    "    bbox_to_anchor=(-0.05, 1.02, 1, 0.102),\n",
+    "    loc=\"lower center\",\n",
+    "    ncol=3,\n",
+    "    handlelength=1,\n",
+    "    columnspacing=0.5,\n",
+    "    handletextpad=0.3,\n",
+    "    fontsize=12,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 4
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }
diff --git a/examples/data/dolfyn/Sig1000_dp_echo.ad2cp b/examples/data/dolfyn/Sig1000_dp_echo.ad2cp
new file mode 100644
index 000000000..37011fdbe
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diff --git a/examples/data/dolfyn/Sig1000_online.ad2cp b/examples/data/dolfyn/Sig1000_online.ad2cp
new file mode 100644
index 000000000..c6bd14c35
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diff --git a/examples/data/dolfyn/Sig100_avg.ad2cp b/examples/data/dolfyn/Sig100_avg.ad2cp
new file mode 100644
index 000000000..e16b2dd98
Binary files /dev/null and b/examples/data/dolfyn/Sig100_avg.ad2cp differ
diff --git a/examples/data/dolfyn/Sig100_raw_avg.ad2cp b/examples/data/dolfyn/Sig100_raw_avg.ad2cp
new file mode 100644
index 000000000..3099caa53
Binary files /dev/null and b/examples/data/dolfyn/Sig100_raw_avg.ad2cp differ
diff --git a/examples/data/dolfyn/dual_profile.ad2cp b/examples/data/dolfyn/Sig500_dp_ice.ad2cp
similarity index 100%
rename from examples/data/dolfyn/dual_profile.ad2cp
rename to examples/data/dolfyn/Sig500_dp_ice.ad2cp
diff --git a/examples/data/dolfyn/sentinelv_b5.pd0 b/examples/data/dolfyn/sentinelv_b5.pd0
new file mode 100644
index 000000000..ac738ccc3
Binary files /dev/null and b/examples/data/dolfyn/sentinelv_b5.pd0 differ
diff --git a/examples/data/dolfyn/test_data/RDI_7f79.nc b/examples/data/dolfyn/test_data/RDI_7f79.nc
index 0d0736649..a43b3d7b7 100644
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diff --git a/examples/data/dolfyn/test_data/RDI_7f79_2.nc b/examples/data/dolfyn/test_data/RDI_7f79_2.nc
index 4874189da..618447a78 100644
Binary files a/examples/data/dolfyn/test_data/RDI_7f79_2.nc and b/examples/data/dolfyn/test_data/RDI_7f79_2.nc differ
diff --git a/examples/data/dolfyn/test_data/RDI_test01.nc b/examples/data/dolfyn/test_data/RDI_test01.nc
index bd5bbc7ff..b930e9595 100644
Binary files a/examples/data/dolfyn/test_data/RDI_test01.nc and b/examples/data/dolfyn/test_data/RDI_test01.nc differ
diff --git a/examples/data/dolfyn/test_data/RDI_test01_clean.nc b/examples/data/dolfyn/test_data/RDI_test01_clean.nc
index 6f2e72ea7..d0599d262 100644
Binary files a/examples/data/dolfyn/test_data/RDI_test01_clean.nc and b/examples/data/dolfyn/test_data/RDI_test01_clean.nc differ
diff --git a/examples/data/dolfyn/test_data/RDI_test01_ofilt.nc b/examples/data/dolfyn/test_data/RDI_test01_ofilt.nc
index 37d75ebca..cf04013d2 100644
Binary files a/examples/data/dolfyn/test_data/RDI_test01_ofilt.nc and b/examples/data/dolfyn/test_data/RDI_test01_ofilt.nc differ
diff --git a/examples/data/dolfyn/test_data/RDI_test01_rotate_beam2inst.nc b/examples/data/dolfyn/test_data/RDI_test01_rotate_beam2inst.nc
index 88d701003..169e39366 100644
Binary files a/examples/data/dolfyn/test_data/RDI_test01_rotate_beam2inst.nc and b/examples/data/dolfyn/test_data/RDI_test01_rotate_beam2inst.nc differ
diff --git a/examples/data/dolfyn/test_data/RDI_test01_rotate_earth2principal.nc b/examples/data/dolfyn/test_data/RDI_test01_rotate_earth2principal.nc
index 95ee62090..9f1865bf1 100644
Binary files a/examples/data/dolfyn/test_data/RDI_test01_rotate_earth2principal.nc and b/examples/data/dolfyn/test_data/RDI_test01_rotate_earth2principal.nc differ
diff --git a/examples/data/dolfyn/test_data/RDI_test01_rotate_inst2earth.nc b/examples/data/dolfyn/test_data/RDI_test01_rotate_inst2earth.nc
index db36c437f..a54aa6ce2 100644
Binary files a/examples/data/dolfyn/test_data/RDI_test01_rotate_inst2earth.nc and b/examples/data/dolfyn/test_data/RDI_test01_rotate_inst2earth.nc differ
diff --git a/examples/data/dolfyn/test_data/RDI_withBT.dolfyn.log b/examples/data/dolfyn/test_data/RDI_withBT.dolfyn.log
index d79f07fcd..1b06d4b89 100644
--- a/examples/data/dolfyn/test_data/RDI_withBT.dolfyn.log
+++ b/examples/data/dolfyn/test_data/RDI_withBT.dolfyn.log
@@ -3,8 +3,9 @@ root - INFO - cfgid0: [7f, 7f]
 root - INFO - {'nbyte': 579, 'dat_offsets': array([ 20,  79, 144, 282, 352, 422, 492])}
 root - INFO - pos 20
 root - INFO - id 0 offset 20
-root - INFO - Number of cells set to 17
-root - INFO - Cell size set to 1.0
+root - DEBUG - Number of cells set to 17
+root - DEBUG - Cell size set to 1.0
+root - DEBUG - Bin 1 distance set to 2.09
 root - INFO - Read Config
 root - INFO - Read Fixed
 root - INFO - id 128 offset 79
@@ -13,12 +14,9 @@ root - INFO - id 512 offset 282
 root - INFO - id 768 offset 352
 root - INFO - id 1024 offset 422
 root - INFO - id 1536 offset 492
-root - INFO - Done: {'prog_ver': 51.41, 'inst_model': 'Workhorse', 'beam_angle': 20, 'freq': 600, 'beam_pattern': 'convex', 'orientation': 'down', 'n_beams': 4, 'n_cells': 17, 'pings_per_ensemble': 1, 'cell_size': 1.0, 'blank_dist': 0.88, 'profiling_mode': 1, 'min_corr_threshold': 64, 'n_code_reps': 5, 'min_prcnt_gd': 0, 'max_error_vel': 2.0, 'sec_between_ping_groups': 0.5, 'coord_sys': 'earth', 'use_pitchroll': 'yes', 'use_3beam': 'yes', 'bin_mapping': 'yes', 'heading_misalign_deg': 0.0, 'magnetic_var_deg': 0.0, 'sensors_src': '01111101', 'sensors_avail': '00111101', 'bin1_dist_m': 2.09, 'transmit_pulse_m': 1.18, 'water_ref_cells': [1, 5], 'false_target_threshold': 50, 'transmit_lag_m': 0.24, 'bandwidth': 0, 'power_level': 255, 'serialnum': 18655}
-root - INFO - self._bb False
-root - INFO - self.cfgbb: {}
-root - INFO -   taking data from pings 0 - 1721
-root - INFO -   1721 ensembles will be produced.
-
+root - INFO - Done: {'firmware_ver': 51.41, 'inst_make': 'TRDI', 'inst_type': 'ADCP', 'rotate_vars': ['vel'], 'has_imu': 0, 'inst_model': 'Workhorse', 'beam_angle': 20, 'carrier_freq': 600, 'beam_pattern': 'convex', 'orientation': 'down', 'n_beams': 4, 'n_cells': 17, 'pings_per_ensemble': 1, 'cell_size': 1.0, 'blank_dist': 0.88, 'profiling_mode': 1, 'min_corr_threshold': 64, 'n_code_reps': 5, 'min_prcnt_gd': 0, 'max_error_vel': 2.0, 'sec_between_ping_groups': 0.5, 'coord_sys': 'earth', 'use_pitchroll': 'yes', 'use_3beam': 'yes', 'bin_mapping': 'yes', 'heading_misalign_deg': 0.0, 'magnetic_var_deg': 0.0, 'sensors_src': '01111101', 'sensors_avail': '00111101', 'bin1_dist_m': 2.09, 'transmit_pulse_m': 1.18, 'water_ref_cells': [1, 5], 'false_target_threshold': 50, 'transmit_lag_m': 0.24, 'bandwidth': 0, 'power_level': 255, 'serialnum': 18655}
+root - INFO - self._BB False
+root - INFO - self.cfgBB: {}
 root - INFO - 17 ncells, not BB
 root - DEBUG -   pos 0mb/1mb
 
@@ -99,3 +97,5 @@ root - DEBUG - Trying to Read 512
 root - INFO -   Reading code 0x200...
 root - INFO - Read Corr
 root - INFO -     success!
+root - INFO - n 4: 768 0300
+root - DEBUG -   pos: 935, pos_: 0, nbyte: 70, k: 0, byte_offset: -1
diff --git a/examples/data/dolfyn/test_data/RDI_withBT.nc b/examples/data/dolfyn/test_data/RDI_withBT.nc
index 5864a5851..d84523619 100644
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diff --git a/examples/data/dolfyn/test_data/RiverPro_test01.nc b/examples/data/dolfyn/test_data/RiverPro_test01.nc
index 719ad6102..b20dadf52 100644
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diff --git a/examples/data/dolfyn/test_data/Sig1000_dp_echo1.nc b/examples/data/dolfyn/test_data/Sig1000_dp_echo1.nc
new file mode 100644
index 000000000..28cef003d
Binary files /dev/null and b/examples/data/dolfyn/test_data/Sig1000_dp_echo1.nc differ
diff --git a/examples/data/dolfyn/test_data/Sig1000_dp_echo2.nc b/examples/data/dolfyn/test_data/Sig1000_dp_echo2.nc
new file mode 100644
index 000000000..d2fb9f4e2
Binary files /dev/null and b/examples/data/dolfyn/test_data/Sig1000_dp_echo2.nc differ
diff --git a/examples/data/dolfyn/test_data/Sig1000_online.nc b/examples/data/dolfyn/test_data/Sig1000_online.nc
new file mode 100644
index 000000000..d028a6ffe
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diff --git a/examples/data/dolfyn/test_data/Sig1000_tidal_bin.nc b/examples/data/dolfyn/test_data/Sig1000_tidal_bin.nc
index bbc4df99a..18b65d846 100644
Binary files a/examples/data/dolfyn/test_data/Sig1000_tidal_bin.nc and b/examples/data/dolfyn/test_data/Sig1000_tidal_bin.nc differ
diff --git a/examples/data/dolfyn/test_data/Sig100_avg.nc b/examples/data/dolfyn/test_data/Sig100_avg.nc
new file mode 100644
index 000000000..bad1802f9
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diff --git a/examples/data/dolfyn/test_data/Sig100_raw_avg.nc b/examples/data/dolfyn/test_data/Sig100_raw_avg.nc
new file mode 100644
index 000000000..2d276cc8e
Binary files /dev/null and b/examples/data/dolfyn/test_data/Sig100_raw_avg.nc differ
diff --git a/examples/data/dolfyn/test_data/Sig500_dp_ice1.nc b/examples/data/dolfyn/test_data/Sig500_dp_ice1.nc
new file mode 100644
index 000000000..2ac223c12
Binary files /dev/null and b/examples/data/dolfyn/test_data/Sig500_dp_ice1.nc differ
diff --git a/examples/data/dolfyn/test_data/dual_profile.nc b/examples/data/dolfyn/test_data/Sig500_dp_ice2.nc
similarity index 74%
rename from examples/data/dolfyn/test_data/dual_profile.nc
rename to examples/data/dolfyn/test_data/Sig500_dp_ice2.nc
index ab63f99da..7c02a066a 100644
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diff --git a/examples/data/dolfyn/test_data/dat_rdi_bt.mat b/examples/data/dolfyn/test_data/dat_rdi_bt.mat
index 6fd6d2ffa..527345893 100644
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diff --git a/examples/data/dolfyn/test_data/dat_vm.mat b/examples/data/dolfyn/test_data/dat_vm.mat
index 32c263ec0..4e68c14fc 100644
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diff --git a/examples/data/dolfyn/test_data/sentinelv_b5.nc b/examples/data/dolfyn/test_data/sentinelv_b5.nc
new file mode 100644
index 000000000..8d4efd2e9
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diff --git a/examples/data/dolfyn/test_data/vector_data01_bin.nc b/examples/data/dolfyn/test_data/vector_data01_bin.nc
index a88effa0d..f7c60a59d 100644
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diff --git a/examples/data/dolfyn/test_data/vmdas01_wh.nc b/examples/data/dolfyn/test_data/vmdas01_wh.nc
index 0ef4a7bca..45aaf8551 100644
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diff --git a/examples/data/dolfyn/test_data/vmdas02_os.nc b/examples/data/dolfyn/test_data/vmdas02_os.nc
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diff --git a/examples/data/dolfyn/test_data/winriver01.nc b/examples/data/dolfyn/test_data/winriver01.nc
index b49bc6205..f6a828e35 100644
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diff --git a/examples/data/dolfyn/test_data/winriver02.nc b/examples/data/dolfyn/test_data/winriver02.nc
index 295415044..ad0f89a2f 100644
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diff --git a/examples/data/dolfyn/test_data/winriver02_rotate_ship2earth.nc b/examples/data/dolfyn/test_data/winriver02_rotate_ship2earth.nc
index 5bebdeaf0..237a7ea1c 100644
Binary files a/examples/data/dolfyn/test_data/winriver02_rotate_ship2earth.nc and b/examples/data/dolfyn/test_data/winriver02_rotate_ship2earth.nc differ
diff --git a/examples/data/dolfyn/test_data/winriver02_transect.nc b/examples/data/dolfyn/test_data/winriver02_transect.nc
index f6ea60590..a00a86582 100644
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diff --git a/examples/data/dolfyn/vector_cabled_imu01.VEC b/examples/data/dolfyn/vector_cabled_imu01.VEC
new file mode 100644
index 000000000..f39de44a6
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diff --git a/examples/data/river/usgs_discharge_TRTS_20090801_20190801_daily.csv b/examples/data/river/usgs_discharge_TRTS_20090801_20190801_daily.csv
new file mode 100644
index 000000000..9c8cea1ac
--- /dev/null
+++ b/examples/data/river/usgs_discharge_TRTS_20090801_20190801_daily.csv
@@ -0,0 +1,3654 @@
+,"Discharge, cubic feet per second"
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diff --git a/examples/data/river/usgs_discharge_TRTS_20100810_instantaneous.csv b/examples/data/river/usgs_discharge_TRTS_20100810_instantaneous.csv
new file mode 100644
index 000000000..60562adeb
--- /dev/null
+++ b/examples/data/river/usgs_discharge_TRTS_20100810_instantaneous.csv
@@ -0,0 +1,97 @@
+,"Discharge, cubic feet per second"
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diff --git a/examples/data/river/usgs_gage_height_TRTS_20100810_instantaneous.csv b/examples/data/river/usgs_gage_height_TRTS_20100810_instantaneous.csv
new file mode 100644
index 000000000..99ee738da
--- /dev/null
+++ b/examples/data/river/usgs_gage_height_TRTS_20100810_instantaneous.csv
@@ -0,0 +1,97 @@
+,"Gage height, feet"
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diff --git a/examples/data/wave/mcr_mhkit.mat b/examples/data/wave/mcr_mhkit.mat
new file mode 100644
index 000000000..2e7850bfb
Binary files /dev/null and b/examples/data/wave/mcr_mhkit.mat differ
diff --git a/examples/data/wave/mcr_mhkit_power.mat b/examples/data/wave/mcr_mhkit_power.mat
new file mode 100644
index 000000000..4b104435e
Binary files /dev/null and b/examples/data/wave/mcr_mhkit_power.mat differ
diff --git a/examples/data/wave/pacwave_cluster_32.csv b/examples/data/wave/pacwave_cluster_32.csv
new file mode 100644
index 000000000..c9158f7b9
--- /dev/null
+++ b/examples/data/wave/pacwave_cluster_32.csv
@@ -0,0 +1,33 @@
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diff --git a/examples/extreme_response_MLER_example.ipynb b/examples/extreme_response_MLER_example.ipynb
index be95fb280..3433d0f72 100644
--- a/examples/extreme_response_MLER_example.ipynb
+++ b/examples/extreme_response_MLER_example.ipynb
@@ -5,11 +5,15 @@
    "metadata": {},
    "source": [
     "# MLER example\n",
-    "Extreme conditions modeling consists of identifying the expected extreme (e.g. 100-year) response of some quantity of interest, such as WEC motions or mooring loads. \n",
-    "Three different methods of estimating extreme conditions were adapted from [WDRT](https://github.com/WEC-Sim/WDRT): full sea state approach, contour approach, and MLER design wave. \n",
-    "This noteboook presents the MLER approach. \n",
+    "Extreme conditions modeling consists of identifying the expected extreme (e.g., 100-year) response of some quantity of interest, such as WEC motions or mooring loads. \n",
+    "Three different tools for estimating extreme conditions were adapted from [WDRT](https://github.com/WEC-Sim/WDRT): full sea state approach, contour approach, and MLER design wave.\n",
+    "This noteboook presents the MLER design wave. \n",
     "\n",
-    "This example notebook shows users how to utilze the most likely extreme response (MLER) method. This method is an alternative to exhaustive Monte Carlo or long-term simulations for finding and evaluating wave energy converter response events at extreme loads. To accomplish this, a given RAO is combined with a wave spectrum corresponding to certain extreme sea states. MLER then generates the resulting wave profile that would cause the largest response for that degree of freedom. See full explanation and derivation of the MLER method in \n",
+    "This example notebook shows users how to utilze the most likely extreme response (MLER) method.\n",
+    "This method is an alternative to running long (e.g., 3-hour) simulations for each sea state in which we wish to estimate the short-term extreme response.\n",
+    "To accomplish this, a given RAO is combined with a wave spectrum corresponding to certain extreme sea states.\n",
+    "MLER then generates the resulting wave profile that would cause the largest response for that degree of freedom.\n",
+    "See full explanation and derivation of the MLER method in \n",
     "\n",
     "> E. Quon, A. Platt, Y.-H. Yu, and M. Lawson, “Application of the Most Likely Extreme Response Method for Wave Energy Converters,” in Volume 6: Ocean Space Utilization; Ocean Renewable Energy, Busan, South Korea, Jun. 2016, p. V006T09A022. doi: 10.1115/OMAE2016-54751.\n",
     "\n",
@@ -92,7 +96,7 @@
     },
     {
      "data": {
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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -132,7 +136,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -142,7 +146,7 @@
     },
     {
      "data": {
-      "image/png": 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YhIiI7Mj3h/Ox+3QZXJxVeOfOePix+dluhPm4YeHkWADAKxsykF9ZJ3FF9oFBiIjITpjNIt7YdBIA8PDYXuwDskN3JEUgPsIbtQ0mvLDuuNTl2AUGISIiO7EpvQiniqqhc3HC7JGRUpdDXUClErBoWn+oBOCHtHzsOFksdUmKxyBERGQnVu85CwD4c2I4dC7cfNRe9Q/RY2ZyJADgn98ehbHJdPkH0GUxCBER2YFzFXXYesIyOnBbYrjE1VBXWzChD/w8tDhdUoP3fj0jdTmKxiBERGQH1h0+B1EEEiN9EMU9ZuyezsXZ2jj99pZMlFQbJa5IuRiEiIjswLrD+QCAqYODJa6Eussf43pgQA8dqo1N1iZ56jgGISIihcurqMPh3EqoBGDyQAYhR6FSCfj7lL4AgE/2ZCOzuFriipRJMUFo8eLFGDZsGDw9PREQEIDp06cjIyPjio/78ssvERsbCxcXFwwcOBA//vhjN1RLRNR9tmVYeoPiwr25b5CDGdHTD+NiA2Ayi1jyU7rU5SiSYoLQtm3bMHfuXOzevRsbN25EY2MjJkyYgJqamjYf89tvv+G2227DPffcg4MHD2L69OmYPn06jhw50o2VExF1rW0nLIc+j+3jL3ElJIWFU2KhVgn4+Vgh9mWVSV2O4ij20NXi4mIEBARg27ZtGD169CWvufXWW1FTU4N169ZZ7xs+fDiGDBmCFStWtOt12ntoGxGRFBpNZsQt2ohqYxO+mzcSg0K9pC6JJLDwm8P4dG8OkqJ88Nn9w3mcCtr/+1sxI0K/V1lZCQDw8fFp85pdu3Zh/Pjxre6bOHEidu3a1eZjjEYjDAZDqxsRkVwdOFuOamMTfNw1GBCil7ocksgj1/WGRq3CnjNl+C2zVOpyFEWRQchsNmP+/PkYOXIkBgwY0OZ1BQUFCAwMbHVfYGAgCgoK2nzM4sWLodfrrbewsDCb1U1EZGvbmvcOGt3bj6eRO7AQL1fcnmTZP2rpzxlQ6GSPJBQZhObOnYsjR47gs88+s/lzL1y4EJWVldZbTk6OzV+DiMhWWoLQmBj2Bzm6h8f2hNZJhQPZFdbNNenKFBeE5s2bh3Xr1mHLli0IDQ297LVBQUEoLCxsdV9hYSGCgoLafIxWq4VOp2t1IyKSI0N9I47lW6bvR/b0k7gaklqAzgWzRkQCAF77+QRHhdpJMUFIFEXMmzcPa9aswebNmxEVFXXFxyQnJ2PTpk2t7tu4cSOSk5O7qkwiom5zMLsCogiE+7ghQOcidTkkAw+MjoabRo20vErraCFdnmKC0Ny5c/Hxxx9j9erV8PT0REFBAQoKClBXV2e9ZubMmVi4cKH140cffRTr16/H0qVLkZ6ejmeffRYpKSmYN2+eFF8CEZFN7W9eKp0Q4S1xJSQXvh5a3N581tzbWzIlrkYZFBOEli9fjsrKSowdOxbBwcHW2+eff269Jjs7G/n5+daPR4wYgdWrV+Pdd9/F4MGD8dVXX2Ht2rWXbbAmIlKKlLPlAID4SAYhOu/ea6LhrBawN6sMKdxX6IqcpC6gvdoz17l169aL7psxYwZmzJjRBRUREUmn0WTGwewKAEBCRNvbiJDjCdK74Ob4UHy6Nwdvb83E+7P5/8flKGZEiIiIzjueb0Bdowk6Fyf0DvCQuhySmQdG94RKADanF+HYOe6HdzkMQkRECpSSZZkWGxrhzf2D6CKRfu6Y0nwA7/Jt7BW6HAYhIiIF2p9tCUJslKa2PDS2JwDgh8PnkFXS9rmcjo5BiIhIgdJyLccMDQljEKJL6x+ix7Ux/jCLwDvbT0tdjmwxCBERKUxlbSOyy2oBAAN6cNNXatsDYyyjQt8cyEV5TYPE1cgTgxARkcIcOWcZDQrzcYWXm0biakjOkqJ80D9EB2OTGav3ZktdjiwxCBERKUxaniUIDezB0+bp8gRBwJyRlpMYPtyVhUaTWeKK5IdBiIhIYVqC0AAGIWqHGwcHw99Ti0KDET+m5V/5AQ6GQYiISGGOcESIOkDrpMZdwyMAAO//eoaHsf4OgxARkYJU1jXibGlzo3QIgxC1z+1J4dA4qXAotxIHmrdeIAsGISIiBTna3Cgd6u0Kb3c2SlP7+HloMX1ICADg/V+zpC1GZhiEiIgU5Gie5bgETotRR80ZZWma/ulIPvIq6iSuRj4YhIiIFOR4viUI9Qvm/kHUMbFBOozo6QuzCHy6h0vpWzAIEREpSHpBFQAgJshT4kpIie5sbpr+bF8OGpq4lB5gECIiUoxGkxmniqoBWP66J+qo6/sFIsBTi5JqI34+ViB1ObLAIEREpBBZJTVoMJnhplEj1NtV6nJIgZzVKvx5WBgA4JPdnB4DGISIiBSjZVqsT6AnVCpB4mpIqf6cGA6VAOw6XWodYXRkDEJERAqR0RyEYtkfRFchxMsV18UGAgA+2XNW4mqkxyBERKQQbJQmW7lzeDgA4Ov9uahrMElcjbQYhIiIFCKj0LJ0nkGIrtbo3v4I93GDob4J3x8+J3U5kmIQIiJSgGpjE3LKLJvgccUYXS2VSsDtSZZRoU92O/b0GIMQEZECtPQH+Xtq4cOjNcgGZsSHwlkt4FBuJdILDFKXIxkGISIiBThV1NwfFMhpMbINXw8txve1NE1/vi9H4mqkwyBERKQAmcU1AIBeAR4SV0L25JYEy55Caw/mwdjkmE3TDEJERAqQ2bzfS09/d4krIXsyuo8/gnQuKK9txC/HiqQuRxIMQkRECpBZ3ByEOCJENqRWCbg5PhQA8HmKY06PMQgREcmcscmE7LJaAEAvfwYhsq0ZCZYgtONkMfIq6iSupvsxCBERydzZ0lqYRcBT6wR/T63U5ZCdifB1x/BoH4iiZYNFR8MgREQkcy39QdEBHhAEnjFGttfSNP1FSg7MZlHiaroXgxARkcydYqM0dbHJA4LhqXVCbnkddp0ulbqcbsUgREQkc9ZGafYHURdx1agxdUgIAMebHmMQIiKSOe4hRN3hT0N7AADWHy1AbUOTxNV0HwYhIiIZE0WRI0LULYaGeyPC1w21DSZsOFogdTndhkGIiEjGCgz1qG0wwUklIMLXTepyyI4JgoDpQyyjQt8cyJO4mu6jqCC0fft2TJ06FSEhIRAEAWvXrr3s9Vu3boUgCBfdCgocJ+kSkbJlFlmmxcJ93eCsVtSPbFKgP8ZZgtDOUyUoMtRLXE33UNR3VU1NDQYPHoy33nqrQ4/LyMhAfn6+9RYQENBFFRIR2Ranxag7Rfq5Y2i4F8wi8G3qOanL6RZOUhfQEZMnT8bkyZM7/LiAgAB4eXm161qj0Qij0Wj92GAwdPj1iIhspSUIsVGaustNQ0NxILsC3xzMw32jo6Uup8spakSos4YMGYLg4GBcf/312Llz52WvXbx4MfR6vfUWFhbWTVUSEV2MI0LU3W4cFAyNWoXj+QakF9j/YIBdB6Hg4GCsWLECX3/9Nb7++muEhYVh7NixOHDgQJuPWbhwISorK623nBzHPISOiOSBmylSd/Ny0+DaWH8AwBoHaJpW1NRYR8XExCAmJsb68YgRI5CZmYnXX38dH3300SUfo9VqodXyLB8ikl61sQmFBstUfTRHhKgb/TEuFBuOFmJtah7+NikWapX9Hu1i1yNCl5KYmIhTp05JXQYR0RWdLbWsGPN110Dv6ixxNeRIro31h97VGYUGI3Zl2veRGw4XhFJTUxEcHCx1GUREV3S2tBYAuH8QdTutkxo3DrL8rvzmoH0fuaGoqbHq6upWozlnzpxBamoqfHx8EB4ejoULFyIvLw8ffvghAGDZsmWIiopC//79UV9fj//973/YvHkzfv75Z6m+BCKidstqHhGK9GV/EHW/m4b2wCd7srH+SAFenG6Cq0YtdUldQlFBKCUlBddee6314wULFgAAZs2ahVWrViE/Px/Z2dnWzzc0NOCxxx5DXl4e3NzcMGjQIPzyyy+tnoOISK7OlrSMCDEIUfcbGu6NUG9X5JbXYXN6EW4YZJ+zKYIoiqLURciZwWCAXq9HZWUldDqd1OUQkQO55Z1d2HumDP/+8xBMaz76gKg7LVmfjuVbMzGpfxBW3BUvdTkd0t7f3w7XI0REpBQtzdIcESKpTB0UAgDYnFGEqvpGiavpGgxCREQyVNtwful8JJulSSJ9gz3R098dDU1mbDxWKHU5XYJBiIhIhrLLLP1BeldneLlpJK6GHJUgCJg62DIq9P0h+zx7jEGIiEiGspobpSP9OC1G0rqxeXpsx8kSlNc0SFyN7TEIERHJ0Fnr0nlOi5G0egV4oF+wDk1mEeuPFkhdjs0xCBERyVAWG6VJRux5eoxBiIhIhqxTYxwRIhlo2WV61+lSFBnqJa7GthiEiIhkiEvnSU7CfNwQF+4FUQR+TMuXuhybYhAiIpKZ+kYTzlVa/urmiBDJRUvT9PeHGYSIiKgL5TQvnffUOsHHnUvnSR5uGBgMQQD2ny1Hbnmt1OXYDIMQEZHMZLWcOu/nBkEQJK6GyCJI74LESB8AwA92NCrEIEREJDNZJewPInm6sXn12I9H7GcZPYMQEZHMtCydj2IQIpmZ2D8QggAcyqmwm+kxBiEiIpk52zw1Fs5GaZKZAE8XDGueHltvJ6NCDEJERDLTcs5YhA+DEMnPlAFBABiEiIioCzSZzDhXUQfAsncLkdxMGmDZXDHlbDkKKpW/uSKDEBGRjORX1qPJLEKjViFQ5yJ1OUQXCdK7ID7CGwCwwQ7OHmMQIiKSkZzmBtQe3q5Qq7h0nuRpcvP0mD3sMs0gREQkIy2bKXJajORsUnMQ2ptVhuIqo8TVXB0GISIiGckpa+4P8naVuBKitoV6u2FwqB6iqPzpMQYhIiIZaVkxFs4RIZK5yQMtTdM/HVH29BiDEBGRjLT0CDEIkdy19AntPl2G0mrlTo8xCBERyQh7hEgpInzd0T9EB5NZxMZjhVKX02kMQkREMlHb0ISS6gYADEKkDFOs02PK7RNiECIikomWRmmdixP0rs4SV0N0ZS3TYztPlaCytlHiajqHQYiISCZapsV4xhgpRbS/B/oEeqDJLGJLRpHU5XQKgxARkUy0rBgL82YQIuWY0M8yKvTzMWVOjzEIERHJBFeMkRJN6B8IANiaUYz6RpPE1XQcgxARkUy0TI2FMgiRggzsoUew3gW1DSbsPFUidTkdxiBERCQTLc3SHBEiJREEARP6WUaFfj6qvGX0DEJERDIgiuIFPUI8XoOUZUJ/S5/QL8cLYTKLElfTMQxCREQyUFrTgLpGEwTBcvI8kZIkRvlA5+KE0poGHMgul7qcDmEQIiKSgZbRoCCdC7ROaomrIeoYZ7UK4/papsc2KGxzRUUFoe3bt2Pq1KkICQmBIAhYu3btFR+zdetWDB06FFqtFr169cKqVau6vE4ioo7i0RqkdBObV4/9fKwQoqic6TFFBaGamhoMHjwYb731VruuP3PmDG644QZce+21SE1Nxfz583Hvvfdiw4YNXVwpEVHH5JZbGqW5hxAp1eg+/tA6qZBdVouMwiqpy2k3J6kL6IjJkydj8uTJ7b5+xYoViIqKwtKlSwEAffv2xa+//orXX38dEydO7KoyiYg6LLd5D6FQ9geRQrlpnHBNbz/8crwIPx8tRGyQTuqS2kVRI0IdtWvXLowfP77VfRMnTsSuXbvafIzRaITBYGh1IyLqai0jQmyUJiVr2WV6w1Hl9AnZdRAqKChAYGBgq/sCAwNhMBhQV1d3yccsXrwYer3eegsLC+uOUonIweU1ByGOCJGSjesbAJUAHD1nsI5yyp1dB6HOWLhwISorK623nJwcqUsiIjsniiLyKpqDkBd7hEi5fD20SIj0AQBsPKaMzRXtOggFBQWhsLD1f4jCwkLodDq4ul76ry6tVgudTtfqRkTUlYqrjTA2maESgCC9i9TlEF0Vpe0ybddBKDk5GZs2bWp138aNG5GcnCxRRUREF2uZFgvUuUDjZNc/lskBTGzeZXpvVhnKaxokrubKFPUdV11djdTUVKSmpgKwLI9PTU1FdnY2AMu01syZM63XP/jggzh9+jT+9re/IT09HW+//Ta++OIL/PWvf5WifCKiS8plfxDZkTAfN/QN1sFkFvHLcfmPCikqCKWkpCAuLg5xcXEAgAULFiAuLg7PPPMMACA/P98aigAgKioKP/zwAzZu3IjBgwdj6dKl+N///sel80QkKy39QT28GITIPlzfPD226XiRxJVcmaL2ERo7duxld6u81K7RY8eOxcGDB7uwKiKiq3N+DyE2SpN9GN83AG9sOokdJ4thbDLJ+tgYRY0IERHZozzuIUR2ZkCIHgGeWtQ0mLD7dJnU5VwWgxARkcTYI0T2RqUSMK5vAABgk8z7hBiEiIgkdOEeQuwRInsyLvZ8n5CcD2FlECIiklB5bSNqG0wAgBAGIbIjI3v5QeukQl5FHdIL5HsIK4MQEZGEWvqD/D21cHGWb0MpUUe5atQY1csPgLynxxiEiIgkxFPnyZ5d19wn9IuMl9EzCBERSYj9QWTPWvqEDuVWoLjKKHE1l8YgREQkofMrxriHENmfIL0LBvTQQRSBLenyHBViECIiklAu9xAiO9cyKiTX4zYYhIiIJMQeIbJ34/tagtCOkyWobzRJXM3FGISIiCTU0iMUyh4hslMDeugQqNOirtGEXadLpS7nIgxCREQSqaxrRFV9EwBOjZH9EgQB11k3V5Tf9BiDEBGRRFr2EPJx18BNo6gzsIk6ZHzzMvrNMtxlmkGIiEgi7A8iRzGylx9cnFU4V1mPY/kGqctphUGIiEgi3EOIHIWL84W7TMtrGT2DEBGRRHjqPDmScX3l2SfEIEREJJGWHiGOCJEjGBdr6RM6lFuJoqp6ias5j0GIiEgiuRWWHqEe3FWaHECAzgWDQvUA5LXLNIMQEZFE8jg1Rg7m/C7TDEJERA6txtiE8tpGANxDiBzHuOZl9L/KaJdpBiEiIgm0rBjTuThB5+IscTVE3aN/iA4BnpZdpveeKZO6HAAMQkREkmjZQ4j9QeRIBEHAtTHNmyvKpE+IQYiISALsDyJHdW3z6rEtGfLYZZpBiIhIArlcOk8OalRvPzirBZwtrcXpkhqpy2EQIiKSQm4FR4TIMXlonZAU5QtAHsvoGYSIiCTAXaXJkbVMj8mhT4jHHRPJmCiK2HmqFJ/ty8bB7ArUNDRBABCoc0G4jxsGh3lheLQP4sK8oVIJUpdLHXC+R4jN0uR4ro3xx/PrgH1ZZaiqb4SnhCsnGYSIZKrQUI/HvzyEHSdLLvpceW0j0guq8PMxy5k9AZ5aTOwfhBkJoRgU6tXNlVJH1TeaUFJtBMAeIXJM0f4eiPR1Q1ZpLXaeKsGkAcGS1dLuIGQwGDr85DqdrsOPISJgV2YpHvn0AEqqG6BRq3BbYhgmDwyGr7sGZhHIr6zDqaJqHMyuwPaTxSiqMuKj3Wfx0e6zSIjwxj2jojCxfxBHiWTqXHN/kJtGDS837iFEjuna2ACs3JmFzelFyghCXl5eEIT2/1AVBAEnTpxAdHR0pwojclQ/peXjkU8PosksIjbIE2/fMRTR/h6trokJ8sTY5r04GprM2JlZgm8P5uGHtHyknC1Hytly9A3W4W+TYjC2j3+Hvnep67Vsphji5cr/NuSwrmsOQlsyimE2i5L94dahqbGvvvoKPj4+V7xOFEVMmTKl00UROarvDp3DXz9Phcks4oaBwXh1xmC4atSXfYzGSYVrYwJwbUwAFk7pi492ncUHu7JwPN+Au1fuQ3K0LxZN64/egZ7d9FXQlbSMCHFajBxZYpQP3DRqFFcZcfScAQObD2Ttbu0OQhERERg9ejR8fX3bdX10dDScnTnkS9ReWzOKsKA5BN0cH4olfxoEdQf/QgrUueDxiTG4Z1QUlm/LxKrfsrDrdCmmvLED94+Oxrxre18xWFHXa2mU5hlj5Mi0TmqM6uWHn48VYnN6kWRBqN3L58+cOdPuEAQAR44cQVhYWKeKInI0h3Iq8PAnB9BkFjFtSAhe7kQIupC3uwZ/n9IXmx8bg/F9A9BoEvHWlkxMWLYNO04W27By6oxcjggRAQBmjYjEKzcPwh3DwyWrgfsIEUksq6QGc1btQ22DCdf09sMrNw+22Vx5qLcb/jszAe/cFY9gvQtyyupw13t78fTaNNQ2NNnkNajjODVGZDGylx9mJITBz0MrWQ3tnhp744032v2kf/nLXzpVTHu89dZbeOWVV1BQUIDBgwfjzTffRGJi4iWvXbVqFe6+++5W92m1WtTX13dZfUQdUVptxOyVe1Fa04ABPXRYfmc8NE62/ftEEARM7B+EUb38sGR9Oj7cdRYf787GjpMlWDpjMBIir9z3R7bV0izNqTEi6bU7CL3++uutPi4uLkZtbS28vLwAABUVFXBzc0NAQECXBaHPP/8cCxYswIoVK5CUlIRly5Zh4sSJyMjIQEBAwCUfo9PpkJGRYf2YKzRILuobTbj3wxRkldYi1NsVK2cnwkPbdVt7uWudsGjaAEzoF4S/fXUIZ0trccs7u/DExFg8OCaa3xvdxGQWkV9h+WOMI0JE0utQj1DL7cUXX8SQIUNw/PhxlJWVoaysDMePH8fQoUPx/PPPd1mxr732Gu677z7cfffd6NevH1asWAE3Nze8//77bT5GEAQEBQVZb4GBgV1WH1F7mcwiHv3sIA5mV0Dv6oxVdyfC37N7hoZH9fbD+r+Oxk1De8AsAkvWp+PRz1LRaDJ3y+s7uuIqI5rMItQqAQHd9N+ciNrWqTH4f/zjH3jzzTcRExNjvS8mJgavv/46nn76aZsVd6GGhgbs378f48ePt96nUqkwfvx47Nq1q83HVVdXIyIiAmFhYZg2bRqOHj162dcxGo0wGAytbkS2JIoiXvjhGDYcLYRGrcJ/ZyagV4DHlR9oQzoXZyydMRjPTx8AJ5WA7w6dw2NfHILJLHZrHY4or6IWABCkc4GTmm2aRFLr1Hdhfn4+mpoubrQ0mUwoLCy86qIupaSkBCaT6aIRncDAQBQUFFzyMTExMXj//ffx7bff4uOPP4bZbMaIESOQm5vb5ussXrwYer3eeuPKN7IlURTx0o/HsXJnFgBg6S2DkRglTY+OIAi4a3gE/jszwRqGnvn2CESRYagr5XLpPJGsdCoIjRs3Dg888AAOHDhgvW///v146KGHWo3YSC05ORkzZ87EkCFDMGbMGHzzzTfw9/fHO++80+ZjFi5ciMrKSustJyenGysme2Yyi/j7mjT8d8cZAMBzf+iPqYNDJK7Kss39sj8PgUoAPtmTjc/38f/5rnSO/UFEstKpIPT+++8jKCgICQkJ0Gq10Gq1SExMRGBgIP73v//ZukYAgJ+fH9Rq9UUjToWFhQgKCmrXczg7OyMuLg6nTp1q8xqtVgudTtfqRnS1Gk1mLPgiFZ/uzYFKAF7+0yDMGhEpdVlWNw4KwWMTLFPdz3x3FGm5lRJXZL9apsYYhIjkoVNByN/fHz/++CPS09Px5Zdf4ssvv8Tx48fx448/trl662ppNBrEx8dj06ZN1vvMZjM2bdqE5OTkdj2HyWRCWloagoOlO9yNHE99owkPf3IA36aeg5NKwBu3xeGWYfKbcn1oTE+M7xuAhiYz/vLZQdQ1mKQuyS5xV2kiebmqtbp9+vRBnz59bFXLFS1YsACzZs1CQkICEhMTsWzZMtTU1Fj3Cpo5cyZ69OiBxYsXAwAWLVqE4cOHo1evXqioqMArr7yCs2fP4t577+22msmx1TY04f4P9+PXUyXQOKmw4s6huC5WnisXVSoBS2cMwYRl23CmpAZLf87A0zf2k7osu9MyNRbCESEiWeh0EMrNzcV3332H7OxsNDQ0tPrca6+9dtWFXcqtt96K4uJiPPPMMygoKMCQIUOwfv16awN1dnY2VKrzg1zl5eW47777UFBQAG9vb8THx+O3335Dv3784U5dr7KuEXNW7cP+s+Vw06jxv1kJGNHTT+qyLkvv5ox/3TQId6/ah/d2nsHkgUGIj+CGi7YiiuL5zRQZhIhkQRA7sURk06ZN+MMf/oDo6Gikp6djwIAByMrKgiiKGDp0KDZv3twVtUrCYDBAr9ejsrKS/ULUbqXVRsx8fy+OnjNA5+KEVXMSMTTcW+qy2u3xLw/hq/25iA3yxLpHRnGZt41U1jZi8KKfAQDHF03iAbhEXai9v7879dNt4cKFePzxx5GWlgYXFxd8/fXXyMnJwZgxYzBjxoxOF01kDwoq63HLO7tw9JwBfh4afP5AsqJCEAD835S+0Ls6I72gCqv3Zktdjt3IbW6U9nHXMAQRyUSngtDx48cxc+ZMAICTkxPq6urg4eGBRYsWYcmSJTYtkEhJsktrMeOd35BZXINgvQs+fyAZfYOVN5Lo7a7B4xMs/X+vbshAabVR4orsA5fOE8lPp4KQu7u7tS8oODgYmZmZ1s+VlJTYpjIihckoqMKMd35DTlkdInzd8MUDyejp3707RtvS7UkR6Besg6G+Cf/Z0vaWE9R+eeVcOk8kN50KQsOHD8evv/4KAJgyZQoee+wxvPjii5gzZw6GDx9u0wKJlGBfVhlmrPgNhQYj+gR64MsHkhHm4yZ1WVdFrRKwcEosAOCT3dnIbf4lTp3HU+eJ5KdTQei1115DUlISAOC5557DuHHj8PnnnyMyMhLvvfeeTQskkrtfjhXizv/tgaG+CfER3vjigWQE6FykLssmRvXyQ3K0LxpMZvz7l5NSl6N4XDpPJD8dXj5vMpmQm5uLQYMGAbBMk61YscLmhREpwRcpOVj4TRpMZhHjYgPwn9uH2lUTrCAIeGJSDG56+zd8fSAXD4yJRq8AT6nLUqxcLp0nkp0Ojwip1WpMmDAB5eXlXVEPkSKIoojlWzPxt68Ow2QWcXN8KFbcFW9XIajF0HBvXN8vEGYReG3jCanLUbSWXaVDOTVGJBudmhobMGAATp8+betaiBTBbBbxwg/HsWR9OgDggTHReOXmQXC24712Hp8QA0EAfkwrwPF8g9TlKFJ9owklzavvODVGJB+d+sn9wgsv4PHHH8e6deuQn58Pg8HQ6kZkrxqaLIenvver5QT5p2/oi4WT+0IQBIkr61oxQZ6YMtByRt/yrZlXuJouJb/S0h/k6qyGt5uzxNUQUYtOHbExZcoUAMAf/vCHVr8ARFGEIAgwmXhYI9kfQ30jHvp4P3aeKoWTSsArMwbhj3GhUpfVbR4e2xM/HM7HusPnsOD6Poj0c5e6JEW58LBVew/ORErSqSC0ZcsWW9dBJGvnKupw98p9yCisgptGjbfvGIqxMQFSl9Wt+ofocW2MP7ZkFOOd7ZlYfNMgqUtSlHPNjdKcFiOSl3YHocOHD2PAgAFQqVQYM2bMFa8/evQoYmJi4OR0VQfcE0nu6LlKzFm1D4UGIwI8tXh/9jAM6KGXuixJzL22F7ZkFOOr/bl4dFwfBOntY5uA7sAVY0Ty1O4eobi4OJSWlrb7iZOTk5GdzTOKSNm2ZBThlhW7rBslrpk70mFDEAAkRPogMcoHjSYR/93BBRMdwRVjRPLU7uEaURTxj3/8A25u7dstt+UIDiIlEkURK7adxisb0mEWgeRoX6y4Kx56Vza5Pjy2J/aeKcNne7Px6Pje0LnwPWmP81NjHEUjkpN2B6HRo0cjIyOj3U+cnJwMV1f+5UPKU2NswhNfHcKPaQUAgFsTwvD89AHQONnv8viOGNPHH70DPHCyqBpf7MvBvddES12SIliP1/BS9tErRPam3UFo69atXVgGkTycKqrGw5/sx4nCajirBTz7h/64PTGcq3wuIAgC5oyKwsJv0rByZxZmj4iEkx3voWQLZrOI/EqeM0YkR/zpRQTLVNjqPdm48c0dOFFYjQBPLT67Pxl3JEUwBF3CH+N6wNvNGXkVddh4rFDqcmSvuNqIRpMItUpAoKdW6nKI6AIMQuTwymsa8ODH+/H3NWmobzRjVC8/rHtkFOIjvKUuTbZcnNW4IykCAPD+zjMSVyN/uc2N0kE6F46eEckMvyPJof1yrBCT/r0dG44Wwlkt4P+m9MWHcxLt5vT4rnRXcgSc1QL2ZZXjcG6F1OXIWh6XzhPJFoMQOaTSaiMe+fQg7v0wBYUGI6L93LHm4ZG4b3Q0VCpOhbVHoM4FNw4KAQC8/ytHhS6nZcUY+4OI5IdBiByK2Sziq/25GP/aNnx/6BxUguXQ1B8fvcah9wfqrDkjowAA6w7no6D5LC26WMseQlw6TyQ/3PaZHEZqTgX++d1RHMqpAADEBnni5ZsHYVCol6R1KdnAUD0SI32wN6sMn+w5i8cmxEhdkixx6TyRfDEIkd0rqqrHy+sz8NX+XACAu0aNR8b1xpyRUdwbyAZmjYjE3qwyfLo3B49c15vv6SVwaoxIvhiEyG6V1zTgf7+exqqdWahpMAEA/jQ0FE9OimEztA1N6B8If08tiquM2HC0AFMHh0hdkuxYT57n1BiR7DAIkd3JLK7GZ3uzsXpPtjUADQ7zwrNT+yEunEvibc1ZrcJtieF4Y9NJfLT7LIPQ71TWNaLK2ASAJ88TyRGDECmeKIpIy6vEpuNF2JxehLS8Suvn+gXrMH98b1zfL5AbI3ah2xPD8daWU9h7pgwZBVWICfKUuiTZaJkW83HXwE3DH7lEcsPvSlKsQzkV+O7QOaw/UmBtRgUAlQBcGxOA25PCcV1sAANQNwjSu2BCv0D8dKQAH+3OwgvTB0pdkmxwxRiRvDEIkeKcKqrCv37KwC/Hzx/t4KZRY0wff1wbG4BrYwLgz2MMut1dwyPw05ECrDmQhycnxcKTp9ID4GaKRHLHIESKca6iDm9uPonP9+XALAJqlYAbBgbjxkHBGN3HHy7OaqlLdGjJPX3R098dmcU1WHswD3clR0pdkixw6TyRvDEIkWyJoojTJTU4mF2BzemF2HC0ECazCACY2D8QT0yMRa8AD4mrpBaCIOCu4RF49vtj+HDXWdw5nAfWAhcEIS6dJ5IlBiGSlRpjE7afKMbG44XYmlGMspqGVp9PjvbFYxP6ICHSR6IK6XJuig/FkvUZOFlUjT1nyjA82lfqkiTHpfNE8sYgRJJqMplxuqQGe8+U4ZfjhfjtVCkaTGbr57VOKgwK1WNohDf+GNcDsUE6CaulK9G5OGN6XA98ujcbH+0+yyAETo0RyR2DENmcKIoorjLiTEkNDudWYuOxQhzLN8BkFqFzdYLe1RkeWieU1zYir6IODU3mVo+P8HXD9X0DMb5fIIaGe3OnYoW5a3gEPt2bjQ1HClBkqHfozSuNTSYUVxkBcGqMSK4YhMhm8ivr8OIPx7HtRDGq6psueU1dowmFBmOr+9w1avQP0WNsrD+u7xuIXgEe7C1RsH4hOiREeCPlbDk+3ZuDR8f3lrokyeRXWA6idXFWwduNq+iI5EhxQeitt97CK6+8goKCAgwePBhvvvkmEhMT27z+yy+/xD/+8Q9kZWWhd+/eWLJkCaZMmdKNFSuHySzibGkNAnQu8NBa/teoNjbh29Q8/HA4HwGeWtwwKATfHzqHLelF6OHtin4hOvQL1sFJJeCNzaesPT0qAQj1dkO0vzvG9vHHqN5+0KjVMNQ3WnbarW+E3lWDUG9X9PByhUrF4GNP7kqOaA5C2Zh7bU84qR1zVO/CpfMM90TypKgg9Pnnn2PBggVYsWIFkpKSsGzZMkycOBEZGRkICAi46PrffvsNt912GxYvXowbb7wRq1evxvTp03HgwAEMGDBAgq+gaxmbTCitbkBpdQNKaoyWf1YbkVdeh5zyWlTVN+GfU/tZT1s3mUVkldZg35ky7D5dil9PlaKk2giNkwoPj+2JvPI6/JCWj9rmYyoAYG3qOeu/pxdUIb2gCt8gz3pf/xAdFk3rj4E9vDil5cAmDQiCr7sGBYZ6/HK8CJMGBEldkiTOrxhjfxCRXAmiKIpSF9FeSUlJGDZsGP7zn/8AAMxmM8LCwvDII4/gqaeeuuj6W2+9FTU1NVi3bp31vuHDh2PIkCFYsWJFu17TYDBAr9ejsrISOp3tGnX3nC7FicIqGJvMaDCZ0dB0we2Cj42X+VzLvxubzKhvNKHaeOnpqAtF+rpheLQvjucbkFFYhfpG8xUf09PfHTMSwnC6uBpbMooxJMwL910TDUNdI47lG3D0XCXKaxsRH+GNR8f15n4+BABYsj4dy7dm4prefvjoniSpy5HE6xtP4N+bTuK2xDAsvmmQ1OUQOZT2/v5WzIhQQ0MD9u/fj4ULF1rvU6lUGD9+PHbt2nXJx+zatQsLFixodd/EiROxdu3aNl/HaDTCaDzfw2IwGK6u8DasTc3Dp3tzbP68zmoBvu5a+Hpo4Ouhha+7BiFeLgj1dsPSn08gq7QWWaW11utdnFUY2EOP5GhfDI/2RXykN5b9chLfHzqHsTH++MPgHhgW6d3msP74foE2/xrIPtyeGI4V2zKx42QJzpTUIMrPXeqSuh13lSaSP8UEoZKSEphMJgQGtv7FGxgYiPT09Es+pqCg4JLXFxQUtPk6ixcvxnPPPXf1BV/BgB56TOrfCK2zChq1Chqn8zfthR+rVdA4qVt9rHX6/edVcHFWw8ddA52LU5uhJcrPHZ/syUaEjxv6BusQG+yJSF93qH/Xn/PkpFg8OSm2y98Dsm9hPm4Y28cfWzKKsXrPWfzfDf2kLqnbneNmikSyp5gg1F0WLlzYahTJYDAgLCzM5q9zR1IE7kiKsPnzXs7w5lEfou5y5/AIbMkoxpf7c/HYhBiHmzZtGREK0TMIEcmVYrpZ/fz8oFarUVhY2Or+wsJCBAVduhEzKCioQ9cDgFarhU6na3Ujos4ZGxOAHl6uqKhtxA+H86Uup1uZzaJ1+TxHhIjkSzFBSKPRID4+Hps2bbLeZzabsWnTJiQnJ1/yMcnJya2uB4CNGze2eT0R2ZZaJeD2pHAAwMd7zkpcTfcqqTaiwWSGWiUgyIE3lSSSO8UEIQBYsGAB/vvf/+KDDz7A8ePH8dBDD6GmpgZ33303AGDmzJmtmqkfffRRrF+/HkuXLkV6ejqeffZZpKSkYN68eVJ9CUQO55aEMDirBRzMrsCRvEqpy+k2uc3TYoGeWofdR4lICRT13Xnrrbfi1VdfxTPPPIMhQ4YgNTUV69evtzZEZ2dnIz///PD7iBEjsHr1arz77rsYPHgwvvrqK6xdu9Yu9xAikit/Ty0m9rdMR3/iQKNCuc2HrYb6cA8hIjlT1D5CUuiqfYSIHMnu06X487u74eqsxp7/Gwedi/0fN/H21lN4eX0GbhraA6/dMkTqcogcTnt/fytqRIiIlCkpyge9AzxQ12jC94fOXfkBdiCnrHlEiLtKE8kagxARdTlBEPCn+FAAwHepjhGEcsstG5eGcsUYkawxCBFRt5g6OAQAsDerzLrRoD3La+4RCuOIEJGsMQgRUbfo4eWKxCgfiCKw7rB9jwqZzaJ11RhHhIjkjUGIiLrNtCGWUaFv7Xx6rKTaiIYmyx5CwXruIUQkZwxCRNRtpgwIhpNKwNFzBpwurpa6nC6T0zwtFqRz4R5CRDLH71Ai6jbe7hok97Scd/fzscIrXK1cbJQmUg4GISLqVhOaN1fcaNdBiEvniZSCQYiIutX1fS07wR/ILkdxlVHiaroGR4SIlINBiIi6VZDeBYNC9RBFYNNx+xwVahkRCuPxGkSyxyBERN1uQj/LqJC99gmdnxrjiBCR3DEIEVG3u76fpU/o11MlqGswSVyNbZnNonUzRQYhIvljECKibtcn0APBehc0NJmxN6tM6nJsqrjaiAaTZQ+hIB33ECKSOwYhIup2giBgdG9/AMD2E8USV2NbLY3SwXruIUSkBPwuJSJJjO5jr0GI02JESsIgRESSGNnLFyoBOFlUjfxK+zmENafMMiLEw1aJlIFBiIgk4eWmwaBQLwDAjhMl0hZjQ9xMkUhZGISISDIt02PbTtrP9BinxoiUhUGIiCQzurcfAODXkyUwmUWJq7EN7ipNpCwMQkQkmSFhXvB0cUJlXSPS8iqlLueqmc0i8iqaR4S4qzSRIjAIEZFknNQqjOxpGRWyh9VjRVVGNJpEOKkEBHpqpS6HiNqBQYiIJHVNn+bpsVPKb5i27iHkxT2EiJSC36lEJKnkaF8AQGp2BeoblX3chvWwVa4YI1IMBiEiklSUnzsCdVo0mMw4kF0udTlXpWUPITZKEykHgxARSUoQBAxvHhXafVrZ545xDyEi5WEQIiLJnQ9CpRJXcnVyKzgiRKQ0DEJEJLnhdtInlFPGESEipWEQIiLJRfq6ne8TOqvMPqFGk9m6h1CEL4MQkVIwCBGR5Fr3CSlzeiy/oh4mswitkwr+HtxDiEgpGISISBaSFd4wnd28Yizcxw0qlSBxNUTUXgxCRCQLLSNCB3PKUdegvD6hs2U1ACxBiIiUg0GIiGQhwtcNQToXNJpE7Fdgn1DLiFAYgxCRojAIEZEsCIKApGgfAMC+LOVNj7VspshGaSJlYRAiItkYFmkJQilnlReEzpae7xEiIuVQTBAqKyvDHXfcAZ1OBy8vL9xzzz2orq6+7GPGjh0LQRBa3R588MFuqpiIOqolCB3MrkCjySxxNe0niiKyGYSIFEkxQeiOO+7A0aNHsXHjRqxbtw7bt2/H/ffff8XH3XfffcjPz7feXn755W6olog6o3eAB3QuTqhtMOF4vkHqctqtorYRVcYmAOwRIlIaJ6kLaI/jx49j/fr12LdvHxISEgAAb775JqZMmYJXX30VISEhbT7Wzc0NQUFB7X4to9EIo9Fo/dhgUM4PYyKlU6kEJET6YHN6EfZllWNQqJfUJbVLS6N0oE4LF2e1xNUQUUcoYkRo165d8PLysoYgABg/fjxUKhX27Nlz2cd+8skn8PPzw4ABA7Bw4ULU1tZe9vrFixdDr9dbb2FhYTb5GoiofRIivQEAKQpqmL5wDyEiUhZFjAgVFBQgICCg1X1OTk7w8fFBQUFBm4+7/fbbERERgZCQEBw+fBhPPvkkMjIy8M0337T5mIULF2LBggXWjw0GA8MQUTdq6RPal1UOURQhCPLfnPB8EHKXuBIi6ihJg9BTTz2FJUuWXPaa48ePd/r5L+whGjhwIIKDgzFu3DhkZmaiZ8+el3yMVquFVsvt8YmkMrCHHhq1CiXVRpwtrUWkn/zDBRuliZRL0iD02GOPYfbs2Ze9Jjo6GkFBQSgqKmp1f1NTE8rKyjrU/5OUlAQAOHXqVJtBiIik5eKsxqBQPVLOlmNvVpkyglDLiJCvq8SVEFFHSRqE/P394e/vf8XrkpOTUVFRgf379yM+Ph4AsHnzZpjNZmu4aY/U1FQAQHBwcKfqJaLukRDpg5Sz5UjJKsMtCfKfmubUGJFyKaJZum/fvpg0aRLuu+8+7N27Fzt37sS8efPw5z//2bpiLC8vD7Gxsdi7dy8AIDMzE88//zz279+PrKwsfPfdd5g5cyZGjx6NQYMGSfnlENEVJEa1NEzL/6iNhiYzzlXWAeDUGJESKSIIAZbVX7GxsRg3bhymTJmCUaNG4d1337V+vrGxERkZGdZVYRqNBr/88gsmTJiA2NhYPPbYY/jTn/6E77//XqovgYjaKT7c0jB9uqQGJdXGK1wtrdzyWogi4KZRw89DI3U5RNRBilg1BgA+Pj5YvXp1m5+PjIyEKIrWj8PCwrBt27buKI2IbEzv5oyYQE9kFFYhJasckwa0vxewu124dF4JK9yIqDXFjAgRkWNRyn5COTx1nkjRGISISJas+wmdlXefUBaXzhMpGoMQEclSy4jQ0bxK1DY0SVxN27JKagAAUQpY5k9EF2MQIiJZ6uHlimC9C5rMIlJzKqQup01nGISIFI1BiIhkSRAsB7AC8l1G32QyW5ullbDxIxFdjEGIiGRrWPP02D6ZNkznVdShySxC66RCsM5F6nKIqBMYhIhIthIiLCNCB86Wo8lklriai51unhaL9HWHSsWl80RKxCBERLIVE+QJT60TahpMSC+okrqci7BRmkj5GISISLbUKgFDIyzTY/tluIy+pVGa/UFEysUgRESyJuc+ofMrxriHEJFSMQgRkazFR5xfOXbhMTpycD4IeUhcCRF1FoMQEcnakDAvOKkEFBjqkVdRJ3U5VsYmk7WeSI4IESkWgxARyZqrRo0BPfQA5LWfUHap5dR5D60T/D20UpdDRJ3EIEREspcQIb8+ofON0jx1nkjJGISISPbkuMN0Vun5PYSISLkYhIhI9loOYD1RVIXK2kaJq7FoGRGK5tJ5IkVjECIi2fPz0CLazx2iCBzIlseoEPcQIrIPDEJEpAjxMusT4qnzRPaBQYiIFGFYS5+QDHaYrjE2odBgBMAgRKR0DEJEpAgtfUKHcipgbDJJWktLo7SXmzO83DSS1kJEV4dBiIgUIcrPHb7uGhibzDiSZ5C0lsxiNkoT2QsGISJSBEEQrH1CKRL3CZ0qqgYA9A7wlLQOIrp6DEJEpBhy6RPKbA5CvQJ4xhiR0jEIEZFitPQJpWSVSXoA68miKgAMQkT2gEGIiBSjf4geWicVymsbrX063a3JZLYunWcQIlI+BiEiUgyNkwpDwrwAAPvPStMnlF1Wi0aTCBdnFXp4uUpSAxHZDoMQESlKS5/QPonOHWtplO7p7wGVioetEikdgxARKUp8pLQrx04Vs1GayJ4wCBGRogwN94YgAFmltSiuMnb767eMCPXyZxAisgcMQkSkKHpXZ8QEWvbvkeLcMS6dJ7IvDEJEpDhJUZY+oT2nS7v1dUVRPD8ixCBEZBcYhIhIcYZH+wIAdp/u3hGhvIo61DSY4KQSEOHL4zWI7AGDEBEpTmLziFBGYRXKahq67XUzCs5vpKhx4o9PInugmO/kF198ESNGjICbmxu8vLza9RhRFPHMM88gODgYrq6uGD9+PE6ePNm1hRJRl/P10Fr7hPae6b7psfTmIBQTxDPGiOyFYoJQQ0MDZsyYgYceeqjdj3n55ZfxxhtvYMWKFdizZw/c3d0xceJE1NfXd2GlRNQdhkdbRoW6c3qMQYjI/igmCD333HP461//ioEDB7brelEUsWzZMjz99NOYNm0aBg0ahA8//BDnzp3D2rVru7ZYIupy5/uEum9EKKPAAADoG6Trttckoq6lmCDUUWfOnEFBQQHGjx9vvU+v1yMpKQm7du1q83FGoxEGg6HVjYjkp6VPKL2ge/qEjE0mnG4+34wjQkT2w26DUEFBAQAgMDCw1f2BgYHWz13K4sWLodfrrbewsLAurZOIOsfXQ4s+gZYl7N3RJ5RZVIMmswhPFycE6126/PWIqHtIGoSeeuopCIJw2Vt6enq31rRw4UJUVlZabzk5Od36+kTUft25jD6j8Py0mCDwjDEie+Ek5Ys/9thjmD179mWviY6O7tRzBwUFAQAKCwsRHBxsvb+wsBBDhgxp83FarRZarbZTr0lE3Wt4tC8+3HW2W/qE2ChNZJ8kDUL+/v7w9/fvkueOiopCUFAQNm3aZA0+BoMBe/bs6dDKMyKSrwv7hEqqjfDz6Lo/YjIYhIjskmJ6hLKzs5Gamors7GyYTCakpqYiNTUV1dXV1mtiY2OxZs0aAIAgCJg/fz5eeOEFfPfdd0hLS8PMmTMREhKC6dOnS/RVEJEt+Xlo0TfYsoJr56mSLn2tY+eap8aCGYSI7ImkI0Id8cwzz+CDDz6wfhwXFwcA2LJlC8aOHQsAyMjIQGVlpfWav/3tb6ipqcH999+PiooKjBo1CuvXr4eLCxsdiezF6N5+OJ5vwI6TJZg2pEeXvEaRoR5FVUaoBFiDFxHZB0EURVHqIuTMYDBAr9ejsrISOh1/ABLJza8nS3Dne3sQqNNi98JxXdLIvOl4Ie75IAW9AzywccEYmz8/Edlee39/K2ZqjIjoUhIivaF1UqHQYMTJouorP6AT0vIsI80DQ/Vd8vxEJB0GISJSNBdnNZKal9FvP1HcJa+RltschHowCBHZGwYhIlK80b39AAA7TnZNw7R1RIhBiMjuMAgRkeKN7mPZhmPPmVLUN5ps+twXNkr3C2GfIJG9YRAiIsXrHeCBQJ0W9Y1m7D9bbtPnbhkN6unvATeNYhbaElE7MQgRkeIJgoBreltGhbaftG2f0GH2BxHZNQYhIrIL1zT3CW1Nt20QOpRbAYArxojsFYMQEdmFMX38oVYJyCisQnZprU2e02wWrVNt8RHeNnlOIpIXBiEisgtebhokRlrOHvvleKFNnvNEURWq6pvgplGjH3eUJrJLDEJEZDfG9wsEAGw8ZpsglJJlGQ2KC/eCk5o/LonsEb+zichuXN/XEoT2ZpWhsrbxqp8vJasMABAf4XPVz0VE8sQgRER2I9zXDTGBnjCZRWzJKLrq50tp7g8aFsn+ICJ7xSBERHbl+ubpsZ+O5F/V8+RX1iG3vA4qAYgLZxAislcMQkRkV24YFAwA2JJRDEN956fHWvqD+gbr4KHlRopE9opBiIjsSmyQJ3oHeKChyYyfj3a+aXrnKcu5ZYlR7A8ismcMQkRkVwRBwNTBIQCA7w6d69RziKKIrRmWjRnHNJ9jRkT2iUGIiOxOSxDaeaoEpdXGDj/+RGE1Cgz10DqpMDza19blEZGMMAgRkd2J8nPHwB56mMwivk3t+KjQ1uYVZ8k9feHirLZ1eUQkIwxCRGSXbkkIBQCs3psNURQ79FhOixE5DgYhIrJL0+N6wE2jxqmiauw9U9bux1Ubm5By1nL92JiAriqPiGSCQYiI7JKnizOmDbH0Cn2yJ7vdj9uWUYxGk4gIXzdE+bl3VXlEJBMMQkRkt25PjABg2VyxqKq+XY/5vnml2eQBwV1WFxHJB4MQEdmtgaF6xEd4o9EkYsXW01e83lDfiM3NjdJ/aF55RkT2jUGIiOza/PG9AQAf7zmLQsPlR4W+PZiHhiYzegd4oG+wZ3eUR0QSYxAiIrs2qpcfhkV6o6HJjLe3nGrzOlEU8fFuSy/R7UnhEAShu0okIgkxCBGRXRMEAX8d3weApWk6o6DqktdtzShGRmEVXJ3VuGloaHeWSEQSYhAiIruX3NMX4/sGosks4m9fHUJDk7nV501mEa9tPAEAuCs5AnpXZynKJCIJMAgRkd0TBAHPT+8PnYsTDuVW4um1aTCbz2+y+MFvWUjLq4S7Ro37R0dLWCkRdTcGISJyCMF6Vyz78xAIAvBFSi4e/Hg/0nIr8fHus3jxx+MAgIVT+sLPQytxpUTUnQSxo3vPOxiDwQC9Xo/KykrodDqpyyGiq7T2YB4e//IQmsytf/TdFNcDr84YDJWKTdJE9qC9v7+durEmIiLJTY/rgV4BHlj2ywnsOV0Gd60T7kqOwAOjoxmCiBwQgxAROZwBPfT436xhUpdBRDLAHiEiIiJyWAxCRERE5LAUE4RefPFFjBgxAm5ubvDy8mrXY2bPng1BEFrdJk2a1LWFEhERkWIopkeooaEBM2bMQHJyMt577712P27SpElYuXKl9WOtlktjiYiIyEIxQei5554DAKxatapDj9NqtQgKCuqCioiIiEjpFDM11llbt25FQEAAYmJi8NBDD6G0tPSy1xuNRhgMhlY3IiIisk92HYQmTZqEDz/8EJs2bcKSJUuwbds2TJ48GSaTqc3HLF68GHq93noLCwvrxoqJiIioO0kahJ566qmLmpl/f0tPT+/08//5z3/GH/7wBwwcOBDTp0/HunXrsG/fPmzdurXNxyxcuBCVlZXWW05OTqdfn4iIiORN0h6hxx57DLNnz77sNdHRtjsAMTo6Gn5+fjh16hTGjRt3yWu0Wi0bqomIiByEpEHI398f/v7+3fZ6ubm5KC0tRXBwcLe9JhEREcmXYnqEsrOzkZqaiuzsbJhMJqSmpiI1NRXV1dXWa2JjY7FmzRoAQHV1NZ544gns3r0bWVlZ2LRpE6ZNm4ZevXph4sSJUn0ZREREJCOKWT7/zDPP4IMPPrB+HBcXBwDYsmULxo4dCwDIyMhAZWUlAECtVuPw4cP44IMPUFFRgZCQEEyYMAHPP/88p76IiIgIACCIoihKXYScGQwG6PV6VFZWQqfTSV0OERERtUN7f38rZmqMiIiIyNYUMzUmlZYBM26sSEREpBwtv7evNPHFIHQFVVVVAMCNFYmIiBSoqqoKer2+zc+zR+gKzGYzzp07B09PTwiCYLPnNRgMCAsLQ05ODnuPLoHvz+Xx/bk8vj+Xx/enbXxvLk9J748oiqiqqkJISAhUqrY7gTgidAUqlQqhoaFd9vw6nU72/zNJie/P5fH9uTy+P5fH96dtfG8uTynvz+VGglqwWZqIiIgcFoMQEREROSwGIYlotVr885//5OaObeD7c3l8fy6P78/l8f1pG9+by7PH94fN0kREROSwOCJEREREDotBiIiIiBwWgxARERE5LAYhIiIiclgMQhJ56623EBkZCRcXFyQlJWHv3r1SlyQL27dvx9SpUxESEgJBELB27VqpS5KVxYsXY9iwYfD09ERAQACmT5+OjIwMqcuSheXLl2PQoEHWjd6Sk5Px008/SV2WbP3rX/+CIAiYP3++1KXIwrPPPgtBEFrdYmNjpS5LVvLy8nDnnXfC19cXrq6uGDhwIFJSUqQu66oxCEng888/x4IFC/DPf/4TBw4cwODBgzFx4kQUFRVJXZrkampqMHjwYLz11ltSlyJL27Ztw9y5c7F7925s3LgRjY2NmDBhAmpqaqQuTXKhoaH417/+hf379yMlJQXXXXcdpk2bhqNHj0pdmuzs27cP77zzDgYNGiR1KbLSv39/5OfnW2+//vqr1CXJRnl5OUaOHAlnZ2f89NNPOHbsGJYuXQpvb2+pS7tqXD4vgaSkJAwbNgz/+c9/AFjOMwsLC8MjjzyCp556SuLq5EMQBKxZswbTp0+XuhTZKi4uRkBAALZt24bRo0dLXY7s+Pj44JVXXsE999wjdSmyUV1djaFDh+Ltt9/GCy+8gCFDhmDZsmVSlyW5Z599FmvXrkVqaqrUpcjSU089hZ07d2LHjh1Sl2JzHBHqZg0NDdi/fz/Gjx9vvU+lUmH8+PHYtWuXhJWRElVWVgKw/MKn80wmEz777DPU1NQgOTlZ6nJkZe7cubjhhhta/Qwii5MnTyIkJATR0dG44447kJ2dLXVJsvHdd98hISEBM2bMQEBAAOLi4vDf//5X6rJsgkGom5WUlMBkMiEwMLDV/YGBgSgoKJCoKlIis9mM+fPnY+TIkRgwYIDU5chCWloaPDw8oNVq8eCDD2LNmjXo16+f1GXJxmeffYYDBw5g8eLFUpciO0lJSVi1ahXWr1+P5cuX48yZM7jmmmtQVVUldWmycPr0aSxfvhy9e/fGhg0b8NBDD+Evf/kLPvjgA6lLu2o8fZ5IoebOnYsjR46wj+ECMTExSE1NRWVlJb766ivMmjUL27ZtYxgCkJOTg0cffRQbN26Ei4uL1OXIzuTJk63/PmjQICQlJSEiIgJffPEFp1Zh+cMrISEBL730EgAgLi4OR44cwYoVKzBr1iyJq7s6HBHqZn5+flCr1SgsLGx1f2FhIYKCgiSqipRm3rx5WLduHbZs2YLQ0FCpy5ENjUaDXr16IT4+HosXL8bgwYPx73//W+qyZGH//v0oKirC0KFD4eTkBCcnJ2zbtg1vvPEGnJycYDKZpC5RVry8vNCnTx+cOnVK6lJkITg4+KI/KPr27WsX04cMQt1Mo9EgPj4emzZtst5nNpuxadMm9jLQFYmiiHnz5mHNmjXYvHkzoqKipC5J1sxmM4xGo9RlyMK4ceOQlpaG1NRU6y0hIQF33HEHUlNToVarpS5RVqqrq5GZmYng4GCpS5GFkSNHXrRVx4kTJxARESFRRbbDqTEJLFiwALNmzUJCQgISExOxbNky1NTU4O6775a6NMlVV1e3+gvszJkzSE1NhY+PD8LDwyWsTB7mzp2L1atX49tvv4Wnp6e1r0yv18PV1VXi6qS1cOFCTJ48GeHh4aiqqsLq1auxdetWbNiwQerSZMHT0/OiXjJ3d3f4+vqyxwzA448/jqlTpyIiIgLnzp3DP//5T6jVatx2221SlyYLf/3rXzFixAi89NJLuOWWW7B37168++67ePfdd6Uu7eqJJIk333xTDA8PFzUajZiYmCju3r1b6pJkYcuWLSKAi26zZs2SujRZuNR7A0BcuXKl1KVJbs6cOWJERISo0WhEf39/cdy4ceLPP/8sdVmyNmbMGPHRRx+VugxZuPXWW8Xg4GBRo9GIPXr0EG+99Vbx1KlTUpclK99//704YMAAUavVirGxseK7774rdUk2wX2EiIiIyGGxR4iIiIgcFoMQEREROSwGISIiInJYDEJERETksBiEiIiIyGExCBEREZHDYhAiIiIih8UgRERERA6LQYiIqAvNnj0bgiBAEASsXbvWps+9detW63NPnz7dps9N5CgYhIioQy78xX7hjad0t23SpEnIz8/H5MmTrfe1FYxmz57d7lAzYsQI5Ofn45ZbbrFRpUSOh4euElGHTZo0CStXrmx1n7+//0XXNTQ0QKPRdFdZsqXVahEUFGTz59VoNAgKCoKrqyuMRqPNn5/IEXBEiIg6rOUX+4U3tVqNsWPHYt68eZg/fz78/PwwceJEAMCRI0cwefJkeHh4IDAwEHfddRdKSkqsz1dTU4OZM2fCw8MDwcHBWLp0KcaOHYv58+dbr7nUCIqXlxdWrVpl/TgnJwe33HILvLy84OPjg2nTpiErK8v6+ZbRlldffRXBwcHw9fXF3Llz0djYaL3GaDTiySefRFhYGLRaLXr16oX33nsPoiiiV69eePXVV1vVkJqa2mUjYllZWZccfRs7dqzNX4vIUTEIEZFNffDBB9BoNNi5cydWrFiBiooKXHfddYiLi0NKSgrWr1+PwsLCVtM5TzzxBLZt24Zvv/0WP//8M7Zu3YoDBw506HUbGxsxceJEeHp6YseOHdi5cyc8PDwwadIkNDQ0WK/bsmULMjMzsWXLFnzwwQdYtWpVqzA1c+ZMfPrpp3jjjTdw/PhxvPPOO/Dw8IAgCJgzZ85FI2ErV67E6NGj0atXr869YZcRFhaG/Px86+3gwYPw9fXF6NGjbf5aRA5LuoPviUiJZs2aJarVatHd3d16u/nmm0VRFMUxY8aIcXFxra5//vnnxQkTJrS6LycnRwQgZmRkiFVVVaJGoxG/+OIL6+dLS0tFV1dX8dFHH7XeB0Bcs2ZNq+fR6/XiypUrRVEUxY8++kiMiYkRzWaz9fNGo1F0dXUVN2zYYK09IiJCbGpqsl4zY8YM8dZbbxVFURQzMjJEAOLGjRsv+bXn5eWJarVa3LNnjyiKotjQ0CD6+fmJq1atuuz7NW3atIvuByC6uLi0eh/d3d1FJyenS15fV1cnJiUliTfeeKNoMpna9RpEdGXsESKiDrv22muxfPly68fu7u7Wf4+Pj2917aFDh7BlyxZ4eHhc9DyZmZmoq6tDQ0MDkpKSrPf7+PggJiamQzUdOnQIp06dgqenZ6v76+vrkZmZaf24f//+UKvV1o+Dg4ORlpYGwDLNpVarMWbMmEu+RkhICG644Qa8//77SExMxPfffw+j0YgZM2Z0qNYWr7/+OsaPH9/qvieffBImk+mia+fMmYOqqips3LgRKhUH84lshUGIiDrM3d29zamgC0MRAFRXV2Pq1KlYsmTJRdcGBwe3u7dGEASIotjqvgt7e6qrqxEfH49PPvnkosde2Mjt7Ox80fOazWYAgKur6xXruPfee3HXXXfh9ddfx8qVK3HrrbfCzc2tXV/D7wUFBV30Pnp6eqKioqLVfS+88AI2bNiAvXv3XhT0iOjqMAgRUZcaOnQovv76a0RGRsLJ6eIfOT179oSzszP27NmD8PBwAEB5eTlOnDjRamTG398f+fn51o9PnjyJ2traVq/z+eefIyAgADqdrlO1Dhw4EGazGdu2bbtopKbFlClT4O7ujuXLl2P9+vXYvn17p16rvb7++mssWrQIP/30E3r27Nmlr0XkiDi+SkRdau7cuSgrK8Ntt92Gffv2ITMzExs2bMDdd98Nk8kEDw8P3HPPPXjiiSewefNmHDlyBLNnz75o+ue6667Df/7zHxw8eBApKSl48MEHW43u3HHHHfDz88O0adOwY8cOnDlzBlu3bsVf/vIX5ObmtqvWyMhIzJo1C3PmzMHatWutz/HFF19Yr1Gr1Zg9ezYWLlyI3r17Izk52TZv1CUcOXIEM2fOxJNPPon+/fujoKAABQUFKCsr67LXJHI0DEJE1KVCQkKwc+dOmEwmTJgwAQMHDsT8+fPh5eVlDTuvvPIKrrnmGkydOhXjx4/HqFGjLuo1Wrp0KcLCwnDNNdfg9ttvx+OPP95qSsrNzQ3bt29HeHg4brrpJvTt2xf33HMP6uvrOzRCtHz5ctx88814+OGHERsbi/vuuw81NTWtrrnnnnvQ0NCAu++++yremStLSUlBbW0tXnjhBQQHB1tvN910U5e+LpEjEcTfT7oTEcnA2LFjMWTIECxbtkzqUi6yY8cOjBs3Djk5OQgMDLzstbNnz0ZFRYXNj9fo7tcgslccESIiaiej0Yjc3Fw8++yzmDFjxhVDUIt169bBw8MD69ats2k9O3bsgIeHxyUbxImofdgsTUTUTp9++inuueceDBkyBB9++GG7HvPyyy/j6aefBmBZJWdLCQkJSE1NBYBLbk9ARFfGqTEiIiJyWJwaIyIiIofFIEREREQOi0GIiIiIHBaDEBERETksBiEiIiJyWAxCRERE5LAYhIiIiMhhMQgRERGRw/p/hIa02PyN6lQAAAAASUVORK5CYII=",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -229,7 +233,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -242,21 +246,11 @@
     "mler_ts = extreme.mler_export_time_series(RAO.values, mler_norm, sim, k)\n",
     "mler_ts.plot(xlabel=\"Time (s)\", ylabel=\"[m] / [*]\", xlim=[-100, 100], grid=True)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "6acc4428af86beefd6565514d05fe9ce8e024621fafadd3627cdac7b7bd68bc4"
-  },
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "mhkit_release",
    "language": "python",
    "name": "python3"
   },
@@ -270,7 +264,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.4"
+   "version": "3.13.2"
   }
  },
  "nbformat": 4,
diff --git a/examples/metocean_example.ipynb b/examples/metocean_example.ipynb
index 1fd2c1b51..4f4540ab8 100644
--- a/examples/metocean_example.ipynb
+++ b/examples/metocean_example.ipynb
@@ -712,7 +712,7 @@
         },
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -884,7 +884,7 @@
         {
           "data": {
             "text/plain": [
-              "<matplotlib.legend.Legend at 0x232d04a1790>"
+              "<matplotlib.legend.Legend at 0x2617ccbb190>"
             ]
           },
           "execution_count": 11,
@@ -893,7 +893,7 @@
         },
         {
           "data": {
-            "image/png": 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aQ6yV/d3Q5dy5c+jfvz9kMhmcnZ1Ru3ZtdcLy+O+lrvdJrVq1KvW8AHDt2jV1Eufo6IjatWurE5DHn9vS0lLnsOWQIUNgY2ODjRs3qh+3a9cuDB8+XCtWY3bw4EGMHTsWPXr0wLx58zT2qZI+Xe+J/Pz8Ss1vefjwIbp16waZTIbly5ejf//+mDhxIvbu3YvLly9rDTGaI86ZMVNP8sHx+GRDlfK+SQMCAtC2bVts2LABo0aNwoYNG2BtbY3BgweX6/El9f6UVC7+fyKtaqLoiBEjdM6hAKDuydqwYQPGjBmDfv364Z133oGHhwekUini4uI0/qos73OXxMfHB/7+/jhw4AD8/PwghECnTp1Qu3ZtvPXWW7h69SoOHjyIzp07w8Ki7L89KhtHeUkkEmzZsgWHDx/Gzp078csvv+Dll1/G4sWLcfjw4Qqti6Jqj2nTpqFHjx4666iSbqVSifDwcERFRems17hx4wqeCTB58mRERkZix44d+OWXXzBz5kzExcVh3759CAoKqvDxSqKvNrl79y5CQkLg7OyMOXPmoEGDBrC1tUVKSgqmT5+uNRG6PM9b0ufA4+9xhUKB8PBwZGVlYfr06WjatCkcHBxw48YNjBkzRuu5bWxsdP6+1qpVC3369MHGjRsxa9YsbNmyBQUFBRo9SMbu1KlTeP7559GiRQts2bJFY5IvAHh7ewMo6sV7/A+s9PT0Sl11dODAAZw9exZLlizRKG/UqBGaNWuG33//vcLHrGmYzJiZevXqQalU4uLFi2jWrJm6/NatW7h79y7q1aunLqtVq5bWVSCFhYVIT09/4jhGjRqFKVOmID09HZs2bULv3r21epH0rXbt2nBycoJCoVD/RV+SLVu2oH79+ti2bZvGB355JihXVHBwMA4cOAB/f38EBgbCyckJrVu3hkwmQ3x8PFJSUhAbG6uX51K178WLF7X2VWTdmo4dO6Jjx46YN28eNm3ahOHDh2Pz5s0al6fqeo4LFy6oJ8jWr18fQNFl5mW1R4MGDXDv3r0y69WrVw+//fYb7t27p5FYlXRuDRo0wNSpUzF16lRcvHgRgYGBWLx4MTZs2FDi8Us63l9//QV3d/cqW5Jg//79yMzMxLZt29C1a1d1eVpaWqWPqXrPPf4+f7yH9syZM7hw4QLWrVuHUaNGqcv37NlT4eccNWoU+vbti6NHj2Ljxo0ICgpST3Q3dpcvX0bPnj3h4eGB3bt360zeVRcHHDt2TCNx+ffff/HPP/9UarLurVu3AOj+Q1Iul2stxmeOOMxkZnr16gUA6qs/VFQZf+/evdVlDRo0UM9FUFm9enWJPTMVMWzYMEgkErz11lv4+++/q+UvM6lUigEDBmDr1q06LxEufsmo6q/a4n/FHjlyBElJSXqPKzg4GFeuXMG3336rHnaysLBA586dsWTJEsjl8nLNlykPb29vBAYGYt26dRpDA3v27EFqamqZj79z545Wj4Lqw/vxbvUdO3ao57wAQHJyMo4cOaK+ksbDwwOhoaH47LPPdCbIxdtj8ODBSEpKwi+//KJV7+7du+oP8169eqnX5FBRKBRYtmyZxmPy8vK0LgNu0KABnJycdA4PqBR//YonAGfPnsWvv/6qfn9VBV2/k4WFhfj0008rfcwGDRoAgMb7XKFQYPXq1WU+txACH3/8cYWfMyIiAu7u7liwYAESExNNplfm5s2b6N69OywsLPDLL7+gdu3aOus1b94cTZs21fqsXLlyJSQSCQYOHFjh51b1PG7evFmjPCUlBefPn9drT6KpYs+MmWndujVGjx6N1atXq7utk5OTsW7dOvTr1w9hYWHquuPGjcOrr76KAQMGIDw8HKdOncIvv/yicRliZdWuXRs9e/bE999/DxcXF40kqip98MEHSEhIQIcOHfDKK68gICAAWVlZSElJwd69e5GVlQUA6NOnD7Zt24b+/fujd+/eSEtLw6pVqxAQEIB79+7pNSZVonL+/HnMnz9fXd61a1f8/PPP6vVi9CUuLg69e/dGly5d8PLLLyMrKwvLli1D8+bNyzy3devW4dNPP0X//v3RoEED5ObmYs2aNXB2dtb6Im/YsCG6dOmCiRMnoqCgAEuXLoWbm5vGUNGKFSvQpUsXtGzZEq+88grq16+PW7duISkpCf/88w9OnToFoGj10x9//BF9+vTBmDFj0LZtW9y/fx9nzpzBli1bcOXKFbi7uyMyMhLPPPMMZsyYgStXriAgIADbtm3TmtNx4cIFPPfccxg8eDACAgJgaWmJ7du349atWxqTy3VZtGgRIiIi0KlTJ4wdO1Z9abZMJtO5LpO+dO7cGbVq1cLo0aPx5ptvQiKRYP369U80hNi8eXN07NgR0dHRyMrKgqurKzZv3qz1l37Tpk3RoEEDTJs2DTdu3ICzszO2bt1aqfk3VlZWGDp0KJYvXw6pVIphw4aV+7Hvv/8+gKK5QwCwfv16HDp0CADw3nvvqeudPn1avVbRpUuXkJ2drX5s69atERkZqa6r6iksa32hnj174u+//0ZUVBQOHTqkfl4A8PT0RHh4uHp70aJFeP7559G9e3cMHToUZ8+exfLlyzFu3DiNHvHynlPbtm0RHh6OdevWIScnB927d0d6ejqWLVsGOzs79ZISZq3ar58ivVJdWlnSpbIhISEal2YLIYRcLhexsbHC399fWFlZCV9fXxEdHa1xaacQRZdRT58+Xbi7uwt7e3vRo0cPcenSpRIvzdYVQ0mXfgohxHfffScAiPHjx5f7fHWdjxBFl4v37t1bqxyAmDRpkkbZrVu3xKRJk4Svr6+wsrISXl5e4rnnnhOrV69W11EqlWL+/PmiXr16wsbGRgQFBYldu3aVeEm0rkt8UcKl7bp4eHgIAOLWrVvqskOHDgkAIjg4WKv+k8axdetW0axZM2FjYyMCAgLEtm3btI6pS0pKihg2bJioW7eusLGxER4eHqJPnz7i2LFjOmNZvHix8PX1FTY2NiI4OFicOnVK65iXL18Wo0aNEl5eXsLKyko89dRTok+fPmLLli0a9XJzc0V0dLRo2LChsLa2Fu7u7qJz587iww8/FIWFhep6mZmZYuTIkcLZ2VnIZDIxcuRIceLECY1Ls2/fvi0mTZokmjZtKhwcHIRMJhMdOnQQ3333Xannr7J3717xzDPPCDs7O+Hs7CwiIyNFamqqRh3Vpdn//fefRnlp74niRo0aJZydnTXKfv/9d9GxY0dhZ2cnfHx8RFRUlHoJguKX9Jb0PtHVxpcvXxbdunUTNjY2wtPTU/zvf/8Te/bs0Tpmamqq6Natm3B0dBTu7u7ilVdeEadOndJa/kDXJeWPS05OFgBE9+7dS633OJSyBEVxpS1ZUfyzSwgh3N3dS7xcurzPHRISolV/+/btIjAwUNjY2Ig6deqI9957T+P3tKLnlJeXJ+bMmSMCAgKEnZ2dkMlkok+fPuLEiRNlxm4OJELoaWYgUQX98MMP6NevHw4cOKC3YRQyvCtXrsDf3x+LFi3CtGnTDB2OyXrhhRdw9OhRXL9+3dCh6N2pU6cQGBiIr7/+utxXMVYF1cKUu3btqrbeYaoanDNDBrNmzRrUr18fXbp0MXQoREZFqVQiJSWlXGv/mKI1a9bA0dERL7zwgkHjSEhIQKdOnZjI1ACcM0PVbvPmzTh9+jR++uknfPzxxya1vgRRVbp//z6++eYb7NixA1evXtWYQ1UT7Ny5E6mpqVi9ejVef/11g9+MdtKkSZg0aZJBYyD9YDJD1W7YsGFwdHTE2LFj8dprrxk6HCKj8d9//2HChAnw9fXFokWL8OKLLxo6JL164403cOvWLfTq1Utvyw0QAYBB58wcOHAAixYtwvHjx5Geno7t27ejX79+Ouu++uqr+Oyzz/DRRx9x5jYRERGpGXTOzP3799G6dWusWLGi1Hrbt2/H4cOH4ePjU02RERERkakw6DBTREREmbeiv3HjBt544w388ssvnKRFREREWox6zoxSqcTIkSPxzjvvlHu564KCAo0VPFV3PHZzc+NEUyIiIhMhhEBubi58fHzKvDedUSczCxYsgKWlJd58881yPyYuLo4Ty4iIiGqI69ev67wLe3FGm8wcP34cH3/8MVJSUirUoxIdHY0pU6aot7Ozs1G3bl1cuHABrq6uVREqVZJcLkdCQgLCwsJgZWVl6HDoMWwf48W2MV5sG/3Jzc2Fv78/nJycyqxrtMnMwYMHkZGRgbp166rLFAoFpk6diqVLl5Z4Hw0bGxvY2Nholbu6usLNza2qwqVKkMvlsLe3h5ubG9/0RojtY7zYNsaLbaM/qtevPB0aRpvMjBw5Et26ddMo69GjB0aOHImXXnrJQFERERGRsTFoMnPv3j1cunRJvZ2WloaTJ0/C1dUVdevW1epJsbKygpeXF5o0aVLdoRIREZGRMmgyc+zYMYSFham3VXNdRo8ejbVr1xooKiIiIjIlBk1mQkNDUZEFiEuaJ6MPCoUCcrm8yo5P2uRyOSwtLZGfnw+FQmHocOgxNb19rKysIJVKDR0GEemB0c6ZqS5CCNy8eRN37941dChmRwgBLy8vXL9+nWsAGSFzaB8XFxd4eXnV2PMjMhdmn8yoEhkPDw/Y29vzQ60aKZVK3Lt3D46OjmUuiETVrya3jxACeXl5yMjIAAB4e3sbOCIiehJmncwoFAp1IsPLtqufUqlEYWEhbG1ta9yXZU1Q09vHzs4OAJCRkQEPDw8OORGZsJr3CVUBqjky9vb2Bo6EiAxB9d7nfDki02bWyYwKh5aIzBPf+0Q1A5MZIiIiMmlMZshsXblyBRKJBCdPnnyi48yePRuBgYF6iak67d+/HxKJhFfyEZHJYzKjBwqlQNLlTPxw8gaSLmdCoSz/2jmVMWbMGEgkEnzwwQca5Tt27NDoNld9WUkkElhYWEAmkyEoKAhRUVFIT0/XeOzs2bPVdSUSCWQyGYKDg5GYmKj1/Fu3bkVoaChkMhkcHR3RqlUrzJkzB1lZWWXGXlBQgMDAQL0kEU/K19cX6enpaNGihUHjICKiJ8Nk5gnFn01HlwX7MGzNYby1+SSGrTmMLgv2If5setkPfgK2trZYsGAB7ty5U2bd8+fP499//8XRo0cxffp07N27Fy1atMCZM2c06jVv3hzp6elIT09HUlISGjVqhD59+iA7O1td591338WQIUPw9NNP4+eff8bZs2exePFinDp1CuvXry8zlqioKPj4+FT8hKuAVCqFl5cXLC3N+qI+IiKTx2TmCcSfTcfEDSlIz87XKL+ZnY+JG1KqNKHp1q0bvLy8EBcXV2ZdDw8PeHl5oXHjxhg6dCh+//131K5dGxMnTtSoZ2lpCS8vL3h5eSEgIABz5szBvXv3cOHCBQBAcnIy5s+fj8WLF2PRokXo3Lkz/Pz8EB4ejq1bt2L06NGlxvHzzz/j119/xYcffqi1b+3atXBxccGuXbvQpEkT2NvbY+DAgcjLy8O6devg5+eHWrVq4c033yxxNdrs7GxIpVIcO3YMQNGlxa6urujYsaO6zoYNG+Dr6wtAe5hJ1ZP122+/oV27drC3t0fnzp1x/vx5jef54IMP4OnpCScnJ4wdOxb5+Zrt/7g7d+5g+PDhqF27Nuzs7NCoUSN89dVXGjFs3rwZnTt3hq2tLVq0aKHVI3b27FlERETA0dERnp6eGDlyJG7fvq3er1QqERcXB39/f9jZ2aF169bYsmWLxjF2796Nxo0bw87ODmFhYVW6ojYRUXViMlNJCqVA7M5U6BpQUpXF7kytsiEnqVSK+fPnY9myZfjnn38q9Fg7Ozu8+uqr+P3339WLhj2uoKAAX331FVxcXNQ39ty4cSMcHR3x2muv6XyMi4tLic9569YtvPLKK1i/fn2Jl8Ln5eXhk08+webNmxEfH4/9+/ejf//+2L17N3bv3o3169fjs88+0/qSVpHJZAgMDMT+/fsBAGfOnIFEIsGJEydw7949AEBiYiJCQkJKjBMo6n1avHgxjh07BktLS7z88svqfd999x1mz56N+fPn49ixY/D29sann35a6vFmzpyJ1NRU/Pzzz/jzzz+xcuVKuLu7a9R55513MHXqVJw4cQKdOnVCZGQkMjMzAQB3797Fs88+i6CgIBw7dgzx8fG4desWBg8erH58XFwcvv76a6xatQrnzp3D22+/jREjRqiTouvXr+OFF15AZGQkTp48iXHjxmHGjBmlxk1EZCrYv15JyWlZWj0yxQkA6dn5SE7LQqcGVbMgX//+/REYGIiYmBh88cUXFXps06ZNART1DHh4eAAo+vJ3dHQEUJRYODk54dtvv4WzszMA4OLFi6hfvz6srKwq9FxCCIwZMwavvvoq2rVrV2KPgFwux8qVK9GgQQMAwMCBA7F+/XrcunULjo6OCAgIQFhYGBISEjBkyBCdxwgNDcX+/fsxbdo07N+/H+Hh4fjrr79w6NAh9OzZE/v370dUVFSp8c6bN0+d8MyYMQO9e/dGfn4+bG1tsXTpUowdOxZjx44FALz//vvYu3dvqb0z165dQ1BQENq1awcA8PPz06rz+uuvY8CAAQCAlStXIj4+Hl988QWioqKwfPlyBAUFYf78+er6X375JXx9fXHhwgXUq1cP8+fPx969e9GpUycAQP369XHo0CF89tlnCAkJUb+uixcvBgA0adIEZ86cwYIFC0p9LYiITAF7ZiopI7f0oYWK1qusBQsWYN26dfjzzz8r9DjVDT6LTxhu0qQJTp48iZMnT+L48eOYOHEiBg0apB62qchNQYtbtmwZcnNzER0dXWo9e3t7dSIDAJ6envDz81MnWKqyknqTACAkJASHDh2CQqFAYmIiQkND1QnOv//+i0uXLiE0NLTUOFq1aqX+v2qZe9Vz/vnnn+jQoYNGfVUCUZKJEydi8+bNCAwMRFRUFP744w+tOsWPYWlpiXbt2qnb9NSpU0hISICjo6P6R5WMXr58GZcuXUJeXh7Cw8M16nz99de4fPlypeMmIjIV7JmpJA8nW73Wq6yuXbuiR48eiI6OxpgxY8r9ONUXZfFeAmtrazRs2FC9HRQUhB07dmDp0qXYsGEDGjdujEOHDkEul1eod2bfvn1ISkqCjY2NRnn79u0xaNAgbNy4EQC0jimRSHSWKZXKEp+ra9euyM3NRUpKCg4cOID58+fDy8sLH3zwAVq3bg0fHx80atSo1HiLP6cq2SvtOcsSERGBq1evYvfu3dizZw+ee+45TJo0SefcIV3u3buHyMhInb0o3t7eOHv2LADgp59+wlNPPaWx//HXnIioJmLPTCW193eFt8wWJa0fKgHgLbNFe3/XKo/lgw8+wM6dO5GUlFSu+g8ePMDq1avRtWtX1K5du9S6UqkUDx48AAC8+OKLuHfvXolzREpar+STTz7BqVOn1L0+u3fvBgB88803eO+998oVc3m5uLigVatWWL58OaysrNC0aVN07doVJ06cwK5du8qcL1OWZs2a4ciRIxplhw8fLvNxtWvXxujRo7FhwwYsXboUq1evLvEYDx8+xPHjx9GsWTMAQJs2bXDu3Dn4+fmhYcOGGj8ODg4ICAiAjY0Nrl27prVfNdm5WbNmSE5OrnDcRESmgD0zlSS1kCAmMgATN6RAAmhMBFYlODGRAZBaVP1y6S1btsTw4cPxySef6NyfkZGB/Px85Obm4vjx41i4cCFu376Nbdu2adR7+PAhbt68CQDIzc3Ft99+i9TUVEyfPh0A0KFDB0RFRWHq1Km4ceMG+vfvDx8fH1y6dAmrVq1Cly5d8NZbb2k9f926dTW2VcNGDRo00OpJ0IfQ0FAsW7YMAwcOBAC4urqiWbNm+Pbbb7FixYonOvZbb72FMWPGoF27dnjmmWewceNGnDt3DvXr1y/xMbNmzULbtm3RvHlzFBQUYNeuXepERWXFihVo1KgRmjVrho8++gh37txRTzyeNGkS1qxZg2HDhiEqKgqurq64dOkSNm/ejM8//xxOTk6YNm0a3n77bSiVSnTp0gXZ2dn4/fff4ezsjNGjR+PVV1/F4sWL8c4772DcuHE4fvw41q5d+0SvBRGRsWDPzBPo2cIbK0e0gZdMcyjJS2aLlSPaoGcL72qLZc6cOSUOhTRp0gQ+Pj5o27YtPvjgA3Tr1g1nz55FQECARr1z587B29sb3t7eCAwMxHfffYeVK1di1KhR6joLFizApk2bcOTIEfTo0QPNmzfHlClT0KpVqzIvza4uISEhUCgUGnNjQkNDtcoqY8iQIZg5cyaioqLQtm1bXL16VesS98dZW1sjOjoarVq1QteuXSGVSrF582aNOh988IF6KOzQoUP48ccf1Vc8+fj44Pfff4dCoUD37t3RsmVLTJ48GS4uLuq7Wc+dOxczZ85EXFwcmjVrhp49e+Knn36Cv78/gKKEcuvWrdixYwdat26NVatWaUwoJiIyZRJR2VmdJiInJwcymQy3b9+Gm5vmVUX5+flIS0uDv78/bG0rP7dFoRRITstCRm4+PJyKhpaqo0fG1CmVSuTk5MDZ2Vn9pWxurly5An9/f5w4ccLobolgDu2jr8+A6iaXy7F792706tWrwlcXUtVi2+iP6vs7OztbfVVtSTjMpAdSC0mVXX5NREREpauZf24RERGR2WDPDJEB+fn5VXr9HiKjkxAHWEiBEB0LUyYuBJQKIKz09aaIKoM9M0REpB8WUiBhXlHiUlziwqJyC6lh4qIajz0zRESkH6oemYR5j7ZViUzYu7p7bIj0gMkMERHpT/GE5sAiQFHIRIaqHIeZiIhIv0KiAKl1USIjtWYiQ1WOyQwREelX4sJHiYyiUHsOjZFQKAWSLmfih5M3kHQ5EwolJ+ObKg4zERGR/jw+R0a1DRhVD0382XTE7kxFena+usxbZouYyIBqXb2d9IM9M2Sy1q5dCxcXF/X27NmzS11F9/H65iA0NBSTJ09+omNcuXIFEokEJ0+e1EtM1cnPzw9Lly41dBjmQ9dk35Coom1dVzkZSPzZdEzckKKRyADAzex8TNyQgviz6QaKjCqLyYyJWbVqFZycnPDw4UN12b1792BlZaV136H9+/dDIpHg8uXLALQ/2P38/CCRSLTunjx58mSNY82ePRsSiQQSiQSWlpZwd3dH165dsXTpUhQUFJQYa2hoqPpxun6effbZyr8QlTBkyBBcuHBBvV1W8qNy7tw5DBgwQP16lfTluGLFCvj5+cHW1hYdOnTQuku1IWzbtg1z5841dBhkLpQK3ZN9VQmNUmGYuIpRKAVid6ZC14CSqix2ZyqHnEwMk5knkRBX8l8aiQuL9utZWFgY7t27h2PHjqnLDh48CC8vLxw5cgT5+Y/+0khISEDdunXRoEGDEo9na2urvit2aZo3b4709HRcu3YNCQkJGDRoEOLi4tC5c2fk5ubqfMy2bduQnp6O9PR09Rf73r171WVbtmwp72nrhZ2dHTw8PCr8uLy8PNSvXx8ffPABvLy8dNb59ttvMWXKFMTExCAlJQWtW7dGjx49kJGR8aRhPxFXV1c4OTkZNAYyI2HRJQ8lhUQZxYJ5yWlZWj0yxQkA6dn5SE7Lqr6g6IkZNJk5cOAAIiMj4ePjA4lEgh07dmjsnz17Npo2bQoHBwfUqlUL3bp1w5EjRwwTrC4GWCCqSZMm8Pb2xv79+9Vl+/fvR9++feHv76/Ry7J//36EhYWVerzx48fj8OHD2L17d6n1LC0t4eXlBR8fH7Rs2RJvvPEGEhMTcfbsWSxYsEDnY1xdXeHl5QUvLy/Url0bAODm5qYuS0hIQKdOnWBnZwc/Pz8sXrxY4/F37tzBqFGjUKtWLdjb2yMiIgIXL14sNc7iLl++jPr16+P111+HEEJjmGnt2rWIjY3FqVOn1D1Fa9eu1Xmcp59+GosWLcLQoUNhY2Ojs86SJUvwyiuv4KWXXkJAQABWrVoFe3t7fPnll+o6EokEn332Gfr06QN7e3s0a9YMSUlJuHTpEkJDQ+Hg4IDOnTure9J0GThwIF5//XX19uTJkyGRSPDXX38BAAoLC+Hg4IC9e/cC0B5m8vPzw/z58/Hyyy/DyckJdevWxerVqzWeIzk5GUFBQbC3t0dYWBhOnDhRYjwqn376KRo1agRbW1t4enpi4MCB6n2hoaF4/fXX8frrr0Mmk8Hd3R0zZ87UWPm4oKAA06ZNw1NPPQUHBwd06NBB43ccAA4dOoTg4GDY2dnB19cXb775Ju7fv6/en5GRgcjISNjZ2cHf3x8bN24sM24yPxm5JScylalHxsGgycz9+/fRunVrrFixQuf+xo0bY/ny5Thz5gwOHToEPz8/dO/eHf/99181R1oCXWPB1bBAVFhYGBISEtTbCQkJCA0NRUhIiLr8wYMHOHLkSJnJjL+/P1599VVER0dDqVRWKI6mTZsiIiIC27Ztq/A5HD9+HEOHDsULL7yAU6dOYfbs2Zg5c6ZGQjFmzBgcO3YMP/74I5KSkiCEQK9evSCXy8s8/unTp9GlSxe8+OKLWL58OSQSzbuYDxkyBFOnTlX3OKWnp2PIkCEVPg+gKIE4fvw4unXrpi6zsLBAt27dkJSUpFF37ty5GDVqFE6ePImmTZvixRdfxIQJExAdHY1jx45BCKGRrDwuJCRE40s+MTER7u7u6rKjR49CLpejc+fOJR5j8eLFaNeuHU6cOIHXXnsNEydOxPnz5wEUDVn26dMHAQEBOHr0KKZPn46oqNJ/j48dO4Y333wTc+bMwfnz5xEfH4+uXbtq1Fm3bh0sLS2RnJyMjz/+GEuWLMHnn3+u3v/6668jKSkJmzdvxunTpzFo0CD07NlTnbxevnwZPXv2xIABA3D69Gl8++23OHTokMZrNWbMGFy/fh0JCQnYsmULPv30U4P3jJHx8XAq393Ry1uPjIQwEgDE9u3bS62TnZ0tAIi9e/eW+7iqx9y+fVtr34MHD0Rqaqp48OBBRcPVtH+BEDHOQsxxL/p3/4InO14Z1qxZIxwcHIRcLhc5OTnC0tJSZGRkiE2bNomuXbsKIYT47bffBABx9epV9ePq1asnPvroI63tjIwM4eTkJL7++mshhBBvvfWWCAkJUdeLiYkRrVu31hnL9OnThZ2dXZkxp6WlCQDixIkTQgghXnzxRdGtWzdx584doVAohBBCvPPOOyIgIEAIIcSFCxcEAPH777+rj3H79m1hZ2cnvvvuOyGEEF999ZWQyWRacf7++++iVq1a4sMPP9SIoaT6FfH4ayiEEDdu3BAAxB9//KFR/s4774j27durtwGI9957T72dlJQkAIgvvvhCXfbNN98IW1vbEp//9OnTQiKRiIyMDJGVlSWsra3F3LlzxZAhQ4QQQrz//vuic+fO6vohISHirbfe0oh/xIgR6m2lUik8PDzEypUrhRBCfPbZZ8LNzU08ePBAKBQKcefOHbFixQqNtnvc1q1bhbOzs8jJydG5PyQkRDRr1kwolUp12fTp00WzZs2EEEJcvXpVSKVScePGDY3HPffccyI6OloIIcTYsWPF+PHjNfYfPHhQWFhYiAcPHojz588LACI5OVm9/88//xQAtNqrOL19BlSzwsJCsWPHDlFYWGjoUEzOQ4VSdJy/V/hN3yXq6fjxm75LdJy/VzxUKMs+mA5sG/1RfX9nZ2eXWddkLs0uLCzE6tWrIZPJ0Lp16xLrFRQUaExKzcnJAQDI5XKtv+jlcjmEEFAqlRXuldAQPA2SA4sgURRCSK0hgqcBT3K8MnTt2hX379/HkSNHcOfOHTRu3Bhubm4IDg7GSy+9hLy8PCQkJKB+/fqoU6eOxrmpzrf4tpubG6ZOnYpZs2Zh0KBB6u5/Vb3Ht4tTKpWQSCRlvn6q/arX+s8//0RkZKRGTJ06dcLSpUshl8tx7tw5WFpa4umnn1Y/tlatWmjSpAlSU1M12qx4nNeuXUN4eDjef/99vPXWWxpx6apf0nmV5vHX8PFzK17v8eO3aNFCva0aemvevLlGWX5+Pu7evQtnZ2et5w4ICICrqysSEhJgbW2NoKAg9OrVCytWrIBSqcT+/fsREhJSapu3bNlSY9vLywu3bt2CUqlEamoqWrVqBWtra3X8HTt21Hl+Ks899xzq1auH+vXro0ePHujRowf69+8Pe3t7dZ0OHTpACKE+ZocOHbB48WLI5XKcOnUKCoUCjRs31jhuQUEBXF1doVQqcerUKZw+fVpj6Eh1XpcvX8aFCxdgaWmJoKAgdYyNGzeGi4uL1vkXp1QqIYSAXC6HVGo69w1SfZaVp5eStL0b0QRvbD4FCaAxEVhSbL9S8bBS85XZNvpTkdfQ6JOZXbt2YejQocjLy4O3tzf27NkDd3f3EuvHxcUhNjZWqzwhIUHjwxV4NA/k3r17KCwsrHSMNkc+ht3/JzISRSHy98xFQYe3Kn28snh4eMDHxwfx8fG4e/cuOnbsiJycHDg6OuKpp57C3r17sXfvXnTp0kWdzAFFH9z5+fnqsuLbY8eOxaeffoqPPvoIhYWFePjwobpeQUEBFAqFxrFUzpw5g7p16+rcV9y9e/cAFA0t5uTkQKFQqF9z1QTiBw8eAChKQPPy8tT/L/4lo1AoUFBQgJycHOTn50MIoRGnak7Oxo0bMWDAAI2EQFf9ks6rJI+/hgBgbW0NqVSKK1euoHnz5uryf/75B25ubhp1i7+uqvkehYWF6jLVa5CdnV1iDJ06dcKePXtgY2ODjh07ws/PD/n5+Th8+DCSkpIwceJE9fEePnyocXylUql1zkqlEg8ePEBOTo5W2xePU9V2uuzbtw+HDh3Cvn37MGvWLMyePRv79u2DTCbDw4cPIZfLNR5bvK3/++8/SKVSJCQkaCUUDg4OyMnJQU5ODsaMGYMJEyZoPXft2rVx6tQp9fEsLB6NngshtNqruMLCQjx48AAHDhzQuELQVOzZs8fQIZislxpLsO2KBe4WPhqCllkLvOCnhOLqcey++mTHZ9s8OdX3QHkYfTITFhaGkydP4vbt21izZg0GDx6MI0eOlHhVSnR0NKZMmaLezsnJga+vL8LCwuDm5qZRNz8/H9evX4ejoyNsbSs5PnpgESySlkAZ+j+g6zsQBxbBbv982NjYAl3fqdwxy+HZZ5/F4cOHcffuXUydOlX9pR0SEoIDBw4gJSUFkyZN0vgyt7CwgK2trbqs+LazszNmzpyJ2NhYREZGwtLSUl3PxsYGUqlUq6fgr7/+wm+//YYZM2bo7EUoztHREUDRl5OzszOaN2+uviLLyckJEokEJ06cQOPGjVGrVi20bdsWDx8+xJ9//qme/5GZmYlLly4hMDAQzs7OsLW1hUQi0YjTwcEBu3fvRp8+fTBkyBDEx8err+Z5vL6qvKzYi3v8NVRp27YtkpKSMGzYMABFCcLBgwe12sDOzk69/fhrAkCdcDs5OZUY13PPPYfPP/8cNjY2mDt3LlxcXNC1a1esWrUKBQUFCA8Ph4ODA4CihN3a2lpnm6tIpVLY2NjA2dkZrVq1wnfffQdra2vY2NggNzcXZ8+e1YpTl+effx7PP/885s2bB1dXVxw9ehQvvPACLC0tceLECY3Hnj59Go0aNUKtWrXQuXNnKBQK5OXlITg4WOex27Zti8uXL5d4KX1QUBAePnyIixcv4umnnwYAnD9/HtnZ2TrbSyU/Px92dnbo2rVr5T8DDEAul2PPnj0IDw+HlZWVocMxSb0ARCkFjl29g4zcAng42aBdvVqQWkjKfGxp2Db6U5E/NI0+mXFwcEDDhg3RsGFDdOzYEY0aNcIXX3yB6Gjdl/jZ2NjovOLEyspK6xdLoVBAIpHAwsJC46+5cktcCOyfD4S9CwvVZN/Q6YBEAouEeYBEUmWTgJ999llMmjQJcrkcYWFh6vhVV44UFhbiueee0zov1fnq2p4wYQKWLl2Kb775Bh06dFCXSyQSPHz4EBkZGVAqlcjMzMT+/fvx/vvvIzAwEFFRUWW+fqr9qtd62rRp6quERo4ciSNHjmDFihX49NNPYWFhgSZNmqBv376YMGECPvvsMzg5OWHGjBl46qmn0L9/f402Kx4nUJQI/PTTT4iIiEDv3r0RHx8PR0dHrfr+/v5IS0vD6dOnUadOHTg5Oen83SksLERqaqr6///++y9Onz4NR0dHNGzYEAAwZcoUjB49Gk8//TTat2+PpUuX4v79+3j55Zc1XhtdcZdV9riwsDBMmTIF1tbW6Nq1KywsLBAWFqZ+TR+/FLu0Nn+8bMSIEZg5cyYmTJiA6dOnIzU1FUuWLCk1pl27duHvv/9G165dUatWLezevRtKpRLNmjVT17927RqmTZuGCRMmICUlBcuXL8fixYthYWGBpk2bYvjw4RgzZgwWL16MoKAg/Pfff/jtt9/QqlUr9O7dGzNmzEDHjh3x5ptvYty4cXBwcEBqair27NmD5cuXo1mzZujZsycmTpyIlStXwtLSEpMnT4adnZ3O8y3eHhKJROfngykw1biNhRWALo09q+bYbJsnVpHXz+TWmVEqlaUu1FatDLhAVFhYGB48eICGDRvC0/PRmzEkJAS5ubnqS7grwsrKCnPnztVYq0bl3Llz8Pb2Rt26dREaGorvvvsO0dHROHjwoLqHoSLatGmDzZs3Y9u2bWjVqhVmzZqFOXPmYMyYMeo6X331Fdq2bYs+ffqgU6dOEEJg9+7d5foFd3R0xM8//wwhBHr37q1xCa/KgAED0LNnT4SFhaF27dr45ptvdB7r33//RVBQEIKCgpCeno4PP/wQQUFBGDdunLrOkCFD8OGHH2LWrFkIDAzEyZMnER8fr9E2+tKyZUu4uLggMDBQ/dqHhoZCoVBoLZxYUY6Ojti5cyfOnDmDtm3b4v3330dcXOnrJbm4uGDbtm149tln0axZM6xatQrffPONxpDbqFGj8ODBA7Rv3x6TJk3CW2+9hfHjx6v3f/XVVxg1ahSmTp2KJk2aoF+/fjh69Cjq1q0LAGjVqhUSExNx4cIFBAcHIygoCLNmzYKPj4/GMXx8fBASEoIXXngB48ePr9S6QkSVpVAKHEnLwvHbEhxJy+LCe9VIIoQw2Kt97949XLp0CUBRN/GSJUsQFhYGV1dXuLm5Yd68eXj++efh7e2N27dvY8WKFdi0aROOHz+u8UFZmpycHMhkMty+fVvnMFNaWhr8/f1Nqou5plAqlcjJyYGzs3PlesaoSumrfUJDQxEYGGiUtxUw1c8AuVyO3bt3o1evXvzr30jwXk/6p/r+zs7OLnM6gEG/QY4dO6b+ixco6qpX/cUllUrx119/YcCAAWjcuDEiIyORmZmJgwcPljuRISIiqmq815PhGXTOTGhoKErrGKrMYmxERETVpax7PUlQdK+n8ACvJ55cTCUz+gnARGTaHr8tAVFNUpF7PXVq4FZiPXoynKhARERUSbzXk3FgMgOUOtRFRDUX3/v0pHivJ+Ng1smM6iqAiqwySEQ1h+q9zyuCqLLa+7vCW2aLkmbDSFB0VVN7f9fqDMvsmPWcGalUChcXF/Wdde3t7bXurkxVR6lUorCwEPn5+bw02wjV5PYRQiAvLw8ZGRlwcXExqfsykXGRWkgQExmAiRtSSrzXU0xkACf/VjGzTmaAopvsAVAnNFR9hBB48OCBepVWMi7m0D4uLi7qzwCiyurZwhsrR7TRWmfGi+vMVBuzT2YkEgm8vb3h4eHBu5xWM7lcjgMHDqBr167s5jdCNb19rKys2CNDetOzhTfCA7yQdCkDvx48gu7BHdCpoQd7ZKqJ2SczKlKplB9s1UwqleLhw4ewtbWtkV+Wpo7tQzVWQhxgIdV977zEhf9/qxrd9/8rjdRCgg7+rsj8U6CDvysTmWpUswbCiYiIymIhBRLmFSUuxSUuLCq34B+2poY9M0REZF5UPTIJ8x5tqxIZXTcPJqPHZIaIiMxP8YTmwCJAUchExoRxmImIiMxTSBQgtS5KZKTWTGRMGJMZIiIyT4kLHyUyikLtOTRkMpjMEBGR+Sk+R2bmf0X/6poUTCaBc2aIiMi86Jrsq2tSMJkMJjNERGRelArdk31V20pF9cdET4TJDBERmZfSFsRjj4xJ4pwZIiIiMmlMZoiIiMikMZkhIiIik8ZkhoiIiEwakxkiIiIyaUxmiIiIyKQxmSEiIiKTxmSGiIiITBqTGSIiIjJpTGaIiIjIpDGZISIiIpPGZIaIiIhMGm80SURERk+hFEhOy0JGbj48nGzR3t8VUguJocMiI2HQnpkDBw4gMjISPj4+kEgk2LFjh3qfXC7H9OnT0bJlSzg4OMDHxwejRo3Cv//+a7iAiYio2sWfTUeXBfswbM1hvLX5JIatOYwuC/Yh/my6oUMjI2HQZOb+/fto3bo1VqxYobUvLy8PKSkpmDlzJlJSUrBt2zacP38ezz//vAEiJSIiQ4g/m46JG1KQnp2vUX4zOx8TN6QwoSEABh5mioiIQEREhM59MpkMe/bs0Shbvnw52rdvj2vXrqFu3brVESIRERmIQikQuzMVQsc+AUACIHZnKsIDvDjkZOZMas5MdnY2JBIJXFxcSqxTUFCAgoIC9XZOTg6AomEruVxe1SFSBajag+1inNg+xstc2uZIWpZWj0xxAkB6dj6SLmWgg79r9QVWCn23jUIpcOzqHWTkFsDDyQbt6tUym8StIq+hySQz+fn5mD59OoYNGwZnZ+cS68XFxSE2NlarPCEhAfb29lUZIlXS4z1wZFzYPsarprfN8dsSANIy6/168Agy/9TVf2M4+mibU5kSbLtigbuFj5IXF2uBF/yUaO1mXOdbFfLy8spdVyKEMIpXRCKRYPv27ejXr5/WPrlcjgEDBuCff/7B/v37S01mdPXM+Pr6Ij09HW5ublUROlWSXC7Hnj17EB4eDisrK0OHQ49h+xgvc2mbI2lZGPHlsTLrbXi5nVH1zOijbX45dwtvbD6lNcSmSmuWDW2NHs09K318U5CTkwN3d3dkZ2eX+r0PmEDPjFwux+DBg3H16lXs27evzBOysbGBjY2NVrmVlVWNftObMraNcWP7GK+a3jadGnrAW2aLm9n5OufNSAB4yWzRqaGH0Q29PEnbKJQC834+X+pcoXk/n0dEq6eM7rz1qSKvn1EvmqdKZC5evIi9e/eyZ4WIyIxILSSIiQwA8KhHQkW1HRMZUOO+0JPLOVcoOS2r+oIycgZNZu7du4eTJ0/i5MmTAIC0tDScPHkS165dg1wux8CBA3Hs2DFs3LgRCoUCN2/exM2bN1FYWGjIsImIqJr0bOGNlSPawEtmq1HuJbPFyhFt0LOFt4EiqzoZuSUnMpWpZw4MOsx07NgxhIWFqbenTJkCABg9ejRmz56NH3/8EQAQGBio8biEhASEhoZWV5hERGRAPVt4IzzAy2xWAPZwsi27UgXqmQODJjOhoaEobf6xkcxNJiIiQ0mIAyykkIZEoVODx6YaJC4ElAogLNowsVWR9v6u5Zor1N5IJj0bA6OeM0NERGbOQgokzCtKXIpLXFhUblH2pdumxlznCj0JJjNERGS8QqKAsHc1ExpVIhP2btH+Gsgc5wo9CaO/NJuIiMycKmFJmAccWAQoCmt0IqNibnOFngSTGSIiMn4hUY8SGal1jU9kVKQWEu25QqSFw0xERGT8Ehc+SmQUhdpzaMisMZkhIiLjVnyOzMz/tOfQkNnjMBMRERkvXZN9i8+hKb5NZovJDBERGS+lQvdkX9W2UlH9MZHRYTJDRETGq7QF8dgjQ/+Pc2aIiIjIpDGZISIiIpPGZIaIiIhMGpMZIiIiMmlMZoiIiMikMZkhIiIik8ZkhoiIiEwakxkiIiIyaUxmiIiIyKQxmSEiIiKTxmSGiIiITBqTGSIiImOTEFd0x3BdEhcW7Sc1JjNERETGxkIKJMzTTmgSFxaVW0gNE5eR4l2ziYiIjI3qjuAJ8x5tqxKZsHd5x/DHMJkhIiIyRsUTmgOLAEUhE5kScJiJiIjIWIVEAVLrokRGas1EpgRMZoiIiIxV4sJHiYyisORJwWaOyQwREZExKj5HZuZ/Rf/qmhRMnDNDRERkdHRN9tU1KZgAMJkhIiIyPkqF7sm+qm2lovpjMmJMZoiIiIxNWHTJ+56wR0ahFEhOy0JGbj48nGzR3t8VUgvJEx3T0Aw6Z+bAgQOIjIyEj48PJBIJduzYobF/27Zt6N69O9zc3CCRSHDy5EmDxElERFQTxJ9NR5cF+zBszWG8tfkkhq05jC4L9iH+bLqhQ3siBk1m7t+/j9atW2PFihUl7u/SpQsWLFhQzZERERHVLPFn0zFxQwrSs/M1ym9m52PihhSTTmgMOswUERGBiIiIEvePHDkSAHDlypVqioiIiKjmUSgFYnemQujYJwBIAMTuTEV4gJdJDjnVuDkzBQUFKCgoUG/n5OQAAORyOeRyuaHCIh1U7cF2MU5sH+PFtjFexto2R9KytHpkihMA0rPzkXQpAx38XasvsFJU5DWscclMXFwcYmNjtcoTEhJgb29vgIioLHv27DF0CFQKto/xYtsYL2Nrm+O3JQDKvjnlrwePIPNPXf031S8vL6/cdWtcMhMdHY0pU6aot3NycuDr64uwsDC4ubkZMDJ6nFwux549exAeHg4rKytDh0OPYfsYL7aN8TLWtnFLy8LXF4+VWa97cAej6ZlRjayUR41LZmxsbGBjY6NVbmVlZVS/WPQI28a4sX2MF9vGeBlb23Rq6AFvmS1uZufrnDcjAeAls0Wnhh5GM2emIq8fb2dARERUw0ktJIiJDABQlLgUp9qOiQwwmkSmogzaM3Pv3j1cunRJvZ2WloaTJ0/C1dUVdevWRVZWFq5du4Z///0XAHD+/HkAgJeXF7y8vAwSMxERkSnq2cIbK0e0QezOVI3JwF4yW8REBqBnC28DRvdkDJrMHDt2DGFhYept1VyX0aNHY+3atfjxxx/x0ksvqfcPHToUABATE4PZs2dXa6xERESmrmcLb4QHeNW4FYANmsyEhoZCiJJnTY8ZMwZjxoypvoCIiIhqOKmFBJ0a1KwLYjhnhoiIyBwkxBXdjVuXxIVF+00UkxkiIiJzYCEFEuZpJzSJC4vKLcpeh8ZY1bhLs4mIiEgH1d22E+Y92lYlMmHvPvHduA2JyQwREZG5KJ7QHFgEKApNPpEBOMxERERkXkKiAKl1USIjtTb5RAZgMkNERGReEhc+SmQUhSVPCjYhTGaIiMxVDb66hUpQfI7MzP+K/tU1KdjEcM4MEZG5Ul3dAmgONRT/wqOaQ9dkX12Tgk0QkxkiInNVg69uIR2UCt3tqtpWKqo/Jj1hMkNEZM5q6NUtpENYdMn7TLy9OWeGiMjc1cCrW8i8MJkhIjJ3NfDqFjIvTGaIiMxZDb26hcwL58wQEZmrGnx1C1UfhVIgOS0LGbn58HCyRXt/V0gtJNUaA5MZIiJzVYOvbqHqEX82HbE7U5Gena8u85bZIiYyAD1beFdbHBVKZu7evYvt27fj4MGDuHr1KvLy8lC7dm0EBQWhR48e6Ny5c1XFSURE+laDr26hqhd/Nh0TN6RAPFZ+MzsfEzekYOWINtWW0JRrzsy///6LcePGwdvbG++//z4ePHiAwMBAPPfcc6hTpw4SEhIQHh6OgIAAfPvtt1UdMxERERmQQikQuzNVK5EBoC6L3ZkKhVJXDf0rV89MUFAQRo8ejePHjyMgIEBnnQcPHmDHjh1YunQprl+/jmnTpuk1UCIiIjIOyWlZGkNLjxMA0rPzkZyWhU4N3Ko8nnIlM6mpqXBzKz0YOzs7DBs2DMOGDUNmZqZegiMiIiLjk5FbciJTmXpPqlzDTGUlMk9an4iIiEyHh5OtXus9qQqvM7Nu3Tr89NNP6u2oqCi4uLigc+fOuHr1ql6DIyIiIuPT3t8V3jJblHQBtgRFVzW193etlngqnMzMnz8fdnZ2AICkpCSsWLECCxcuhLu7O95++229B0hERETGRWohQUxk0RzaxxMa1XZMZEC1rTdT4WTm+vXraNiwIQBgx44dGDBgAMaPH4+4uDgcPHhQ7wESERGR8enZwhsrR7SBl0xzKMlLZlutl2UDlVg0z9HREZmZmahbty5+/fVXTJkyBQBga2uLBw8e6D1AIiIiMk49W3gjPMDL9FYADg8Px7hx4xAUFIQLFy6gV69eAIBz587Bz89P3/ERERGREZNaSKrl8uvSVHiYacWKFejUqRP+++8/bN26VX3l0vHjxzFs2DC9B0hERKZFoRRIupyJH07eQNLlzGpbOI3MV7l7Zr788ks8//zzcHd3x/Lly7X2x8bG6jUwIiIyPcZyrx4yL+XumdmwYQPq1KmDzp07Y8GCBfjrr7+qMi4iIjIxqnv1PL4yrOpePfFn0w0UGdV05U5m9u3bh/T0dLz22ms4fvw42rdvj0aNGmHq1Kk4cOAAlEplVcZJRERGzNju1UPmpUJzZmrVqoURI0bgu+++w+3bt7Fs2TI8ePAAw4cPh4eHB0aNGoUtW7bg/v37VRUvEREZoYrcq4dI3yo8AVjF2toaPXv2xKefforr168jPj4efn5+mDt3LpYsWVKuYxw4cACRkZHw8fGBRCLBjh07NPYLITBr1ix4e3vDzs4O3bp1w8WLFysbMhERVRFju1cPmZdKJzOPa9euHebMmYNTp05hxowZ5XrM/fv30bp1a6xYsULn/oULF+KTTz7BqlWrcOTIETg4OKBHjx7Iz+ebgYjImBjbvXrIvFR4nRkhBLZs2YKEhARkZGRozJWRSCTYunUrrKysynWsiIgIRERElPg8S5cuxXvvvYe+ffsCAL7++mt4enpix44dGDp0aEVDJyKiKqK6V8/N7Hyd82YkKFoZtrru1UPmpcLJzOTJk/HZZ58hLCwMnp6ekEiqZpW/tLQ03Lx5E926dVOXyWQydOjQAUlJSSUmMwUFBSgoKFBv5+TkAADkcjnkcnmVxEqVo2oPtotxYvsYL2Ntm3cjmuCNzacgATQSGkmx/UrFQygVBgiumhhr25iiiryGFU5m1q9fj23btqlX/q0qN2/eBAB4enpqlHt6eqr36RIXF6dzzZuEhATY29vrN0jSiz179hg6BCoF28d4GWPbvNRYgm1XLHC38NEfujJrgRf8lFBcPY7dVw0YXDUyxrYxNXl5eeWuW+FkRiaToX79+hV9WLWJjo5W3y8KKOqZ8fX1RVhYmHq1YjIOcrkce/bsQXh4eLmHJqn6sH2MlzG3TS8AUUqBY1fvICO3AB5ONmhXr1a136vHUIy5bUyNamSlPCqczMyePRuxsbH48ssvYWdnV9GHl5uXlxcA4NatW/D2frRq5K1btxAYGFji42xsbGBjY6NVbmVlxV8sI8W2MW5sH+NlrG1jBaBLY88y69Vkxto2pqQir1+Fr2YaPHgw7ty5Aw8PD7Rs2RJt2rTR+NEXf39/eHl54bffflOX5eTk4MiRI+jUqZPenoeIiIhMW4V7ZkaPHo3jx49jxIgRTzwB+N69e7h06ZJ6Oy0tDSdPnoSrqyvq1q2LyZMn4/3330ejRo3g7++PmTNnwsfHB/369av0cxIREVHNUuFk5qeffsIvv/yCLl26PPGTHzt2DGFhYept1VyX0aNHY+3atYiKisL9+/cxfvx43L17F126dEF8fDxsbblOARERERWpcDLj6+sLZ2dnvTx5aGgohCj5Ph0SiQRz5szBnDlz9PJ8REREVPNUeM7M4sWLERUVhStXrlRBOEREREQVU+GemREjRiAvLw8NGjSAvb291mzjrCzeRIyIiIiqT4WTmaVLl1ZBGERERESVU6mrmYiIiIiMRbnmzNy/f79CB61ofSIiIqLKKlcy07BhQ3zwwQdIT08vsY4QAnv27EFERAQ++eQTvQVIREREVJpyDTPt378f//vf/zB79my0bt0a7dq1g4+PD2xtbXHnzh2kpqYiKSkJlpaWiI6OxoQJE6o6biIiIiIA5UxmmjRpgq1bt+LatWv4/vvvcfDgQfzxxx948OAB3N3dERQUhDVr1iAiIgJSqbSqYyYiIiJSq9AE4Lp162Lq1KmYOnVqVcVDREREVCEVXjSPiIiIyJgwmSEiIiKTxmSGiIiITBqTGSIiIjJpTGaIiExBQhyQuFD3vsSFRfuJzFS5rmY6ffp0uQ/YqlWrSgdDREQlsJACCfOK/t/57UfliQuLysPeNUxcREagXMlMYGAgJBIJhBCQSCSl1lUoFHoJjIiIigmJKvo3YR4sFAoAAbA4+CFw4IOiREa1n8gMlSuZSUtLU///xIkTmDZtGt555x106tQJAJCUlITFixdj4cISukCJiOjJ/X/CIk2Yhz4SS0jFQyYyRChnMlOvXj31/wcNGoRPPvkEvXr1Upe1atUKvr6+mDlzJvr166f3IImITJFCKZCcloWM3Hx4ONmivb8rpBal926XKSQK4sAiSBWFEFJrSJjIEFVsBWAAOHPmDPz9/bXK/f39kZqaqpegiIhMXfzZdMTuTEV6dr66zFtmi5jIAPRs4V35AycuhERRCIXEElJFYdGcGSY0ZOYqfDVTs2bNEBcXh8LCQnVZYWEh4uLi0KxZM70GR0RkiuLPpmPihhSNRAYAbmbnY+KGFMSfTa/cgf9/sq+i6wzsCvwSiq4ziib/lnSVE5GZqHDPzKpVqxAZGYk6deqor1w6ffo0JBIJdu7cqfcAiYhMiUIpELszFULHPgFAAiB2ZyrCA7wqNuRU7KolZee3gd27oQyeVnRzX9VVTuyhITNV4WSmffv2+Pvvv7Fx40b89ddfAIAhQ4bgxRdfhIODg94DJCIyJclpWVo9MsUJAOnZ+UhOy0KnBm7lP7BS8Wiyr1z+qFyVwCh5JSlVs4S4oiUDdCXRiQv//3c2ulpCqXAyAwAODg4YP368vmMhIjJ5GbklJzKVqadW2pcCe2TIECxK6BU0wNpHlVoBeP369ejSpQt8fHxw9epVAMBHH32EH374Qa/BERGZGg8nW73WIzJaIVFFCUvxeVvFE5lqTLIrnMysXLkSU6ZMQUREBO7cuaNeJK9WrVpYunSpvuMjIjIp7f1d4S2zRUmzYSQouqqpvb9rdYZFVDWKJzRzaxskkQEqkcwsW7YMa9aswbvvvgtLy0ejVO3atcOZM2f0GhwRkamRWkgQExkAAFoJjWo7JjLgydebITIWIVGA1BpQFBb9a4BhzwonM2lpaQgKCtIqt7Gxwf379/USFBGRKevZwhsrR7SBl0xzKMlLZouVI9o82TozRMYmceGjREa19lE1q/AEYH9/f5w8eVJjVWAAiI+P5zozRET/r2cLb4QHeOl/BWAiY/L4HBnVNlCtPTQVTmamTJmCSZMmIT8/H0IIJCcn45tvvkFcXBw+//zzqoiRiMgkSS0kFbv8msiU6JrsW+yGqBrbVazCycy4ceNgZ2eH9957D3l5eXjxxRfh4+ODjz/+GEOHDq2KGImIiMjYFF/7qDgDrH1UqXVmhg8fjuHDhyMvLw/37t2Dh4eHvuNSy83NxcyZM7F9+3ZkZGQgKCgIH3/8MZ5++ukqe04iIiIqgxGtfVSpdWYePnyIvXv3Yv369bCzswMA/Pvvv7h3755egwOKeoL27NmD9evX48yZM+jevTu6deuGGzdu6P25iIiIyPRUOJm5evUqWrZsib59+2LSpEn477//AAALFizAtGnT9BrcgwcPsHXrVixcuBBdu3ZFw4YNMXv2bDRs2BArV67U63MRERGRaarwMNNbb72Fdu3a4dSpU3BzezSxrX///njllVf0GtzDhw+hUChga6t5eaOdnR0OHTqk8zEFBQUoKChQb+fk5AAA5HI55MXvZ0IGp2oPtotxYvtUnsWBBYBECmWw9h94Fgc/BIQCyq7TK318to3xYtvoT0VewwonMwcPHsQff/wBa2trjXI/Pz+9D/04OTmhU6dOmDt3Lpo1awZPT0988803SEpKQsOGDXU+Ji4uDrGxsVrlCQkJsLe312t8pB979uwxdAhUCrZPxTW+eRnN0rfhwsULuODVr1j5DjRL34Y/vV/AhXu7n/h52DbGi23z5PLy8spdt8LJjFKpVN/CoLh//vkHTk5OFT1cmdavX4+XX34ZTz31FKRSKdq0aYNhw4bh+PHjOutHR0djypQp6u2cnBz4+voiLCxMoyeJDE8ul2PPnj0IDw+HlZWVocOhx7B9nkQvKA42RrMDH6Bxo8ZQBk+DxcEPIT2xDYquM9AweBp0/zlWPmwb48W20R/VyEp5VDiZ6d69O5YuXYrVq1cDACQSCe7du4eYmBj06tWroocrU4MGDZCYmIj79+8jJycH3t7eGDJkCOrXr6+zvo2NDWxsbLTKrays+ItlpNg2xs0s2ichrugOwLquwEhc+P+XoJZy5YYuz0YDUimkCfMg/X1J0cqoYe9CGhIFqX6iNo+2MVFsmydXkdevwhOAFy9ejN9//x0BAQHIz8/Hiy++qB5iWrBgQUUPV24ODg7w9vbGnTt38Msvv6Bv375V9lxEZGYspJp3/lVRLQpmUcn0wwjuWUNkDircM1OnTh2cOnUKmzdvxunTp3Hv3j2MHTsWw4cPV1+mrU+//PILhBBo0qQJLl26hHfeeQdNmzbFSy+9pPfnIiIzpWvVUl2rm1aUrnvWMKEh0rtKLZpnaWmJESNG6DsWnbKzsxEdHY1//vkHrq6uGDBgAObNm8fuOyLSr+IJzYFF6mGhJ0pkjOCeNUTmoFLJzPnz57Fs2TL8+eefAIBmzZrh9ddfR9OmTfUaHAAMHjwYgwcP1vtxiYi0hEQ9SmSeZFjIiO5ZQ2QOKjxnZuvWrWjRogWOHz+O1q1bo3Xr1khJSUHLli2xdevWqoiRiKh66BoWqozS7lkT9m613rOGyBxUuGcmKioK0dHRmDNnjkZ5TEwMoqKiMGDAAL0FR0SGoVAKHEnLwvHbErilZaFTQw9ILSSGDqtq/X9vyrXWb+OE/ysISluDupXtRTGie9aUh0IpkJyWhYzcfHg42aK9v2vNb2+qUSqczKSnp2PUqFFa5SNGjMCiRYv0EhQRGU782XTE7kxFenY+ACm+vngM3jJbxEQGoGcLb0OHVzX+P5FZLR2K+UeeBo6cBPA0/ucwFONr+LCQZnsXqfHtTTVOhYeZQkNDcfDgQa3yQ4cOITg4WC9BEZFhxJ9Nx8QNKRpfbABwMzsfEzekIP5suoEiq1qXbt7FEvlAzL//vEZ53P3nsUQ+EJdu3jVMYFXMXNubap4K98w8//zzmD59Oo4fP46OHTsCAA4fPozvv/8esbGx+PHHHzXqEpFpUCgFYnemQujYJwBIAMTuTEV4gFeNGoJQKAVG/v0c0hX5WvsEgGWKF/D937Y4pBQ17rzNsb2pZqpwMvPaa68BAD799FN8+umnOvcBRSsD67rtAREZp+S0LK2/0IsTANKz85GcloVODWrOrUF43rrV1POmmqlS92YioponI7fkL7bK1DMVPG/91CMypArPmSGimsnDyVav9UwFz1s/9YgMqdzJTFJSEnbt2qVR9vXXX8Pf3x8eHh4YP348CgoK9B4gEVWP9v6u8JbZoqTZERIUXeXS3t+1OsOqcjxv3WrqeVPNVO5kZs6cOTh37px6+8yZMxg7diy6deuGGTNmYOfOnYiLi6uSIImoZAqlQNLlTPxw8gaSLmdCodQ1pbNsUgsJYiIDAEDrC061HRMZYDSTQc35vFVrAB1JyzKb8yYqTbnnzJw8eRJz585Vb2/evBkdOnTAmjVrAAC+vr6IiYnB7Nmz9R4kEemm7zVCerbwxsoRbbSO6WVk647wvPWzBpCpnDdRWcqdzNy5cweenp7q7cTERERERKi3n376aVy/fl2/0RFRiVRrhDz+d7lqjZCVI9pU+gsuPMALSZcy8OvBI+ge3MGoVgCu6vM21pVwzfW8icqj3MNMnp6eSEtLAwAUFhYiJSVFvc4MAOTm5vJO1kTVpKw1QoCiNUKeZAiig78r2roLdDCiL7bqOO9ODdzQN/ApdGrgxvMmMhHlTmZ69eqFGTNm4ODBg4iOjoa9vb3Gir+nT59GgwYNqiRIItJUkTVCahKet2419byJyqvcw0xz587FCy+8gJCQEDg6OmLdunWwtrZW7//yyy/RvXv3KgmSiDSZ6xohPG/91COqacqdzLi7u+PAgQPIzs6Go6MjpFKpxv7vv/8ejo6Oeg+QiLSZzBohCXGAhVT3TRoTFwJKRel3mH6Mh5MtJltugUJYYJniBa39b0i3QSpRwsOpo45Hmy6TaW8iA6nwonkymUwrkQEAV1dXjZ4aIqo6JrNGiIUUSJhXlLgU9/93qYaF9mdJadr7u8LexhpTrbbgDek2jX1vSLdhqtUW2NtYG/689cxk2pvIQLgCMJEJMpk1QkKigLB3NRMaVSIT9q7uHptSSC0kqNt/NpbIB2okNKpEZol8IOr2n23489Yzk2lvIgOp8L2ZiMg4mMwaIaqEJWEecGARoCisVCKj0rOFNzDsfazebomp2IzXLXfARvIQq6VDETBwtvGct56ZTHsTGQCTGSITZjJrhIREPUpkpNaVTmRUerbwhiJgFZTvb4ONshBKC2uMfXeV8Z23nhn7GkBEhsJhJiITZxJrhCQufJTIKAq159BUgvTgIlgoi45poSyE9OAiPQRq/Ix1DSAiQ2IyQ0RVq/gcmZn/ac+hMZZjEpHJ4jATEVUdXZN9i8+hKb5tyGMSkUljMkNEVUep0D3ZV7WtVBjHMYnIpDGZIaKqU9qCeJXtPamKYxKRSWMyQ0RVTqEUxn/FFRGZLCYzRFSl4s+ma62N4s21UYhIj3g1ExFVmfiz6Zi4IUXrjs83s/MxcUMK4s+mGygyIqpJmMwQUZVQKAVid6ZC6NinKovdmQqFUlcNIqLyYzJDRFUiOS1Lq0emOAEgPTsfyWlZ1RcUEdVIRp3MKBQKzJw5E/7+/rCzs0ODBg0wd+5cCMG/5IiMXUZuyYlMZeoREZXEqCcAL1iwACtXrsS6devQvHlzHDt2DC+99BJkMhnefPNNQ4dHRKXwcLLVaz0iopIYdTLzxx9/oG/fvujduzcAwM/PD9988w2Sk5MNHBkRlaW9vyu8Zba4mZ2vc96MBEV3fG7v71rdoRFRDWPUw0ydO3fGb7/9hgsXLgAATp06hUOHDiEiIsLAkRFRWaQWEsREBgAoSlyKU23HRAZwvRkiemJG3TMzY8YM5OTkoGnTppBKpVAoFJg3bx6GDx9e4mMKCgpQUFCg3s7JyQEAyOVyyOXyKo+Zyk/VHmwX46SP9nmuiTuWDW2N93f/hZs5j96XXjIbvBvRFM81cWf7VwLfO8aLbaM/FXkNJcKIZ9Nu3rwZ77zzDhYtWoTmzZvj5MmTmDx5MpYsWYLRo0frfMzs2bMRGxurVb5p0ybY29tXdchEpINSAJdzJMiRA85WQANnAXbIEFFp8vLy8OKLLyI7OxvOzs6l1jXqZMbX1xczZszApEmT1GXvv/8+NmzYgL/++kvnY3T1zPj6+iI9PR1ubm5VHjOVn1wux549exAeHg4rKytDh0OPYfsYL7aN8WLb6E9OTg7c3d3LlcwY9TBTXl4eLCw0p/VIpVIolcoSH2NjYwMbGxutcisrK/5iGSm2jXFj+xgvto3xYts8uYq8fkadzERGRmLevHmoW7cumjdvjhMnTmDJkiV4+eWXDR0aERERGQmjTmaWLVuGmTNn4rXXXkNGRgZ8fHwwYcIEzJo1y9ChERERkZEw6mTGyckJS5cuxdKlSw0dChERERkpo15nhoiIiKgsTGaIiIjIpDGZISIiIpPGZIaIiIhMGpMZIiIiMmlMZoiIiMikMZkhIiIik8ZkhoiIiEwakxkiIiIyaUxmiIiIyKQxmSEiIiKTxmSGiIiITBqTGSIiIjJpRn3XbKKaRqEUSE7LQkZuPjycbNHe3xVSC4mhwyIiMmlMZoiqSfzZdMTuTEV6dr66zFtmi5jIAPRs4W3AyIiITBuHmYiqQfzZdEzckKKRyADAzex8TNyQgviz6QaKjIjI9DGZIapiCqVA7M5UCB37VGWxO1OhUOqqQUREZWEyQ1TFktOytHpkihMA0rPzkZyWVX1BERHVIExmiKpYRm7JiUxl6hERkSYmM0RVzMPJVq/1iIhIE5MZoirW3t8V3jJblHQBtgRFVzW193etzrCIiGoMJjNEVUxqIUFMZAAAaCU0qu2YyACuN0NEVElMZoiqQc8W3lg5og28ZJpDSV4yW6wc0YbrzBARPQEumkdUTXq28EZ4gBdXACYi0jMmM0TVSGohQacGboYOg4ioRuEwExEREZk0JjNERERk0pjMEBERkUljMkNEREQmjckMERERmTSjT2b8/PwgkUi0fiZNmmTo0IiIiMgIGP2l2UePHoVCoVBvnz17FuHh4Rg0aJABoyKqoRLiAAspEBKlvS9xIaBUAGHR1R8XEVEpjL5npnbt2vDy8lL/7Nq1Cw0aNEBISIihQyOqeSykQMK8osSluMSFReUWUsPERURUCqPvmSmusLAQGzZswJQpUyCRcNVUIr1T9cgkzIOFQgEgABYHPwQOfACEvau7x4aIyMBMKpnZsWMH7t69izFjxpRYp6CgAAUFBertnJwcAIBcLodcLq/qEKkCVO1hzO2iUAocu3oHGbkF8HCyQbt6tWr+7Qc6vw0LhQLSAx+gj8QSUvEQiq4zoOz8NmDEbWVOTOG9Y67YNvpTkddQIoQQVRiLXvXo0QPW1tbYuXNniXVmz56N2NhYrfJNmzbB3t6+KsOjGuZUpgTbrljgbuGj5MXFWuAFPyVau5nM26bS+px8uSiRkVhiV+CXhg6HiMxMXl4eXnzxRWRnZ8PZ2bnUuiaTzFy9ehX169fHtm3b0Ldv3xLr6eqZ8fX1RXp6OtzceE8cYyKXy7Fnzx6Eh4fDysrK0OFo+OXcLbyx+RQef3Oo0pplQ1ujR3PP6g6r2lgc/BDSAx9AUbxnJniaocOi/2fM7x1zx7bRn5ycHLi7u5crmTGZYaavvvoKHh4e6N27d6n1bGxsYGNjo1VuZWXFXywjZWxto1AKzPv5vFYiAwACRQnNvJ/PI6LVUzVzyClxIXDgAyi6zsCu3AD0cUqF9MAHkEpLuMqJDMbY3jv0CNvmyVXk9TP6q5kAQKlU4quvvsLo0aNhaWky+ReZqOS0LKRn55e4XwBIz85HclpW9QVVXVRXLYW9q+6JUQZPK5r8q+sqJyIiI2ASmcHevXtx7do1vPzyy4YOhcxARm7JiUxl6pkUpeLRVUvFJ9+pemSUCt2PIyIyIJNIZrp37w4TmdpDNYCHk61e65mU0hbE4xATERkpkxhmIqpO7f1d4S2zRUmzYSQAvGW2aO/vWp1hERFRCZjMED1GaiFBTGQAAGglNKrtmMiAmjn5l4jIBDGZIdKhZwtvrBzRBl4yzaEkL5ktVo5og54tvA0UGRERPc4k5swQGULPFt4ID/BCcloWMnLz4eFUNLTEHhkiIuPCZIaoFFILCTo14GKLRETGzGyGmY5dvQOFkldEERER1TRmk8y8sv4EuizYh/iz6YYOhYiIiPTIbJIZALiZnY+JG1KY0BAREdUgZpXMqAaZYnemcsiJiIiohjCrZAao4ffVISIiMkNml8yo1Mj76hAREZkhs01mauR9dYiIiMyQ2a0zI0HRKq68rw4REVHNYFY9M7yvDhERUc1jVj0zXjJbxEQG8L46RERENYjZJDNrRgahW2AD9sgQERHVMGYzzNSuXi0mMkRERDWQ2SQzREREVDMxmSEiIiKTxmSGiIiITBqTGSJTlRAHJC7UvS9xYdF+IiIzwGSmghRKgaTLmfjh5A0kXc7kDSvJcCykQMI87YQmcWFRuYXUMHEREVUzs7k0Wx/iz6Yjdmcq0rMf3dfJm2vXkKGERBX9mzDv0bYqkQl799F+IqIajslMOcWfTcfEDSl4vB/mZnY+Jm5IwcoRbZjQUPUrntAcWAQoCpnIEJHZ4TBTOSiUArE7U7USGQDqstidqRxyIsMIiQKk1kWJjNSaiQwRmR0mM+WQnJalMbT0OAEgPTsfyWlZ1RcUkUriwkeJjKKw5EnBREQ1FJOZcsjILTmRqUw9Ir0pPkdm5n9F/+qaFExEVINxzkw5eDjZ6rUekV7omuyra1IwEVENx2SmHNr7u8JbZoub2fk6581IUHRH7vb+rtUdGpkzpUL3ZF/VtlJR/TERERkAk5lykFpIEBMZgIkbUiABNBIa1a0rYyIDeCNLql5h0SXvY48MEZkRo58zc+PGDYwYMQJubm6ws7NDy5YtcezYsWqPo2cLb6wc0QZeMs2hJC+ZLS/LJiIiMiCj7pm5c+cOnnnmGYSFheHnn39G7dq1cfHiRdSqVcsg8fRs4Y3wAC8kp2UhIzcfHk5FQ0vskSEiIjIco05mFixYAF9fX3z11VfqMn9/fwNGVDTk1KmBm0FjICIiokeMepjpxx9/RLt27TBo0CB4eHggKCgIa9asMXRYREREZESMumfm77//xsqVKzFlyhT873//w9GjR/Hmm2/C2toao0eP1vmYgoICFBQUqLdzcnIAAHK5HHK5vFripvJRtQfbxTixfYwX28Z4sW30pyKvoUQIYbRr8FtbW6Ndu3b4448/1GVvvvkmjh49iqSkJJ2PmT17NmJjY7XKN23aBHt7+yqLlYiIiPQnLy8PL774IrKzs+Hs7FxqXaPumfH29kZAQIBGWbNmzbB169YSHxMdHY0pU6aot3NycuDr64uwsDC4uXGuizGRy+XYs2cPwsPDYWVlZehw6DFsH+PFtjFebBv9UY2slIdRJzPPPPMMzp8/r1F24cIF1KtXr8TH2NjYwMbGRqvcysqKv1hGim1j3Ng+xottY7zYNk+uIq+fUSczb7/9Njp37oz58+dj8ODBSE5OxurVq7F69WpDh6Y3CqXgpd5ERERPwKiTmaeffhrbt29HdHQ05syZA39/fyxduhTDhw83dGh6EX82HbE7UzXuyO0ts0VMZAAX4SMiIiono05mAKBPnz7o06ePocPQu/iz6Zi4IUXrXk83s/MxcUMKVxUmIiIqJ6NeZ6amUigFYnem6rxppaosdmcqFEqjvdCMiIjIaDCZMYDktCyNoaXHCQDp2flITsuqvqCIiIhMFJMZA8jILTmRqUw9IiIic8ZkxgA8nGzLrlSBekREROaMyYwBtPd3hbfMFiVdgC1B0VVN7f1dqzMsIiIik8RkxgCkFhLERBatbPx4QqPajokMqPR6MwqlQNLlTPxw8gaSLmdyIjEREdVoRn9pdk3Vs4U3Vo5oo7XOjNcTrjPDtWuIiMjcMJkxoJ4tvBEe4KW3FYC5dg0REZkjJjMGJrWQoFODJ78BZllr10hQtHZNeIAXb5dAREQ1CufM1BBcu4aIiMwVk5kagmvXEBGRuWIyU0Nw7RoiIjJXTGZqCK5dQ0RE5orJTA3BtWuIiMhc8WqmGoRr1xARkTliMlPDcO0aIiIyN0xmaiCuXUNEROaEc2aoRFW5do1CKXAkLQvHb0twJC1LL3NwOK+HiMg8sWemJkmIAyykQEiU9r7EhYBSAYRFl/twVbV2jeYcHCm+vnjsiefgcF4PEZH5Ys9MTWIhBRLmFSUuxSUuLCq3kFbocFWxdo1qDs7jPT6qOTjxZ9MrFGNVHZOIiEwHk5maJCQKCHtXM6FRJTJh7+rusSmFvteuKWsODlA0B6ciw0NVcUwiIjItTGZqmuIJzdzalU5kAP2vXVMVc3B4TyoiImIyUxOFRAFSa0BRWPRvJRIZFdXaNV4yzaEkL5lthS/Lroo5OLwnFRERcQJwTZS48FEioygs2n7ChEYfa9dUxRwc3pOKiIiYzNQ0j8+RUW0DT5TQ6GPtGtUcnJvZ+TrnuEhQ1ONTkftHVcUxiYjItHCYqSbRNdlX16RgA6mK+0dV9T2piIjI+DGZqUmUCt2TfVUJjVJhmLiK0eccnKo8JhERmQ4OMxmKnhe4A1B6/ScYYtI31RycpEsZ+PXgEXQP7oBODT2eqPdE3/ekIiIi08FkxlBUC9wBmolG8aGiGkxqIUEHf1dk/inQQU9Jh77uSUVERKaFyYyhqBKY4gnNEyxwR0REZK6Mfs7M7NmzIZFINH6aNm1q6LD0Q48L3BEREZkrk+iZad68Ofbu3avetrQ0ibDLJyQKOLBILwvcERERmSOTyAosLS3h5eVl6DCqhp4XuCMiIjI3Rj/MBAAXL16Ej48P6tevj+HDh+PatWuGDkk/is+Rmfmf0awHQ0REZEqMvmemQ4cOWLt2LZo0aYL09HTExsYiODgYZ8+ehZOTk1b9goICFBQUqLezs7MBAFlZxnWjQYs/PoH0j4+g6Pw2lC3GApmZQIuxsMi5D2n8+1Dk3Iey85uGDrNKyeVy5OXlITMzE1ZWVoYOhx7D9jFebBvjxbbRn9zcXACAELrWd3+MMDF37twRzs7O4vPPP9e5PyYmRqDoZsn84Q9/+MMf/vDHxH+uX79eZm4gEaI8KY9xefrpp9GtWzfExcVp7Xu8Z+bu3buoV68erl27BplMVp1hUhlycnLg6+uL69evw9nZ2dDh0GPYPsaLbWO82Db6I4RAbm4ufHx8YGFR+qwYox9mety9e/dw+fJljBw5Uud+Gxsb2NjYaJXLZDL+YhkpZ2dnto0RY/sYL7aN8WLb6Ed5OyGMfgLwtGnTkJiYiCtXruCPP/5A//79IZVKMWzYMEOHRkREREbA6Htm/vnnHwwbNgyZmZmoXbs2unTpgsOHD6N27dqGDo2IiIiMgNEnM5s3b36ix9vY2CAmJkbn0BMZFtvGuLF9jBfbxnixbQzDJCcAExEREakY/ZwZIiIiotIwmSEiIiKTxmSGiIiITBqTGSIiIjJpNT6ZWbFiBfz8/GBra4sOHTogOTnZ0CGZvdmzZ0MikWj8NG3a1NBhmaUDBw4gMjISPj4+kEgk2LFjh8Z+IQRmzZoFb29v2NnZoVu3brh48aJhgjVDZbXPmDFjtN5LPXv2NEywZiYuLg5PP/00nJyc4OHhgX79+uH8+fMadfLz8zFp0iS4ubnB0dERAwYMwK1btwwUcc1Wo5OZb7/9FlOmTEFMTAxSUlLQunVr9OjRAxkZGYYOzew1b94c6enp6p9Dhw4ZOiSzdP/+fbRu3RorVqzQuX/hwoX45JNPsGrVKhw5cgQODg7o0aMH8vPzqzlS81RW+wBAz549Nd5L33zzTTVGaL4SExMxadIkHD58GHv27IFcLkf37t1x//59dZ23334bO3fuxPfff4/ExET8+++/eOGFFwwYdQ2mv1tAGp/27duLSZMmqbcVCoXw8fERcXFxBoyKYmJiROvWrQ0dBj0GgNi+fbt6W6lUCi8vL7Fo0SJ12d27d4WNjY345ptvDBCheXu8fYQQYvTo0aJv374GiYc0ZWRkCAAiMTFRCFH0XrGyshLff/+9us6ff/4pAIikpCRDhVlj1diemcLCQhw/fhzdunVTl1lYWKBbt25ISkoyYGQEABcvXoSPjw/q16+P4cOH49q1a4YOiR6TlpaGmzdvaryHZDIZOnTowPeQEdm/fz88PDzQpEkTTJw4EZmZmYYOySxlZ2cDAFxdXQEAx48fh1wu13j/NG3aFHXr1uX7pwrU2GTm9u3bUCgU8PT01Cj39PTEzZs3DRQVAUCHDh2wdu1axMfHY+XKlUhLS0NwcDByc3MNHRoVo3qf8D1kvHr27Imvv/4av/32GxYsWIDExERERERAoVAYOjSzolQqMXnyZDzzzDNo0aIFgKL3j7W1NVxcXDTq8v1TNYz+dgZU80RERKj/36pVK3To0AH16tXDd999h7FjxxowMiLTMnToUPX/W7ZsiVatWqFBgwbYv38/nnvuOQNGZl4mTZqEs2fPcu6fAdXYnhl3d3dIpVKtmeO3bt2Cl5eXgaIiXVxcXNC4cWNcunTJ0KFQMar3Cd9DpqN+/fpwd3fne6kavf7669i1axcSEhJQp04ddbmXlxcKCwtx9+5djfp8/1SNGpvMWFtbo23btvjtt9/UZUqlEr/99hs6depkwMjocffu3cPly5fh7e1t6FCoGH9/f3h5eWm8h3JycnDkyBG+h4zUP//8g8zMTL6XqoEQAq+//jq2b9+Offv2wd/fX2N/27ZtYWVlpfH+OX/+PK5du8b3TxWo0cNMU6ZMwejRo9GuXTu0b98eS5cuxf379/HSSy8ZOjSzNm3aNERGRqJevXr4999/ERMTA6lUimHDhhk6NLNz7949jb/i09LScPLkSbi6uqJu3bqYPHky3n//fTRq1Aj+/v6YOXMmfHx80K9fP8MFbUZKax9XV1fExsZiwIAB8PLywuXLlxEVFYWGDRuiR48eBozaPEyaNAmbNm3CDz/8ACcnJ/U8GJlMBjs7O8hkMowdOxZTpkyBq6srnJ2d8cYbb6BTp07o2LGjgaOvgQx9OVVVW7Zsmahbt66wtrYW7du3F4cPHzZ0SGZvyJAhwtvbW1hbW4unnnpKDBkyRFy6dMnQYZmlhIQEAUDrZ/To0UKIosuzZ86cKTw9PYWNjY147rnnxPnz5w0btBkprX3y8vJE9+7dRe3atYWVlZWoV6+eeOWVV8TNmzcNHbZZ0NUuAMRXX32lrvPgwQPx2muviVq1agl7e3vRv39/kZ6ebrigazCJEEJUfwpFREREpB81ds4MERERmQcmM0RERGTSmMwQERGRSWMyQ0RERCaNyQwRERGZNCYzREREZNKYzBAREZFJYzJDROUyZswYrvxbzG+//YZmzZqp71A9e/ZsBAYGlvqYoUOHYvHixdUQHZF5YTJDRJBIJKX+zJ49Gx9//DHWrl1r6FABFMW7Y8cOrXJVwnXlypUyz0l1LgkJCejVqxfc3Nxgb2+PgIAATJ06FTdu3Cg1hqioKLz33nuQSqXljvu9997DvHnzkJ2dXZHTJaIyMJkhIqSnp6t/li5dCmdnZ42yadOmQSaTwcXFxdChlouvr69G/FOnTkXz5s01yoYMGYLPPvsM3bp1g5eXF7Zu3YrU1FSsWrUK2dnZpfagHDp0CJcvX8aAAQMqFFeLFi3QoEEDbNiw4UlPkYiKYTJDRPDy8lL/yGQySCQSjTJHR0etYabQ0FC88cYbmDx5MmrVqgVPT0+sWbNGfTNXJycnNGzYED///LPGc509exYRERFwdHSEp6cnRo4cidu3b+v1fKRSqVb8lpaWGmWZmZl488038eabb+LLL79EaGgo/Pz80LVrV3z++eeYNWtWicffvHkzwsPDYWtrq7Vv/fr18PPzg0wmw9ChQ5Gbm6uxPzIyEps3b9br+RKZOyYzRFRp69atg7u7O5KTk/HGG29g4sSJGDRoEDp37oyUlBR0794dI0eORF5eHgDg7t27ePbZZxEUFIRjx44hPj4et27dwuDBg6s99u+//x6FhYWIiorSub+0XqiDBw+iXbt2WuWXL1/Gjh07sGvXLuzatQuJiYn44IMPNOq0b98eycnJKCgoeKL4iegRJjNEVGmtW7fGe++9h0aNGiE6Ohq2trZwd3fHK6+8gkaNGmHWrFnIzMzE6dOnAQDLly9HUFAQ5s+fj6ZNmyIoKAhffvklEhIScOHChWqN/eLFi3B2doa3t3eFH3v16lX4+PholSuVSqxduxYtWrRAcHAwRo4cid9++02jjo+PDwoLC3Hz5s1Kx05EmiwNHQARma5WrVqp/y+VSuHm5oaWLVuqyzw9PQEAGRkZAIBTp04hISEBjo6OWse6fPkyGjduXMURPyKEgEQiqdRjHzx4oHOIyc/PD05OTuptb29v9bmr2NnZAYC6t4qInhyTGSKqNCsrK41tiUSiUaZKFpRKJQDg3r17iIyMxIIFC7SOVZEeEicnJ51XBN29excymaxcx2jcuDGys7ORnp5e4d4Zd3d33LlzR6tc1+uhOneVrKwsAEDt2rUr9JxEVDIOMxFRtWnTpg3OnTsHPz8/NGzYUOPHwcGh3Mdp0qQJjh8/rlGmUChw6tSpcvfuDBw4ENbW1li4cKHO/Xfv3i3xsUFBQUhNTS13vMWdPXsWderUgbu7e6UeT0TamMwQUbWZNGkSsrKyMGzYMBw9ehSXL1/GL7/8gpdeekm9+Fx5TJkyBZ9//jk+/fRTXLx4ESdPnsT48eNx584djBs3rlzH8PX1xUcffYSPP/4YY8eORWJiIq5evYrff/8dEyZMwNy5c0t8bI8ePXDo0KFyx1vcwYMH0b1790o9loh0YzJDRNXGx8cHv//+OxQKBbp3746WLVti8uTJcHFxgYVF0cfR2rVry5zLMmzYMHz++ef48ssv0bZtW/Ts2RM3b97EgQMH1PN0yuO1117Dr7/+ihs3bqB///5o2rQpxo0bB2dnZ0ybNq3Exw0fPhznzp3D+fPny/1cAJCfn48dO3bglVdeqdDjiKh0EiGEMHQQREQqMTExSExMxP79+w0dSqneeecd5OTk4LPPPiv3Y1auXInt27fj119/rcLIiMwPe2aIyKj8/PPPJc5jMSbvvvsu6tWrpzXBtzRWVlZYtmxZFUZFZJ7YM0NEREQmjT0zREREZNKYzBAREZFJYzJDREREJo3JDBEREZk0JjNERERk0pjMEBERkUljMkNEREQmjckMERERmTQmM0RERGTSmMwQERGRSfs/3k+Je5dZRpgAAAAASUVORK5CYII=",
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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -948,7 +948,7 @@
       "outputs": [
         {
           "data": {
-            "image/png": 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",
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",
             "text/plain": [
               "<Figure size 1200x800 with 1 Axes>"
             ]
@@ -977,7 +977,7 @@
       "outputs": [
         {
           "data": {
-            "image/png": 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",
             "text/plain": [
               "<Figure size 1200x800 with 1 Axes>"
             ]
@@ -1011,7 +1011,7 @@
         {
           "data": {
             "text/plain": [
-              "<matplotlib.legend.Legend at 0x232d0335690>"
+              "<matplotlib.legend.Legend at 0x2617e6fa5d0>"
             ]
           },
           "execution_count": 14,
@@ -1020,7 +1020,7 @@
         },
         {
           "data": {
-            "image/png": 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+ygcffMDgwYP56KOP8Pb2xsbGhlGjRuX43Lm9nv3792fFihXs3LmT4OBg/vjjD15//fXHvuKruGRkZPDcc89x5MgRNmzYQJ06dUy2e3t74+jomOOl6VllD46HyovZs2fz7LPPZhub1rVrV8aMGcOFCxeyJeki7yTxEflSuXJl/vrrL55++uli+dLU6/WcP3/e5Av/33//BTBeqVMYMa1Zs4b09HT++OMPk7+cH+wmg9y/HLP+wk5ISDC5GiM/rSSVK1cmOTnZ5HLZ/MpqUbk/zgdfs9xkXQ11+vRpk5a2jIwMoqOjHyuuLFnHtbe3z9PxvL29GTRoEIMGDSI5OZlnnnmGSZMmPVbi86AKFSqg1+s5c+aMyRxVcXFxJCQkGF+X4rRy5UoGDBjAl19+aSxLS0szubouv0qVKpXt8RkZGdm+uFeuXEmrVq344YcfTMoTEhJMBvc+Svv27SlTpgxLliwhJCSE1NRU/ve//xU4/uKk1+vp378/mzZtYvny5bRo0SLbPjY2NgQHB+c4seHu3bupVKlSvgc2g+F9l9MfTFqtFjBcgSgKrmSk3cJs9O7dG51OZ2y6vl9mZuZj/VLOzbfffmu8rSgK3377Lfb29rRu3brQYsr6a/nB7oQFCxZk29fV1TXHY1auXBnAZBxOSkoKixYteuTzZ+nduzdRUVFs2LAh27aEhIQ8/cK7du2ayVVISUlJLF68mPr165t0c+WkTZs2ODg4MHPmTJPX4ocffiAxMZFOnTrl+Vxy4+vrS8uWLfnuu+9y/Ev5/kuwb968abLNzc2NKlWq5Ni18Dg6duwIkO2qua+++gqgUM47v2xtbbO11nzzzTePNXNv5cqVs40TmzdvXrZj5vTcK1asMI69yis7Ozv69u3L8uXLWbhwIcHBwY/dTVlcRo4cybJly5g9ezbPPfdcrvv16tWLvXv3miQ/p0+fZvPmzQWeU6hatWpERkaavP91Oh3Lly/H3d3d+LtGFIy0+Ih8adGiBcOGDSM8PJxDhw7Rrl077O3tOXPmDCtWrGDGjBn06tWr0J7PycmJiIgIBgwYQEhICH/++Sfr1q3j3XffNXZhFUZM7dq1w8HBgS5dujBs2DCSk5OZP38+vr6+2b6cGzZsyJw5c/j444+pUqUKvr6+PPvss7Rr147y5cszZMgQ3nrrLWxtbfnxxx8pU6YMly5dytP5vvXWW/zxxx907tyZgQMH0rBhQ1JSUjh69CgrV67kwoULj/yLu1q1agwZMoS9e/fi5+fHjz/+SFxcXI5J3IPKlCnDhAkTmDx5Mu3bt6dr166cPn2a2bNn07hx40IblDpr1iyaNWtGcHAwr7zyCpUqVSIuLo6oqCiuXLlinC+mVq1atGzZkoYNG+Lt7c2+fftYuXJloc/GXK9ePQYMGMC8efNISEigRYsW7Nmzh0WLFtG9e3datWpVqM+XF507d+ann37C09OTWrVqERUVxV9//ZXjGJu8evnll3n11Vfp2bMnbdu25fDhw2zYsCHbe6pz585MmTKFQYMG0bRpU44ePcqSJUuyjbfLi/79+zNz5ky2bNnC1KlT8/y4NWvWGN8HWq2WI0eO8PHHHwOGLp/7E6is8uPHjwOGGej//vtvAN5//33jfllTNWzZsuWhc01Nnz6d2bNnExoaiouLCz///LPJ9h49ehgnvXz99deZP38+nTp1Yty4cdjb2/PVV1/h5+dnHByf33N65513eOmllwgJCWHo0KE4Ozvz66+/sn//fj7++GOT6TlEAah0NZkoIR68nD3LvHnzlIYNGyrOzs6Ku7u7EhwcrIwfP165du2acZ8KFSrkeCkyoAwfPtykLKfLjwcMGKC4uroq586dU9q1a6e4uLgofn5+ysSJE7Ndavu4MSmKovzxxx9K3bp1FScnJ6VixYrK1KlTlR9//DHbpeixsbFKp06dFHd3dwUwuXR7//79SkhIiOLg4KCUL19e+eqrr3K9nD23OO7cuaNMmDBBqVKliuLg4KD4+PgoTZs2Vb744gslIyMjx8c8eNwNGzYodevWVRwdHZUaNWqYXCatKPcugd67d2+Ox/n222+VGjVqKPb29oqfn5/y2muvKbdv3zbZ53EuZ1cURTl37pzSv39/xd/fX7G3t1fKli2rdO7cWVm5cqVxn48//lhp0qSJ4uXlpTg7Oys1atRQPvnkk0e+Do+6nP3BmBVFUbRarTJ58mQlKChIsbe3V8qVK6dMmDDB5JJ7RXn893VONm/erADKqlWrjGW3b99WBg0apPj4+Chubm5KWFiYcurUqWyXnudWl1mXmt9/abdOp1PefvttxcfHR3FxcVHCwsKUs2fP5ng5+9ixY5WAgADF2dlZefrpp5WoqKhsdZvTZfg5qV27tmJjY6NcuXLlofvdL2s6i5x+Hnwv5bbfg7+7xo4dq2g0GpNL7PP73A9+lhVFUS5fvqz06tVL8fDwUNzc3JTOnTsrZ86ceaxzioiIUFq0aKH4+PgoDg4OSnBwsDJ37tw8v34idxpFKcCoOyGKwcCBA1m5ciXJyclqh1JiVKxYkTp16rB27Vq1QxH58Mcff9CtWzf++usvYxeuJWnQoAHe3t5s2rRJ1TiaNGlChQoVWLFihapxCHVJV5cQQqhs7969gKFrz9Ls27ePQ4cOsXDhQlXjSEpK4vDhw/kacycskyQ+Qgihko0bN7Jt2za+/PJL2rZtS0BAgNohFZpjx46xf/9+vvzySwICAujTp4+q8Xh4eBT6oHhRMslVXUIIoZLw8HC+/fZbunbtmm0AbUm3cuVKBg0ahFar5ddffzXOZCyE2mSMjxBCCCGshrT4CCGEEMJqSOIjhBBCCKshg5sxTE1+7do13N3d87xWjxBCCCHUpSgKd+7cITAwMM9rwEnig2GK/wdXVRZCCCFEyXD58mWeeOKJPO0riQ8YF5G7fPkyHh4eKkdj2bRaLRs3bjQuKyHUIfVgHqQezIPUg3koSD0kJSVRrly5fC0GK4kP91bb9vDwkMSniGm1WlxcXPDw8JBfMCqSejAPUg/mQerBPDxOPeRnmIoMbhZCCCGE1ZDERwghhBBWQxIfIYQQQlgNSXyEEEIIYTUk8RFCCCGE1ZDERwghhBBWQxIfIYQQQlgNSXyEEEIIYTUk8RFCCCGE1ZCZm4UQQghRJHR6hT3Rt4i/k4avuxNNgryxtVF3MXBVW3y2b99Oly5dCAwMRKPRsHr16mz7nDx5kq5du+Lp6YmrqyuNGzfm0qVLxu1paWkMHz6c0qVL4+bmRs+ePYmLiyvGs3jAlnDYNi3nbdumGbYLIYQQFi7iWAzNpm6m7/xdvLn0EH3n76LZ1M1EHItRNS5VE5+UlBTq1avHrFmzctx+7tw5mjVrRo0aNdi6dStHjhzhgw8+wMnJybjP6NGjWbNmDStWrGDbtm1cu3aN5557rrhOITsbW9jySfbkZ9s0Q7mNrTpxCSGEEMUk4lgMr/18gJjENJPy2MQ0Xvv5gKrJj6pdXR06dKBDhw65bn/vvffo2LEj06bdSyIqV65svJ2YmMgPP/zAL7/8wrPPPgvAggULqFmzJrt27eKpp54quuBz02K84f8tn4CiwBON4Mo+2PoptHrv3nYhhBDCAun0CpPXnEDJYZsCaIDJa07Qtpa/Kt1eZjvGR6/Xs27dOsaPH09YWBgHDx4kKCiICRMm0L17dwD279+PVqulTZs2xsfVqFGD8uXLExUVlWvik56eTnp6uvF+UlISYFgZVqvVPn7wTUdjo8vEduunxiLdM++gbzoaCuP4JVjW61sor7MoMKkH8yD1YB6kHgrX7uhb2Vp67qcAMYlpRJ2NJyTI21hekHooSJ2ZbeITHx9PcnIyn332GR9//DFTp04lIiKC5557ji1bttCiRQtiY2NxcHDAy8vL5LF+fn7Exsbmeuzw8HAmT56crXzjxo24uLgU0hnUpisaNCgoaFh7pxasX19Ixy75IiMj1Q5BIPVgLqQezIPUQ+HYf0MDPHpYx8Ydu7l5Mnu7UH7qITU1NT+hAWac+Oj1egC6devG6NGjAahfvz47d+5k7ty5tGjRosDHnjBhAmPGjDHeT0pKoly5crRr1w4PD4/HC/w/Nju++C/pAQ0KnTWb0Xf4olCOXZJptVoiIyNp27Yt9vb2aodjtaQezIPUg3mQeihcpaNvsfjMvkfu1655SLYWn/zWQ1aPTX6YbeLj4+ODnZ0dtWrVMimvWbMmf//9NwD+/v5kZGSQkJBg0uoTFxeHv79/rsd2dHTE0dExW7m9vX3hvOm3TYPtn0Gr99DcPAtHlmF7YCG2nmVljM9/Cu21Fo9F6sE8SD2YB6mHwhFaxRc3RzuS0zNz3K4B/D2dCK3im+MYn/zUQ0Hqy2wnMHRwcKBx48acPn3apPzff/+lQoUKADRs2BB7e3s2bdpk3H769GkuXbpEaGhoscZrlHX1VtZA5qZv/LdBk/PVXkIIIYQFWb7v8kOTHoCJXWqpNp+Pqi0+ycnJnD171ng/OjqaQ4cO4e3tTfny5Xnrrbfo06cPzzzzDK1atSIiIoI1a9awdetWADw9PRkyZAhjxozB29sbDw8PRo4cSWhoqDpXdAHodaZXb/nXgSpt4OxfULahYbsQQghhgSJPxPHe70cB6FDHn0OXE0wGOvt7OjGxSy3a1wlQK0R1E599+/bRqlUr4/2scTcDBgxg4cKF9OjRg7lz5xIeHs4bb7xB9erV+e2332jWrJnxMV9//TU2Njb07NmT9PR0wsLCmD17drGfi1GrCdnLnn7TkPjEnYAXlxd/TEIIIUQR23/xFiN+OYBegd6NnmBqz7roFcxu5mZVE5+WLVuiKDld6X/P4MGDGTx4cK7bnZycmDVrVq6TIJqFis0hsAFcOwh75uecHAkhhBAl1Nn4OwxeuI/0TD3P1vDl0x7BaDQabDUQWrm02uGZMNsxPhZFozG0+gDsmQcZKerGI4QQQhSS2MQ0+v+wh8S7WuqX8+LbFxtgZ2u+6YX5RmZpanaFUhXh7i04uETtaIQQQojHlnhXy4Af93AtMY1KPq78OLAxLg5me8E4IIlP8bGxhaYjDbejvgFdziPehRBCiJIgTavjlcX7OB13B193RxYNboK3q4PaYT2SJD7FqX4/cPGBhEtwYrXa0QghhBAFotMrjF52iD3Rt3B3tGPhoCaU8y6slQ+KliQ+xcneGUKGGW7/M8OwiKkQQghRgiiKwuQ1x/nzWCwOtjZ8178htQILZ9WD4iCJT3Fr/DLYu0DsETi/Ve1ohBBCiHyZteUsi6MuotHAV33q0bSyj9oh5YskPsXNxRue7G+4/c8MdWMRQggh8mH53st8sfFfACZ2rkXnuoEqR5R/kvio4anXQWML57dAzGG1oxFCCCEeadPJOCb8Nyvzay0rM/DpIJUjKhhJfNRQqgLUec5w+5+Z6sYihBBCPMKBS7cZ/ssBdHqF554sy/iw6mqHVGCS+Kgla/HS47/D7QuqhiKEEELk5mx8MoMX7iVNq6dFtTJM7VkXjUbdZScehyQ+agmoC5WfBUUHUSquLSaEEELkIi4pjQE/7iEhVUu9JzyZ3e9J7M14Vua8KNnRl3RZy1gcWAwpN9WNRQghhLhPUpphVuarCXcJ+m9WZldH856VOS8k8VFTUAsIqAeZd2Hv92pHI4QQQgCQnqlj6OJ9nIq9Qxl3RxYPbkJpN0e1wyoUkvioyWTx0u8gI1XdeIQQQlg9vV5hzLLD7Dp/CzdHOxYMbFxiZmXOC0l81FazG3hVgNSbcEgWLxVCCKEeRVGYsvYE647GYG+r4bv/NaROWU+1wypUkviozdbuvsVLv5XFS4UQQqhm7rbzLNx5AYAve9fn6Sola1bmvJDExxzU7wfO3obL2k/+oXY0QgghrNDK/VeYGnEKgA8616JrvZI3K3NeSOJjDhxcZPFSIYQQqtlyOp63fzsCwLBnKjGkWcmclTkvJPExF41fATtniDkE0dvVjkYIIYSVOHQ5gdd/NszK3KNBWd5uX0PtkIqUJD7mwrU0PPk/w21ZvFQIIUQxOH/dMCvzXa2O5lV9mNqzLjY2JXdW5ryQxMechA4HjQ2c2wSxR9WORgghhAWLv5NG/x/3cCslg+Cynsx5qSEOdpafFlj+GZYkpSpC7R6G27J4qRBCiCJyJ03LwB/3cuX2XSqUdmHBoMa4WcCszHkhiY+5yVq89NhvkHBJ3ViEEEJYnPRMHa/+vJ8TMUn4uDmweHATfCxkVua8kMTH3ATWh0otZfFSIYQQhU6vVxi34gj/nL2Jq4MtCwY2oUJpV7XDKlaS+Jgj4+KliyD1lrqxCCGEsAiKovDxupOsOXwNOxsNc//XkOAnLGtW5ryQxMccVWoF/sGgTYW9P6gdjRBCCAswf8d5fvwnGoAvnq9H86plVI5IHZL4mCONBp4eZbi9ey5o76oajhBCiJLt94NX+HS9YVbm9zrWpHuDsipHpB5JfMxVre7gWR5Sb8ChX9SORgghRAm17d/rvLXCMCvzy82CeOWZSipHpC5JfMyVrR00HWG4vfMb0OvUjUcIIUSJc+RKAq/9vJ9MvULXeoG827Gm2iGpThIfc9bgJXAuBbej4eQataMRQghRgly4kcKgBXtJzdDRrIoPXzxfz+JnZc4LSXzMmYMrNBlquC2LlwohhMij63fS6f/jHm6mZFA70IM5Lz1pFbMy54Wqr8L27dvp0qULgYGBaDQaVq9eneu+r776KhqNhunTp5uU37p1i379+uHh4YGXlxdDhgwhOTm5aAMvTk2Ggp0TXDsAF/5WOxohhBBmLjk9k0EL93DpVirlvV1YOKgJ7k72aodlNlRNfFJSUqhXrx6zZs166H6///47u3btIjAwMNu2fv36cfz4cSIjI1m7di3bt29n6NChRRVy8XP1MXR5gSxeKoQQ4qEyMvW89vN+jl1NorSrA4sGN6GMu/XMypwXqi7M0aFDBzp06PDQfa5evcrIkSPZsGEDnTp1Mtl28uRJIiIi2Lt3L40aNQLgm2++oWPHjnzxxRc5JkolUuhw2PcjnI2E2GPgX0ftiIQQQpgZvV5h/MrD7DhzAxcHW34c2JggH+ualTkvzHpFMr1ez//+9z/eeustateunW17VFQUXl5exqQHoE2bNtjY2LB792569OiR43HT09NJT0833k9KSgJAq9Wi1WoL+SwKgXs5bGt0webk/6H/Zwa6riV3KYus19csX2crIvVgHqQezIOl1MNnEadZfcgwK/M3L9Sjlr9riTqngtRDQc7PrBOfqVOnYmdnxxtvvJHj9tjYWHx9fU3K7Ozs8Pb2JjY2NtfjhoeHM3ny5GzlGzduxMXF5fGCLiJe+ga04P/g6G9s0T/FXQcftUN6LJGRkWqHIJB6MBdSD+ahJNfDlmsaVl+0BeCFSpkkn9nD+jMqB1VA+amH1NTUfB/fbBOf/fv3M2PGDA4cOIBGU7iX302YMIExY8YY7yclJVGuXDnatWuHh4dHoT5XYdIv2YTNhR20dv0Xfdv+aodTIFqtlsjISNq2bYu9vQy2U4vUg3mQejAPJb0e1hyJYXXUUQDealeVoc2DVI6oYApSD1k9NvlhtonPjh07iI+Pp3z58sYynU7H2LFjmT59OhcuXMDf35/4+HiTx2VmZnLr1i38/f1zPbajoyOOjtkHe9nb25v3m77ZKLiwA9uDP2Hb6h3DHD8llNm/1lZC6sE8SD2Yh5JYD3+fucHbq44BMOjpirzeqmqhNxYUt/zUQ0Hqy2wv6v/f//7HkSNHOHTokPEnMDCQt956iw0bNgAQGhpKQkIC+/fvNz5u8+bN6PV6QkJC1Aq96FRuDX51QJsii5cKIYSVO3Y1kWE/7UOrU+hUN4APOtUq8UlPcVC1xSc5OZmzZ88a70dHR3Po0CG8vb0pX748pUuXNtnf3t4ef39/qlevDkDNmjVp3749r7zyCnPnzkWr1TJixAheeOEFy7mi634aDTz9Jqx6xbB4aegIsHdSOyohhBDF7OLNFAYu2ENKho6mlUvzVW+ZlTmvVG3x2bdvHw0aNKBBgwYAjBkzhgYNGvDhhx/m+RhLliyhRo0atG7dmo4dO9KsWTPmzZtXVCGrr3YP8CwHKdfh8K9qRyOEEKKY3UhOZ8CPe7iRnEHNAA+++19DHO1s1Q6rxFC1xadly5Yo+ViG4cKFC9nKvL29+eUXK1q93NbeMK9PxDuGxUuf7A828oYXQghrkJKeyeCFe7lwM5UnSjmzaFBjmZU5n8x2jI94iAb/AycvuHUOTq1TOxohhBDFQKvT89qSAxy5koi3qwOLBzfB10OGO+SXJD4lkaMbNHnFcPuf6bJ4qRBCWDhFUXh75RG2/3sdZ3tbfhjQiEpl3NQOq0SSxKekajIMbB3h6n64uFPtaIQQQhShqRGnWXXwKrY2Gmb3e5IG5UvudCZqk8SnpHIrAw36GW7L4qVCCGGxfvw7mrnbzgHw2XPBtKrh+4hHiIeRxKckCx0BaODMBog7oXY0QgghCtmaw9f4aJ3h9/tbYdV5vlE5lSMq+STxKclKV4ZaXQ23d36jbixCCCEK1c6zNxi7/DCKAgNCK/B6y8pqh2QRJPEp6Z5+0/D/0eWQeFXdWIQQQhSK49cSGfrTfjJ0ejoG+/Nhl9oyK3MhkcSnpCvbECo2B30m7JqtdjRCCCEe0+VbqQxcsJfk9EyequTNV73rYyuzMhcaSXwsQVarz/6FcDdBzUiEEEI8hpvJ6fT/cQ/X76RTw9+def0b4WQvk9QWJkl8LEGVNuBbCzKSYd+PakcjhBCiAFIzMhm8aB/RN1Io6+XMosFN8JBZmQudJD6WIGvxUjAsXqpNUzceIYQQ+aLV6Rm+5ACHLyfg5WLPosFN8JNZmYuEJD6Wok5P8HgCkuPgyDK1oxFCCJFHiqIwYdVRtpy+jpO9DT8MaEwVX5mVuahI4mMpbO0h9HXD7Z0zQa9XNx4hhBB58sXG06zcfwVbGw2zXnyShhVkVuaiJImPJXmyPzh5ws2zcHq92tEIIYR4hEU7LzBri2FW5k971KF1TT+VI7J8kvhYEkd3aPyy4bYsXiqEEGZt/dEYJq05DsDYttXo07i8yhFZB0l8LE3W4qVX9sKlXWpHI4QQIgdR524yaukhFAX+91QFRjxbRe2QrIYkPpbG3Q/q9zXclsVLhRDC7JyMSWLo4n1k6PS0r+3PpK4yK3NxksTHEoWOBDTw758Qf0rtaIQQQvznyu1UBi7Yw530TJoEeTP9BZmVubhJ4mOJfKpAzc6G27J4qRBCmIXbKRn0/3EPcUnpVPdzZ77MyqwKSXws1dOjDP8fWQZJ11QNRQghrN3dDB2DF+3l/PUUAj2dWDi4MZ7OMiuzGiTxsVRPNIIKT4NeC7vmqB2NEEJYrUydnhG/HODgpQQ8nQ2zMgd4OqsdltWSxMeSZS1jsW8BpCWqG4sQQlghRVF47/djbDoVj6OdDT8MaERVP3e1w7JqkvhYsiptoUxNyLhjSH6EEEIUq68j/2XZvsvYaOCbvg1oVNFb7ZCsniQ+lszGBp5+w3B71xzITFc3HiGEsCI/7brIzM1nAfi4ezDtavurHJEASXwsX51e4B4IybFwZLna0QghhFWIOBbDh/93DIBRbaryYojMymwuJPGxdHYOsnipEEIUoz3Rt3jjv1mZ+zYpz5utq6odkriPJD7W4MkB4OgJN/6FfyPUjkYIISzW6dg7vLxoLxmZetrV8uPj7nVkVmYzI4mPNXDygMaDDbdlGQshhCgS1xLuMuDHPSSlZdKoQilm9m0gszKbIUl8rEXIq2DrAJd3yeKlQghRyBJSDbMyxyalUdXXje8HyKzM5koSH2vh7g/1XjDc/memurEIIYQFSdPqeHnRPs7GJ+Pv4cSiwU3wcnFQOyyRC0l8rEnTNwANnF4H1/9VOxohhCjxDLMyH2Tfxdt4ONmxeEgTAr1kVmZzJomPNfGpCjU6GW7vlFYfIYTIL51eIercTf7v0FWizt3gvdXH+OtkHA52Nnw/oDHVZFZms6dq4rN9+3a6dOlCYGAgGo2G1atXG7dptVrefvttgoODcXV1JTAwkP79+3PtmumCm7du3aJfv354eHjg5eXFkCFDSE5OLuYzKUGcSxn+P7IMkmJMt22bBlvCiz8mIYQoASKOxdBs6mb6zt/Fm0sP0Xf+bpbtvYwGmPlCA5oEyazMJYGqiU9KSgr16tVj1qxZ2balpqZy4MABPvjgAw4cOMCqVas4ffo0Xbt2NdmvX79+HD9+nMjISNauXcv27dsZOnRocZ1CyeP13yRaugzYPfde+bZpsOUTsJHBeEII8aCIYzG89vMBYhLTsm1T7vtXmD87NZ+8Q4cOdOjQIcdtnp6eREZGmpR9++23NGnShEuXLlG+fHlOnjxJREQEe/fupVGjRgB88803dOzYkS+++ILAwMAiP4cSp8V4w3w+R1cYlrFoPgZ2f2dIelq9Z9guhBDCSKdXmLzmRK6pjQaYvOYEbWv5y+XrJYCqiU9+JSYmotFo8PLyAiAqKgovLy9j0gPQpk0bbGxs2L17Nz169MjxOOnp6aSn31u3KikpCTB0r2m12qI7AXPRZRZ25zajSb2JMjUIjaJD98w76JuOhiI+/6zX1ypeZzMm9WAepB7Mw6PqYXf0rRxberIoQExiGlFn4wmR7q4CK8jnoSCfnRKT+KSlpfH222/Tt29fPDw8AIiNjcXX19dkPzs7O7y9vYmNjc31WOHh4UyePDlb+caNG3FxcSncwM1UVc9nqZW6wpD0aOxYe6cWrF9fbM//YGueUIfUg3mQejAPudXD/hsa4NHDADbu2M3Nk9Ll9bjy83lITU3N9/FLROKj1Wrp3bs3iqIwZ86cxz7ehAkTGDNmjPF+UlIS5cqVo127dsakytLZ/LYSYkBBg62SSWf3E+ibjyvy59VqtURGRtK2bVvs7e2L/PlEzqQezIPUg3l4VD2Ujr7F4jP7Hnmcds1DpMXnMRTk85DVY5MfZp/4ZCU9Fy9eZPPmzSaJib+/P/Hx8Sb7Z2ZmcuvWLfz9/XM9pqOjI46OjtnK7e3treOXz7ZpcOoPADRe5aHBS9hu+QRbW9tiG+NjNa+1mZN6MA9SD+Yht3oIreKLj5sDN5IzcnycBvD3dCK0iq+M8SkE+fk8FORzY9bz+GQlPWfOnOGvv/6idOnSJttDQ0NJSEhg//79xrLNmzej1+sJCQkp7nBLhqyrt0JHGO4nXYPm4wwDm7d8YtguhBDCSKdXcLTLuasrK82Z2KWWJD0lhKotPsnJyZw9e9Z4Pzo6mkOHDuHt7U1AQAC9evXiwIEDrF27Fp1OZxy34+3tjYODAzVr1qR9+/a88sorzJ07F61Wy4gRI3jhhRfkiq7c6HWGJKfZGNg1G/RaSIm/19Kj16kbnxBCmJlZW85yNeEurg62uDraEX/n3sUx/p5OTOxSi/Z1AlSMUOSHqonPvn37aNWqlfF+1ribAQMGMGnSJP74w9AdU79+fZPHbdmyhZYtWwKwZMkSRowYQevWrbGxsaFnz57MnCmzEueq1YR7t90DIekKJF4xrOUll7ILIYSJY1cTmbXF8Af6Zz3r0jE4gD3Rt4i/k4avuxNNgrylpaeEUTXxadmyJYqS+wj4h23L4u3tzS+//FKYYVkPz7L3Ep8nGj16fyGEsCIZmXrGrThMpl6hQx1/OtcNQKPREFq59KMfLMyWWY/xEUXM8wnD/4lX1I1DCCHM0Lebz3Aq9g7erg581L0OGo207FgCSXysmUdZw/9JV9WNQwghzMyxq4nM2noOgCndauPjlv1KYFEySeJjzTzLGf5PvKxuHEIIYUayurh0eoVOwQF0risXy1gSSXysmed/LT6J0uIjhBBZvvmvi6u0qwNTutVWOxxRyCTxsWYyxkcIIUwcvZLI7P+6uD7qXofS0sVlcSTxsWYe/yU+KfGQmf7wfYUQwsKl39/FVTeAjsEyN48lksTHmrl4g52z4bYMcBZCWLlZW89xOu6/Lq6u0sVlqSTxsWYazX3jfKS7SwhhvS4lw7wdFwD4WLq4LJokPtbOOM5HWnyEENYpPVPPkrO26PQKnesG0EG6uCyaJD7WzkMGOAshrNu3W84Re1fz31VcddQORxQxSXysXVaLT5IkPkII63P4cgLzdkQDMLlLTbxdHVSOSBQ1SXysnYzxEUJYqTStjnErDqNX4MnSesJq+6kdkigGkvhYOxnjI4SwUjM2neFMfDKlXR3oGaRXOxxRTCTxsXYyxkcIYYUOXU7gu23/rcXVtSZu9ioHJIqNJD7WLqurK+MOpCWqG4sQQhSD+7u4utUPpF0t6eKyJpL4WDsHV3AuZbgtrT5CCCsw/a8znI1PxsfNkUldZKJCayOJj5BxPkIIq3Hw0m3mbTd0cX3aow6l5CouqyOJj7hvnM9ldeMQQogidH8XV/f6gbSr7a92SEIFkviI++bykRYfIYTl+jryX85dT6GMuyOTZC0uqyWJj5C5fIQQFu/ApdvM33EegE97BOPlIl1c1koSHwGe5Qz/yxgfIYQFur+Lq0eDsrSVq7ismiQ+AjyyWnxkjI8QwvJ8Ffkv5//r4prYpZba4QiVSeIj7hvjcw30MnupEMJy7L94r4srXLq4BJL4CAD3ANDYgF4LKfFqRyOEEIUiTavjrRWHURR47smytJEuLoEkPgLA1s6Q/ICM8xFCWIwvN57m/I0U/DwcmdhZruISBpL4CAMZ5yOEsCD7L97i+7+jAQh/LhhPF1mMSxhI4iMMZC4fIYSFMHRxHUFRoOeTT/BsDeniEvdI4iMMZC4fIYSF+GLDvS6uD+UqLvEASXyEgXEuH0l8hBAl194Lt/jhH0MX12fP1cXTWbq4hClJfISBh7T4CCFKtrsZ967i6tXwCVrV8FU7JGGGJPERBjLGRwhRwn2+4TQXbqbi7+HEB52li0vkTNXEZ/v27XTp0oXAwEA0Gg2rV6822a4oCh9++CEBAQE4OzvTpk0bzpw5Y7LPrVu36NevHx4eHnh5eTFkyBCSk5OL8SwsRFbikxwHmenqxiKEEPm0J/oWC3b+dxVXz2Dp4hK5UjXxSUlJoV69esyaNSvH7dOmTWPmzJnMnTuX3bt34+rqSlhYGGlpacZ9+vXrx/Hjx4mMjGTt2rVs376doUOHFtcpWA6X0mDnZLiddE3dWIQQIh/uZugYv9LQxfV8wydoVV26uETu7NR88g4dOtChQ4cctymKwvTp03n//ffp1q0bAIsXL8bPz4/Vq1fzwgsvcPLkSSIiIti7dy+NGjUC4JtvvqFjx4588cUXBAYGFtu5lHgajWGcz61zhnE+3kFqRySEEHkybcMpYxfX+9LFJR7BbMf4REdHExsbS5s2bYxlnp6ehISEEBUVBUBUVBReXl7GpAegTZs22NjYsHv37mKPucSTcT5CiBJmT/QtFu68AMBn0sUl8kDVFp+HiY2NBcDPz3TiKT8/P+O22NhYfH1NmzTt7Ozw9vY27pOT9PR00tPvjWNJSkoCQKvVotVqCyX+ksjWPRAbQHfrIvoieh2yXl9rfp3NgdSDeZB6eDypGZmMW3Hovy6usjxdqVSBXkupB/NQkHooSJ2ZbeJTlMLDw5k8eXK28o0bN+Li4qJCROahevxdagCXjkVxJGl9kT5XZGRkkR5f5I3Ug3mQeiiY36JtuHTLBi8HhYY2F1m//uJjHU/qwTzkpx5SU1PzfXyzTXz8/f0BiIuLIyAgwFgeFxdH/fr1jfvEx5uuJp6ZmcmtW7eMj8/JhAkTGDNmjPF+UlIS5cqVo127dnh4eBTiWZQsmoM3Yf1qKnjZ8kTHjkXyHFqtlsjISNq2bYu9vTRJq0XqwTxIPRTcngu32B61D4CvXmhI86o+BT6W1IN5KEg9ZPXY5IfZJj5BQUH4+/uzadMmY6KTlJTE7t27ee211wAIDQ0lISGB/fv307BhQwA2b96MXq8nJCQk12M7Ojri6OiYrdze3t663/TeFQCwuXMNmyJ+Haz+tTYTUg/mQeohf1IzMpnw+wkAXmhcjmdrBTziEXkj9WAe8lMPBakvVROf5ORkzp49a7wfHR3NoUOH8Pb2pnz58owaNYqPP/6YqlWrEhQUxAcffEBgYCDdu3cHoGbNmrRv355XXnmFuXPnotVqGTFiBC+88IJc0VUQWYObZfZmIYQZm/rnKS7dSiXQ04n3OtVUOxxRwuQ78UlPT2f37t1cvHiR1NRUypQpQ4MGDQgKyv/lz/v27aNVq1bG+1ndTwMGDGDhwoWMHz+elJQUhg4dSkJCAs2aNSMiIgInJyfjY5YsWcKIESNo3bo1NjY29OzZk5kzZ+Y7FsG9ZSvSkyAtEZw81Y1HCCEeEHXuJouiDGN5PutZF3cnaaER+ZPnxOeff/5hxowZrFmzBq1Wi6enJ87Ozty6dYv09HQqVarE0KFDefXVV3F3d8/TMVu2bImiKLlu12g0TJkyhSlTpuS6j7e3N7/88kteT0M8jKMbOHlBWgIkXpXERwhhVlLSMxn/22EA+jYpxzPVyqgckSiJ8jSPT9euXenTpw8VK1Zk48aN3Llzh5s3b3LlyhVSU1M5c+YM77//Pps2baJatWoyMr4ky1qlXebyEUKYmakRp7h86y5lvZx5t6N0cYmCyVOLT6dOnfjtt99yHURUqVIlKlWqxIABAzhx4gQxMTGFGqQoRp5lIe4oJF5WOxIhhDDaee4Gi41dXMHSxSUKLE+Jz7Bhw/J8wFq1alGrlkwZXmIZBzhLi48QwjykpGcyfuURAF4MKU/zqtLFJQrusa7qSk5ORq/Xm5RZ8zw4FiFrgLNc2SWEMBOf/XmKK7eli0sUjnyv1RUdHU2nTp1wdXXF09OTUqVKUapUKby8vChVqlRRxCiKk4zxEUKYkZ1nb/DTLkMX17RedXFzNNvp50QJke930EsvvYSiKPz444/4+fmh0WiKIi6hFs+sFh8Z4yOEUJfhKi5DF1e/kPI8XaXgszMLkSXfic/hw4fZv38/1atXL4p4hNqMK7RfA70ebPLdKCiEEIUi/M+Txi6uCdLFJQpJvr/VGjduzOXL0hpgsdwDAA3oMiDlutrRCCGs1D9nb/DzrksAfC5dXKIQ5fud9P333/Pqq69y9epV6tSpk+0S97p16xZacEIFtvaG5OfONUi6Au5+akckhLAyyfddxfXSU+VpKl1cohDlO/G5fv06586dY9CgQcYyjUaDoihoNBp0Ol2hBihU4FnWkPgkXoGyDdWORghhZT5df5KrCXd5opQzEzpIF5coXPlOfAYPHkyDBg349ddfZXCzpfJ8Aq7slbl8hBDF7u8zN/hlt6GLa1qvurhKF5coZPl+R128eJE//viDKlWqFEU8whzIXD5CCBXcSdPy9n9Xcf3vqQo0rSxdXKLw5Xtw87PPPsvhw4eLIhZhLoxz+UjiI4QoPp+uP8XVhLuU83bmnQ411A5HWKh8t/h06dKF0aNHc/ToUYKDg7MNbu7atWuhBSdU4iktPkKI4rXjzHV+3fNfF1fPetLFJYpMvt9Zr776KgBTpkzJtk0GN1sIWa9LCFGM7qRpefu/q7gGhFYgtHJplSMSlizfic+Da3MJC+TxX+KTHAeZGWDnoG48QgiL9un6k1xLTKO8twtvSxeXKGIyLa/IztUHbB0BxXBZuxBCFJHt/17n1z2GSXGn9aqLi4N0cYmilafEZ+nSpXk+4OXLl/nnn38KHJAwAxqNjPMRQhS5pPuu4hrYtCJPVZIuLlH08pT4zJkzh5o1azJt2jROnjyZbXtiYiLr16/nxRdf5Mknn+TmzZuFHqgoZjLORwhRxD5Ze5KYxDQqlHZhfHtZ/1EUjzy1KW7bto0//viDb775hgkTJuDq6oqfnx9OTk7cvn2b2NhYfHx8GDhwIMeOHcPPT5Y5KPGyxvnIKu1CiCKw9XQ8y/b918XVU7q4RPHJ8zuta9eudO3alRs3bvD3339z8eJF7t69i4+PDw0aNKBBgwbYyErelsO4Sru0+AghCldSmpYJq44Chi6uEOniEsUo3ym2j48P3bt3L4JQhFmRMT5CiCLy8doT0sUlVCNNNCJnMsZHCFEEtpyOZ/m+K2g08HmvetLFJYqdJD4iZ8YxPtLiI4QoHIl3tUz4zdDFNahpEE2CvFWOSFgjSXxEzrK6utITIS1J3ViEEBbh47UniE1KI8jHlbfCpItLqEMSH5EzR3dw8jTclgHOQojHtOVUPCv2Z3Vx1cXZwVbtkISVynfiM2XKFFJTU7OV3717N8f1u0QJlrVKu4zzEUI8hsRULe+sMkxUOPjpIBpVlC4uoZ58Jz6TJ08mOTk5W3lqaiqTJ08ulKCEmfDIurJL5vIRQhTclLUniEtKJ8jHlXHtpItLqCvfiY+iKGg0mmzlhw8fxttbsniL4ikDnIUQj2fzqTh+OyBdXMJ85Pk6wlKlSqHRaNBoNFSrVs0k+dHpdCQnJ/Pqq68WSZBCJVkDnGWMjxCiABJTtbzz31VcQ6SLS5iJPCc+06dPR1EUBg8ezOTJk/H09DRuc3BwoGLFioSGhhZJkEIlxjE+0uIjhMi/yWuPE38nnUo+royTq7iEmchz4jNgwAAAgoKCaNq0Kfb29kUWlDATHjJ7sxAi73R6hT3Rt4i/k8blW6msOnDV0MX1fF2c7KWLS5iHfE+Z2aJFC/R6Pf/++y/x8fHo9XqT7c8880yhBafT6Zg0aRI///wzsbGxBAYGMnDgQN5//31jV5uiKEycOJH58+eTkJDA008/zZw5c6hatWqhxWF1toSDjS3U7WO4n3QV9HqwsYFt00Cvg1YT1I1RCGFWIo7FMHmNYSmK+7Wu4UvDCtLFJcxHvhOfXbt28eKLL3Lx4kUURTHZptFo0Ol0hRbc1KlTmTNnDosWLaJ27drs27ePQYMG4enpyRtvvAHAtGnTmDlzJosWLSIoKIgPPviAsLAwTpw4gZOTU6HFYlVsbGHLJ4YEBw3oMiD1BuxfaChv9Z7aEQohzEjEsRhe+/kASg7bNp2MJ+JYDO3rBBR7XELkJN+Jz6uvvkqjRo1Yt24dAQEBOV7hVVh27txJt27d6NSpEwAVK1bk119/Zc+ePYChtWf69Om8//77dOvWDYDFixfj5+fH6tWreeGFF4osNovWYrzh/y2fgIMbZCTDlk9h/wJD0pO1XQhh9XR6hclrTuSY9GSZvOYEbWv5Y2tTdN8XQuRVvhOfM2fOsHLlSqpUqVIU8Zho2rQp8+bN499//6VatWocPnyYv//+m6+++gqA6OhoYmNjadOmjfExnp6ehISEEBUVlWvik56eTnp6uvF+UpJhSQatVotWqy3CMypBmo7GRqfDdvtnhvv7F6B75h30TUfDY7xGWa+vvM7qknowD5ZQD7ujb2Xr3rqfAsQkphF1Np4QM12byxLqwRIUpB4KUmf5TnxCQkI4e/ZssSQ+77zzDklJSdSoUQNbW1t0Oh2ffPIJ/fr1AyA2NhYAPz8/k8f5+fkZt+UkPDw8x8kWN27ciIuLSyGeQUlXi66ABtBjw9o7tWD9+kI5cmRkZKEcRzweqQfzUJLrYf8NDfDogcsbd+zm5smHtQupryTXgyXJTz3ktJLEo+Qp8Tly5Ijx9siRIxk7diyxsbEEBwdnu7qrbt26+Q4iN8uXL2fJkiX88ssv1K5dm0OHDjFq1CgCAwONV5kVxIQJExgzZozxflJSEuXKlaNdu3Z4eHgURugWwWbHF2Q1TNugp7P7CfTNxz3WMbVaLZGRkbRt21auDFSR1IN5sIR6KB19i8Vn9j1yv3bNQ8y6xaek14MlKEg9ZPXY5EeeEp/69euj0WhMBjMPHjzYeDtrW2EPbn7rrbd45513jF1WwcHBXLx4kfDwcAYMGIC/vz8AcXFxBATcGzgXFxdH/fr1cz2uo6Mjjo6O2crt7e3lTZ9l2zTI6uYCaD4O2+2fYWtrWyhjfOS1Ng9SD+ahJNdDaBVfAjydiE1My3Gcjwbw93QitIqv2Y/xKcn1YEnyUw8Fqa88JT7R0dH5PnBhSE1NxcbGdFUNW1tb4yX0QUFB+Pv7s2nTJmOik5SUxO7du3nttdeKO1zLsW2aYWBzo8Gw70dwLQOtPwA7R0M5yABnIQQAtjYaJnapxWs/H0ADJslPVpozsUsts096hPXIU+JToUKFoo4jR126dOGTTz6hfPny1K5dm4MHD/LVV18ZW5s0Gg2jRo3i448/pmrVqsbL2QMDA+nevbsqMVsEvc5w9VbpKobEx7uSoTwr2dEXXqueEKLka18ngDkvPZltHh9/Tycmdqkll7ILs5Lvwc1//PFHjuUajQYnJyeqVKlCUFDQYwcG8M033/DBBx/w+uuvEx8fT2BgIMOGDePDDz807jN+/HhSUlIYOnQoCQkJNGvWjIiICJnD53FkTU6440vD/6Xuq09p6RFC5KB9nQDa1vJn0II9bD9zgxcal+OTHsHS0iPMTr4Tn+7du2cb7wOm43yaNWvG6tWrKVWq1GMF5+7uzvTp05k+fXqu+2g0GqZMmcKUKVMe67lEDm6dN/yf1eIjhBAPYWujoWagB9vP3MDZwVaSHmGWbB69i6nIyEgaN25MZGQkiYmJJCYmEhkZSUhICGvXrmX79u3cvHmTceMe7+ofYQZuXTD87104LXhCCMtXxs1w4ciN5AyVIxEiZ/lu8XnzzTeZN28eTZs2NZa1bt0aJycnhg4dyvHjx5k+fbrJVV+ihLr936D2UpL4CCHypoy7IfG5fif3SQ2FUFO+W3zOnTuX41w3Hh4enD9v6BqpWrUqN27cePzohHq0dw2Lk4J0dQkh8kxafIS5y3fi07BhQ9566y2uX79uLLt+/Trjx4+ncePGgGFZi3LlyhVelKL43b5o+N/RA1zMc9IxIYT5udfik/6IPYVQR767un744Qe6devGE088YUxuLl++TKVKlfi///s/AJKTk3n//fcLN1JRvIwDm4OgCBeiFUJYFp//WnwS72pJz9ThaPfo5SyEKE75TnyqV6/OiRMn2LhxI//++6+xrG3btsbJBmUOHQsg43uEEAXg6WyPva0GrU7hZnIGgV7OaockhIl8Jz4ANjY2tG/fnvbt2xd2PMJc3N/iI4QQeWRjo8HHzZGYxDSu30mXxEeYnTwlPjNnzmTo0KE4OTkxc+bMh+77xhtvFEpgQmW3/mvxkYHNQoh8ykp8biTLOB9hfvKU+Hz99df069cPJycnvv7661z302g0kvhYCunqEkIUkAxwFuYs34uUqrVgqShGukxIuGS4LS0+Qoh88nFzACTxEeYp35ezZ8nIyOD06dNkZmYWZjzCHCReBn0m2DqCuywuKITIn6wWH+nqEuYo34lPamoqQ4YMwcXFhdq1a3PpkqFlYOTIkXz22WeFHqBQQdbA5lIVwabAubEQwkplTWJ4XRIfYYby/a02YcIEDh8+zNatW01WQG/Tpg3Lli0r1OCESm7LwGYhRMH5ZLX43JHZm4X5yffl7KtXr2bZsmU89dRTaO6b2K527dqcO3euUIMTKjFe0SUDm4UQ+SctPsKc5bvF5/r16/j6+mYrT0lJMUmERAkml7ILIR7DvRYfSXyE+cl34tOoUSPWrVtnvJ+V7Hz//feEhoYWXmRCPXIpuxDiMWQNbr6TnsndDJ3K0QhhKt9dXZ9++ikdOnTgxIkTZGZmMmPGDE6cOMHOnTvZtm1bUcQoipOiSFeXEOKxuDva4WhnQ3qmnhvJ6ZTzdlE7JCGM8t3i06xZMw4dOkRmZibBwcFs3LgRX19foqKiaNiwYVHEKIrTnVjIvAsaW/Asp3Y0QogSSKPRGBcrlXE+wtwUaK2uypUrM3/+/MKORZiDrG4uzyfAzkHdWIQQJVYZd0euJtyVSQyF2clz4pOUlJSn/Tw8PAocjDADxsVJZWCzEKLgslp8ZBJDYW7ynPh4eXk99KotRVHQaDTodDKQrUST8T1CiEIg63UJc5XnxGfLli3G24qi0LFjR77//nvKli1bJIEJlRhnbZbERwhRcJL4CHOV58SnRYsWJvdtbW156qmnqFRJukQsiszaLIQoBGX+W6hUurqEuZGFmIQp6eoSQhQCafER5koSH3FP6i1ISzDcLlVRzUiEECXcvcHNsl6XMC+PlfjIEhUWJquby80fHFzVjUUIUaLd3+KjKIrK0QhxT57H+Dz33HMm99PS0nj11VdxdTX9gly1alXhRCaKn6zRJYQoJFktPne1OlIydLg5FmjaOCEKXZ7fiZ6enib3X3rppUIPRqhMxvcIIQqJq6MdLg62pGbouHEnXRIfYTby/E5csGBBUcYhzIEsTiqEKERl3B25eDOV68npVPSR7nNhHmRws7jHOGuzJD5CiMdXJmuAs1zZJcyIJD7iHunqEkIUIlmoVJgjs098rl69yksvvUTp0qVxdnYmODiYffv2GbcrisKHH35IQEAAzs7OtGnThjNnzqgYcQmVkQLJsYbbMrhZCFEIZC4fYY7MOvG5ffs2Tz/9NPb29vz555+cOHGCL7/8klKlShn3mTZtGjNnzmTu3Lns3r0bV1dXwsLCSEtLUzHyEuj2BcP/Tl7gXOphewohRJ7IQqXCHJn1MPupU6dSrlw5k4HVQUH3umEURWH69Om8//77dOvWDYDFixfj5+fH6tWreeGFF4o95hJLurmEEIVMWnyEOTLrFp8//viDRo0a8fzzz+Pr60uDBg2YP3++cXt0dDSxsbG0adPGWObp6UlISAhRUVFqhFxyGQc2SzeXEKJwGBMfmb1ZmBGzbvE5f/48c+bMYcyYMbz77rvs3buXN954AwcHBwYMGEBsrGFMip+fn8nj/Pz8jNtykp6eTnr6vb9AkpKSANBqtWi12iI4E/Nnc/MctoDOswL6InwNsl5fa32dzYXUg3mw9HrwcjL8bX09Kc2sz9HS66GkKEg9FKTOzDrx0ev1NGrUiE8//RSABg0acOzYMebOncuAAQMKfNzw8HAmT56crXzjxo24uLgU+LglWejZvfgChy/f4fL69UX+fJGRkUX+HOLRpB7Mg6XWw610ADviku6ybt16zH2VI0uth5ImP/WQmpqa7+ObdeITEBBArVq1TMpq1qzJb7/9BoC/vz8AcXFxBAQEGPeJi4ujfv36uR53woQJjBkzxng/KSmJcuXK0a5dOzw8PArxDEoOu1kfAFC3RVeCy4cW2fNotVoiIyNp27Yt9vb2RfY84uGkHsyDpddDulbH5AOb0Ckamj/bFg9n8zxHS6+HkqIg9ZDVY5MfZp34PP3005w+fdqk7N9//6VChQqAYaCzv78/mzZtMiY6SUlJ7N69m9deey3X4zo6OuLo6Jit3N7e3jrf9JkZkHgZALsyVaEYXgOrfa3NjNSDebDUerC3t8fdyY47aZncTtNT2sO8z9FS66GkyU89FKS+zHpw8+jRo9m1axeffvopZ8+e5ZdffmHevHkMHz4cMKwOP2rUKD7++GP++OMPjh49Sv/+/QkMDKR79+7qBl+SJF4GRQ92zuDur3Y0QggLkjXAWS5pF+bCrFt8GjduzO+//86ECROYMmUKQUFBTJ8+nX79+hn3GT9+PCkpKQwdOpSEhASaNWtGREQETk5OKkZewtx/Kbu5d8ILIUoUHzdHzl9PkUvahdkw68QHoHPnznTu3DnX7RqNhilTpjBlypRijMrCyKXsQogiInP5CHNj1l1dopgYV2WvqGoYQgjLU0ZmbxZmRhIfIbM2CyGKjLT4CHMjiY+Qri4hRJGRFh9hbiTxsXZ6/b0FSktJi48QonD5uDsAcF0SH2EmJPGxdneugS4dbOzAs5za0QghLEwZN8MVttLVJcyFJD7WLmt8j1d5sDX7i/yEECVMVovPzeQM9HpF5WiEkMRHGK/okm4uIUThK+1qGOOTqVdIuCuLgAr1SeJj7WRgsxCiCDnY2VDKxbCsgAxwFuZAEh9rJ5eyCyGKmI+bXNIuzIckPtZOWnyEEEVM5vIR5kQSH2umKHIpuxCiyPnIXD7CjEjiY81Sb0J6kuF2qQrqxiKEsFjS4iPMiSQ+1ixrfI9HWbB3VjcWIYTFMiY+0uIjzIAkPtZMLmUXQhQDGdwszIkkPtbMOLBZEh8hRNGRri5hTiTxsWZyKbsQohj4uBlmb76RnKFyJEJI4mPd5FJ2IUQxyGrxuZWSjk6WrRAqk8THmskYHyFEMSjt6oiNBvQK3EqRVh+hLkl8rFX6HUi5brgtXV1CiCJka6PB29XQ3SXjfITaJPGxVlnje1xKg5OnurEIISye8couuaRdqEwSH2sl3VxCiGKUNc7nhrT4CJVJ4mOtZGCzEKIYlZEWH2EmJPGxVnIpuxCiGEmLjzAXkvhYK+nqEkIUIxnjI8yFJD7WytjiI11dQoiiJ7M3C3MhiY81ykyHxCuG29LVJYQoBsauLmnxESqTxMca3b4IKODgBq5l1I5GCGEFZKFSYS4k8bFG94/v0WjUjUUIYRWyWnxup2rR6vQqRyOsmSQ+1sg4vqeiqmEIIayHl7M9tjaGP7RuymKlQkWS+FgjmcNHCFHMbGw0xlXapbtLqEkSH2skl7ILIVQgA5yFOZDExxpJi48QQgWl/1uodOOJWKLO3USnV1SOSFijEpX4fPbZZ2g0GkaNGmUsS0tLY/jw4ZQuXRo3Nzd69uxJXFycekGaqy3hsG0a6HX/XdXFvUvZt00zbBdCiCIScSyGPdG3Afh1z2X6zt9Fs6mbiTgWo3JkwtqUmMRn7969fPfdd9StW9ekfPTo0axZs4YVK1awbds2rl27xnPPPadSlGbMxha2fAKRH4BeCzb24FH2v6TnE8N2IYQoAhHHYnjt5wPc1epMymMT03jt5wOS/IhiVSISn+TkZPr168f8+fMpVaqUsTwxMZEffviBr776imeffZaGDRuyYMECdu7cya5du1SM2Ay1GA+t3oOoWYb7pSrCji8NSU+r9wzbhRCikOn0CpPXnCCnTq2ssslrTki3lyg2dmoHkBfDhw+nU6dOtGnTho8//thYvn//frRaLW3atDGW1ahRg/LlyxMVFcVTTz2V4/HS09NJT783uC4pKQkArVaLVqstorMwA01HY3NlP7ZnIlBunkWz5RN0z7yDvuloKKbzznp9Lfp1LgGkHsyDNdTD7uhbxCSm5bpdAWIS04g6G09IkHfxBXYfa6iHkqAg9VCQOjP7xGfp0qUcOHCAvXv3ZtsWGxuLg4MDXl5eJuV+fn7Exsbmeszw8HAmT56crXzjxo24uLg8dszmzFdfh1Ai0KCgx4a1d2rB+vXFHkdkZGSxP6fITurBPFhyPey/oQEe3ZW+ccdubp5Ut9XHkuuhJMlPPaSmpub7+Gad+Fy+fJk333yTyMhInJycCu24EyZMYMyYMcb7SUlJlCtXjnbt2uHh4VFoz2OObHacgHP/3UZP59Rl6HstKrbn12q1REZG0rZtW+zt7YvteYUpqQfzYA31UDr6FovP7Hvkfu2ah6ja4mPp9VASFKQesnps8sOsE5/9+/cTHx/Pk08+aSzT6XRs376db7/9lg0bNpCRkUFCQoJJq09cXBz+/v65HtfR0RFHR8ds5fb29pb9pt82DbZ/Bi3fhbu3YPdcbE+vw/b3l6F38SU/YAWvdQkh9WAeLLkeQqv4EuDpRGxiWo7jfDSAv6cToVV8jTM7q8WS66EkyU89FKS+zHpwc+vWrTl69CiHDh0y/jRq1Ih+/foZb9vb27Np0ybjY06fPs2lS5cIDQ1VMXIzlHX1Vqv3oOXb0P4zaDjIsO3Ealg5WNXwhBCWydZGw8QutQBDkpOTiV1qqZ70COth1i0+7u7u1KlTx6TM1dWV0qVLG8uHDBnCmDFj8Pb2xsPDg5EjRxIaGprrwGarpdeZXr2l0UCnryAzHQ7/Asd+h7p9oFqYunEKISxO+zoBzHnpSSavOZFtoPMHnWvRvk6ASpEJa2TWiU9efP3119jY2NCzZ0/S09MJCwtj9uzZaodlflpNyF5mYwPdvgVdOhz7DZb9D15cBpVbFX98QgiL1r5OAG1r+bMn+hbxd9L4eddF9l64zf5LtxmMLJ8jik+JS3y2bt1qct/JyYlZs2Yxa9YsdQIq6Wxsocd3hpafU2vh177w0m9Q8Wm1IxNCWBhbGw2hlUsDUN3fnfbTd7DuSAwjn02ihr9lX1gizIdZj/ERxcTWHnotgKphkHkXfukNl/eoHZUQwoLV8PegU7Chi2t65BmVoxHWRBIfYWDnAL0XQ6WWkJEMP/eEqwfUjkoIYcHebFMVjQYijsdy/Fqi2uEIKyGJj7jH3gle+BUqPA3pSfBTD4g9qnZUQggLVc3PnS51AwGY/pe0+ojiIYmPMOXgYhjg/ERjSEuAxd0h/pTaUQkhLNQbratio4HIE3EcvSKtPqLoSeIjsnN0h34rIaA+pN6AxV3h5jm1oxJCWKAqvm50r18WgK//+lflaIQ1kMRH5MzZC/73O/jVgeQ4WNQFbl9QOyohhAUa2boqtjYaNp+K5+Cl22qHIyycJD4idy7e8L/V4FMdkq7Coq6QeEXtqIQQFibIx5UeDQytPjLWRxQ1SXzEw7mVgf7/B96VIOGiIfm5E6t2VEIIC/PGs4ZWn23/Xmf/xVtqhyMsmCQ+4tE8AmDAGvAqD7fOGZKf5OtqRyWEsCDlS7vwfMMnAPha5vURRUgSH5E3nk8Ykh+PsnDjNPzUHVLlrzIhROEZ3qoK9rYa/j57g93nb6odjrBQkviIvCtVEfr/AW5+EHfMMM9Pmlx+KoQoHOW8XejdqBwgV3iJoiOJj8gfnyqG5MelNMQcgp97QfodtaMSQliI4a2q4GBrw67zt9h57oba4QgLJImPyD/fGoYBz05ecGUP/PICZKSqHZUQwgIEejnzQhNDq8/0yDMoiqJyRMLSSOIjCsY/GP63Chw94OLfsPRF0KapHZUQwgK83rIKDnY27Llwi3/OylgfUbgk8REFV7ahYYZne1c4vwWW94fMDLWjEkKUcP6eTvQLKQ/AV5GnpdVHFCpJfMTjKR8C/ZaDnTOc2QArB4FOq3ZUQogS7rWWlXGyt+HApQS2/SvTZ4jCI4mPeHwVm8ELS8DWAU6thd+HgV6ndlRCiBLM192Jl0IqAPB15L/S6iMKjSQ+onBUaQ29fwIbOzj2G/wxEvR6taMSQpRgw1pUxtnelsNXEtlyOl7tcISFkMRHFJ7q7aHXj6CxhUNLYN0YkL/ShBAFVMbdkf5NDa0+X0mrjygkkviIwlWrGzw3D9DA/gUQMUGSHyFEgQ17pjKuDrYcu5pE5Ik4tcMRFkASH1H4gntBt1mG27vnwF8TJfkRQhSIt6sDA5+uCMDXf51Br5ffJeLxSOIjikaDftDpK8Ptf2bA1s/UjUcIUWK90rwSbo52nIxJYsPxWLXDESWcJD6i6DQeAmHhhtvbPoMdX6kbjxCiRPJycWDwf60+06XVRzwmSXxE0Qp9HdpMMtzeNBmbPXNVDUcIUTINaVYJdyc7TsfdYf2xGLXDESWYJD6i6DUbDS0nAGAb+T4Vr29SOSAhREnj6WLPy80qAYZWH520+ogCksRHFI8WbxsSIKDelUVoDi1ROSAhREkzqFlFPJ3tORufzNoj19QOR5RQkviI4qHRQOuJ6BoPA8B23Sg4skLdmIQQJYqHkz2vNA8CYMZfZ8jUySSpIv8k8RHFR6NB3/Zjon2eRYNiWNri+Gq1oxJClCADnw7Cy8We8zdS+L9D0uoj8k8SH1G8NBqOPNEffd2+oOjgtyFw+k+1oxJClBBujnYMe6YyADM3S6uPyD9JfETx09ig6zQd6vQCfSYs7w9nZcCzECJv+odWoLSrAxdvprLq4FW1wxEljCQ+Qh02ttDjO6jZFXQZsPRFiN6hdlRCiBLA1dGOV1v81+qz6QxaafUR+WD2iU94eDiNGzfG3d0dX19funfvzunTp032SUtLY/jw4ZQuXRo3Nzd69uxJXJys6WL2bO2g5w9QrT1kpsEvfeDSLrWjEkKUAC89VQEfN0eu3L7LtIhT/N+hq0SduymXuYtHMvvEZ9u2bQwfPpxdu3YRGRmJVqulXbt2pKSkGPcZPXo0a9asYcWKFWzbto1r167x3HPPqRi1yDM7B3h+EVRqBdoUWNQFruzPvt+2abAlvPjjE0KYJWcHW1pWLwPA/B3RvLn0EH3n76LZ1M1EyASH4iHMPvGJiIhg4MCB1K5dm3r16rFw4UIuXbrE/v2GL8fExER++OEHvvrqK5599lkaNmzIggUL2LlzJ7t2SetBiWDvBC/8Ap7lDd1eCztAzJF727dNgy2fGLrHhBACiDgWw2/7r2Qrj01M47WfD0jyI3Jl9onPgxITEwHw9vYGYP/+/Wi1Wtq0aWPcp0aNGpQvX56oqChVYhQF4OACr0eBxxOQmQ4/tIO4E/eSnlbvQYvxakcphDADOr3C5DUnyKlTK6ts8poT0u0lcmSndgD5odfrGTVqFE8//TR16tQBIDY2FgcHB7y8vEz29fPzIzY251V809PTSU9PN95PSkoCQKvVotVqiyZ4AWB8fXN8nW0c4ZXt2H7XFJvkWJQ5oWgA3TPvoG86GqRuCs1D60EUG6mHgtkdfYuYxLRctytATGIaUWfjCQnyfuTxpB7MQ0HqoSB1VqISn+HDh3Ps2DH+/vvvxzpOeHg4kydPzla+ceNGXFxcHuvYIm8iIyNz3WYf9CEdjg5Hg4ICnDobzdmkdYbZn0Whelg9iOIj9ZA/+29ogEd3fW/csZubJ/Pe6iP1YB7yUw+pqan5Pn6JSXxGjBjB2rVr2b59O0888YSx3N/fn4yMDBISEkxafeLi4vD398/xWBMmTGDMmDHG+0lJSZQrV4527drh4eFRZOcgDNl5ZGQkbdu2xd7ePsd9bHZ8YUh6NDZoFD21ry2jZqlMw9w/9s7FG3AR0el0ZGZmoijqNMVnZmayc+dOmjZtip1difk1YHEsrR40Gg12dnbY2hbteLzS0bdYfGbfI/dr1zwkzy0+j/q9JIpeQeohq8cmP8z+k6YoCiNHjuT3339n69atBAUFmWxv2LAh9vb2bNq0iZ49ewJw+vRpLl26RGhoaI7HdHR0xNHRMVu5vb29vOmLSa6v9bZpsP0zaPUemmfeMlzifmYDNsd/w+bWuf8GQZct/oALiaIoxMbGkpCQoHoc/v7+xMTEoJGWNNVYaj14eXnh7+9fZOcUWsWXAE8nYhPTchznowH8PZ0IreKLrU3eY5DvAPOQn3ooSH2ZfeIzfPhwfvnlF/7v//4Pd3d347gdT09PnJ2d8fT0ZMiQIYwZMwZvb288PDwYOXIkoaGhPPXUUypHL/Ilp4HM/ZbD6tfh0BKIOQTzWkKfn6B8yazbrKTH19cXFxcX1b7s9Ho9ycnJuLm5YWNT4q5xsBiWVg+KopCamkp8fDwAAQEBRfI8tjYaJnapxWs/H8i2LesTNbFLrXwlPcJ6mH3iM2fOHABatmxpUr5gwQIGDhwIwNdff42NjQ09e/YkPT2dsLAwZs+eXcyRisem1+V89Vb32eDkBUeXQ0o8LOwMnb6EhgNUCbOgdDqdMekpXbq0qrHo9XoyMjJwcnKyiC/cksoS68HZ2dAdHR8fj6+vb5F1e7WvE8Ccl57kvd+PcTMlw1ju7+nExC61aF+naJIuUfKZfeKTlzEQTk5OzJo1i1mzZhVDRKLItJqQ+7b2n0Krd2H1a3DyD1jzBsQehfbhYFsymqazrj6QAfTC0mW9x7VabZGO92lfJwC9XuH1Xw5SsbQL4c/VpUmQt7T0iIeyjD8xhHVwdIPei6HV+4b7e+fDTz0g5aa6ceWTJY3lECInxfkev55saO2pGeBBaOXSkvSIR5LER5QsGg20eMswyNnBDS7sgPktDa0/olht3boVjUaj+kBtYd3ikgzz+fh5OKkciSgpJPERJVONTvDyX1AqCBIuGWZ6PvF/akdVbHR6hahzN4ttYcaWLVsyatQok7KmTZsSExODp6dnkT53QcyaNYuKFSvi5ORESEgIe/bsMdk+bNgwKleujLOzM2XKlKFbt26cOnXqkcddsWIFNWrUwMnJieDgYNavX2+yXVEUPvzwQwICAnB2dqZNmzacOXPmkcfdunUrTz75JI6OjlSpUoWFCxfm+5weNHDgQLp3757jc2UlrC1btkSj0eT6kzW2MjY2lpEjR1KpUiUcHR0pV64cXbp0YdOmTY88t6IWK4mPyCdJfETJ5VsTXtn83wKnqbC8P2z+BPR6tSMrUhHHYmg2dTN95+9SdWFGBweHIr1kuaCWLVvGmDFjmDhxIgcOHKBevXqEhYUZrzQCjGv6nTx5kg0bNqAoCu3atUOn0+V63J07d9K3b1+GDBnCwYMH6d69O927d+fYsWPGfaZNm8bMmTOZO3cuu3fvxtXVlbCwMNLScp9l+OLFi3Tp0oVWrVpx6NAhRo0axcsvv8yGDRvydU4FsWrVKmJiYoiJiTEmUn/99ZexbNWqVVy4cIGGDRuyefNmPv/8c44ePUpERAStWrVi+PDhj/X8hSE+yTALv59H9ilKhMiRIpTExEQFUBITE9UOxeJlZGQoq1evVjIyMgrvoJlaRflzgqJM9DD8/PKCotw1v7q8e/eucuLECeXu3bsFPsafR68pFd9eq1R44Kfifz9/Hr2Wp+PodDrl9u3bik6ne+S+AwYMUDCsAmD8iY6OVrZs2aIAyu3btxVFUZQFCxYonp6eypo1a5Rq1aopzs7OSs+ePZWUlBRl4cKFSoUKFRQvLy9l5MiRSmZmpvH4aWlpytixY5XAwEDFxcVFadKkibJly5aCvDyKoihKkyZNlOHDh5uca2BgoBIeHp7rYw4fPqwAytmzZ3Pdp3fv3kqnTp1MykJCQpRhw4YpiqIoer1e8ff3Vz7//HPj9oSEBMXR0VH59ddfczymTqdT3njjDaV27dom5X369FHCwsIe65wGDBigdOvWLVv5g/WWJTo6WgGUgwcPmpR36NBBKVu2rJKcnJztWA8eI0thvNfzqs2XW5UKb69V/j5zvcDHKJLfSyLfClIPBfn+lhYfUfLZ2hmu+uo+B2wd4fR6+KEt3DqvdmSPpCgKqRmZefq5k6Zl4h/HH7ow46Q/TnAnTZun4yl5nDV6xowZhIaG8sorrxhbAsqVK5fjvqmpqcycOZOlS5cSERHB1q1b6dGjB+vXr2f9+vX89NNPfPfdd6xcudL4mBEjRhAVFcXSpUs5cuQIzz//PO3btzd2EV26dAk3N7eH/nz66acAZGRksH//fpNFi21sbGjTpk2uixanpKSwYMECgoKCTM6rYsWKTJo0yXg/KirK5LgAYWFhxuNGR0cTGxtrso+npychISEmz92yZUvjVBwAe/fupXXr1rketyDnVFhu3bpFREQEw4cPx9XVNdv2B9dIVIOM8RH5ZfaXswuRZ/VfBJ9qsLQfXD8F81rB8wug8rNqR5aru1odtT7c8Ogd80DBMN4heNLGPO0fNeYp8jI6x9PTEwcHB1xcXHJdBiaLVqtlzpw5VK5cGYBevXrx008/ERcXh5ubG7Vq1aJVq1Zs2bKFPn36cOnSJRYsWMClS5cIDAwEYNy4cURERLBgwQI+/fRTAgMDOXTo0EOf19vbsCzBjRs30Ol0+Pn5mWz38/PLNoZn9uzZjB8/npSUFKpXr05kZCQODg7G7ZUrV8bHx8d4PzY2NsfjZk2qmvX/w/YBKF++vMnEfvHx8Tk+Jikpibt373L79u08n1NhO3v2LIqiUKNGjSJ9noK6m6EjKS0TkK4ukXeS+AjL8kQjGLoVlr0EV/fBzz2h3cfw1OuyyGkxcHFxMSY9YPhyrlixIm5ubiZlWWNTjh49ik6no1q1aibHSU9PN07yaGdnR5UqVQo91n79+tG2bVtiYmL44osv6N27N//88w9OToaWg6IauLt48eIiOW5RyGuroFqyWntcHGxxc5SvM5E38k4RlscjAAaug3VjDEtdbHgXYo9B56/B3ryaw53tbTkxJSxP++6JvsXABXsfud/CQY1p8oiFGfV6Pdq7KXl63vx4cN0cjUaTY5n+vwHoycnJ2Nrasn///mwT3WUlS5cuXaJWrVoPfd53332Xd999Fx8fH2xtbYmLizPZntOixZ6ennh6elK1alWeeuopSpUqxe+//07fvn1zfA5/f/+HHjfr/7i4OJMWnbi4OOrXr59r7L6+vjke18PDA2dnZ2xtbfN8Tvfz8PDg4sWL2coTEhKwtbXNsevqQVWrVkWj0RR5y1JB3d/NZW6D7IX5kjE+wjLZO0G3WdD+M9DYwuFfYGFHSCreK58eRaPR4OJgl6ef5lXLEODpRG6/3jVAgKcTzauWydPx8vNF4eDg8NArngqqQYMG6HQ64uPjqVKlislP1pd6VlfXw35effVVY5wNGzY0aa3R6/Vs2rQp10WLwdCyoSgK6enpue4TGhqarRUoMjLSeNygoCD8/f1N9klKSmL37t0Pfe7GjRuzefPmXI9b0HOqXr06x48fz3ZOBw4cICgoKE+LO3p7exMWFsasWbNIScmeKKs9h1PcHcO5+bpLN5fIO0l8hOXSaOCp1+Cl3wxrfV3db1jk9Mo+tSMrkKyFGYFsyU9RL8xYsWJFdu/ezYULF7hx44axxeZxVatWjX79+tG/f39WrVpFdHQ0e/bsITw8nHXr1gH3uroe9pM1xgdgzJgxzJ8/n0WLFnHy5Elee+01UlJSGDRoEADnz58nPDyc/fv3c+nSJXbu3Mnzzz+Ps7MzHTt2NB6ndevWfPvtt8b7b775JhEREXz55ZecOnWKSZMmsW/fPkaMGAEYkthRo0bx8ccf88cff3D06FH69+9PYGCgyXw6/fv3Z8KEe8uzDB48mPPnzzN+/HhOnTrF7NmzWb58OaNHj87zOeWkX79+aDQa+vfvz/79+zl79iw//vgj06dPZ+zYsXmuo1mzZqHT6WjSpAm//fYbZ86c4eTJk8ycOfOhiVdxiP+vxcff07xacoV5k64uYfkqt4KhW+DXF+H6SVjQAbrMMAyGLmGyFmacvOYEMYn35oYp6oUZx40bx4ABA6hVqxZ3794lOjq60I69YMECPv74Y8aOHcvVq1fx8fHhqaeeonPnzgU6Xp8+fbh+/ToffvghsbGx1K9fn4iICOPgYCcnJ3bs2MH06dO5ffs2fn5+PPPMM+zcuRNfX1/jcc6dO8eNGzeM95s2bcovv/zC+++/z7vvvkvVqlVZvXo1derUMe6TNVh66NChJCQk0KxZMyIiIozjhsDQdXf/gqQVKlRgzZo1jB07lhkzZvDEE0/w/fffExZ2rwv0UeeUEy8vL3bs2ME777xD165dSUxMpEqVKnz11VcMGTIkz69npUqVOHDgAJ988gljx44lJiaGMmXK0LBhQ+Mi0mqJTZQrukT+aRRzH71WDJKSkvD09CQxMREPDw+1w7FoWq2W9evX07Fjxzw1tReq9Dvw+6twaq3h/lOvQ9uPDJfDF4O0tDSio6MJCgoy+SIsCJ1eYU/0LeLvpOHr7pTvhRn1ej1JSUl4eHhYzKrgJZGl1kNhvtcfZuSvB1lz+Brvd6rJy80rFfg4qv5eEkYFqYeCfH9Li4+wHo7u0Psn2DYVtn0Gu2ZD/AnotQBcHj4Y2NzY2mgIrVxa7TCEUJXM4SMKwnL+xBAiL2xsoNUEwyrv9q5wfivMbwXxJ9WOTAiRTzLGRxSEJD7COtXqBkM2gld5uH0Bvm8DJ9eqHZUQIo8URbm3QKm7JD4i7yTxEdbLvw68shUqNoeMZFjWD7ZOtfhFToWwBElpmaRpDZ9VX5m1WeSDJD7CurmWhv/9Dk2GGe5v/RRWDID0ZHXjEkI8VFY3l6ezPU72to/YW4h7JPERwtYeOk6Drt+AjT2c/AN+DDN0gQkhzFJckmHyQlmjS+SXJD5CZHmyv2GpC1dfiDtmWOQ0ervaUQkhciBXdImCksRHiPuVDzEschrYAO7egsXdYfc8kOmuhDArsZL4iAKSxEeIB3mWhUF/Qt0+oOjgz7fgj5GQmfs6TkKI4hVvTHykq0vkjyQ+QuTE3hl6fAftPgaNDRz8CRZ1gTtxj36sldi6dSsajUb1hSqFdbo3xkdafET+SOIjRG40Gmg6EvqtACdPuLzbsMjp1QNqR1bsWrZsyahRo0zKmjZtSkxMDJ6enuoElYvt27fTpUsXAgMD0Wg0rF69Ots+AwcORKPRmPy0b9/+kcfeunUrTz75JI6OjlSpUoWFCxdm22fWrFlUrFgRJycnQkJC2LNnzyOPe+TIEZo3b46TkxPlypVj2rRp2fZZsWIFNWrUwMnJieDgYNavX//QY06aNIn69etnK79w4QIajYZDhw7l+Drc/1OxYkXAsCzAe++9Z3x+f39/2rRpw6pVq1Br1aO4O9LVJQpGEh8hHqVKG3hlC/hUhzvXDIucHlmuTixbwmFb9i9FwFC+JbzYQnFwcMDf3x+NpvBXg38cKSkp1KtXj1mzZj10v/bt2xMTE2P8+fXXXx+6f3R0NJ06daJVq1YcOnSIUaNG8fLLL7NhwwbjPsuWLWPMmDFMnDiRAwcOUK9ePcLCwoiPj8/1uElJSbRv354KFSqwf/9+Pv/8cyZNmsS8efOM++zcuZO+ffsyZMgQDh48SPfu3enevTvHjh3L46uSsxkzZpi8BmBYNDbr/t69e0lISKBp06YsXryYCRMmcODAAbZv306fPn0YP348iYmJjxVDQcXJAqWioBShJCYmKoCSmJiodigWLyMjQ1m9erWSkZGhdij5dzdRUZb0VpSJHoafDe8rii4z7w+/e1c5ceKEcvfu3YLHsHWq4bm3Ts1beS50Op1y+/ZtRafTPXLfAQMGKIDJT3R0tLJlyxYFUG7fvq0oiqIsWLBA8fT0VNasWaNUq1ZNcXZ2Vnr27KmkpKQoCxcuVCpUqKB4eXkpI0eOVDIz771uaWlpytixY5XAwEDFxcVFadKkibJly5a8viIPBSi///57jufUrVu3fB1r/PjxSu3atU3K+vTpo4SFhRnvN2nSRBk+fLjxvk6nUwIDA5Xw8PAcj6nT6ZQvvvhCKVWqlJKenm4sf/vtt5Xq1asb7/fu3Vvp1KmTyWNDQkKUYcOG5RrvxIkTlXr16mUrj46OVgDl4MGD2bbl9Hq99tpriqurq3L16tVs+9+5c0fRarXZygvlvf4QOp1eqTxhnVLh7bXKtYTUxz5eif69ZEEKUg8F+f6WFh8h8srJA174FZqPM9zfORN+6Q13bxf8mIoCGSl5/wkdDs+8BVs+gc0fG8o2f2y4/8xbhu15PVYeuyhmzJhBaGgor7zyirEloFy5cjnum5qaysyZM1m6dCkRERFs3bqVHj16sH79etavX89PP/3Ed999x8qVK42PGTFiBFFRUSxdupQjR47w/PPP0759e86cOQPApUuXcHNze+jPp59+mu+XfuvWrfj6+lK9enVee+01bt68abK9ZcuWDBw40Hg/KiqKNm3amOwTFhZGVFQUABkZGezfv99kHxsbG9q0aWPcBwzdbC1btjTe37t3L82bN8fBwcHkuKdPn+b27dt5eu6iotfrWbp0Kf369SMwMDDbdjc3N+zsin+t61upGWTqFTQa8HGTwc0if2R1diHyw8YGWn8AfrVh9etw9i+Y3xr6LoUy1fJ/PG0qfJr9CyVPtn9u+Mnt/kPYAAw/CTx6fI6npycODg64uLjg7+//0H21Wi1z5syhcuXKAPTq1YuffvqJuLg43NzcqFWrFq1atWLLli306dOHS5cusWDBAi5dumT8Yh03bhwREREsWLCATz/9lMDAQA4dOvTQ5/X29s7DWd/Tvn17nnvuOYKCgjh37hzvvvsuHTp0ICoqCltbwyzA5cuXJyAgwPiY2NhY/Pz8TI7j5+dHUlISd+/e5fbt2+h0uhz3OXXqlPF+QEAA+vuWRYmPj6dKlSrZHpP1nKVKlcr1uWNjY/N13vl148YNbt++TY0aNYr0efJDp1eIPGE4b3dHe2zMrKtVmD9JfIQoiDrPQekqsPRFuHUO5j4NtbpDz/nZ9902DfQ6CB1d7GEWNxcXF2PSA4Yv54oVK+Lm5mZSljXm5ejRo+h0OqpVM00a09PTKV26NAB2dnbZEoPH9cILLxhvBwcHU7duXSpXrszWrVtp3bo1AIsXLy7U58wSHl5847Ael2Jm81dFHIth8poTxPw3vicpTUuzqZuZ2KUW7esEPOLRQhhI4iNEQQXUNUx2uLw/XPwHji6HlHj432rDFWHw34DjT6DVezkfw94F3r2W/+f++2tD646tA+gyDN1czfKeWOn1eribmf/nfQR7e3uT+xqNJseyrBaP5ORkbG1t2b9/v7GlJUtWsnTp0iVq1ar10Od99913effddwscd6VKlfDx8eHs2bPGxOdB/v7+xMWZTmcQFxeHh4cHzs7O2NraYmtrm+M+D2sp8/X1zfExWc/5sOd+2HE9PDxyHHicNf1AXq7GK1OmDF5eXiYtVmqJOBbDaz8f4MFULDYxjdd+PsCcl56U5EfkiYzxEeJxuPpA//+DRkMM989vhdmhkJFqmvS0GJ/z4zUacHDN30/ULEPS0+o9+OC64f/tnxvK83OcfHQRODg4oNPpHv/1ekCDBg3Q6XTG7p77f7K+1LO6uh728+qrrz5WHFeuXOHmzZsmXVsPCg0NZdOmTSZlkZGRhIaGAobXqGHDhib76PV6Nm3aZNwnJ40bN2bHjh1otVqT41avXp1SpUrl6blzUr16da5cuZItYTpw4ABOTk6UL18+18dmsbGx4YUXXmDJkiVcu5Y9QU9OTiYzs/AT6Afp9AqT15zIlvQAxrLJa06g05tXC5UwT5L4CPG4bO2h81fQ+WvDZIfXTxrG7Twq6SmInJKpFuMN97d8kvul7o+pYsWK7N69mwsXLnDjxg2TMSqPo1q1avTr14/+/fuzatUqoqOj2bNnD+Hh4axbtw6419X1sJ/7x/gkJycbEyIwXIZ+6NAhLl26ZNz+1ltvsWvXLi5cuMCmTZvo1q0bVapUISwszHic/v37M2HCBOP9V199lfPnzzN+/HhOnTrF7NmzWb58OaNH32tpGzNmDPPnz2fRokWcPHmS1157jZSUFAYNGmTcZ8KECfTv3994v1evXjg4ODBkyBCOHz/OsmXLmDFjBmPGjDHu8+abbxIREcGXX37JqVOnmDRpEvv27WPEiBG5vrZhYWFUr16dvn37snPnTs6fP8/KlSt5//33efPNN7O1sOXmk08+oVy5coSEhLB48WJOnDjBmTNn+PHHH2nQoAHJycl5Os7j2BN9y9i9lRMFiElMY0/0rSKPRZR8FpP4FGTSMCEKVaPBhkVOAVAM3VCFmfSAYaxQTslUVvKjL/xWGTAMOLa1taVWrVqUKVPGmEQUhgULFtC/f3/Gjh1L9erV6d69O3v37s1Ti0RO9u3bR4MGDWjQoAFgSEYaNGjAhx9+CICtrS1Hjhyha9euVKtWjSFDhtCwYUN27NiBo+O9K4QuXbpknNsGICgoiHXr1hEZGUm9evX48ssv+f77702SpT59+vDFF1/w4YcfUr9+fQ4dOkRERITJwOSYmBiT18/T05OIiAiio6Np2LAhY8eO5cMPP2To0KHGfZo2bcovv/zCvHnzqFevHitXrmT16tXUqVMn19fBzs6OjRs3Ur58efr27UudOnWYOHEib775Jh999FGeX09vb2927drFSy+9xMcff0yDBg1o3rw5v/76K59//nmxTGAZfyf3pKcg+wnrplHMbfRaASxbtoz+/fszd+5cQkJCmD59OitWrOD06dP4+vo+8vFJSUl4enqSmJiIh4dHMURsvbRaLevXr6djx47Zxn5YhKwWmayxN/clKWlpaURHRxMUFISTk7qTrun1epKSkvDw8MDGxmL+/ilxLLUeCvu9HnXuJn3n73rkfr++8hShlUvn+/gW/3uphChIPRTk+9siPmlfffUVr7zyCoMGDaJWrVrMnTsXFxcXfvzxR7VDE9bk/m6orLE3Rdj9JIS1aBLkTYCnE7mNStMAAZ5ONAnK37QGwjqV+Ku6siYNu78vPqdJw+6Xnp5Oevq9lbaTkpIAQ7Z5/wBDUfiyXl9Le51tdnyB7fbP0D3zDvqmo0GrhaajsdHpsN3yCTqdDm3jESiKgl6vL7QxMgWV1dCbFY9Qh6XWg16vR1EUtFptnscSPcp7HaozculhNGAyyFlz33a9LrNAvb2W+nuppClIPRSkzkp84nPjxo08TRp2v/DwcCZPnpytfOPGjbi4uBRJnMJUZGSk2iEUquoxp1ACnuPfO7XAZPHIWlQLeA7Nv6c4d3c7/v7+JCcnk5GRoVqs97tz547aIQgsrx4yMjK4e/cu27dvL9SrvgZV07Dqgg0JGffafjwdFJ6rqEd3cT/rLz7e8S3t91JJlZ96SE1NzffxS3ziUxATJkwwuWIiKSmJcuXK0a5dOxnjU8S0Wi2RkZG0bdvWwvrSOwKQ8zR7hm1l09K4fPkybm5uqo/xURSFO3fu4O7ubnaLjFoTS62HtLQ0nJ2deeaZZwr1vd4RGK9X2HfxNvF30vF1d6RRhVLY2jzea2e5v5dKloLUQ1aPTX6U+MTHx8cn35OGOTo6mly9kcXe3l7e9MXEGl9rnU6HRqPBxsZG9YGsWd0qWfEIdVhqPdjY2Bgnryzsz7k90Kya3yP3K9CxrfD3kjnKTz0UpL5K/CetoJOGCaEWC7iQUoiHkve4MGclvsUHDPN0DBgwgEaNGtGkSROmT5+ebdIwIdSW9ZdJamoqzs7OKkcjRNHJGnchrSfCHFlE4tOnTx+uX7/Ohx9+SGxsLPXr1882aZgQarO1tcXLy8u4QKeLi4tq4zr0ej0ZGRmkpaVZVBdLSWNp9aAoCqmpqcTHx+Pl5VVoV3QJUZgsIvEBGDFixEOnbxfCHGSNO8tKftSiKAp3797F2dnZogbVljSWWg9eXl4PXUBVCDVZTOIjREmg0WgICAjA19dX1TlDtFot27dv55lnnpHuCBVZYj3Y29tLS48wa5L4CKECW1tbVb8cbG1tyczMxMnJyWK+cEsiqQchil/J71QWQgghhMgjSXyEEEIIYTUk8RFCCCGE1ZAxPtybbKsgU1+L/NFqtaSmppKUlCRjGlQk9WAepB7Mg9SDeShIPWR9b+dn0kxJfLi3QGC5cuVUjkQIIYQQ+XXnzh08PT3ztK9GkbnF0ev1XLt2zeIWCjRHWQvCXr58WRaEVZHUg3mQejAPUg/moSD1kLXQb2BgYJ4nAZUWHwwL6j3xxBNqh2FVPDw85BeMGZB6MA9SD+ZB6sE85Lce8trSk0UGNwshhBDCakjiI4QQQgirIYmPKFaOjo5MnDgRR0dHtUOxalIP5kHqwTxIPZiH4qoHGdwshBBCCKshLT5CCCGEsBqS+AghhBDCakjiI4QQQgirIYmPEEIIIayGJD6iwLZv306XLl0IDAxEo9GwevVqk+2rVq2iXbt2lC5dGo1Gw6FDh/J03BUrVlCjRg2cnJwIDg5m/fr1hR+8BSmKeli4cCEajcbkx8nJqWhOwEI8rB60Wi1vv/02wcHBuLq6EhgYSP/+/bl27dojjztr1iwqVqyIk5MTISEh7NmzpwjPouQrinqYNGlSts9DjRo1ivhMSrZH/V6aNGkSNWrUwNXVlVKlStGmTRt27979yOMWxudBEh9RYCkpKdSrV49Zs2blur1Zs2ZMnTo1z8fcuXMnffv2ZciQIRw8eJDu3bvTvXt3jh07VlhhW5yiqAcwzJ4aExNj/Ll48WJhhGuxHlYPqampHDhwgA8++IADBw6watUqTp8+TdeuXR96zGXLljFmzBgmTpzIgQMHqFevHmFhYcTHxxfVaZR4RVEPALVr1zb5PPz9999FEb7FeNTvpWrVqvHtt99y9OhR/v77bypWrEi7du24fv16rscstM+DIkQhAJTff/89x23R0dEKoBw8ePCRx+ndu7fSqVMnk7KQkBBl2LBhhRCl5SuseliwYIHi6elZqLFZk4fVQ5Y9e/YogHLx4sVc92nSpIkyfPhw432dTqcEBgYq4eHhhRWqRSusepg4caJSr169wg3OiuSlHhITExVA+euvv3Ldp7A+D9LiI8xKVFQUbdq0MSkLCwsjKipKpYisV3JyMhUqVKBcuXJ069aN48ePqx2SRUlMTESj0eDl5ZXj9oyMDPbv32/yebCxsaFNmzbyeShEj6qHLGfOnCEwMJBKlSrRr18/Ll26VDwBWoGMjAzmzZuHp6cn9erVy3Wfwvo8SOIjzEpsbCx+fn4mZX5+fsTGxqoUkXWqXr06P/74I//3f//Hzz//jF6vp2nTply5ckXt0CxCWloab7/9Nn379s11McYbN26g0+nk81CE8lIPACEhISxcuJCIiAjmzJlDdHQ0zZs3586dO8UYreVZu3Ytbm5uODk58fXXXxMZGYmPj0+O+xbm50FWZxdCZBMaGkpoaKjxftOmTalZsybfffcdH330kYqRlXxarZbevXujKApz5sxROxyrlZ966NChg/F23bp1CQkJoUKFCixfvpwhQ4YUdagWq1WrVhw6dIgbN24wf/58evfuze7du/H19S3S55UWH2FW/P39iYuLMymLi4vD399fpYgEgL29PQ0aNODs2bNqh1KiZX3ZXrx4kcjIyIe2Mvj4+GBrayufhyKQn3rIiZeXF9WqVZPPw2NydXWlSpUqPPXUU/zwww/Y2dnxww8/5LhvYX4eJPERZiU0NJRNmzaZlEVGRpq0Pojip9PpOHr0KAEBAWqHUmJlfdmeOXOGv/76i9KlSz90fwcHBxo2bGjyedDr9WzatEk+D48hv/WQk+TkZM6dOyefh0Km1+tJT0/PcVthfh6kq0sUWHJysslfPNHR0Rw6dAhvb2/Kly/PrVu3uHTpknGOjNOnTwOGVp2sDL1///6ULVuW8PBwAN58801atGjBl19+SadOnVi6dCn79u1j3rx5xXx2JUdR1MOUKVN46qmnqFKlCgkJCXz++edcvHiRl19+uZjPruR4WD0EBATQq1cvDhw4wNq1a9HpdMZxCd7e3jg4OADQunVrevTowYgRIwAYM2YMAwYMoFGjRjRp0oTp06eTkpLCoEGDiv8ES4iiqIdx48bRpUsXKlSowLVr15g4cSK2trb07du3+E+whHhYPZQuXZpPPvmErl27EhAQwI0bN5g1axZXr17l+eefNz6myD4P+boGTIj7bNmyRQGy/QwYMEBRFMMl0TltnzhxovEYLVq0MO6fZfny5Uq1atUUBwcHpXbt2sq6deuK76RKoKKoh1GjRinly5dXHBwcFD8/P6Vjx47KgQMHivfESpiH1UPWVAI5/WzZssV4jAoVKpjUi6IoyjfffGOsiyZNmii7du0q3hMrYYqiHvr06aMEBAQoDg4OStmyZZU+ffooZ8+eLf6TK0EeVg93795VevTooQQGBioODg5KQECA0rVrV2XPnj0mxyiqz4NGURQlf6mSEEIIIUTJJGN8hBBCCGE1JPERQgghhNWQxEcIIYQQVkMSHyGEEEJYDUl8hBBCCGE1JPERQgghhNWQxEcIIYQQVkMSHyGEUMkHH3zA0KFD8/WYjIwMKlasyL59+4ooKiEsmyQ+Qlg5jUbz0J9JkyapHWKhq1ixItOnT1c1htjYWGbMmMF7772XrXzkyJFUqlQJR0dHypUrR5cuXYxrFDk4ODBu3DjefvttNcIWosSTtbqEsHIxMTHG28uWLePDDz80rucF4ObmpkZY+aYoCjqdDju74vu1lpGRYVzfKb++//57mjZtSoUKFYxlFy5c4Omnn8bLy4vPP/+c4OBgtFotGzZsYPjw4Zw6dQqAfv36MXbsWI4fP07t2rUL5VyEsBbS4iOElctarNTf3x9PT080Go1J2dKlS6lZsyZOTk7UqFGD2bNnGx974cIFNBoNy5cvp3nz5jg7O9O4cWP+/fdf9u7dS6NGjXBzc6NDhw5cv37d+LiBAwfSvXt3Jk+eTJkyZfDw8ODVV18lIyPDuI9eryc8PJygoCCcnZ2pV68eK1euNG7funUrGo2GP//8k4YNG+Lo6Mjff//NuXPn6NatG35+fri5udG4cWP++usv4+NatmzJxYsXGT16tLFVC2DSpEnUr1/f5LWZPn06FStWzBb3J598QmBgINWrVwfg8uXL9O7dGy8vL7y9venWrRsXLlx46Ou+dOlSunTpYlL2+uuvo9Fo2LNnDz179qRatWrUrl2bMWPGsGvXLuN+pUqV4umnn2bp0qUPfQ4hRHaS+AghcrVkyRI+/PBDPvnkE06ePMmnn37KBx98wKJFi0z2mzhxIu+//z4HDhzAzs6OF198kfHjxzNjxgx27NjB2bNn+fDDD00es2nTJk6ePMnWrVv59ddfWbVqFZMnTzZuDw8PZ/HixcydO5fjx48zevRoXnrpJbZt22ZynHfeeYfPPvuMkydPUrduXZKTk+nYsSObNm3i4MGDtG/fni5dunDp0iUAVq1axRNPPMGUKVOIiYkxafHKi02bNnH69GkiIyNZu3YtWq2WsLAw3N3d2bFjB//88w9ubm60b9/eJJG7361btzhx4gSNGjUyKYuIiGD48OG4urpme4yXl5fJ/SZNmrBjx458xS6EQFZnF0Lcs2DBAsXT09N4v3Llysovv/xiss9HH32khIaGKoqiGFe7/v77743bf/31VwVQNm3aZCwLDw9Xqlevbrw/YMAAxdvbW0lJSTGWzZkzR3Fzc1N0Op2SlpamuLi4KDt37jR57iFDhih9+/ZVFOXe6s+rV69+5HnVrl1b+eabb4z3K1SooHz99dcm+0ycOFGpV6+eSdnXX3+tVKhQwSRuPz8/JT093Vj2008/KdWrV1f0er2xLD09XXF2dlY2bNiQYzwHDx5UAOXSpUvGst27dyuAsmrVqkeej6IoyowZM5SKFSvmaV8hxD0yxkcIkaOUlBTOnTvHkCFDeOWVV4zlmZmZeHp6muxbt25d420/Pz8AgoODTcri4+NNHlOvXj1cXFyM90NDQ0lOTuby5cskJyeTmppK27ZtTR6TkZFBgwYNTMrubzUBSE5OZtKkSaxbt46YmBgyMzO5e/euscXncQUHB5uM6zl8+DBnz57F3d3dZL+0tDTOnTuX4zHu3r0LgJOTk7FMUZR8xeHs7Exqamq+HiOEkMHNQohcJCcnAzB//nxCQkJMttna2prct7e3N97OGjPzYJler8/3c69bt46yZcuabHN0dDS5/2C30Lhx44iMjOSLL76gSpUqODs706tXr1y7nbLY2NhkSz60Wm22/R58vuTkZBo2bMiSJUuy7VumTJkcn8vHxweA27dvG/epWrUqGo3GOID5UW7dupXr8YUQuZPERwiRIz8/PwIDAzl//jz9+vUr9OMfPnyYu3fv4uzsDMCuXbtwc3OjXLlyeHt74+joyKVLl2jRokW+jvvPP/8wcOBAevToARgSkwcHGjs4OKDT6UzKypQpQ2xsLIqiGJO3Q4cOPfL5nnzySZYtW4avry8eHh55irFy5cp4eHhw4sQJqlWrBoC3tzdhYWHMmjWLN954I1uClZCQYDLO59ixY9lav4QQjyaDm4UQuZo8eTLh4eHMnDmTf//9l6NHj7JgwQK++uqrxz52RkYGQ4YM4cSJE6xfv56JEycyYsQIbGxscHd3Z9y4cYwePZpFixZx7tw5Dhw4wDfffJNtYPWDqlatyqpVqzh06BCHDx/mxRdfzNbaVLFiRbZv387Vq1e5ceMGYLja6/r160ybNo1z584xa9Ys/vzzz0eeR79+/fDx8aFbt27s2LGD6Ohotm7dyhtvvMGVK1dyfIyNjQ1t2rTh77//NimfNWsWOp2OJk2a8Ntvv3HmzBlOnjzJzJkzCQ0NNdl3x44dtGvX7pHxCSFMSeIjhMjVyy+/zPfff8+CBQsIDg6mRYsWLFy4kKCgoMc+duvWralatSrPPPMMffr0oWvXriaTJX700Ud88MEHhIeHU7NmTdq3b8+6dese+dxfffUVpUqVomnTpnTp0oWwsDCefPJJk32mTJnChQsXqFy5srG7qGbNmsyePZtZs2ZRr1499uzZw7hx4x55Hi4uLmzfvp3y5cvz3HPPUbNmTYYMGUJaWtpDW4Befvllli5dapKUVapUiQMHDtCqVSvGjh1LnTp1aNu2LZs2bWLOnDnG/aKiokhMTKRXr16PjE8IYUqj5HdEnRBCPKaBAweSkJDA6tWr1Q5FNYqiEBISwujRo+nbt2++HtunTx/q1avHu+++W0TRCWG5pMVHCCFUoNFomDdvHpmZmfl6XEZGBsHBwYwePbqIIhPCskmLjxCi2EmLjxBCLZL4CCGEEMJqSFeXEEIIIayGJD5CCCGEsBqS+AghhBDCakjiI4QQQgirIYmPEEIIIayGJD5CCCGEsBqS+AghhBDCakjiI4QQQgirIYmPEEIIIazG/wOKiPakP9ReMwAAAABJRU5ErkJggg==",
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",
             "text/plain": [
               "<Figure size 640x480 with 1 Axes>"
             ]
@@ -1065,7 +1065,7 @@
   ],
   "metadata": {
     "kernelspec": {
-      "display_name": "Python 3 (ipykernel)",
+      "display_name": "base",
       "language": "python",
       "name": "python3"
     },
diff --git a/examples/power_example.ipynb b/examples/power_example.ipynb
index 1f9c0b710..516c9c980 100644
--- a/examples/power_example.ipynb
+++ b/examples/power_example.ipynb
@@ -329,7 +329,7 @@
    "metadata": {},
    "source": [
     "## Power Quality\n",
-    "The `power.quality` module can be used to compute current or voltage harmonics and current distortions following IEC/TS 62600-30 and IEC/TS 61000-4-7. Harmonics and harmonic distortion are required as part of a power quality assessment and characterize the stability of the produced power. "
+    "The `power.quality` module can be used to compute current or voltage harmonics and current distortions following IEC TS 62600-30 and IEC TS 61000-4-7. Harmonics and harmonic distortion are required as part of a power quality assessment and characterize the stability of the produced power. "
    ]
   },
   {
@@ -374,7 +374,7 @@
    "metadata": {},
    "source": [
     "### Harmonic Subgroups\n",
-    "The harmonic subgroups calculations are based on IEC/TS 62600-30. We can calculate them using our grid frequency and harmonics."
+    "The harmonic subgroups calculations are based on IEC TS 62600-30. We can calculate them using our grid frequency and harmonics."
    ]
   },
   {
diff --git a/examples/river_example.ipynb b/examples/river_example.ipynb
index 964b14048..cb9302d1e 100644
--- a/examples/river_example.ipynb
+++ b/examples/river_example.ipynb
@@ -53,33 +53,35 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Data request URL:  https://waterservices.usgs.gov/nwis/dv/?format=json&sites=15515500&startDT=2009-08-01&endDT=2019-08-01&statCd=00003&parameterCd=00060&siteStatus=all\n",
-      "                           Discharge, cubic feet per second\n",
-      "2009-08-01 00:00:00+00:00                             59100\n",
-      "2009-08-02 00:00:00+00:00                             59700\n",
-      "2009-08-03 00:00:00+00:00                             56200\n",
-      "2009-08-04 00:00:00+00:00                             51700\n",
-      "2009-08-05 00:00:00+00:00                             52100\n",
-      "...                                                     ...\n",
-      "2019-07-28 00:00:00+00:00                             66000\n",
-      "2019-07-29 00:00:00+00:00                             63900\n",
-      "2019-07-30 00:00:00+00:00                             63500\n",
-      "2019-07-31 00:00:00+00:00                             64700\n",
-      "2019-08-01 00:00:00+00:00                             64600\n",
+      "            Discharge, cubic feet per second\n",
+      "2009-08-01                             59100\n",
+      "2009-08-02                             59700\n",
+      "2009-08-03                             56200\n",
+      "2009-08-04                             51700\n",
+      "2009-08-05                             52100\n",
+      "...                                      ...\n",
+      "2019-07-28                             66000\n",
+      "2019-07-29                             63900\n",
+      "2019-07-30                             63500\n",
+      "2019-07-31                             64700\n",
+      "2019-08-01                             64600\n",
       "\n",
       "[3653 rows x 1 columns]\n"
      ]
     }
    ],
    "source": [
-    "# Use the requests method to obtain 10 years of daily discharge data\n",
-    "data = river.io.usgs.request_usgs_data(\n",
-    "    station=\"15515500\",\n",
-    "    parameter=\"00060\",\n",
-    "    start_date=\"2009-08-01\",\n",
-    "    end_date=\"2019-08-01\",\n",
-    "    data_type=\"Daily\",\n",
-    ")\n",
+    "# Here we load 10 years of daily discharge data \n",
+    "data = pd.read_csv(\"data/river/usgs_discharge_TRTS_20090801_20190801_daily.csv\", index_col=0)\n",
+    "\n",
+    "# The previous data was created with the following mhkit call:\n",
+    "# data = river.io.usgs.request_usgs_data(\n",
+    "#     station=\"15515500\",\n",
+    "#     parameter=\"00060\",\n",
+    "#     start_date=\"2009-08-01\",\n",
+    "#     end_date=\"2019-08-01\",\n",
+    "#     options={\"data_type\":\"Daily\"},\n",
+    "# )\n",
     "\n",
     "# Print data\n",
     "print(data)"
@@ -99,7 +101,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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H9O+/f0b/cOwe3dsYxJLdQ4MMDQCNKtSRocG5BUAtg309s2YciLyeCGh84hOf0BNPPKH58+frxBNPdB4fGhrSpz/9aZ166qnabbfdJElLlizR5Zdfrttuu01ve9vb9Oijj2rKlCmSpPXr10uSZsyY4ft7zOPr1q1zHmvmNX7GxsY0NjbmfL9hwwZJUjabVTabrflaxJfZt+xjAH44R2CyGM/lXd9zzNeP80RJwYqK5fOFSf/3AAzOEfUx2Rd9qeAJyfFsnr8jehLniXD0JRP8DdGzGjlPdOJzEPuAxre+9S1deeWVetOb3qRf/vKXrp/Nnj1bn//8512PHX300brlllt01FFH6b777tOPf/xjXXjhhZ3cZF9f/vKX9bnPfa7q8VtuuUVDQ0Nd2CJ0kjeDCABsnCPQ65YtT8quhLpgwYLubUxMTfbzxJo1KUmlZvIvvfyyFix4sbsbBETMZD9HTGRsvHQOyY6OyJxLvP709NNasOlPHd0uoJM4TzSulLhVmlp98c/PaMHI013dHqDd6jlPjIyMtH07Yh3Q+Pa3v60LL7xQ++yzjxYtWqRZs2bV9bp0Oq1zzjlH9913nxYvXuwENEw2hcm68DKPb7HFFs5jzbzGzyc/+UldfPHFzvcbNmzQTjvtpHe84x2aPn36xP9TiKVsNquFCxfqhBNOcPrAAIDBOQKTxU3rHpXWrJAk7bDFgObPP7q7GxQjnCdK/mP5A9KGtZKkHXfaWfPn79PlLQKigXNEff7loUVSPq8tZ07X65s3+j5n9z330vzjdu/wlgHtx3mieWO5gnTvHyRJBx2wr+YfulOXtwhoj0bOE6bqUDvFNqDxjW98QxdddJH23XdfLVq0SLNnz27o9VtvvbWkUt8MY8qUKdphhx20dOlSLV++vKqPxnPPPSdJ2muvvZzHdt99d6VSKb3wwgvK5XJVfTT8XuOnv79f/f39VY9nMhkuKJMA+xlALZwj0OuK1mrYYlEc702Y7OeJhHUMJZOJSf23APxM9nPERExT8IFMjSmSRJK/IXoa54nGDWfHna+n9Pfx90PPq+c80YnPQSw71vzbv/2bLrroIh144IG67bbbGg5mSNK9994rSU5vDeP444+XJP3+97+ves3NN9/seo4kDQwMaO7cuRoZGdEdd9xR12sAAABQUbQardJyFc1wNQUvdHFDAMSSOW/U6qFh9+oBAEkaHq/0gUsl/cvVAQhf7AIaX/jCF/SJT3xCBx98sBYtWqStttoq8LkPP/ywCj53NIsWLdLXv/51SdIHPvAB18/OO+88SdIVV1yhtWvXOo+/9NJL+s53vqP+/n6dffbZrtecf/75kqRPfepTGh0ddR5/4IEHdO2112rrrbfWaaed1uD/KQAAwORQsAIa9tdAvezDhmMIQKMqGRrBUyQ5AhoAPLK5ypwj4w+gc2JVcurqq6/WZz7zGaVSKc2bN0/f+ta3qp4zZ84cnXXWWZKkiy++WM8995zmzp2rHXfcUZK0ZMkS3XrrrZJKwZG5c+e6Xj937lxdfPHF+trXvqb9999fp59+usbHx3XttddqzZo1uuqqqzRnzhzXa84880zdeOONuv766/WWt7xFp5xyilavXq1rr71W+XxeP/rRj+iDAQAAEKBQ9P8aaAbHEIBG5csnjv50ynlsSl/KtfqayUoAXvZZgVME0DmxCmi8+OKLkqR8Pq9vfOMbvs855phjnIDG3/7t3+q///u/9cADD+jmm29WNpvVNttsozPOOEMXXHCB5s2b5/seV155pfbbbz995zvf0Q9/+EMlk0kddNBBuvTSS3XyySdXPT+RSOjXv/615s6dq5/+9Ke66qqrNDAwoKOPPlqf+tSnqoImAAAAqHDfDHI3iMbZR02emlMAGmCXkkqnKiVjthjq0/D4Zuf7PNFSAB5FsoyBrohVQOPyyy/X5ZdfXvfzP/ShD+lDH/pQU7/rrLPOcgIj9Uin07rooot00UUXNfX7AAAAJitXDw3uBdEE+xjKcwwBaEDeOn9krB4aWwxltHRdJaDBZCUAr2LA1wDaK3Y9NAAAANBb6KGBVtlHDY17ATTCzrxIJ+0MjYzreVyeAHjZ5wWyjIHOIaABAACArrIrBDEXjVZRFgZAI+xAetrK0BjMpAOfBwAl9qKcLm4GMMkQ0AAAAEBXFVwlp7gbROPswybPMQSgAbmADI3+tHu6hIAGAC93hkb3tgOYbAhoAAAAoKu4GUSrKDkFoFlBTcH7qgIaHdskADHhGn8wiAU6hoAGAAAAuooeGmiZqyk4xxCA+tll6uym4H0p93QJpxYAXq4s4y5uBzDZENAAAABAV3EziDDRQwNAI+wgaDIRnKFBSUQAXjQFB7qDgAYAAAC6yp5/JkMDzaDkA4BmFQqlf1PJhKx4hk/JKc4tANwomwp0BwENAAAAdJV7Mrprm4EYczUF5yAC0ACToZFKJJSsGdDo5FYBiIOiKJsKdAMBDQAAAHSVK0Wfe0E0wTWhUOjihgCIHdMUPJVMaO7uWzmP95OhAWACZGgA3ZHu9gYAAABgcqMpOMJEU3AAjchbAY0j99hK//Ghw7Xb1lP0P48udT2PUwuAWhjDAp1DQAMAAABdZa+o52YQzaDkFIBmmSCoKTd11J6lLI2+FBkaAGrjtAB0ByWnAAAA0FXuDI0ubghiy55QYNIRQCPsklO26pJTHdskADFBDw2gOwhoAAAAoKu8939FbgjRIPuIIUMDQCOcpuCegIa3KTjXJgBeBXpoAF1BQAMAAABd5V3Rxg0hWkFAA0AjzDkjmZgooNGxTQIQE0WyjIGuIKABAACArvIGNEjZR6OKNJYH0CTTx8mboTHU5245yrkFgFfR9TXnCKBTCGgAAACgq6pKTnVnM9AjyNAA0IhKU3B3QGPaAAENALUVKTkFdAUBDQAAAHQVGRpolbspePe2A0D85AOagk8fyLi+59wCoFrlxECfHaBzCGgAAACgq7y3f9wPohVkaABoRCGgKbg3oMFkJQAvFlQA3UFAAwAAAF1FU3C0yq5bTUADQCMqTcHdj1eXnOrUFgGIC1cPDc4RQMcQ0AAAAEBXmYaszvfcEaJB7hWSHD8A6lcIKDk1lR4aACbA+APoDgIaAAAA6CpvGQ9uCNEo+4ghQwNAI4KagmdSSR28y0zney5NALzsMSunCKBzCGgAAACgq7zzz9wQolF2UIyAGIBGBDUFl6TrPnKEvnL6/pI4twCoZp8W6LMDdA4BDQAAAHRVVQ+NQsATgTqQoQGgEUFNwSUpmUyoL12aNmGuEoCX3cOLcwTQOQQ0AAAA0FXe+WdWwaIRqzeNuSYRCGgAaES+HET3lpzy4toEoAo9NICuSE/8FAAAAKB96KGBZi1+dqU++NP7XY8RzwDQiFolp6RKoINrEwCvYsDXANqLDA0AAAB0VVXJqS5tB+Ln27f9ueoxMjQANMIpORWQoVEJaHRskwDERJEMDaArCGgAAACgq7y3f9wQom4+h0qe4wdAA0wQNBkwO2ISN2j4C8DL7qHBihygcwhoAAAAoKsKnmWvzBmhFbl8gYlHAHWr1RRckhJkaAAIQIYG0B0ENAAAANBV3vs/7gfRikJR2jCa6/ZmAIgJJ0MjsORU6V8mKwF42ecFThFA5xDQAAAAQFd5J4mYNEK9igH1HVZtGuvwlgCIq3qbgnNpAuBlnxbI4gI6h4AGAAAAusp7A0hAA61avWm825sAICYmbApenjWhlB2AKq4WGpwjgE4hoAEAAICu8gYwmDNCs8wK69VkaACoU75Q+jdJDw0ADbKDGIxfgc4hoAEAAICuoocGmuU9Vob6UpKkzdl8F7YGQBzlJ8rQcAIaXJwAuNmnBbK4gM4hoAEAAICuoocGwtKfLgU0xnOFLm8JgLgoTNBDwzxKhgYAL3vIyjkC6BwCGgAAAOiqqpJTXdoOxF9/unR7k80T0ABQH9MUPKjkVKUpOFcnAG5F19ecI4BOIaABAACArvLe/pGhgXp5j5S+ckBjjAwNAHWqNAX3/7mJc3BtAuBlnxfI0AA6h4AGAAAAuqZYLPr00OCOEPXxHit9KZOhwTEEoD4TZWjQFBxAEHcPje5tBzDZENAAAABA19g3fwlnFWx3tgXx158p3d7QQwNAvSZuCl76l2A7gGqV8wLnCKBzCGgAAACga+xU/ZRTp7xbW4O4qSo5laKHBoDGTNQU3GRucG0C4EWGBtAdBDQAAADQNXY2hplMok45mmV6aIwT0ABQJ3O6CG4KXvqXaxMAL/uswDkC6BwCGgAAAOgaV4YGAQ20yAloUHIKQJ0mKjlFDw0AQVwZGt3bDGDSIaABAACArin6ZGgQz0C9vMeKKTlFhgaAek1YcipBsB2Av6IVxuAcAXQOAQ0AAAB0jX3zlyaggRaZDI0sGRoA6mQyNJITNgXv1BYBiAvXeYFzBNAxBDQAAADQNZScQpjooQGgUZUMDf+fJ8S1CYA/+7zAOQLoHAIaAAAA6Br71s8pOdWdTUEMeY+V/nRKkpQloAGgTvlyQCOoKXiCpuAA6sAZAugcAhoAAADomqI175xOloamTBqhWf00BQfQoImagptSVFyaAHjZ54UC5wigYwhoAAAAoGvs4EU5nqEis0ZoUqXkFMcQgPpM2BS8fG1ishKAl90UnPEr0DkENAAAANA1rh4arIJFozwHi2ksny+QoQGgPhM3BTfXJi5OANzs0wKnCKBzCGgAAACga+wVr0lnMpo7QjQnXe7qmyNDA0CdTMudwAyN8sN5ZisBeLgCGnTRADqGgAYAAAC6puisjK1kaDBphGZlyjOPOYJiAOo0UcmpVLnmFMF2AF72WYHkUKBzCGgAAACga8z8UDKRcCaTuCFEvbzTi5lyDw0CGgDqNVHJKSfYznkFgIddio4MDaBzCGgAAACgawrWRFKSDA20KOOUnCIqBqA+lQwN/5+nUgQ0APizh6ycIoDOIaABAACArjEBjURCVoYGd4RoToaJRwANIkMDQLPsrAzW4wCdQ0ADAAAAXVO0Sk7RFByN8k4emAyNLBkaAOqUn7CHBr15APhzNQUnogF0DAENAAAAdE0loCGVF9dTcgpNMwENgmIA6mUyBYMCGmnrcTIIAdiKAV8DaC8CGgAAAOgau4cGJafQKlNyKpvnGAJQHxMADSo5lbQCGmRpALC5e2hwfgA6hYAGAAAAusbuoUFTcDSq6FkP2UeGBoAGmQp1dWVocH0CYLHHIQw9gM4hoAEAAICuMTd/yWQlQ4PJaDQrXQ5o5Ar00ABQH6fkVFBTcDI0AASghwbQHQQ0AAAA0DVFv5JT3BCiSabkFJOOAOrllJyaoCm4/VwAkDw9NDg9AB1DQAMAAABdY+aGErJKTrG4HnXyTh6YpuA5emgAqFOlKbj/z+3MDQIaAGx2Voa3DCaA9iGgAQAAgK6p9NCoZGhcct1j2jia7eZmIaZMQCNLVAxAneppCm5+RDk7ADZXU3BOD0DHENAAAABA1xScklPuyaSrbv1ztzYJMWZKTrGKGkC9zPkiqCm4VGkMzoQlABsZGkB3ENAAAABA15j7wFIPjcrjS9dt7s4GIdacklOFIs05AdRloqbgUiXgToYGAJs90mAtBdA5BDQAAADQNZWARu3VsYCfoB4aElkaAOozUVNwqRLQ+Luf3t+RbQIQD65xCMMOoGMIaAAAAKBr7B4arvrl3BSiCabklFTK0gCAieTLp4paGRqbs3lJ0vMrh+nRA8DhztBg3AF0CgENAAAAdI3TQyPpztDgphDNsDM0CGgAqEehjh4aNrK/ABjuHhoAOoWABgAAALqmYPfQsFbHEs9APbwLql0BDVZRA6hDPSWnbATcAfjh3AB0DgENAAAAdI1Z2ZZMJOqeTAIMb0AjTckpAA2qpym4jQwNAIYdxCCeAXQOAQ0AAAB0jZkXSiTck0lFEvdRh4TcE5DJRMIpG5PLcwwBmFglQ6O+5xdI/gJQZgcxikQ0gI4hoAEAAICucZqCy13ugwWwqId3QXVCUtoENJh1BFCHfKMZGkxaAigrBnwNoL0IaAAAAKBrClbJqRQjUzTIO/2YSFgBDTI0AEygWCzqhZXDkmgKDqBxdnyTHhpA53DbCAAAgK4p0hQcrfCsqE4ooXQ5MkYPDQATue/FNc7XNAUH0Ci7RCqnBqBzCGgAAACga4pWDw33ZBJ3hZhY1fRjQsqkKDkFoD4vrhp2vi7UGQQlQwOA4c7Q6N52AJMNAQ0AAAB0javkVIIeGmhMVQ+NhGgKDqBuM4f6nK/rve4Q0ADgh6bgQOcQ0AAAAEDXOAGNZP31ywHDO3dQagpOySkA9RnPVzK5DtllZl2voeQUAMMOYnBqADqHgAYAAAC6xu6hYZecYpUb6uE9SpKJhNLlklN5Sk4BmMB4rnSeOGavrevuoUGwFIBhnw6KlEsFOoaABgAAALrGrHRNeJuCd2uDEC+ewFciIaXLk5JZSk4BmMBYLi9J6k/Xnhr50FG7Ol/X22sDQO+jhwbQHQQ0AAAA0DUFJ0NDngyNLm0QYsV7mCSUcEpOUecewERMhkbfBAGNfz7pTc7XeS5QAMrsrAyyi4HOIaABAACArrGbgtvFPrglRD2qemgk5JScyuYpOQWgtrFyQKM/nar5vL50UrOn9UsiWAqgwh6HEM8AOoeABgAAALrGrGZLqBTU8D4O1OJXr9qUnGLSEcBE6s3QkKRU+dxCex4ARjHgawDtRUADAADEwjf/8JxO/97devaNjd3eFISoYDUFH+xjaIrG+GdolI4jemgAmEi9PTSkStCdklMAHNb5oMC5AegY7hoBAEDk5fIFff0Pz+rBl9fq+ode6/bmIETm3i+RkAYztUt+AF5VAQ0lnFXUZGgAmIjJ0OjP1J+hwbkFgOHK0ODUAHQMAQ0AABB5y9ePOl+nk4kaz0Tc2D00BqyARiLBfsbEqpqCJ6RMuYdGjrowACbg9NBINVByillLAGUFMjSAriCgAQCIpOGxnBY/u1IFVsFB0itrRpyvOSR6ixPQSMoV0CBuhXp4e60kEwmlkqVbnBwlpwBMoJEeGua6RIYGAIOm4EB3ENAAAETSMV+5TR/86f267ZkV3d4URMD6zVnna1Y/9Zai3UPDCmiwm9GMhCpBjoVPvdHdjQEQeeP5UkAj00iGBgENAGXuklOcG4BOIaABAIic4bGcVm0alyQ9vnR9l7cGUZCzJg9Ydd1bTIAqkUhosC9V9ThQi19T8DueWyVJ+v2Tr3dhiwDEiQlOpOpIC3SyvwhoAChzZWh0bzOASYeABgAgcrL5St3zGYOZLm4JoiJnHRN56uL3lIKToSEdMmdmdzcGsVP0TB/QewVAI3INBTRK/+YJuAMos8chLMYBOoeABgAgcqhNDC87K4OJhN5iNwXvT6f0tTMOcD0O1OJ3mGw9rb/zGwIglsy1pq6ARoKSUwA86KEBdAUBDQBA5NgT1gwMIUnZgp2hwUHRS4pOQKP0faVGebe2CHHidzb4yd8dIonG8gAmZhZM1BPQSJafwzgEgGGfDTg1AJ1DQAMAEDn2RCartCF5MjS4W+gpld1ZmihKllfAeksJAX78GnDuNHNIUunYssvVAYCXk6FRR7k6J0ODsSmAMvc4hHMD0CkENAAAkWNnaHDTCMndV4VmnL3FfMTNXJL5l92MevgdJlP6087Xw2P5zm0MgNhppIdGJUOjrZsEIEbs8SpjV6BzCGgAACLHrk3MwBCSOyuD2tW9xWRimLkkk6HBIjfUxec46Usn1Vfu3rtpPNfhDQIQJ/lGmoKXr0/08gJg2KcDv6xRAO1BQAMAEDn25DXlhSC5szLI0OgtZncmnJJT5nH2MyYWdJRM6U9JkobHCGgACNZQU/AkTcEBuNklUjk1AJ1DQAMAEDnupuCMDOEuOUWQq8eUP+OV8uXUKEf9gq4RpuzUJgIaAGqgKTiAVpChAXRHeuKnAADQWQVXhkYXNwSRQVPw3mX2pik1laTiFOqweTyve15Ypc1Z/x4ZU01AY5SABoBgJnierqvkVOlfSk4B8MOpAegcAhoAgMixbxS5aYQkZQtkaPSqQqXmlKRKYIPdjFouue4x/e7x5YE/NxkalJwCUIspY5lM1FNyqlTggnEIAMPOyuDMAHQOJacAAJFj3yiSugvJk6HBMdFTzN40U0lOT3D2M2qoFcyQrAwNAhoAajBB9XSqnoBG6V8CGgAM+2xAuVSgcwhoAAAix1qMz00jJNEovpeZe7+EU3Iq4XocaMZUMjQA1KGxDA16PAFwc/fQ6N52AJMNAQ0AQOTYK/CZu4ZEU/BeVumhUf6i/C8TRmjFlP6UJGl43L/HBoDJ6eL/elSHXvEHPfzKWkmVMUU6OfHUiAl6MA4BYBRc962cG4BOIaABAIgc+0aRgSEkd8mpHBMJPcWUljLxDHpoIAxTKDkFwMeNDy/Vyo1j+t7tz0uqjDnriGc4GRoENAAYxYCvAbQXAQ0AQOS4Vrpw0wi5m4JzTPSW6pJT5nH2M5pHySkAXvZ15ZFX1kmqZAXXk6GRSlByCoCbu+QU5wagUwhoAAAix9UvgYEhRIZGLyvKnaGRED000DoyNAB42ePL9ZvHXY+l6pgZSToZGuFvG4C4qpxXGLsCnUNAAwAQOfbKNwaGkKRcgR4avSowQ4PEfbRgChkaADzsBRHZfFHFYrFScqqepuBkaADwsE8HnBuAziGgAQCIHGvumslrSPJk7XBM9JSCE9Aw/9JDA62bapqCj9EUHECJd7Jx41iusabg9NAA4OEqOdW9zQAmHQIaACLvmvtf0T9fv4Sbh0nELjPFShdI7sltzgW9parkVPkLPvtoxZQ+Sk4BcPOWrFw7PN5gU/DSv4xDABjFGiWn8oUifTWANiGgASDyPnHj47r2wVd1y5Ovd3tT0CF202cmNSG5m+zRV6W3mN2ZdEpOOTWngKbRFByAVz7vvrCsG8k2lKFhSk4R0ABgeG9LzD1LLl/Q3/zoXh1/5R81liNbFAgbAQ0AsbFuc7bbm4AOobwQvMjQ6F3mxs/EMZJkaCAE9NAA4OVdEDEynnceq6cpeKoc9GBhBQDDe1tivr/vxTW678U1enHVsJavG+38hgE9LlYBjdWrV+vHP/6x3vOe92iPPfbQ4OCgZsyYoaOOOko/+clPVLCLrlvuvvtuzZ8/X7NmzdLg4KD2339/feMb31A+Hxwlvemmm3TsscdqxowZmjp1qg4//HBdffXVNbfv6quv1mGHHaapU6dqxowZOvbYY3XTTTe19P8MTHZFmkNPSu6SU13cEESGHcTIBVzvEU+VpuDuf/nsoxUDmVIPjbEc5wsAJd4FEcNWD41UPRka5acUuEABKCt6UorN/MXG0cpiTIKgQPhiFdC47rrrdO655+q+++7T4Ycfrv/3//6fTjvtND3xxBM655xzdMYZZ1TVp/vNb36jo48+WosXL9Z73vMeXXDBBRofH9dFF12kM8880/f3fPvb39Ypp5yiJ554Qh/4wAd07rnnatmyZTrrrLN0ySWX+L7mkksu0VlnnaXly5fr3HPP1Qc+8AE9/vjjOuWUU/Ttb3879L8FMFnYExGs1p08XCWnuGmE3J//XJ5jopdU9mYpkmGagntvEIFGZFKl42icgAaAsqqAxrgV0DDR9BpoCt77Xli5SVf87imt2MiKetQpIEOD7HKgvdLd3oBG7LXXXvrtb3+rd73rXUpaKyi+9KUv6bDDDtMNN9ygG2+8UaeddpokacOGDTr33HOVSqV0++2365BDDpEkfeELX9Dxxx+v66+/Xtdcc40rsPHSSy/pkksu0axZs/Tggw9qzpw5kqTPfOYzOvTQQ3XllVfqtNNO0xFHHOG85u6779aVV16p3XffXQ888IBmzpwpSbr00kt18MEH65JLLtHJJ5/svBeA+m0er2RSMQyYPGgKDi/7MMgS0OgplR4apX/NlBKJOGhFX7p0rzCe50ACUOKdVBwZz1cCGqmJAxpODw3Gpj3rvd+7W+tGsnpuxSb9/OzDur05iAHv2cAsyLHvYQloAOGLVYbG8ccfr1NOOcUVzJCkbbfdVuedd54k6fbbb3cev/7667Vy5UqdeeaZTjBDkgYGBvTFL35RkvS9733P9V4//elPNTY2pgsuuMAVgJg5c6b+5V/+RZL0/e9/3/Ua8/1ll13mBDMkac6cOfroRz+qsbEx/exnP2vy/xqY3IbHK7WvR8dppjVZuHpoMP6DPBkazHT3lEJVD42JJ5WAidgBDW8GN4DJqWbJqTquPaly5J3s4d61bqRUJujhl9d2eUsQF94xRpEMDaAjYhXQqCWTyUiS0ulK0smtt94qSTrppJOqnn/00UdraGhId999t8bGxup6zTvf+U7Xc1p5DYD62BkadnADva1AhsakdMXvntKHfv6A70SBfRxkKSHTU8yeTZRzM0xAg88+WtGfKvXQKBalHBMJAFR9Ltg0lnMey9SRoZEkQ2PSMEFxTG4rNowqN0GmZ1WGRtH8S4YG0E6xKjkVJJfL6Re/+IUkd1DhmWeekVQqVeWVTqe166676sknn9QLL7ygN7/5zRO+ZrvtttOUKVP02muvaWRkRENDQxoeHtbSpUs1depUbbfddlWv2XPPPSVJzz77bM3/h7GxMVdgZcOGDZKkbDarbDYb9DLEnNm37ONg60cqn4uNm8f5W00S49lK8CqXy0/a/T7ZzhE/uuNFSdK9z6/UoXNmun5m3whkC8VJ8zeZDPL5UuC6WCwom80qny99/gvs57pMtvPERJy/Q7FyHRnePKYp/T1x2wM0jHNExdj4uOv71VafhET5GlRTsTSxmc3V8VzEWiaVnFT7mPNEtUdeWaczfnS/jtpjS/3s7w4OfF7eE/AYGx9XOpFW1rqfHWNeDz2gkfNEJ473nhjZf+ITn9ATTzyh+fPn68QTT3QeX79+vSRpxowZvq8zj69bt66h1wwPD2v9+vUaGhpq6nf4+fKXv6zPfe5zVY/fcsstGhoaqvlaxN/ChQu7vQmR9dz6hKTSKsun//yiFix4vrsbhI54dEVlvy9//XUtWLCguxvUZZPnHFEaltx9z71a+ZR7JdPq1SmZ7grZXH7SHxO95LlXkpKSeuXll7VgwYt6bViS0to8Osp+bsDkOU8Y/rcx5pgplSssPWfB72/RlEyHNguIqMl3jqi2tHx9MZ5+4WWZohW3/eEWTbQo/8+vlcanL7/yihYseKlNW4luWD0qXfNCaTwiSdmxzZNyDMJ5ouLqZ0vHw51/Xl3zWFi2rHLcSNL//d8tGkhLj6ys3M/eedfdWj69vdsLdEo954mRkZG2b0fsAxrf+ta3dOWVV+pNb3qTfvnLX3Z7c5r2yU9+UhdffLHz/YYNG7TTTjvpHe94h6ZP58zXq7LZrBYuXKgTTjjBKZsGt7ueXy099ZAkacttdtD8+ft1eYvQCRsffE16/ilJ0tazt9H8+W/p8hZ1x2Q6RxSLRV14T2lw9Na3Hq7Dd53l+vnVS++XNq4rPVcJnXTSO5VM0muhFzy98Dlp6YuaM2eO5s9/k55+faO+suQe9fX3a/78Y7u9eZE3mc4TtgvvucX38fnz5ztfX3LfLSoUpWOOf5tmT+vv1KYBkTJZzxF+nli6QVpyr/P9zK23lVaukCSd8q53KjFBH41XF7+o3736nLbfYUfNn79vW7cVnfXBnz2oZ9evcb6fMW2q5s8/sotb1FmcJ6r938bHpNVvSHKPLWo9T5JOeMcJmjaQ0eaHl0p/flKSdNjhb626twHippHzhKk61E6xDmh8+9vf1oUXXqh99tlHixYt0qxZ7hOEyY4wWRRe5vEtttjC9ZpVq1Zp/fr12nLLLQNfY967md/hp7+/X/391TdamUyGC8okwH6uIVFZ7bBxLMffabKw9ntRiUm/3yfDOcKuT9vn8/9bVXk2lVImnWr/hqHtEsnS5z2VSpaP9dLwtFhUzx/3YZoM54l62H+DTCqpsVxB31v8or54KgsiMLlxjpASKfe4YXO2NPboSyfV19c34ev7Mub1jE17zYqNY67vU8nJuY85T1QUVQlw1vqbJBLu1K5UqvQ3TCYr55tEMsXfFT2jnvNEJ4732HY6+sY3vqGPfexj2nfffXXbbbdp2223rXrO3nvvLcm/f0Uul9OLL76odDqt3Xbbra7XLF++XMPDw9pxxx2dMlBTpkzRDjvsoE2bNmn58uVVr3nuueck+ffkADAxu27+bc+s1Gtr25+6hu6jKfjkYzfq9Eu88PbSy+Y5LnpFUFNw9jBaNZYrTVb+x72vqEBDTmDSyxfcte6Hx0o17vtT9U2LpMoBeG9zccRf0pOdQxNn1HsMFD0jVvN9gabgQFvFMqDxb//2b7rooot04IEH6rbbbtPs2bN9n3f88cdLkn7/+99X/Wzx4sUaGRnR3LlzXZkRtV5z8803u57TymsA1Md7w/DLe1/u0pagk8ZzlRtOBoCTg/1Z9yv5UPQEtnKeBnyIL7NrTSDL7H2CmQjTxtHcxE8C0NO8Q4fhsbykUoZGPVLlC1Se61PP8QY02MWodxzqfZq5pbFvYTlnAOGLXUDjC1/4gj7xiU/o4IMP1qJFi7TVVlsFPvf000/XVlttpWuuuUYPPvig8/jo6Kg+9alPSZLOP/9812vOPvts9ff369vf/rZeeukl5/G1a9fqS1/6kiTpvPPOc73GfH/FFVdo7dq1zuMvvfSSvvOd76i/v19nn312c//DwCTnncye2hfrSnmo05gV0GD8NznYAQr/DA33gUCGRu8wwSozl2ACWnz2Eab1m7Pd3gQAXZbzZmiMlwKddQc0ygMUMr56j7cvGxPQqDtDo+j93idDg/sWIHSxmhm8+uqr9ZnPfEapVErz5s3Tt771rarnzJkzR2eddZYkafr06frRj36k008/Xccee6zOPPNMzZo1S7/97W/1zDPP6PTTT9f73vc+1+t33XVXfeUrX9HHP/5xHXLIIXrf+96nvr4+XX/99Xrttdf0j//4jzriiCNcr5k7d64uvvhife1rX9P++++v008/XePj47r22mu1Zs0aXXXVVZozZ067/ixAT/NmaEwdiNVpC00aI0Nj0pkoQ8MzB1E1KYH4ckpOJUzJqdL3ZGggTAQ0AHiHDiPjjWVomElvxqa9x7uYJscE9KRX7yHgLTllTg92djkBMiB8sZoZfPHFFyVJ+Xxe3/jGN3yfc8wxxzgBDUk69dRT9cc//lFXXHGFbrjhBo2OjmqPPfbQ1772NX384x/3nTT52Mc+pjlz5uirX/2qfvGLX6hQKGifffbRF7/4Rf3d3/2d7++98sortd9+++k73/mOfvjDHyqZTOqggw7SpZdeqpNPPrnl/3dgsvLWuuUGYnIYy+adr5nUnBzsG0e/fV6VoZHjuOgVZGigEwhoAPAuhthU7qHRV28PjfL1ibFp7/GWnLIXV2FyqjcTqypDw+mhUXmMOQwgfLEKaFx++eW6/PLLG37dkUceqQULFjT0mlNOOUWnnHJKQ68566yzXMEUAK3zro4xtW7R28jQmHyyVskpvxuIqoAGGRo9w+zuSlPw0vfevilAKzaMEtAAJjvvmNL0bCNDA96SU2M57jknu/qbgnu+d3po0BQcaKfY9dAAMLl4JzFHsjT1nAxGrQyNLAPAScEe6PsN+r0PUQqgd5jTvFkcmXRWwHZpg9CTyNAAEDSpWH9T8HJAg+tTz0l5CneMk6Ex6dVbJsq7AKcS0LDei0EtEDoCGgAizdtDY/M4q2UmAztDw24Wjd5ll4Hwu4Gobgre/uOiWCzqugdf1ZLX1rX9d01mJjXfWwSUkh5oVcpacUtAA0DQpGI/TcEnPb+SU2SKTm7Nlpwy49ciGRpAWxHQABBp3ov/CAGNScHO0GAl/uSQtfaz3/2j9zFvsLMd7vzzKl16/RK9+9t3tf13TWZm35rJBFP2gU8+WjV/v+2crwloAAhacd2XTtX1ekpO9S6f1qqusSkmt1qf+aqSU+V/CzQFB9qKgAaASPNOZm8uT3Tf9vQKPbF0fTc2CR1gZ2jQK2FymLjklPuxTmTuPPP6xrb/Dvg0Bfc8DjTrivfsq6G+0kQlAQ0AgSWnGmwKzuRk73ngpbVVj9FHY3Kzs3ZqZYZ7x6sms4OSU0B7EdAAEGnei38uX9Azr2/U2T9/QCdfdWeXtgrtRobG5GPfKNRTcor7gt5hdqW5baSHBsIyfSCjS0/cWxIBDQCVsYS3xFSjJaeYnOwty9Zt9n18jD4ak5qdtVMzoBHwOE3BJ4disajLf/ukfrT4hW5vyqST7vYGAEAt3rIy2XxRf1q+oUtbg06hh8bkY3/W/VbmexN1OnFjkPCrP4DQObvblJxKmMe5+UPrpg9kJEkbR3Nd3hIA3WbGEn3ppGusWXdTcAIaPWndiH/Am8bgk5udoVFrgV1wD43KY5wzeteflm/Uz+9+SZJ0zrxduX/sIDI0AERavnznYVZOZfMF1+p9mvL1JnsVTJZ9PCnYNwp+MSzv5Lb9/epNY/rn65fooZerywUg+kxTcKd/c/lfPvoIw0CmVHLKHjsAmJzyToaGu2dG3SWnyk8zE5abxnL65I1LdPefV4W3keg4bxawQYbG5Fa0ci8aydAwh1NhgnK6iC/7PnTcOjZGs5wzOomABoBIM9eHwXIN7PGcO6CxmQmKnmQP+sjQmBxyVgqGfw8N9/d2Wapv3/ZnXfvgqzrte3e3bfvQPmbfJmQyNCorm8jSQKsGMqXbnTHGC8CkVwwoOVVvhoa5PplxytcXPqtf3/+q/ubH94W4lei0oMlqMjQmN/t+ZLyRHhpFemj0sst/+6SO/NdbtW5kXJKUTlbuWzaOUt60kwhoAIg0k6ExWF5hmSsUtdmKfBPQ6E3ugAYDwMnA3s9+K+XMY0mf1fvUxo83s7tNHMMd0OjCBqGnVDI0mJgCJjszdjCBTqPZklN/XrEpvI1D13hLHBtk9k1u+TozLLxjVfOtq4cGA9qe8fO7X9Ky9aP670eWSnJncm2gvGlHEdAAEGlmgGkmJLL5givyvXmcgWYvsgeNWW/zBPQk+2bSP6BR+jedLA1d7DTuXbec4nwd5jmBCqid4g5W2X/3oDIQQL3MxOVojvECMNmZ8eWUfncr0boDGuWAu7k21SpDg/jIBmRirC2vwMbksnE0q1fXjChfZ4ZF0VN0quhkaFByqpeZ68aYNb4kQ6OzaAoOINLynoDGeK7gunkgQ6M32atYyNCYHCZaBWVuCtKphMbz7psEeyIiWyhoUKmq1yO6TMwykfApORXS73h59bB+++gyfWjerhrqY/jbi75y+v6au8dWVY+bWvmstAVgJhpN5rfhLUEVJOnJ0CCg0RuC+vWt2kRAYzI6/z8e1p2evji1FthUZWiYHhoENHqOvXBuSvl+ws7Q2EiGRkdxRwcg0syq7cFMpSm4vZKbDI3eZK++zxWKKhaLzmQnepNdf7ZWyam0ZzJBck+AF7hhiB3vyraENa8URoZGsVjUMV+5XVKpH9M583Zr+T0RPe/YZ1vNGMpUPU7JKQCGGTsM9XmagjdYcsoMNcZZdNMTgvr1rd401uEtQRR4gxlScFkyqTqgUSi6/5UIaPSKNVbWViZlerRVzh+bxghodBIlpwBEmjdDI1coulbsjxDQ6EneQWOtQSR6g72L/aqMmUCFGTwGTXSHeawQQ+sMbw+NhM/PWrHSmpB4fOn61t8Q0RTweXVKTpGhAUx6ZojgzdTrSzXXFDyoVBHiJSjTZhUBDZQ1VHJKlJzqVWuHKwGN8XxpXGmXnBqjvGlHEdAAEGm58sym00MjV3Aek5ig6FXeVfbrRqhH2evsmwG/xnnmIe/qyNLX3DDEmdljSb+SUyHszhdWDjtfkwreu4ICkGb8MJYruDLBAEw+ZryQSSWUSVVOGvWWnPI2BafkVG/IBmTasNoaRiNNwc1Uhf04TcF7w/rNlTkJk5lhl5waJ8jdUQQ0AERa3ik5Ve6hkS+6Bp3j3Ej0JO+g79Ar/qA3Nox2aWvQCe4MjeCSUyZDw76xsJ9ONk/8mH1rppbsiekwSk69uKoS0GC1Ze8KSqgasGrlj3GjCUxq5pqSTCRcwfN6S055y14ySdkbggJTVAKYfIKOhdoZGt7vyxkaE/QHRPy8/8f3OV+beSgCGt1DQANApJnyUk6GRr7gqnPKRaM3+Y0lf33/K53fEHSMvXLab4LA3AekUyZDw7/nBj00YshTcirspuAvrNzkfB20ChPx4pdpEdRnacCaqKTvFjC5mSFCMplwTULN3X2rul7vlJzyzSTl+hJXuYCxAQGNyWdzQPWH2hkanpJT9NDoSd57TDMPNZa1S04xN9VJBDQARJoZVEwfLNW6zeULyloXE1K9e1Pep4nC6k3jPs9Er7DvBfzG/CZoUSk5VXmSfSNBhkb8mD2WKK+xt+elw7gBtEtOBTX+RLz4zRsGZWikU0nnmMr6NegBMGmYa0rSc8LYadZQXa93xiAFd2ahRNZ4nAVdGwiCTz6jAfu8kZJTlYAGGRq9xC43JVWCF64MDa4DHUVAA0Ak/OGpN/TJGx+v6olhvp8+kJFUWl1Lhkbv8xv05ZiI6mkTZVmYH2eS5abg1uHgXgEV3nFCT/DOMAEpM+mcsiIaYWTcPLtio/M1QfDe4HdUBPXQkCrnjaBVuAAmh6JVcsrwBjdqMb3D/TI0WJkbX0HN3UfG6aEx2QRl5TRScsrc0xQJaPSU1cPuxZVmHirL3FTXENAAEAnn/OJB/fr+V/TV/3vG9bgZVEwfLAU0xvMF14QEk1O9yW/MZxpvoTdNlJbtzdDIB5Sc4pQQP2Z3m5JBKWt2qdWMm+GxnF5ds9n5npJTvcG35FSNEKQpVUdAA5jc7JJTc3ffUpL0VwfvVPfrnZJTPtcm76IsxEfQWMNvcvu1tSO6/ZkVZHz2qMCSUzVKylWVnCr/67q3oSRd7K3xBDRMENtefEVgu7MIaACIlJufeN31vVNyaiBd9ZhUahKO3uOXjTGa40axlxUDAhTexzLlicliQEYHmTzxUyk5Vf43kXBWzLbaFNxbqo4geG9oNEPDNPK1y4q8umZEn//fp7R03eaglwHoMXbJqW/99Vv0vfcfpE+fsk/dr/eWnLLLixAwj6+gMjF+k9vv/e7dOutnD9Dbr0c11UPD870Zu7oWXHF+iL01w2Ou7002hh0QJUOjswhoAIgU7+qmzZ4MDckT0OCi0ZP85qSpY9vb3D00gpuCOxkagSWnwrthCGoyjHBVSoBUHkubEkEt7s/xvPu8QY+V3tBonCuTqi45de4vHtRP73pRZ/30/jA3DT3uZ3e9qGO+cpteWjU88ZMROeZ6k0oktNXUfr1zv+00tT89wasqvE3B7fsQVuzHlzd7zyye8cvQWLGxNKn56Kvr279h6Lig+4jmemhYrydDI/bWDLt7aJh7DHvfMjfVWQQ0AESK91Jvghcz7ICGNbhktW1v8hv0jVJyqqfVKhtlZ2OkyxOTwSWnuGGIG7P77ABSOZ7Rcg8Nb+p3UJ1sxEvRJ0ejZoZGeXLKHjM8/Xqpt8pzKzaFu3HoWcViUZ/736f08uoR/foBVmfHkRk7NLtgoZKhUfrevsaQoRFf3mDUYCYlqXoxlT0eJXO8NwWNOxvJ0DDHCT00eos3QyObK2fikKHRNfUvRwCADigWixrPFfT3P39A+2w/3RlI2qunyNDobcVi0XfQF5QCjN5g73FvhoZ9PJjSMe4SVf7PRTyYyelEVYZGoeX96b1GZClJ1hP8FjrW7KHhk/GTSiY4X6Ah60YqqzO3nzHYxS1Bs5weGi0GNHwzNLi+xJY3e3NKf1obRnPanM2rWCw6ATD7mjHGfUlPCsqkqDle8LxmxcYxjeXyLLjqMaYpeDJRupaYewpXySkW23YUGRoAIueuP6/SnX9epR8ufkHDYzlJ0lBfyjf9lwyN3mOP9z5y9G7O1zRb7G32oN+7Osr+1pSOsW8M7OAGJYXix8nQsB4z5adaLjlVnmwyQXFW0PYG34BGjfnJjE+Ght2b6+XVlA/CxOzjpz/NbXQcmbFGqsndZzcFLxaLrgwNb9kixId3snmoL+U8bk9Q2mMSFlr1pqDKULVKRnmHqv/wq4c1/5t3UHKqx5i+fLOnDUiqnDfI0OgeRmIAIqUoad3mShPX4XLwYqgv7UxkjloBDW85EcSffVPxD8ftoR/87cGS/Pf1+s1Z12Q24svejd5JbDvYYSYm7afUCoaEt30cZ+3iV3LKlBZrtSm4mYiwJyfYl/HnW3KqxvPN8WRPSKespi33v7gmtG1D73Jfd7q3HWieGSM0m6GRts4b2bw7o5hFVvHlDWgcu/ds5+ugUseUwu1NQePO2iWnqn/2/MphmoL3mBdWlUqU7rb1FEmVILYdrGJuqrMIaACIlGJRWrp2c9XjUwcqAY2RLBkavcwe/KWTCe2wRamsg7eO7dOvb9ABn7tF//hfj3V0+9Ae7hJSRc/PKl9X6lf7l5wKM0PDnu9g8qp9zP62/95msqnVFa/eDA2JLI1e4J+hETxB6dcUfJ/tZzhfTxvIVL0G8LInLVhtG09OyalkcwEN+3XeFfpkiMaX+Ty/a7/t9Kl3vVmXnri3b2UAe1KbzPHeFPQxbqQpuN/jXDPiLV8o6tk3SgGNfXcojR/NPJR9bFByqrOaCmikUqmW//v85z8f9v8LgB5QLBa1fnPW9VgiIQ1lKiWn8qT19TRvjfOBcmM+b/O9Hy5+QZJ04yNLO7dxaJtaQQn7JsBv5b59TmhXjdpWMwUQzPxl7R4IZhVsyxka5WvEUH/KecwbCC8Wi1UNQRFtfkdFrelJM36wa9zPGqoEMcZo7oo6uALpTF7HUt7J0Gju9XZml3dCm0VW8WU+z7ttPUXnzNtNA5mU0xjcXeq48rkfGee60YuCxp21ApZBQ1V6aPSOkfGcc0+x48zSYkuzT+mt0z1NNQUvFovaZZddNGfOnKZeu3jx4mZ+LYBJoKjqFN6p/WklkwlnhaWNm4feYw8KkomEBjKl/e7N0JjOitqeUqwx6Le/7zM9NKzn13pteNvXlreF7JJTlcfMpFEzK17zhaL++OwKHTpnlpP6PaWvMuT1Zn189rdP6toHXtX/fuwo7bXNtIZ/HzrPr2xYrQoyJkBmT0bZhwErbVEP12pbJqdiyZw7mm4Kbr1uzHO/Qg+N+DJjSjtgNdRXagy+cuOYXlw1rKP32sr1uX9x1bCGx3Ka0t/UlBoiKqgsaa0gdtBPClwzeoaZn0okSucGScqW9ylNwbun6bPv2Wefrc985jNNvTaZpNIVgArXBb5YvVJyWnmg6B/QYHDQawoBGRpjuYIKhaKT7j/Naug6Mp5zBheIJ1eGRj44oGFWWtv3G20rOeXaPs417VO9YtZMKjRzA3j7Myv0oasf1LSBtD79rn0kyTXhYN9srB0e1y/ueVmSdM39r+ozp+zT8O9D5/lmaNSYoEz7lJyyrzXUQkc9XP2auCbEklNyqsmAhj2N4Z24sjPAEC9mrGEHrEzvrb/72f0azxV09pFz9PdH7up63ad/84S+dsaBHdtOtF/Qx7hWyajAIAgZGj3DLHzpTyede9FsedEUTcG7h8gCgK7zZll4JxZMbWtz8bDReKn35FwZGnJSviX3/rYnr1ZvqjSSRzzZg37vpEDeFeRKVj3WiRsG5q7ax+wyu+RUKwGNJ5dtkCRtHM1p9XDp3NCXSvqWHVpnlTh8Zc1ww78L3dHo59Fv39tfk6GBeuQJaMRePsQMDe/EFYus4sspRWatrBgsBzTMfr7m/lerxiQ3PkzZ217TTFPwIJ3IIEf73f3nVZr377dJkgYyKece5Z4XVuvnd73oyvgloNFZTS1nXblypYaGhpr+pa2+HkBvsQMaRVVnaEwdqJWhwUWj1xSstO9EopKhIZUmncwNxshYznn8G394Tm9/82y9c7/tOruxaAtvloU5JtLJhMxpwB3QqDy3XU33ioEJ5WiVc8MXUobGFlZvhI2jpYBFXzqpdDKpbD6vbM6qdZvzr42NiGtwV6XLgVBXySlr+ECGBurhnpzq4oagaZWSU8293i5J5L0HoeRUfJld6S45lap6nnfBTZ/PvSniLcym4K4efwTBY+tvfnyf8/VAOuWMKSXp8v99SvP329b5npJTndXUGXjLLbfU4OBg07+01dcD6C32DUCxWKzKuphWI6BBFLz3eNO+U8mEc8Ow2VpFO2z11Ljh4dd0/q8e7uBWImyuLAvPpEDOWjlnVlXaE0vuSaYQzwnWSkwWVrVPpSl4hfn8NxPQsFO/TdPOPjtF3DpG7BrolAuJj0YDjE6GhnWjaZ9zRmkKjjrYpyMyNOLJbyV+IxKJhDM0oORU73AWUyXsDI3qtb/eBTcn7btt1XMQb81kaAQ2EnctouCa0QsGMsmqqiH2vn159Yi+tei5Tm/WpBVKSPlf/uVfdMghh2jNmjWux7PZrF577bUwfgWAHmZPLuWLxarSD1OdHhrVNx9kaPQeJ6Bh3Wz2lxuD28fGsJWhYXizexAftfpgmAnqtBXQCCr90a4VkkH1cdE6v5rmToZGE393+xDYVD5PlAIaZpW+FdCwguJkaMRHo4dFJUPDDmDZPTS4dmBirh4aTE7FUqs9NKTKAqvN4+7zBteQ+Mr53HsMZdwZGomET483xoY9p5mARtBP7KDnQy+v1WtrR1rZNESAXXLK8E5HfW3hs/rzio0d3KrJK5SAxqJFi5TP5zVr1iznsUceeUQ77LCDdtllF+222266/fbbw/hVAHqQPTjM5v0yNEwPDZ8MDQIaPccvoGH6aNhlQUbGqwMa60ayVY8hHuz7B2+WRc7K2jHHhavMlPX0dq2aZe6qfUywKOFbcqrxc7w90WgCn32ppNLOKn3/klOsnouPRveU2fdZmoKjBZQPib9KWdPm36M/XXrxiCegkeOeJLYKfvceviWn3J/7LJUCek7QUNC7721Bi568cxrvt0oXIZ76M6mqOalQqwOgIaEENF566SUdcsghrscuu+wyrVq1SkceeaTWrVunk08+Wc8//3wYvw5Aj7FXTOYLxaoVT5ScmlxMkMrOyDF9NFwlp8aqV9SuGaY5eFy5m4J7VsC5Sk6Vn289p1jjta2w19+QodF+dkAj6QQ0Gn8f+xgwGRr9QRkadskpJqNio9HPoylbGNQUfIwMDdTBPuzI0Igns99aydAwY1JvpnCWYyK2ajUFN4rF6nEClQJ6T9D4otY5P+gn3uPj5dVkaMTdQDqptDdDw3MAnPQX22qP2dM6uFWTVygBjQ0bNmibbbZxvl+3bp0WLlyoM844Q4sXL9b999+vYrGof//3fw/j1wHoMd4U7Y2j7huEaeWSU37ZGGRo9B6z/00zeKmSoWFPOvllaKwdIaARV8UaZaPspuBJJ0PDv+RUu1bZE89oH/O3tSeY0hNkaNz2zAqd+cN79IrPzaF9PNzx3CpJ3pJTlZ/b1xDKhcRHOBkalZ8zEYl6uK41XBRiyXzUEy0ENCoZGu5xKEHx+DKfZ7uHhrfk1OZsXn/1g3tcjzFu6D3mPL/rVlN0+K6ztO30AUkTnPMDfsTCy94zkEk5Y0rDe6/il92F9ggloLHDDjto1apVzve33HKLCoWCPvzhD0uS9thjD7373e/WwoULw/h1AHqMt4nexlF32SAzse3Xv4+VMb3HrHib2p9xHhvIVDcF3+TTQ2PTaPVjiAf7PsGbZWECHHZTcHeZqcrX7Qpo0AC2ffz+timf/Ww7+2cP6N4X1uiT/72k6md+x0C/1RQ85+qhQcmpOGr085g2GRp5/wlpyoagHkGlDhEflYnr5t/DydCoKjnFNSSu7F5txpDPpKT30sPCut5jpiV2mjWkaz9yhE78i9LC7aZ6aPiMLd7YMNrqJqKL+tJJpy+b4T02/Pq+oj1CCWjsv//++r//+z/l86WL+q9+9SsNDQ3p6KOPdp6z2267admyZWH8OgA9JptzXwQ2Z70lp4J7aLy6ZrNWbhxr38ah4zY5AY3KjcSAbw+N6hIhTDrHl3uiqOD5mdVDI9G5DA37nZjrbh+z+xI+TcG9AW8vv/O/3zFg34CMB5ScylIDNz4a/DxmfI4nO3A60XEGSO5zC2UI48nst6TfKqk6ORkaVSWnOI/EVc635FQ66OkOFtb1HnNPYQ6FSgnUiXtoeBO//AJeH/v1IyFsJbqlL52sytDwnvoTIqDRKaEENC666CK9/PLLetvb3qZzzz1XN910k+bPn690unIR2LRpkwYGBsL4dQB6jPcGwJu+O7VccursI+c4j+2wxaDz9ZW3PNO+jUPHVQIalWuIfw+N6myMMPsnoLNq9dBwmoLbPTSK9sRS9XNDYf+OhovcoF7mb2sP/1M+pcUMO8PCrw6632v6UnaGht0U3O6hwT6Oi8ZLTlWXG7PrYY+z71GHYgfKG6K9zC1HKz00TEDDm6FBX5X48msW75eh4UVAo/d4y6Cm6wloyP0awy/78/4X17S+keiaTDJRlaHhXRSzhhLYHRNKQGPevHn64he/qLvuuks/+clPNH36dH32s591PefBBx/UdtttF8avA9BjJppEml4uOXXc3rP1u48fpZs+dpS+/4GDnZ+v2sRFo5eYslFTXAGN0uVqtBzQKBaLVTeSEhMMcWbvOe9+zNsBDZ8bi05kaLAYt338emikajQF/9V9r1Q9z+YX1OpLp/ybglNyKpYa/TymfcqN2ccJJadQD1cmIReFWMo7q69bbwru7aHB3HZ8OU3BE3aGRh0BjVxRTy5br4v/61G9tpaGz72gmQyNSkDMfV7xy9DwNpRGvGRSSdd952AmVdUUfPUmqod0SigBDUn6l3/5Fy1dulS33367/vznP2ufffZxfvbCCy/o3nvv1WGHHRbWrwPQQyZa3WJ6aCQSCf3F9jO07w4zNGtqn/Pz6QMTpwQjPkyGxjSfpuAmoDGWK/gOLJmQjK+6m4I7Jaeqfy6FHNAo+n+NcFVKTlUeS9VoCn7TkmVVz7P5rZK1U8TtBtCbx+2m4MxGxUWjGVOZ8mo6O4hhHyfse9TDVeqQ8UYsFT2Tlc1wMjTG3Atr/K5XiAd74YxRK0PDTEpn8wW961t36saHl+qzv3myvRuJjvAGPU2p21oZ4Oaj7w1WjPkslpg5pa/qMcRHOpV0VYwYyCSrzv3vPWjHTm/WpBXqLODs2bM1e/bsqsc3bNigs846S6eeemqYvw5Aj5hoIsH00LDZA4apBDR6ylPLNkiSdpw55Dw24A1oZP2PGQIa8eUuOeXev3ZTcKcUUcEOYljPDfEYCOrTgXDVKjnld3noS1fW4/itsg3qoeFkaFg3mMPWCltK1sVHsxka9njD1RScgAbqUHBlBnZxQ9A0ZyV+CxENpym4p/QpWTvxZY6LiZqCG9MG0lo7knWtwH9p9XD7NhAdY87t3pJTte4D/I4fyX9ssXZ4XMVi0dU3DvHRl0po3+1nKJVMKF8olv8r/ey77z9IWwxmdPhuW3Z3IyeRpjM0LrzwQi1evLiuhmgHHnigfvKTn+iUU05p9tcB6GETlZyyeykYdjmioTqatiE+nli2XpJ0yC4znce8TcH9UnglJp3jzN513glpuym4Gf+7e2jYwY3wJibtbeLYah/fDI1EcIZGJlV7+Oo3qVTqoWFW6Rf0p+UbNJrNa+OoFdBgUjs2Gv00+pUby7syNPh8Y2KUnIo/72RlMyo9NCg51Sv8SpENZoLvL1PlrL9xa4HE1tP627R16CQni6s81DTBz5oZGiaT3DM+HffJ0MgViq4V/oiXTCqpvnRSt/7jMZJK40ez2GHGYEZz99jKN3sc7dF0QOM73/mOjjvuOG2zzTY655xz9Lvf/U7j49SxB9C4iTM0qgeUU/vTOnROacKbicbesrncG2PGUCUzx9sUPOiYYYV1fNm7zrsf7VIAzkR3QPZEmBMK9NDoDPOntVerpVLBN5B2Mz6/gLdfKZhiseg0Bb/hoaV65zfv0DlXP+haYcv5Iz7qWVBlM6sm7QUUeUpOoUGUnIo/b338Zpgswc2ebGFKTsVXocGSU2be2l5gxe7vDeZYSHgzNJrooWG/xD6esjmuH3HhHW+aoJW5DuQKBaeyQCuBcjSn6YDGsmXL9L3vfU8HH3yw/uM//kPvfve7tdVWW+nMM8/Utddeq40bN4a5nQB6WDZggHD6wTvq48fv4Uxme721nM7nt/oB8WXqjQ6kK/vd20PDTD71pZI6ef/tnOcxwRBfdk38Wk3Bzc2CO3vCfm6YGRp2FkhobwsPcyNo3wZkfCagjY2jWedrv/O/38rpNzaMOjch97+0RpJ0559XOT17gn4XoqnRz6OToVEgoIHmtatfEzonaOKxEeZaMpb19tBofrvQXU6GRp0BDTNxOWL1URnLseq+F3izuOppCh5Ucsr49t+8RQ9/+gTn+6BKA4ge737vS5lAl8n8Lfr24EFnNB3QmD17tj784Q/r5ptv1sqVK/XLX/5SJ554ohYsWKC//uu/1uzZs3XyySfrJz/5iVauXBnmNgPoMUFlPj57yj66+B17B77OpHwzgOwtZn/2ZyqXqIGMWQ1X+pmZxJzSn9K3/+Ygvasc1GCCIb7sCUrv5KI9UEwkqm8s3P032tQUvOEiN6hXpeRU5UYg7UxAV18f7BvBUZ/zv9954MR9t3WCJDZXQIPllT3LZOfkKDmFFrj7KnVxQ9A0c5pvpX69uZaMegIaZIzHl7k02BPSg3UENOzxiF8DaMSPN4srXUdAw/woaEI7oYQGMqnKWITxZmx4F0mZBTJ9VnkxMz9h9/hDZ4TyF582bZr+5m/+Rtddd51Wrlyp3/zmNzrzzDN133336dxzz9X222+vo48+Wt/85jf18ssvh/ErAfSQoFWxdlkRP/3lFfxBDaIRP/lC0ZlYcmVolG8qTDkqcwNhBhWmDBElY+KrELByWqrs11LJqfLzA7InwszSsYMYHFrtY/609n1gZQK6+g8f1Ach6LFb//EYbTdj0Lf3xiarh0ahSJZXXDTeFLyyks6gKTgaZc9BMXkdT3ZPrmaZa4l3AptJyvgy2b32cVGrR6PfxDWr7nuDObU7GRo+pW69ChNkaFSCI+UyRSyiiA3vPYUpiZtOVfa1ydTqm6DHH8IX+l+8v79fp5xyin72s5/pjTfe0KJFi3T++efr5Zdf1kUXXaTddttNBx98cNi/FkCMBQ0AJ0rbMyv4WRHTO+xsGztDY/pAqZ/GhnKpGTMp5QQ0TH1TJhhiy95z3sCUPQGR9NnX7crQsN+KY6t9TGkve36pctPn01AxoA9C5TH397tuNaX0nj43Gt4Vtn4ZIYieRjOmnB4ahaAMDfZ7L9o8ntd1D76qVZvGQnk/Sk7FXxg9NMy1xHv94DQSX+bzXG/JqY8dv0fVYwwTe0PBMyZNJSdeNGeCHX7jTPu9zGIdgl/x4d3vpv+JnY2xsZzt3Zem5FSntTWElEwmddxxx+lb3/qWXn75Zd1///3653/+Z23evLmdvxZAzASVnApa5WBQcqr3jFrZNv1WhsaMwVJAY/1mE9Ao99BIuwMaZGjEV62JIrvklFkpZc8752tkd7TCVXKKQ6ttnJJTVheNWp9p+0bQ7+fe4JMpLdKXqr6mBDWgR7Q120MjuCk4+70X/evNf9Kl1y/R3/zo3lDez9WviYtCLJl92FLJqfK1xLugiqbg8eVXMqjfp3zMB4/YRXf803Gav992VT9j4UtvyHuyuBppCh40d2HON35jEURb3rOvzLxTJpWsWnzblwoOgqI9OpoTc8ghh+hLX/qSnnrqqU7+WgAR5zchlUi4V8n4cUpOkaHRMyqDhIRrkDC9HNB4YukG5fIFZXMF53lSZdBJuZj4su8DgyaZ7YBGPqDkVKgBDdm/g2OrXZy/s1/JKZ/92UjJqb/Yfrrz9aBP+Qjv65nYjodGJ45MaQD72LGvF6yW7E2/e/x1SdKzb2wK5f3cpQ45V8RRGM1bTQYhGRq9w8nQsAJdfkGvKf1p7TRryLeEJQsiekNVyak6Fs2ZWGZwD40Sv7EIos273+15pwFP0DNDhkbHNRzQeOKJJ/SjH/1ITz75pCTpqaee0rnnnqsPfvCDuuWWW0LfQAC9z28iYaLsDKmycmY0m9cVv3tK//XAq6FvGzrLZGjY/TOkSoaGJP3Po8s05s3QSJmGbZ3YSrRDMSBDo1AoukoBmHtIezKyXWVAXL05uE9tG+/No2T3PGim5FTpsb8+bGdd+5EjnMen9levnNrsberKjo6FRveSmYDMBmT3MLnQq8L9PFNyKv6KoZScMqUv3Y+zQj++6g10mfvTjE/GJ7u/NxSce47S9/VkaDhZHXVmaDDmiA/ved2uDDKQcd9X0EOj8xr6iy9YsEAHHXSQPvGJT+jggw/WzTffrGOOOUYvvfSSli5dqvnz5+sPf/hDu7YVQI/yS7usZ+WU6bHwwEtr9aM7XtQ/3bAk9G1DZ5lBgt0/Q5KmD1ZWVt/29AorQ8PdFJx0//iy7xPMQP83jy7V/p+7Rbc+s0JS6abC3BR0ooeGHWRptGY/6ueUALEey5gVceXrwwsrN+nPK0qrrOvN0Nhn++ma2l85d/g1+NxQLmPnvJYZiVhovORU6Xh6+JV1uuS6xyRVZ3kxQY2JuEpOMdyIJbMPJ8oCr8VvMlui7Gmc1dss3tyfJhKJquOAgFZv8JalM4ttamZomL4bAT83pxun5BTnitioytCwymOnPeeAjE+ZOrRXQ3/xL37xi/qnf/onrV69Wj//+c/1/ve/Xx/+8Ie1cOFCLVq0SJdeeqn+/d//vV3bCqBH+fXQMKspa+lPV6+2pQRAvJkMDe++3WpKv/P19lsMBDYFZzIyvvxWvl54zaPaNJbT75Ysl1S6qXCCVwHZE2EeA64MDSav2sa3Kbhz01dQNl/Q8Vf+UW//2h81Mp7TeM5eZV+9Y7z1jw07uFF5vft4YUIiLhrbT/Yquusfek1S9WpLVkz2nrA/zsWAQDriw6+0UKOC7lHI8Isvp1n8BLefdgUBb9kpguK9oeDJ4qpkZPnv32KxWMk0DszQKP1rgmBZymXHhreHxrYzBpyv39gw5voZGRqd19Bf/Mknn9RZZ50lSTrjjDO0ceNGnX766c7P3//+92vJElZIA2jMuE+Ghjfi7cevWZu3fAjixdQj9mZoJJMJ/eMJe0mSnn59Y6UpuDeg4Rkf/uGpN3T0v9+mu59f1c7NRgjss0DQyqXpA2lnX7uyJ+xgSIg9EOzNIEOj/eym4JU6w0WNjFfO6+tGsq7jw28CoeCUjnA/PuRTcqr6tQ1tMrqk0bnkeoJZ9NHARFzlEAloxJJ3srIZQRkaTGjHlzn9TxToSlsDC29Ag93fGypl6dwZGkGfb/vhwKbgMg3GyyWnOFhiw144dcYhO+ojx+we+FwCGp3X8F88Wf4QJpNJDQwMaMaMGc7Ppk2bpvXr14e3dQAmBb8yQfX10KienNo0lgtlm9AdI+Ol/ec3+WRW2N7x3Cp9c9Fzkqym4E5Aw30sfWPRs3plzYj+5kf3tW2bEY6gHhq2Y/be2lnl5J5Yqjwn1JJTrqbgob0tPCo9NCqPZcrjzVy+oDErUF0oFgP7IBh5z82oMcWn5FTQaxFtje6lKZ5rit9KalZMYiLuklOcK+Ko3tJCtXgnss21izIy8eWdxA5SK0ODKgG9wSlLl3DfYwZ9vu1rQXAPjdK/piQR4434MPt35lBG/376Ab5zFEYrpQzRnIYCGnPmzNFzzz3nfH/PPfdo5513dr5/9dVXte2224a3dQAmBVM+aMBald9IDw3bplECGnE2PFaauBzqqw5W7b3tNOfrF1cNS5JSydoZGiy4jQ87FpUvFH1vDLcY6nP2dVBz1jBXzdr3LqzGbR/nb+sqOVXO0CgUnVJ0UimLK2s3BffJyDHHgzfTzzup7bstTEjFQqMfx2kD7n3vNzGRDTG7C9EQtEf/8NQbeuc379DTr29o6P2CejchPrz18ZuR9kxkm/5MHBPx5QS6PLeWR++1tet7O6DR5xljsCCiNzj9MEzJqQmagtuf+6BydE5TcCc4wg1qXFQavpN9EUUN7ZWPfOQjGh8fd77fd999lU5XbhB+97vf6dhjjw1t4wBMDuaibq90qWdiwS+tz0yII56Gyxk2fiup5+25VdVjm7Ol5wc1BZ85lHG+9uvVgujwlnTKFYpVmVp9qaSzYsre1e1qCi5XWavw3hZu5k/rKjllgpT5oquU4CbPOd43QyOgRvqMwUzVc4Nei2hrtARcVYaGzweaHhq9J2jF9Dm/eFB/Wr5BF/znI02/H+eKeKpMXIfXFNwswuGYiC+z77yBrn87bT996l1v1tbTSr38Ttp3O+dn3gbALIjoDWY3pupsCu4KaASUo0t4fs4CivjIlfdVPdVD0HkTL1Wz/MM//EPNn3/5y19uaWMATE7mQrH71lP16KvrJElrhsdrvKLEL0Nj41g21G1DZw2Xa+X7raROJBJ607bT9PTrG53H3lm+sUgGNAW332fNyLhmTxsQosl7n5DLF6tuHvrSVkAjINjgV8KuWfZvZ+Vl+1RKPVQes5uCuwIaniw8vxWRZmLCO2G1zfT+CbeFFZbx0OhuGsq4s/5GrWOqL5XUeL5AQGMSWjeS1RsbRnXhNY/o/YfvolMO2L7m8+1DhAW28VQoVF9vGuVdhT2lPy1tHCOgEWNFzyS2sd2MQZ0zbzedNXeORrJ5TR+oLIyoLjnV9s1EB1QaxJf7XkzQFNxVciog88vcu5hjhvFGfATdU0il+wpvY3B0FnkzALrOrFI4YZ9tGnqdXw+NUZqCx9qIydAIaN5rByiG+lJ6/+GlsofpgJJT9mrKVRsnDpKhe7w3CmO56s9yfzrpTELkA0p/hDmh4C4vEtrbwsOpOJWoztDI5ovabDUF3zjqDlr7NgUPqJE+YzCj/rT/0HeikgKIlkYnjrx1je0gmVkcwYrJ3lPPHv32rX/WvS+s0cd+PXG2BiWn4i+cklNkaPSaSrN4/+MinUq6ghlSdUCDBRG9oeBk65S+dzI0AsYIdnB7wh4aZrEO443YyNUIaFx/3txObw48CGgA6DpTcipooimI3/NZ8BBv37q11KfJL1gluZuFnzV3jnNDGtQU3J6gGh6nv0qkecb2m32Ck/3pZKWHRkDfjDAnFNz3ptx8tItTcspuCm5laIxawa2NY54MDZ9+K07JKc/NRyKR0DbT/bO0+srXEyYk4qHRklOSdN4xuztf231ZBsrZG6yYnJwaWQjjKjnFuSKWaq22rVdgySmOidgy+66ROJe3hwZBzt7gbQpuMrKC9m9dJafKD5ty2X6LthBNTl8+n2vGTrOG9KtzDteOMwf1/96+Z6c3DQo5oDE+Pq61a9eG+ZYAJoFcjQtFLf4BDSYk4swEIF5aPez7czugMWiVEAlqCm5PUI1lOTaizHujYK/KN9wlpyqP2y8dy7Wr5FRobwsPM0loXwHsOsOj48Elp6TqIJaJY/ql/k8f9K+2agIorLCNh2bmjT7xzjc5GV72+cWMJcYJaPSciY+ToraaVilFN1G5U/v0QDZXPPmVOGyUt+TUYLnvG9eP+HL6JjRwYHgXX7H7e0PBc44wH/egHhp2IDMow8f0iDNj0PWbKZEdFxMFwY/cYyvd+c/H6/+9fa9ObhbKQglovPrqqzr++OM1NDSkrbbaSltssYWOPfZYXXzxxfqP//gPPfXUU4FN2QDANGtOp5LaadagJGnu7ltO+Lp0Kll1cWE+Ir7s68TfvnUX3+fYpagG+6yARkBTcDull3Jk0ea9Txj1CUD1pZOVfikBGRobfSa8m98mmoJ3QiVDwy45ZdLy3T00hseq96/3JtOcB1I+K+Wm9PkHNEyGBjHx3mYCVybrJ5VMOPueEhCTk72Y5nePL6/5XPu6w2r8ePKuvm6GdxX2FEpOxV6lt0oDAY0MTcF7UdFTfszcYwbtX7svT2BAo/zwzKE+SdLaEQIacRFGVh/ap6Gm4EH+4R/+Qbfffrt22mkn7b333nr55Zd1xx13aPHixc7N6eDgoA444ADdddddYfxKAD3ETCJkUgn99O8O1a/ue0X/cNzuE7yqJJ1MuG4gcsxGxZa9sv6wXWf5PsfOyrADGpWm4O7n2ytuw1y5j/B5bxP8Sk71pZKVG4uA/hYbwlz1ZK/GZfKqbQo+pR5MSY9coej67A6PVyaizbm/KkOj/HS/DA07y8tmygAwSRkPzX4e+1JJjeUKToA7lUwok6RJ52RVLEojVrbO+pGJMjSs6w6HSyzli41PXHv1eXonDJGhEXveVfn1GMh4MzTY/73A22cnbY1H/by8ZkRSaTwRdPyY883MKSagQV/HuDBzSwQ0oimUgMYdd9yhQw89VHfeeacymVKzpOHhYT3yyCN65JFH9PDDD+uhhx7SAw88EMavA9Bjsk7JqaT23GaaLn/3X9T92kx5csKoNZj8+V0vqj+T0l8ftnPzG4u2sfdjX0A/FfvmYcgKaAQ19LUDXGRoRJv3s+u3v/qspuBBfTM2jGZVLBZbavhpuEtOcaPaLk5TcOuxtNU40b6JHCn3wsmkKgEN701mocZqqik+AY10MuGUFGA/x0OzuymTTkpjlfNLKpFQJl06Tig51XvqqRBgBzT8MgPd71f5mnNFPDmrr1uoU5GuCmiQoRF3TuZOA5OW1QGNMLcI3eINbpmM4aDP9199/x5JpRKpO88a8n1OJUOjNFe6jgyN2KjVQwPdF0pAo7+/X8cee6wTzJCkKVOm6KijjtJRRx3lPDY+TiQSQLVKyanGLxTe1wSVjFi5cUyX/+9TkqT3HrRDYNNpdM+4HdBI+d9p2n1T7GwNcwPizdCxjwcyNCLO2xQ8qIeGCV65emhUvsnmixrNFlwZPM1yBci4UW0bJ6BhBaEy1mfa3g/DY6Xjoi+VdCYfq3toBK/A9QtopJKJCUsKIFqa3Usm88ccO6lkwilDleUa0XPqWVG5ebxSxm6iRq1BgXTEh1M+pJWSU57jaqhcDpUgVzzZ1/1GMncGM9X3KoVCsaGgCKLH6adiSk4lTU+36jHCuGfccN6xu2vFxjFtHMvpd0sqJQzNETF72oCk4F6RiB5KTkVbKD003v72t+uZZ56Z8Hl9fX1h/DpMIk8t26Bv/OFZZ0UmelOl5FTjpyRvY76gmwn7GKJMQDSZiYS+dDJwdX2/q+RUZWIy7fRVcD/fXnFLhka0VTUFDyg5lfSZePbOK20YDWflE03BO8u+VzA3Drl80TVxuDlrMjSSzoo378RirQyNqf3Vga5ShkZ1bxZEV7O9+cw4Y7NVuswJaNBDo+fYk5N+x0xRlTJ20sQZGnZJOsrTxZO3nEwzvPcrQ5nSeDSoJA2izR5/NhLo8mZoeN8L8VTJ4nKXnPIbH67b7F6wPdSX1r+etr/e/ubZrsfN+eagnWcqkZBeWDmsVZvGQt92hK+SoRHK1DlCFspeueyyy7Ro0SLdf//9Ybwd4Jj/rTv0jT88px8ufqHbm4I2aqU2YcaboVHHzQQ3odFkAg79AeWmvD+zMzT8+ipIZGjEST0lpxKJhFI+pYG8rw2rjwblRTrD6aFhFZ0yJT2yhYLrb2/KwySTCSuQ6S0111jJKTtDg+tDPHj30pu3m17X60z232arh4Z5jB5cvceemwwaHtrXmokyNOzTA6eKeGqmV4JXVVNwk6FBQCOW7N2WaGB2zD+gEcIGoavMmNJcP8xEtt8cw9ph//sN7+S3ea8ZQxlNLS/IC7XnH9qm1j0Fui+UgMY+++yjX//613rPe96jX//618rnWQWLcD2/krS8XmYuFN7gRD28NxX13EywAjeaLvjPRyRJG0eDM7KCemhUSk65922WDI3Y8H4u/TI0pMoqp7wroOF+TlgZGvZEOmeN9qmUnKo85jQF92RojJRLTiUTVhaHZyK6Vnr4lL7qgEYmlXSey5x2PJhjZqup/frqXx2g//jQYXW9Lu2UnKoENMxj3tIR6AWVc4BfULpYdJ9fJsrQsMeYjCXjqVYGX70yyYCm4ES5Ysk+NzRScmrAZwEWi1/iz+mnYpqCOxnD7uvDM69v1DcXPev7Ht4sLvu4qpXxgeih5FS0hdJD44033tD3v/99rVixQh/4wAf08Y9/XMcee6wOPvhgHXLIITr44IM1c+bMMH4VJinTQAm9yZR5aCaVz3tTUVeGBgOISHr69Y0TPsfO0JgxWDkvBDUFz5KhERtVAQ1PDw3TaK/S66DyM5Me3pdOajxX0IbN4Zcp5Ca1fYo+4SJzPcjmi65JouFy+cBUIlF+TqG65FQxuEZ6UIaGX6AMUVbaT0N9KZ1+8I51vyqVdJeXSiUoOTVZ5AtF+Syodp0/JsrQcJWcYiwZS97JymZ4F1OZBTZBffwQbc2WnOqn5FRPKnqyuMzn3TvHcOI3Fge+h3eRpv1dmjFHrJChEW2hBDTOP/98LViwQFtssYXmzJmjl19+WTfccINuuOEG5wZxl1120SGHHKL/+q//CuNXYhKwbxS2GKL/Si8Lsyl40A2m/TBlJXrDDCvQGdgUvECGRlx4bxTMStltpw/onHm76t0HbC+pMgnhV3Jqi8GMVmwcC6+Hhqu+SChvCR9+E0xOhoanKfhmq+RUKiAzy1wH/GLkQz7N4lNJq5QZk5Sx4JfVUw+zn002hl1yyq/hJ+LNXXIqaHxY/8IH+/TAxGU8maBUK42bgwIaHBPxZN87NnJNoeRUb6qUpTMZGsElp4KkU/4lpyQpE3DPimgy9wVpAhqRFEpA49Zbb9V+++2nu+66S1OnTpUkvfzyy3rooYdc/91www1h/DpMEutGKk2W7JXY6D2VklOtNwUPCmjkrUEDq+ria9SabJjq0xTcOzbMWs9n4Bht3s/laHml7FbT+nTOvN2cx81H3n6++XqLoVJA48JrHtU7991OfTX6sdSDyavO8JucNjeDpZJTlcedDA0roBGYoeFz8+F3TEzpT1d6aHB9iAWzlxq9vTQZGuN5uyl46V0IaPQe+/jw+2jnC0XXJNVECx+KPoF0xIt39XUz+rxNwU3JKa4fsWTvtoZKTmWqxxMcA/FXcMak1U3Bi8Wi83gt3gwN+7hKOWMOjpU4IEMj2kIJaKRSKZ100klOMEMqZWTssssueu973+s89sorr4Tx6zBJ2BcLTh+9zUwiNBP59g4YgsqF2PMUDDbja8yabLBX1yUDysVkXZPebd44tMT7uRwrZ2h4A51OrwOf5qxbDFay+W59eoVO2nfblrbJLoXEaaOd3A0YJatmcaHg+lybHhqphJWh4bkpzNdYTeWdiJKkKX0p53xCyal4KHomHOplhgxOyakkJacmC3NesIMSm8ZyrqysiTM0GFPEXTglpzwBjXJTcO4v4sk+JzQyaTmQrs7QKDKGiL2Ct+SUdUzkCsW6en7WWqRpymV7e3Igmsyi2GYqiaD9QmkKPm/ePL3wwgsTPm/nnXcO49dhkpg1pU+nHlgqMcIqqN5WmXxqIkPDM2DIB0xI5MjQ6AnbTB/wfTyo9Iy94jZPhkakBWVoePvk1Co5NW2gsk6jv8XsDKkyaVr6mvNGuxR9JpgyVoaGPeFoMjSSyUQlM8uzb8x5wG/Cyi9Doy+dtHqzsJ/jwHweG729NOMMU3IqmZAyaUpO9Sr7HGA+2/Y4oVCUJ0Oj9jFgHyLcm8RTvsb1oV7eYLkpOUVAPJ7s8Wcja+soOdWbzMfY3Fvacw31ziF4zxHuDGT/e1ZEU617CnRfKAGNz372s1qwYIGefPLJMN4OcCQDJinRW5ym4M300PAMGIIzNCqPczxF0347zJAkfeioXQOfM3+/7fTR43bXz84+1PW4X1PwUmqwrO9D3FiEzvu5dDI00v5p2+6ARulfuzlw2KnBnDbap+AzOW0HKfM++9qVoeEtOVUjPdwvoFEsigyNmHH2UoMfcxMfNSvx08mkU8+agEZvM+cZ735eM1wpcTtRU/Bi0T3GQLzY+6+VIYJ39bXJ/OOYiKdKiaHGsv4oOdWbnDGp00OjckzUO07wniPsyfBmenKge2plfaP7Qglo/PrXv9bb3/52nXDCCVq4cGEYbwlIEjWtJwmTPVFPCqeXd8AQtLo2V+AmNOpMQOvwXWcFPieVTOjSE9+k4/ae7XrcL/jpHXSSoRFt3tWuToZG1U1B6d+8a5Vt6ev9d9oi1Aa/9haRodE+Tj8Eu2midT0Y9ykDY2doeD/bTtNXn4mJg3eZqTdtO01vf3PlHLLnNlMrGRrs5ljwNu2sV1WGhlVyapyARs+xywaa80I25/6Qr9g45nw9NkGGhl9mIOLDHje0sujB+9qgfk6Ih2KT1xO/DA3GivFXq+RUrc/4B95aqUbjvXfxG99Scioe8s4iqVCmzhGyUHpofPWrX1UikVCxWNRJJ52k3XbbTW9/+9t18MEH65BDDtG+++6rdDqUX4VJxm7ChN5l6p83V3LKPfgMWu3gXbmP6MnXWFU9Eb8MjaqABrs90rx9EEy/FO95wRwf7nJQpX+TCWm/HWfooZfXhlIP3/U7Wn43BKn8na0VbNbNoN+q6VRSgT00THzD71ySSSV184XzJEl/XrFJP1z8gv7h2D30yRsfL7+WPR0LZkVtgy8zwW8TvEglrZJTOfZ9r7E/zua84A1c2WPCsVxeY7m8Xlw1rDdtO32C9+N4iRt7lzXaf6cWAhrxlvdMYNfLL0ODQyD+zCXCBLhSrgwN/x38mZP30d9bFQa88xMJn/EtfbviIUeGRqSFEmW49dZb9fDDDzv/Pfvss/rBD37gDBT6+vq033776ZBDDtF3v/vdMH4lJokkGRqTgtMUvKmSU54eGgErY8jQiD6nrnETAwa/puDVjYJZCRNlVU3Byyuo+zwlp0zJoNFsXsViUYlEwrVa224mHSZW47ZP0Wcywb5x8Fs1nUoknPO/99gx54Gg4KgZn+65zTR95a8OKD8m3/dCNPll9dTDHFfjuUrA1OnXwjWi5xR9MipqZe+NZQs69Tt360/LN+j6847QIXPcGaP2+YHydPFTCKnklJe51jBOiKdmG8X3+zQF57wQf94xaaJ8b5ErFAPHiFsMZVzf9wVkl0t2SVXGHHHgLLikKXgkhRLQOPbYY3Xsscc634+MjOixxx5zBTkee+wxPfTQQwQ00JCghp/oLSbY4E3PrIe3TFVQU3B6aERfKzUq/VbHVZecYr9Hmfcm0Exie4OWU/pLQ5dcoaixXEEDmZTz2kSiEvAIpx5+ZZu4DLWP+dvaK2YzrgwN/5JTfqXmisViU9lezjmEHR0LzjHTYI6GmbCq9NBIqC9FD41eZV/2zXmh1n7eOJbTn5ZvkCSd/v179Ml3vkkfOWZ35+euAAmHS+zY95Nh9dnKpIL7OSEeTLZVGCWnyNyKP28PDal0vsgVis71w3tPue30Adf3VRkaviWnOFbiwJzXUyFm9SE8bakDNTQ0pCOOOEJHHHGE89j4+LieeOKJdvw69DCagve+ZiefjHSqmQwN7kKjyAwgmxkw+AY0vKu2OY9EmpOhkyhNQgX10JjSVxm6DI/l1J9OWiWnKhkaYadyE1hvH2e1vfVYKplQIlGauPYtOZWwe2jYq7Ddz6mX00OD80QsmN4IzWdoVHpomHHEOCWneo5fz4tGAldfvvlpV0DDVXKKa0Ls2Puv0cnrIP3plHP9KBZL15BmMo3RPd6eCfXqT1cvxOO0EH9+GTuZVFJjuYIz3vT2dttmhiegkfT20KhuCs4iingw80ZhBcERro51Nunr69NBBx3UqV+HHsEEQ++zAw2ZJnpoZDwXl6BJazuIwYqIaDLHQjM3gt6AxiurR/Te797leg4BjWgz+8cEMEyGhrfkVCqZ0FBfaVXcprGc6+Yxlag0+A2lKbhPnw6Er1j0n5zOeBo425JJ/1Wx9ue8kXNJkgyNWGl2N6U8AY10MtxzBqKl4BPsbCVwZQcxOFfEj319CGuxbTLhnujiuIgfZwK7wfsPv2OI/R9/zgI7a1rCWybKOy6dPa3f9b235JR9qGToERsrZmhID41oolU7Ii3FCb/n2cGFZnpo9HlWxwQHNKyvGWxGUqGFTB0T/DT7/1O/eUJvbBhzPYdMr2gz+87pkZHzbwouSSPjpZ997n+f8tTErkxOhhG4dDcF5/hpF/OX9a6YNdcEv5JTwRkazZUUYQFFvFR6aDR2vXACGvnKijtKTvUu+xxeT8mpidjnl2LRXYIK0Wfvr7DKhwyP590BDa4hsVPJEG7smNjamsSeWi6HSuZW/NlZ34ZTJspkaFjXkR9/8BBNG3D30KguOeWTocG5IhacDA16aERSUwGNffbZp6VeGK2+HpOHGWzmCkWNZqtLTiD+slbmRDMBDW/90noyNLjZiKaJGvnW4q1/v35kvOo53GREm1n1ZFL4TYZGrd46tz69wlVCIpGs3HSEPTlJpbr2Cfpsms+1b0DDytDIB2RoNFRyyue9EF1OVk+Dr3OOqaxfhkZRY7k8k9Q9xB2ACCGg4Xkp54t4aUfJqXyh6Bq3MtaMn2KT9x/96ZQe+8w7tOTydzj3sFw/4i+oh4ZUWSxlAhp96aTevs82Ve/hndOwD62000ODG4s4MEEsMjSiqamAxtNPP61Vq1Y1/UtbfT0mD3PxuOGh17TPZ36v3zy6tMtbhLDZq6ibKTnlrV8adHNpr85npX40mYmCMHpo9Ps06qPUWLSZcb2ZXHR6aKRrHw/eDI20NTnZKjsrg0mK9qk0BXc/7pQf8+mhUep94C4BILkz8BqZnDC/m70cD5UMjcZeV52hkXSOs5UbR3XYFYv0kV8+FNZmostcTcGL1StrG38/T28urgux0mxJwonYwRHuMeKn0jOh8dfOGMpo+kDGyhQPccPQFX49VUxWhZOhUV5o0x+w6Mo7p5Gwll+EmUmO9nN6vdIUPJKabgp+++23N/1LG00Px+Rlbjw3juUkSZdet0R/eeAO3dwkhMysTkgkmru58J5PgjM0rBW8DCAiqZXm8N6AhjdzR2JCOupMFpVTcqqclecX6Dxwpy306KvrtMMWg66SIsmElRYedg+Nlt8NQQJLTnlW09tSCUnlYyPo/N7IucT8buaiYiIgCDYRc0M6ljUl7RLqz5SOo8deWy9JuuWpN8LZRnSdq+eFU3KqlR4a7u8ZVsRL0Weisln/ec7huvT6JbriPfu6Vu5StjB+zLmhlTmqhDOGYP/HnV9T8LRTBt3dQyPj0xheqvR5qxxblZ+ZsWmW1O9YyDnzE3RriKKWAhqtBDWAengjodvOGOjSlqBdzEWimewMqXoyI2i1nGvCi8FmJDlNwVvI0DA3kgM+A0xWzUWb2T99zqr84JJTH3/bHvr7nz+oLaf2BfbQCLvkFGUE2iewKbjnWLClkgklyrskF3B+b2TSyjyXyah4MNlTiQaLTiW9GRqpRFXzTvQOV1C6/PUTS9f7Pncgk9SoT/DUVpWhwfkiVvLF5seZXnP32Ep3feJ4Se7xAcdE/DhNoFs4LpwxBGPF2CsUqgOfThAi7y5dWGv8kA4IaFQWXnGsxIE5HpopjY72ayqgcdttt7X8i+fMmdPye6D3eZvv2M230BvMxbzZi4T3VUGT1q4JL242IsncBDRzLNj9dqSADA32e6SZ/WMmsc09oV/Jqf50af+OZQuuCexEItxme/Y7cI/aPk7JKc8ZvdIU3KfklHV36GoKbt2INrLaMsnqylgJKlM2EW/WTylDo/p64a2Lj3jyZmiMjOf0lf97xve5O2wxqOdXDtf9fhILZOLGWXkd8mc7kUgomSi9P/cY8VNpAt38e1QWVoWwQegqvx4aGU9GsFlo0xeQoSGVgh3meX5NwVloFw+VDA3GhFHUVEDjmGOOCXs7AF/elRKcRnqPSbdsttGSd5VV0KS1fYPBACKa8q1kaJhU4PIg1NtbRWK/R52ToeHZd37ZW07j8FxeRevmMZVIOAGQrM+q/kbZc1UcPu0T1A/BmXwOyNBI1sjQaPTGw0xwsZ/joRLQaGw/O03BTYZGwj9DY3M2r6n9TSeyIyLsAMRffucu/fVhOwc+d/u6Ahqe7zlhxIrfyuuwpJIJFfJFglwxlPeZwG4UiyJ6h1/JqUqGhrvkVK2Ahr1AL+HzOE3B48HMT9AUPJrIsUakEQntfSZDw6+sTD28Y8/6MjQYQERRoYUVECb4WSstlFVz0ZYPCmj47EsnQyNXqC451aaVT9ykts9EJaec5ovWsZFMJpxAZsF1fm8uMEq5iHhxVlA2+DqnKXiu4HxvemjYRsZzLW0fosH7af71/a84X++61RTXz/acPW3C9/OeHxhWxEsYpYWCmGsOY834KTS5EMKWYAzRM4rO8VB5LJNyf75N2cpa8xdp62f2mNRpCs65IhZaKYmN9iOggUjzDiw4j/Qes9IhrJJTQavl3BNeTf0qtJmT0tlCDw3zHn4xK24yos2skPOulvZruGcmIMc9AY1EonIuCaOHRtGaDuPoaR9zeg4uOVW9Ei6VSDirpfxKCjYaJDc3KvRKiYegrJ6JeMeV6YAeGpvHq8ucIV6KxWJgqcDpA2ldNv/NrsfeutusCd/TO8Zk8jpe/FZeh8Vcjzgm4ieMzJ0UWZ49wxmT+mZolHto1FlyyrBPOelkePcpaD+zEJYeGtFEQAORRoZG78s5aXzNnY5mDPX5vp9XngyNyHNKxTTTQ8M6VxQKRd9BIithoqtQqEw8eW8OBn3q21dKThWsG4/SzQdNwePLO8eU8tQstjM0UsmE87m3z+nN1rpNJJiMiJNK35XGeI+LVDKhAd8MDQIacVfrlD1tIKOhPve1ZcupfQHPrqjO0OCEESd+DXrDkiSgEVth9Fah5FTvMPvQDnyabAtvhkZ/zQwNq+SUHdAwGRo0BY+FfJP3FegMAhqItKoMDbpo9JxWo95/c9jOOm7vrXXILjMlBQ8k7ceZ2I6mQisZGtZrcoWib0Noal1Hl/2Z9K6W9k46SXbJqbwTaDA3HhmnNm0I+9vuocHx0xZ2oMj7yc94xgB9nvT9WhkaDffQoFxEzDRX89x7fUknk+pLVZ9jKDkVf7U+y/2ZpIY8PVKG+ibumeK9DDB5HS/FEEoLBams0OeYiBu/CexGOSWnOCfEXiWTq/JYZbzZQA8N6w3sOSzveyHa6KERbQQ0EGntqHGKaDGpm81eJAb7UvrZ2Yfpg3PnSAq+ubTvL7gBjSaTodFMso6d1VEoFn0bQhPIii57AsBbYmqgRoZGNl8JXplTiNN3IZSSU/5fIzz2udk7meANdLtKTiWtDA4reGUCWY0HNMjQiJMwMzT8e2iQoRF3tT7L/emUpva7ry1+wXMvb6Yek9fx0omSU4w14yeUklOMIXqGd6GUVJ1VYQIafn3+jExQyakwF16h7SqZ30ydRxF7BZFWlfpJfKPnmDrVg3XcSNZiBpJBNxJ2LXwCGtFjlxxqNUMjH1ByilVT0WXvr/46Sk7ZE9vmHGJWarcrlZvDpz28PVBs3j4YfVUlp0pf+2VoNBokN0+ntFg8hNZDI+nfQ4OARvzVzNBIJzXTU7K0ngwN7/iRBbbxUik5RVNwVIQR6KLkVLz9532v6GO/fkTZfMG3NJ03q2IsP3GGhj3ecDUFT9IUPE4qmd9d3hD4mnjk1qDh4WE9++yz2rRpk+bNmxf222OSqZqQ4LzfczaNlco6TO1v7XTkpHoHNQUnQyPS8tYNQDP9VOxBY65Q9F2dz8AxurJW8MF7c+A3yWRPdI/lShOPJqhlyhSF0hTcOi6Z6G4P+69a1RTcW3LKE9BIe3psSFYvnqZ7aLCf46CSodFgySnPcZEMzNCg5FTc1foo96eTmjGYcT0WlKFRKBSdBVZVJac4X8RKodi+ialKTyeOibgJteQU54RY+pf/flySdPSeW/n30PBkYFWaggcvyHSXnLIeT9EUPE4qmd9ENKIotL3y2muv6bTTTtPMmTN1yCGH6LjjjnN+duedd2qfffbR7bffHtavwyThvfGk1mDvCTugETRpTQ+NaLNvAJsqOVWjKTh1jaPP7K9UMlHVN2Gwr/qAsFO8Tdq3t+SUXx+VRrlKTnH4tIX9d014dnXam6FhfZ9OJn3P+05fJkpO9TQn67LB+SdveYh0MuH05LGRoRF/ta75felk1fnFmxFm2EEL73syeR0vYUxcByGgEV+tlLw1UgFBT8TL2pFxZ1zqLjnl/nybhXN+GZ6GfW9KU/D4MucHemhEUygBjeXLl+vwww/Xb37zG5188sk64ogjXCsZDz/8cK1YsULXXnttGL8Ok4h3wMlEdO8ZDimgkZ5g0poeGtFm77dmmjXaL8kViq4V/06TaPZ7ZJmARiaVqCo16NdDI2E1hB5zAhqm5JSpTRtuAJyAWHu4Sk55fuadfLYzNNLJhO95v/keGtXbg+hqtoeGd9K6VLqs+l02E9CIvdolp0rXlekDlbFn0DnDHjN635LMvXhpZw8NFs/El9MsPoySU9xrxFqh6J/JZTKCs54eGrWbgts9NOySUzQFj5NKySkCGlEUSkDjc5/7nFasWKGFCxfqxhtv1AknnOD6eSaT0bx583TXXXeF8eswiXgjoVki2T1n42gpoDGlxYCGmQQNWu1g33QS0IgeV4ZGEzcUiUTCdTNpZ2i8advpVb8D0WLO7ZlksuqGMqiuuQlcmJsK8zKzWiqcklPW1y2/GybirWvuLT9np/anUpXPvH3eb/bGw1xDmIuKh2Z7aHhX5QetuCNDI/5qNgUvlxmbOaXSRyPolFGoMX6k5FS8FEJYiR/E73qEeDDDxVZ6q7AoIr7MfYRUOseb03zCp+SUyQI2YwS/Pn9GUIaGeZx5rXhwmoK3IRCO1oVyOV+wYIHe/e53u8pMee28885atmxZGL8Ok4h3pW7YK27RfU6GxkB7MzTse1BW6kePvUuaXQGRshoymsHpDlsM6nPv/gvncUSTk6GRTlbt/6CbBbPS2snQSLqbgod9o8BNanu4Sk55flbVQyPln6GRt1a5OTceDc5YOfWvOU/Eglmk0GgPjT5P1o85Tn7z0SN1+sE76qS/2FaS9LWFz+rVNSMhbCm6pVb2RL9PyY+gyUx7zEjJqXgz5/e2ZGiYMShjhdiprMhvIaBByanYsntmFQpF/x4aKXcQYtNYVpI0rcb8Rdoab7iagqeq+78husw9RipFQCOKQglovPHGG9pzzz1rPieTyWh4eDiMX4dJxDuZwUR073F6aASswq6XGSjU00MjT4pn5NgTD83eaNr1i80E+ZVnHKAtp/Y5jyOaapec8h+qmBuC8YCSU6FkaNhfc/i0RVHBn/205+ah39UUPOlMRrt7aDRX65YeGvHU6OXCW3LKHCcH7LSFvvpXB2jVpjHnZ/e/uKbl7UP31Posm+Ngr22mTvw+NUpOMZyMF7Mr27HS1skS5piInaIzgd38eyStRVWIFzMXIUmbs3mrh0blOWa8afavqTBRK6DhytCwHqcpeLyY3UQPjWgKJaAxa9YsvfrqqzWf8+yzz2rbbbcN49dhEvFObHHij69n39ioE7++WDc/vtz1uNNUq0YNynpUbiQm7qFBYCx67F3S7HjBDmiYfZxJJZ2yNdxkRJdZ8ZT2lJwazKQCV82a/gpjuVLatzlu+kJstmcH2qiV3h72x9K7q6uaglvXiUwyUdWk0f6aHhq9za9pZz28AQ3vOPOYvbZ2vh7NUXYqzmp9ls254/N/ua9O2GcbXXfeEYHPzdfI0OB8ES9mX7ajcohTcoqIRuyEWXKKsWL82CUmN47mfDM0Mp7+fJvqCWhYr3dle/gsxkF0ORkaBDQiKZSAxpFHHqnf/va3ev31131//txzz+n3v/99zZJUgB/vChrqksbX/7vmUT3zxkad/6uHXY87q2lT4QQ0ggYH9gCTkiLR42oM3OQNhRln5ItFZU2ztlTSqZVMGYDo2Dye148Wv6CXV5cyN3NWYNOeYBzsC65Na24IxpweGuUMDVObNuRJBU4b7VHr5j9To+RUKpXwzczLtZihwWREPJhrRqsZGt4SVOfM2835msbg8Vbro2yOg51mDelHHzxEh86ZJUm67ZJjddn8N7uea48dvOMIxhXxUgyhtFAQmoLHl1NyKoym4Oz+2Bm2MjQ2jmZ9xxfeeQaToTG1PxP4vva5wLUgxxMcQbRV7iva0HwJLQtlr1x66aUaHR3VMccco5tvvlkjI6Was8PDw7r55pt1yimnKJlM6h//8R/D+HWYRKqbgnPijys7ndPW7OSTl7063w89NKKtEEK6d9qqSTpumkynE84NSrFIMCsq/vXmP+mKBX/Syd+6U1IlUyuTSrgmGGs128t4moKb/ZxJh9gU3P6aQ6ct7D9rIxka7h4a1Rka3pX3E0kwGRErzX4e+9LesmbuY2ywL6UzD91JkjSaJaARZ7WCk0Fjzl23mqIzyvvfsGPj3vMDY4p4MburHT00KiWHQn9rtFkYzeKd/c9gMXbGrKbgG0dzzmfYr++FE9AYmzhDw16HawdR29XrD+1Ryfzu8obAV2tF68sOP/xw/eAHP9D555+vk08+2Xl8+vTppV+STuunP/2p/uIv/iKMX4dJxDshMcJqudgKmlsyqxNaXS3lN7Flc/fQYAARNc2WD7HZ9WsrPRmSrhUV+WJRyQabyCJ8tz2zUlLlhsAuOWWvoK6VoVFpCu4uOZVJhldyyp5tZ9Vle9h/1ol6aPR5emiYn+ZdGRqlz37jGRqlf9nP8WD2UqMZfd4MDe/3kjRQDqRuJqARazV7aNQoczrFc92xSwh5gySMJ+Ml76y8Dn8cWLkPIaIRN34lhhplbjXI8owfewHUxrGc1VOlcjw4GRp5k6FRago+tUZAIyjgnaE8XayYfZ4iQyOSQgloSNLf//3fa968efrud7+re++9V6tXr9aMGTP01re+VRdccIH23nvvsH4VJhHvhMTmbF6FQrHhlZfovqBBYrMNXIPeP2hljP0oGRrR46yqbuFmwg5qOQGNZNK14ipfKKrGon90yMi4O2PLBDYzaXdAY6hWySlPU3Cn5FSbmu1xk9oe9t/V++nPJIMzNDJWsCOcHhpkaMSJOW4avWJUBzSq38EEUjePM9kQZ7WCk95ydrZ0Kqn3H76zfnXfK6X3cWVoUHIqzpzSQm2Yl0o6Y9Dw3xvtVfBZkd+oyhiCc0LcuAIaVg8N+zzhDUKYyhPTawU0Ao6FicpkI1rMfqQpeDSFFtCQpD333FNf//rXw3xLTHJ+gYvRXF5DfaEeuuiAoDGiU3KqxbsLv+awNleGBimekdNsPXRbyi+gkU64MzQYPEaCN9uuEoBKuFbODtSIPvU5TcFLrzW7ORNiKnfRCoVy5LSHfb/nXTVblaFh99Cwxgf2Krfme2iY7als0MbRrBY/u0rHvWlrxh0RU8nQaOx13gCGX4aGKXVHU/B4q90UvPaY84r37KffPrpMG8dy7h4anslq5i7jxW/ldVhSlByKrbxzXDT/HpQci69xV8mprLOwJeHK0KiUnCoWi3X10Ai65wyzNC7aL9fkQil0BnkziDS/5lzDY9xgxlFQenfoGRqBTcGt38nNRuSYXdLKYMFMaOcKRWcyO5PyZGiw7yPBG9AYt/ZXvT00vBka5hxgJr3DKBdjHy6sumsP+6/q/fh7J5v7MxP30Cg4Nx6NDXETPqsrP3nj4/rofz6sT9zweEPvhQ4wEw4Nvsx7TPmNPQbKx9koZU5jrdYp2xss9ZOscX4xWCQRL3618cNSWVjFJGXchNEsnrKV8TVuLYDaNJbzLUGWthp5j2YLzrm/Vg+NoGPB3KdkcxwrUVcsVhZJkqERTbELaFx//fX62Mc+pnnz5mn69OlKJBL6wAc+4Pvcl156SYlEIvC/M888M/D3XH311TrssMM0depUzZgxQ8cee6xuuummwOfn83l9/etf1/7776/BwUHNmjVL8+fP1913393y//Nk5jew2MwNZiwFXQOyIffQCKpXWXStsGMAETVh1K81mRhj1qraqh4aZOdEkl1yyi4rVKvkVMaboVE+drac2iepFOj41qLnQttGThvtYd/weQPf3uuCO0Mj6VoxZ1RWUjW2HX4lp25aslyS9NvHlum/Hni1sTdEW5nsqXb00Bikh0ZPqDWx2FfHCcKcf1wZvpScirVCCCvxg7BCP778VuQ3yux/ypPGT9bTFLxQqD5PpK0yUaZ/RjJR+z4lMEMjRYZGHBQKRb33e3dr3Uh5fxPQiKRQ8ud32223CZ+TTCY1ffp0vfnNb9Z73/tenXbaaU39ri9+8Yt67LHHNHXqVO244456+umnJ3zNAQccoFNPPbXq8X333df3+ZdccomuvPJK7bjjjjr33HM1Pj6ua665RqeccoquuuoqXXDBBa7nF4tFnXnmmbr++uu1995764ILLtCaNWt07bXX6uijj9YNN9ygv/zLv2zq/3ey85vkHsnmfJ6JqGt3D42J6lHaD1OzMnoqNxPNv4c5hMaylQFiXyrpGpAy+RBNrpJT1kRTrZVPlabgpodG6fEp/ZXXfG3hs/r42/Zservsw4VDpz1qrqL2XBf6PT00zOSUXw+NdIMZGn6rK5OJyrnpn25YojMO3amh90T7mN3Ueg8NmoL3qlpDvXrGnN4msFL1BFXQIhpEU2Wisn1NwTkm4ifvM4HdKHqoxJcdWMgXihouL571awqeLxS1ccyUm0rXDIIFnQrMwq1xDpZIW7Z+sx55ZZ3zfT0LIdB5oQQ0CoWCcrmcli1bVnrTdFpbbrmlVq9erVyu9IHffvvttWLFCj366KO65pprNH/+fP3P//yPUqnGurN+/etf14477qg99thDf/zjH3XcccdN+JoDDzxQl19+eV3vf/fdd+vKK6/U7rvvrgceeEAzZ86UJF166aU6+OCDdckll+jkk0/WnDlznNdcc801uv766zV37lwtWrRIAwMDkqTzzjtPRx11lM4991wdf/zxmjZtWkP/r/APaFByKp6CLvhh9dBwBhoBs2OuFXakg0dOmBkao1k7Q6OUkZdKJpQvFMnOiShTIiydcgc0pg8E16Y1kwcmI8evRGGYWHXXHpWV9tU/804229k7qWRCiWL1hGOztW4rqysrj82a0q9Vm8Yaeh90RrM9NLw3pH6lhypNwRlvxlkrPTSkyjXFfp+cZ/zIkCJezP5qx0rb5AQLq9Bd60bGtWrTmPaYXT0fQ8mpyS0oU6LPtYimnBGcr/TPmFbjHkUKPhZMhjkZGtG2bN2o63u77C2iI5S9smTJEu2www6aN2+e7rzzTo2Ojmr58uUaHR3VHXfcoXnz5mnHHXfU0qVL9cwzz+ikk07SggUL9M1vfrPh33Xcccdpzz33bCklsJbvf//7kqTLLrvMCWZI0pw5c/TRj35UY2Nj+tnPfuZ6zfe+9z1JpewRE8yQpEMPPVTve9/7tHLlSl1//fVt2d5e55uhMU6GRhwFjRHDztCoq4cGNxuRUwyhDIC5mTSrahOJynHhNGtk30eK2d/2qvo+V4ZG8M2CN0PDDoZtP6N0Ld5qan9L20dT8A4wE0w+47qqpuDWzWVQDw0TsG70mpLwmYyYPkgj8Kiq7KYGS06lG2gKToZGrNUKQnubw/vxG1eaeIa5TjGmiJd2lpyiKXi0HfzFP+jtX1us597YWPUzSk5NbuMB5Yj7PYtopFJQe5MT0Kg9Rgy6PvR5egAiml5dM+L6fqBGX0d0TygBjcsuu0zr16/XokWLNHfuXCXLq2STyaSOPPJILVy4UOvWrdNll12mPffcU9ddd5122GEH/epXvwrj109o2bJl+sEPfqAvfelL+sEPfqAlS5YEPvfWW2+VJJ100klVP3vnO9/peo4kjY6O6u6779bQ0JDmzZtX12tQP78Vt2vLdewQL0Er783K2lZ7aNg3nn6DSXpodN9Lq4YD//aFGpOa9TLzUqPlklOZVNK5OZko4IXuGOor3QzkrVX19qR1PSWnxj0lpyTpc39ZKim548zBlrbP1RScY6ctnIkEn59lPGWjBtKVm4lSD43qCaRWMzTM9ty0ZJleWDnseg7nj+ioldlTS3/afUPqN7FdCWgw2RBntUtOTXwLbJ6S98nQMNcpVmPHSxjZwEFSpik4q64jyVy/73lhdeDPWjkuktxnxFZQpoQ9gZ1xmoJXemhMFNAIOhTM/UuhyPESZd5F1ANpAhpRFEpA47//+7/17ne/W+m0/4e6r69Pp5xyim688UZJ0tDQkN72trfp2WefDePXT2jhwoU677zzdNlll+m8887TAQccoOOOO06vvPKK63nDw8NaunSppk6dqu22267qffbcs1SH297u559/Xvl8Xrvttpvv/7/fa1A/vwmJVRsp/xBHQXNLuSZX03rZwS+/sQE9NLrrxodf07FfvV2XXveY78/NTWYrq6NMg2CToZGxjikCGtFkSrvYAY36e2iU9um4T4aGWVU1FuLqJw6d9qg1Me3N0Nh6WiXjxs7QyLlWUDcb0Ci/vnwuuuA/H6l6DqvpoqPZHhoDnpIBfhPb/fTQ6Am1gg2ZdAMlp3x69JjrD2OKeCmEUFooSCVDI/S3Roj8TgvOcdFSHz/3ogjER9DYzs4YN/eYOU8PjVr+Zf6bJEkfOmpX9/ta1x/KTkWX9x6ynsxOdF4oufSrV6/W+Ph4zedks1mtXl2JiG+77bZOf412GRoa0qc//WmdeuqpTuPyJUuW6PLLL9dtt92mt73tbXr00Uc1ZcoUSdL69eslSTNmzPB9P/P4unXrnMeaeY2fsbExjY1VJuo3bNggqfR3y2Ynb0ZCsVB9M7lyw+ae+ZuY/49e+f+pl/3/mytfyIvFfEt/h0SxcqwMbx5zJkorv6fy82yuMOn+5t32tYWloO6NjyzVv733L6p+Pl5eBZFMNP95SJYnRjeMlM6lA5mU815O9sZ4vM6pvXiOsLOlhsr7aCxb2v8JFV2f5SmZZOD/u5mPGDWvTRQr+ztROq+MZVs7rxQKdqPA1t4L/rLZnPW1+++bKLpvJmYOVs7rGzePOQ3gc/nKvhk3JedUbGh/Fcu/K5cL3s/Do2NKJ2rXTO6GXjxPTKRyTW9sP6c8s1kJn7FHJlF6zubx3KT6m/aasfHKvjty9y111/OV+9BEceJxoLnGjFn3YpWARmlQkc3F4xiZjOcIP+PmelNs7LxRj0R5DBqXY2Kyyuerz/nZci+2lo4LM4bwef+4mKzniVGfcuZ96aTy+ZzMUMOMR7O5vNYNl+4zp/Slav6t3jpnCz182XGaNpBxP8+6txgZHVNK0RtXovq4aPfcdVw0cp7oxLkklIDGbrvtphtuuEFf+MIXfBtfb9iwQTfccIN23bUSnVy+fLlmzZoVxq8PNHv2bH3+8593PXb00Ufrlltu0VFHHaX77rtPP/7xj3XhhRe2dTvq8eUvf1mf+9znqh6/5ZZbNDQ01IUtiob145L3MH3k6ee1IPtcV7anXRYuXNjtTWi79etSMmspFyxY4Dy+YWPp8Qfuu1ernmr+/UsrokrHyk03/5+meMYGL7+SlElKW7FypWsb0H4bNvnvf+O1YUlKa2xstOl9s6F8jC3503OSklJuzHmvXLb0s9v/+Ec9E8NTai+dI8bykvms5kaHtWDBAv3ptYSklJYvfU33519xfv7c4w8q+5L/+yxfWvpMv7FyjaSENqzf4OzvlzaWfse6jZta+qy//kblvPHiiy9pwYIXmn4v+FszJklpqVCo2ldLVpeOC+PuxbfLHBt3PvCItuqXpJTWrqvs+6fL5/rXXn1FCxa8VPd2PPV6+Rh8/fXye1UPkW/+v4Wa3lf3W3ZcL50nJvJEeX+94eyv+qUSKeXLDeXvumOxnvVUpluxWZLS2rh5jLFCjL28SZLSmtlX1Bmz39CGNUk9vrZ0Pn/skYdVeLn2UuqR4dK44e577tOqp0rPHR0rPZYbH5WU0MOPPKrUa9XZXFE1mc4Rfh5dUTpvrF4V/n3A8mWla8+TT/1JC9a3cEODNild05988kktWPOE6yd/Wlo6LpYtW6oFC15t6t1fd/b/U1qw7skWt7W7Jtt54pmXK2N9I1XMu84Rj68qHSMrVq3WI+MrJaW0ZsUyLVjwWsO/r2jNWSz4v4WaRjwjkp56zX0PwnjQrZ7zxMjIyITPaVUoAY0Pf/jDuuiii3T44Yfrsssu05FHHqltttlGb7zxhu68805dccUVWrZsmb72ta9JKq3OvP3223XggQeG8esblk6ndc455+i+++7T4sWLnYCGyaYwWRde5vEtttjCeayZ1/j55Cc/qYsvvtj5fsOGDdppp530jne8Q9OnT5/4f6pHrd40ps889EdJpfTgfKGorbbZXvPn79/lLQtHNpvVwoULdcIJJyiT6e2r2dVL79dLm9ZJkubPn+88/u9/WiyNjWrekUdq/x39M53q9U/3L1SuUNS8447XttMHXD+7+zdPSm8slSTNmDlL8+cf1tLvQmM+/citUnllnL3/jSeWbpCW3KuhwUHNn390U7/jP19/QM9vXKvZO+wkLV+qLWdM0/z5cyVJX3z8dg1vGtfcI+fpzdtVB96jqhfPESs2jkn3l87rW2+5hebPP1zP3/a89Orz2mWXnXXMYTvpK0vukSS956Tjtd2MAd/3eeCmP+meFa9qaNp0adNGzZw5Q/Pnv1WS9KflG/X1J+5Rum9A8+cf0/S2/nbtI3pi7UpJ0s677KL589/c9HvB32trN+tzD9+hVDql+fNPdP2s708r9LNnH3W+f8cJb9enH7pdknT8EQdran9a3/3TgxqaMlXz5x8pSXrqluekpS9q913naH453b8e6x94Vde9+CfNnr2N5s9/iy6855aq58w79jjtsEVrfVnaoRfPExNZfe8r0otPa7vtttP8+Qc09NpPPXKrNpabep7wtuO0vWefvr5hVFc8uli5YrLqmER8PPjyWunxBzR96hTNn3+UFm5aosfXvi5Jeuthh+iYvbau+frvvXC3Xt+8SYccepiO2mNLSdInH1ok5fPaYtoUrRkb0X7776/5b9mh7f8vrZqM5wg/ww+9pl8//5S22Wa25s8/KNT3vvN/ntT9K5dqz7321vxjdgv1vdE6c01/8z77aP4Ru7h+9uriF6VXntPOO+2k+fOrs8jrcfuNT+iBVcu0195v0vx5u078ggiarOeJRxY8LS1zl6KfOtiv+fOPdb5PP/WGfv7cY5q+xUxtt+MM6bWX9Rd77qb5J+7V1O+89IGFyuaLOvrY4PscdNczf/iz9GplIZvf/MVk1Mh5wlQdaqdQAhoXXnihnnnmGX3/+9/XBz/4waqfF4tFffjDH3YCBytWrNBf//Vf64QTTgjj1zdl661Lg9jh4UrDxylTpmiHHXbQ0qVLtXz58qo+Gs89V8oK2Guvyolr9913VyqV0gsvvKBcLlfVR8PvNX76+/vV399f9Xgmk5lUFxSv/r7K6qm+VFKbC3kVpJ77m0yG/WzXq7X/X03t8/6+1v8G/emkcuN5FZT0eS+7XmWx5//eUTNq1aH0+9snU6UVEMlEoul9ky6XgNg0XvpdUwbSznuZ2qfJVCqW+76XzhG5YqVEZcLs70Rp//SlU8pZn9XtZ05x9qtXX/l6O543NbErn/uhgdIy+rFcoaW/m93TJZHwO6+gVel0KR05oerP/kCf9/s+XXfeEXrs1XU6cd/t9cBLayWValab1xbL+6wvk25of5njSTXOQf7XlujopfPERJLlc7r9ua/XQCblBDQG+/uqXj+9HN/IFYpSMuXq64P4KJavJf2Z0jEyZNU732LKwITHTaq83xPWMWZKTvWVm4MmEvEaU0ymc4SvRGm/pZLh77eMaRjLWCHSkn77vjxuSKea33emH1OiiWtS1Ey284TJ2LT1Z9zHSb+5BhSlzbnSdWDaYPX4oV59qaSy+bwUs2vIZOItfM9+cqvnPNGJv1loI/Tvfve7Wrx4sc4++2y95S1v0W677aYDDzxQZ599tm6//XZ9//vfd567zTbb6Mtf/rKOP/74sH59w+69915JcnprGGabfv/731e95uabb3Y9R5IGBgY0d+5cjYyM6I477qjrNahfymq+Yxoo5ei2Fkv2xKBdQ9/cHHqbvzZjoNzIczRb3WDLbg4ZZqNg1GeiZrpm//j0Z62bCVqYiaohq49Kyqd5MLrD3gXm63yh0th73+2n65BdZuqMQ3YMDGZIUiZd2qdj5drHKZ+m4K02cS66tpVjpx2c5s51NAVPJqVD58zSOfN2UyKR8P1c55wAV6NNwSdu6DlO88bIcHZTE0OHjL3AwuccM9BXeYzG4PE1Vv68mn1s3z/sOXviTE1zDin4jFnNPQnXhXipNAUP/73NGIRxZrT57R2zy5ItNIt3zhfs/9gZGS/3XrN2f3/afZIw49FcvuDcdwxkmj+RZMx9CuPKyMrm+CzHQSgZGosXL9b06dN11FFH6aijjgrjLUPx8MMP68ADD3RWcRmLFi3S17/+dUnSBz7wAdfPzjvvPP3yl7/UFVdcoVNPPVUzZ86UJL300kv6zne+o/7+fp199tmu15x//vm644479KlPfUqLFi3SwEApbeyBBx7Qtddeq6233lqnnXZau/43e5o9QeUENBgoxJI9RMwVisqk3AP/dCsz2WVm8GEGGjb7qMkyeIgc52bCb1azTmbuc9NoacX3YKZyiTM3GnnOH11nBzTN1+YjmU4mlE4ldf35cyd8n0zSHbRI+gQ0/M4FzeLIaQ8nmOnz2fdeF7xBCnMdsc/pzoRVg+cS8/RaE5StBsgQHicQ1sRr7SDF9MHq1WN9qaQSidLvGM3mNX2AVXlxlM25Axpv2m6a9Ii006xBzRiaeJ+ac5I9ZMx5moLnCWjESq3rTauY0I4uv3GnrXJcNP87EnUsikA0bRor3TduP2NQS9dtllRZJGmY8Wi+UNRYeeGk9zmNMNcQxpXRxXxRPIQS0DjuuOP0kY98RN/97nfDeLua/ud//kf/8z//I0l6/fVSHdR77rlHZ511liRpq6220le/+lVJ0sUXX6znnntOc+fO1Y477ihJWrJkiW699VZJ0he+8AXNneueNJk7d64uvvhife1rX9P++++v008/XePj47r22mu1Zs0aXXXVVZozZ47rNWeeeaZuvPFGXX/99XrLW96iU045RatXr9a1116rfD6vH/3oR5O6D0Yr7MmLvvKJn5NLPNnjO9dqt7wJaHQuQ4PVENFTDOEm05uhMaW/MtBME9CIDHeGhglolD6TjayqN6ulzM2Ae2VVyvlduXyhZqZHLfbR4ncTjNaZv6rfns94MzQ854dB55xfmaDONXEs2e9tjk8zoW3jxjM6nOOmiWvG8FjlePE7ThKJhAYzKY2M5zU6zj6PKzPWMwuizpq7q/bcZprevG1992SVcUPpfeyJanNuYvI6Xsz+amUlfhBn4QxjhciZaOzvHBct3IMk61gUgWgyY4JtZww4AY2gDI2slaHhfU4jmNeKPnvM/9bdZnVxS1BLKAGNrbbaSoODnWmS+Oijj+rqq692PfbCCy/ohRdKDVt22WUXJ6Dxt3/7t/rv//5vPfDAA7r55puVzWa1zTbb6IwzztAFF1ygefPm+f6OK6+8Uvvtt5++853v6Ic//KGSyaQOOuggXXrppTr55JOrnp9IJPTrX/9ac+fO1U9/+lNdddVVGhgY0NFHH61PfepTVUET1M++0TQXGSYk46/gs9qt0cknP321MjSsw4ZJqeixJxGbZQaW6zaXVtrYJaeSBDQipLIPzLnAjOcbOQ+Y1U1jPhkafdZNxliu+YCGrcBpoy2KNZbae/eb9/jwC2I7ZQwbDWgk3dszlElpeNx9LaFcYXSY/dTMJaOeRQ0moEHJqfgyE0Vm4qgvndRxe8+u+/WVcUPpeztD3PTQYEwRL2FkAwchEzi6JqruEEqWOBk6sbVxrLQQblurOffsae5G3fbCODMWNIunmmHuUwhoRJe9b77zNwd1cUtQSygBjWOPPVZ33313GG81ocsvv1yXX355Xc/90Ic+pA996ENN/Z6zzjrLyfqoRzqd1kUXXaSLLrqoqd8Hf3bJiF22HNJDL6+lh0ZcWbvNXr1kVtOG0UOjv0aGhr26OssxFDlhlAEwAY21w6Wm03bJqbRPLWx0R1gZGhlPhoZdncg+n7RyzXCVKaDoVFuYv6p/ySn3Y94yUiagsTmbV7FYVCKRqPTQaPCaUsnQKAc0+tNVAQ2C4dHTzCVj3x2m64mlG7Tt9IHA59jHFuLJ1L/ua3IVrTnf5J3rlBXQMBkaXBZixezDEG45qjg9nbjHiBz7s+t3G5AP4R6knj5ciKbhckBjO2tMsP0W7sXaZoFNzio51UqGhnMPQ0Ajssy++ewp+2jLqf1d3hoECaUl1he/+EU988wz+vSnP61sNhvGWwKS3CnBB+60hSQpyzLZ2DMDy2Kx6AQX/BpzNmqgRoaGPcBkUip6wqhf259x99mxS06ZGw168HSf381kM5lamZR7f9s3ovZEeCs3C+5SeU2/DWpwVtr77HrvdcFbJmTQysIyK+aazdBw6l9b/Vy8yNCIjlZ6aPz0rEN16oHb66t/dUDgc8yxtXmcgEZcVZqCNzew8K64thfjmHMTiyTixVuGLEwsnIkue+zvtzgljGbx9fThQnv9/K4X9c5v3qFVm8YmfO6msZxTXmpTuVTxdlYQY/st/DM0cvmiRk3JqVaagtNDI/Kyzhgi/OsFwhNKhsaXv/xl7bvvvvrSl76kn/zkJzrggAO07bbbVtW1TSQS+slPfhLGr8QkcuM/zNXoeN65eLDyJZ6KrjIzpa/tTIkwLha1MjTooRFtZve0lqHhTv21JzvT1LuODPuzaL42/zYyCe0tR2SPORKJhDKphLL5opMF1ipuUtuj1sS0HeDyC3YNWJNSo9m8BjIp3wBXPbz1r+3rk1nRT2mA6DBjimZ6aMyeNqBvnPmWms/x68+CeDFNwfuaLAviLVWZt84JZmUuiyTiZTzXvoCGuUZxnYieiTI0wrgH8WZ0ofMu/9+nJElXLXpOn/vLfWs+98SvL9bSdZt19yeOdzI07KzNHWd6MzQqC+OcpuAtlJzKOD00OF6iyuybPgIakRZKQOPnP/+58/Xrr7/uNOv2IqCBZhy080xJ0uJnV0ri5iGu7PFd3pkwqgz6w7hYTOsvndI2jlZninl7aJjyJOiMVDJRs66w+Vkr+8Sb+juUqQw0udGMDvuzaA4JE6hupFFnn2fVrXcRbiaVVDafb7HklP1N02+DGmo1d7ZXVnvLTUmloJYJXK3aNK5MKulcXxruoVF+f7PPTSDsNx89Uv/+f09LIqgVJa1kaNSDgEb8jbeYoeHUTC8fbHZw3EyI0y8hXtq54tbJGmWCMnLsz65fwCGMexAzfmWY0H0TLVxcuXHMyc54atkG5/lbTu1znlNVcspkaBSspuAtZGjQQyP6nOtFmvmiKAsloPHiiy+G8TZATU5knBN/LOV9VmXbF/Fmbzht0wczkqT1m30CGp7ZyGy+qD4uUB2TSiSUrzEjHErJKW9Ao69yifOWJ0L3+GVoNDMJnU56yhF5bkTNe4VXcopjpx0qKyOrf2Zn4QSVIxvIpJTN5/T2r/1Rg5mU3rrbrNLzG5yw8mZomEmpGYMZpZJMXkaNsyfadBk3ExX00IivrNO4tblJJ3NNyXtKTiUTVj11Jq9jxdsoPkxODw2uE5FjX7vzPp/ZUEtOsf+7bqLA1Eurh52vh/pTzvGxxVDGeXwbT48tMw7M5UNqCp4ioBF1JqPPe7+JaAkloLHLLruE8TZATeZkwkAxnuwBnlkoYyYa7ZvDVsyoEdDwVp3J5gttSTmHv2RSUnleKF8oVk1OmknNRnooePVn3APLIauHBgPH6LDjAubrfBNlgtKeIKj3Bibs1ZJcedqjEijyydCwzgdB5+uBTEoby/WPN2fzenzpekkt9NAob4+5PqVTCSf7h/FHdIRRIqSWQZqCx954i6vxzcucgIbTnydZyd6gr1+shNm3z8tewY1osceBWZ/rOCWnestEw7+s1bcily86+2zWlD695y07KJGQZk9zN4Ee6quMCcz9RxhNwenNFl1mzE8PjWgLJaABdEKldiEn/jiyB3iVklPhXihMQGPD5lzVz7yrq8dzBU3pr3oa2qQvlXR6m2wazWmGtQpGquyfUEtOWT00zMCR5mvd5+qnU/ROFDVScsqboeH+eSaEIFbRlU3S9NugBqd00AQZGkHHxqAnkLlq07ikxoOjSSegUfo+Z02GmpV5rLyMDuea0ab3pyl4/JmJouYDGu4gZ6U0Ious4qrVIFctZAJHl52h4VfpIZSSU56yleieiQJTdlCrVIa69HU6mdTX33eg72umDZSmTfOFotaNlBZOtpKhEcY9Ctor18S9KTov1IDG8uXLtWjRIi1dulRjY2NVP08kEvr0pz8d5q/EJJJJkt4dZ3YcymkKngs39btWhob3qKExeGfZN49vbBz1CWiU/g2z5NRgprrkFM3Xus++1/dmaDQyCe1dsV9VcioVbt8USk61h9Pc2edndhZO0ETDLlsO6ZU1I1WP+/XcqMUcesViUflC0TlOSwGN0tdMVEVPu1ph0UMj/p5cVsrW2nnWUFOv95aceub1jZKk0WzBOTdRhi5eOtEUnNLI0WNfu/2u407JqZYCGu73QvdMFNCwP6Ojuco1vtb+H8ykqvpBDrTQQyNjemiw0C6yTAZmKoSy6Gif0AIan/3sZ/Wv//qvyuUqK6PtprvmawIaaFalqS8DhTiyB3hmMOCslArpxsKsnvBvCl6doYHOsTN0Xl8/qr22meb6eaWHRvODBu8N6hSr5FSG5muR4c56MM1WTUCj/nOBd2WU96V9Ya+W5NLTFrVKPWSsnRp0ajhyj610x3Orqh73liSbiJ2hYWeCppIJZzU2ExXRUXQyNNpzozlAyanYMwGIg3ae2dTrU0l30OLj1zxS9TOCnPGSbbFRfC0Zp9cjx0TU2JPQfvcBYfTxSyQIckbFRLeS9lzSWNYa79U4LyQSCU0bSDvZGVJrGRr9LLSLPHMuJ0Mj2kKZRfzVr36lL3zhC5o3b56uv/56FYtF/d3f/Z3+8z//U+eee66SyaTOPPNM3XrrrWH8OkxSGWc1FBOScWTPA5nJ7fFcuDcWZgLCrx6ld3xJhkZn2U343tgwWvXzYgg3E96BpV1yih4a0eGXoVFwAhr1v0+/Z2WUdwW/k6HRQvDSPm8xmd0etUtOVR4MOjccuftWvo83WnIqYa2utHd1KplQMslEVdTUOm7CYEpOjWa5ZsSV2XdTB5pbv+cNaIxY5cfSSSYv48hpCt6WDA1KTkWVvUjBr3SkkyXcwv1opURd02+BkEyYoVFoPENDqiycNLz3IY0wlQOYj4iuZqoHoPNCuZp/73vf04477qjf//73es973iNJmjNnjs4880x9//vf10033aT/+q//0oYNG8L4dZik0iE3eEVn2ZOBTsmpkGvZmpJD/gENMjS6yc7QGPGpSV5wJqfC66Ex2GeXnCr30GDgGAHhZGgMeAJY3hsRp8xYSHeXBDTao2bJKesmImglflA5mUZXVNkZGva+TiYq78UxEB1OK/k23Wea8wsZGvHVankhb5PffbabXvmZkzXOmCJOxnPta/KaoddjZNmBR7+m3WHUyrfLVqK7JtqN9lySvWhhoonr6QPucsmtlMzOpOntGHXmXNFKKTq0XyhX88cff1zz589XOl2ZPMrnKzcAJ554ok488UR95StfCePXYZIyg4wsA8VYcgU0yl+aNMuwemhUAhrVExDe8SU3oZ1lr1jzmyAKI93bu1JmiqspuKlVyo1Gt9nxBbPfzb+N3Ex697f3pWlnn7eQoWEFX7hHbY9ijWCm/VjQoeFdMWc0EhwrvX+lRKp9jCYTCednrLyNjsrnsT03moN9peNnlKbgsVQsFp0FDM2OMZ0V1+XP/R6zp0qS/u6IXcjQiKmwF1LZ0vR6jCz72u13+5dvYlGNFyWnoiM5wb2EPQdg98maKKAxc6jP+bovlZzw99RCU/DoM5/lRkvYorNCuZpns1ltueWWzveDg4Nav3696zn77ruvHnvssTB+HSYpGvDFm6vklCdDI6zU735TcsqnRAQZGt1lp3hvrpGh0UoPDW/JqUG/gAYDx65zl3Eq/WsmABq5OfBm5HiPnb4QVkv6bSvCZc7NE330g7K3go6ZxjM0KttjXy8SCcrLRJGT2dPmpuBkaMSTnY3Z7Bgz6XzuS9+bia+9tp1WyRrnnBAr7S05ReA7quxrt1/JqTBq5VNyKjomLjll9dCw5gMm2v3bTB9wvm6l3JRUOQdxXxpd5rzQSqAT7RfK3tluu+20fPly5/udd95ZS5YscT1n2bJlrgwOoFFm5Us2XySdM4bcGRqepuAdKDnlPWQIaHSWPXgc9c2gab0puD3BnUomXKsyGThGh30uMPs930yGhieAVdVDI2nq04Z1veC60w71lg5q9H6i0ZVzCbvklHW+SiUS1sQmx0BUOJk9bXp/moLHmz3G8wa/6+UtOWXGlv3pFEHOmHLKkLWzKTjHRORMXHKqdFy0UiufklPdZf/dzb4YGc/9f/bePF6Ssjz7v6r302edMzsMMMMim4Aosu8q6qiJURLJ+2qC5tVX4xI1aDZ/0bxBja/gEjESd6Nv1IhxBVRwARSUxQVBYJBhgBlmhlnPfnqpqt8fVfdTT1VXd1d31/JU1/39fPgwZ5tT01Vd9Tz3fV/XhU/85BE8unfB9b1NaS9Ys5/x+ZzW1fZ43WRZ/HmQQHDAUQ5yPUJd9BCs6JjoCaWKeOqpp+K+++4TH1988cW47bbb8MUvfhELCwu4/vrrcd111+HUU08N49cxGUUOjuYNRPqQT5lQaIQcCk6b1rpfwdxTjOQshfjwTkP5WXgEndLuhLy4rJbyroUpZ2iog7zXoz/TPaGXhlalq+UUBTmzQkNlnMJ053Pf7eteBlNoyJ/XuHipIFFnaJDCz09RyKiPXCQKw3Jq18wy9i3UAFhrTZ7GTydRWk6Ja4LXmcrhl+MoQ8/2QfajOU8DlIkX+V5M5+IjNz+MD3zvQfzxtXe4vrfhytBwGhrdWDMuKTQGVHk5oeB8vaiKk+/IDQ2VCeVp/uIXvxj33XcfHn30UQDA3/7t32JychKXX345JiYm8Ad/8AcwTRNXXnllGL+OySjyzYQ3EOlDXkzqHoVGISyFBllO+YWCez7FExHx4X2/+mZo2KdjIIWGVOCultyTM2w5pQ6mj1qLzksvGwTvdFSr5dTgftamT4A5EzbB8nM6ff1PTtvg8/09NjRylKGBFsupPDc01IOa4FFlaNjriWVWaKQSRwGs9e1zTu/7Xz5+AGe+/4e4b8csAEu94zQ5eU2RJmoDBsV3ohjCmoOJBpftcYdQ8EGsZXKSypOJH/l9RwNtP3xgNwBg73zN/b3SfZvuCUGCn0fLjtvMoJZTvC9VH3q+s0JDbUJ5ml9++eVYXFzEpk2bAACHHXYY7rrrLrzhDW/AJZdcgte97nW46667cOaZZ4bx65iMIk/TNA2Tp+ZShmvS2V7t1Zu9FzE7IVtOeSW/LRkavICIDe9rv+zJOFmq6+GEghfkhobb4pBDwdVBPgO08as3e5+a9BYkvPvQQsiqHO5nRANdA92k/p2+/r4/Oglve+7TXJ/zKni64ZehoWnW7xUNDb4IlCFqhQY1TP0GJBj1oSy1ftUZgNPQeGDnrOvzlkLDscFl0gMN1HiHXsLAUe3wPUM1XEN1EWVoyGsIJn4a0vvO7zTKdQE/hUaQcy+vKwe2nGIrZOVhhUY6iCzUYtOmTbjmmmui+uuZDCI/aL5y5+O48voH8H8vPRl/ctphCR4VExTTZzEp+xGHgVzQruuG6+/1ri95AREfnRQan75tK668/gFceOxqAOGFgnubZCWehFGGznk6wc9/PqehmNfExsRb8HamJcOynOJNahQEzULo9PVCPocjVlZdn5Mn6YLgZGiY4pjofsQKDfWIOkOjJCws+ZmRRuohhD/T82jBM0DlVmjwPSFN0DAcZeSECWdoqIvRZS0XSoaGZFHHxI9LoWGvDArSpNNiXRfrwqaroWGd+yBKvpFi+31mr1COD68x1MXJ0OBQcJXhs8OkBnmRceX1DwAA3nndve2+nVEM92LS+r9QaAwo2yTkgrZ3qrIlQ4MXELHh3fDLFh70Xv7JQ3sAdJ/S7oQ3FFyGMzTUQb4caF8pfK173CBUpPe8dxLXkXMPYjkl/Zn3qJFgdqlMU+Hw6DVjHf8e7+ZypMeClbCLMNCiGOOGhno4KppoWhqy4pPg858e6iFYC5XyedffRZQLOSejia+JVEENDa+KNwyo6MWWU+rhsjr12QaEmaHBwy/JIA8v0Z5fblLI93FZRbXcDK7QkNeVva4xvbDllPrQfSEfUtYrEw3c0GBSgyYFczLpw28qu2YvIsohZWgU85qwn6h5bI1oz0mXEDc04sM7reQ9NzIDWU51aIwVWdqrDL4ZGrYVWK/2IPI5955/2pg2QrJ/8DZFmXCgV7WdOutzr342/vT0w/F3m4/v+Pd4J257tRShe48phYJTsZy8lbmgrQ5RW06VPA2NL/78MZz0nu/jrm37o/mFTKiEoQBu1wypFPNSk5PXFGmCFMKDFiP9YMspdeEMjeGnYchNq9aTIO//5EGnWg8KjbLc0BjQto5DwdXHUWhw/VFlQmto3HLLLXjxi1+MNWvWoFgsIp/Pt/xXKETmcMVkhAJ3SFOLvLYQllONcBUamqZJU5VuiwAqnFLRixcQ8eGdYOzkQz+I5VSnYngY0/pMOMinv0Wh0WtDQypWVTyFq0IYAZ2u7J/+/xqmPWLSvs3XzztmNd7/spOwadVox7/H28Do1XJKLkbQZpgVGuriCHuiVmhYa4n/75v3YbGu4y//3y8j+X1MuISi0Gjzs+VCjqfxU0i9aYj1aBQNDQ4FVxfXUJ3PczyMwqXI0OB1QiLICg3aZ8rnQlZbyt/bt0JjwIaGyNDgAUslMU2TMzRSQigdhuuvvx4vfelLoes6Dj/8cBx77LHcvGAiwdpA8I0/jbgyNIRCI9wMDfq7lhtGq+WUSV/PYbGus0IjRrzy604WDYPYVBakYri3Z8IZGuogKx0GydAA3DZD3gn9or0ADeucs0IjIoQaYrC/Rm5gaFrv/sb0+3UpFDzvydBgexl1oPdj1AoN71phZqkRzS9kQoUaUYOEgpfaPI/cCg2+J6QFOb9t0GKkH/ycUBfXUJ3PUBUNO4WSocGWU4ngZ20tq6Vky2H5PSoUGgEWE/J9IyzLKbZCVhP5espHtdBkQiGUrsN73vMeFItFXH/99bjkkkvC+CsZxhdWaKQXv+kYeogPGqwlI6YqG96Ghml/PQ+gwQ2NGGlRaHQYdR9EoSHj3VCIhSOf98SRTz+dpn6naUuuhkYUGRpy86Xvv4bpQDfLqaDICo1qMd9ztgL9ftlyyhsKzpOXChFxKDgNWtSahmsgg58h6YCCXgcpXHdWaJClId8T0gLlt+Vz2kBZCe0QoeBSgfKubftx28N78ZaLj3YN3TDxIu8J/JqQtC8ZTKHBllNJYvrUGeRzLT+75UEnqkUEqTHJ+4xebU29lArhDl0x4SI3wzhDQ21CaWjcd999uOyyy7iZwUROYZDxbSZR/C2n7Am6MBsaxXaWU9b/aTHCC4j48BYBO8nxe7Ucaod34kXkKfB5Txz57BumCdM0haKq12laWZXhVWgUQlDluO2xeJcaBWFNM45JCo2RPgJfXZZTInDa+hpP3qpH1BkashUmWxWmj+UQshI6ZmjkOUMjbdyyZQ+A/hreQRA2l9Jz4o+vvQMAsGqshD87a2Pov5MJhrzMkNccpmnidV+8B1t2zwPAQE0nYTnFa8VEkF91qjM02zQ0aq4/243OIAoN6XkyaE2KB+3URm6GcYaG2oRSORobG8P09HQYfxXDdMR7Q4liwoaJBncouPV/x3IqTIWGM1UpQ4saJ0ODFxBx0arQaL/YD2vR4C1iO6HgvNFIGve9wMSnbtsqPu49Q6O9QqPkMy05CLxHjQaRhTCoQkNqaLSziukEXXrUZAMcC4kCW0kohymaTtGsA+XmqndAglEfamh4nwu9UMr7N0PKhRyKnKGROh7aNQcAmKs1I/n7Cx0a3w/bBXMmGcw2Co0nZ5Zx0+92i48HsZzK8zohUfysreVzLe8LZfu5XkLB5cGZpcZg9xG2QlYb+T7OGRpqE0oV8TnPeQ7uuOOOMP4qhumIVw7IN5j04DcdU48kQ4MUGu4FAqkEyH6AJyLiw1tQ7tjQCEmh0Wi6fwcvHNXBfS8A3nfDg+LjYo/NzXKxeyh4fSDLKQfepEaDmLQf8O+RJ+f6Uf1RYdwwWi2n6P+s0FAHJxQ8GuRmKa8X0seSaGj0v75sNzSVy2mcoZFCFuxGxlufe0wkf39Buia8ik5+diSLK19Bup179yeDDFU5a4i+/wpmAFx7iy4KjeW61NBoBrcbk/eoe+bqfR8rwIN2qmO4FBrsEKMyoZydD3zgA3jkkUdw5ZVXsiUDEyne6V0O6UkPfv6ltT598zvhZGi4JyrJC5E8L1mhER/exVqnjV1YqqsWhQaHrylDp3VCqKHgZP8wkOWUZE3Q99/CdMJRQwz298gDDv00Rp0MDed5RX8lDVNwhoY6iDMR0TJQ0zTRCPcOSDDqsxSh5RTg3BO4oZEe5u2GxkSlGMnfLxe9esmOY6LHqwwmFuvuveJAoeD2j/qFjjPR47Kc8lFo1KS9wLKkuqRGR685bpSB0S8ltpxSGvkezvPTatNXhsZrXvOals+deOKJePe7343PfvazeMYznoGpqamW79E0DZ/5zGf6+ZUMA6B1ocFLhvTgt5gkG4c4LKdoUUObW15AxEfTCK7QCEt1xRka6tLpvl3ssaotNzHKLaHg4U7Vc90qGpxJ+/B2DDNLjZ5/Rva/pluW1qLQ4PuHKkRx3XgpF3Ko6wY3NFIIhYIPotDo1NDgXJ30QQoN2TYmTGQXgaZuQr70eOmZLIaPHREALNbdtkGD5PjlxVAE3xOSQH7Z6f0mDzTJ+/4ll0LD+nOQUHAA+Pj/eCY+edtW/M0LjhvgaJ1rjfelakK1ikJOi8zalAmHvp7on//859t+bdu2bdi2bZvv17ihwQyKVw7IFiDqY5ombn7gKbG5BFoVGt5C5CA4oeCeIrrpydDgAkVstCo02r/2YYWCkxLH+/d6raiY+Ol03w7iYSsjN0OrniBoel4MosqRj5Q3qdFgwh3AHQb9NMn9QsGpQFFgexnliOK68UJe+8/70C3R/RImEkQoeKn/hkan+4jIS+BiVGoghcZoRA0NeSDHWuc61x4rNJLFz44IABZq4Sk0NGkNwcSPKa3Y6Ry3z9Bw/uy1GO3Gi05ejxedvH6QQwXgNMy5HqEmNKzQ676UiZ++nuiPPvpo2MfBMIHwFjt50aA+tz+yD6/9j7tdn2tpaESSoeFepOp2UZ0K3TwRER/e1zrKUPBP/dlpeN8ND+BDf3KK6/OlAk/CqEKYfQE58HVyxG0jQf60g1lO+f+ZCY8wsxA+e/lp+Kfv/A7/9+Un9/yzmqTQMMUG1/4/NzSUI+oMDQA4feM07ty2n6fwUwhN4A6WocEKjWFBN0zM28XrsUo0DQ35evGGxbNNfrLIxW5ZobHgCYgfZA8iqzyZ+JF7hnSO/TI05pYbeGDnbMvPD7r/7BXR0OB9qZLQ3rEU0qAlEx19PdGPOOKIsI+DYQLRMjnBawbl+e2OmZbPOZZT4T8shOVUw71AaLLlVGLQxq6Y19DQzY4FgEFDwZ93wlo874S1LZ/nDA118G72Vo2VsXe+hv99/pE9/13jkhd2S0MjF27gHm9So0EUpkMYtb/4uLW4+LjW938Q5AwN2gxrHoUGFy/Vo1ff6174yGXPwNn/8qPI/n4mOpZFKHj/awqv0hMADpmsAHDWFNzkVJ+P//j3+MRPHhEKjbFyeENUMvmcBk2zniGcoaEWrmK39OeFEDM06Gc5aysZuik0aN//5Tsf9/35uCfx5Ywu0zTZ1kgxaAAyrGxPJjq45cSkCu9NhQtM6uPnVUvrCwruDtNyqlRwW06RTQxdKxUOBY+dhr2TqNjNJr1DgbkY0YKSMzTUwXvbrttqqpecckjPf5fcDJ3wTF0WC4Ofc/lQeY8aDYZoHiR7HI7llOmEgtuXlyhU8JpDGeK4bg6ZGsF5x6yK7hcwkRGGAnh6tOz6+NXnbMQNf3UeAFZopIkPfv8h0cwAorOcAuTmt2eoiiUaieLKcZTes94MjUEC43NsOZUorgwN04RpugfoaN/fbsgpboWGXPvgmoR61Js0jMnlctUJ5Qx97Wtfw8UXX4wnn3zS9+s7duzAc57zHPz3f/93GL+OyTCFnNdyilcNquPX0KCJiXqEllO/fPwANv7t9dj0dzdgy+45saipFguu381ED23kyrY6JkqFRjtK+XCn9Zn+MT3SOjonQQP5ZOTQVu+1U8iFazPGGRrRQK9q0jNQsl0EnWsqUIjiJd8/lCEOyykAePbG6Yh/AxMFtMbrFOzdjamRomti+/SN05iqlgBwrk6aGS1F2dAgq0v3dcH71WTxFrsJUnIREyP9XxuybSWTLFYWmvtz9ExYOVry/ZlB1Dn9IGc0eXM/meRxFBrc0FCdUM7Qpz/9aRw8eBCHHOI/XXnooYdiZmYGn/70p8P4dUyG8Ra8eB+hPn6BjF7LqX4CXNtBzZGbfrdbfO6q7z8kVAEjJbYeihvyoSTrhygzNNoh20NwASJZvM4LNMnYz7lfN1Fp+zVhMzbIRkHamPIeNRrCtJwaBM0VCm59TjQ0NFZoqIZojEZ83YxH5LfPREtD+F8P4Imf01zFL3k9m28zic+oj9+gVVjI9oTyWpOVPMnSTqHhXR8Osg6h9QLvMZLBG/zuPQ+072+3/4+7oSErzL022UzysOVUegilivjb3/4Wp512Wsfvefazn4177703jF/HZJi45YDM4PjVf7yh4INM0Hnxs69abhpiIoczNOKHFo8jQqHR/rXvZ0o/CEXpGuNznyzeWwIpNPK53u8Df/iMQ/DHz9qAq/74lJavTduFqH0L9Z7/XsJ0/Zk3qVHgqCGSPQ7599MzimobbC+jHnEpNMJcnzDxQeuOQc/f6nHHdkq2Kmo3ic+ohZ+yMlLLqTyp+Qy2OFUIt32o85E8Gb9qzG0x1yu0TuC5h2SQz6vf8Brt/ah54FVq5GMeqtE0TQx08pCletRZoZEaQjlD+/fvx5o1azp+z8qVK7F3794wfh2TYfzsaHgSQm38JlqdhoadoRGqQqP175J9NEdKbDkVN7Thr9gNDcNsH5oXleVUtehMVno9c5l4aTfl3k/DupDP4YN/fAoufdaGlq+RemPXzHIodlH8qIkGx3Iq2Y6GHC5Nz6i8x3KKwz7VQVw3EV82YVpiMvFBa7xBixHHrBkTfx4p+ik0TLYjVBg5OwOwGlxRNilpDds0TG5oKISr2O3T0DhksoJvvemcgX4HW04li7dp5R2eEw0Nu/ZQLbuf7XErNAAp99NjfcYkD9UuuKGhPqGcoVWrVuHhhx/u+D0PP/wwpqamwvh1TIbxK3hxYVpt/CZaabEnMjSKYWZotP5ds8tNUaAiqfkSLx5igxaVFUk9o7dZ8EcVCp7LaajadhELNT73idJmrxe2OmfNhDVtV2samFlq9PV3uCTsvEmNBNPpaCSK3NCg51ZLhgY3NJTBUWhEe+H4FT+5saU+QqExYDHiaevGxZ+rkuWUbEPBl4O6eNf6UdpNAZLllG5yZptCyO9Ruc5NheRLn7UBh06NDPQ7hOUUrxUTQW4sG2brwCs1GKn24M3SSaKhQTULztBQD2E5xSpd5QnlDJ1zzjn49re/jQcffND36w888AC+9a1v4bzzzgvj1zEZxm96mxsaauO38TdMa+ERTYZG6981v9wQCxsKfFusc1E7LhoehQbQXlkVlUIDAKr24nWBFRqJ0q4xEPZmolLMY9wuXuzv03bKZTPFe9RIoNc4acspTbr16HbFw2s5xYpQlXDbgkWF35qCG1vqE1YxQi5yylZF8vOKczTUxetNP1qOVnFFgxkNwxD5cQA/O5LG9NgREbUQB+vYcipZTM9H3ue0o9Cw/l8tJa/QoPUFNzTUI4wcLiYeQqkcXXHFFWg2mzj33HPxr//6r9iyZQsWFhawZcsWfPSjH8V5550HXddxxRVXhPHrmAzjN73NvoNq47eI1w3Tdd6iztCYXXYK2BOVIoBWGToTHbSpk9Uz7Wwaogzfoo0sW04lS7u9XqGPDI1u0CY1jM0CKzSiwYhp0r4bLoWG7q/Q4KKUOiSZocEFbPVpNK0LpDzgkMR0m1Bw+XnF9wV18e4RvVPZYUPXhXefw1krySIP18kKioOLlno3jMG6HFtOJYpbUd16X6b3I+0HvFk6iTQ07JoFD+eqR50tp1JDKE/1Zz/72fi3f/s3vPGNb8Tb3vY2vO1tb3N9PZ/P4xOf+ATOOOOMMH4dk2H8LEm4oaE2fhs9wzBdBcZBLQFk/CynZLuZyRGroVFvWoF9/KCKHlJoyMUAXW8NbAOiXVCSQmOeLacSJS6FBuDYnPXb0PBukJjwocZmzHmMLciXn7Ccsh8PebaSUA7R0GCFBuNDPSSFxoqq09Co+mRoAHw9qIy3UDheicdyqqEbriYGN0GTxW05ZX2wUGvie/fvAhBOQ0PTePAhSdyWU60KjZo3Q0MBhQbVP+iYGHVohJTDxURPaE/11772tTj33HPxb//2b/jFL36BgwcPYmpqCmeeeSbe8IY34Pjjjw/rVzEZJu8zwctdbbXxKwDppikeFEB0llOTI0XMLDVc1wgpNADLdmpyhB9UUdOgDA3p3DQNA7rZ+tpHMaVPjJFCg9U5idKuJtxPKHg36H6w3GdmjnysJntORUryDY32GRo0TMGFCnUwheVUtBeO3/pE52lr5QkrFPyE9RM4/2mrsaJadFliys8rnr5XF+8e0TuVHTZ0jeiGO5SY8zSSRX71aV/6mycOis/51Rd6hQYfeO4hGdyh4K3PaWE51WiToZHAIlSoyBtcy1INYVvJllPKE+pT/fjjj8fHPvaxMP9KhnHhd1PhhobatFNo0OK+kNNCLUjIxYdVY6WWMOByMYdiXkNDN7FYbwrFBhMdtNkv5HPI5zTohqXO8Fv0R7lwcDI0eBImSfysxoBopqPCDNzjActooMshl3BHQ/71ToaG9ckcT14qR1xFIz/VJ0/kq09YoeC5nIb/eM3pvp/XNOs65Ol7dWmxnIq4oUFr2IZuuFQBfI0ki1+Gxrg04Da73Gj5mV6hdQJbTiWDawDJNFvec1SgrukKWU5xhoayOA0NHnxVHT5DTKrwm95usOWU0vgt7HTTFOfNz0ZsEORgt6lqqSVotpDTnMI2T+rHQlOacqAFY9Pwt5zyKx6FBWVo8HlPlvYZGtFZTvWt0AjzYBhfVNn8+2Vo0ONJ9kVn1IDORNR9MM7QSCci0LMQ3QVS5PuC8ngnn8ciztCg4le9abqebaziSRb5XNAf5fv4gcX6wL+DnkV8P0gGr+VUS4aGR6GhguUUNTTqOg/aqQZnaKSHSM/Qvn378I1vfAPf//73ofMblQkBv+I3d7XVxm8RrxvOhGMxZIsh2daoWsr7TmCMlqiwzfelOJAXBQUpXNfPjizMgHgvo0KhwQ2NJDHabPaUV2goUngfNpwsBHVCwXWP5RQ9prhQoQ5xKXt8MzS4OKk8VLwq5aMbkhADGnw9KIu3UBirQkNadrCqK1nkl5+e47IN2JlHrhz4d9D9gE91Mrgsp4zW95wTCk4ZGiooNNhySlVYoZEeQjlDFPi9f/9+8bl77rkHxx13HC699FJs3rwZZ599NhYWFsL4dUyG8ZvgZcsptfErApqSQmPQwEYvsoS4XMhjXNq85DSraEYqDg6Uj4empMaRFRqmz8sfaUPDvhYWuZGVKH6bvXzI1nPEwAoNz8QXEz5i0j7Ro2gTCk4ZGjyJrRwiTD7i3+P3TOLrQH2cNWZ0V4g8oMGoiXePOBZxKHjJLlDWm4ZHocH7jSSRzwUNU8kODxc+bfXAvyMnMjT4fpAERlCFRpMsp9RRaPBwrno0Y1B5MuEQSuXoq1/9KjRNw/T0tPjcO97xDhw4cACvfvWrsXnzZtx111249tprw/h1TIYp+HRJuSitNn4bPd2QLKdCXkCMS5uVSjHnmsaiolRJSML52omDhuRl7RQADH+FRoSTECQvnmfLqUTx2+pFYTcFhKvQ4D1qNNDmP4G9pAvNR6FBn6Lbkt89i0mGuCyn/Kbz2HJKbeSctijXFPm8M6DBqIn32T9Wjk6xAwAlSaEhPy44FDxZ5HNBKmGqH5x06GQoAzW0huF1QkK4MjRa6w/kFkD3hBaFRhKh4KKhwYN2qsGWU+khlDP08MMP4+STTxYf7927F7fccgv+4i/+Ap/+9Kfxne98B89+9rPxn//5n2H8OibD+BW9eOpFbfw2elaGRjQPiglJoZHPaa5pLJq+KBd5AREnDYMC4HPI202ldhkasSg02HIqdO5/cgafvPWRQJlGftNrUTU0SKFR61OhIcM1q2hQxXIKcAoSXoUG3bd0Lkopg6PQiPa68bPF5AK22jSkhlPYKmCZglCc8j5EVbyDS1FbTtEa1goFlxQafI0kiumn0Gg6+X5hkGPFVqK4LKdMs9Vyyj7f9XYKjZAzPYMg6hFsOaUcbDmVHkJ5qu/btw9r1qwRH//sZz8DAPzRH/2R+Nx5552Hz3/+82H8OibD+GVo8NSL2vj55RuG6QqKDhO5gdHUTYyVfRoaBV5AxInYNBQ0pwCgm76FbT+/8rAQ2Sl1bmSFzYv+9acArIXfq8/Z1PF7/YbXopJ6D6rQMF0TX/ysiQIT8VgHBSGnabZVgXW9UC2bJvd48lId4lJo+K07OTNBbeR9QZQKDSp0NJp8PaiKV8U/FnmGhmMhw6Hg8fD2//o1yoU83v+yk9p+j+Fay1nruXrIBUtqfvO5TgaXCsfXcsra+7XN0EhEocEW2KriNDy5oaE6oZyh6elp7N27V3x8yy23IJfL4eyzzxaf0zQNy8vLYfw6JsP4TcoFmQhmksOvABSlQkMujJYLOdfmhb5EE1S8gIgHmpIp5XPi/CQRCl4VGRqs0IiK+5+c7fo9flkUfnaCYVAeNENDmvniLWo0qKXQILsQj0KDrWWUI67ekt8ahSdw1Uaeyo+yGOGsJXlIQlW8g0ujpYgVGtTk0k1XEZ33qtGwa2YZ//3LHfjynY93VF97152y9XFY+w7eWyaLfI5NtKqiRCi4fU+gITciKqV4J0qcoaEsjl128nsTpjOh3MGPP/54fOc738G+fftw8OBBfOUrX8Gzn/1sTExMiO/Ztm0b1q1bF8avYzKM3xQvy3jVxj9Dw3lQRFXIBIBLTlznamhMVUsApKltVmjEQl3KSylIhUFfy6kIrwcKiD+w2IjsdzDd8SsFRqXQKErFhUHhUPBoMERDI9njkI/BydCwGxr2//0Uh0wyOAqNiC2n/BQavO5UGlpf5nNapEGvpTwXo1THu86MOhScLM7qTcOl6uRmeDTIg1GdGs3eL+mmKZRVYTU9qTitt9nfMNHitpxqvR7ofIsMDY9aK5dkKHgItrhMuFCGRpR1KiYcQjlDf/VXf4WdO3diw4YNOOyww7B792785V/+pet7fv7zn+OUU04J49cxGcZvY8lSb7XxW9QZhikKAmFbTgHAl197Jv75D0/E809c6/LLXTdRASBtQnmKJhaEvVjBrdDwqwlFqdA4cvUYAOCRp+bZPihBfBUaEW0kZD/rfnBbToVxRIwX1SynAOeeRY+nfI4VGqpBzaWoh+f8GiZkKfLgrln85KGnoj0ApmdIoRHlgAQgTWRzQ0NZvPfskWLUoeDOmkP+zWxDFA3y3bnTa+xd8xsGJMupcB4i8v6F7wnxI59jU8rQKHuUM2Q5pYJCo8wKDWXhDI30EMoZ+oM/+ANce+21OPHEE3Hsscfiqquuwitf+Urx9Z/85CeYn5/H85///DB+HZNh/LqkDZ6UUxo/WyHDNFEPeTJG5qyjVuJVZ22EpmkYl6ax1k1aDY1yiEHBTHeEvVgu5wrR9CtsR5mhceTqUeRzGuZqTeyerUX2e5jOxJmhUZSKC/c8th9v/+qvsX+hHvjnvSGDTPg4llPJHgfQKRTcOThWaagBDUvkE9hs0u9+wUduw+WfuwsP7uputcfER9iFynZwQ0N9dM8eMernDF0TSw3d9azgvWo0yE/jToMr3uWbYcqWU+E0ueQGKt8T4se7XtftvWfVblx4Q8FHPA2NZBQa7mNj1KER0zqCGZzQdJeve93r8LrXvc73axdeeCEOHDgQ1q9iMoxf0avBDwGl0X0mZnTTUWhEPREhKzRWjZUB8ERE3IhFQUFDPidJsmPMUgCs4vbkSBH7F+qYWWqIBhcTL3GGwZP3aUM3cNknf46GbmLPfA1f/Iszev67uJ8RDfSy5hToaAiFhtdySnpONQ0TpQQ2vowben4kEeTZ8DS1Htg5i+PWTbT5biZuhEIjpEJlO6iAyZ756uJVaBw+XY3091Hx6zM/fRS/3T4jPm/aFjhRWqBlkab03vPel2VaMjSkhkZYBUv576npOoBiKH8vExA5FNxw3vsjxTwOoCFs4GjvXynmUchp4vuSzNBYbvKApWqQ4itK5wgmHPgMManC38uYq0wq46vQiCCMrR1yhgZ55/JUXbyIvBSXQsNMZNKZrod5DgZPDL/GwNqJaJpLRcnjnJRCtz28N/DPeyXsTPjQ66pAP0OEf5N6j9YcLoUGXwdKIBQaCexkvFPfnMelFnGFefJaUm1M08kyuOSEtfjRX18gsvSiQr4X3Lltv+trHAwePnINoNOAo3e7YRhm6NZ0mqbxPSFB5LWZIb33SYlR1w3XIGNJskEGkhmqqQjHCL5eVKPOllOpoS+Fxmte8xpomob3ve99WLt2LV7zmtcE+jlN0/CZz3ymn1/JMACsgqiXMMJemejwK1rrpnPeop6IcDU0ytaiRoSC84IzFoTlVF7K0NDNlg1GHIxyQyNx/M77uogbGvJzot8JSb/mLDM4wnJKgRQNul6W7IYGXSsFj0KDSR7dYwsWJ16vdp7QV4u4BmaEQoPXkspxYKGOF3/sp9hxcAkAsGFFVeSoRcnWvQttv8bPjvCR78WdLac8Cg3DFKG/YRYsy/kc6k2D7wkJ4M28IyeIasmpAyxIe79ywRqyIwPiJBQaFTvTZ4ktsJWDMzTSQ18Njc9//vPQNA1/8zd/g7Vr1+Lzn/98oJ/jhgYzKH4PG554UZt2Cg1ahEZpMQS4Gxq0qCkJyyleQMRBU5J1y+G6foHxUTNuXw8L3NBIDBOt531NVA0N+72+KJ3viUrwpY/Lk5cfNZFgOh2NxKEC5WKdFBrWx3LRPIn7FtMKTWMWEvA31g3TVSDj4pVa0LBK1IWIUoEtp1TlC3dsE80MIL77xNRIe5uhJl8nodOUFmadBhw7W06Fd58oFXJAje8JSdCSoSFZThFzy85eoJR3KzSSsIOjugQ3NNSDMzTSQ18NjUcffRQAcOihh7o+Zpio8St+ywvEp2aXUcjnMD0araSYCc7Du+dbPqfLllMRbzjlUPBRodDgqbo4qfspNAwzEesWugbml7mhkRR+9eDRUjRe52X7/nJwqSE+11MTVTpWVmhEg0oZGlT0WrY3lwUfhQY3NNQgSYVGw9OQZ7WnWjRi8r5mexl18d6m4ypWvuMFx+Jnj+zF7tlay9fYUSB85PtwT6HghmNRVSyEd23wPSE5TI/lFCmiKtL+gtT55UIOmqa59gNJhIJTs2Wpzg0N1Wg07XUEKzSUp6+GxhFHHNHxY4aJCr8JGyqWLtSaOP19PwQAPPr+zSLMk0mOHz/0FH7xqOUhe9y6cfzRqYfi/Tc+CMM0xTRCuRjtg0L25h8pWrc8tpyKl6Y0BeVkaBiJFAbHKtb0HFtOJYdfFkVUSi3aqM4tOw2NfqckuZAdDfSyqvDE9lpO0Zojxw0N5aBiRRJTlbphuOxjajxdqRT1uBQaUkYToxbe20JcdjLrJ0fwyVedhj/8+M9avtZkmWfoyE2iTq+v97EtKzTCLFhyQyM55FNsms5ajZQYumFi1t4L0GCjvH5IwlpopGSHgvMaQjlIZRW1kwgzOH01NNrx2GOPYc+ePdA0DatXr8bhhx8e5l/PML4LUipOPbZvUXyuoZsohThxwfTH//v5Y+LPlz5rg7B/MkxTFBgnKu3l2WGwbtJpaJAigBec8dLwsZxKSqHBoeDJ43faoyo20AZFlpn3Iu32HqphmIlMcQ0zKoWCFz2WU3JuVyGnJWaVx7RC+Vz5hDI0mqzQUJa4FMC0lmTrW/Xw3hfibHy2G9TyZu8wgyM/j+vN4JZThmGKgmWoDY0829AlhTwsZcK5Ngo5DeVCDot1HbNL1l6gZA82FqX7QhKT+JyhoS5sOZUeBn7n7t27F29/+9uxfv16HHnkkTjjjDNw+umnY9OmTTjkkEPwjne8A/v37w/jWBmmTSi4dcORFw+8uVADef04UsqLQqBumKLAON6Dn30/VCTvTDqeMmdoxIocCl6QMjSSaWhY1wNnaCSH33mPyt/ar6Gx3DBEMbQbLUGSbDsVGSpYTtHGheT/8nUpnl98DSgBnYckFBpNw3QpvdhKRi3qEVjJ+MHDMeriHTyIM/CXVOBeeG8aPu4MjeCh4IZpigZIMURrOr4nJId8iuUMjXxeE9ZOM0t1AJJCI5+wQoMtp5QlrsEIZnAGOkMPP/wwTjvtNHz0ox/F7t27kc/nsWbNGqxevRr5fB67du3Chz70IZx22mnYunVrWMfMZBi/LmnDfmDJiwdeNKqBXLisFPJiYko3gd2zywCib2gAwF8/72k475hVeO4JawDIoeB8ncRBQ8g2NeTtpqRumJDfpqccNoWvvO7MyI9lrGwpgua4oZEYfqW/qIoNtBD1KnKW+2xm8nR++NBzIvl2hrOh9WZoyH/WuXitBEaCllNNw3Q1MUzfuxqTFFGE/frBxUt18fbH41RWltsUyJu8fgidZr+WU0aEoeDge0ISeEPBm5JCo2oPsx1YtC2nbBWVS4WbwCT+iJ3vsdzg60U1xDBmxFlczOD0fYYMw8D//J//E48//jguuOAC3HzzzZifn8fOnTuxa9cuzM3N4Qc/+AHOP/98bNu2Da985SvDPG4mo8gb14r9MKIpucW6U7DihYQaGB6FBp2/maUGvn//bgDAeMSWUwDw5uccgy/+xRliaopDweNFnnLwU2gctXoU33rjOTjzyJWRH8soKzQSx1+hEVGGRpu/N+g0VIvlFE/nh454SRXoaNDQhLCckq4fpyHP14AK0HlIwgJO1w1X8YytZNTCKWRFW4gos72MsmhIUqHhf93xsF349G055crQCDEUnO8JieFSaBhW1hVg1Y6qdobmgUVSaOTF14gkJvFJoVHXjb7z/ZhooGy0dvdzRh36PkM/+MEPcPfdd+NP/uRP8MMf/hAXX3wxSqWS+Hq5XMZzn/tc/OhHP8Kll16KX/ziF7jppptCOWgmu8jFKXoINEQouFOg4oWEGsjLx5GiYzm1Zdec+Lwc1hsXHAoeL1TsKeRzQt6r647tT5xWM6QIml/mhkZi+Ow5o5qybud92q9fLSs0wsfpZyTf0aA1xr4Fa9Mr+yuLexcHuyoBPVcSydAwTFcTgwuVaiFP5kYJT2Ori/fU5yNubsmUi/6WU7x+CB9Z9dLRcsrzsW5Eo+Tie0JyyEpJ06PQICXEwQV3KLj8jEjCckq2xV7ma0Yp6HxU2tzPGXXo+5379a9/HeVyGR/72MegddhMaJqGa665BsViEdddd12/v45hALjlgNWSVZikBcl8zSmM80JCDWTP0nLRmc6XF/VnHbkq9uPiDI14qUvBWrJCIwkPdLKc4lDw5PBTOUQVutausLAcsKHhPVSuZYcPvcYqZK3ThlZ4L+d8FBp8DSgB3UfimLz+9pvOwRsvOgovOmk9ALKc4gwNVWlKNpdRwsVLdfEOyqih0OD7RNjIU+2dLKda8tAME3Up3y8shEKD7wmxI/cLDdOxB83ncqiWyHLKGlahe7f8jEjCcqpcyAl7PM7RUAtWaKSHvs/QL3/5S5xzzjlYvXp11+9ds2YNzj33XPzyl7/s99cxDAC3fJwmrWlTKYe+8qJRDVwZGsW82FDICpqTNkzGflxsORUvNCVTzOdE80I3TLH4jFOhQZZT3NBIDj/HnqimJ0faNDSW6sHe+15vfLYbCh+RoaFEQ8NTBJM+zotmLD83VICaTnFYTp28YQrveP5xGCsXxO/WA04GM/HTjKBQ6QcVL2t8/pXDe1+Ic3CmkNN8G/RsKRM+LoVGJ8spz0tvhYJbBctSFKHgfK5jR25atWRo2EOwBxfdCg1575GEQkPTnMDyoINWTDywQiM99P3OfeKJJ3DiiScG/v4TTzwRjz32WL+/jmEAuCdsaGNJGxd6SAFcqFYFufaX07SWxcKJh0zEfEQWHAoeH4ZU+Cl6MzREQSq+46H7BmdoJIef60JU05PtGhp3bdvf19/HlhHRoYLllDfLpejT0OB+hhrQWzHOQiXZjjV1dyg4NzTUoikUVlErNGz/c15LKof3zMep0NA0TVjbynAoePi4sow6vL5eZbAVCh6BQoMH5pTAMGWlrSYUGvs9GRpFl+VUMmtQ2qf0a4XLhE9TN8T1wwoN9en7DM3OzmJqairw909NTWFubq77NzJMB+RpSVJo0BQEyQjlzzHJ4p249W4uk+p6l3kTGhsNabNRyDtNrXrTWSzE6YFeEZMwfO6Twqt6AKIrNlRK/suc//Pd3+HxfYtdf77FcooVGqFjKqTQ8IZCuiynWKGhFHQe4lT4FaRrQL4OWBWsFk3J5jJKuHipLt5ndZyNT8Cy2fXCjc/wkbOMOilovb0OXQ4FL4QfCs4Dc/Ejn35vhoZoaNj5aBX7/UnZGkAyCg3rWOyGBltOKYOcZ+LXnGbUou93br1eRz4f/ATncjnU6/Xu38gwHZAfNmMVywufFRrqIi8uDpmstPhTJtX1po0GLzijRy70lPI5p5mkG2LzEYdlCFER554Xjknht+eMyruWZOZ+7Di41PXnvcfKCo3wode4Ux5bXHgLoL4KDW5qKQH1E+KcvCbb06bBCg2VaYhCVsSWU9zQUBbvtH7sDQ2f/U2TG5+hI6/JjA7rM+8gjWma4n3LCo3hQD7HhmlCtxcJ+ZwmmgbU0KAGhzxYmVRDg5oqrNBQh5p0LlihoT4DnSEVNp9Mtsj7WE7RRpIeUvLnmGShxfufnXUE1kxUWhYLSSk0xAQNLx4iR/YMLuZzTjOpYYjJ7DgVGtRQYYVGcnjDGYHoCk/tLKeA/ppa3NAIH0M0NJI9DqDV9qrgp9DgopQS6DHZCslQ41U3TNezjdecaiEXsqKklG/NhWPUwPusjr+h4Wc5xddJ2DSk89xpfdY6nOLctyNpaPA9IXbkt5dhuq0HvfUGGnaquhQabDnFWNDAaymfi3XokumP9qOLAXjPe96D97znPSEdCsN0pygVF+gBQIuZp+aWxdd4c6kGZDd0ztGrALRuKJJWaPCCM3roNdY06/yXC45Cgl7+OC1D6NwvN3WYpsmN+RDoNBXn+/0xZmjkcxpKhZzvtFw/E3Q8nR8+NFWnwjvRO8UpX5fUeOVg+PDYPbuM6dFSXwUlofCL8R4uN7XkCXBucqmFEwrOllNZxfuejFqt48Vvf8PWdOGjS/u4TuszztAYfuQzbJpOfmMhp7nsQwGnkTGigkKDrJDZckoZKKDdzzqQUY+BzpJpmj39xzCDItuSjNje6E3dQK2pY5vkh84LCTWQFxOAuyEFJK/QaOhmz8VYpje8GwaaWqs1HQ/yqOyG/KBrzjS5oRUWvRb5fTM0ItxItHseBLGc865dWKERPo7lVLLHAbQqeuQmPP2Zr4Fw+NXjB3DG+36It//Xb/r6+UQUGq4MDec64GeJWtC6I8rnCgCU8pzHpiq6Rw2hQoYGKzTCx9VY7hgK7v54sd7ErhlrEHLlaCm04ynn3Q2N6+/dibu37Q/t72faI6/XTZdCIyfshgmVLKcqbDmlHLQ/5PyMdND3O9cwjJ7/03V+ozKDIS9IhUJDN/DE/kVXkYE3l2rg3VSqk6HhPKD4WokWEc5pv3cdhYYhJuji3GhWpMUJ206FQ68T6357zriLDUB/GTqs0AgfM4FJ+3aMlt3C5Uf3Log/O6HgfA2EwT9/93cAgO/85sm+fj6ZhoaToSEXTLlQqRZ0bqLOV2F7GXXx3qfjzNoB/AthrNAIn2ZAyynv2u23O2ZQ1w2sGS/jiJXV0I5HVmg8vHsOb/zPX+LSa+8I7e9n2iOfYcN0BhYL+faWUyWpDpGU5VTVPra55WYiv59pRSg0OD8jFfBZYlKF3D2nh1NTN7FUd28meFpKDbzFbO+GIrGGhvR7a1zUjhThUWu/5k6Ghi42H3FOxRTzGugy5AyVcOi1luen2IxyI3GubXn35ouPxuvOP1J8PkiGhvdIuW4VPvQaJ9/OaG1oPOf4NeLP9PxiVV84PL5/aaCfN5JoaFCGhm667gWNJl8TKsGh4EzyGRqt1x5fJ+ETNBTcu5ibt4vH06OlUK1n5Sbn9gPOM47XDdEj7y28GRpe9S0pNORBmqgVfe04ZGoEALDj4GBrIiY8SC0jZ6ww6jJQhgbDxI28IF0zUQFgLRrqHvUPT9GqQdPwKjTci4VSQg2NQk6DplmSVKuoWUzkOLKAUOnk/Cyn4i9IaZo1qbNY1/ua0Gda6dlyKmaFxif/7Fl4aNccTjp0EjlNw08f3ovf7Zztq7jAdkPh41hOJd/SOHbtuPjzT664EBtXjYqPc6zQCJVBTzcpw/IJZGg0DMN13+PcNrWgYZqo7SzlTDBGLbxqiLjtZLihEQ/yvbeTWti7TqWCZdj7UNrj1JuG6xm3UG9ivMJ7zSiRT7HhydDwWsCN2IXqrXvmxedGEypeHz5tNTQel6zTmfj46M0PQzcMvP2SY8XnSKExwg2NVMANDSZ1/OufnorZpQY2rbQKDU3dbClMsqxXDciGId9GoZHUNISmWeHUyw2Di9oRQ5uNUt5rOaU7GRoxT85RQ2OZFRqh0LvllE+GRoSTtNVSAacevkJ8fMIhE/jdztmAGRruj7lZHj4iFDz5fgaec/wavOP5x+LEQyZczQyAFRphM+htn4oVcWb9FqQcFfk6aLDllFI0pUJWlND05hKHuSqHN0Mj7gEqP8sptiYLn6AKjXYNjbAbXZTVsNTQXcH0c8vc0Igadyi4W6HhtZwatS2n/ujUQ/GD3+3GOUevTGyohhQaO2eXE/n9WWah1sSHb94CAPjzszdi5VgZAITzS1JZr0xvcEODSR1/cMohAIAHd80CsAqm3gYGT9GqQVMEQtsNDc+0XDEB33yilOeGRhx4c1T8MjTibmzRMXCGRjj0WuD16wnEGQxPhY0gdnPeAHN+toQPvaSaAqZTmqbhjRcd7fs1siZghUY4DJqZosdkKyRTkFQ6ciOXJ6/VIq61BfmwL9R1GIYpVFxM8njv07E3NHxCwfk+ET79hoJTE7IU8j1ixL4nLNV1LNSdTATOR4ieVoWGMzTXznLqBU9fh2/85dk4dt04kmJixGp0zS03EjuGrCLfM+SGMzU8vdcNoyacocGkFtrENnSjZZHIBQc18NoNeQsPSSk0ACcYnK0CooVe35LI0LBe918/cdAlB44TmrhY5nMfCr3ebv2+P85rYBCbkF7VKEx3HMupZI+jG9R0Y5VOOAzc0KAw+RiXEXl7zWJlaHBDQ1VI/Rl1yOuYlLmzxIpPpfAOH8Qd+OtnOcX7jfBpBrSc8ma3CYVGyI0uKoAuNXTM1+SGBhero0YeQDJMUzS287lcy6Q9WQlpmoZTD18hmtNJMF6xfjc3veJHHsiTHxnc0EgX3NBgUgtNVTR0s8W/uMmyXiXwbiq9G4o4p7K9lDnMMRYWa9aigMJ2yaPUNIFt+xYAxN/QcBQavLkMA5fkP0Cx16t6AOJtbsoex91osZziZnnoCMuphI+jG0KhwZaWoTBIP8M0TfHejDNDw1FoGK57A6v91MIpZEV7bVSKOXEdy9PYTPJ4B9v8GgxR4ms5xfuN0GkGtpxyf7wYkUJDtqFbqLFCI07kc2yYcA3NVTyKqdGyOiY1ExVWaCSFfP/QpbX9cp0zNNIENzSY1ELF8KbBCg1V0T2yf+/mshjnaKWHkmR9xEQHbfKpkXHKYVPia1v32A2NmBtbQqHBRahQkJsYQYbXfS2nElBofPnOx3HBB3+MLbvn2n6v91DZcioC7JdUdbsWkZ/ACo1QGEShIa/xErOcko6h1tRbJoCZ5KDrI+o1pqZpwot9ocYDEirhfVaX8vEWpjgUPHoMw8T9T86KjzvNMrYoNKihUQh33VFxKTScewIPUMWA5xw3pAwN76S9SpP3pNBYbhgtA7pMtDSlrCU5C40UGpyhkQ64ocGklqKk0PDKeHmCUg0antBnb/hasgqN4FPaTP+Q5JqmYYr5HJ55+BQA4MBiHUC8BSnACe3jDUY4yIWDIAV/v8JfnA0NamYu1HU8tm8Rr//iPYF/lovZ4UMNMbXbGU5Dnpta/WOaJv7jjm2445F9Ayk05Oe2n1d9VNCaRfdkaBgmWrLcmOSgolAca8zRsrWWlKexmeTxFgaVyNDgYmWofOSHD+POR/eLjzsphFsVGtb7NfQMDamhsSjdE3h4Lnq8Z7/RdJ4D3sJ0VaHJe9m6kJU88SLXCxucoZFa1NFbMUyPyIvFec9klG7wwkEFnGBGOxQ857WcYoXGsEObfHnBNm7La6mhEbUthBdqZvG5Dwd5Exmk4O+foRGn5ZT7d23du9D2e1stp6I4omwjXmPFOxrc0Bic2x/Zh3/81v0AgE2rRsXnTdOE1kOHQ753h12Q6kReym7z2pvUmnrsRVPGH12azI0aS6FRExY2jBokn6HRWgjjNWe4/OsPH3Z93OnZ7G12kELDO2g3KC7Lqbrc0OD7Q9R41+s0fZ/TtJYGo0qWU4V8DtVSHot1HXPLDUyPlpI+pMwgq30bTefPS8Jyitd0aYDPEpNa5K7p7JLbd5Atp5LHNE1xHqhY2Wo5lXyGBi8yo2VeZGg471eS1x5YtN638YeCs0IjTOQif5CMCb/viFOtVe5p4sZdGGOFRvg4/Qy1Oxrc0Bicx/cvij/L/Yte1Q303C7lc7FalRWka8B7GXCxUh1omCbsYqUfVVZoKIl3Hxi7QoMtp2Kn097fu3RbbJDlVMgKjZK/5RQ/H6LHq/6u01Clj+VU3Jk63eBg8GSQB6DreqtFHCs00oFa72aG6YFiPic2l7PL3NBQDbnoQ5NRmqa5pqSSVGhwKHg8LHgspwBHoVEXcuC4LacoQ4MbGmEge5AGCQX3+544VTr9bGQoeJhDwcOHrgfFIzTEdD6vL8JBztBo9ih9qtn5R3EXJVwZGp77GD9P4qNbeKrX7jRKRIYGh4IrRUuGBjc0hp7OoeDu4ZTFiBQaVJjWDRO/fuKA+HyNM/six3v6yXIqn2u1nOpFERoHtC/21rOYaJGHaeqyQoMzNFIFNzSYVEM3mtkl90aCMzSSRy76yMVKWYYdtwRcpsyWU7EgLKdKTkNjouKW+sau0GDLqVBxZ2gE+AFFQsGDQDVLns6PDnqNFdtftkCKwib7oPeNfIpzAyk07IZGjPkZgDtDo9Vyiq+LOPjhA7tx0nt+gGtveaTt99B9Op4MDWs9s8ih4ErRotCIeXBGVoLSXoczNKKlk4KWvkTXATWXwm50VUsFPP/EtQCAJ/Yvic+zG0D0eM9+XcpSikOtNwis0EgGeU/nytAQllPc0EgDar+7GaYL1NCY8VhOcYZG8sgPBnkhUZEKEHGHQctwKHg8eEPBAWfhRsQdDs+WU+EiFyOHVqHBllORo7rlFN2nWKHRP+2aVt4A325QccjPpz5KnAwNs6W5yc+TePjrr/0GAPAvNz7Y9nsawmokBsspu+Axz5ZTSuHdB8Y9kS2vM8QQDU/pR0oQhYZ3kC6KRtfLnrmh5XPc8I6eFsspodBwn+O4m5tBmLAVGtzQiBd57cmh4OlFvXc0w/QAhfV4JXoNLjgkjrzZL7RRaMRdyJYpcYZGLHQKBSfiz9Bgy6kwkd/r/TY04iw29FIEpSOlhgZbToWPmRLLKbLG67X4zvgj3zd6VdUKhUbMNjJFodQyWu5jD+yci/VYskoQlVxTmsyNGlrbLLLllFL0qvoKG3mdQWoNVmhESyCFhueZEcXkvndoC+CGRhLQHs+7x0zSHaIdjkKDLafiRF5PyAOunKGRLrihwaSaEWE55VFosOVU4sibCXn6WlZoKGE5xRNTkbJg2zBUfULBCe/0TNSw3Vi4NKRJyCDFpqRFDr1ZTjmhggArNKJAXDKKe045llN8DYSBfKvoWaHRiMYupBt5KUPD29C4wlYOMNESpKksLKdiUWhQhgYPSKhE0vaQLoVGkTP74qCTetJRaHim9SN4hoyWfBoaPEAVOd7leU3K0JBRLT8DcAb9ZIWGV3HChI8rQ8NHoVFhy6lUwA0NJtXQpDU9AKjB0WDLqcRpSqGM8uJBDlhK1nLK3mDwxFSkUFCmXyg4EbdKpswKjVDpXaER5dF0x893v9vGIccZGpFh2joY9baYbkihwZZT4SAHgff6HBaWUzFPzwnbMd30zQti9U70BHn/iVDwWDI0rGtwgS2nlIKukxPWT+DDrzgl9t8vrzNo38MNjWgJZjnlVWiEf48YLbc+l3iAKnq8+w9aJ3gVGiraA054FBpv+NI9eP5HbmUXiYhxZ2hIoeB1VmikCW5oMKmm4lFokJctF52Sh6ZYvRtKV0NDCcspXmRGib/llHt6ae98LdZjciyn+NyHgVzEC9ZLTvb+XMoH32zSkdKGKEjDhumNtISCFzgUfGDknBRZSdtroS8pyylSE+qSQuMlpxwivs5N8mipNfVAazZafxZjGJqhYY0FDgVXCsrQeOcLjsUfndqaaRA18l5HKDT42REafvv8Tnt/+pK3gRHFM6QqKTRWVK0BLt5rRo/37NMejxQaG1dWAQAbVozEeViBoH3x7FITyw0dN963C1t2z+O+HTMJH9lwIw9Ay3tZuna4oZEOuKHBpJpRu4ExZxdNR+yPeYIyeegceDeUbsup5EPBWQYcLdv2LQJwS7BbGhpz9ViPiUPBw0XeRAaxZEr69uyn0Gh3LdA/R4SC8540dOhyyCne0eBQ8HCR7xW93oudUPB41xDU1KrrhpgGHivnRTNuiZ8pkbJ1z4Lr43bNRXqPeq1GooD2IZyhoRbNGIPh/ZALYdWiteZlhUZ4+L2WnQZO6CslT4ZaFPtQWZG+YYVVROdJ++hpd/rpHvD5V5+OF5+8Hv/+qmfFeFTBEJZTtQa2H1gSn+flZrTIgzW+oeBsOZUKWk3+GCZFTI+WXR+TQoMnKJOnXShjRQ4FTzAFli2noudXjx8Qf5abGJtWjWLtRBm7Zy1lxphPgF6U0DW4zJvLUJAzBYL4myftC+tXBO2m1uFQ8Oig60HtdoazKWZboXCQG6G9quWomFWKeShC9sLXRZi9hkohj6WGzplcEeNtGtSahrCCk6H1Zxw5bTSNraKNSZbRY2xq+SEXwqZ4Sj90/BoanRQatM4oee4JUTQ0qtK5XzlW6npsTDiYbdTfQqGxahTX/I9nxnlIgXFCwZt44sCi+PzMIoeER0mzTQYkDdlUCtzQSAOs0GBSzSp7oUCM2BsLXjgkT0NYTnkVGs7DoZzgg6LEoeCR84g0TSlLfKulAm7/2+fg0392Gi4+bg3e8fxjYz2uCmdohEozZRkakyPFls+1V2i4iyIcCh4+abGcKkr5CczguDaPPU6v0j0nbttKoexs6qK5mc9ponjJCo1o8Ta+2hWIm23Wn1FA09iLHAquFEndIwhZobGiau1V6zylHxo1vfW17PRojjMUXP4dJ6yfAMDrhjhoq9BI0N46KKTQmF1uuoLBZ5a4oRElcm6GvCYVQzMxq4CZ/mCFBpNqVo15FBpFtpxSBTkUXEaeoDh0KjkfS5rSrvG0bWTM2+FmLzppvSsYHrCKQM89YS2ee8La2I+rzPkpoSIr4oLs2ZK+O/s1NNpN3XszNLhZHj5OQ0PtTScVRxvBgmIYP6RTLG8ke7V+TMpOhuzqah6FBhUvl7ioHSne17edjYsoZsdhOcWh4EpCe5CkFBrylD5NX/OaMzz8FRrtX1/6UmsoeDTPkK+9/izMLDZw0C5Ic10ietqpv5O6B/TCuBQKviw957ihES0uy2T7z7phivcrNzTSAZ8lJtVMj7oVGrSxYEuI5Gk3HSVbRExWWwuLcVESGRp8rUTFgr0okwPBVYAUGpyfEg4uhUYKLKf8CufdrOco34FDwcOHmtyK9zO4qRUydZ8AxqAkrdAwTeeYc5omGh2s+osWrwKm3XUjBmpitJxa4AwNpdD1+JpafshqdFJw1XUj8fXPsCA3hyjsuXMouK3Q8BQoo8phevbGaTz3hLVC2cnrhuhp99bKq764hNtySn7OyWoNJnwaroE86wKSm6Xc0EgHfJaYVDPhmbSljUWDpZ2JQxOU3lDw1194FDQNeF4Ck/kyzpQ+FyCighZio8o1NLj4FCbtPEjbocJ+3rtIbRvWaR9rgTelkUEvqaZ4igZNcrJ1RP/IZ7jhamj0qtBIZvpaLn6RxVA+59jLsOVUtHivE7/1m2majuVpDAoeGthYrPG5VwlHpZNMqUO+V1BzwzR5Uj8saM22aqyEd77gOACOCqMTcWRoyNAzqsnKzshp985Kg0JjgkLBlxuu5xzXKKJF9xnIczU0Ys5pY/pDrSoTw/QIKTKcj61Lum1xiomNdqHgx62bwB1/+xxMjCR7+ylL4Z5MNJAFQ9yh391wMjT43IeBKxQ8UIaGM5GfVHOjUsi53vvdmuA04cUNjfBJS4YGbYpZAdo/sjpKfu/33NAw/AcmokYuUi7ZE/m5nGM5xU3yaGlpaPg8w+VbdBzT+WQtxAoNtdATztCQ73XyfaPeNCIvomcB4XGfzwkFbaeMM1p3erMbo57ApnsQD0JET7ozNKx98nLDcKky2KYuWpouyynr/3I+TzEF1w7DCg0m5YyX3QqNMbacUoaGCMxsvc2sm6wINU1SUNedFwvRMU8NjXJy4e9+CIUGT76EQq+h4PQtScrAZTsIoHuGBhWz2XIqfMhySvUhOhEKzk2t0Fnu8TlMxaF8zJtNTdNEcVIoNFyWU7yeiBKvAsZv/Sbfy+MoZNFk7XLD4IaWQjTFHiT5B4tsu8pDVOFAloWlQk6c486WU9b/6V5NRF2wpD0wrxuip936XIV7QDfke8SeuZr4M98vokW+ZryWU6VCTvlsP8aCGxpMqmGFhrpQOJuq3e2yXdDkayU6qKGhmuUUTWhx8SEcmj6hap2gBeRRq8ciO6ZueBsa7e4D5HftbJijPa4sIhQailtOkXUJD0yEzzd/taOn7xfriwQKFdTQoOJ6TtPEgES3LB5mML5293bXx352HPLzKI5JeFltfMNvd0b++5hgCIVGgsXMV515BI5eM4Y/eMYhYg3B94hwaEhFxyD5VrTu9K79IldosF1pbLR7hZOyneuFQj4n1H5PzS2Lz7PlVLR0spyKKl+HCR+1qkwM0yNeKxvqcHPBIXkaCQfydcNRaPBiISqcRYFaCg15mtY0TZ7AGJCmdL8NImCg7/nfFxyJ3+6YSSRPx9tka1dkoH8ObYhYoRE+pmRBpjJUmGDriPCRJxKD0EkBGjXlYh5YbgqFRi6niaIYD0hEy8NPzbs+9rOc0qX3ZxyTufL64e3/9Ru87JkbIv+dTHcaCeXsyPzzS58u/lzK57Bk6HyPCAlasxXzOeQCNDRo6eYtUkbtkS8sp7ihET0pVmgAlu3UYl3HU6zQiA35nkHvUbq3cEMjPfCZYlKN13JqtMyh4KpARZ+Col6xVNRmy6noaEiScJWQJ7R4Wm5w5AJvJw9jgiyGRop5vPslJ+Lso1ZFdmzt+MDLTwpkA0ENDJ6yi460vKI07c3XQP+0Kyv02syic5CEAtTPcqpUYMVnEvhaTknhu0kM1Jjc9FYCPeFQcC90r+I9RzjQurOYzwn70k4DJ+0yNKLeozrqXj7vUdNuaabqYKWXtRMVAMDDu53GPe9Ro0X3sUyW83mYdMBnikk1lWLO5btNXra8YEyepuqWUzxRGTmioaHYNVCRNjTseT44vVtOWf9PUhlz8oYp/Obdl+C8Y6xmStsMDftYg1gaMP1Br3FOcYkGXQMNLkz0Tbt3T69DKElOX1NDnELB8zmw5VQMmKYp1vvHrRsHAGzdM9/yfQ3dWXvG9Yz50l+cIf68a3a5w3cycWCapliXqLIHoaYnOwiEg/w+p55VEIVGxZOhMTlS9Pnu8KCGGis7o8dss8JIi0Lj2LXWc01eR/ipEJnwkIfwdI/llGrDmEx7+EwxqUbTNIxI09a0MOEFY/KI0E5FpqO8UEODm1/RQYuCOHyse6GY10RhpJ8iCvwAANxGSURBVMY5GgPTu+WUGiHQ+ZzWtbFJ/x66htlyKnyMlFhO0TXAhYn+aff+afa4ZhPT1wk8W7wKDctyym528XoiMpqGKZrhq8fLAICrb9qCueWG6/uSmK4895hVWD9pTdfumuGGRtLIDdKiIkUpHqIKl4bRqtDopBD2y9BYN1HB9GgpwqN0iulsORU9aVdoPHvTdMvneEgiWgyfgbwaNzRSB58pJvXIG1pqaPCCMXmaCYZ2BoFkx5yhER11SRKuEpqmiU0NKzQGJ40KDYKuzZ1tilC0CWa7oeigVzT5q6EzVJjggYn+aWfH0+jxfZVkRheHgifDsjR8IA8ybdu76Pq+pKYryS7kj/7tdrz/hgdi/d2MG/kerYptiMjZ4XtEKFDzuJDPCUtQo8cMjYuOWxPdAdoU2a40NtoNTKRFofEcn+uRFRrRIt+OWyynuKGRGvhMMalH3iCzQkMdRMFBEbm3FznEkz2Po6Ghq6nQAJxNzTI3tAZG3qgFy9CwUGGPQfeBj/7wYdy3Y6bl63SswgeZ7xXho1CDqxMiFJwLE33T7qXrXaFBxawkGhpkOSVnaPD0ddS0Gz7Yv1h3fZzUdOU6u6EBAP9+61Y8uGs21t/POMh7QFXWn1TY5ntEOMiWtmRX2enZ7KfQqJby7b49NByFBp/3yPE5/flcfNaDg7Ki2qoWqnE9K1LkbBvayy7YdqLVYsH3Zxj1UOMpzzAhMVq2FidNw+w4qcFEDxUolA0FL5CFDBeoosIJBVdvMekoNLihMSjy+yfIfddUyGJIDgz94Pcfavm6NxScnyvhQ77HKjS4OuFYTvEGs1/aTVAaZm/vrWaCCg3yYKf7nmU5xRaWUUPP6nIh53rm7Pao6+oJDVKsm6y4Pn5i/1Ksv59xoGsgp6kznU33iL3ztYSPZDhwWU7luq/PnFBw574QR0ODMzTiw299ocr7Pwg5n2PlBmi0+GVoHFi0bCxXjEabr8OEh5qVRobpAfnxJU9ksaw3WUihoarllOta4QVDJDQUzdAAnIYGF6AGR95EBMmYoG9RYWpK9l8fK7dO44gMjRxZTsVyWJlCDEgpcD10osBe2APT6aXrJWydzkEhgYwuUmgQpbwmnnG87owOsgetFPOue/UBj0KjkZRCw9PQWLSnPJn4cRTi6qw9yfrqr77ya3zp548lfDTpR7acygXI0HBCwWWFRvQT2JyhER9+L3Fa8jPawbbY0SIv2URDY8FaU/gpZhg1UedJzzD9Ij3AuKGhDhSYORLDgrEfZF9dbmhEA2VoqOhDKSynWKExMPLkWbAMDVuhEdkRBedZR6wQf/Y2t2QrujwpNNhyKnRIoaHC9dAJDgUfHK+9o1xr6OV1bSZpOVV0P89KhZx4xnEoeHSQ5VSlmMM7nn+s+Ly3UFgXVjTJWU4BwEKN1xZJ0UggGL4b8jr4Xd+8L8EjGQ4cS1vNsQTtcPul20TcCg3O0IgPv+V5XvFBGS/nHbMKgHOdcn0iWuQ9HTVEaUhixSg3NNKCOk96hgmBYo6L1KpAgZlxLBj7wZrqsf7Mza9oUDlDg0PBw0P2IA2yZ6P1Y06Bjcb/Ou9InLJhEgCwUHNP1Mr/lmKON6VRodL10AknQ4PvGf3iff/I07I9NTT0JBUa7t9ZzOc4FDwGaPigUszjsOkq/ucZhwNozcyjtb/3PEXNmvGy62Pv84SJD7nYrQqlgpp7obRCjcySbDnVUaHRmqERR8GSMzTiw9dySqF7QBA+8cpn4a3PPQb/99KTAbCLQNTI606yrDtIllNVtpxKC+pVmbpw3XXX4c1vfjPOO+88TExMQNM0vPKVr+z4M7fffjs2b96M6elpjIyM4OSTT8ZHPvIR6Hr76Znvfve7uPDCCzE5OYmxsTGcccYZ+MIXvtDx93zhC1/A6aefjrGxMUxOTuLCCy/Ed7/73b7+nUxw5FDfXE4T/sYU2MgkA20+R4rqLuI5yDNa6gpOyRF0n2CFxuC4MjQCKBgMhTI08jkNf/XcYwA4QXCE/G8h6woOBQ8funxUuB46QcXzBis0+sbbD5SnlvuznEouFJwo5nM8TRkDQqFhv/7tFFP1hKwuJ0bcxQ/v84SJD2F5q9Das5Sywqrq1JuOSk80DTo0lGk9Jw/ZPXvjinbfHhoFYVfK64YkSJvl1Fi5gLc+92k48RBr0IrXFNHiVmhY/z9oKzSmRlihkRbU9ILpwJVXXonf/OY3GBsbw4YNG/Dggw92/P5vfetbePnLX45KpYJXvOIVmJ6exne+8x287W1vw89+9jN87Wtfa/mZa665Bm9+85uxcuVKvPKVr0SpVMJ1112Hyy+/HL/97W9x1VVXtfzMFVdcgauvvhobNmzAa1/7WtTrdXzlK1/BS17yEnzsYx/Dm970ptBeA8aNt7gwXiliuVHDrOSNzsQP+QePKKrQAKxC+3LD4AmIiFBZoUFFKW5oDI68Uetl06bKRP6obYs336LQkBoaAUInmX5Jh+VUIUDRhOmM13KqkMuhkNPQNMweLafIIz+5UHCimM85GRq8logMJ0PDeq3p/dii0CDLqZgVGpOehsYiD1UlhoprTxWtV9OMfI7JVqjT8oy+Nl4p4l0vOh7lQg7rJ0eiPkzRbGnoJkzTVCI7blihNXsxr4naUJpCwWVoSIIzNKJF3rPS/m5myaofeocUGHVJ3dP1wx/+MLZs2YLZ2Vl84hOf6Pi9s7OzeO1rX4t8Po+f/OQn+MxnPoMPfvCD+PWvf42zzjoL1113Hb7yla+4fmbbtm244oorMD09jbvvvhsf//jH8eEPfxj33nsvjjrqKFx99dW44447XD9z++234+qrr8ZRRx2Fe++9Fx/+8Ifx8Y9/HPfccw+mp6dxxRVXYNu2bWG/FIzNv7zsJFSKOfzn/zoDADBesYpTc8s8HZUUpmniv+7eDkB1hYZ1bFyECB/DMEXRSSXZPyEUGnzuB6ZvhUZkR9Qbo3bArNciRP6nFNgHOTLMtCg07GugwddA33jvD/mc9Lr20CiiplISxYqWUPACh4LHASk0yvaaklRz3qGmekKh4JMeewpvg5yJj0ZCTa1OyEplFVXLacPPcqrT+oyePTnNshp91VkbIz9GwL3/4aVDtDgqHGdeOwlbyjBg1Wc8yKp73dPQ8A4pMOqSunf5RRddhGOOOSZQh/u6667Dnj17cNlll+G0004Tn69UKrjyyisBoKUp8tnPfha1Wg1vetObsHHjRvH5FStW4O///u8BANdee63rZ+jjf/iHf8CKFY58cePGjXjjG9+IWq2Gz33uc739Q5nAXHb64fjdP70AZx9tBSmNV6wbEDc0kuO+HbPizyorNMSCgYsQoSPbhxQV2lQS5KNbY4XGwLgmXAJs2ByLITUq2E5Dw30tuBoaObacigp6RVW5HtpBRWtuavWP96Ur5HIi+8wb7twJp1muRoaGyFdhO7LIkDM0AMfCx+tNn1Qo+FjJbXqwyA2NxKgrmKEhN0LjzncZRmTLqRw1NDqtzxLK6pKb7r007ZneodM/KtUd0qrQoGasYbIqOEpk1X1TNDSsZzc3NNLDUD9Rf/SjHwEAXvCCF7R87fzzz0e1WsXtt9+OWq0W6Gde+MIXur5nkJ9hwiUnPbAmhEKDLaeSoi7l06hs6cMZGtEhv6YqTqORDzfbjQ1Or5ZTpjQppwKjZetaWKg3XZY4bDkVD6opdtpRkKZAvdZJTDC894d8TpOaAelQaFTL7sK1bDnFwa/RQXl5VAzuptCIe5Ai57kW52vqrn2HHRUzNCZGnPtGuajOcaUVX8upQAqNeJ8Z8jXIDY1oodM/WpYVGqqvLP2R1WW8T40Or8OAaZqYJYUGh4KnhtRlaPTCQw89BAB42tOe1vK1QqGATZs24f7778fWrVtx/PHHd/2Z9evXY3R0FNu3b8fi4iKq1SoWFhawY8cOjI2NYf369S0/c8wxVtjoli1bOh5rrVZzNVZmZ60J90ajgUaDC/O9QJ35gws15V87Oj7Vj7NXmpLn4+6ZJWX/fUV7obOwXFf2GNPK7KJ1P8tpQM7U0WiotSCjAZ6FZbXvsWm4RzQ8Hq+1Wr2luCNDm05d15X4d5Vz1vGYJjC7uCzk6vWGM2Gr2eN9Dd1Q4piHCcMuAhuGGtdDWwznOl9critlZ5KG+wTgXhsA1vOBCg6LteDPYWpoaGb878eJsvu852BAM63jqTf5/hAVC/aQUimvodFoIGffk+vNpus1X6pbfy5q8b8f/v6Fx+ID398C3TCxUFNrbZGWe0QYLNWsUNd8Tp1/b1FaE5ULOWWOK63U7WdJDiZMw1qr6abZ9nWlhoauN+N97aWC6eJyHRV1TQsApPs+odvDlHLwe9F+XqQNTRqOWFiuoZTjIZookNekTV3H3GJNKPyqhXS+D+Kgl/tEHK/hUDc0ZmZmAACTk5O+X6fPHzx4sKefWVhYwMzMDKrVal+/w4/3v//9+Kd/+qeWz//gBz9AtVrt+LOMm4N7cgByuOfe+7Fy/31JH04gbrrppqQPIVQentEAWAuKlbMP44YbHk72gNqwtJAHoOH2X9yJ2S28WAiT3UsAUEA5Z+LGG29M+nBa2PG4dZ94YMvvcUO9c8NZBVS+R+zYab2WxPU33ohOTg/z9vvuFz+/A3t+F/nhdcU0AQ15mNDw7Rt+gImS9XlLdWwtk36/5UEAeex48knccMP2pA51KHnqKev6uffee1HZ+ZukD6ct1sC1dT1cf+P3UFawMKHyfQIAHtrurA0AYGlxAZaIU8Mtt/0U28aC/T37Dlj3kF/eczeWHon32f37A+5/w5133I7ZhvW5fQcO4oYbboj1eLLCvTus13jvLuse/PCT1sfbHt+OG254XHzfffY1tnvnDtxwwxOxHuNaAK85RsOnHsrjyaf2KXktqH6PCINf77OugfmZGWXOwe8fd9ZJtaUlZY4rrWx7zHo9H3n4Ifzo4IMACjBN4LvX3+Cr/m02rWfGT37yY0yX4z3WHPIwoOH7N92MyVK8v7tf0nifePwJ65pYmjsAeq8tzs+l9r1G182N378ZUzFfs1nhiR3OfXn3U3vwjeu/D6CAHEzccvMPlM/2S5og94nFxcXIj2OoGxpp4u/+7u/w9re/XXw8OzuLww47DJdccgkmJiYSPLL08ZsbH8LPn3oM6484Cpuf36q0UYlGo4GbbroJz3ve81AsDo+07ZYte4Df/QqHTlVw+cvPT/pw2vKFHXfiiYWDOPkZz8QLTlyb9OEMFb/dMQP8+heYGhvB5s3qXQNbfvh7/GjnVhxy2BHYvPn4pA+nLWm4R3xr/6+A/XvEx5c8/wUdPaKvfug2YHkJZ599Np55+FQMR9idd/3qR5ivNXHmeRdg48pRAHYw3F0/BgA8/cQT8M3HHsKateuwefMzEjzS4eNre+4BZvbh1FNOweZnHJL04bSl3jTwzjtvBgA857nPw4RC/rppuE8AwCM/egR44hHx8eT4OJYaOg7Wl3DGmcHvBx9/5HZgcR5nn3k6zjpyZURH68+h22fw7w/+Qnx80QXn4am5Oj754D0YHRvH5s1nx3o8WWHLD38PPL4VR2+yntl77ngM33rsIaxdtx6bN5/i/r4ntuKoTck826e37senHrobhZExbN58DgDgxvt24St3b8cHX34S1ownU5lKyz0iDJq/2Qls+S3Wrl6JzZtP6/4DMfDgzQ8DOx4FAOTLFWzefEHCR5RufvBf9wJ7d+GkE0/A859xCP7hbmut9vwXvMDXauyKO28CdBPPufhirJ+sxHqsf3v3zVhqGDjvgouwYcVIrL+7V9J8n7j1G/cBTz2Jw9evxZYZa0+yeuUKbN58esJH1h9/d88PsVjXce4FF+LwaR5ujoIffNW6jwDA9MpVOO3sY4Ff3oHJagkvetFFCR+duvRynyDXoSgZ6oYGqSNIReGFPj81NeX6mb1792JmZgYrV7ZukryKjH5+hx/lchnlcusit1gspu6BkjSTVet1XKgbqXnthu0863a3e+1ERel/F4X0GdCUPs40smy79YyVC0q+ttWydUx13VTy+LyofI/wmonl8wUUi+3H12meulhU59oYLecxX2ti+0wdx6ybAgAUGs7kd6VkHadh8r0ifKwRqEJBnevBj3xeUgLk8koeq8r3CQBAzl1oKuRzKNpWIKaWC3zsTftnygn8e1dPuAtSI+USKjXreJpGOp4naYRcK6v2moLuyboJ12vetN+mlYSeL5Oj1h5kqa6L3/+Wr94LALj21m3455c+PfZjklH+HhECdA1US+o8U8ZHnNH8ejM9+1NVoTiKcqmIcsl5LXNt1p8Ue1UuxX/9F/M5LDWMnp5xSZPG+4SmWeuLsYpz3OWCmmu1IJQKOSzW9VRdN2lD3r8aJrBguyNNVUv8mgcgyH0ijtdRHQPgCDj22GMB+OdXNJtNPProoygUCjjyyCMD/czOnTuxsLCADRs2CBuo0dFRHHrooZifn8fOnTtbfubhhy2rHb9MDiYaxjkUPHEowIoaBqpCHugcuBU+8zWrozFWUbNvXilyKHhYeIN+9S6ByWQNm1dIy7t71sp8ee0X7hafk/9ZdL3UOdQxdEy7xaXQ5eBLLqcJKwvvNc8EwxumXshrKNpNjqYe/DWl7y0kEPp76NSIa8LXCgW3g835uoiMmuVNJu7F4jVvEwqeVMYNZTAt1FtDwTkUOB6W7e5XpcNgRdy88swjsMIOmV1WLFMujdB7qZTXkJc8pow2609alyaxzqB7UaOHZxzTO3Tu5VDwokJZZ71CSne+X0SH7gkFp0BwlRTYTHfS+y4PwMUXXwwA+N73vtfytVtvvRWLi4s4++yzXcqITj9DPvD0PYP8DBMdTkOj2eU7maigjadKoal+0GKhzkXt0BENjbKqDQ1aKLYWHZje8BaU9C6bNtp05BSsYDc9i1uC7hU1vl5Cp0v/SymogN7gwnVfeBtBOU1DwS5MN4zgz2H6ewp+ZukRU8jncPFxa8THpUJOXBe9NGWY3vAWqQs5//di0g2NUTtcZ6HWhGmaWJIaG2sm4rW6ySJPzS3j5gd2A1CroTFRKeL7b7PsV5ebektzl+kNet8X8znXWtJv2MA0TbHOSGKQhiyweK8ZLXSO5X1nqVOgn+LQM4wHqaJD3uc1DdOyGgYwoegwJuOP2tXGAbn00kuxatUqfOUrX8HddztTl8vLy3jXu94FAHjDG97g+plXv/rVKJfLuOaaa7Bt2zbx+QMHDuB973sfAOD1r3+962fo4/e+9704cOCA+Py2bdvw8Y9/HOVyGa9+9atD/bcx7Rm3pYazrNBIDHr4dvLRV4ESNzQig6YTR0tqLgoqtnpomc/9wPSs0KCGhkK3h4uOXQ3AXSClf4amSc1P3liEjsoNLi/FHE2F83XQD95aUyGn9dUMoOncQkLFiuPWO9l2lWJe3Dd4Aj86lpvWmoLuxaIR5nmGJ93QIIVG0zDR0E2XWlz1NXHaaeoGTn/vD3Hbw3sBACMltV5varCYJq8lBoXe94V8zrVu82toyJ9KYp0hGhp8ziOFmoRVad+ZZjVtKU+DVHzdRIV8feiGiYW62sOYjD+pO1vf/OY38c1vfhMAsGuXFeJyxx134PLLLwcArFq1CldddRUAYGJiAp/61Kdw6aWX4sILL8Rll12G6elpfPvb38ZDDz2ESy+9FK94xStcf/+mTZvwwQ9+EG95y1tw2mmn4RWveAVKpRKuu+46bN++HX/913+Ns846y/UzZ599Nt7+9rfjQx/6EE4++WRceumlqNfr+OpXv4r9+/fjYx/7GDZu3Bjp68I4TLBCI3Ho4VtWaDrKD55+iI6kiwrdKLNCIzS8DYxml0lrerupVMB+14tPwI8fusU10WlKhXZufkaH3DhSHav4rnPhuk+8U8m5nAYS9vfSJHIUGsk8X176jEPwuydncdTqUUyOFLGLmjIpLp6oDj2ry8Jyil5zT0NDWNEkc22MSM+QpYbuWl+yxWW0bN274Pp4RLE9iNzQWm4Yytvyqkw7yym/Arb8uVwCqj6yx+N1Q7TQaSaVHJDuZzLdH7hGER3yHE1TN8U6Y6TE9+Y0kbqGxq9//Wt84QtfcH1u69at2Lp1KwDgiCOOEA0NAHjpS1+KW265Be9973vx9a9/HcvLyzj66KPxoQ99CG95y1ug+eyg3/zmN2Pjxo246qqr8B//8R8wDAMnnHACrrzySvz5n/+573FdffXVOOmkk/Dxj38cn/zkJ5HL5fDMZz4T73jHO/DiF784xFeA6Qb53pFsjImfpDeUQWHLqejQ7SJDEpYgQSCFBlsIDY53w9DNOYaKmnmFro1VY5b15HytieWGjkoxLzZHGljNFSV09WhQ53poh3MdpHeTnCRef/NCToNhn/YgNl6maeLTtz2KfQt16+cTUmiMV4p4/8tOEh8XuGAVOcJyyn4PUkPD60tfFxluyaw/i3aBVTcsuylZecTrjWjxZieqZDkFWHsiTbOa+LWmDoB92vtFtpzSNE28rn4KYfm5k8Sy07lX8fMhSgyfvUWabSB53xE9urRhbRoGlurWx6o1w5nOpK6h8Z73vAfvec97evqZc845BzfccENPP/OSl7wEL3nJS3r6mcsvv1woRZjkmOSGRuI4Cg21Gxol9jUdmNnlBi79xO245IR1uOL5x4rPU5E7qYJTN2ijy2Frg6N7OhhdFRpC+RDZIfXMqDSNQw0NCqvOaZqTocH3ivCx95sqXQ/tKHFhYiC8PYt8TkMewW28fvn4Abz3hgfEx6o0zPsJNmd6Y9kTCl4QQexqWU5pmoZqMY+5WhNLDd11XfPzI1oWau6GkWoNDU3TUCnksdTQ2UZmQGTLKcDKxmiapu9AjdzQSGKQhtaPv378IM49epXvMC0zOHSWZfX3YoqbyM6+I73/BtWR1VtN3cSSZ53BpAO1q40M0weTVauhUW8abCeTEHXdDgVXXKHBllOD86WfP4Ytu+dxzY9/7/o8FXbyKgUlSIhQcF4oDkxLKHiXSWvDUC8zoZDPiY0uFZ2EQkOTpN9ckAodKjYodDm0hZ8Zg+G9N+RzmmhKBGkG7Juvuz4uKLLGaFdcZ8KD8q6o0NCuiSQUwgnaXVbsBvlivelSkPCeJFoW626rYRWnbCtsdxoKNFRAdk60fvO7B6uSoXH1TVvwtXu2x/77s4Lps5ZcqKXXfpwVGtEj3y4ahlM3VPHZwbRHjZ0Aw4TIWKkgJj1ZpZEMqVFo8GJhYBZr/psyodBQZILWCxVFeEpucFpCwbs1NMREvlrXhpiGsq8JarxoGheyo8S5WtS6HvxgVd9geJUthVxONCUaAZoBBxY9DQ1Fni+O5ZTZkhPC9I5umNg5s+T6XE1MTrpDwb33ZGpIl/LJFSSqJVKAuvN2WKExOFt2z2HzR2/D9+7b2fK1ec96VEUfdFYHh0NTspyS/+/XGHdlaCTY0ACAT966NfbfnxWcPDZJoZHihgYrw6NHtqjjDI30ona1kWH6IJfTOEcjYWiDWVZkerIdtOHlxUL/tCtCiQwNZS2nWKERFr02NOjrKmVoAO3l3TlN40J2hPhN1alKseBfRGWC4S02FXKamLANotDY61VoKHIPKUpKxG73P6Y7r//SPTjr/T/CrVv2iM/VvAqNNgXMpC2nAGe6c6luuBoaPJU/OO+9/gH8bucsXv+lX7Z8zavQGC2r56xN6wxeew4Gvc/pPuAoNFrvv2bCllOyUP3YdeOx//6sYPjY2c6nuqHByvCoke8XDclyKqkMLqY/+GwxQ8mYvYhNs9QwzTgKDbU73KzQGJxGm3BcKjKoUnDyQgtFLjAMjncD6behlFHVYoiuCcdyyrHG4ntFdPj5HqsKN7YGw9sAL+Q1FHLBc0laLKcUsTSUG/fd7n9Md2763W4AwGd++qj4nMjQKFBDwz97RYWGBjVdvJZTPDwzOEYHBZQ3Q2PVWCnqw+kZVgeHA1lL0X2g2MH2z63QiOHgPCzWneuSrWyiQ9jEQsORq0cBAOc/bXWCRzQYvO+IHkPO0JAtp1ihkSrUG11gmBAYLVmXtryIYOJDeBirrtBgG5mBaVeEckLB1bwGysLH2IBpmhzSNwC9W04pqtAouhUazubIfa8wDBM5xY49zcivs+oUORR8ILzT9MV8TjQ2gzQC5mtu1a0qCkDZUqShGxwoGRJy8XrZazlFjTDPdaPC+pMsp5Yauuta8CoImN5ZNVYWf27qhmuN6X195e9VhXKRh2nCgBqFdO8ttMnUAdx5aEms9eXnA5/36KDHRU4DvvzaM/HtXz+JPz5tQ7IHNQAcCh498n5V100s2Y1mbjymCzUrTQwzINRZZYVGMtDDNz0ZGrxY6Be5sNf0+bOqCg15scJTk4PRb4ZGXrEmkjdDQ7ZCkuXHVDDjqamQUFSx4wdPzA2Gd3o2n9OkcOfur6nXPkKVpqhcsApincUEQx7Gp8yBcgoUGo7llDtDY4mHrAbGteb0rDW8xWIVGxoVtpwKhUYPllOy2jYJqiW5qcnnPSpM6TyvnajgtecfiamqeiqtoPB6M3rkoYmGYYi6ITc00oXa1UaG6ZPRMsm9eeGQBFQQVF2hUWb7kIGR7RTqPhtNVQpOXkZLBVFAnVvmxucgeDeQepdQXGp4qKaKabWcsj6fy2mu4lhdN3DPYwfwtHfdiI/e/HDsxzls0NWi2OXgS5k3mAPRaFFoaK5A7W54Q3+Liqwx8jlNNO+5QR4eW/fM445H9sE0TVEAdkLBSS3ln6GRpAf2iKTQkI8vzX7uqiDfe71KOe+1MGXnKaoEh4KHA9kX0n23XYMTkHLbElpk/PnZG8WfedAyOkRxOgVrySBQDYXXFNEhD+A1dRM7DiwBANZPjSR1SEwfqLETYJiQqbLlVKKIUPC0KDTYPqRv5OkG2ROYFgmqFJy85HKayNqZWWrgX258EF+58/GEjyqd6J6p604KjaTDGTvhlXcLhQbczdl608A/fed+AMCHb94S70EOIWIPmoKOBltODYa32FTI5ZxwZx/vcy/zy27LKZVuIRW2kgmdJ2eW8aef+jnuf3JW3CfKRbdCw/teFJZTKig0Gm6FBu9JBsc1OONtZtlf++NnbcDP/+45SlpDso3M4BiGKRpC1Dxs1+AEks9tu+jYNbjikqcB4HtAlKQpjy0Ijg0urzejwtXQMEzsml0GABw+XU3qkJg+4AwNZigZLTmBfEz8OAoNtSV7LOccHHlzWXNNzqmt0ACAiUoRc8tN/PThPbj2lkcAAJedfnjCR5U+RF5KTkPTMDtariQdztgJ7+ZB3hxpmoZSPoe6bqDWNJS+rtOGITWOVKdU4A3mIHjVXLKyIYhVkxz6e+jUiFJNsEoxh/kaW8lEwd3b9os/k0LDaYT5KzSSVAgLhYbHcoqnswdHbhg2PE1QsiHatHoU6yYrsR5XUFihMTgL0t6eBpPoOeI3UEOXSZLrtmdvnAbgPnYmXNKUxxYERzXOa4qo8HMUGCnmsaKqnrqPaQ83NJihpGovcBZq/BBIghopNBKckAtCiS2nBqYmbS7lRZfukYOryMRIETsOLmHHwSXxuYZuKKsqURXaQJYKOTTruku140Xea6o2PSk2Dw2ynHJbY5ULVkOj3jSUy/9IM+lUaHBOQj+0hoJryOeCv6Zk2XPVH5+CMzZNh3+AA+C9fzDhsWBPNWuas26TC5imaYr7hxIZGlJDQ77mF+u661iZ3pEn3L33jIYCgfDdoIYcK7n6h54DhZwm9pnCutBH6UdruSTXbaN2XWKR6xKRITI01H3790RV5MHyNRMVfg3QqWqRn9EpY0je8gzjhhUayUJF7iQ3lEHgadvBkafM5NdRntpXlYlKqzXdPOdp9ExTamjIH/shNztUk4V7rSBoX0yHKSu6VGvGpBlHCZPoYQSCVX2D4bWVKuRzjvd5F8up7QcWRfP5zCOncZhilgBlLlRGBuVcVQp5UWgoSEVrKmybpqmU5dRiQ2/JFvvA9x5K6rCGAvneu2XXHOYkGzq6DlTee5BCo8b3ib4hpdNYpeDcD+wqtp/ST0/YcgqQitNcl4gM2l6otrfoF1IfcS0rOgyf/eqkgtlLTGfUfeIzzACMcIZGotRTotDggNfBkYs38mQqbSryCk/KTdiLlv0LdfE5DgjvHZpwofeTN1PD73uBZKfl/Ch7GpymXWrP+TQ0VG7UpQ0nq0T911So+nReW/SDd6K6kNNEIaqbQuP+J2cBAMetG8eGFWo1MwCr2A4Ay7yeCJ2nbF/ripTLRo0wwJnMl5sHSRa1qXi5XNdbcmOuveURzuAZAHlg4tWfvwsv+Mht4mM6/yqrbIXlFN8n+obW6aMlx2jEUWy1vq60xkjSckooNGyVFhM+ndThaYTdRqLHbwBvghsaqUPdJz7DDMAoT0IkisjQULyhwQqNwZH9wuUJ23QoNKxFy267WAIAs57QWaYzpmm6LKcAoFOtRt5wKNbPkPxq7YaGZ9pLNDR0nTM0QsSxnEr2OIJA1wBbTvVHi0Ijl0Ox4B/u7IXUc2smVPXGZ4VGGPgV+3bOWM9oukcD7qI1DVDIwymJZmi4QsFb/z2P7JmP+5CGBm+DSLYMpfOvckNDDE7wfaJvqMA7XpEaGmQ55afQsC+ZJCf3qcmpGybvOSNi2BQaXMuKHr8mGCs00oe6T3yGGYAqe1Umhm6Y2LdQAwBMVUsJH01nnAIlLy77RbackhcG6cjQsO4TT83VxOdYodEb8nALFZs6KTTkL6nWFBCh4HahwRtWTQWyWoNDwcOElDBpeEU5d2kwvHYghbzW0khsB/mmj5fVjP8L+u9gOuO3Hts7bz2jZYWGvLYg33xVGho0hb9YdyynnnfCWvF1trbsH7+JWrINaQiFhrpPEw4FH5z5mjV4NCo9C6iJ5WddaIhshSQbGs6xsntENIjzPCwNDaHQ4OdFVPhlaHBDI31wQ4MZSrirnRyP7VvAcsNApZjD4Yp5XHthy6nBWZIW5nKxSig0FN5YkkLjqVmnoTHPC8eekDeP5R4VGspbTnnCqkXDQ2fLqTBJYyg4N8H7w6vCKOQ0UaTuNrFM9+YxRRsarNAIB7/12B7R0HAUGpqmifuwUGhIBW0VipdLDScUfPV4GSesnwDABc1B8MtIIGVtGkLBaZ0hq5uZ4PzX3U/g9V/6JQBH9QA4AzK+GRqG2z40CfLSs44L1NGQJrVvEEbZPj1yuKExHKj7xGeYARCbCX4IxM4vHz8IADhu3YTyU8ylvLUY5oZG/9SkTZkuFatpU0H+6CpCPplycZKLUb0hLwadUPAOGRopspxypvqsr8vT+cMyAaYChgKBnUHhUPDB8E5XF/I58b7r5ilPRUvZZkQlOOw3HPzeWwcXrXNflhoagGwz41ZoJF3QHilZv3+prruK7KNlUm5wQbNf/BQapOCppygUnNea/fHO6+4Vf5Yt6EQouM/1QcvOpIdouEAdLd7cu7RTtZ8XPGgXHT63C25opBB1n/gMMwBVodDgRUPc3PPYfgDAWUetTPhIusOWU4PjspySXkY9FRkarYUx3mT2hrx5dBQa7fMFHEm4ehP5jkKDLKesz1NYtSsUXGHlUdqgqyUNryg3NAajxXKqF4WGbdMzpnhDg61kBqNTPk3FU6guikB5d0OjmHBBe6ToKDTIDquY1zBS4pDXQfEbmJi3X89GCjI0+D4RHrIFHdmMeTNWAGeQJuk1JxWo2T0iGowUqX2DIDfAOEg+GvyeJ9zQSB/qPvEZZgB4Cio59szVAQCHrVDbbgqQQ4zNjkVYpj1yA0CevqdNvMoqnRU+GS/dpoQZN7ouKzSc0MN20NpRRYWDk6FB14B72ov+ffWmgbzCyqO0IYIcFb5XECXPRDjTGy2WU31kaKhuOVVjK5mB6PTeGim5FRrFgnsqm66hctINDfs4l+o6Gk2y38wJO1zem/SP7tPwIjW+k6Gh7vPZOzjB9I9sQScsp3zWn7QmTXo/Ui1yvmeUeHPv0g7VsjhIPjpoTyoPOHJDI32o+8RnmAGoljhIKSkOLFoNjRVV9R8IsiydJ257p6kbrs2DIf1ZWD8oLP0/zCfjhe1CekM+/1TsDaTQULB43Wo5ZX2emi8iFLxpQBZo8OTUYFARSmU1F8EKjcHwFpuKuVzg7Ik5W6GhquWUsM7iyeuB8CtIEt5mlmwDCDhr/tGEm16kEl9q6GICtJjPib0JW870T8NnopbuHaS2LhXUfZawQiM83AoNu7np0/CiNVriDQ1WaESK6Vmzpx0Oko8eGsSclGpW3NBIH+pWmhhmAOhmNLPU4GJTzIiGxmjr9LtqyD7LXKDqHa+aQS5kO5OS7olKlThseqTlc2w51RvUoCjkNOFv3mnhrUI4Yzu8oeCG4zllfb1IxTPdpdDoZJHCdIcKECpP1RIcCj4Y3un7fM5RaHR7BgvLqbKam80yh4KHgp9lDOFtaJQ9qphF+7WvlpJdd4wUJYUGqQZympi4ZTvc/vEbmKD3XDosp/g+ERYVV4aGrZ70aXjRNZN0nduxEOKGRhSYKcpjCwIHyUeLaTruHBMVqaGRgoFcxo26T3yGGQCykmnoJm8cYubAAik01G9oFKUx65rO10mveDdksuUUfU2eoFKNaqnQoiTiqbneaEpS/jXjZQDA7rnltt9PDZCkwxn98BbH6Gqmaa+yVMyW6yVc3B4MmmBOQy4JKzQGoyVDI68FV2jU1FZoVES4Oa8lBqEXhYZoQtvPbbJykSdbk4Cm8JcaumiQFws5YUW1yMWpvjBN03eAYMm+d9RSoAyma4MtZAZHtpyi9YOfJRndUpJed4p8T7acigTvmn0YoGceq3rCR15qyKqMVaPlBI6GGQR1n/gMMwAjpbzY6FCBnYkewzAxs9QAkA7LKU3TuEA1AN4ClGw5RY0BecOhIivH3AuXJZ6a6wnaPBZyGtZNVAAAu2c6NTSs/6u44Wi1nPJmaDj3Ctm6gO8dg9EQ15D6S9ISKzQGwhvAWCnme8jQsNYWqoeC17gpPhB+ljGE99x7rx0q+owmrNCQFSKkLCrkNDGhzYNW/dGu10XrNlqTjii87hSNT15rDow8MEXrh4bPReKs5ZJdd5IVHis0osEYMoUGIFuo8/0ibGS1n6zqWzWu/kAu40b93SPD9MlU1bGdYuJhdrkhNhxTKVBoANLUNRcle8arZpAVGjTlnnQ4ZzdWjbmvU95k9kZTCn9fN2k1NHbNtm9oCMspBT2nvJZTpnCcsjM0pK/LToZ87xiMpghyVe+a8EIhxBwK3jt+09WVYr73DA1FQ8Hp/uG1YmR6w88yhvAqNLxB7KR8qCZ8jciDHLQHKRVyTrYGFzT7ot19d7lhwDRN0dhQuaHhKEH5PjEofgoNP8s6VbLbWKERLfTo0Iaoo+FcM/zMCBtD2sjJz4ykFZ5M76hdaWKYASDLI8p0YKJnv62GGSsXlJZ8y4ipay5Q9UyL5ZScoZFShQZbTvUGnfNCPoe1tkJjVweFhirhjH6IaV/7uhYNDcrQkBQa8rXODY3BaEjXkOpwA7x//KarR4p5YcOz1NDb5ieYpulkaCiu0OCm+GB0VGi0WE55FRq25VTC6w7Z+3xWKDRyYkKbFRr94ZefAVjvubpuiHtMJWGFTidYoREe5WJrhobfNaJKdhu9/z/6w4eTPZAhxbGcSvQwQkVYTnFDI3Tke8VLTz0UgJp7U6Y7au4KGCYESKFxYJEVGnFBr/WKUfXtpgi2nOqfWtO/oaEbpmgQqa7QWDtecX3M/ue9IWdokOXUrtllmKbpOyWlm2psLP3w3gu8NgWyQsPV0OD8nYFoSqG5quMoNDgIvlf8pqtHinmsGi2jVMih3jSwc2YZh01XW75vrtYU95qpETXVnxWevA4FuanlqHeszx2xctT1vUIVYxeHl+xGwagCKp5qqYDlRh0z9lBVMe9YnLLlTH+0a3Yt1XXXMIocFq0asiKt3TqJCcZYWVZotH8203BK0sXK49aNiz/vX6hjelTNZ1laMRWxFgsTGuCY54ZG6MiuEhcftwafvfw0HLaidf3JqI/alSaGGQDa9B5khUZs0GudhkBwghsa/dNiOWUXnOTXUnWFxuHTI66POayzN+ic5zXHcmq5YWB2yf91JEm4ihsOr+WU14+3lLeu5bpueOzV+N7RL4ZhiqnaNCg0KEOjxtO1PeMX9lwp5pDLaThshXUffnz/om/uGam+JioFoehQDVZohAMpto5cPYpf/n/Pc60zzjxy2vW9XvseytCoKnCN0DGQ5VQxn2M/9AHxZvAQSw1dvO/yOU1p+0JSFRgmN8YHZZWksKaBCL9rhNapSTePXvbMDcLm9p7HDiR6LMOIWLMnfBxhQgoNbmiEzxP7F8Wf8zkNFx+3FsesHe/wE4yqqL97ZJg+IZXAQVZoxAY9cMcVtYTwo8QWIn3TEgpuLyblz6uu0Dh8pXsagxeNvSErNCrFvAhjbWf1p0o4ox9ljx+7I193KzTYciocDMPEdb/cLj5OenoyCCL4mc95z/jZSdHreciU1dB47/UP4NR/vgnf+NV21/dRQ4OapioiGqLc0BgI3S5IjpULqJYK2LTKUWV4C5KOTaD1M4s1dRQa3gDwYj4nno+s0OgPv6YoYK05SZ0zUswnXrjuhBxk7VU5M72xctRpaOTtUHC/a4QGUFToc52xaSUA4LF9CwkfyfDh2MQqcKJDguopZLnJhMOXfv4YXvSvPxUfp2D7wXRA7UoTwwzAFGdoxA7JwQu59NxaRBGTMzR6xlvUo5eQPl/IacpPXZ++aSVOOWxKfDzHi8ae0KVQcKB7Jo2RigwNCgV322O1tZzi4nZffPPXO/DO6+4VH6s8VUt4LW6Y4PhNI5PaYqJiDaD8bucsAOCD33vI9X1PzdUAQOT0qIicBcL0T0OsI637wSdf9Sw8/dAJfPby01q+1xsKTgoNFUKhq2X3MRTymggrX+QMjb5o19BYaujCLlRuGKhIKZ8Tqk/ObBuMVeOOG0CnUHCVstvIUlGeDmfCwauqHgZYoREN3/zVDvHnnDZcTbAskvwIC8NExNQIKzTiRgQEK7BoDAorNPrH64muexQaqttNAdZi8VtvPAe/evwA/ujfbueGRo+IJma+VcXghyP9j+HgeqTFcor+CfbBOqHgOiTHqbbNG6Yzv9i63/VxGhrhrNDoHz8rEHo9J0bc2xFvVsKSXaimqXcVYTuhcHCeKdb94Ji14/jum8/z/V5vKLiToZH82sNre1WSFBoc8NoffsVqwK3QUH3dqWkayoUclhsGN8b7QNOcSfxpyd64KBoafqHg9LPJLzyPXG092+5/cjbhIxk+6LpQUQHeL2Nlq5Y1x8+MUClKw5YqNDqZwVB/98gwfUI5DpyhER80PZVL0cOBMzT6xztxa9jnnyblVLebkhm3J4R5CqY36D1ftIvRRRHMmEaFhqMuMQxTspyy/i/fKwyTFRqD4t1zpkGhQdO/TcNsW1xj/PErNFXs9xTdf8XnPVPWNMms8vR1lRUaoUCNryD3A69iysnQSL7x5T2GQl4TVljc9OqP9pZThnjfqaDO6YbTGOfroFdorfnff3m2SwEexHJKhWG7s460LKd+/cRBXjuGjHfNPgyMseVUJMhrSc4ySj/q7gwYZkCmqtYG+QArNGKD7GdUWDQGpWRP+PHCsndaFBr2RqImik/qbywJ4VNaawp5OtMdOUMDCKLQsP6fV3CCqixdr3XdwKwd5lqx7xFyw6PJllOhks9pSkxPdoMmwgFgmc97T3ifF+OVgihITXhyt/I5b0ND/elrKqRyPsJgNHqwLvWq6hYVUmiMehQaVoaGdZ3XdYOfG31ATdFyIYfXnLMJf37WEQAsZU4a7hEErSnYcqp3qOF5qJ27RAiFho8SkIYPVBikOXRqBIWchqZhYt9CLenDGSqG0XJqnC2nIqGouB020xt8NpmhZYoVGrHjLW6mAbKcYguR3vFOKHtDwcsKT9N6IZ9S3TB5wrYH6BqgzWRJKDT8m0K6wvcIWVFUaxjYsnsOAHD0mjEA0r2i4cnQ4En9vpD7hmlpgruvEb5P9AKtDyYqBXzu8mfjeslGaGLErdDw7jUdf3x1i5U0fb/suT8wvUHPlCD3BGpC0xAFWTkpodDwBJMX85qr0cK2U71DxeqpahH/+JITcO4xqwHYGRr2NZAOhYY7+4UJhmmaMNrYClED1G/tKZTEChQxczkN06NWfWLvHNcnwsRZU6ZjPRkEVmhEQzsXASadJH9nZ5iIWMEKjdhJY4aG7IvP9Ebds3HQheWUtVCQp5lVp1rKC5kyLxyD01ahofu/n1RuaBRymrgGak0dj+2zQhuPsj2P5cBztpwKFxUKDUHI5TRxHbBCozdoA1kp5nHRcWtw+Mqq+Nq4R6HhbQhQsVLlJrmcmcBN8f6hZ0qhB8spKgxTjoI3vyIJqsVWhUYhnxPHvMBKnp5petQ71BhwZWgocO67UWaFRl/IjwXvGpL2nX7NZLpuVFl3rhorAwD2skIjVNJYg+gGDdtxhka48CDacKHuzoBhBoQUGrPLDZ6WiwmnuJmeW4tcpGR6w6vQaArLKZqmTc91oGkaLxz7wBvgSoXperONQkPhDA0rrNMpNCza1zH5+5clezo5D4DvHf0hD1gGKV6qQsXj288EwylGtp7rCU+GhlcxKexkFG6Slws5cU2z7VT/CIVGgCYnKTSoMKxSjoJXoUFF+DHO0egbb7NrRJx/XZz7Sgqy2+RGDBMceS/f0tCwrwm/yWu9h1yeOFg1bjc05rihESYqD0z1i1Bo1Hg4N0x4EG24UP+pzzB9MmlbGJgmhBc6Ey1pnI4oiQIsP9x6xbtxMDwKDZWLT35Q4XqOFRqBaXpyc2jD2K7ITxtLVTccNNm72Ghi2Z74HLE/1y4U3C/smOmNIH75qlCWimhMcMS9wqdQ7Q0Fb21oqJ/LpGmamMqnaXGmd4Q9TBDLKY9Co6aQOtSboVEqWP+eUfZE7xtvFgLdDyzLKffzWmW8jTgmGPK6q7WhYYeC+6zHGrpaw3bkIDHDtYlQoYEprx1ZmhEZGrwvDRWu+QwXatzZGSYCSoWcmIQ6wDkasSBkvYpMwQShW4gx0x6vVy0tJmspzNAAnMlJXjgGx6vQKNmFpEa3UHBFGxqjYnq2Kfn207/NyduRJwWbrAAcGFUmJ4Pg+J/zM6MXRNizz7meGHFPs7c0NJrpUP1Vefp+YBqeZ0onvKHgtI4rKTCl306hIT9jmN5wml1kOeU0BpYVUud0o8JN8b6Q11r5lgyN9pZTeg9N0jiggcuDbIkdKkYPdoVpwVFoNGGavNcIC16/DxfJr/gYJkKm7CmIgzwFEQu6Z1o7DYgiJdvG9AxN3BLpV2iwtLdXvKqsUlCFhqITVM70rOTJbRcfRCi4p6GhG3zvyBKVAhej+oGan0WfKdkWhYbntXVsDNV+ppDCa6nBxep+oSn8IE1OUmLQPZkKnmUVGhreDI0CWU5Zn+eGRu94M7tGSo5CYykl9whAugbYmq4n5HWX9zFCa9CDS/UW9XhDMWXw1AgrNKKA7g/DpNCgQbuGbnIRPkR4iHW4SH7FxzARIhoarNCIBe9mIw2wQqN/hk6hYTc0ZlmhEZiGp4lJ7yc/H2PAUWjkFL1HyMUmrx+7c6/QxbUOsEIjDIwUTZ5RwazGdiE9Ie4VfgoNTyi493nsWE6p/Uyhe8UiW071TaOHdWS56FhOydeMCgqN0bI3FJwtpwbF2+yi91u9aQhVVBoaGuNltjftB0Naa3ltKim/bcvueTz/w7e6vqb3oPqKg4kRHraMAiOFNYhujJactRE/M8KDm0PDhRp3doaJiBV2MDjLOuOBMzSyhbdoTR/WUqrQoPvFgQVugAZF90i8i13eT97MDdWQi01e336a+q3rBuRLX+cMjYFJU0+ozKHgfbHHDkCdHi21fE3etAPpDAUHZDshvjb6xVH6dt+iOmopQ+RoAIooNDzXdJEtpwbGCQW3XkvZXurzt29r+ZyqjIvhGd6b9oI8SOJdQspF7K17F1xfayi2N52y9xqs0AgXuj5UOc9hkMtpbIccAbKLwBErqwkeCRMGya/4GCZCaJPMG4d4cBQa6bm1sEKjf1pCwe3FpCg+KT5N62WlXWjbxw2NwAi/c/s9LxqEbRQadI2oOkHlytDwKDTK0r1CtpnSU6QuUAlZ2ZImb2Ch0OBnRk88sX8RAHD4dOvm0avYap+hoXaxki2nBkdYkwWxnPJRaOQ0NSaxSSFOCMsp2pewiqdnRE6ffb/wa1yNlJI/990giz1WaPSGLiyFAM2bodHhfkHKHlWyFSbZcioShOWUovuLfhljVV/o0HrhRSevx5f+4oyEj4YZFPWf+gwzAFXhU8obhzhIo0JDnrpmeqPptZyyzz8Vo8qKF5+8rBovAwD22pPETHd0j42MyKRpY8fjLUioxljJp6FRcltOGaa7AeoXQsl0R34N0/QSUqOWFRq9sePAEgBgw4ru03DytD3gWE6pbmPIllOD008ouKXQsK8RRVQ8pPgkaF1M+xIuTvUOKTyp2eVXuPTm8agIKTS4odEbegdLIa8yRx64Um1vSs3OGbbDDhVhOTVEGRqAY4fM94vwoDXm377gOBzmM2TDpAu1dwYMMyBjLO2OlaZiwWtBYIVG/zjT+db5pk2DYw+SrkcMKTT2zHNDIyjea2Ciy+SZUGgouuEghcbMUkNMe5GtiezLLhcsOUOjP+SCQ5oyNMocCt4XpLLwZgv40dZySvEmuVBocEOjb0TROsA6Ul7j0zWjQn4GAKzwWKuRHSPvS/rHGYhof47HPXk8KuI0NHhCvxc6NTTofUXIz5BemqRxwAqNaCC1dIpMIgLBCo1wMQxTDMnQwBqTbobsLc8wbqol9jOOE9WmYILAGRr9QwVJKuLQx8siFDxdCwXydufMneDoHk/rFfbk2cE2k2dUw1a16UnF1v0LzjVABbKStBlekorZrNDoD7flVIIH0iOkEljmZ0ZP9KLOqjcNlw2ZyLNRZPq+HVXO0BiYXoqPNI2/WNdFE0mF/AwAGPUUSui651Dw/gnS7JpIhUKDLaf6odNAzJinkVVzrdHUym6bkhoaBq8fQ8EwTLGODJK/lCaoATpf471pGMjNzjRkLjHdGa53PMN4oA3FYp0XjXEgChaK+JQGgYpTbDnVO7S5pM0ZFZ0c64d0PWJ4aqp3hDexvVEkm439bZpCuuIqLlmhQZCdViGfE8ctT+d7rdeYYDRTqtAQGRptbNUYf6iBVQxYbKDnyCN75rHXVs2pnstEa04uPPQP3ReCPCNktc9+u4muikLD6/FPcCh4/zih4O2vjTQoNCYoFJzXmj3RKSNh1KPQkAcOnFBwNe4NpGQ2TGCe6xOhIGfZqaoA7xe6p83wsF0oyANpqqt+mWCocWdnmIgQGweW/8cCLShUmYIJQinPAa/9Um9a55vksGQpkhZ7EC/c0Ogd70aRGhrtFRpqZ2g4DQ3n+OX7Gak0GlITQw4IZ4KTVoUGqQSWm7yu6IVml/f+u150vOvjWtPA7tllXPLhW8XnVH+mTI9aOUyywovpDXpGBAoFL+RFA4Oyr1QapPA7lrEy56z0S1PvXphOR4YGKzT6wejwDBktdVBo6N0bYXFSKeZFc56L1OEgK6UV6VuFxkp7XbFvgTNXwoAaGiVpSI1JN0P2lmcYNzS9xZNQ8eAUK9Nza+EMjf4hhQZJvWkDIQJcFSosBIEaGrPLLAMPiu6ZmFwxSpZT/pu0bkXNpKFiEx1/Pqe5Jm29U4AAZ2j0i6xsSZNCo8yh4H0hbD/aFJX+13lH4qErXwB6u9WbBrYfWHIVKlRvaFAO074FzmHql16nqcftezK95iWFbMn81AJUeGXLqd4hW1P5HvLRy57h+h4KXFYZztDoj05Dc941pStDQzHLKYAHqMJGXkOqosQJi5Vj1rpi7zw3NMLAGbocrusky/CZZIYaJ0ODNw5x0ExjhgY3NPqGNpek0KANRK2ZToUGycBNE5jje0YgGh7LKSr4t7P5Ex7Iit4jqNhEm0zvvWzCp0DFGRr9oaddocGWUz3RCDBdXS7kRSO81tRdU7aA+htQKjzs50nKviHLqSAKDcApDtP0qkqDFA0fO0IOBe8fP4XnHz7jULzs1EPFxys9YewqQtfsQl3n9UMP0GuVC2ApJDc0VFQGT41wZl+YNIdYobFqzFJokPUmMxiUt5W2GgXTniF7yzOMmzFRXONJyjjQewj9VAW5eML0Bm3WaXO2nHKFhiwDb2eZxLjRPU3MbtOnQqGhqMctPTMO2g2NoieY1m/ilhUa/dEw0pmhMVKynxms0OiJoEWlcsGxgfTaeqkeCj5NCg2epOybXkLBAUchSq+5KhkagP/09SgHx/dNuxyeUw6bEn9ul12iErIt1jzbTgWml8aEX86Zdz2XJKzQCBdZVa/q/qJfVvGgRKjQvWGkpPZ6kgmOOnd2homAaoktp+IkjQqN0RJvLvuFJimdhoY7FDyN0w9rxisAgN2zPAkTBG/xie65yw3Dd/LQ8FhUqQYVm0ix5d04+/lz84Rlf6RWoVHkDI1+CDp5TwXpWsNwqWAOmaz4hsGqBE1SsuVU/zR7tIehJvS+efUyNP7PH54IAHjH848Vn6NnDFtO9U6zTRbCn55+ON79khNw89svSOKweqZUyIm10gEenglMvwoNuqeoNGw3WeWGRpjI60mVznMYjJWta4VrWeFAGRojKaxRMP6os+pjmAhwQsH5IRAHKi4au0HTfby57B0qZotQcHuRUEtpKDgArJuwGhq7ZpcTPpJ0oHve83LGhJ/tVLOHDWkSeDMyvMXXiRFWaIRFI6UZGnRfW2LlZ08Ezc+RVZP0THn6oRP43tvOj/YAQ4AUGssNo63tHtOZdkXrdlCxx7GcUmfd8aozj8Ct77gIf3nhUeJzsuWUmaL7ngq0a3aVCjm8+pxNOHrNWBKH1Rerx63m5x62kQkMrRPa3Ru+/oazxJ9lBWVDKDTUWXeSQuPgEje0wsBpdqVDpdULlAfLdYpwEC4SKaxRMP5wQ4MZaoSfO0/fxwL56ask+e/GmNT04iDo3nAyNKyFOU1EURFKpUnJoKybtBUaM9zQCELTs1EsF3KiYOln9WcoruIa8zQ0vH7/42U/hQZnKfSDnlbLKWposOVUTwS1/ShLuVa08Vw/OYIJH3WUalRLTgYI2071h1O0DrZ+mPBYTqm07tA0DYevrLoKbFScahqma4qc6Y5QgStkHdQvq20111OsBg6MveVoayn0rCOmcdaRKwEAy9J7iyxkJ0fUyVeZYsupUNEVz+cbhFHOXQoVUatSqMHJDEb6VwQM04FRspyq8yRUHDSa1mtcStFmgwqYpgkscoGqJ0RDw5OhkWbLKZqa4/C1YDhT19Z7XtM0YaXgN00kFBqKbjqo2ER4N0deBQfgFGqZ3mi6FBoJHkiPCMspDgXviaAKTleGRsrUfpqmSbZT3NDoB2+TvBsiQ2NBPcspP6ol5xnCBareINs6VQciemHNhK3QmOPhmaDQM6TT+rFcbM24ouDtFVV1muIiQ6NLKDgP2gWjFzuytOG4jXCNIgwawv5U7bUCExw+k8xQU7UfAobJxYc4qKXwIVEpOhPlvLnsDSpOt4aCp1ehIUJduRgVCNpgysWnsQ7TRDRFpWpBYrTU2XLK2/AAOEOjX9Jq1TUiGhq8uewFep90K1SLgpQUCl5J0bOEniH7OUejLxpCxRcwFLzszvBSXSGcz2niHsLZbb3R6NGOTGWEQmOO7xNBMbooNADZstDZ8++3FRorRhVSaATI0HjH136Ds//lR12bHoyzvlB1bzEIYyUn24+K8Uz/eLMfmfTDZ5IZaqrSVB/naERPo5mODaWMpmlCyTO3zNdIL9D5HrcLCrPLTXzp548JqXdapmplqBh1gBsagaBpWnnqms67n52Grqut0MjlHIUJ0LrgrZY4QyMsmindmI2UrGuCLad6o6G71VztcGdopO9ZsnLMeobsZcupvhBT+AGL1uMeK7I0DFJwMHh/6Eawe0gaWGPntXFDIzhBbIW860/dMEXTYEVVnYbGBGVodGhWfO2e7dg1u4xv/npHXIeVWnTF1d+DUJUGqXjwcnCabDk1dKR/RcAwHZCLU5yjET31FCo0AGdhOcPhbD1Bk5Rk+QAA7/rmfWJhWSmm6zoAWKHRK1TML0oFBprA9pskEhtShWXhctPCO+3FCo3wSGsjiCyRWKHRG0EnKEtkOdUwhG1Imp4ljkKDnyH94PhbBzvnq8bcRco0DNSMlR07XCY4QhE6BEVLEQrODY3AGEb3hgY1NOn5vP3AIkzTeu5MKWg5ddButtzw25046d3fx/tvfACAe/1ca/JaoxvGEGdoFPM58VzjJvjgNIR1ofprBSYYfCaZoYeKU/wQiB4nFDxdC4o19sZiN4fz9URThIK3Tq0DTuEvTazkYlRPNH02mLRIbPhkS6RBFj5WlhUa7uMc8ZkUb3IoeF/IDY1P/M9nJngkvTFiD0kssZ9xT4iw526WUxQKrhuYs9dtftk1qkLPkH2cw9QXZMNUDXjO101WXB+nYd3BCo3+aA6RVchqse/gDI2gBMlgkzOYAODOR/cDAE49fEqpYbs149Z9a9fMEgDgYz/6PeZqTfz7LVsBuFXibJndHer/qLy3GATaZy/yunNgaG9aTMHwAxMMPpPM0EMTtYs8CRU5wnIqr/6GUmb95AgAYNcMbyx6gRYF45V2DY30PWJ4urY3/OxBaJHY8LGcUj0UHHAXT70TPH6FVVZo9AddO99763l44UnrEz6a4FBTa6mhwzT53AelGbCZKSynGrrwDlfJKqQba20rmcf3LyZ8JOmE1uqjpWDryPWehkYaFBr0HGHleG8EvYekgQ1T1r5jx4Elfo4ERFiOdTj9smUh4Fj/HbFyNNqD65FNq6zjObDYwIGFeks4/C6p0cUqnu6IwHiF1d+DQLUsboIPjggFH4LnCGOh/qqPYQaEQl4XuKsdOcJyKmUKDSpAPHlwKeEjSRe0KPDzNy8VckoXrduxctSampuvNVnmHYCmT4Ar+ZL6KRfqdpND5Slad0PDfQ1XfYpsHOzaH3TtVIvpmb4HnAZuQzd9c2IYf8R0ddcMDWfC9oAd5qqSVUg3nnXECgDAz7fu52ZnH/Sq0Fg9nr6GBk3bsh96bzR6zFdRmQ0rqgCAuVqzY44C40C2Qp2eISJDw1Y19NogjYuRUh6H2M3YK772m5bMpR/cv1v8mZ6DTHtEYHwK951BELUsfmYMjNi3DsFzhLFQf9XHMANCXW1+CERPvdmb97EqHL9+HABw17b9CR9JuujkdZ1GdQYATIwUxIL4wAJvMrvhp9CgzWbdx3JK3CMUvj5kCzXvgtdPoXGQN5t9kdZNxWipABoCnF3me0RQqMHZreBQLtKErSEKfVMpUmicdOgkxssFzCw1cN+OmaQPJ1XUm4YYjBkrBWtoTFQKIrcJ8G86qwZbTvVHGiwrgzJSygvbqe0HeJgqCE7wc/vv8So0em2QxsmRq8cAAD988CnX5w8s1LFl95zzMa8xuxIkMD7NjIomOA9QDUojpXmvTHv4TDJDT5W72rGR1lDw85+2Gvmcht9sn+ECREAMwwQNn/r5GfupNtKApmnC3mTfAsu8u+FXYOhkOZWGhobctPDey/yKZQd4urIvRDMsZRvQXE4TTa+5ZV5XBIUUGt3WB3JBipqFUyPpUWgU8jmcaqs0Htg5m/DRpAs5l2YkYGNCfmYDziSryozxoFVfNIYoQwMAVo9ZDY29nLcTCD1IKDg1xG2FBr3H2mX9JQnZTnk5619+iB/8TlJo8HBVV/SAAxNpZZRVfaHBDY3hg88kM/TQIsYr52T6wzDMtn6vtNlI23T+2okKzj16FQDgnscOJHw06aAh2QkVfSas03YNyHAweHD8CgzkS+prOWUvJMsKLyTlUHDv5qjqUyxbauhYbvDUVC90a4iqzkTFKrC/87p7YbCtUFdM0xSKnG4Fh1LBKUgdXEpfhgYAYSWyiwN/e2Letocp5XM9Nb0p+wpwVNkqQ02Xec726wkqWqatCd6OVbZCYw83NAIhFBodchKE5ZQ9PLNgv8dUVG4ds3bM9/PeEHBWaHSHBiaGtqFhX78L/MwYGGe4ZjivlSySvl0kw/QIBQZ+4HsP4ju/eTLho0k3v39qDsf94/fwge891PI13TDFYjONXe8TDpkAADy4a67LdzKAU8gG/M/3SEoVGgAHg/eCr0Ijn27LKXnCtyUUXNoUF/Oa+HfzhrM3mlITII0bUFIO3PPYgRa7CKYVuefTbRNJGRrztSYW7Yn9yRRlaABOLtdubmj0xKI9fVrtsSkhNzT8ms6qUeVp275Iq01hO1aN2WpgHrgLhG52txyjYSoaMqFniIrKrWPXjgf6vpklVmh0g/akabO8DgpbToVHXVglD+e1kkX4TDJDz6vP3ST+/OYv/wq3btmT4NGkm8/8dBvqTQPX3vJIy1QqSfgAx3ImTWxcaQX07ZxhL9sgNKXz7be5mEyRRYiXad5kBqbhMzFJ73/5GiFS0dCQrAm8SiPZh7lpmBircGGqHwxJ5ZfGaVvyvgY4eykI8vqgWwOrYluG7JyxmgE5DRhX0C6kE+smqaHBk9e9QJk045XezrdboaH+teJYTnFxqhdEKHinEIUUwZZTvWEEsZwqeBQafTZJ4+CUw6Zw+sbprt+3WNdZCdoFsRcZkmanlzFugodGUPtTJj3wmWSGnkOnRvC/LzhSfHyT5EvJ9IZceNo95548rEl++WmckKBwvj1zvLEIAk04aJq1ufAuytPc0GDLqeA09daJSbKcavg0NGodguRVQb52vQ0NWXmU0zRU7M2z1yKA6Ywubc472UeoyrtfcoL4M6v6uiOf726bSLKX2rp3HoAVCJ5LWdOL/g0HWbnVEzSJ3Ov6YWXaLKc4FLwvhikUHHCUZzyBHwxSaHRaM7SEgtsWPSo2OivFPP7r9Wfhd//n+XjjRUeJZr4fbDXUGbEXGZJmpxeyTONnxuCQHXJxSJ4jDDc0mIzwzMNXiD8vsdd538jqBe9k2YN2+OWqsVIqfQlXj1kTlTwpFQwx4ZDLQdM0/PurnoVnHeG8z9Lc0KBi1H4uRnXFKTBIGRp2wbLhYzlVs++/Kis0piR7m7LHOk2eDMxrmtiAcoZGb+iSQiON+8/TNk7j6284GwDw0C4Ofu5GUw9uMUbF6Sf2W+uNNAWCE/T840Jlb9DrRRk1QZkeLYs/q2gt44Wnbftj2ELB6Trn+0QwgoSCezM0FmvqWk4R1VIB73j+cXjeCetavkb/VFZzdaaRgmGpQaCG3CI3tgam3rTrFwrvQ5ne4DPJZILnHb8Wa+wJ/AM8dd03j+9fFH9eqrsXV3fbYdpnHrkSWgonbleNW0WUvfN1lvYGgBaP1LxaMVrC2UetFF+fSGERilhpW07tZ8uprtB1kHdZTrVXaJCyJzUNjQ7Hmcs5m2dWaPSGfI/Np/B5AQDHrrP8r3fP1nhd0QWaiAO6T1dTUC4xlbL8DEBuaHDxoRce22etMXsdiKBnDpCO64WKq9zQ6I2hU2hw47MngjQ0vBkaKoeCe5GVZgSruYIh7OhSOFAZhDHO0AiNpo9VMpNu1K0oMEyI5HIa/s8fPh0Ah7f2i2maYmISaJ0SINuNEw+ZjPW4wmKlPeGnGyZfIwHwm5STJ2PSrNDgUPDg0AZTtpEhtYZvQ8OemuvUKEiayRFnU1npEG6vQRMKDlZo9IbcM06j5RRgbTBpUGLHQc5e6kRTKkR1G3ggX3liqtpa5FEdspKZXWrANHlAIigfuflhAMCjexd6+rnnHb8W5UIOrzv/SIz3qO5IAi5S9sewFS1pnTzLDY1ABGpoFMlyijI0rLXZmIKWU14oe4l47XmbWM0VkOaQqbe88DMjPJyBzOG8VrKI+nd3hgkJmto6yAvHvphZarjsuhY9Co1Hbb/rY9aMIY2UCjlMVYs4uNjA3vk6VnqKKowb4UEpNzQKw9XQ2LfA9mPd8FNo0HXgZzmVhlDwoAqNpYaOCk0DNrmh0QuuDI0UT0lNj5bw1FwN+7j52ZFmgEIUQQo5Ig0T917o+VfXDdSaRsfGKNNKr4Mxx6wdx/3/9PzUFLPGhH0IPzd6wc/iMs2wQqM3DLs53EnVKULBGwZM03QUGinI1jl8uir+/OFXnII/OnUDfvzQHgDc0OgG7UlLQ9Ls9DJqK4xomHTnzBJWVEu8tuiDebKhS0GTkwnGcKwIGCYATkgjLxz7wTsV4N2IzS1bX09j8YGgyVAOBu9Ogzwo862FbCDdDQ1S67BCozuOQkOynMp3t5xSWaEhe/b75QHRVP54ucCWU30iChMpbmYAwKoxulfwM6MTushc6n6+q6WCyx5kaiR9Co3RUh5Uc5td5jVnEGQlyxsuPKrnn09LMwNwgst52rY3qDE6bAoNbmgEg85/pyEIkWvW1LHcMEC3FZUzNAi5ofG0tZalJRVdF7j52ZH6kIeC03Vw17YD+NcfPoxzP/BjvPO6exM+qnQyZ6/Jxivq3xOYYAznu55hfBAKjUXOSOgHb2aG13KKpkfGUvyAWG0XKjkYvDsNo1X6XxwSyym6Dg4sNlDjyfuONHwmr+k6IMm/TK1BU1TqThXJ167fv+E//uJ0nLFpGp/+89M4FLxPgkxapgGh5uK8nY7Q8yJoA0tWaaRxSELTNFSkSWGmO7Kib/X4cCtkZRsZtiQLjrCcSnkjnKC1xmJd9x0AYdwYATJUZIUGNQw1DRhJwST78esn8LJTD8WbLz5aqNTG7OYnKzQ60xwyOzovKyTrzQ/dtAW6YeLbv3mS9x59QAO43NAYHrihwWQGWjgaJjDHC4Oe8U6HtFNopGEKph2rWKERmEazi+VUCotQxIpqUfxbnprla6ETfhkadA9Y9NxnDcMUtnUjCgc0ypO+3kYuABy3bgJf/d9n4YwjV0oKDd5U9AJdNynvZ0j2dNzQ6ITffaITqyTLxzUpLW7TPY7vDcGQLU3TUHwcBJq2NUxW9/XCsFlOTUjDE6zS6A71fDopNEj9W2vqoglQLeZTYW2Zz2n40Cuegb++5FjxOVpPs5qrM5ShMay5CCceMoFTDptq+fwje+bjP5iUQwqNiRTkbTHBGM53PcP4UCnmxSZphm2neqZFkSF93LB9ooF0d7xZoREckn4XpY2lbCOU5oWCpmlYO2FdC7tmlxM+GnUxTdM3pJFUWuRTSsg5E1WFGxoy3bIxxBS2j5KDaY89sD8EllNWQ2M/KzQ64pe104mnrRl3/rxuvMN3qgutN5e4oREIah4XcprSGUthMFJ0LMnmarwfCUoaMrh6IZ/TMG43t7ih0R09SIaGfd81TOc1rabYK59DwYNRF0HP6V5TtiOX0/Dl156B5xy3xvX5J/YvJXRE6YUVGsPHcKwIGCYgK+yp8QOLXHzoFe+kstwUkhdaaQ5ZYoVGcMTiseAsHsn/tZjXcMhUJZHjCov1EyMAgF0z3NBoR1Oy7pMbW7QBm/cUamRVV1omcDV03hzRFLafkoNpz/BYTlnPjH2codGRXhUal5y4FgAwUSnguJQ2NMq2HR3fG4Ih1HspeTYMQi6niaymAwtcyA7KsDU0AEelwQ2N7ugBrAvlwSrKwRtNyQCNH1W2nAoEKTTSlKXUK9VSAc88YoXrc4/tW0joaNJJUzfEXnQ8xYOXjJv0Vh4Zpg8mqyU8ObOMg7xw7Bmv5ZQcmExS2HIhl2q5Jyk09rBCoytNnwC2Uw9fgRv/6jyMlQupXyisnbQaMrtZodGWpuR5npemopyGhnsDtlhzClaqy///YfPx+PKdj+ONFx3d8ftIabJQ581mL9CkZcr7GWw5FRDKRwiq0Lj4uDX41hvPwarxMqoptbFkhUZvkApYZTvCMJkeLeHAYsNuhqazaRc3pIQsD1FDY3KkiB0HlzDL+9Ku6AGUnfK1Qc/lNA/ajZb9Fc+Mm6bd7CoqvrcYlBPWT7g+vuexAwkdSTqR96Ws0Bge+EwymWKFFAzO9MaSp2B3QFJoULc7LTYy7RANDVZodKXZRt57vGexlVbWkeUUKzTaQhsIwB3SSJZTC54N2GLD9jNOwX3itecfideef2TX7xtlO4C+MHysytKIsJzihkZH9ABhrjKapvn6RaeJEZGvw3Z0QVhOQb5SmKwcLeORPQt87wiIaZpCGTxMCo1JVmgERig7OzxHNE1DuZBDrWnggFBopLfcNVbiNWYQHMup4bk3+HHaRrdC497tMwkdSTohu6lKMd0DuIwbPpNMppgSDQ1eOPYKNS1oISnbdg2LDJyKU3vZD70rw754XDthKTQ4Q6M9skJDLlTS5pEWjgTdQ4apYOX4G/P0XC/oAQoTaUAoNPiZ0RFqgBeG1N/aj0qRQ8F7QTwfMmA5BTj3Dm5oBEPOqRomhcbEiLWGYIVGd6gxnusi7aTrgxQaZNuURoRCg1XAHcmC5RRg2STd+o6LcN3rzwJgOUrokv0v05lZOxA87S4SjJvhftczjIepqrWB4IZG79Bm4tCp1myBxpAUt0mhsX+BFwjdGPbF4zq2nOqKnKEhF6bHK/4TZeQln+ZpOS9sOdUfJO7pVphQnZV2hsZ8rYlalwD5LNMUipzhfF74UWHLqZ6gwv4Ke50+7EzbAzTfvXcn9rLNaVdoiAZI//CUDCs0gqOL50jn76N773472yrNllMcCh6Mdq4Bw8jhK6s49fAVyOc06IbJz48euP/JWQBsNzVsDM+KgGECIEL42HKqZxp2Q+PEQyxLoR0Hl4QXIfljl1Je3J6ulqBpgGFyyGs3qIlVGtLF4zpWaHSFLKcKOQ2aVJimzeNSQxebDMDZkA2nQoM3m71A1hFpb2hMjBSEOoknrdvjhIKn+3z3At3n+N4QjP/8xeMAgFX2YMmwM203bu58dD9e9Zk7Ez4a9alJ1m1p32vIcEMjOHrAxni5aH19/4L1mqY5FJxtTYNR12mNMTz3hk7kcxpWj7E1ci8cXKzjndfdCyD9ew/GTTbe9QxjQ5NfvHDsHZqOWjNeFkqGrXvmAciTEem+pRTyOayyFwhPzXJDoxMNozUUfJggy6ndszWYJqt1/HBUOu6F4agk75etmKgBOkyTMVW2nOoLfUgyNDRNY9upAFADPO3nuxc2rLDUrI/uXUj4SNSn3jTwi0f3A8hOxh3dNwDggZ2zrArugpyfoQ1RMYobGsERVpVdzn+lYK1B98ynX6FB62kOBe9MFm0tV43ba08ewAzEW7/6a/Hn3z81n9yBMKEznJUohmnDZJUVGv0i52SstQOTSeYo8hQK6V9IiMl8nnjoiGhiDZH0X4YaGvWmgQNsUedLs01Tq1zIiwlK2feXPKInhsi7dKzMllP9IBQaQ3D7oAb/U3P8zGiHUGgMwwkPyHHrxgEAD+2aS/hI1Ed+7wxTw7sTEyPu5yDbhnSmZlu3DVN+BsANjV4wAlpOkULj8X1WM5nW82mEVMCLvMbsSDODa4xp2/KUh2m6Y5omfvLQHvHxXz3nmASPhgmb7LzrGQbASp6k7BvK0ChKKoa9c9br2BgiqSctfHey1VBHRG7KkE7clgo5ERLPzS1/dKP9RNSYXZSal4LBZ+0/UwjmMMB2AP0xLJZTALDeztt58iDfJ9rRGBJFTi8cuWoMALBt32LCR6I+clbVP7zohASPJD42raq6PuZ1RmdocGrYGhoT3NAIDBWtc12eI2VboUHDSOtS3NDgNWYwGhnK0CCopsVDut3ZKT1fn3n4FN76XG5oDBPDtSpgmC7QJOWeOZ6E6hVZ7k1BqHttmeOwhIIDjk0ETfYw/jTa2A0NE2tFjsZSwkeiJuIa8Nlc0lTZvLQJm1u2NpfjQ6TQoIBztpzqDYpW6WYdkQYOmbKeGTtn+D7Rjk7Nz2Hl8GmrYL13voalOt8fOrFrxlpLnnbEChxqv5+GnWcevgL/99KTMWIHGHNeV2coQ4OK1cOCo9DggnU3DKP9mlOmUnTvRYdBoTHPDY2ONITlVPrrEEEhG/V9nN/WlScPOuvzT/3ZaUNlW8hwQ4PJGKQs2LfAvvi90pAsp8i30VFoDM9kxLG2TcSDbBPRkWFqYrWDJq938OS1LyJDw0fiPeqzCZu1N+wTQ2QpIgegswd6cPSAk5ZpYP2k3dDg+0RbOjU/h5XJahHj9v1hx0FWaXSCptNXSLkSw46mafiT0w7D+U9bBcCtUmFakYeqhglqaMyyQqMrekBlZ8XT9Hra2rHIjilqqnag+XLDEFa/TCtN4RSRnTXGSttFYD+7jnRl0R4qOX79BFbatUBmeBiuVQHDdIFu/g3dZHlvj4jNRD6HVaOeDI3m8BS3j15jLXwfY5uIjjSHyGasHUettq6FLdzc8qVptA/6nbRtpf7r7ifEVN2srdDweoenGdpsApyj0QumKEwkfCAhcMgUNT5ZodGOYVoj9ALlts0u872hE/M169kwluLw3n7h3LZgLHOGRubRA1oXyo3RUzZMprqAKQeaL7DSry2NNpl+w8y0fZ3vZ4VGVyiDRt6zMcNDdt71DANLqkyLR7ad6g0qSJQlhcY+YTk1PMXt1aTi4YDGjgyTKqcdx3Koa0dECJ/PNUAS/+vv3YkPfO9BAMCcXdQbptDXciEnps4X2XYqMEEnLdOAUGhwQbItjSGdru7GGPufB4KylrLY0Fg7SdaWfP/w43v37cI7vvYbUbQbtoIU7Unna01xn2T8CdrQWCk1NP7yoqMjPaaokdeY/BxpD7lIFDO0xqCGBltOdYcUGsP2/GAssvOuZxgbCvrdwwXrnpAnLEWGhsdyqjQEDQ1S8SzUdfa97oCToZH+c94Ox35sli3qfCCVjt/mUg5h/PdbtwKQFBpDlKGhaZqvvRbTmaCFiTRA1nS7ZpaFGolxU29mu6ExzwqNjszbzeDRDDY06FnJllP+vP5L9+Br92zHv99irSOqpeG6RlZUS2IohK+BzgRdN8hq2ecevzbSY4oaeY3JDY32kGK8OARryqBwKHhwuKEx3GRrZ8EwcHI0WKHRG7J/rZxFAgzXtP5YuSAk7Xu56dUWsXgc4obG0WvGkM9pmF1uYvsBtpPx0ukaoCKvDCk0hslyCgBG7QXyIltOBYb6g8PQ0Fg3WYGmWc9InpTzR1Z4ZomxCjc7g0CWU8Ok3gsKNTRY4eWwUGviH77xW7zja78Rn/v9U/MAhq8glctpWDfJ10AQDHvhkO+i7JQHaoZhjcHB4N3JwpCdF1Jo7J3jXNhuLImGRvbWGFkgO+96hrFZPU75D1x46AV5wpJULvsW6tANc6gspzRNEw0bbmi0RzSxhmCz0I5yIY+TDp0EANyxdV/CR6MezQ7Tci855RBceOxq8XGtqYvQy2ErWrFCo3do0lIbAsupYj4nrAp3znDj04+6PvwNcD+4EBWMBVuhkWXLqd1czBb84He78P9+8Ti+ds928Tm6hwxbQwNwbAuf5BymjtC6Iddl33H5OZvwp6cfhi/+xelxHFbkjJata36BbU3bMkyDlUHZsKKKQk7DQl3nZmgXSLU1MoTPD4YbGkwG4WJ1f8ih4NOjJWiaNWV7YLHuLCSGZPqSGjbc9GpPVqZhzjhyGgDw2+0zCR+JejQ7XAMrx8r43OXPRqVofW3XzPJQWk4BQFXYAfBmMyi6mLRM+EBCYv0UF6Q6IQYihvx54YUtp4IxZzd8smw5tVDXMbfMwdAAsKODInZkCCdsD2GFRiDEEE2XQYixcgHvf9nJOO+Y1R2/Ly3w0Ex3mkM0WBmUUiGHI1ePAuCsx26QQmOUGxpDSXbe9QxjQwoNtpzqjboUuFXI57CiSkX/2lBlaABWMRbgYPBOZGUa5jgpR4Nx07SvgUKbaTlN03CIPXm4de+CaIINm+XUWJktp3rFGKIMDQA4fLoKAHhs32LCR6ImtYxmaNC97taH94jpYqYV8obPokJjtFwQqkXOULDoFJA+jAUpaojv5IZ4R8hyqjDk+w4vY5yh0ZWG0Xk/MqwcuWoMAPDYvoWEj0Rdtu1dwOwyKTSyt8bIAtnaWTAMIKwhWKHRG1TALttNCwqj2j1bkwLDh2Mh4Sg0+BppR1amYY5ZYzU0Ht3Li0Uv9QCNzPVT1uThFnt6KKcNX0GCPFl5ei44VNsdBsspANi00mpobONNpS8NPZsNjUPtQuVd2w7gIzdvSfho1IUULFlsaACOSmPXDK85AWtf0Y5htJwihcaTrNDoiLCcGpJ1Q1AqReuaX26yCrgdw+YUEZTD7bXn4/u5GerH9ffuxIVX/QRfvvNxAMP5/GC4ocFkkFXjXKzuh7pnwvL49RMAgLse3S+mL8uF4XhQkIqH5d/tyYonOi0W987XeTrKg/ee4Ad5Q5McerxSHJoiNsHTc72jBwz3TAsbV1myf258+hPkXjGMrLSHIwDgxvt2JXgkakPN4LEhy1cKCoVCs0LDotOzdBhtyWidxBlMnbGH8IdG2RkUGhakQTKmFTFkl8vWGuOwaWposDrYj2t+/HvXx8M2UMdYZOtdzzBwMjTYcqo3vAWJi46zvEm/evcTWG5YUyPlISlWHL3GknA+vHs+4SNRF2E3NCSqnHZMVIqYqlq2IU8c4AWjTD3A1DVNHj5oNzQmRoavGMGBjb0zbJZT1NDYtpfvEX4EUXMNI2dsWin+vHt2GabJBSk/REOjnM1iA2f7uaEhKT8OXTES45HEAylZdx7khlYnaBAiawqNvF2kb7JtYVucXMdsXRtkd/oENzR8oVoFwZZTw0m2dhYMA2f6ft98XRRVmO54J/Kfc/xaAFZj6OCiFWRYLg7HLeXYtZb65MFds1yAaAMtrIfFZqwTtGB8nP3xXQRSaNiWK7/baWWQjJeHKz8DAEZLrNDoFfLCzg1JQ2PTSquhsWt2WYQPMg5ZVWisHi/jF3//HADA3HJThF8zDqZpSg2N4Xs+BIEsXLmhYVHrYK1D67FhgrLG9i3UxYAY0wrtO7KWk1DMkUKjfaMv6zSN4bK+DopoaBxY5HqFD94mIFtODSfZ2lkwDICVo1ZDo2mYOLjUSPho0oO3IDFWKoCGZPYv1AEMj+XUUWtGkc9pmF1udgwnzDJObsrwP0ZY0uuPN1fHj/W2QoMYToWG3dDgQnZgHC/shA8kJKaqRUzYdjmP7WfbKS9i/ZCB54WXtRMVcW3sZhvLFmpNQ9wPRrOq0JAGrRjnfuFF04Aj7ObxMDFVLWJyxGrmkZqVaWXYlJ1BIdUBKzTa02hSKHi21hiH2OquxbouhksZB2p0EdzQGE6y9a5nGFgFeVo48jRUcLyWEbmchqodVLZ/kRoaw3FLKRfyONK2ENnCtlO+OJNSw3HOO0ETMNsPsL+xTJCm1rOOWOH6eKIyfBO4tEBmhUZwjCHL0NA0DZvsZ8Znbns04aNRjyD2dMMMZSTwgEQrc8vOfXM0o3YQwgqX9yQAHMuptzznGNEMBIBNq0bF/m2Y0DQNp2+aBgDcvW1/wkejLmIQInMNDeu52WCFRlvIcipra4xyIS/ukfsW+PnhRde9Co1srjGGnWy96xnGhmyn9nKORmDqIvjbuW3QZPIBUmgMieUUAGywfXp3HuQith8NUaAa/o3FYStYoeFHEBuZ8UoRb3vu08THVPQdJjgUvHdoXz5MhQnK0bjt4b0s/feQJUWfH2snbI98Vmi0sCDspgpDdT/oBQqPZ4WGRa1h3S8uOWEtfvPuS8TnjxhCuymCBmc437E9wzYIERTHcorXFX6YppnpoQlS+O3l50cLXlETKzSGk+y96xkGwCp788DTUMFo6oZ4KJR8Ghr7hsxyCuCJym6IALYMKTS4oeGmFnADccIhE+LPx64bj/SYksCxnOKGRlD0IfQ7/scXnwDAemawmsuNnqHMJT/W2Q0NtpxqhfIzsmo3BQCrORTcBWVolAs5aJqGd73oeKwZL+MfXnR8wkcWHSuqlvLkwCIXJdvRzKjlFIeCd6YuKVcy2dAY5edHO4qeocsRbmgMJdl71zMMJHk3T8IEQl4syBOW3g3osFhOAc5E5RP7uTDlR1PPzsStCF3bvyg8fBmg0Qwm8T55w6QoZF507JrIjytu6D64UOMMjaBQQzQ/RA3RlWNlHGc37H7/1Dy+etfjOOv9P8Q9j7GFSMMYvvPdCzwg0Z55SaGRVWhPsm+hzmsMOJZTNCT1v847Enf+w3Nx9JrhG4ggVtjB8PsX2Ae/HVnN0KD1M4eC+yNn7mQxp2vavneQWwbj4L0ehtH2mOGGBpNRyHLqKW5oBIIKl4C7eOn1IhwmhcbJGyYBAN+/fxf7lvrQ0Idvwrod66cqyOc01JoGq7ok6rpVwO+2gVg7UcF1rz8bP77iQrFpHybI950tp4IjJvaHrDBx5GrLduqRPfP4m6//FjtnlvEXX7g74aNKHlLkFDLwvPCDBiR2c0OjhfllbmhQQUo3TJ7Qh9TQGCIb226sqFrXwEE+/23RyXIqO5cFAA4F70bWGxqkEp/noaquDGMGE8MNDSajbFxpFR1+/5QT+PzU3DLmlnkyxo+aXbjUNKAgFaDGPRvQYdp8XPi0NRgrFzBfa2LrnoWkD0c5hOVUBhaPxXwOh0xZBSm2nXIIkqFBnHLY1FDmZwBsOdUPDbvAPWyTliIY/KdOMPhygzeZTWFROFznOygrR6lYyWtML3TfHKtkt6FRKuSwdsIatHoi43Z1Td0QDe9hUn13Y2qELae6IULBM5ahQda+PFznT10asMtiDtOYrRJf5D1ICw1P7kwWLcmyAJ9VJpOQLcQDO2dhmiZ2HFzCuf/yY1z+ubsSPjI1EYXLvOVnS0x7pq2HaTIil9OE3/8DO2cTPhr1yJJCA5ByNPZxQ4OoC1uI4Xnf94NoaPB0VGD0IW2I0rCEHP7c1M3M28jQZGkWMpf8oKnAmSVuaHiZsxUao6XsNjQA4Aj73vHYvmwP0NSkaethUn13g9YRS3VeR7RDz+hzhPZZesbXEe2QaxRZpMp7kLZwEzAbZPOdz2Sepx86iUJOw86ZZTy+fxHf+OV21HUD9zx2QITRMQ7tFgsrbd9fYthCHU89bAoA8POt+5I9EAVpipDXbDxGRI7GAW5oEPUM5ah0YtQOmVuoN2GavOEMQsMYzon9Ew+ZbPlc0zCxL+PexuT9nVXLqQluaLRlgTM0AAAbV1prjG17s73GkBVtWZqmrdrriEVW9LVFKDSyc1kAcLKnvNPmjEUvavFhhPYgrNBohRsa2SCb73wm84yWC3jm4SsAAD/7/T48sHNOfG1HxuXeftAiyrtYWDXmVmiMlYfLm/C0jdMAgPuenEn4SNSj0cxWMfswUmiw5ZSA7gtZuQbaQZOVpgksN3jxHIRhzVQ44ZAJXPnSp7d8fm/Gs3eaQ9rACgorNNpTz2Begh+s0LBYtBUKlWJu6CwJOzEiipLc0GiHYWY8FNzg9aUftaw3NESGBjc0vMj5Ki99xiEJHgkTJdl85zMMgHOPWQUA+Oavd+AXj+4Xn9/ODY0W2k0/rPIoNIbNA5n80J/Yz9eEF/LAz0qBap0d6vrUbLYLkzJZn7omRoqOMo03FMEY5kyFV555BO5513Nx2zsvwtFrxgCwL7pzvrO57SCFRq1p4PdPzXX57mzBSj8Lsqvbxg0NANmzIKN/b71piLUV44YUGvnMZWjYDQ1WaPhCz5DMNjTsewc3Q1uhwbvP/PlpuOqPT0n4aJioyOY7n2EA/PFpG5DTgDsf3e+ansz6JKUfdTsUvFtDo1ocLsupw6ZHAFhTlTMc5umi2Ua1M6zQtc73BwfHdixbm0svuZzm2EWw5DsQYmJ/SIuYK8fKOGy6iumqpWI8sJDt54dzvrN5rxiX7JSuv3dXgkeiHqIYNaT3gqAcYVtOPZbxnC4KiSfFQlaQ/71sO+UPPUeyptCgdRLb5/jDGRq27S0PVLVA75mnHzo5tPsNJiMNjY0bN0LTNN//1q1b5/szt99+OzZv3ozp6WmMjIzg5JNPxkc+8hHoevtFxne/+11ceOGFmJycxNjYGM444wx84QtfiOqfxQzI+skRnLxhquXzXLBspdbGXmilZDk1Vi4gN2SLzGqpIGy1ODvBwTTNzFmI0LW+dz7bk9Yy1NTKZ3TqWoaDwXtDqHuG/P4xVbUm8zOv0MiYos9LLqfh8rM3AgB2HOS1hEzW/c8JamjsW6hjdjm7DdCljCo0yoUc6PbIweD+GFltaOQ4FLwTzjMkW01QghUa/hiGmbm8z6ySmdXC5OQk3vrWt7Z8fmxsrOVz3/rWt/Dyl78clUoFr3jFKzA9PY3vfOc7eNvb3oaf/exn+NrXvtbyM9dccw3e/OY3Y+XKlXjlK1+JUqmE6667Dpdffjl++9vf4qqrrorin8UMyP84/XD8+omDrs9xwbIVkaGRb6/QqAyZOoM4bLqKvfN1PL5/EU8/tDXwNYvIwXRZmXhYbV/r+xdq0A0zcxsqP2hzVeTXAqOlPPaAFRpBcRqiw33/WGErNA5mvKGh68OtyAnCqYdP4fO3cw6TlwZbTgEAxitFrBorWevNfdldb9KUMU0dZwVN01AtFTBfa3Jhsg26naGRy5rlFCk0uKHhSyPrllNioIr3HzJ1SdGUdSeBYSczDY2pqSm85z3v6fp9s7OzeO1rX4t8Po+f/OQnOO200wAA//zP/4yLL74Y1113Hb7yla/gsssuEz+zbds2XHHFFZiensbdd9+NjRs3AgD+8R//Ec9+9rNx9dVX4+UvfznOOuusKP5pzAC87JmH4p1fv9f1OVZotNJugm5F1QkBrw2pRPrw6Sp+9fhBbN0zn/ShKIMse86KxHflWBmFnIamYeKpuWWsnxxJ+pASh3JUuLljqbkAztAIishUGPJNxopRq6GxP+OWU1nLXPJjwwprAp8zudywQsNh48pR7J2vY9u+hcw2NLKaoQEA1VIe87UmFybbQJnYWVtzilBwtpzyhZ4h5YzsR72Q5e0CD1S5aLgaGtm8NrICn10P1113Hfbs2YPLLrtMNDMAoFKp4MorrwQAfOITn3D9zGc/+1nUajW86U1vEs0MAFixYgX+/u//HgBw7bXXRn/wTM8U8jlc/5Zz8YrTDsNbn3sMAO5w+9FuwylPW9aGdKF16mFTAICrfrAF3/zVjmQPRhGaLoVGNjYW+ZyGQ1dYTYz//MXjLP2GpNDghSLGyiz57oWsWNZR0z/zCo2MZ2gA1nAEADw5syTWVIxUjOKGBo6wg8Ef3p3dAZqsZmgATmFyaUgHxAYlq9aFpGTlUHB/aBK/WMjWdUGQQmORLW9dyG4SWRm+zCqZObu1Wg1f+tKX8L73vQ8f/ehH8eMf/9g3D+NHP/oRAOAFL3hBy9fOP/98VKtV3H777ajVaoF+5oUvfKHrexj1OPGQSXzg0pOFfy0XpFqhUHC/DSctLJ95+FSchxQbl5zo5Ox86KYtCR6JOsgyzixtLKgg9bEf/R7X/Oj3CR9N8jT0bPoZ+8GhfL3RzIi6Ryg0MtzQME1T3CuG3WKsE6vGShgp5mGawOu+eDe+85snkz4kJaBrgxvjli0ZAHzn3uxdG6Zp4pO3PoLbtuwFYNk4Zo0R9sJvi2maoDmiYctr7AYNAtC6iXFTy3go+Kik0DBNbnoRDSmrL2v3jKyRGT3nrl278KpXvcr1uU2bNuFzn/scLrjgAvG5hx56CADwtKc9reXvKBQK2LRpE+6//35s3boVxx9/fNefWb9+PUZHR7F9+3YsLi6iWq36Hl+tVnM1SWZnZwEAjUYDjUa2rQriomQ/B+eX43vN6feofo6X7CJdXms91uv+9xn41G3b8HcvfJry/45+WD1awM1vPRfP/chP8eTBJdTrdWgZ82/1slSzinPFvIZmMzsF3P91zhHYsmsOu+dq+OLPt+EN5x8R+bWg8j2iaTc6NdNQ8vjiZMRu9s4u1TP/WgSh0bSunRzMoX69JuyFxf6FGt721V9hdqmBj112SujFW5XvE7KazTSaaDSyWXQAgMNWjGDLU/P4yUN78JOH9uCS41ZlfqO93KD15XDfC4JwxkbLZmrXzHLor4XK9wgA+P79u/G+Gx4UH1eKOWWPNSqqReveOLdYy9y/vRvyc8TQM/YcMa3CbEOPfq2t+n3Cj6WadayFnJaq4w6LUs56bximde/IorrNj4Vlq65azGfzuoiSXu4Tcbz2mWhovPrVr8Z5552HE088EePj49i6dSuuueYafPKTn8QLX/hC3HHHHTjllFMAADMzMwCsEHE/6PMHDx4UnwvyMwsLC5iZmWnb0Hj/+9+Pf/qnf2r5/A9+8IO2P8OEy4MHNQB57Np3EDfccEOsv/umm26K9ff1yq92Wa/N/r1P+b42l4wD9/x0e/wHFhOWirOApmHim9+9ERnLKmxh3zIAFKCZRuzvlaR55wnA396Vx975Oj739RuxLqbbs4r3iJnZPAANd935c+x/sOu3DzX7n8oByOFXv70fq/bfl/ThKM/2J63X64Hf3Y8bhvj12joLAAU8unsG9263BlX++Yvfx5lropmiU/E+0TAA2m78+OabUcnEzsOfUsO67omvf+dGjBbbf38W2LHTvhfc/1vc8NS9Xb9/mJlvAEABi3Ud373+BkTR61LxHgEAP3rS2mcQu7c/jhtu2JbY8STBwoz1Xrjj7l9Cf4wnrWWa0nPkhzffjGqGniMP2PWJ/QdmYttzqXqf8OPXO63XZ99TuzK3JwVgK5esN8S3b/w+xjO+piB2LgJAATlTz+R1EQdB7hOLi4uRH0cmHgfvfve7XR8//elPx7XXXouxsTFcffXVeM973oNvfOMbCR2dxd/93d/h7W9/u/h4dnYWhx12GC655BJMTEwkeGTZYf3jB/GJB+5EvlzF5s3nxfI7G40GbrrpJjzvec9DsajuE2j37Y8Bjz6Eww89BJs3n5z04cSOaZr4h3tuRkM3ceb5F2P9ZCXpQ0qUrXsWgF/9DJVSEZs3Pz/pw4mdL+z4Oe7dPosNJzwLl5ywNtLfpfI94qoHbwOWl3DuOWeLrJmscs/1D+IXex7HYRuPxubnHZP04SjPt/b/Cti/B6eechI2P2tD0ocTGQ/umsNH778Dcw2nMjl+yFHYfEmroncQVL5PLNSawC8s29UXvuD5mZ4e/I32EO67/THx8bPPvQAb7dyErPLlXXcBMwdw2qnPwOaT1yd9OIlSa+j4h7t/CAC44DnPw3glvPeyyvcIALj3ew8BjznvjROPPRqbn3N0gkcUP989+Gs8OPMUjjn+6dh8+mFJH45SLNV14BfWe+OFz79E5AZkgRVb9+HaB+7ByOgYNm8+J9Lfpfp9wo/ttz0KbHsYRxy+AZs3Pz3pw0mEv7/nZiw1DJx13oXCHjnr3Lt9BvjNLzA5OoLNm89P+nCGil7uE+Q6FCXZeRr48PrXvx5XX301br31VvE5UlmQ6sILfX5qasr1M3v37sXMzAxWrlzZ9mfaKTgAoFwuo1wut3y+WCym5oGSdiZGrdd/qa7H/pqrfp510yrGVEoFpY8zSiZHitg7X8dCw8zsayDIWQWpYj6XydfiiJVjuHf7LJ6cqcf271fxHkEWAJWSescWN+Mj1r9/qcn3hyBQVl9Jwes6TMZGWtd1O2Zqkf2bVbxPaJLafKRSynRWwv848wh8+a7tIvR3tsb3i4ZtCz9SLmX+tSgUCpY9hm6iZmiYjuD1UPEeAQA7DtZcH4+PZO96GLMbWHWd7wteaoYzFFApl1AsZqcxXilZWVy6iUzvOdohahTF7NYoysU8lhoGTC2b+3I/GvZ1MVLK82sSEUHuE3G89tndVQBYvXo1AGBhYUF87thjjwUAbNnSGv7bbDbx6KOPolAo4Mgjjwz0Mzt37sTCwgI2bNjA1lGKM2qHsS3Us5MJEJS6HbiV5ULEhF2wnFliH0YK2srq9XD49AgA4PH90csoVaZpcNAvUaXnB4eCB4KaYcX8cOcHVIqt740dB5YSOJLkkINMCxnPizh6zThu+5uLcPSaMQDAwQyHxRO0viwX+DmiaZqYPJ9fztazxLueqmZoAp+o2uo1DgVvRc7QyGUsx5BDwTvDzxBnP15vslUdQYMjWVYFZ4XsvvMB/PznPwcAV3Pi4osvBgB873vfa/n+W2+9FYuLizj77LNdaopOP3PjjTe6vodRF1pILjcM18KJcQrYWV4sTHJDQ0DXQ2HIi5HtOGyF1Zx+4gA3NIDsXgcyY2VuiPcC3UPyQ17grhRaN1KzGXuG0H0in9OgZawQ5ceqsbKwrZxdzta14EfWByS80LNkPmPN8Sc8DY3RDBahuKHRHkPalw/7usELDQI0da5N+FG3nyGlDNcoStTQ0LnpRSzb99GRDKm5ssrQv/MfeOABlwKD2LZtG970pjcBAF75yleKz1966aVYtWoVvvKVr+Duu+8Wn19eXsa73vUuAMAb3vAG19/16le/GuVyGddccw22bdsmPn/gwAG8733vA2DZWzFqI/txLnJRysWy3eXOckNjosINDYIKVKWMFiDInzTrCg3R2MrY5tIPmsSvNXgzEYSsFDErPhuprBWx5YYGY1G2G118v3Cma7NcjJIRzfFadoraS3Udc54GTtYav4DzvKg1snPug9J0KTQSPJAEIBV0gxsavohnyJCvJztBz88GNzQEpNDwW4czw8XQ6zm/+tWv4uqrr8b555+PI444AuPj43jkkUdw/fXXY3l5GZs3b8YVV1whvn9iYgKf+tSncOmll+LCCy/EZZddhunpaXz729/GQw89hEsvvRSveMUrXL9j06ZN+OAHP4i3vOUtOO200/CKV7wCpVIJ1113HbZv346//uu/xllnnRX3P53pkXIhh5wGGKY1HRNmGF/aWW7aDY0MPxRIoZHFTZaXRjPbCo1DV1iWUzsPLid8JMmis+WUgDYTtSZvJoKQlYk6vyGA2aUmTNPMjFqhSc2rrFWhOlC2G6DLXLgU98xhvxcEhZq8WSpMHbCt1wo5DStGS9gzV8OzjphO+Kjih6es22OY2VX6FdlyqiP8DHGukTrvQQTCcirDtausMPQNjYsuuggPPfQQfvWrX+FnP/sZFhYWMDU1hXPPPRevetWr8KpXvarlwfjSl74Ut9xyC9773vfi61//OpaXl3H00UfjQx/6EN7ylrf4Pkjf/OY3Y+PGjbjqqqvwH//xHzAMAyeccAKuvPJK/Pmf/3lc/1xmADRNw2ipgLlak33QPSzbU4R+fuBZgRsaDo2MF7JJrbPU0NHQjaGfMm8Hyd+z2tiSKeWtBTNvJoKRlYm6XE5DqZBzXRd13UCtaWRmaoymSlmh4VDmBqjAUWvx9QE4z9MsNjSmqiV8/63n44n9izhpw2TCRxU/Rb4vtIUGaPIZa2YAQMFeJ+ms0PCFVX7Ov52boQ5Ldc7QyApD39C44IILcMEFF/T8c+eccw5uuOGGnn7mJS95CV7ykpf0/LsYdaiW85irNdm/1ANNEfr5gWcFztBwIIVGMaOLx7GK8+icX25ixWgpwaNJDpoWY8spSaHBm4lAUJE7CxvQsqehAViN8aw0NJwA+OE/10Ghc7/MllOiAJNlS1MZep5mKcvv4KK1rp4eLWJ6tITpjK6pSnm2FmoHvR+yOEdF94QGKzR8EYrfDK8xnFBwvkaIZVZoZIbsvvMZxofRklWo5IaGG0ehkd2HAjc0HKiQnVULkWI+JxZIc8vZVHMZhgmqtxQyvIkgqBjHm4lgZEWhAXCORlYC4HvBUWjwWpPuBdzwshB++RlqaOxfcBQaWUZMWfN9oQWynMqiMpxUWxwK7g+9X7IwINOOUgatCrvBGRrZIbvvfIbxoVq2bnoLHArugjbdbDnFDQ0AqOs8cTtuqzSyVJiUkQMauVDJhYheyZLnsd9zc2YpO2sMVmi0wgoNh0ZG8nSCQsVLPUPT2Adty6kV1WxnF5Z4MKIttObM4nKTmjhNw4RpclPDCw2hjpazW7jme0crS3XrtWDLqeGHV48MI1ElhUaNi1Iyy9zlxgQ3NAQU8prl7ARqaGRVoSGHE7L3OfvX9go1frJQ5N60aqzlc1lqhNK9ghufDqzQsDAM07Gfy8C9IAjCXiZD09gHhOVUthUaZV5HtMUwspvFJK+xmxlSbgVl3s49JZeNLMIKjVY4FDw78OqRYSRG7S7u+254AH/22Tu5023DoeCOQuOXjx8UBf2s0uQCBMbtYPC5DBUmZVih4abE/rU9QcW6LPjmv+mio1s+N5uhxjg9L7LcAPfCCg0LuXCb1UwuL3l7GjtLGRpsOWVBDf5GMzvnPii6md2GhmzrmqX7QlDm7cEyOd8wa3CGhsOBhTpe9m8/w5fvfBwANzSyAK8eGUaiWrYehjsOLuHWLXvw09/vSfiI1IAVGk5DAwD+9r9/m+CRJE+dFRqs0JCmR4sZ9DT2whkavVHPkM3M6Zum8bE/PRX/5w9PxHOPXwMAmM3QfYOan3yfcGCFhoU8TZrlAQmZovDLz86zhC2nLOg9UMvQuQ+KnmGFRkH6N/MEfiuk0BgrZ7eh4ajEueF12+/34pePHxQfV9hyaujh1SPDSIx6bnocwGWx3OSGxqS00brunu14ePdcgkeTLI7lVHYfIROZV2hY14CmAbkMbjC9lESBkjeb3dANUxQnslLEfMkph+DPztqIFfYEcqYUGhkuRLWDFRoWcgM4K/eCbtD7JEvWMvtty6kVGVdosA9+e0RDQ8vec0RuaHBdohVuaLBCQ+ap2WXXx6zQGH549cgwElWP/+LBDBUdOiEspwrZfSisGiuhKjW8rrtne4JHkyzsec0KDdpU8dS1Rdm+N/JmojuuImYGFBoyZIlAG/AsQA1wztpxYIWGhVB75jRujNtQYSpLhUtHocENDcDJmGIcqKGRxfuEPAzQMHiNKWOaJjc0AJQK1jXCexBgz1zN9TE3NIafbO0kGaYLo2X3TY98XbOOYzmV3VtGuZDHj6+4EH921hEAgAd3ZVehQQvqQgY3FgSFxM9lqDApk2X5vx9UiGgapgivZPxx+eZnrCnqTOZnp2DFCo1W6DqoZVyhQVkBWWtsdiKLCo0D1NAYzbjlFIeCt8XIcIaGpmliIIAzNNzM15qwLw3O0ACgc8OrtaFR4vXFsMNnmGEkvAoNbmhY1EQoeLa73GsnKnjh09cDAJ7Yv5jw0SQHFSGyHOI5XiaFRjZVXA3OUXEhF+S4GNEZeYIsa1P7pHLMktWQEwqe3eeFF1ZoWNR169/PDQ2HLGZoHFhgyynAUT1zKHgr9HbIouUUABRy2VNuBWHnjGUvNF4ptNRwskQWG+HteMrT0Mh67SoL8AqSYSS8GRr75rmhoRumKNDxQ8GxGlqoZ3MyH3DyE4oZnJQi6DrIUrivDE2JZVmlIyPbr3GORmfkQHAtY8UJUjnWMqXQYEWfF87QsKB7ZdaUWp3IWmGq3jSEZUzmGxqs0GhLM+NDNPT85FBwNzsOLAEANqyoJnwkyUKNPlbwtCo0Dsv4tZEFeAXJMBKtCo1am+/MDvIEYZYtp4hRezJ/sZadgpSXBk/cYswOBZ/PaEODrwE3stKAPWw7Q69POYPXzsj/3959h0dVpm0Av6dPek9IgxQgoUhXSgBBFAQbimV1dUVdXXXFthb0U1HXhpV1basiuLpiQcQGqCCgNAENndASIBDSe512vj/OnDMzyaSXmcy5f9flJZlJZt6Bk/ec8z7v8zz2TRP1CtqZzwyNppihIWI/rqbkndgKKR0i9c9QqRylPJVKz8a+zTLLm2iUOVdIgRylBDrb6lS5GNCID/Xz8Eg8S8PjQ1ZY5WgKrlYBieEMaPg65eZmEbkxsm8o/PUa1JrEm0yWnHLdQajkpuASqc9KjckCQRAUt8MYcOwQUvKuykDpOFB4Dw3uuhapVCrotWqYLDburmyFtFijxJJ1Siw5xbmiKYMCjwN35OCmAueC5mgVlqFRUuNoCK7E/gjOHE3BlT0vuGOR7zuUeYxIGwKYoeHqVJlY/jkhTNkBDem8ofQMDbPVhrJasYThkrlnI61PkIdHRD2BV5BETgbEBOGPxy/AZ7eNA+C40FYyqXmpXqOGWuE3GwAQYM/isQnKXYxQ+o0FAAQa7BkaCg1oyI3hFXwMNGbg7so2kW7Ilbgr22DPclRSU3AzS041IZceU3yGBjdHNCYtXCqlVn6Z/T4rPEDZ5aYA15JTgqCMf/+2UnpWsI4L1m45Sk4pO6ChUUtNwZV9fDhXTZg0IBJxCs/cUQplnhWIWmDUaRARKF5YVym0nIwzaeHFwHJTAAA/pz4iSu2jYbLfWCh5EULK1FFqQMOq8PR/dwxcpGwTqW6+EhsBO3onKOcYkecKBj9lzNAQmRQ8FzRHaTttSxjQkDlfU5sVEtBqK6X3YnJkaPC4cHaaJacAKC+zrznSPblRp1Zs8FOJ+C9N5IZ0s8mdto4bbjYEF6nVKrl5fEvlhpZtP4kbFv/mkwveSm/OBziagvviv29bSDtrlXpz6Q7rX7eNkhcxldgMWt5Zy+CnzDlDQ8k7sZUc3GyOdF2llNIyUmnfCAY0XEqvsXSlK4vCN1LJC9Y8LlxIGRrxis/QkALhyj4+pHtyqYoCKYMyzwpErXBu2Kjkm03A0byUDcEd/O2NwWuaaQxuswl4ZMVe/HqkGJ/vyO3JofUIaQeIEkvGSOSSU/UWRc4R0u5Rpde8dsb6121jUnCZGaP9GFFSU3CrwnfWuiNtmrEJyt5xa2b5yiaYoaFcztfUvI5wZVb4Rio2BW+q3mxFYVUDACAhTNmNn91laOw7XYFXfzqMrcdKPDWsHucIaHATrpIo726SqA2cbzaVfvFQb2+QzobgDlKGxoPLd+P5VQebPJ+VXyX/2RePHhN358slpyw2Qd5lqiRK3y3njnNAw2YTUGdSzqJ1e5gVvCvbz37uUNKx4ah9rtzzRWPOJTyVXKLOka3F60uJ0krLlNaIC5IMaIgZ4NJ1NQMariwKL3MqfW6lr0k4K6wU5w6jTo0wf2XvyNe4CYTf99kuvL7uCK59bxsKKus9NbRuVWuyoKLOLH8tBzTsVRRIGZR5ViBqhevNprIvKivrxRNFsJ+yLxacBdgzNPbnVeI/v2SjtlEvjS3HiuU/O59ofYWj5JRyTyFSc3hAmWWnLMzQaEJaoG+w2vDQl3sw/KkfkZVf6eFReR8pIGpQ4PwR6icu2pXX+t55oTnSwhyDnw4GrRoq+9SppOBWY9JcoORsz8ak68uKOpOHR9IzymrEuZABDZF0HaGUkmNtZVF4Npf0uVlyyqHcPkeG++uhUinzuJA0ztCwWG3ILq6Rn994qMgj4+pOhVX1mLhwPcY885N8ryU1BQ80MKChJLyCJHLD+eaqQUHNO92RFuRDGNCQOS9mA0BZo8UpqUkZAJRUN/TImHqStHNQyYsQbe2l4quUfnPpjvT7sCOnFMt/PwWT1YYvdp7y8Ki8j5J7aIQGiOfROrNVMY3BpYU5Jf57N0elUsk33JX1yjt/SBzHBs8jkuSIAABwWYzyZSXM0HAhBX6VvpmuMUemnzLPI0rL3GqLyjrx3MkNl4DGfnzY7AGNMxX1LtkazpUjfMWy33JRWmOC2Spg0xFxI6mj5BQDGkqizLMCUSvUapW8OKX0i0oGNJryb1SbsazGdSedc1ZGsU8GNJRdy1YipbRWKXBBSunp/+5IC7bLtp+UHzt4hhkajTl27Ctv/ggyaOWddErJ0lByAKslwUbxmqqqXhnHgTvysaHQRUp3kqPEgMbJklpF7MZmhoYr9uJyz2LvxaRTaFawRt6Bz+NCIleQMHJ9onGGxqmyOpfnjxT6XkBj01FH1snBM+Lnq2FAQ5F4BUnUDEdjcGVfPDCg0VSAoXGGhmtAo9IloOF7ZQPMCm7q60w6DhatPeLhkfQ86aZK6UEtZ1LvJeeMraz8KkU2jW+JScE79lUqFUL9xYW70hrfOze4o+QSYy0JUnBAXNLAYFcTscFGGHVqWGwCchstSvkiqWxMmD8DGoAjuGdSQDCrPZTei0naALLxUBH++d0BRWaGNyatTwT7cfFao3LtoVFYJfbMkAId2UW+lfFnswnYd9qxYWzH8VIAjuupxus05Nt4BUnUDKmPhpIbNgLOFwwMaEgaL1A2XpiS0mAB3yw55WgIrcwbi8bWHixAdlG1p4fRo6RjQMmN4RtztyhXWmNCkQ/OAZ2h9EbAIX5SqSFl7MxnhoZ7jgwN5S5McXNEU2q1CsmRgQCgiOsKKVONm6ZEBvbQcMui9JJT9mzoL34/hcWbcvDP7w54eESeV8n1CZmmUYZGUZV43zE2JRwAkFdR51NlTnPLalFn/zwqFXCytBbXv/8b/rVO3GDIpuDKosyzAlEbSLttP92e6+GReFaFfXGeNxsOu06Wu3x9z6e7cP9nu7BwTRbMVlujklO+twvXzHJDAFx3vBRUKmvR2tEUXNnHgLMgpwvoMH8dkiPF0iGHfLB2bWcovRGwFMhRSkkRExet3XJkaCgjsOUOg13u9Y8WAxpHCn07oFFvtspZOqH+vMcA3Jecqjdb8cx3B7Bkc46nhuVxSi851XgD2a7ccs8MxIuw5JSDlLlktf+eSAGNtJhghPjpIAhAjg/1ZTpRUgsAGBgTiDH9wgAAm44Wy88H6hnQUBJeQRI1Q0rXW7rluCLq2DaHJaeaumR4XJPHVmSextsbjmF9ViFKnUpQVTdYfC7LxyzVwFf4IsSVoxPkP+dX+n5pCGfSnMgMDYc+wUb5z/FhfhgcFwwAWL0v31ND8kpKX8TUK2wHrtL/vZvDklM8NpqTFiMGNLJ8vAeTlJ2hUatY89zOoBMD3rUmx33Dij9O4/1NOXjq2wPILa311NA8SvFNwRttHuKcyQoSzuQMDatrhkZ0sAEp9r5MvlR2Kr9SXKPrE+KHOaMSmjzPDA1l4WxI1Azp4gkAahp8a0G6PRjQaOruaQOw6JoRuCkjSX5seGIoAGB/XqV8ISGp9rEFC6XvlJI8fGE6/PXizWd+hbIyNEz2+ZE3VQ59QhwBjYgAA/50diIA4Id9+bDZ2EdD4mgErMz5Q/rcisnQ4KK1W0FsCo56+2YPo0LLzzVnaHwIAGDniTKf7sEk9c8I9dNBpVLm+aCxiACpx5LjmnJrdon85wM+HuRqjryJRqHXDZpGn7vEB7P/20sq7xzMxWt5c5mjh4Y9oBFkQGqUlPHnO9niBRX2gEawAVeOTsCg2GCX59lDQ1l4d0HUBlUNyr3hrGRAo4kAgxazR8bjvgsGYsaQGLxz/ShMS48GAGy2pzz66TTws++08rWAmBTsU3qGRlSQATeM6wcAKFZYnwQuUjYV45ShERlowNjkCPjrNSipMSGLZadkZgU3BQecSoooJUND4SXGmiNlaFT62IaH9qg3i8eGUceAhrOzk8KhUatwqqwOZ+wLN75I7p/BclMyKaDhXK72ZIljZ/VhhV5LSGVOdQotc9p4A1lJjbLuOdyRSk5xfcJR/tfSqCl4VJAB6X2CAPhW+dsC++eLCTZCq1Hj679n4NWrh8vP85hQFmWeFYjaYPGNY+Q/Vzco94bTkdLJaHdjwUYd/nPDGFw4NFaul7/zRBkAICHMT0559LWAmJnlhmSRgQYADGiQa8mpyEA99Fo1xiaLDfm2HCtu7scUp0Hhx47US0IpGRpKD2A1J4hNweUmpUYdjw1nAQYtUqN8vweTFNAI5eKTLMLNNaVUXgUATii25JSyMzQal9qqN9tQa1LuuQNgU3Bn0v24TXAtORUVZECaDwY0SmvEgK90D67XqpHex5GlMapvmEfGRZ7BK0iiZkwbFCPvoPO1kkFtJQgCS061kVSjUhIf5ocge8qjr2VoSAuSBpaJQESguJtOaenfJqt4THPXtYNzyamEMD8AwGh7s7r9ecosE+GOY8e+MucP6XfGuaylL3OUGONc4YxNwZmh0ZI0++KML2f3VUglp/z1Hh6J94gKEhfoCivFBUmL1eZSxvZEie/UwW8PqTeATqHnEXefW2n3HY3JGy7ZFNylh4bz2k24v14OaBwvqZE3EfR20rEfFuA4d6T3CcLlI+Pxt3NT5HmUlEGZZwWiNuob7g8AqFJohkaNySrXY2RAo2UpkYFwLgEcH+on13Cs9rEMDe6qdGCGBo8BiXQsAEBcqBjQUMKiVHtJx45Oq8ydllKpPpPFN24sW8O5wj02BQcaLLyWaI6jTIjvBsOZodFUP/t9Z06xGLgoqTHBuQXX8RJlZmgovXShu3vwkhplBzSkco2sIOHaQ6PObJXnjECjFlGBBoT562ATgKOF1R4cZdeRMjQinAIaarUKr10zAo/MHOSpYZGHKPOsQNRGgQZlZ2hIEX6dRiX3gyD3/PQaOQAGiBka8vHjaxka3FUpkzI0ihW2U0raXa7Um0t3NGoVrj2nL85JCsfEAZEAIJcNOVFS49PNXdvDrPCFCYPCMjSUXmKsOdKu0kpFZ2iwKXhz0mLEgIYvB8PL69hDozEp2zunuAY2myBnakhBv6KqBkWWGpKCnwaFBj/D3PyOlCq8jwZ7fDrIGRo2m7xJQq0S+3mqVCqk2BuDS4HS3k4KaIQHMLuPGNAgapFcckqhGRrOFwsqlTJ307aHtKMOABLC/B0ZGj4UELPaBHmnFAMaQJR9V35pTYOczaQEXKR07/krzsLnt4+Xy7HFh/lBpQJqTVbF76aTmOSSdco8duQeGgppCi4FsJRaKqQ5zNBgyamWSGVCjhVVy79DvsaRocFFKUliuD+0ahXqzFYUVNXLzX37Rwci1L6ofUKBWRpKL10Y5qYsm5JLTtWbrfJ9CHtoOAc0BPmaItCgldduUux9PrOLen9Aw2YTUFbbNEODlEuZZwWiNmKGBhtutUeaU0Oq+FA/ecGixocCYg1OZVJYJsJRv9MmQL7AUgKWkWkbg1aDWHuz8JMKbebZmNKPHanUlsliw7Giasz/cg9OlfnusSEFenUKbebaHEdTcOVmaNSZlb3ruiUJ9ixfs1XwmV21jTl6aPAeQ6LTqOVs7+yiGkdz30AD+kU4Mj6VRu7dp9C5wvl3RNoMouRNMlJmo0oFBOpZckraFNBgtsmbcIOceotIGRrZxb2/5FR5nVkuqRXGgAaBAQ2iFgVKO+h8aEG6PdgQvH1G9Q0FIO6USIkMQIBBvMDwpeNH2lEJsEwEIN58SqngStotZeKu6zZLtC9O5DKgAYDHjtQM3WS1Yfabm/Hpjlw8t+qgh0fVfaTSWhq1Mv+9m+OcoaHUcnSOfly8lmhMpVLJJQuzi3r/IpQ7coYGAxoupMXHo4XVKLQHNKKDjHJ/DSVmaMhZwRplzhXJ9h32AJDRXyxpWqrkgEadfdHeoIVazc0Sjp6dFnkTrnSNATiOH1/I0JCO+yCjVrH3EeSKRwFRCwIN4kW2UjM0yuwnDXeprtTUuQOj8MHcMVhxxwSEBejl48eXMjSkBQi9Rs2LSDslNgY3K3yXfXtIAY2TClyEcIcZGo4MDak0wKq9+S7Zb75EytDQ8nzhQlpssNgEl40CSsIeGi2TFraP+cAilDuOgAbvMZyl9RH/3bPyq+QMjehgA5IixGsJJTYGb1B4qcoBMUH45Nax+PS2cTg7KRyAsjZRNcYKEq6kiiI1JoucvSI9Bogl6wAxSGrr5eWR3TUEJ2VT5lmBqI0cPTSUWRJAWqCNDORJoy1UKhXOS4/B8MRQAE7Hjw8FxOpZIqIJR2Nw5QQ0pF32Bu6OaZVUPiLXh8sKtYeJTcEBACWN5otPt+d6YjjdzmIT/721LDnlIkCvhdSaTIllpyxWm7wwwWtM93yp7rk75fYynaFclHQxJC4EALDuYIFcqjIqSNklp5S+EQIAJqRGYlxKhLyQW6LgpuBlbArtQgpeCAJQUCn23Ql0ytBIivCHXqtGndna68vfSvfaEfbNhETKPSsQtUGgQdlNwYvtuz940uiYAL2467Da5DvHD5t4NuXI0FDObineXLadFNDo7TcRXUXpx46UIp9XXu/y+Kq9ZzwxnG5nkTM0lPnv3Ry1WiVfY1b60KaHtiquNsEmiCU6eY3pXrJUcqpR3fN6sxVvbziGrPxKTwyry5TXseSUO9MGRSM+1A+FVQ3YeLgIABAdZEC/CCWXnLJvplLodYOzmBCxL9vpsjoPj8RzShnQcGHUqeXG4Pn2gIZzDw2tRo0B0Y7Mr95MOu7jQv08PBLyFjwrELVAutmsUuDNJuCcocGbzY4INPpeybI6ueY1Tx8S6fdD2nFdZ7LicEFVr0/rbYm0KM36pa2TSk7tOF4Gi1WZpWWcyQENhR47fvZAt9QIXNql/8fJMtT6UPBbYrGy5FRzpIVcqTmykkiLLtFBBnkhhlylSiWnCqtd+qy8uf4oFq7JwtwPdvTac0qDxYpak3g9GerHRUlnBq0G910w0OWxQbHBcoZGXkWdz5YobI7SN0I4kzK3TpTUyiUdlaa0lgENZyqVSt5EmV9hz9AwuDZLT+sTBAA41MsDGtK1czwDGmTHswJRCwKNys7QkJrYxtp3g1D7BNqbgvtiDw0/ZmjIpN+Pk6W1sNkEXPPuVkx/7Re8tvawh0fWfeotDGy1Vf/oQOi1alhtAsY8u7bX76rtLOnYMSh0DpECGnn2m84RiaGID/WD2SrgF/tuXF/CklPNk/qTldWIO9Uras34YFOOXB/cl0mLLjHBvL5sTkpUADRqFSrrLXIACAC+2HkKgBgU2nO6wlPD6xTpGFepXJvXkujykfEIsi9IGnVq9IsIQGSgHgF6DQQByC1V1u58Rw8NZV43OIsL9YNeq4bJalNsloacocH+OzIpI+NMhZSh4TqvpksBjYLefQ9yulw85hPCGNAgEVciiFogXUz60g77trLZBBwuENPcpag+tY/cVN6HAhpStlLjnR9KNtBp18vvJ8uw55S4wPCfX7JRWFnf0o/2WtIxzeOgdSF+Osyb2h+A2AT1lqU7Fbe70pl0PlXqIpa/3nVBJsiow8XDYgEAy38/5YkhdStHhgZvORqTAxr23aaPfLUHT393AI+v3OfJYfUIqc43N8w0z6DVINm+G1sqE3K6vM4luLH5SLFHxtZZFfaG4CF+OqiZodOERq3CBzedjaHxwVh849kAxF3YUpZGTrGy+mgwQ8NBo1bJDeKPNSpHpxRSQ/Rw9l+Shdh7EUmbURvfn6X3CQbQ+0tOnbIH8eIZ0CA7nhWIWqDkDI3i6gbUma1Qq4Ak+wU0tU+APUPDl44fKdsk0Miax5KBMWJAI6e4BntPOXZLmiw2uf6xr5EWpQMVuijdXvOmDcDLVw0HIC5ILfvtpIdH5Dk1DWIwR6nBMD+d6+cONGhwbloUAOBooW8tTgiC4OihwQyNJsLsJafKak2w2QSs2psPAPhmdx7qTL4d9DzDDI02SW9UJmTLUdcAxqajvTOgIfXPCGFD8GadnRSO7+ZNQkb/SPmxATFiGbLDBb17UbK92EPDVUqkeBxkFykrsCUptTdEj2DJKVlkkFj++EwzJacGx4kBjeyiGrmkeG8kZ2iw5BTZ8axA1IJABWdolNVKzfr0rG/cQUE+GBCrMUk785n2LekTbIReo4bFJmBdVoHLc729Vmlzapih0W5Xjk7AM7OHAgDe35TjUhNdKRosVpjsNd8DFHrsBDSaOwMNWrlWfm5ZnbwT1Rc41/dmD42mwgPEBYjiapN8ky7ZcbzUE0PqMdIu0j7M0GhR44DG/jyxXMj5g2IA9N7eO/IOay5Itouv1MFvD5tNQL1ZPC/66XnvATgCW0cUFtiSOJqCs8enJLJRtkpEo68jAw0YHCsGNTb11sy+OrNcKYIZGiRhQIOoBVI9wmqTxacb/LojXSxIOwip/aQFu5oGi88sXrLkVFMatUqu5bn5aAkAYObQPgCAQz54s2GzCagxKXuXfUddMSoeeo0ap8rqcKKk1tPD6XFSdgag3GOnccmpQIMO0UEGBOg1sNoEnCz1nR2XFueAhkKbwLckOUrMfj1SUNWkhMz6Q4WeGFKPEAQBv+WI58qRiaGeHYyXS2tUJkTqwXTh0D5y750dx8s8Nr6OknYIR3BBsl3624Pf2QoqNVTjFLBT6nVDY1Jg6+CZ3t0PoaPYFLypyEDXudRd9qOUDdxbqwdIPWPCA/Tw13MuIBHvLohaIO2wFwSg1uzb6f+NlddKAQ1eLHSUdOFttoq7i3whKCbtzFfq7urmJIb7u3z957H9APT+WqXuON9c8jhoH3+9FkPixQWqvb20mWtnSNmO/nqNYjP/3JWcUqlUSLEvVB3zoRISFmZotGiQfVFq7+lKZBeJC5TS39NnO3JRVNV7y0K0pN5sQ7F9h75UBoPckzI0jhVWw2oT5J356X2CkNE/AgDw3e48j42vo6SARlQQ7zHaQzpP5BTV+MxGqdbU2jfQaNQqlpyyG54QCgDYl1eJCnv5NqUQBEHO8GLJKYc+jQIY7vpTnTtQDGj8crioV65JSJms8Sw3RU54ViBqgUGrht6+q1Ba4FcKafdDKAMaHRbgtHtg0BNrcM5za3GqrHfvypbKZwVxIdtFX6eARkKYH0b1C4VaBRRVNSCvUSmR3k46BnQa3lx2hLRAtU+JAQ0GRJtmaNg3TvSPFheqvvShxuAWq6N8FgMaTQ2ND4Feq0ZxdQOe/PYAAOCWSckYnhiKWpMVb64/6uERdg9pAU6jVnHHdSviQv2g16hhstqw+1Q5ymrNUKvE+eKasxMBACsyT+NkL8v4kxYkG+8qppb1DfeHRq1CjcmKQh8NeDYmXTf468XgP4mbqPpHB8JqE3pt+aCOKq0xodZkhUrFkoXO+jbaWOcuQ2N0vzAEGrQoqTFhX17vuAc5kFeJN9cfRVW9GcftmayJ4QxokANXIohaoFKpEGVvslRQWe/h0fSsAntTKenzU/up1SqXhf/iahNW7T3jwRF1XkkNA13uOF9InhUfAn+9FsPtpTRW78v30Ki6h7QQEeqv581lB4xLEXfVLv/9lEuPASWorBcXMoMU3Ew+tFEZR6kG9LiUcADAjwcK8OGW4z09rG7hnKGh1Iyclhh1Gky1l4CQDI4NxsMz0gAA//vthNxrwpdUODWE5jmkZRq1Sq4VLgU7B8YEwajTYHS/cIxPiYDVJuCngwUtvYzXkRrXRvMeo130WjUS7cfDsSJllJ1izzb3pHOHL5cndOek1H8p2Aijjj1VJEmRjvvQ6CCD278bnUYtZ/atPeD954yaBgv+9O5WvPTDITy/Ogv77UGYQX2Y2UkODGgQtUKK/udXiDthTBYbfssuwaYjxSip9t3dMdIFQ78I/1a+k1qyeO7ZLl//d+uJXp0mLgW6uCvG1VkJIfKfJw6IBABcMSoBAPD5jlyPjKm7SMHdxunN1DYXnRWLIPsOqf29ZIdUVymvFRcylVzKMMRP55LZJAVDLx4WJwd6FnyzHy//cKjL3/t4cU2PNhC2WMVznVat4sJ1M97+82iXRd0JqZGY0D8SGf0jYLYK+MKHMnYkzgENal2S/Tp8ZeZpAEBG/0j5uSn2Rc0tR3vXLm2pdEhCGO8x2kvK5juQp4z+CVLvLSVndrozNS0aALA+qxBmp2xIXyetTzQu9at0yZGB8p91LfQsu9De43FVL9hs9/3eM6i0l6r9bnce9tgz24fGh7T0Y6QwDGgQtUJatMuvrEe92Yrpr23ENe9uw/WLf8Plb21Bg8U3e2uckC4YeLPRKeckh+Od60fhQfuOy1NldVh3sPfupsm3L2a7S2VVslF9w5AcGYCUyADMHhEPQFy4BsTG4IVVvpPhxWOgc7QaNcbaszSkJvJKUVFnz+5R8EKmc+Yn4EidDzBosenh82DUiZfmb6w/ih/2d90N555T5TjvlQ2Yu2RHjwXVLTZxkYXZGc1Tq1V47ZoR0GlU+Mv4fvKxccmwOADAj/vze/UmCHekgEawgjO12uOcZPF8UWPvJTDRKaAhBTe2Zpegorb31NKXyq9K2SfUdmPtx8OWY8q4fmDvPvfOTg5HZKAeJTUmrM/qvfeV7SWV12tcYknpNGoV5k5IgloFPDwzvdnvmzYoBnqNGkcLq/HHybIeHGH7SUF8AKistyDb3mNuCHtvkRMGNIhaEW5vOFVRZ8ab64/iuFOd2pOltT55ESEIAo4WiKnM0k4g6rgLh8bizimp8s37X/+706W2eG9hswkorBSzkpih4UqvVeOn+yZj9b2T5Juu8AC9fNHlSzVuHVk6LBXRUZPsWTxf/qGsslNShkaIv3IDGoA4X0iinGrIh/jpsHvBdHkjxcI1WV12rvh2dx5sArA9pxT7TvfMzl4pQ6Ol3YIkLkrvemI6nrxkiPzY9CF94K/XICu/CrtP+VYmV5m9R1uIgjO12kMqEQKI2U7nJIfLXw+ODcbAmEDUmqz4Zk/vaA5eWW9GlX3XLZu7tt8E+/GwLbtEETvzqxrspSoZ0HCh06gdmeA7fSsTvCVShgYDGk09cfFgHH5mJi4dHtfs9wQbdbhshPj8mz97b5+u6gYLdhwvBQBcOTpBfrxfhD+iuaGOnPAOg6gVwX7iBVR2UTUWb8oBANyckYy5E5IAiPWufc3p8jpUNVig06iQEhXg6eH4BJVKhaU3nyN//dG2Ex4cTceU1ppgstqgUrHusTtajRoGrWvN0mnpYkr4+7/mwOYjC9f5LDnVabNHxiPET4ejhdX4ymkHkq8rt+/MDvVT9kKmc4NsbaPFfoNWg5/un4wwfx2yi2rw4PI9XfKeRwod9dY3H+uZAKvUQ4MZGq0LMGihdvp7Cg/Qy4HPnw54f2mI9iiUsvx4HdEmQ+JC5PJcI/uGuuxUV6tVcjZPb6iJDgCny8RyU2H+Ou6674BBfYIRbNSi1mTFkQLf76NRVmO/blD4Rgh3rh6TCAD4OasQh/KrPDyannHgjLghIzWKGy4bU6tVTa4p3bljSirUKmBdViF+P1HaAyNrv63HSmC2Cugb7o+nLxuC4Qkh0GvUeGTmIE8PjbwMAxpErQg2ihdQ3+05g1qTFcMTQvD4xYNwiT36/fWuPGT7WGM26aIoNSqQOyu70Ki+YRjTLwwA8OKarq+P3t3y7TvzIwIMPC7a6KaMZAQatDhwphLbj3vnRWN7Sc08WXKq40L8dLhjSioA4ONeGNzsqPJaqaG8shcmtOqW588gow5/n9ofAPBV5mn87aOdOF5cgzpTx0tcnnRqLr2/h2qvSyWndBoGNDri0uFi+cJl23N9JiAOOAXFmenZJhq1Su7NNWlAVJPnLxoWC7UK2Hi4yOtLiACOgAbLTXWMWq1Cur0p7qEC3++jIV03KLn3VnP6Rwfi/EExsAnAX/+7A5X1vafsXEdUN1iQZV+jGNk31LOD6cVSogIxx57d8+DyPag3e1/59G93ixmHU9Ki4K/XYsWdGdi14AK5BwiRhCtSRK0IMrouvDx0YTpUKhVG9wvDeenRsNoEvPLTYQ+NrntIFwtpfYI8PBLf89wVZwEA6sxWvPvLMQ+Ppn3kZtAsNdRmYQF6nGfP0vjZR8rTFXAxqktcMDgGgBhA9qXFypZIJaeUHtCIasPO9BsnJGFovLho9cP+Akx5eQMGPbEGr/x4CO/+cgxf7Gz7IrfFasOp0jr56z2nynvkmJNKTjFDo2OmD4lBgF6D0hoTMnPLPT2cLpNfIZauZFC87R6/aDAemZmOWyelNHkuJSpQLsnx4posr++5IvfPYLmpDpPuz7LO+P6u/FIpoBHAgIY7C+echcRwP+SW1uHlH3rfZrn22Hy0GFabgH4R/ojj/NEpj100GNFBBmQX1eD5VQe96rzx04ECfGMPaEhl1TRqFfz1zOijphjQIGqFVHIKABLC/OQGfADw4Iw0qFTA93vO4ExFnbsf75WkgIa0A4i6zsCYIIyy7yp5blUWqu3N7noDlhrqmIuGic3Bl2zOkUtt9GZSpg6Pg85JigiAUadGndkq14n1dXIPDQU3BQeAJy8dgv7RgXjxymHNfo9Oo8bKOzNw4ZA+CHJqnvzvn4/iuVVZeHD5HgxesAZz3t6CO/6XieJ6oKTGhJLqBtSbrbDaBJRUN6CkugEbDhXBZK+17qfT4ERJLVb0QKkzadefn07TyneSOzqNGjOGiLsR/+dDmVzH7FnNSREsadpWfUKM+Nu5qfDTu/9duuf8gdBr1NiWXYq1B71788TpcvF+KSGMNfA7Sg5oKKDMUJn9uiFM4RshmhMRaMALV4jXEh9tO9ErsrQ6asOhIgDA1LRoD4+k9wvx12HhHPG4+XDrCTz7vfcENZZsFku8z52QhBGJoZ4dDHk9BjSIWhHslKHReAFvUGwwhieEAgA2Hy3pyWF1q0P5YgpzOjM0usUb142S/9ybdtMUsNRQh0wfHINhCSEwWwVMfXmDV6b2tlWdyYpKezPPGGZodIpGrcLMoWKwa52PZO+0Ru6hofDSEcmRAVh7/7ly/evmaDVqvHPDaOx9cga+v3sirhmTiCtGxSMpQlwIrDfb8PuJMqzNKsI/M7UY98IGjH5mLdIfX4PUR1dh9DNrMfqZtfjrf3cCAC46Kxa3nyuWOnvgi91Ytv1kt37OKnvAnnXyO07aef/LkWKYLL2/AXCtyYLjJTUAmAXcleJD/XDD+H4AgFv/uxPfeXGDcCmgwQyNjhscJ244291D2XaedMZ+vEQH8ZqzORn9I3HFqHgIAjD/yz2dKk/prcprTXIZIinznTpnano0HrtI7Enx/qYcryg/VVpjwm854iavmzOSPToW6h0Y0CBqRbDTTtLo4KZlIibaMzY2HSnqsTF1J5PFhuwi3mx2p7hQP1x0lriQuXTLcdSaekeWBjM0OkalUuGJiwcDAGpMVny9q/c2gZaOAX+9BkFcpOy0YQkhAICTJbWtfKdvqJB6aCg8Q6MjhsSFYOGVw/Dq1SOw/oEp+OTWsXjn+tG4KSOpTT0qQv11uOu8/rhlUjL0WvHy/5EVe/HZju4LatTYAxqBnCs6bHRSGCID9SiubsBnO3M9PZxO23m8DIIAxIUY21R6jdpO6rsDAHd9kombl+7AiZIaWKzeFQg7WmjP0IlkhkZHnRUfggC9BuW1ZrlJsq/KKRbvSXm8tOyxiwYjMlCPwwXVeGTFHq/Zbd9V/rXuCKobLBgUGyyvvVDn/XVSCp67/CyoVcDy30/hyne2ILfUc/ckaw8UwGoTMDg2GH0j+DtPrWNAg6gVwU6lHqICm958TR4oNuj7YX8BympMPTau7nKsqBoWm4Agoxax3IHdbZ66bIj850dX7PXgSNouv9Je95rHRbuNSQrHPdMGAOjdvTScy02pVKyL31l9w8WL9RMevHnoKRarDcXV4jkynLWwO0WlUmFCaiQuHNoHCy4Zgv0LzseicRYcfvoC/PrQVFw6PA4XDumDT24di5znZyHn+VnIfPwCDIoNRqBBi22PTJODGg9/uRev/tg9mYLV9mwu55JZ1D4GrQbzzhPPHf9ed6RX77zde6oCf/lgOwD3za2pc8ID9Nj2yDRIp+afswpx7ksbMPCx1bjjf5nYVaJCcXWDR8foummKZW07SqdRY1xKBACxGbyvOlVWi7JaM1QqlqhrTXiAHq//aSQ0ahVW7srDu79ke3pIXWbPqXJ8uOU4AGD+zHSo2ZerS103ti8+umUswgP02He6ErPf3IwjBZ4pZ7d63xkAwEw2/6Y2YkCDqBXOGRqRbgIaZyeFYWBMIOrMVvziA1kaUi33YQkhXLDsRpGBBlx7Tl8AwMpdeb2it4JUcoqBro6ZPkRsAv3D/oJuL/XSXdgQvGtJWXBHCqp6VT+djjheUgOT1QZ/vYalRrqYSqWCSiX+PzHcH69fOxLv3DAaE1Ij7c+pXM7n4QF6bH90mtys+/Wfj+KF1VldPq5qlpzqEtee0xcJYX4orGrAUvuiTm+y43gp7l6WiUve2CQ/duvkps2tqfP6hBix8//Ox93TBkBa87MJwNqsIiw5rMH4hRtx97JM3PNpJh5ZsReFVT177bkrtxwWm4CIAD3ieB3RKdI15cfbTvjcbnzJt7vFxc2xyeE8j7TBhP6R+L9ZYgmh51dnYY19cbg3s9kEPP71ftgEYPaIOJw7kMHw7pDRPxLfzZuIIXHBKKkx4er/bMXvJ3q2v19lvVku4X4hAxrURgxoELXCeWehu2Z8KpUKU+zNqTYfLe6xcXWXTUfEzzAhlemc3e3JSwfLf35/U44HR9I2UuN7lpzqmCFxIZg+WLwBfWTFXjy+ch8uev1XDHp8DQY9vgZjnvkJy7af9OqFbam0AY+BrpEQ5o++4f6w2ARsz/GdPkzu7M6tACAGcbi7zvNC/fXY/9QM+Xf5nY3H8MAXu7t0YayaJae6hF6rxv0XDAQALFyThTlvb5GDy96qoLIez3x3AMOf+hFXvbMV3+x29HRY+fcM9I8O9ODofFtEoAH3XzAQWf+ciczHL8Cfx/bFqL6h8vPf7M7D17vysGz7SZzz7Dq8sDoLDZaeyfz51L6ZY+KASG6a6qTLRsTDqFPjTEU9Dp7xvebghZX1+GjrcQDiZ6W2mTshCdeNFTfM3fVJJj7edqJX91n5evdp7M4tR6BBi0ft/R6oe8SF+uF/fx2L4QkhKKs147r3fsOP+/N77P3XZxXCZLUhJSqA1wjUZgxoELXCoHUEMQy6pgENQIxqA8D6Q0Ueb6bUGRarDVuzxUU11qfsfgatBrdOEhtebTnm3cGwqnozm0F3gRevHCbviv5o2wnsz6tEndmKOrMVxdUmPLJiL8568gf8Z+MxWL3sBsRmE+QUdh4DXSdD7sPk2wGNzfY5TiqTQZ5n1Gmw4cEpSAgTM2aW/34Kl76xucsyBqWSUwxodN5lI+Ix3N5z5/cTZRj73Do8+c1+VNSaPTyyppZszsHY59bh/U05qKgTx6dRq/DwhelY/8AUjEgM9ewAFUKvVSMsQI9nLz8Ln916Dh4eZsGjM9Pw2EWD5AVPQAxmnvPsOuw7XdGt4/l0+0msyBR7iN0ykc1eO8uo02CyvXTbx7+d8PBoutaZijpc8+425FXUo1+EPy4bEefpIfUaarUKT186BJcOj4PFJuCxlftw0b83YePhol6XyVPdYMHC1WJJzL9P7c/G8D0g1F+PZbeNw3np0Wiw2HD7x79j4ZosmCzd34tpzT4xeDJzaB8GvKnNGNAgaochce7rvY5LCUefYCOKqhp6bSkZANiXV4mqeguCjVoMjQ/x9HAUQSq7sO90ZY/ugmivncfLAAAJYX4INrKhb0dJu6KnpUdjUGwwhsQF48EZafjfX8diaLw4vwiCmCo+6PE1XtXsMSvfsQOQN5ddJ6O/uMDvCxl+zbHZBPnzMVjuXYw6DX55cKrcoH7v6Qqc89w6/GvtEZg72Uw4z57VF8OMrk7TqFX48o4JmHeeo/Hz0i3HMfzpH3Hbf3fiuL1xbkt+OVyEc19aj39+d6BbxpiVX4lxz63DU986Xn/ywCisvX8yDj59Ie6YkorkSNbB95S4AOCmCf3kJrA7/u98ObhUUWfGxf/ehPs/29XlWaJHC6twzX+2Yr69X9yss/pgWEJol76HUkmBqQ1ZhW1arK43W/H9njOY+vIGTH5xPT7bcdKju/frzVbszi3HzuOl2JZdgs1Hi/FV5ilc9c5W5BTXID7UDx/fMhb+egbF20OrUWPRNSPw6Kx0BBm1OHimEjd+sB2TX1qPW/+7E6/8eAjf7s7DofwqlFQ3eFWgQxAENFisqKgz48lv9iO/sh59w/1xU0aSp4emGP56Ld69YTSuPacvbALw9oZjuPKdLW26zuioWpMFGw6JpdsvHBLbbe9DvodnB6I2+PauiTheUoNRfcPcPm/QanDHlFQs+GY/vt2dh5syeufOI2nBaXxqhLyLnLpXdJARUUEGFFU14POduZg+xDtrRnIxsusYdRosnnt2k8e/mzcJ27JL8Oz3B7H3dAVMVhsue2sbrklRYZYHxtnYy/bGwVPSopDOZp5dRirvd6igCoVV9T65A+2ngwUoqGxAkFGL0f3cn0fJc9RqFVbemYGXfjyEtzccAwC8tvYwlm7JwWMXDcYVo+I7tFvupL3ZfWK4f5eOV6m0GjX+MT0Nl4+Mx2trj+BbexmnHw8U4McDBZg8MAoPzUhzuyHlREmN3JB78aYczDortkt/F9//NRvPfH9Q/jopwh9LbzoHSQxgeK2oIANW/j0D3+7Ow7xlmQCAFZmnsSLzNP52bgqCjTrcOCGpwxlWO46X4rlVB5F5slx+LMRPh2dmn9UVwycA5ySHQ69RI6+iHsdLalsMGAqCgL9+uBObnDZPPPzlXvxnYzauOTsRN05IgrGZSgRdzWYT8Mn2k/jndwfQ0MzO777h/lh22zj23OogtVqF2yan4qrRiXhz/VH8d+sJ5JbWIbe0Dj8dKHD53gC9Bv0iAhDqr4VQpcaJjdlICA9AZKABUUEGhPjpYBMECIK46comCBAAp8cE2ARAgICqegvqTFbUm60orzOjwWKDxWpDnVkMUpgsNtQ0WFBVb4HJYkO9xYr8inrUNFjlQEbjGNszs4f22LFJIq1GjeevOAvnDozEw1/uxZ5TFZix6BfcNjkFd53X36WCSVdY/vsp1Jmt6BfhL2/wI2oLBjSI2uCshBCcldByxsK0QdFY8M1+/HGyHB9tO4EbxvXrodF1Hal/Bhete9Y714/GnLe3YO3BQlTVmxHkhRkQ0g1QBo+NbjUuJQLf3JWBHw8U4G8f/Q4A+Cxbg+2LNuGt60cjvU9Qj6bhltaYUFFnRnW9BT9nFQLg/NDVwgP0GBIXjP15ldh6rMQna0Wvtx87V41O5E2pl1LbSwLdMK4fbl66A1n5VSirNeMfX+zGc6sO4p7zB+DK0Qlt3ilrsdqQUyTu5usXwYBGV0qJCsS/rx2JR2el419rj+DTHbkAxAyMXw4XoX90IAbFBuO+8wcgMsiAIIMW93++2+U15ry9BZ/cOhbnJIVDq2l/wn5xdQOq7CXFvtiZi7fsgTAA+OfsofjzOX3ZK6eXuGR4HKakReGRFXvx3R6xifB/NorlJV/64RAGRAdiYEwQ/jF9IIw6DWJDjG6vQ0wWG06X1+FMeR2eW30Q+067ZpjeOikZD8xI6/KFMCXz12sxql8otmWXYtPR4hYDGhsOF8nX8sFGrVxGNru4Bs+vzsLzq7Pw1p9H4fxBMdBru7aIR73ZijMV9bAJAp5fdRAbDxfBbHWsWseH+sGoU0OrVkOvVePcgVG4eWIywgP0XToOJQoL0OOxiwdj3nkDsD+vAocKqnAovwpZ+VU4VlSNqnoLakxWp4xwNbauPerRMUtC/HR46tIhmMxG4B5z4dBYDEsIxT8+342t2SX4989H8f6vObhxQhJmj4zDwOjO98UzWWzyOefmjGSWm6J2YUCDqIskhPljSloUNhwqwsLVWbjorNhedSFWUWvG7yfEskITuGDZo0YmhiLUX4fyWjPOevJHPDorHTeMS3LbhN4TTpfXyeWGJqSy/n13U6lUmDGkD9bePxnXv/8b8isbkFNSi5n/+hWpUQH4YO7Z6BfRtbteTRYblv9+ClX1jnrsB89UYuWuvCbf++exvS9Y6+0y+kdif14lNh0p9rmAhiAI+NUeLJ80kOcWbxcX6oc1907G93vO4J/fHUB+ZT1Kakx44uv9eOLr/Zh3Xn/MHBqLwc2U4ATEYMbjX+9DjcmKED8dUqPY3LE7xIb44YU5w/DkpUPw1vqjeP1ncRHqaGE1jhZWyxkcFw+Lla/vnrv8LDz6lVj657r3fkOov7hgdOnwuFYXESrqzPjqj1PYcbwM3+890+T5IIMWmx85j2Upe6Egow5vXDcKN08sw3e7z+BoUTV+OSyW/zhSWI0jhdXyv/mIxFDMHNoH/gYtrhqdgB3HS7HvdCUWrT3sdrf9Qxem4bZJKR0KnFHrMlIjsS27FD8fLGh2M53NJuClNWKW7W2TU/DorEEQBAEf/3YSX2eexk77/HDn//6ATqPCvecPhLaLApImiw2vrj0Md1WNUqIC8PjFgzE1LbpL3ouaF+Kvw4T+kU3u8U0WG06W1iK3tBaFlbX4efteBEYn4ExFA0prTMivrEdtgxUqFaBSAWqVCmqVCirYv1aLf1arVFCpgACDFgF6LfRaNUL8dPDTaaDTqqHXqBHqr4NRp4afToNgPx0MWjUMWg2igwzy10FGHfwNGug1ahi0ai5ue4G4UD98cutYrN6Xjye+3ofiahPe2XgM72w8hhA/HaakReG89GikRgUiMtCAmGBDm//d6s1WPPP9AZwur0NkoAHXnJ3YzZ+GfI1K8KaieSSrrKxESEgIKioqEBzMtKveos5kxaQX16O4ugGRgQas+8e5CPFr/sbObDZj1apVmDVrFnQ6z94AvrA6C+9sPIb0PkFYfc8kXkD0sDfXH8VLPxxyeWx0vzD46zV4+MJ0xIYYEWDQemR3872fZmLlrjyMT4nAstvG9fj7K1ldfQMe+uAH/Jinc1koeHBGGu44N7VDu2LqzVbU2Gtkv/trNnYeL0PmybImKd7Ogoxa6DRqPDIzHVeN4cVmV9t4uAg3frAdcSFGbJ5/XqfmX0EQUFZrbrUmsr9e2yNB099PlGHO21ug06iw64npCGCD6C7XXdcSVpuA33JK8NhX+5DdqHbyiMRQaNQqxIYY8dhFg1FntuKZ7w6gvM6MPafK5d23156TiOevGNZlY6LmldeacPBMFT7fmYv1hwpR3qhh+O3npmL+zHRsPFyE+z/bhZIak/xcgF6DJy4ZjMtHJsi7s202AWW1Jvx4oABf/XEa24+XNnnPIKP4+5waFYhlt47zmo0Y5Kojc8TRwmoUVTVg+e+nsC6roMnx1Jwgg3humTdtAGaPiPPKrGNfcrSwChe89gsEAfjmrgy3/Uk+35mLh5bvQZBBi18emoqwRhvufj1ShIeX70FeRX23jlW6lpw5tA+uHpOIYQkhvN/0It60LkHeyWSx4eesAizbnovtOaWoM1ubfE/fcH+clx6NUH8dgo1iuTKbIMBqg/3/AixWGw4VVOG3nFL53PKfG0ZjhpeW3iaH9swTPbGmzYCGl2JAo/f69UgRbli8Xf76h3snI61PkNvv9ZYLB+lCFwDe/8sYnD84xmNjUbLdueV49vuDbhcNJDdnJGNAjPvdrpGBBpw/KLrLbg4EQcDfP/kDq/aKzcq/vGM8RvcL75LXprZxniM++u0UnnZq5hpo0OLjv46Vm3q25LfsEmQX1+BUWS3eXH+s2e8bHBuMQbGOc06QUYv7zh+IEH/e2HSnOpMVo/75E+rMVrz3lzG4oINz8JGCKtyweDvyK9u2KHFTRhJum5yC2JDuqVEtCAKufW8btmWX4qrRCXjpquHd8j5K1xPXEgfPVGLp5uP4bGdum39mVN9QvH/j2b0qW9WX5JbW4u2Nx9BgtiHMX4f7LhjoElBcvfcMHv1qL8qcFqpVKuDeaQMRGaTHyz8ccnlOktE/Aul9gnF/o9cj79UVc4TNJuDtjceQXVSDY0XV2JVbLj+n16hx6Yg4XDo8juVhPOD+z3ZhReZpnJcejQ8a9Wj75LeTclbW/RcMxN3TBjT7OluOFuOrzNMtbnDpCI0auHpMIsYk8R7Cm3nLugT1DmarDbtzy7FmXz525ZYju7gGZbUmt9lYLYkP9cM/pg/EFaMSumeg1KUY0KA2YUCjd/vX2iN4be1h+evmggTecOHwyIo9WLZdXKAY3S8My28fz90yHrY7txz5lfU4WliNN34+6nb3Q3PiQoxI6xOEuFA/PDJrEPQaNXQaVbv/TaXmgevste8vGByD9/4ypl2vQZ3XeI44XV6H2z/6HXtPV8jfc05SOPrHBOLRWYMQoNfI/9ZWm4Alm3OwctfpJrWsJSoVMD4lAjdOSEJkoJ4BKw96cU0W3tpwDANjArH6nsnQtCP7RhAEvPLjYbyxvmN1jxfOOQtXjEqArotLgvzzuwNYvCkHeo0a6x+cwuae3aQnryVKqhvw+4kyCADWHSzAysw8mKxi9pheq8ZNGUkY1TcMYf56nJPM+cTbWaw2/LC/AM9+f6DF3dnxoX547KJB6BcR0GK5MfJO3TFH7MotR0FlPXQaFcanRDI7x4OOFlbh/Fd/gU6jwu4F0+VeR9lF1TjvlY0AxPn5j8cv6HCTd/J93rAuQb1brcmCH/bn4+CZKlTbm7+rVYBGpYJarbL/XyxRlhjuj9H9wjAyMZQlCXsRBjSoTRjQ6P1yimsw9eUN8tdD4oJxx5RUl++xWqzIzMzEyJEjoWlDk7zoIGOXLRDYbAJu/nAHNhwSa+RGBOjx6W3jMCDGfTYJeVZhVT1eWJWFKnupoMa255Sios59OYAgoxaPzhqEIKMWGpUK41MjEOrf/I7Z/Ip6XL/4NxwtrAYg7sb8+JaxDHR5QHMXDeuzCnHT0h1Nvj88QI/HLhqEA3mVeH9TTpPnLxgcAz+dBg/OSENiOBv1epOKWjMmvfgzKusteOWq4Zgzum07lX45XIS5S7a77Kj8159GtNiLo6S6AU99ewDf7HbtkTJpQCT+dHZfjO4Xhj4hxg59DkAMsNz1SaZcc/2Wicl4/OLBHX49ahkXIagrHC2swqK1R+TyhioAc0YnsASED+Ac4dsEQcDkl9Yjt7QOM4bE4NnLz8K6gwV4+EsxM8NPp8Hm+ecxW45axHmCiFrDgAa1CQMavuGPk2W44f3fUGNq+w771qT3CcLALgg6bMsuQWFVAwBgWEIIvrxjQpfvzqWeY7UJ+OlAPqrqLdhwqMhtw05nlw6PQ1SQAQ/OSINRp8GvR4rw5e+nYLYJ+H6P42enpEXhgxvP7lCvBuq8li4aymtNWH+oED9nFcnNX91J7xOEO6ak4oLBMfKuPfJOb284hoVrspAQ5od1/zgXhhYC3XUmK+7+NBM/HSiQHzsnKRxvXDcS0cFtC0ZkF1XjxiXbkVta1+S5G8f3w0MXpre7pEzj7K7UqACsumdSi5+FOoeLEETUEs4Rvu/jbSfw2Mp9bp/78b7JXXLvSL6N8wQRtcbbAhpc2SDqRqP6hmHvkzPwn1+yseFQYZPnBUFAaWkpwsPDW939vvNEGaw2AVn5VcjKr+qyMU5Lj8biRvVWqffRqFW4cGgsAOCqMYl42WSFyWrDHyfK8MHmHJgsNjRYbHLNY2ln9uJNOTBo1S4NpyUPTB+Iu85rvtYueVaovx6Xj0zA5SMT8OKcYViXVYBl20/CYm/GG+KnwxOXDEZCGDMxeou5E5KwZHMOTpXV4bnvD+Kpy4Y2+R6bTcA/vtiNrzJPy4+pVcCHN5+DSQPaV7s8JSoQvzw4FVUNFiz66Qj2na6Qe/h8uPUEPtx6AkadGkFGHR6+MB3RQYYWX6+kpgEPfrEHFnu6yNjkcHx62zhmdxEREXWja85OxLbsEnzntClpSFwwnr5sKIMZRETkkxjQIOpmarUKd0xJbVJuCnCOcJ7daoTTZLHhq8xTqG7oumyP6CADLh4W22WvR97DT6+BHzSYmh6NqenR8uN7TpVjx/EybM8pwQ/7xZ3dUjDDoFXjH9MHQqtW49y0KKRGuW8+Tt7HT6/BxcPicPGwOE8PhTrBT6/B3dMG4LGV+/Dh1hPYc7oCY/qF4dbJKYgOMiK3tBZzl2zHsaIa+WeuGBmPZy8/q8P1y1UqFYKNYvALAI4WVmPR2sPyoki92YZ6cwMe+GJ3u153aloUFt94NoMZRERE3UynUeON60bh+nEl2J9XibSYIEwcEOnpYREREXUbBjSIegm9Vo1rzu7r6WFQLzcsIRTDEkJxy8RklNaYUOPUkyM2xMimXEQeds3ZifhgUw6yi2uQebIcmSfL8d6vOYgOMshlAgGxmftbfx6FsC6uid0/OhBvXDcK/7zMhOoGCw6eqcRbG465zeJyx6BVY955/TFtUEyXjouIiIhaNi4lAuNSIjw9DCIiom7HgAYRkUKFB+jZIJDIy+g0aqy+dxI+3nYS+09XYIW9tJRzMOPBGWm4c0pqt2Y/hAXoERagR2K4P6azKTARERERERF5CQY0iIiIiLyIQavBLROTAQDzZ6WjyCmY0SfYiIjAlntZEBEREREREfkqBjSIiIiIvFR0kBHRQUZPD4OIiIiIiIjIK7BYOhEREREREREREREReT0GNIiIiIiIiIiIiIiIyOsxoEFERERERERERERERF6PAQ0iIiIiIiIiIiIiIvJ6DGgQEREREREREREREZHXY0CDiIiIiIiIiIiIiIi8HgMaRERERERERERERETk9RjQICIiIiIiIiIiIiIir8eABhEREREREREREREReT0GNIiIiIiIiIiIiIiIyOsxoEFERERERERERERERF6PAQ0iIiIiIiIiIiIiIvJ6DGgQEREREREREREREZHXY0CDiIiIiIiIiIiIiIi8HgMaRERERERERERERETk9RjQICIiIiIiIiIiIiIir8eABhEREREREREREREReT0GNIiIiIiIiIiIiIiIyOsxoEFERERERERERERERF6PAQ0iIiIiIiIiIiIiIvJ6DGgQEREREREREREREZHXY0CDiIiIiIiIiIiIiIi8HgMaRERERERERERERETk9RjQICIiIiIiIiIiIiIir8eABhEREREREREREREReT0GNIiIiIiIiIiIiIiIyOsxoEFERERERERERERERF6PAQ0iIiIiIiIiIiIiIvJ6DGgQEREREREREREREZHX03p6AOSeIAgAgMrKSg+PhLqT2WxGbW0tKisrodPpPD0cIvIynCOIqDWcJ4ioJZwjiKg1nCeIqDXtmSektWxpbbs7MKDhpaqqqgAAiYmJHh4JEREREREREREREVHbVFVVISQkpFteWyV0Z7iEOsxmsyEvLw9BQUFQqVSeHg51k8rKSiQmJiI3NxfBwcGeHg4ReRnOEUTUGs4TRNQSzhFE1BrOE0TUmvbME4IgoKqqCnFxcVCru6fbBTM0vJRarUZCQoKnh0E9JDg4mBcORNQszhFE1BrOE0TUEs4RRNQazhNE1Jq2zhPdlZkhYVNwIiIiIiIiIiIiIiLyegxoEBERERERERERERGR12NAg8iDDAYDFixYAIPB4OmhEJEX4hxBRK3hPEFELeEcQUSt4TxBRK3xtnmCTcGJiIiIiIiIiIiIiMjrMUODiIiIiIiIiIiIiIi8HgMaRERERERERERERETk9RjQICIiIiIiIiIiIiIir8eABlELSkpK8P777+Pyyy9H//794efnh5CQEEycOBGLFy+GzWZz+3NbtmzBrFmzEB4eDj8/PwwbNgyLFi2C1Wpt8r3l5eV46aWX8Oc//xmDBw+GVquFSqXC2rVrWxyb1WrFa6+9hmHDhsHPzw/h4eGYNWsWtmzZ0iWfnYjaxhvnCUEQsGbNGsybNw8jRoxAWFgYjEYj0tLScO+996KgoKBL/w6IqGXeOE+4U1xcjNjYWKhUKkycOLHDn5eI2sfb54j8/Hzcd999SEtLg5+fH8LCwjBq1CjMnz+/05+diNrGm+eJnJwc3H777UhPT4e/vz9iYmIwfvx4vPvuuzCZTF3y+YmodT0xT+zatQtPPvkkMjIyEBsbC71ej/j4eFx77bX4448/mh1bl69hCkTUrLffflsAIMTGxgrXXXedMH/+fOGmm24SQkJCBADCnDlzBJvN5vIzK1euFDQajRAQECDcfPPNwgMPPCCkpaUJAIQrr7yyyXtkZmYKAAQAQkJCghATEyMAEH766admx2Wz2YQrr7xSACCkpaUJDzzwgHDzzTcLAQEBgkajEVauXNnlfxdE5J43zhN1dXUCAEGv1wvnn3++8I9//EO49957hWHDhgkAhJiYGOHw4cPd8vdBRE154zzhzhVXXCEEBgYKAISMjIxOf24iahtvniM2bdokhIaGCnq9Xpg9e7bw8MMPC3fffbcwa9YsoV+/fl3510BELfDWeWL79u1CQECAoFarhVmzZgkPPfSQcPvttwvx8fECAGH69OlNxkVE3aMn5omxY8cKAITRo0cLd955p/DQQw8J06dPFwAIWq1W+PLLL5v8THesYTKgQdSCdevWCd98841gtVpdHj9z5oyQmJgoABCWL18uP15RUSFERUUJer1e2LFjh/x4XV2dMH78eAGAsGzZMpfXKi0tFdauXSuUlJQIgiAIN954Y6sXDZ988okAQJgwYYJQV1cnP759+3ZBr9cLUVFRQmVlZac+OxG1jTfOEyaTSXjmmWeE0tJSl8etVqvwt7/9TQAgXHzxxZ363ETUdt44TzT24YcfCgCEt956iwENoh7mrXPEmTNnhIiICKFfv37CoUOHmjxvMpk69HmJqP28dZ6YNWuWAEBYunSpy+PV1dXC4MGDBQDCxo0bO/y5iajtemKeeP3114UjR440ee+PP/5YACBEREQIDQ0NLs91xxomAxpEHfTss88KAIS77rpLfmzx4sUCAOEvf/lLk+9ft26dAECYPHlyi6/blouGSZMmCQCEn3/+uclzN9xwgwBA+OCDD9rxaYioO3hynmjO6dOnBQBCYGBgu3+WiLqeN8wTJ06cEEJCQoRbbrlFyMnJYUCDyIt4co6YN2+eAEBYvXp1xz8AEXU7T84T6enpAoAmG6kEwTGHOC+gEpFndNc84WzAgAECAGHnzp0uj3fHGiZ7aBB1kE6nAwBotVr5sZ9//hkAcOGFFzb5/smTJ8Pf3x9btmxBQ0NDh9+3vr4eW7Zsgb+/PyZNmtTk+ZkzZ7qMhYg8x1PzRHvHRESe4+l5QhAEzJ07FyEhIXj11Vc7/XpE1LU8OUcsW7YMYWFhmDFjBg4cOIB///vfWLhwIZYvX47q6upOvTYRdR1PzhNDhgwBAHz//fcuj9fW1uLnn3+Gv78/xo8f36n3IKLO64l5wt17dNcaJgMaRB1gsVjw3//+F4DrL/6hQ4cAAAMHDmzyM1qtFsnJybBYLMjOzu7wex87dgxWqxUpKSluFyQHDBgAADh8+HCH34OIOs+T80RLPvjggyZjIiLP8IZ5YtGiRdiwYQMWL16M4ODgTr8eEXUdT84ROTk5KC4uRv/+/XHfffdhyJAhuPvuuzF//nxcddVVSEpKwqpVqzr8+kTUNTx9LfHMM88gNjYWc+fOxaWXXor58+fjzjvvRHp6OkpLS7F8+XLExcV16j2IqHN6Yp7Ytm0bDhw4gPj4eAwdOlR+vLvWMBnQIOqA+fPnY9++fZg1axZmzJghP15RUQEACAkJcftz0uPl5eUdfu+eeA8i6jxPzhPN2bFjB5566ikEBQXhmWee6fLXJ6L28fQ8ceDAATz66KO4/fbbcf7553fqtYio63lyjigsLAQA/PHHH3j33XfxxhtvoLCwEHl5eXjxxRdRUVGBOXPm4ODBgx1+DyLqPE9fS6Snp2PHjh2YMGECvv32WyxcuBBvv/028vPzcf3112PcuHGden0i6rzunidKS0vxl7/8BQDw2muvQaPRdPl7NMaABlE7vf7663jllVeQnp6Ojz76yNPDISIv5I3zxOHDh3HJJZfAbDbj448/RmpqqqeHRKRonp4nzGYzbrjhBsTGxuLFF1/s8fcnopZ5eo6w2WwAAKvViieeeAJ///vfERUVhdjYWDz44IO4++67UV9fj0WLFvX42IhI5Ol5AgAyMzMxYcIE1NXV4ddff0VVVRVyc3Px9NNP49VXX8XYsWPlBU0i6nndPU/U1NTgsssuw5EjR/DQQw/hqquu6vL3cIcBDaJ2eOONN3DPPfdg8ODBWL9+PcLDw12elyKLzZ2wpcdDQ0M7PIaeeA8i6jhvmCcaO3z4MKZOnYrS0lJ8+umnuPTSS7vstYmo/bxhnnj++eeRmZmJJUuWIDAwsMOvQ0RdzxvmCOefvfzyy5s8Lz22ffv2Dr8HEXWcN8wTFosFV199NYqKivDtt99i4sSJCAwMREJCAubPn4958+bhyJEjeO211zr8HkTUcd09T9TU1OCiiy7Cpk2bcP/992PhwoVNvqe75iIGNIjaaNGiRZg3bx6GDh2K9evXo0+fPk2+Jy0tDYD72m8WiwU5OTnQarVISUnp8DhSU1Oh0WiQnZ0Ni8XS5PkjR44AcF8Dj4i6l7fME84OHjyIKVOmoLi4GF988QXmzJnTJa9LRB3jLfPEH3/8AUEQMGXKFKhUKvm/5ORkAMDmzZuhUqm4QYKoh3nLHJGamirXunY3D4SFhQEA6urqOvweRNQx3jJPZGVl4ejRoxg0aJDbMUydOhUA8Pvvv3f4PYioY7p7nqiqqsLMmTOxceNGPPTQQ3jllVfcjqO71jAZ0CBqg4ULF+K+++7DiBEjsH79ekRHR7v9vvPOOw8AsGbNmibP/fLLL6itrcWECRNgMBg6PBaj0YgJEyagtrYWv/76a5PnV69e7TIWIuoZ3jRPSPbu3YspU6agtLQUK1aswGWXXdbp1ySijvOmeeKCCy7ALbfc0uS/a665BgAQExODW265Ra6HS0Tdz5vmCL1ej0mTJgEA9u3b1+R56TEpCEpEPcOb5omGhgYAQHFxsdvni4qKAIjzCRH1nO6eJyoqKjB9+nT8+uuv+L//+z+3mRmSblvDFIioRU8//bQAQBg9erRQUlLS4vdWVFQIkZGRgl6vF3bs2CE/XldXJ4wfP14AICxbtqzF17jxxhsFAMJPP/3U7Pd88sknAgBhwoQJQl1dnfz49u3bBb1eL0RFRQkVFRVt/IRE1FneOE9kZmYKERERgp+fn7BmzZr2fSAi6nLeOE+4k5OTIwAQMjIy2vVzRNQ53jhHrFixQr7nqK6ulh8vKysThgwZIgAQPvzwwzZ+QiLqLG+bJ+rr64XQ0FABgPDee++5PFdWViakp6cLAIQ333yzjZ+QiDqru+eJ0tJSYcyYMQIA4amnnmrTmLpjDVMlCILQvhAIkXJ8+OGHmDt3LjQaDebNmyfXfnOWlJSEuXPnyl+vXLkSV155JYxGI/70pz8hPDwc33zzDQ4dOoQrr7wSn3/+OVQqlctrPPDAA/Kuhk2bNuHYsWOYPn06YmNjAQCzZ8/G7Nmz5e8XBAFXX301li9fjvT0dFxyySUoKSnBZ599hvr6enz55ZfciU3UQ7xxnigrK0P//v1RWlqKadOmYeLEiW7Hfu+997KcDFEP8MZ5ojnHjx9HcnIyMjIysGnTps59cCJqE2+eI26++WYsWbIEycnJmDlzJqxWK7777jucPn0ac+bMweeffw61moUfiLqbt84TH374IW666SYIgoBp06Zh5MiRKCsrwzfffIOioiKMGzcOGzZs6JLscyJqWU/ME1OnTsWGDRuQmpqK66+/3u04Zs+ejREjRshfd8saZrvCH0QKs2DBAgFAi/+de+65TX5u06ZNwsyZM4XQ0FDBaDQKQ4cOFV599VXBYrG4fZ9+/fq1+B4LFixo8jNms1l49dVXhaFDhwpGo1EIDQ0VZs6cKWzevLmL/xaIqCXeOE9IO6xb+y8nJ6d7/lKIyIU3zhPNYYYGUc/z5jnCZrMJ7733njBmzBjB399f8PPzE0aPHi288cYbgtVq7eK/CSJqjjfPExs3bhQuv/xyoU+fPoJWqxUCAgKEUaNGCc8//7zLbmwi6l49MU+0NkcAEJYsWdLk57p6DZMZGkRERERERERERERE5PWYG0pERERERERERERERF6PAQ0iIiIiIiIiIiIiIvJ6DGgQEREREREREREREZHXY0CDiIiIiIiIiIiIiIi8HgMaRERERERERERERETk9RjQICIiIiIiIiIiIiIir8eABhEREREREREREREReT0GNIiIiIiIiIiIiIiIyOsxoEFERERERERERERERF6PAQ0iIiIiIurVli5dCpVKhaVLl3p6KERERERE1I20nh4AERERERGRRKVStev7lyxZ0k0jISIiIiIib8OABhEREREReY0FCxY0eWzRokWoqKjAPffcg9DQUJfnRowYgeTkZIwbNw6xsbE9NEoiIiIiIvIElSAIgqcHQURERERE1JykpCScOHECOTk5SEpK8vRwiIiIiIjIQ9hDg4iIiIiIerXmemgkJSUhKSkJ1dXVuO+++5CYmAg/Pz+MGDECK1euBABYLBY8++yzGDBgAIxGI1JTU/HGG280+14//PADZs2ahcjISBgMBqSmpuLBBx9EeXl5931AIiIiIiICwJJTRERERETkw8xmMy644AKUlpbisssug8lkwrJlyzBnzhz8+OOPeOutt/Dbb79h5syZMBgM+OKLLzBv3jxERUXhmmuucXmtp556Ck8++STCw8Nx8cUXIzo6Gnv27MHLL7+MVatWYevWrQgODvbQJyUiIiIi8n0MaBARERERkc/Ky8vDqFGjsGHDBhgMBgDADTfcgMmTJ+Oqq65Camoq9u3bJ/fmuP/++5Geno4XXnjBJaCxfv16PPnkkxg/fjxWrVrl0stj6dKluOmmm7BgwQK89tprPfnxiIiIiIgUhSWniIiIiIjIpy1atEgOZgDApEmTkJycjLKyMixcuNAlOJGSkoKMjAzs27cPVqtVfvz1118HALz33ntNGpPPnTsXI0aMwP/+979u/RxERERERErHDA0iIiIiIvJZoaGhSE1NbfJ4XFwccnJyMHr06CbPxcfHw2KxID8/H/Hx8QCArVu3QqfT4YsvvsAXX3zR5GdMJhOKiopQUlKCiIiIrv8gRERERETEgAYREREREfmukJAQt49rtdpmn5eeM5vN8mMlJSWwWCx46qmnWny/6upqBjSIiIiIiLoJAxpEREREREStCAkJgc1mQ2lpqaeHQkRERESkWOyhQURERERE1Ipx48ahrKwM+/fv9/RQiIiIiIgUiwENIiIiIiKiVtx3330AgFtvvRV5eXlNnq+pqcG2bdt6elhERERERIrCklNEREREREStmDZtGl544QU88sgjGDBgAGbNmoXk5GRUV1fjxIkT2LhxIyZOnIg1a9Z4eqhERERERD6LAQ0iIiIiIqI2ePjhh5GRkYHXX38dmzZtwtdff42QkBDEx8fjtttuw3XXXefpIRIRERER+TSVIAiCpwdBRERERERERERERETUEvbQICIiIiIiIiIiIiIir8eABhEREREREREREREReT0GNIiIiIiIiIiIiIiIyOsxoEFERERERERERERERF6PAQ0iIiIiIiIiIiIiIvJ6DGgQEREREREREREREZHXY0CDiIiIiIiIiIiIiIi8HgMaRERERERERERERETk9RjQICIiIiIiIiIiIiIir8eABhEREREREREREREReT0GNIiIiIiIiIiIiIiIyOsxoEFERERERERERERERF6PAQ0iIiIiIiIiIiIiIvJ6/w8xJdYJ2Xl7fwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -142,7 +144,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -184,7 +186,7 @@
     },
     {
      "data": {
-      "image/png": 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oy8uDhf/bexmp7G354/VPyA81kirCOX+Fqk2hzrFQpvwBb1shKYEbezU7ur2XEoYa0i/MmjWL3/72tyQnJ3PBBRfwzDPPRLslSZIkSZIkSYUtayusmQM7NkKbc4NaKATv/wq2rs2/rkwlqHdcMPuiYZe973HcZUXXrwBDDWkfgwcPZvDgwdFuQ5IkSZIkSVJhCYdh8wpY+TmsnBG8fvgKwrlQoQa0PicINEIhaDcQdm3NX0qqeguIi4/2J9BPDDUkSZIkSZIkSSVLbjbEJ+af//cSWPT+vtcl14N6HSF7O5SpENT6/aFoetQRMdSQJEmSJEmSJMW2LWvzZ2Gs+gK+/xLu/hbKJgfj1ZoGm3nXORYadIYGJ0L9EyGlXnT7VoEZahRz4XA42i1IMcc/N5IkSZIkSaXAkokw999BkLF5xb7jq2dB07TguPudkHYvJJYr2h5V6Aw1iqn4+GCNtuzsbMqV8w+aVBDZ2dlA/p8jSZIkSZIkxbDtG2HVzCC86HBJMOsCYOMSmD/yp4tCUOsYqH9C/kyMqk3y71G+apG3raPDUKOYSkxMJCkpiYyMDCpVqkQoFIp2S1JMCIfDZGRkkJSURGJi4qHfIEmSJEmSpOIjLw82LP7Zht6fw/pF+eMp9fNDjSZp0Os3QYBRryOUTYlOzypShhrFWPXq1Vm9ejWrVq0iJSWFxMREww3pAMLhMNnZ2WRkZLB161bq1XM9REmSJEmSpGIvZxfk7MgPJJZOhFfP3/e6qk2DGRjVmuXXqjWFXkOLpk8VG4YaxVhycrCJzfr161m9enWUu5FiQ1JSEvXq1dvz50eSJEmSJEnFyM5MWPU5rJgOy6fB6pnQ+Xo49cFgvF5HSKwAdY+DBj8tJVX/BKhQPbp9q9gw1CjmkpOTSU5OJjs7m9zc3Gi3IxVr8fHxLjklSZIkSZJU3OzaDuMfhOWfwQ8LIJy39/gPX+Ufl6sMQ1dAvF9da//8b0aMSExM9MtaSZIkSZIkScVXOAwbvoMV0yB3F5wwJKgnloMv34AdG4PzyqmQ2hUangQNu0L15nvfx0BDB+F/OyRJkiRJkiRJBZebA2u/DEKM5Z8FS0ptXx+MVaoDna6GUCh49bkfyiZDwy6QXDe6fSumGWpIkiRJkiRJkg4tJwsSkvLPR5wNKz7b+5r4pGBfjNQukJsNCWWCeqcri65PlWiGGpIkSZIkSZKkfW3f+LNZGNPgh6/hV0ugTIVgvG4HWPcVNDgpCDEadgk2+P558CEVMkMNSZIkSZIkSVJg5Rew4C1I/zTY1Jvw3uNr5kKjbsFx2r3Q9w8QF1fUXaoUM9SQJEmSJEmSpNJo64+wfGqwYXel2kFt9UyY8ff8a6o1D0KMhj9t7F25Yf5YUsWi7VfCUEOSJEmSJEmSSoctP8DyTyF9ajATY/2ioH7O03D8oOC4aW84YQikdgtelWpFr19pPww1JEmSJEmSJKkkWzsfRl4JGxbvO1bzGIgvk39eoyWc+XjR9SYVkKGGJEmSJEmSJJUEGauCWRjLP4Xa7eHEa4J6cr2fAo0Q1G4Lqd3zl5SqUC2qLUsFZaghSZIkSZIkSbFo0/JgT4zdQcam9Pyx1O75oUb5qnD5GKhzLJSrEpVWpcJiqCFJkiRJkiRJsWBnJpRNDo7zcuHZHpCVkT8eioM6HYJZGE167f3eX55LMcpQQ5IkSZIkSZKKo60/QvonsGwyLJsSBBm3fxmMxcVD4x6w9YdgQ+9G3aFB5/zQQyqhDDUkSZIkSZIkqbhY9gksfC8IMdZ9tfdYKA4yv4fkOsH5ha9AXFzR9yhFkaGGJEmSJEmSJEXDrm2wYjo06gEJZYLa12Pgi3/kX1OrLTTuCY1PhtQuUDYlf8xAQ6WQoYYkSZIkSZIkFYWcXbDqi2AWxrIpwXFeNlz5AaR2Da5pdSaEc4MQo1EPqFA9uj1LxYyhhiRJkiRJkiQdTSu/gEl/hOXTIGfH3mPJ9WH7xvzzpmnBS9J+GWpIkiRJkiRJUmHIy4MfvwlmYdRuD426BfW4OFgyITiuUCOYhbH7VaUxhELR61mKMYYakiRJkiRJknSkNqXDkomwbHKwyff29UH9uEH5oUbtY+H0/4PGPaBGK0MMKQKGGpIkSZIkSZJUUFlb4NkesGnZ3vXE8tCwCzQ4Mb8WnwCdry3a/qQSylBDkiRJkiRJkg4kZxes+jyYjZGzE/r9IagnVYL4MhCXAPVPgCa9oHFPqNcREspEtWWpJDPUkCRJkiRJkqTdwmFY9w0snRgEGcunQvb2YCyhHPS5HxKSgvMLR0ByPSibHL1+pVLGUEOSJEmSJEmSdhs5GL5+e+9ahRrBTIwmaRDOy6/XbF2EjUkCQw1JkiRJkiRJpc2ubZA+NZiNsXQSXD4GKtYMxup2gG8/hNSuQYjRNA1qHgNxcdHsWNJPDDUkSZIkSZIklWx5ubBmzk9LSk2ClTMgLzt/fOkkaH9hcHzCEOh8AySWjUankg7BUEOSJEmSJElSyZOXC3HxwfGCt2DUNXuPV26YPxOjSa/8elKlImtRUsEZakiSJEmSJEmKfVlbIf1TWDIevvsYOl4J3W4NxhqfDGVToFGPn0KMNKjaBEKh6PYsqcAMNSRJkiRJkiTFnnAYfvgqP8RYMR1yd+WPL5mQH2pUqg2/WpY/c0NSzDLUkCRJkiRJkhQbcnZBQpmfjrPghVMgZ0f+eOWG0OxUaNYnmJ3xcwYaUolgqCFJkiRJkiSpeMrLhdWzg5kY330MOTvhhqnBWGJZaH5qUGt2CjTtA9WauqSUVMIZakiSJEmSJEkqPjK/z19SaslE2Ll57/Eta4PlpAAufNkQQyplDDUkSZIkSZIkRU9OFsQlQlxccD7hYZj7av542ZRgY+9mfYLZGLsDDTDQkEohQw1JkiRJkiRJRSccho1L4bufZmOkfwKD34N6xwfjzU+FHxcGIUazU6Du8RDv15iSAnHRbuBQNmzYwAsvvMB5551Hs2bNKFeuHCkpKXTv3p0XX3yRvLy8w7rPSy+9RCgUOugrPn7vzYLS09MPev3FF198ND6yJEmSJEmSVLLs2g6LPoT37oKnOsBfj4cP7oHFH0H29iDY2O2Y/nDNeEj7LTQ40UBD0l6K/f8ijBw5khtuuIE6deqQlpZGw4YN+eGHHxg1ahRDhgzhgw8+YOTIkYQOMdWsQ4cODBs2bL9jn3zyCRMmTOD000/f7/ixxx5L//7996m3bdu2wJ9HkiRJkiRJKhVydkFCmeB49Sx47aL8sbhEaHhSMBOjWR+o5fdskg5PsQ81WrRowTvvvMOZZ55JXFz+xJI//vGPnHjiibz11luMGjWKAQMGHPQ+HTp0oEOHDvsd69KlCwDXXnvtAd/7wAMPHFH/kiRJkiRJUqmQkwXLP4PFY4NX0z5wxp+CsYYnQa120LBzEGQ06gFJFaPbr6SYVOxDjd69e++3Xrt2ba6//nruvfdeJk2adMhQ40Dmz5/P9OnTqVevHmeeeWYkrUqSJEmSJEmlS8Zq+G4cfDsWlk6C7G0/G/zZyirxiXDDp0XdnaQSqNiHGgeTmJgIQELCkX+M559/HoCrr756nz01dluzZg3PPfccGzZsoFq1anTp0oX27dsf8TMlSZIkSZKkmBQOw+5l4MNh+OdpkLEif7xirWCj7+Z9oUladHqUVKLFbKiRk5PDyy+/DMBpp512RPfYsWMHr776KvHx8QwZMuSA140bN45x48btVevVqxcjRoygYcOGB31GVlYWWVlZe84zMzOPqFdJkiRJkiQpKrath+/GB5t6r54NN88MNu8OhaBFP/h+LjTvF4QZtdvDz5aQl6TCFrOhxtChQ1mwYAFnnHEG/fr1O6J7vPHGG2zevJkzzzyTBg0a7DNevnx57rvvPvr370+TJk0A+PLLL3nggQeYOHEiffr0Ye7cuVSoUOGAz3jkkUd48MEHj6g/SZIkSZIkqcjl5cHaebB4HHz7UbDJN+H88VWfQ2rX4Pj0PxliSCpSoXA4HD70ZcXLU089xW233UarVq2YOnUqVatWPaL7dOvWjc8++4x33nmHs88++7Dfl5OTQ/fu3ZkxYwbDhw/ntttuO+C1+5up0aBBAzIyMkhOTj6iviVJkiRJkqSj5pPHYfzv967VbhcsKdW8L9TrFMzUkKQjlJmZSUpKyhF9Tx5z/+vz9NNPc9ttt9GmTRvGjx9/xIHGV199xWeffUb9+vU544wzCvTehIQEhgwZwowZM5gyZcpBQ42kpCSSkpKOqEdJkiRJkiTpqAiHYf1i+PaDYEbGSTdCq5++I2vcC8o8AU16/RRknArJdaPYrCTli6lQY/jw4dxxxx20bduW8ePHU7NmzSO+1+FsEH4wNWrUAGDbtm1H3IMkSZIkSZJUZHKzYcU0WPRhEGZsXJo/VrVxfqhR9zj41TJIKBOdPiXpIGIm1HjssccYOnQoHTp0YNy4cVSvXv2I77Vz505eeeUV4uPjufrqq4/oHtOnTwfYs9eGJEmSJEmSVGxtWw9/PR52ZuTX4hKhcQ9ocVqw4feeehzEGWhIKp5iItR46KGHuP/+++nYsSNjx4496JJT2dnZLFmyhMTERJo2bbrfa0aOHMmmTZs466yz9rtB+G6zZ8+mQ4cOxP1is6Px48fz5JNPAnDZZZcdwSeSJEmSJEmSjpINS2DRB5CVCWm/DWoVqkPFWhCXAM37QcvToGlvSKoU3V4lqYCKfagxYsQI7r//fuLj4+nRowdPPfXUPtc0atSIwYMHA7B69Wpat25Namoq6enp+73n7qWnrr322oM++84772Tx4sV07dqV+vXrA/Dll18yYcIEIAhbunbteoSfTJIkSZIkSSoEuTmwckawpNSiD2HD4qCeWB663wmJZYPzy0YFe2PEFXwpdkkqLop9qLFs2TIAcnNzGT58+H6v6dmz555Q41C++eYbPv3008PaIHzQoEGMHj2aL774gg8++IDs7Gxq1arFhRdeyM0330yPHj0K8lEkSZIkSZKkwjXlzzDtadixKb8WlwCp3aDl6ZCXk1+vfOAVSyQpVoTC4XA42k2UJpmZmaSkpJCRkUFycnK025EkSZIkSVKs2JQezMRofyGU/2l59k+fhI8fgLKVg30xWpwGzfpA2ZQoNipJBxfJ9+TFfqaGJEmSJEmSVCrl5cKqmfnLSv34TVAvVwWOvSg4bn8R1D8RGnSGeL/qk1Ty+b90kiRJkiRJUnGyKR0mPQaLx8L29fn1UDykdoVylfNryXWDlySVEoYakiRJkiRJUjRtXQfbN0LNVsF5QlmY95/gOCkFmp8CLU4Pfi1XJXp9SlIxYKghSZIkSZIkFbUNS2Dhe8Fr5Qxo0gsufzsYq1QbTn0I6naAhl0gPjGKjUpS8WKoIUmSJEmSJBWFNXPgm/8FQcaPC/cey94e7KERFx+cd7u16PuTpBhgqCFJkiRJkiQdDbk5e2/ePf73sGRCcByXAI16QKszoeUZkFIvOj1KUowx1JAkSZIkSZIKy85M+O7jYDbGd+PgxhmQXCcYazsQkpKh1VnQ/NS9N/yWJB0WQw1JkiRJkiQpElt+gEXvB0HGssmQuyt/bPFH0HFwcHzcpcFLknTEDDUkSZIkSZKkI7V4HPz7AiCcX6vaFFqfFczIqNcpaq1JUklkqCFJkiRJkiQdSl5esNH3wnehWrP8GRf1T4D4RKjdLtgfo9VZUL0FhELR7VeSSihDDUmSJEmSJGl/crMh/VP45n/B8lJbvg/q9U/IDzXKVYa7FkH5qlFrU5JKE0MNSZIkSZIk6ZfeuxsWvAk7NuXXylQMNvhuddbe1xpoSFKRMdSQJEmSJElS6Za1FZZ/Bi365te2rg0CjfLVg2WlWp8NjU+GhKTo9SlJMtSQJEmSJElSKbRjM3z7YbC01HcfQ85OuHkmVG8ejHe/EzpfDw27QFx8VFuVJOUz1JAkSZIkSVLpsH0jfPNOEGQsnQx52fljVRoFe2bsDjXqHR+VFiVJB2eoIUmSJEmSpJIrHIZQKDheMQ3+d1v+WI1W0PocaHMO1Gqbf50kqdgy1JAkSZIkSVLJsnFZMBvjm3egSRr0vjeoN+0dLCfV7JQgzKjRIrp9SpIKzFBDkiRJkiRJse/HRfD1O/DNGFg7P7+etSU/1EgsB1d9GJ3+JEmFwlBDkiRJkiRJse3FfrByev55KB4adQtmY7Q6K3p9SZIKnaGGJEmSJEmSYkM4DKtnw5LxcPI9+XtgVGsKa2YHS021PhtangEVqkW3V0nSUWGoIUmSJEmSpOJrd5Dx9Wj4agxkrAjqzU6BescHx71/B6c9AmVTotenJKlIGGpIkiRJkiSp+Nm4FGb+E74eA5tX5NcTK0CLfhBfJr+WXLfo+5MkRYWhhiRJkiRJkqIvHIbsHVCmfHC+eSV89tfgeHeQccx5wQyN3ddIkkodQw1JkiRJkiRFRzgc7IXx1dvw9dvBXhinPxaMpXaD4y6D5n2h2akGGZIkwFBDkiRJkiRJRSkchjVz4KvRQZDx86Wlvvs4GA+FID4Bzv1b1NqUJBVPhhqSJEmSJEkqOv86A1Z8ln+eWD5YWqpN/2BWRigUtdYkScWfoYYkSZIkSZIKXzgM38+Fhe9Dz18HMy8A6hwb1H8eZLi0lCTpMBlqSJIkSZIkqXDsDjK+Gh3sk7F5eVBv1A2a9AqOT74H+txvkCFJOiKGGpIkSZIkSYrM5hXwxYtBmLE7yABIKBfMyEhKzq9VqFb0/UmSSgxDDUmSJEmSJBVcThYkJAXH29bD1OHB8e4g45j+Py0tVSFaHUqSSiBDDUmSJEmSJB2eTemwYFTwqnMs9P9bUK97HHS6Ghr3MMiQJB1VhhqSJEmSJEk6sMzv4eu3YcFbsOqL/PqW7yEvF+LiIRSCs56IWouSpNLDUEOSJEmSJEn7N+ZmmPMqEA7OQ3HQqAe0HQCtzw4CDUmSipChhiRJkiRJkmBnJix6H9r0h8SyQS25LhCGBp2h7UBocy5UqhXNLiVJpZyhhiRJkiRJUmm1azss/ihYWurbsZCbFeyH0frsYPyEIXDcZVC5YXT7lCTpJ4YakiRJkiRJpUnOLlgyARa8CQvfh+xt+WPVW0A4L/+8Ys2i70+SpIMw1JAkSZIkSSpNNi+H1y7KP6/cMNgjo+1AqHVMsOm3JEnFlKGGJEmSJElSSZSXB6s+D5aWysuFs54I6tWbQ/O+ULUptBsI9ToaZEiSYoahhiRJkiRJUknyw1fw5RtBmJGxMqgllIVTHoCyycH5pSOj1p4kSZEw1JAkSZIkSSoJ5r0OU/8C677Kr5WpFGz63XYAJJaPXm+SJBUSQw1JkiRJkqRYtH1jMAOjzE9hxdYfgkAjvkywvFS7C6BFP0gsF90+JUkqRIYakiRJkiRJsSJ7B3z7IXw5EhaPhXOegg6XBGPtLgiWl2pzLpSrEt0+JUk6Sgw1JEmSJEmSirO8XFg2BeaPhK/fgV1b8sdWfp4faiTXgY6Do9KiJElFxVBDkiRJkiSpuNq1Df7aCbasya+lNIB2A6HdhVCrTfR6kyQpCgw1JEmSJEmSiouNS2HlF3DsRcF5mQpQtQlkb4djzoP2F0KDkyAuLrp9SpIUJYYakiRJkiRJ0bT1R/hqVLC81KovIBQHTdOgYs1g/PznoEJNSCgT3T4lSSoGDDUkSZIkSZKKWtZWWPgezH8DlkyEcG5QD8VBk16wY1N+qJFSP2ptSpJU3BhqSJIkSZIkFbX5I+Hd2/PP6x4fLC11zPlQqVbU2pIkqbgz1JAkSZIkSTpawmFY+yXM+y/U6ZC/V0abc2HGs9CmP7S7AKo3i2aXkiTFDEMNSZIkSZKkwpa5Br58IwgzfvwmqNXrlB9qlK8KN82IXn+SJMUoQw1JkiRJkqTCMv9NmPMqLJ0EhINafBK0OgOO/X/BzI1QKJodSpIU0ww1JEmSJEmSjlReHsTF5Z/PHwlLJwbHDbvCsRcHS02VqxyV9iRJKmkMNSRJkiRJkgpq3Tcw77VgZsaVH0CV1KB+4jX5m35XbRzdHiVJKoEMNSRJkiRJkg7H1h9hwZtBmPH9vPz6gjehx13BcbNTgpckSToqDDUkSZIkSZIOZvMKeO9u+O5jCOcGtbgEaN4vWF6qRb/o9idJUiliqCFJkiRJkvRzeXmwbR1Uqh2cl68G6Z8GgUa9jsGG38ecDxWqRbdPSZJKIUMNSZIkSZIkgA1LYN5/4cvXIbE83DgNQiEoUwH6/w1qHgM1WkS7S0mSSjVDDUmSJEmSVHpt3whfjYJ5r8Oqz/PrZSpB5hpIqRecH3NedPqTJEl7MdSQJEmSJEml0/S/w7j7IXdXcB6Kg6a9g+WlWp4BZcpHtz9JkrQPQw1JkiRJklQ6/PgtJFWC5DrBebVmQaBRq20QZLQbmL+PhiRJKpYMNSRJkiRJUsm1MwMWvAVz/wOrvoBut8OpDwZjTdLguk+gTvuotihJkg6foYYkSZIkSSpZ8nJh2WSY829Y+C7k7AzqoXjYsSn/uvgEAw1JkmKMoYYkSZIkSSo5wmF4viesnZ9fq9EKOlwK7S+CSrWi15skSYqYoYYkSZIkSYpdWVtg0YfBfhihUPBqcBJsXgHtLoAOl0Dd44O6JEmKeYYakiRJkiQptuTlwfJPg+WlvnkHsrdDSj1I7RqMp/0W+j4MiWWj26ckSSp0hhqSJEmSJCk2bEqHua/BvP8EMzF2q9Ycdm3LPy9ftchbkyRJRcNQQ5IkSZIkFX/fz4PnTs4/T0qGtudDh8ugfieXl5IkqZQw1JAkSZIkScVLOAwrpkHmmmCvDIBa7aBqE6icGmz63fosSCwX3T4lSVKRM9SQJEmSJEnFQ+b3MPffwWvjUihXFVqfAwllIC4ObvjMIEOSpFLOUEOSJEmSJEVPbjZ8+xHMeQUWj4VwXlAvUxFanQFZWyChWlAz0JAkqdQz1JAkSZIkSdEz+TGY8n/55w27wPGXQ5tzoUyF6PUlSZKKJUMNSZIkSZJUNHZtg6/HQPWWUL9jUGt3AcwaAR3+Hxw3CKo3j26PkiSpWIuLdgOHsmHDBl544QXOO+88mjVrRrly5UhJSaF79+68+OKL5OXlHfa9GjVqRCgU2u+rdu3aB3zfZ599xhlnnEHVqlUpV64c7du3Z/jw4eTm5hbGR5QkSZIkqeQKh2HVLPjfbfDnlvD2DTDt6fzxGi3hzm/g1N8baEiSpEMq9jM1Ro4cyQ033ECdOnVIS0ujYcOG/PDDD4waNYohQ4bwwQcfMHLkSEKh0GHdLyUlhdtvv32fesWKFfd7/ZgxYxgwYABly5bloosuomrVqvzvf//jjjvuYOrUqYwcOTKSjydJkiRJUsm0bQN8+XqwV8a6r/PrVRpDveP3vja+2H89IUmSiolQOBwOR7uJg5kwYQLbtm3jzDPPJC4uf2LJ2rVrOfHEE1m5ciVvvvkmAwYMOOS9GjVqBEB6evphPTszM5NmzZqRkZHB1KlT6dSpEwA7d+6kd+/eTJs2jddee42LL774sD9PZmYmKSkpZGRkkJycfNjvkyRJkiQppvy9O/wwPzhOKBvskXHcIEjtBnHFfuEISZJ0FEXyPXmx/1tE7969Ofvss/cKNABq167N9ddfD8CkSZOOyrPffPNNfvzxRy6++OI9gQZA2bJlefjhhwH4+9//flSeLUmSJElSzNi8Eib/CXZtz6+1vxDqHAtn/BnuWgTnPw+NexhoSJKkiMT0/M7ExEQAEhIO/2NkZWXx6quvsmLFCipUqED79u05+eSTiY+P3+faCRMmAHDaaaftM3byySdTvnx5PvvsM7KyskhKSjrCTyFJkiRJUgzKyYJF78Psl2HJRCAMKQ2CDb8ButwE3W6NaouSJKnkidlQIycnh5dffhnYf+hwIGvXrmXQoEF71Ro3bsy//vUvevbsuVd90aJFALRo0WKf+yQkJNC4cWO++uorli5dSuvWrff7vKysLLKysvacZ2ZmHnavkiRJkiQVOz98HeyTMe+/sGNjfr1RD6hUO/88bt9/PChJkhSpmJ3zOXToUBYsWMAZZ5xBv379Dus9V155JePHj2ft2rVs27aN+fPnc91115Gens7pp5/OvHnz9ro+IyMDCDYX35/d9c2bNx/wmY888ggpKSl7Xg0aNDisXiVJkiRJKnYyVsPfu8D0Z4JAo1Id6HE33DoHBr8LTdOi3aEkSSrhYnKmxlNPPcXjjz9Oq1ateOWVVw77fcOGDdvrvG3btjz77LNUrFiRxx9/nAceeIDRo0cXaq+/+c1vuPPOO/ecZ2ZmGmxIkiRJkoq/cBjWzIY1c+CEIUEtpR407Q2J5eH4y6FpH4iPya8WJElSjIq5v3k8/fTT3HbbbbRp04bx48dTtWrViO95/fXX8/jjjzNlypS96rtnYuyesfFLu+uVK1c+4L2TkpLcb0OSJEmSFDt2ZsD8kTDrJVg7H+ISoNVZ+UtLXfqmS0tJkqSoianlp4YPH84tt9xC27ZtmThxIrVr1z70mw5DjRo1ANi2bdte9ZYtWwLw7bff7vOenJwcli1bRkJCAk2aNCmUPiRJkiRJiopwGFbNgjE3w+Ot4L27gkAjPgmOOR9yduZfa6AhSZKiKGZCjccee4w77riDDh06MHHiRGrWrFlo954+fTrAPuFE7969Afjwww/3ec+UKVPYvn07Xbt2dSaGJEmSJCm2zfsvvNA72AA8eztUbwn9HoG7FsKAf0CVRtHuUJIkCYiRUOOhhx5i6NChdOzYkfHjx1O9evUDXpudnc3ChQtZsmTJXvVvvvlmn5kYAOnp6dx8880AXHbZZXuNDRw4kOrVq/Pf//6XmTNn7qnv3LmT3/3udwDccMMNR/y5JEmSJEkqcuEwrPwc0qfm11r0g7KVof3FcOWHcNMM6HIjlI98yWdJkqTCFAqHw+FoN3EwI0aMYPDgwcTHx3PLLbfs2efi5xo1asTgwYOBIKRo3LgxqamppKen77nmgQce4PHHH+fkk08mNTWVSpUqsWTJEt577z127tzJGWecwejRoylTpsxe93777bcZOHAgZcuW5eKLL6Zq1aq88847LFq0iIEDB/LGG28QCoUO+/NkZmaSkpJCRkYGycnJR/R7IkmSJElSge3YBF++EeyVse5rqNcJrhmfP569ExLLRq09SZJUekTyPXmx3yh82bJlAOTm5jJ8+PD9XtOzZ889ocaBpKWlsWjRIubMmcPUqVPZtm0blStXpnv37gwaNIhBgwbtN5zo378/kydP5g9/+ANvvfUWO3fupFmzZjzxxBPceuutBQo0JEmSJEkqUuEwrJwRBBlfjc7fGyOhLFRvATlZkPDTksoGGpIkKQYU+5kaJY0zNSRJkiRJReZ/twWBxm41j4GOg6H9BVCuSrS6kiRJpVyJnqkhSZIkSZIOQzgMK6ZD1SZQqVZQa9oH5r0ObQcEYUb9TuCKA5IkKYYZakiSJEmSFMu2b4R5/w1mZKxfBGn3Qs9fBWMtT4e7F0HZffenlCRJikWGGpIkSZIkxZpwGJZ/FgQZX4+B3Kygnlg+2Cdjt/hEiDfQkCRJJYehhiRJkiRJsSQvD17oDWvm5Ndqt4OOV0K7C6Cs+zdKkqSSy1BDkiRJkqTibt03ULN1cBwXB7WOgR8XQbuBQZhR9zj3ypAkSaWCoYYkSZIkScXRru3w1Sj44kVYMxuunRSEFwC974N+f3SvDEmSVOoYakiSJEmSVJz8uAhm/gvm/Qd2ZgS1uET4fl5+qFGpdvT6kyRJiiJDDUmSJEmSioPM72HUNZD+SX6tcip0uhI6XAYVa0SvN0mSpGLCUEOSJEmSpGjJ3gGJ5YLjCjVgw3cQioMWp0Gnq6Fp72APDUmSJAGGGpIkSZIkFa28XFgyIdgr44cFcOtciE8IXuc/D1UaQ+UG0e5SkiSpWDLUkCRJkiSpKGz9Eea8ArP+BZtX5NdXTodG3YPjxidHpzdJkqQYYaghSZIkSdLR9OMimPwYfP0O5GUHtbKVocOlwX4Z1ZtHtT1JkqRYYqghSZIkSdLRtGsbLHgrOK7XMdgro+35+XtpSJIk6bAZakiSJEmSVFjWzIWZL0JCOTjjT0Gt3vGQdi807wt1O0SzO0mSpJhnqCFJkiRJUiRysuCrt+Hz52H1zKCWUA7SfgvlKgfnPX8Vre4kSZJKFEMNSZIkSZKORMYqmPlPmDUCtq8PanGJ0OacYImpsinR7U+SJKkEMtSQJEmSJOlIzHkVPnk8OE6uF2z6ffwVULFmdPuSJEkqwQw1JEmSJEk6lJ2ZMO+/UKMlNOkZ1DoOhhXTglkZLc+AeH/EliRJOtr8G5ckSZIkSQeybiF88Y8g0Ni1FZr0yg81KtWGy8dEtT1JkqTSxlBDkiRJkqSfy82BRe8HG3+nf5Jfr94CWp0F4TCEQtHrT5IkqRQz1JAkSZIk6efeGBSEGgChuGBpqROvgcY9DTMkSZKizFBDkiRJklR6hcOwaibUaAFlU4Jaq7Ng5Yxgz4yOV0LlBlFtUZIkSfkMNSRJkiRJpU/2DljwVrDE1Pfz4LRH4aQbgrF2F0DbAZBYNro9SpIkaR+GGpIkSZKk0mNTOnzxIsx5BXZsCmrxSbB9Y/41CWWi0pokSZIOzVBDkiRJklTy5eUFe2UsfA8IB7WUhnDCVXDc5VChWlTbkyRJ0uEx1JAkSZIklUzZOyCxXHAcFxds+k0YmvaGE6+F5n0hLj6qLUqSJKlgDDUkSZIkSSXL+sUw4zmY91+4bjJUaxrUe98HfYZB9WbR7U+SJElHzFBDkiRJkhT78vJgyXiY8Sx893F+fcEo6HlPcFyjRXR6kyRJUqEx1JAkSZIkxa7sHTD7Ffj8Odjw3U/FELQ4DU66Hhr3jGp7kiRJKlyGGpIkSZKk2BXOg4l/gJ2bISkZjhsEJw6Bqk2i3ZkkSZKOAkMNSZIkSVJsCIdh6UT45l0483EIhaBMBeg1FOIS4NiLIalStLuUJEnSUWSoIUmSJEkq3nZtg3mvwYznYf2ioNb6bGiaFhyfdEP0epMkSVKRMtSQJEmSJBVPm9Lh83/AnFdgZ0ZQK1MROlwKVRtHtTVJkiRFh6GGJEmSJKn4+f5LeL5nsGcGBHtknHgddLgEyiZHtzdJkiRFjaGGJEmSJCn6dm2HH7+Beh2D89rtoHoLSK4Lna+HZqdCXFx0e5QkSVLUGWpIkiRJkqInY1WwxNTsEcFG4Hd+A2XKB5uAXzMh2AhckiRJ+omhhiRJkiSpaIXDsGIazHgWvnkXwrlBvXJD2LQMah0TnBtoSJIk6RcMNSRJkiRJRWflF/DenbD2y/xaox5w0g3Q4jSIi49eb5IkSSr2DDUkSZIkSUdXOBwsJwVQrgqsnQ8JZaH9RdD5uvyZGZIkSdIhGGpIkiRJko6Odd/AtL9BXg6c92xQq94MBv4TmvSC8lWj2p4kSZJij6GGJEmSJKnwhMPw3XiY/jdYMiGoheKgz/2QXDc4b3t+9PqTJElSTDPUkCRJkiRFLnsHzPsvTP87rF8U1EJx0OpMOOkmqFQnuv1JkiSpRDDUkCRJkiRFbtYI+PDXwXGZSnD8oGC/jCqNotqWJEmSShZDDUmSJElSwX3/JWRvh4YnBecd/h/Mfhk6XBIEGmVTotufJEmSSiRDDUmSJEnS4cnLg28/hOnPQPonUOdYuHYyhEJBiHHD1OBYkiRJOkoMNSRJkiRJB5e1Fea9FoQZG5cGtVA8VGsezNYoU+GnmoGGJEmSji5DDUmSJEnSgc1+Gcb+DnZmBOdlU6DjYDjxWkipH9XWJEmSVPoYakiSJEmS9paXC3HxwXH56kGgUbUJnHQjHPv/IKlidPuTJElSqWWoIUmSJEmC3BxY+G6wxFTT3tBraFBvcRpc+lZQi4uLbo+SJEkq9Qw1JEmSJKk025kZLDE14znIWBHUMlbDyfcEszXi4qD5KdHtUZIkSfqJoYYkSZIklUYZq2DGszBrBGRlBrXy1aDT1XDCkPzlpyRJkqRixFBDkiRJkkqjSY/CnFeC4+otoMtN0P4iSCwX3b4kSZKkgzDUkCRJkqSSLhyGJeMhpSHUaBHUutwMm9Kh6y3Q7FT3y5AkSVJMMNSQJEmSpJIqJwvmvwnTnoZ1X0P7i+H854Kxmq1g8LvR7U+SJEkqIEMNSZIkSSppdmyCmf8KNv/eujaolakIlWoFszZCoej2J0mSJB0hQw1JkiRJKkkm/x98+iRkbwvOK9WFztdBx8FQrnI0O5MkSZIiZqghSZIkSbHu57Mv4uKDQKNW22C/jGPOh4Qy0e1PkiRJKiSGGpIkSZIUi/Ly4NsP4bO/wknXQ5tzg3qnK6FuB2iS5jJTkiRJKnEMNSRJkiQplmTvgHn/hWl/gw2L8+u7Q41yVaBp7+j0JkmSJB1lhhqSJEmSFAu2bYAvXoDPn4ft64NaUkowM6PzddHtTZIkSSoihhqSJEmSFAveuByWfxocpzSAk26E4wdBUqXo9iVJkiQVIUMNSZIkSSqOVkyHGi2D5aQATrwGdm2BrrdCm/4Q749zkiRJKn38W7AkSZIkFRd5efDtB/DpcFj1OfQZBj3uDMbanBu83PxbkiRJpZihhiRJkiRFW04WfPkGfPYUrP82qMWXgawt+dcYZkiSJEmGGpIkSZIUVdP+Bp/9FbZ8H5wnpcAJV0Pn66FSrej2JkmSJBUzhhqSJEmSFE1r5gaBRqW60OVGOP4KKJsc7a4kSZKkYslQQ5IkSZKKyvrvgiWmutwUbAIO0OMuaNIL2l0ACWWi2p4kSZJU3BlqSJIkSdLRtmomTB0O37wLhCEvF/r/LRir2Sp4SZIkSTokQw1JkiRJOhrCYVg8Dqb+BZZ/ml9vcTocf3n0+pIkSZJimKGGJEmSJB0NL58Dy6YEx3GJ0P5C6HqrszIkSZKkCBhqSJIkSVJh2LUNEspBXFxwntodVs+GjoPhpBshpV5U25MkSZJKAkMNSZIkSYrEtvUw4zn4/Hk471loeXpQP+kG6HwtlKsS3f4kSZKkEiQu2g0cyoYNG3jhhRc477zzaNasGeXKlSMlJYXu3bvz4osvkpeXd9Tuk56eTigUOuDr4osvLuyPK0mSJClWbFwG790FTx4DU/4EOzfDgrfyx8smG2hIkiRJhazYz9QYOXIkN9xwA3Xq1CEtLY2GDRvyww8/MGrUKIYMGcIHH3zAyJEjCYVCR+0+xx57LP3799+n3rZt28L6mJIkSZJixffzgs2/vxoN4Z/+cVTd46H77dDqrKi2JkmSJJV0oXA4HI52EwczYcIEtm3bxplnnklcXP7EkrVr13LiiSeycuVK3nzzTQYMGFDo90lPT6dx48ZcccUVvPTSS4XyeTIzM0lJSSEjI4Pk5ORCuackSZKkIhIOw3Mnw9ovg/OmfYIwo1EPOMQ/tJIkSZIUiOR78mK//FTv3r05++yz9woiAGrXrs31118PwKRJk4rsPpIkSZJKkbw8+OZdyNoSnIdC0ONOaHcBXP8pDBoFjU820JAkSZKKSLFffupgEhMTAUhIiOxjHOo+a9as4bnnnmPDhg1Uq1aNLl260L59+4ieKUmSJKkYy82G+SPh0+GwfhH0fRi63hKMHXNe8JIkSZJU5GI21MjJyeHll18G4LTTTjuq9xk3bhzjxo3bq9arVy9GjBhBw4YND3r/rKwssrKy9pxnZmYeca+SJEmSjrJd22HOq/DZU5CxMqglpUCo2E9ylyRJkkqFmP2b+dChQ1mwYAFnnHEG/fr1Oyr3KV++PPfddx+zZs1i06ZNbNq0icmTJ5OWlsakSZPo06cP27ZtO+j9H3nkEVJSUva8GjRocMS9SpIkSTpKwmH49EkY3g4+uCcINCrUhFMegDsWQJebot2hJEmSJGJgo/D9eeqpp7jtttto1aoVU6dOpWrVqkV6n5ycHLp3786MGTMYPnw4t9122wGv3d9MjQYNGrhRuCRJklTcvHEFfP02VG4IXW+F4y6DxHLR7kqSJEkqcUr0RuG/9PTTT3PbbbfRpk0bJk6ceMSBRiT3SUhIYMiQIQBMmTLloNcmJSWRnJy810uSJElSlG1eAe/dDRuW5Nd6/grOex5umQ0nXmOgIUmSJBVDMbWnxvDhw7njjjto27Yt48ePp2bNmlG7T40aNQAOufyUJEmSpGJk3UKYOjzYBDwvB/Ky4ey/BGO1jglekiRJkoqtmAk1HnvsMYYOHUqHDh0YN24c1atXj+p9pk+fDkCTJk2O6P2SJEmSitDqWfDJE7Dw3fxak17QdmDUWpIkSZJUcDERajz00EPcf//9dOzYkbFjxx50qajs7GyWLFlCYmIiTZs2PeL7AMyePZsOHToQF7f3Kl3jx4/nySefBOCyyy47wk8lSZIkqUi8cTl8PSb/vNVZ0ONOqNcxej1JkiRJOiLFPtQYMWIE999/P/Hx8fTo0YOnnnpqn2saNWrE4MGDAVi9ejWtW7cmNTWV9PT0I74PwJ133snixYvp2rUr9evXB+DLL79kwoQJQBCSdO3atfA+rCRJkqTI5eVBKBS8AKq3gFA8tL8Qut0ONVtFtT1JkiRJR67YhxrLli0DIDc3l+HDh+/3mp49e+4VRhTWfQYNGsTo0aP54osv+OCDD8jOzqZWrVpceOGF3HzzzfTo0aOgH0eSJEnS0ZKbDQvegk+fhH5/gGanBPWTboTjBkGV1Oj2J0mSJClioXA4HI52E6VJZmYmKSkpZGRkkJycHO12JEmSpNiXvQPmvApTn4KMFUGteV+4dGR0+5IkSZK0X5F8T17sZ2pIkiRJ0n7tzIQvXoDpz8C2H4NahRrBzIwTro5ub5IkSZKOCkMNSZIkSbHp3wNh5YzgOKUBdLsNjrsMEstFty9JkiRJR42hhiRJkqTYsPVHSKqYH1qcMAR2bILud0K7gRCfGN3+JEmSJB11cdFuQJIkSZIOKnMNfDAUhreD2S/n19sOgBtnQIf/Z6AhSZIklRLO1JAkSZJUPG1Kh0+Hw9x/Q+6uoJb+CXS+LjiOi49WZ5IkSZKixFBDkiRJUvGyfjF88gR8+TqEc4Naajc4+W5okhbd3iRJkiRFlaGGJEmSpOJl3DBY9F5w3LQ39LgbGnWLbk+SJEmSigVDDUmSJEnRtXo2VKoNyXWD85PvgnAenHwP1O8Y3d4kSZIkFStuFC5JkiQpOlZMh1cHwD/SguWmdqvXES75r4GGJEmSpH04U0OSJElS0QmHYdkUmPJ/wabfAKH4YCPwcBhCoej2J0mSJKlYM9SQJEmSVDS+Gw+THoVVnwfncYnQ4RLofjtUbRLV1iRJkiTFBkMNSZIkSUVj2eQg0EgoC8dfAd1uhZT60e5KkiRJUgwx1JAkSZJU+HJz4KvRULUx1O8U1LrcHPx60k1QqVb0epMkSZIUsww1JEmSJBWenF3w5evw6ROwcSk06QWXjwnGKtaEU38f1fYkSZIkxTZDDUmSJEmRy8mCuf+GT56EjBVBrVxVaNQd8vIgLi66/UmSJEkqEQw1JEmSJEVm/pswbhhkrgrOK9QM9svoeCUkVYxub5IkSZJKFEMNSZIkSZHJ3hEEGpXqQLfboeMVkFgu2l1JkiRJKoEMNSRJkiQdvuwdMGsEVKwBbQcEtWMvhnAetL8IEstGtz9JkiRJJZqhhiRJkqRD27UdZv0Lpv4Ftv4AlRtC63MgPjF4dbwi2h1KkiRJKgUMNSRJkiQd2K5tMPOfMPUp2LYuqKU0gO53RLcvSZIkSaWSoYYkSZKk/fv6HXjvTtj2Y3BeuSH0uAuOvQQSykS3N0mSJEmlUlxBLo6Pj4/49fvf//5ofRZJkiRJhalizSDQqJwK5/wVbpkNHQcbaEiSJEmKmgLN1AiHw6SmptKoUaMCPygcDjNlypQCv0+SJElSEcjaAp//A/Jyoec9Qa3hSXDJG9C0d7BvhiRJkiRFWYGXn7ryyiu5//77j+hhcXEFmhgiSZIk6WjbmQmfPwfT/gY7NkFCuWDT74o1g/EW/aLbnyRJkiT9jHtqSJIkSaXRzgyY8VOYsXNzUKvWHE6+B8pVjWprkiRJknQgBQo1fvzxR8qXL3/ED4v0/ZIkSZIKweKP4a2rgmADoHoLOPlX0PZ8iIuPbm+SJEmSdBAFCjWqVasW0cMifb8kSZKkQlCrDWTvgBqtgpkZx5xnmCFJkiQpJkS0ycVvf/tbOnXqxMaNG/eqZ2dns2rVqogakyRJklQItm+E8Q/Bm1fn15LrwpCP4YZp0G6ggYYkSZKkmBFRqDF+/Hhyc3OpWjV/zd05c+ZQr149UlNTadKkCZMmTYq0R0mSJEkFtWMTTPgDDG8Pn/wZFrwJa+fnj9c5FuIi+nFAkiRJkopcRD/FpKen06lTp71q9957L+vXr6dbt25s3ryZs846iyVLlkTUpCRJkqTDtDMDJj0Kw4+FKX+CXVugVju46FWoeUy0u5MkSZKkiEQUamRmZlKrVq0955s3b2bcuHFceOGFTJkyhc8//5xwOMyf/vSniBuVJEmSdAirZ8PwdjDpEcjKgJpt4MJX4Lop0PpsZ2ZIkiRJinkF2ij8l+rVq8f69ev3nI8dO5a8vDyuvfZaAJo1a8Y555zDuHHjIutSkiRJ0v6FwxAKBcc120BiBahUB3oNhdbnGmRIkiRJKlEi+gmnffv2fPTRR+Tm5gLw73//m/Lly3PyySfvuaZJkyasWbMmsi4lSZIk7W3XNpj6F/jXGZAX/H2cxLIw+F244TM45jwDDUmSJEklTkQzNe644w569uxJnz59aN68Oe+++y4DBw4kISH/tlu3bqVs2bIRNypJkiQJ2LUdZv4Tpg6HbT8Gta/HQNvzg+NqTaPWmiRJkiQdbRGFGj169ODhhx9m2LBhTJkyhZSUFIYNG7bXNTNnzqROnToRNSlJkiSVetk7Yda/4NMnYesPQa1KI+j5a2h9TlRbkyRJkqSiEgqHw+FIb7Ju3ToWLlzIMcccQ7Vq1fbUly5dSvPmzbnssssYMWJEpI8pETIzM0lJSSEjI4Pk5ORotyNJkqRYkPk9/CMNtnwfnFduCCf/Co69GOITo9ubJEmSJBVQJN+TRzRTY7eaNWtSs2bN/TY2ePBg+vfvXxiPkSRJkkqPn28AXqk2VE6FUDycfDd0uBQSykS3P0mSJEmKggLP1LjtttsYMGAAPXr0ILT7hywdNmdqSJIk6aBys2Huf+Dz5+GK/0H5qkE9YxVUqAEJSdHtT5IkSZIiFMn35AUONRISEgiHw1SrVo1zzjmH8847j1NPPZUyZfyXYofDUEOSJEn7lZsDX/4XJv8JNi8Pamm/g573RLcvSZIkSSpkkXxPHlfQh61Zs4a///3vdOzYkVdffZVzzjmH6tWrc/HFF/P666+zZcuWgt5SkiRJKr1yc2Dua/B0JxhzUxBoVKgB/R6BrjdHuztJkiRJKlYi2ih8y5Yt/O9//2P06NF89NFHbN26laSkJPr06cN5553HOeecQ40aNQqz35jnTA1JkiTtkbMLnusBPy4MzstXh263wQlXQ5kK0e1NkiRJko6SIl1+6kCysrIYO3Yso0aN4t1332XDhg3Ex8fTpUsXBgwYQP/+/UlNTS2MR8U0Qw1JkqRS7ucbgAOMuRkWvgfdboUTroGkitHrTZIkSZKKQLEINX4uLy+PyZMnM3r0aMaMGcPKlSsJhUJ06NCBWbNmFfbjYoqhhiRJUikVDsOi92HSI3De81CrTVDftj7Y/DupUnT7kyRJkqQiUuxCjV+aOXMmo0aN4u233+brr78+2o8r1gw1JEmSSplwGJZMgAkPw5rZQa3dBTDghej2JUmSJElREvVQY9euXWzbto0qVapEeqsSz1BDkiSpFFn+GYx/CFZ8FpwnlofO10PXW6B81ej2JkmSJElREsn35AmRPHjlypVcccUVTJkyhXA4TKVKlejQoQPHH3/8nlfr1q0J/XzNYEmSJKk0eOsamP9GcByfBCcMge53QMUa0e1LkiRJkmJYRKHGjTfeyKRJk2jQoAEtW7Zk+fLlfPLJJ0yZMmVPkFGuXDmOPfZYpk6dWigNS5IkSTGhdjv4ahQcfzn0uBtS6kW7I0mSJEmKeREtP1W5cmVatmzJp59+SmJiIgDbtm1jzpw5zJkzh9mzZzNr1iwWLlzIrl27Cq3pWObyU5IkSSXQ+sXBBuBtB0CrM4Na9g7YshaqNo5ub5IkSZJUzERt+amkpCR69eq1J9AAqFChAt27d6d79+57agYakiRJKpE2LYfJf4J5/4FwHqxbCC3PgFAIEssZaEiSJElSIYso1DjllFNYtGjRIa8rU6ZMJI+RJEmSipfM72HK/8HslyEvO6i1PAPSfhsEGpIkSZKkoyKiUOPee++lc+fOfP7555x44omF1ZMkSZJUfM14DsbdDzk7g/MmadD7d1C/U3T7kiRJkqRSIC6SN7dp04bXXnuN8847j9dee43c3NzC6kuSJEkqniqnBoFGg5Ng8Htw+dsGGpIkSZJURCLaKPyHH37g6quv5qOPPiIvL4+qVavSq1cvOnbsSKdOnejYsSNVqlQpzH5jnhuFS5IkxZCsLTDjWUhKgc7XBrVwGFZMg4ZdXGpKkiRJko5AJN+TRxRqnH/++bz99ttUrlyZRo0asXz5cjZt2hTc+Kcf8FJTU+nUqRNvvPHGkT6mRDHUkCRJigHZO+CLF+HTJ2D7BihbGW7/EsqmRLszSZIkSYp5kXxPHtGeGhMmTKBdu3ZMnTqVihUrArB8+XJmzZq11+utt96K5DGSJElS0cjZBbNHwJQ/w9a1Qa1q02AD8DIVo9ubJEmSJCmyUCM+Pp7TTjttT6ABwcyM1NRUzj///D21FStWRPIYSZIk6ehb9gmMuRE2//R315QG0PPXcOz/g/iI/tosSZIkSSokEf101qNHD5YuXXrI6xo2bBjJYyRJkqSjr2ItyFgV/HryPXD85ZCQFO2uJEmSJEk/E1GoMWzYMLp3785XX33FMcccU1g9SZIkSUdXOAxLJsD386DHnUGtRgv4f69Do+5Qpnx0+5MkSZIk7VdcJG9+7bXXOOWUUzj11FMZN25cYfUkSZIkHT0rv4ARZ8Or58OEh+DHRfljLfoaaEiSJElSMRYKh8PhI31zXFwcoVCIcDhMKBSiSZMmnHLKKXTs2JFOnTrRtm1bEhJcf/jnItnVXZIkSRH44WuY8DAsei84jy8DJwyBHndDhWrR7U2SJEmSSpFIviePKHGYMGECs2fP3vP69ttvee655wiFQgCUKVOGdu3a0alTJ5555plIHiVJkiQdma3rYOx98OXrQBhCcdDhEug5FCo3iHZ3kiRJkqQCiGimxi9t376defPm7RV0fP311+Tk5JCbm1tYj4lpztSQJEkqYjs2w1+OhZ2boc25kPa7YP8MSZIkSVJURG2mxi+VL1+eLl260KVLlz21Xbt2sWDBgsJ8jCRJknRgOzbD/JHB0lKhEJSrDGf/BSo3hHrHR7s7SZIkSVIEjvqGF2XKlOH44/3hUZIkSUfZru3w+XPw6fBgVkZKfWh5ejB2TP8oNiZJkiRJKizu4i1JkqTYlpsNs1+GyX+CrWuDWo1WkFg+un1JkiRJkgpdXEEubtOmTUQbfkf6fkmSJGmPvDyY/yY8fQK8d2cQaKQ0hP7Pwg2fQZOe0e5QkiRJklTICjRTY+HChaxfv/6IHxbp+yVJkqR8YZjyZ9i0DCrUhJPvgY5XQEJStBuTJEmSJB0lBV5+atKkSUf8sFAodMTvlSRJklg+Dep2gMRyEBcPfR+C7+dC5xsgqWK0u5MkSZIkHWVHFGpEEmxIkiRJBfb9lzD+9/DdODj1Ieh2a1BvfmrwkiRJkiSVCgUKNSZOnBjxAxs1ahTxPSRJklRKbFgCE/8AC94KzuMSYMfG6PYkSZIkSYqaAoUaPXu62aIkSZKKQOb3MPkxmP0yhHODWtuBkPZbqNY0ur1JkiRJkqKmwMtPSZIkSUfdh0Ph67eD4+b9oM99ULtdVFuSJEmSJEWfoYYkSZKiL3sn5OyEcpWD816/ga3rgjAjtWtUW5MkSZIkFR9x0W7gUDZs2MALL7zAeeedR7NmzShXrhwpKSl0796dF198kby8vALdb9WqVVx11VXUrVuXpKQkGjVqxO23386mTZsO+J6vv/6aCy+8kJo1a1K2bFlatmzJsGHD2LFjR6QfT5IkqXTLy4W5r8HTnWDc/fn1mq3gqg8MNCRJkiRJewmFw+FwtJs4mGeffZYbbriBOnXqkJaWRsOGDfnhhx8YNWoUGRkZDBgwgJEjRxIKhQ55ryVLltC1a1fWrVvHueeeS6tWrfj888+ZOHEiLVu2ZOrUqVSrVm2v98yYMYPevXuTnZ3NwIEDadCgARMmTGDmzJl069aN8ePHk5SUdNifJzMzk5SUFDIyMkhOTi7w74ckSVKJEA7D4nHw8QOw7qugltIAbvocypSPamuSJEmSpKMrku/Ji32oMWHCBLZt28aZZ55JXFz+xJK1a9dy4oknsnLlSt58800GDBhwyHv169ePsWPH8tRTT3HLLbfsqd955508+eSTXHfddTz77LN76rm5ubRr145vvvmGMWPGcM455wCQl5fHhRdeyFtvvcUjjzzC0KFDD/vzGGpIkqRSb9VMGDcMln8anCelQI874MTrDDQkSZIkqRQo0aHGwfzxj3/k3nvv5eabb+avf/3rQa9dsmQJzZo1o1GjRixZsmSvgGTLli3UqVOHcDjMunXrqFChAhAEKn369OHkk09m8uTJe91v6dKlNG3alNTUVJYtW3ZYM0XAUEOSJJVyc1+Dt68PjuOToPO10P1OKF81un1JkiRJkopMJN+TR7Snxg8//BDJ2yOWmJgIQELCofc7nzhxIgB9+/bdK9AAqFSpEt26dWP79u1Mnz59T33ChAkAnHbaafvcr0mTJrRo0YLly5ezdOnSAz43KyuLzMzMvV6SJEmlys//DU3L06BcVehwGdwyC/o+bKAhSZIkSTpsEYUaDRs25KKLLtrz5X9RysnJ4eWXXwb2Hzr80qJFiwBo0aLFfsebN28OwLfffhvRe37pkUceISUlZc+rQYMGh+xVkiSpRNiZAeMfgn8PzA82ylWB2+ZC/79BZf9eJEmSJEkqmIhCjRYtWjBy5EhOPfVUWrRoweOPP86GDRsKq7eDGjp0KAsWLOCMM86gX79+h7w+IyMDgJSUlP2O765v3rw5ovf80m9+8xsyMjL2vFauXHnIXiVJkmJaThZMewb+0gE++TN89zEsn5o/Xnb/f7eSJEmSJOlQIgo15s+fz6effsqgQYNYvXo199xzD/Xr1+fSSy9lypQphdXjPp566ikef/xxWrVqxSuvvHLUnlMYkpKSSE5O3uslSZJUIuXlwbzX4a+d4KPfwI6NUL0FXPRvSO0W7e4kSZIkSSVARKEGQNeuXXnppZdYs2YNf/nLX2jWrBmvvfYaaWlptGnThr/85S9s2rSpMHoF4Omnn+a2226jTZs2TJw4kapVD28N5t2zKnbPvvil3fXKlStH9B5JkqRSKWM1PHcyjL4WMlZApTpw9lNwwzRofRaEQtHuUJIkSZJUAkQcauyWkpLCLbfcsmf2xuWXX87y5cu58847qV+/PoMHD2bmzJkRPWP48OHccssttG3blokTJ1K7du3Dfm/Lli2BA+9/sXjxYmDv/TOO5D2SJEmlUsVakJcNSSnQZxjcMhs6XgHxCdHuTJIkSZJUghRaqPFz1atXp0qVKpQtW5ZwOExWVhYvv/wynTt3pn///mzcuLHA93zssce444476NChAxMnTqRmzZoFen9aWhoAY8eOJS8vb6+xLVu2MHXqVMqXL89JJ520p967d28APvzww33ut3TpUr799ltSU1Np0qRJQT+OJElSbNuwBN69A7J3BufxCTDgxWAT8B53QpnyUW1PkiRJklQyFVqokZ2dzX//+1/S0tJo3bo1w4cPp0aNGjzxxBOsX7+eCRMm0K9fP9555x1uuummAt37oYceYujQoXTs2JHx48dTvXr1g/axcOFClixZsle9adOm9O3bl/T0dP72t7/tNTZs2DC2bdvGoEGDqFChwp56z549ad26NVOmTOGdd97ZU8/Ly+PXv/41ANdffz0hl1OQJEmlxZYf4N074ekTYOY/4Yt/5I/VbgvlD29pUEmSJEmSjkQoHA6HI7nBd999x/PPP89LL73Ehg0biIuL45xzzuHGG2+kT58++1w/cOBAxo8ff9j7bIwYMYLBgwcTHx/PLbfcsmefi59r1KgRgwcPBiA9PZ3GjRuTmppKenr6XtctWbKErl27sm7dOs4991xat27NjBkzmDhxIi1atOCzzz6jWrVqe71nxowZ9O7dm+zsbAYOHEjDhg0ZP348M2fOpFu3bowfP56kpKTD+80CMjMzSUlJISMjw03DJUlS7MjaAlOfgmlPQ/b2oNbitGCpqVptotubJEmSJCmmRPI9eUSLHPfp04dJkyYRDoepU6cO9913H9deey1169Y94Hs6duzI6NGjD/sZy5YtAyA3N5fhw4fv95qePXvuCTUOpmnTpsycOZP777+fDz/8kPfff586depw2223MWzYMKpUqbLPezp37swXX3zBsGHDGDt2LFu2bCE1NZX777+foUOHFijQkCRJijnhMMx8ESY9Ctt+DGr1T4BTHoRG3aLbmyRJkiSp1IlopkZcXBxpaWnceOON9O/fn/j4+EO+Z8GCBcyaNYsrrrjiSB8b05ypIUmSYs5/LoJvP4SqTeCUB6D1OeDym5IkSZKkIxTJ9+QRhRqLFi2iZcuWR/r2UslQQ5IkFXurZkJKA6hUKzhftxCWTYaOV0JCmej2JkmSJEmKeZF8Tx7RRuEzZszgyy+/POg1CxYs4OWXX47kMZIkSSoKG5fCyMHwQh+Y9Mf8es1W0Pk6Aw1JkiRJUtRFFGoMHjyYt99++6DXjBkzhiuvvDKSx0iSJOlo2r4RPhgKT58IX40GQpCXG+ynIUmSJElSMRLRRuGHIzc3l5BrLkuSJBU/2TthxrPwyROQlRHUmvaBUx+E2u2i25skSZIkSftx1EONb7/9lipVqhztx0iSJKmgPn0SJj8aHNdqB31/D017R7cnSZIkSZIOosChxlVXXbXX+dtvv016evo+1+Xm5rJixQo++eQTzjzzzCNuUJIkSYVo1zYoUyE47nwdLHwXutwM7S+EuPjo9iZJkiRJ0iGEwuGCLZYcF5e/DUcoFOJgbw+FQnTu3JlXX32VJk2aHHmXJUgku7pLkiQdsR++gnH3B0tODX4Xdi8PGg7nH0uSJEmSVAQi+Z68wDM1li1bBkA4HKZJkybcfvvt3HbbbftcFx8fT5UqVahQoUJBHyFJkqTCkrkGJv4B5v4HwnkQlwA/LoSarYNxAw1JkiRJUgwpcKiRmpq653jYsGGkpaXtVZMkSVIxsDMTpv4Fpv0NcnYEtTbnQp9hUK1pdHuTJEmSJOkIFXj5KUXG5ackSdJRt24hvHQmbF8fnDfoDH0fhgYnRrcvSZIkSZIo4uWnJEmSVMxVawblq0HZFDj1QWh1lstMSZIkSZJKhAKFGldddRWhUIg//vGP1KpVi6uuuuqw3hcKhXjxxRePqEFJkiQdwsrPYfrf4bxnISEJ4hPg0jcguR7EJ0a7O0mSJEmSCk2Blp+Ki4sjFArxzTff0KJFC+Li4g7vIaEQubm5R9xkSeLyU5IkqdBsXAofPwhfvx2c9/0DdL05qi1JkiRJknQoRbb81LJlywCoV6/eXueSJEkqQjs2wZQ/w4znIC8bCMFxl0Lb86PdmSRJkiRJR1WBQo3U1NSDnkuSJOkoCofh8+dh0iNBsAHQtHewCXitY6LbmyRJkiRJRcCNwiVJkmJFKATfjQ8CjRqtgzCj+SnR7kqSJEmSpCJzeJtiHMDIkSPp3bs3a9as2e/46tWr6dOnD6NGjYrkMZIkSaXXmrmwdV3+ed+H4Kwn4fpPDTQkSZIkSaVORKHGCy+8wObNm6lbt+5+x+vVq0dGRgYvvPBCJI+RJEkqfTLXwOgb4PleMPEP+fUaLaHTVRDvhFtJkiRJUukTUagxf/58OnXqdNBrTjjhBL788stIHiNJklR6ZG2FCX+Ap46Hef8BwpC9E/Lyot2ZJEmSJElRF9E/8du4cSM1a9Y86DXVqlVj/fr1kTxGkiSp5MvLhbn/gQkPwdYfglqDk6DfH6F+x+j2JkmSJElSMRFRqFG9enUWL1580GsWL15M5cqVI3mMJElSyffZU/DxA8FxlUZw6u+h9TnB5uCSJEmSJAmIcPmpbt268c4777Bw4cL9jn/zzTeMGTOGHj16RPIYSZKkkikvN//4+CuCMKPvH+Cmz6HNuQYakiRJkiT9QkShxt13301OTg7du3fnqaee4ttvv2Xbtm18++23/OUvf6FHjx7k5uZy9913F1a/kiRJsW/benjvLnh1AITDQa18VbhlNnS9GRKSotufJEmSJEnFVCgc3v2T9JH5xz/+wU033URubu4+Y/Hx8TzzzDMMGTIkkkeUKJmZmaSkpJCRkUFycnK025EkSUUpeyfMeBY+eRyyMoPa1R9DgxOi25ckSZIkSUUoku/JI9pTA+Caa66he/fuPPPMM8yYMYPNmzdTuXJlTjrpJG644QZat24d6SMkSZJiWzgMX40K9szYvCKo1W4fbAJuoCFJkiRJ0mGLeKaGCsaZGpIklTKZ38Mbg2DVF8F5pbrQ535ofxHERbQSqCRJkiRJMSmqMzV+bsuWLWzevJmUlBS/sJckSQKoUAN2ZkBiBeh+O3S5GcqUj3ZXkiRJkiTFpIj/eWBOTg6PPvoozZo1o3LlyjRq1IgqVarQrFkzHn30UXJycgqjT0mSpNiwMwOm/B/kZAXn8Qkw4AW4dTb0/JWBhiRJkiRJEYhopsauXbs47bTTmDx5MqFQiAYNGlCnTh2+//570tPTuffee/nwww8ZO3YsZcqUKayeJUmSip+8XJg9Aib8Abavh4Sy0PWWYKzOsdHtTZIkSZKkEiKimRpPPPEEkyZN4swzz+Sbb74hPT2dadOmkZ6ezqJFizj77LP55JNPeOKJJwqrX0mSpOJn6WR4tge8e0cQaFRrDjVbR7srSZIkSZJKnIg2Cm/fvj0Ac+fOJW4/G13m5eXRoUMHwuEw8+fPP/IuSxA3CpckqQTZsATG3geL3gvOy1aGtN9Cp6sgPjGqrUmSJEmSVFxF8j15RDM1vvvuO04//fT9BhoAcXFxnH766SxZsiSSx0iSJBVPH/02CDRC8XDidXDrHOh8nYGGJEmSJElHSUR7apQpU4atW7ce9Jpt27aRmOgP9pIkqQTIy4WcnVCmQnB+yoPBr6f+Hmq0jF5fkiRJkiSVEhHN1Gjfvj1vvvkmP/74437H169fz5tvvsmxx7o5piRJinG7980Y+7v8Ws1WcMnrBhqSJEmSJBWRiEKNm2++mR9//JETTzyRF198kaVLl7Jjxw6WLVvGv/71Lzp37syPP/7IzTffXFj9SpIkFa0NS+C1S+Dlc2DdV/D1GNiZGe2uJEmSJEkqlSJafurCCy9k7ty5PProo1x77bX7jIfDYX71q19x4YUXRvIYSZKkordjM0z5P5jxHORlB/tmnDAEeg2FsgXbxEySJEmSJBWOUDgcDkd6k+nTp/Piiy8yZ84cMjIySElJ4bjjjuOqq66iS5cuhdFniRHJru6SJKmILJ8Gr18K2zcE581OgX5/dJkpSZIkSZIKQSTfk0c0U2O3k046iZNOOqkwbiVJkhR9NVpCXg5UbxGEGc1PjXZHkiRJkiSJQgo1JEmSYtqGJTB/JPT8NYRCUL4qXPEu1GwN8YnR7k6SJEmSJP3EUEOSJJVev9w3o04HaHlaMFanfTQ7kyRJkiRJ+1GgUOOqq646ooeEQiFefPHFI3qvJElSocvNgTkvw4SHf7ZvxqlQtUl0+5IkSZIkSQdVoI3C4+LijuwhoRC5ublH9N6Sxo3CJUmKsqWT4MPfwrqvgnP3zZAkSZIkqUgV2Ubhy5YtK9DNJUmSipXcHHjvLtjwHZStDGm/hU5XuW+GJEmSJEkxokChRmpq6tHqQ5Ik6ejYmQGJ5YPgIj4B+j0C330MvYYGG4JLkiRJkqSYcWTrSe3Htm3bmDNnDp988klh3VKSJOnI5eXB7Jfhrx3h83/k11v0hTP+ZKAhSZIkSVIMijjUWLVqFQMGDKBKlSp06tSJtLS0PWOffvopbdq0YdKkSZE+RpIk6fCtmAH/SIN3boFtP8KCt+DwtxGTJEmSJEnFVIGWn/ql77//ns6dO/PDDz9wzjnnsG7dOqZNm7ZnvHPnzqxbt47XX3+dXr16RdqrJEnSwWWugXHDYP4bwXlScrDM1InXQigU3d4kSZIkSVLEIpqp8eCDD7Ju3TrGjRvHqFGjOPXUU/caT0xMpEePHkydOjWiJiVJkg5pwVvw104/BRohOP5yuGU2dLnJjcAlSZIkSSohIpqp8f7773POOefsteTULzVs2NB9NiRJ0tFXsw3k7IQGneH0x6DucdHuSJIkSZIkFbKIQo0ffviB5s2bH/SaxMREtm3bFsljJEmS9rXuG1j+GZxwdXBeszUM+TgIM1xqSpIkSZKkEimiUKNq1aqsXLnyoNd8++231K5dO5LHSJIk5duxCSY9Cp//Izhv2AVqtQmO6x0fvb4kSZIkSdJRF9GeGt26deOdd95h7dq1+x1fvHgxH3744UGXp5IkSTosebkw85/w1PEw41kI50LL0yGpYrQ7kyRJkiRJRSSiUOOee+5h586d9OzZkw8++IDt27cDsG3bNj744APOPvts4uLiuOuuuwqlWUmSVEot/wye7wnv3gE7NkKN1jDobbj431C5YbS7kyRJkiRJRSSi5ac6d+7Mc889xw033MBZZ521p56cnBzcPCGBf/7znxxzzDGRdSlJkkqvrC3w2sWwMwPKpkDavdDpaoiP6K8xkiRJkiQpBoXC4XA40pssXryYZ555hunTp7NhwwZSUlI46aSTuPnmm2nZsmVh9FliZGZmkpKSQkZGxp7wR5Ik/UJOFiQk5Z/PeC7YGLz376BC9ej1JUmSJEmSIhbJ9+QFDjXGjBmzZ1kpFZyhhiRJBxEOw9djYOzv4MwnoEXfaHckSZIkSZIKWSTfkxc4mTjvvPNITU3l/vvvZ/ny5QV9uyRJ0v6tXQAjzoaRV0DGSpj2dLQ7kiRJkiRJxUyBQ41TTjmFNWvW8PDDD9O0aVNOP/10Ro8eTW5u7tHoT5IklXTbN8J7d8FzPSD9E0goCz1/Df/vvwW6zbasHJ6Z9B2/GTWfZyZ9x7asnKPUsCRJkiRJipYj2lNj+fLlvPDCC7z00kusXr2aUChEzZo1ufLKKxkyZAhNmjQ5Gr2WCC4/JUnSz8x/E96/G3ZsCs7bnAt9H4bKDQt0m21ZOZz3zFS+W7eVuFCIvHCYZjUrMvrGblRIckNxSZIkSZKKkyJdfgogNTWVhx56iOXLl/POO+9w1llnsWHDBh599FFatGhB3759efPNN8nJ8V9ISpKkgyhTIQg0ah4DV/wPLny5wIEGwIhp6Xy3bit5YcjJC5MXhu/WbWXEtPTC71mSJEmSJEVNRLt9x8XFcdZZZzFmzBhWrFjBww8/TGpqKh9//DEXXXQR9erV49e//jWLFy8urH4lSVIsy1gN343PP29xGlz0b7huCjQ++Yhvu3LjDuJCob1qcaEQKzfuOOJ7SpIkSZKk4ieiUOPnateuzW9/+1uWLFnCuHHjuPDCC8nMzOTPf/4zrVu3LqzHSJKkWJSTBZ88Dk93gjevhG3rg3ooBK3PgvjIlohqULUceb9YUTMvHKZB1XIR3VeSJEmSJBUvR2WR6Z49e7Jx40aWLVvG559/fjQeIUmSYsXicfDBr2HjkuC89kmQlQkVqhfaI67o0oi356zeZ0+NK7o0KrRnSJIkSZKk6CvUUGPRokW88MILvPzyy6xfv55wOEyjRo0YMmRIYT5GkiTFgo3L4KPfwqL3g/OKteDUh6D9hcEMjUJUISmB0Td2Y8S0dFZu3EGDquW4oksjNwmXJEmSJKmEifgn/Z07d/LGG2/wwgsvMHXqVMLhMImJiZx//vlcc8019O3btzD6lCRJsWTbBvh7V8jeDnEJ0Pl66PlrKJt81B5ZISmBG3s1O2r3lyRJkiRJ0XfEocbcuXP5xz/+wWuvvUZGRgbhcJimTZsyZMgQrrzySmrWrFmYfUqSpFhSoRp0uATWL4bT/wQ1W0W7I0mSJEmSVAIUONR49tlneeGFF5gzZw7hcJgyZcpwwQUXcO2119K7d++j0aMkSSrufvwWxt4LfR+GGi2DWr8/QnyZQl9qSpIkSZIklV4FDjVuvPFGAFq0aME111zDFVdcQfXqhbfRpyRJiiFZW2DyYzD975CXE9QuHRn8mpAUvb4kSZIkSVKJVOBQ45JLLuGaa66hZ8+eR6MfSZIUC8JhmD8Sxt4HW9cGtRanBbMzJEmSJEmSjpIChxqvvvrq0ehDkiTFirXz4f17YMW04LxqEzjtUWjRL7p9SZIkSZKkEu+INwqXJEml1JKJQaCRWB5Ovhu63OxSU5IkSZIkqUgYakiSpIPLy4UtayGlXnDe+XrY8j10uQlS6ke3N0mSJEmSVKrERbuBw/Hmm29yyy230KNHD5KTkwmFQlx22WUFusdLL71EKBQ66Cs+Pn6v96Snpx/0+osvvrgwP6YkScXPqpnwQh94pT/k7ApqCWXgtEcMNCRJkiRJUpGLiZkaDz/8MPPmzaNixYrUr1+fhQsXFvgeHTp0YNiwYfsd++STT5gwYQKnn376fsePPfZY+vfvv0+9bdu2Be5DkqSYsPVHGP8AzPlpL62kZFj3FdQ9LqptSZIkSZKk0i0mQo0nn3yS+vXr06xZMyZPnkxaWlqB79GhQwc6dOiw37EuXboAcO211x7wvQ888ECBnylJUszJzYGZL8KEP0BWRlA79hI45QGoVCuqrUmSJEmSJMVEqHEkIcbhmj9/PtOnT6devXqceeaZR+05kiQVe9s3wktnBTMyAOocC2f8GRqcGN2+JEmSJEmSfhITocbR9PzzzwNw9dVX77Onxm5r1qzhueeeY8OGDVSrVo0uXbrQvn37w7p/VlYWWVlZe84zMzMjb1qSpKOhXBWoUD34tc/9cPwVELf//2+UJEmSJEmKhlIdauzYsYNXX32V+Ph4hgwZcsDrxo0bx7hx4/aq9erVixEjRtCwYcODPuORRx7hwQcfLJR+JUkqVLk5MOtf0G5gEGSEQnDu01CmIpSvGu3uJEmSJEmS9hEX7Qai6Y033mDz5s2cdtppNGjQYJ/x8uXLc9999zFr1iw2bdrEpk2b9uzpMWnSJPr06cO2bdsO+ozf/OY3ZGRk7HmtXLnyaH0cSZIO34rp8HxPeP/uYP+M3So3NNCQJEmSJEnFVqmeqbF76anrrrtuv+M1a9bk97///V61k08+mbFjx9K9e3dmzJjBCy+8wG233XbAZyQlJZGUlFR4TUuSFImtP8K4+2Hef4LzclWgdrvo9iRJkiRJknSYSu1Mja+++orPPvuM+vXrc8YZZxTovQkJCXuWq5oyZcrRaE+SpMKVlwuf/wOe7pgfaBx/Odw8CzpeEd3eJEmSJEmSDlOpnalxOBuEH0yNGjUADrn8lCRJxcKU/4NJjwTHtdvDmU9AgxOi25MkSZIkSVIBlcpQY+fOnbzyyivEx8dz9dVXH9E9pk+fDkCTJk0KszVJko6OE4bAvNegy83Q6SqIK3igL0mSJEmSFG0lbvmp7OxsFi5cyJIlSw54zciRI9m0aROnn376fjcI32327Nnk5eXtUx8/fjxPPvkkAJdddlnkTUuSVJjy8mDWSzD6egiHg1qF6sFSUydeY6AhSZIkSZJiVkzM1Hj77bd5++23AVi7di0A06ZNY/DgwQBUr16dP//5zwCsXr2a1q1bk5qaSnp6+n7vt3vpqWuvvfagz73zzjtZvHgxXbt2pX79+gB8+eWXTJgwAYCHHnqIrl27RvLRJEkqXGvmwHt3wepZwXm7C6BZn+A4Pib+b1+SJEmSJOmAYuLbjblz5zJixIi9akuXLmXp0qUApKam7gk1DuWbb77h008/PawNwgcNGsTo0aP54osv+OCDD8jOzqZWrVpceOGF3HzzzfTo0ePIPpAkSYVtxyYY/xDM/CcQhjKVoPe90LhntDuTJEmSJEkqNKFwePe6FCoKmZmZpKSkkJGRQXJycrTbkSTFurw8mPcfGHc/bN8Q1NpdCH0fgkq1o9ubJEmSJEnSfkTyPXlMzNSQJEkHkLsLpvw5CDRqtIIz/gyNnUkoSZIkSZJKJkMNSZJizc4MSKwQ7JGRWBbOfBx++ApOugHiE6PdnSRJkiRJ0lETF+0GJEnSYQqHYd7r8NdOMPPF/HqzPtDtVgMNSZIkSZJU4hlqSJIUC374Gl46E0ZfC9vWwZevByGHJEmSJElSKeLyU5IkFWdZW2DSozD97xDOhYRy0PMe6HIzhELR7k6SJEmSJKlIGWpIklRcLZkIb98AW74PzludBac9CpUbRLcvSZIkSZKkKDHUkCSpuKpQHbaugyqN4Yz/g+anRrsjSZIkSZKkqDLUkCSpuMjeCSs+g6a9g/Pa7eDSNyC1OySWjW5vkiRJkiRJxYAbhUvS/2/vvuOrLu///z9OEpIAIcGwkZAwBETEAR8UkO0CtYigUHette6i9vetoy24a0VBbV110WodICBoXYUACqiIigMBGVFQGTICBAgZ798fp0RjgiaQ5OQkj/vtllvPdb3X650bXH17nlzvS6oOVs6Ch3rCM2eGFwXfq/3xBhqSJEmSJEn/40wNSZIiafs6eP1G+PTFcDupOeRsADpHtCxJkiRJkqTqyFBDkqRIKCyAhY/DrFshdxuEYqDHJTDgRkhMiXR1kiRJkiRJ1ZKhhiRJVS0I4J9DIeutcLvl0XDqeGh5ZETLkiRJkiRJqu4MNSRJqmqhEHQcDN9+DMf/Gbr9CmJiI12VJEmSJElStWeoIUlSZQsC+GQyNGgObfqE+3r8Fg4/E5KaRrY2SZIkSZKkKGKoIUlSZfpuBbxyLayeA6nt4LL5UCcRYuMMNCRJkiRJksrJUEOSpMqQtwveuhfmTYCCPRCXCEf+MrwguCRJkiRJkvaLoYYkSRXti//Cf34PW1aH2+1PgCF3Q2qbyNYlSZIkSZIU5Qw1JEmqSGveg2eGhz83aAmD/wKH/iK8OLgkSZIkSZIOiKGGJEkVqdX/QYfBkNoWBtwACQ0iXZEkSZIkSVKNYaghSdKBWPs+zLoVRjwJ9VLDMzJG/RtiXDtDkiRJkiSpovmNiyRJ+2PXFpgxGh47HlbNhtl3fr/NQEOSJEmSJKlSOFNDkqTyCAL4+Hl4/SbY+V2474izoe//i2xdkiRJkiRJtYChhiRJZfXdF/DyNZD1VrjduCOcei9kHBfZuiRJkiRJkmoJQw1Jkspqwd/DgUZcXej3/0HPqyAuPtJVSZIkSZIk1RqGGpIk/ZT8Pd8HF4P+DHt2wMA/wkEZES1LkiRJkiSpNjLUkCSpNDmb4I0/Qs4GOGcyhEJQLxWGPxbpyiRJkiRJkmotQw1Jkn4oCGDxs+GFwHdtBkLw7UfQ8qhIVyZJkiRJklTrGWpIkrTXjxcCb3oYnHafgYYkSZIkSVI1YaghSVJ+Lrw9Ht66Bwr2hBcC73899LwCYutEujpJkiRJkiT9j6GGJElBAJ9MCgca7Y+HU+5xIXBJkiRJkqRqyFBDklQ77dwMCckQGwd1EuEXD8D2dXDYsPCi4JIkSZIkSap2YiJdgCRJVSoI4KN/wwPd4N2Hv+9P7wVdzjDQkCRJkiRJqsYMNSRJtcd3X8DE02DaZbBrMyx5CQoLI12VJEmSJEmSysjXT0mSar6fWgg8xnxfkiRJkiQpWhhqSJJqtq8XwZRLYNOKcNuFwCVJkiRJkqKWoYYkqWarUw+2ZEFSMzj5Ly4ELkmSJEmSFMUMNSRJNUsQwDcfwsFHh9tND4Wz/hVeCLxuw4iWJkmSJEmSpAPji8QlSTXHdyvCC4E/Ngi+Xfx9f6chBhqSJEmSJEk1gDM1JEnRLz8X3p4Ab437fiHwDUuhxRGRrkySJEmSJEkVyFBDkhTdsubBy6Phu+XhdrtB4YXAU9tEtCxJkiRJkiRVPEMNSVL0ev0mWPC38Of6TWHwX+CwM1wIXJIkSZIkqYYy1JAkRa+DMsL/2+1COP5m182QJEmSJEmq4Qw1JEnRI3st7NgABx8dbne/CA7u9n1bkiRJkiRJNVpMpAuQJOlnFRbAu4/C34+BSRfCnpxwf0ysgYYkSZIkSVIt4kwNSVL1tn4JzLga1i4Mt5sdBru2Qnz9iJYlSZIkSZKkqmeoIUmqnvJ2w9y7Yd4EKMyH+AZw/Bjo/muIcaKhJEmSJElSbWSoIUmqfnZuhsdPgE0rwu2Op8CQuyHl4MjWJUmSJEmSpIgy1JAkVT91D4LGHSF3RzjM6PyLSFckSZIkSZKkasBQQ5IUeUEAS16CjOOgfmMIheC0CRAbD3UbRro6SZIkSZIkVRO+lFySFFnZa+HZUTDpAnj9xu/7k5oaaEiSJEmSJKkYZ2pIkiKjsAAWPgYzb4E9OyCmDhzUJjxrIxSKdHWSJEmSJEmqhgw1JElVb/1nMP1q+Pr9cDvtGDjtfmjaKbJ1SZIkSZIkqVoz1JAkVa2l/4EXzoPCfEhIhuPHQLeLIMY3IkqSJEmSJOmnGWpIkqpWei+o1xhadYchd0Nyy0hXJEmSJEmSpChhqCFJqly7tsCHz0DPK8JrZdRtCL+dCw2aRboySZIkSZIkRRlDDUlS5QgC+GwKvHo95GyAeo3gyF+GtxloSJIkSZIkaT8YakiSKt7WNfDKdfDF6+F2446Q2jayNUmSJEmSJCnqGWpIkipOYSEsfAxm3gx7dkBMHej7ezjuGohLiHR1kiRJkiRJinKGGpKkijP9SvjomfDntGPhF/dDk46RrUmSJEmSJEk1RkykC5Ak1SBHnQcJyTBkHPzqVQMNSZIkSZIkVShnakiS9t/XH8B3X8ARI8Pt9J5wzaeQmBLZuiRJkiRJklQjGWpIkspvz06YfScs+BvExkOr7tCoXXibgYYkSZIkSZIqiaGGJKl8Vs+F6VfDltXhdqdTDTIkSZIkSZJUJQw1JElls2srvPln+GBiuN2gJZw6HjqeHNGyJEmSJEmSVHsYakiSfl5+LjzSB7Z+FW53/zUcPxYSkyNaliRJkiRJkmoXQw1J0s+LS4Ajz4WPn4dfPAAZvSNdkSRJkiRJkmohQw1JUklBEA4wmnSElkeF+467BnpfDXXqRrY2SZIkSZIk1VqGGpKk4rZ+BTNGw8qZ0KwLXDIbYutAXHykK5MkSZIkSVItZ6ghSQorLISF/4D/3gx5ORCbAF3OiHRVkiRJkiRJUhFDDUkSbFwG06+CNe+G2617htfOaHxIZOuSJEmSJEmSfsBQQ5Jqu28+gsdPgII9EJ8EJ9wM3S6CmJhIVyZJkiRJkiQVY6ghSbVd865wcHdIaACn3gsprSJdkSRJkiRJklSqqPhnuJMnT+aqq66iT58+JCcnEwqFOPfcc8t9noyMDEKhUKk/zZs33+dx8+fPZ8iQIaSmplK3bl26du3KhAkTKCgoOJDbkqTI2LMT5vwVcneE2zExcPbz4R8DDUmSJEmSJFVjUTFT47bbbmPx4sUkJSXRqlUrli5dut/nSklJYfTo0SX6k5KSSt3/pZdeYvjw4SQmJjJy5EhSU1OZMWMG11xzDfPmzWPSpEn7XYskVblVc2DG1bAlC3ZuhsF/CfcnJke0LEmSJEmSJKksoiLUGD9+PK1ataJ9+/bMmTOHAQMG7Pe5GjZsyNixY8u077Zt2/jNb35DbGwss2fPpnv37gDceuutDBw4kMmTJ/Pcc88xatSo/a5HkqrErq3w5p/gg3+G28kHQ7v9H0slSZIkSZKkSIiK108NGDCAQw45hFAoVKXXnTx5Mhs3bmTUqFFFgQZAYmIit912GwAPPfRQldYkKfrk5Obz4OwV3DDlEx6cvYKc3PyqLWDZa/Dgsd8HGt1/DZe/Ax1Oqto6JEmSJEmSpAMUFTM1KlJubi5PP/00X331FfXr16dr16707duX2NjYEvvOmjULgJNPPrnEtr59+1KvXj3mz59Pbm4uCQkJlV67pOiTk5vPsAfnsWLDDmJCIQqDgGkffs3Uy3tTP6EKhuB3H4FX/1/4c2o7+MUDkNG78q8rSZIkSZIkVYJaF2qsW7eO8847r1hfmzZtePLJJ+nXr1+x/mXLlgHQoUOHEueJi4ujTZs2fPbZZ6xatYpDDz201Ovl5uaSm5tb1N62bduB3oKkKDJxQRYrNuygMIDCIABgxYYdTFyQxeX921d+AZ2Hwuy/wFHnwoAboU7dyr+mJEmSJEmSVEmi4vVTFeVXv/oVM2fOZN26deTk5PDJJ5/w29/+lqysLAYPHszixYuL7Z+dnQ2EFxcvzd7+rVu37vOad955JykpKUU/aWlpFXMzkqLCms27iPnRq/NiQiHWbN5VORfcsRHe+8f37QbN4XcfwYm3GmhIkiRJkiQp6tWqUGPMmDEMHDiQZs2aUa9ePbp06cLDDz/Mtddey65du8q8gHh53HDDDWRnZxf9rFmzpsKvIan6SkutWzRDY6/CICAttYIDhiCAjyfB33vAf34fXkdjr8TSg1lJkiRJkiQp2tSqUGNfLr30UgDmzp1brH/vTIy9MzZ+bG9/w4YN93nuhIQEkpOTi/1Iqj0u6JlB+6ZJxIQgLiZETAjaN03igp4ZFXeRbd/Cc2fDlIth12Zodjgkt6y480uSJEmSJEnVRK1bU6M0TZo0ASAnJ6dYf8eOHXn//fdZvnw53bp1K7YtPz+f1atXExcXR9u2bausVknRpX5CHFMv783EBVms2byLtNS6XNAzo2IWCQ8C+OgZeO1GyM2GmDrQ7w9w3GiIrXPg55ckSZIkSZKqGUMN4J133gEoEU4MHDiQZ555htdee41f/vKXxbbNnTuXnTt30rdvXxISEqqsVknRp35CXOUsCv7SFeFQA6Dl0TD079Csc8VfR5IkSZIkSaomatzrp/Ly8li6dCkrV64s1v/555+XmIkBkJWVxZVXXgnAueeeW2zbiBEjaNy4Mc899xzvv/9+Uf/u3bv54x//CMBll11W0bcgSWXT6VSIS4QTboFfv2mgIUmSJEmSpBovFAQ/WsG2Gpo2bRrTpk0DYN26dbz++uu0bduWPn36ANC4cWPGjRsHhEOKNm3akJ6eTlZWVtE5xo4dyz333EPfvn1JT0+nQYMGrFy5kldeeYXdu3czZMgQpk6dSnx8fIlrjxgxgsTEREaNGkVqairTp09n2bJljBgxghdeeIFQKFTme9m2bRspKSlkZ2e7voak8tm8GjavhPbHf9+3fR00aB65miRJkiRJkqRyOpDvyaPi9VMfffQREydOLNa3atUqVq1aBUB6enpRqLEvAwYMYNmyZXz44YfMmzePnJwcGjZsyHHHHcd5553HeeedV2o4cfrppzNnzhxuv/12XnzxRXbv3k379u259957ufrqq8sVaEjSfikshPcegZm3hNfKuOK974MMAw1JkiRJkiTVIlExU6MmcaaGpHL57gt46UpYE177h4w+cPpD0DAtsnVJkiRJkiRJ+6nGz9SQpFqnIB/e+Ttk3gH5uyE+Kbx2RrdfQUyNWw5JkiRJkiRJKhNDDUmqbvL3wFNDYO3CcLvdQDjtfmdnSJIkSZIkqdYz1JCk6iYuHg7uBhuXw8l3wJHngOv3SJIkSZIkSYYaklQtfPsxJCRBattwe9CfoffvILllZOuSJEmSJEmSqhFfzC5JkZSfC7Nug38MCC8IXlgY7o+vb6AhSZIkSZIk/YgzNSQpUr5eBNOugI2fh9v1G0PezvCMDUmSJEmSJEklGGpIUlXL2w1z/gLz7oOgEOo3gSHj4LDTI12ZJEmSJEmSVK0ZakhSVdr6FTxzJmxcGm4ffiYM/ivUS41sXZIkSZIkSVIUMNSQpKqU1Bxi4qB+UzhtAnQ6JdIVSZIkSZIkSVHDUEOSKtu6T6BJJ4itA3HxcObE8MwMZ2dIkiRJkiRJ5RIT6QIkqcbKz4WZt8Aj/eDt8d/3N25voCFJkiRJkiTtB2dqSFJl+OZDmHY5bFgSbm9eBUEAoVBk65IkSZIkSZKimKGGJFWk/FyY89fwzIygAOo1hlPHQ+dfRLoySZIkSZIkKeoZakhSRVm/BF789fezMw47A4aMg/qNIluXJEmSJEmSVEMYakhSRYmND79mql5jOOUeOOz0SFckSZIkSZIk1SiGGpJ0ILavhwbNwp8bt4ez/gkHd4P6jSNblyRJkiRJklQDxUS6AEmKSvl7IPMOmNAFvnrn+/4OJxloSJIkSZIkSZXEmRqSVF7ffgzTLof1n4Tbn8+A1sdGtiZJkiRJkiSpFjDUkKSyyt8Db90Db42DwnyomxpeO6PLGZGuTJIkSZIkSaoVDDUkqSzWfQJTL/t+dsahv4BT7oWkJpGtS5IkSZIkSapFDDUkqSy+XRwONOqmwpC7octwCIUiXZUkSZIkSZJUqxhqSNK+5O2GOonhz0eeAzvWw1HnQVLTyNYlSZIkSZIk1VIxkS5AkqqdgjyY81f4ew/YtTXcFwpBn+sMNCRJkiRJkqQIMtSQpB9a/xk8Nggyb4etX8LHL0S6IkmSJEmSJEn/4+unJAmgIB/mjYfZd0FhHiQ2hCHj4PARka5MkiRJkiRJ0v8YakjSxuUw9bfwzQfhdsdT4NTx0KBZZOuSJEmSJEmSVIyhhiS9dU840EhIgSF/ha4jw2toSJIkSZIkSapWDDUk6eQ7gQAGjYGUgyNdjSRJkiRJkqR9cKFwSbVLEMD7T8DUS8OfAeqlwhmPGmhIkiRJkiRJ1ZwzNSTVHtlfw/QrYeWscLvLcDjkhMjWJEmSJEmSJKnMDDUk1XxBAIufg1f/ALnZEJcYftVUu0GRrkySJEmSJElSORhqSKrZdmyAGaNh2Svh9sHdYdjD0PiQiJYlSZIkSZIkqfwMNSTVXEEAz5wJ334EMXWg//XQezTEOvRJkiRJkiRJ0ciFwiXVXKEQnHALND8cLsmEvr830JAkSZIkSZKimN/uSapZlr8Ou7Oh61nhdtt+cMlciDHDlSRJkiRJkqKdoYakmmH3Nnj9BvjwaahTH9J6wEEZ4W0GGpIkSZIkSVKNYKghKfqtmgMvXQHZa4AQdP8VJDWLdFWSJEmSJEmSKpihhqTotScH/jsW3ns03D4oA05/CNJ7RbIqSZIkSZIkSZXEUENSdMrbDY/2h++Wh9vdfx1eFDwhKaJlSZIkSZIkSao8hhqSolOdROh0Knz8PPziAWg/KNIVSZIkSZIkSapkhhqSosc3H4YXAW/SIdzufz30/h3UbRjRsiRJkiRJkiRVjZhIFyBJP6sgDzLvhH8MgqmXhNsAcQkGGpIkSZIkSVIt4kwNSdXb+iUw9bew7uNwu2E65O2C2DqRrUuSJEmSJElSlTPUkFQ9FRbAgr/DrFuhYA/UPQhOuQe6DI90ZZIkSZIkSZIixFBDUvWTswleOA++nBduH3IS/OJ+aNA8snVJkiRJkiRJiihDDUnVT2IK5O+G+CQ46Q44+nwIhSJdlSRJkiRJkqQIM9SQVD3s2AiJyeHFv2PjYPhjQAhS20S6MkmSJEmSJEnVREykC5AkPn8ZHjwWMm//vi+1rYGGJEmSJEmSpGIMNSRFzu5smHoZPH8O7PwOVs6C/NxIVyVJkiRJkiSpmvL1U5IiY/VbMO0yyF4DhKD372DAjeHXT0mSJEmSJElSKQw1JFWtvN0w8xZ45+/hdsN0GPYIpPeMbF2SJEmSJEmSqj1DDUlVK2cDfPDP8OejL4CTboeEBpGtSZIkSZIkSVJUMNSQVPmCAEKh8OeGreEX90Gd+tDx5MjWJUmSJEmSJCmquFC4pMq1aSU8fiKszPy+r8twAw1JkiRJkiRJ5WaoIalyBAG89w94+DhY+x68fiMUFka6KkmSJEmSJElRzNdPSap4276Bl66ElTPD7TZ94fSHIMYcVZIkSZIkSdL+M9SQVLE+mQyvXAe7t0JcIhx/M/S4xEBDkiRJkiRJ0gEz1JBUcb5cAC/+Ovy5xZFwxqPQpGNES5IkSZIkSZJUcxhqSKo4rY+Fw8+C1LbQ9/cQWyfSFUmSJEmSJEmqQQw1JO2/PTkw5y7odTXUbwyhUHh2RigU6cokSZIkSZIk1UCGGpL2z9r3YcolsHklbF4FI58O9xtoSJIkSZIkSaokhhqSyqcgD+b8Fd66B4ICaNASul8U6aokSZIkSZIk1QKGGpLKbuNymPIb+PajcPvws2DIX6HuQREtS5IkSZIkSVLtYKghqWxWZsKzv4T8XeEQ49TxcNiwSFclSZIkSZIkqRYx1JBUNgcfDfWbQOP2MPRBSG4R6YokSZIkSZIk1TKGGpL27csF0PrY8OLfiSlw0WvQoAXExES6MkmSJEmSJEm1kN9MSiopdztMuwKePBkWPfl9f8rBBhqSJEmSJEmSIsaZGpKK++pdmHoJbMkCQrBjQ6QrkiRJkiRJkiTAUEPSXgV5MOcueOseCAohpTUMexgyeke6MkmSJEmSJEkCDDUkAXy3AqZcDN98GG53HQVD/hpeR0OSJEmSJEmSqglDDUmwcxN8uxgSG8Kp46HLGZGuSJIkSZIkSZJKMNSQaquCPIitE/7c+hg4/SHI6BNeDFySJEmSJEmSqqGYSBdQFpMnT+aqq66iT58+JCcnEwqFOPfcc8t1jk2bNvHYY48xbNgw2rdvT926dUlJSeG4447j8ccfp7CwsMQxWVlZhEKhff6MGjWqom5RqlpL/wP3HwUbln7fd8QoAw1JkiRJkiRJ1VpUzNS47bbbWLx4MUlJSbRq1YqlS5f+/EE/MmnSJC677DJatGjBgAEDaN26NevXr2fKlClcfPHFvPrqq0yaNIlQKFTi2COOOILTTz+9RH+XLl3253akyMndAa/fAB/8M9x+ezyc8Uhka5IkSZIkSZKkMoqKUGP8+PG0atWK9u3bM2fOHAYMGFDuc3To0IHp06dzyimnEBPz/QSVO+64gx49evDiiy8yZcoUhg8fXuLYI488krFjxx7ILUiRt2YhTPkNbFkNhKDXVTDwj5GuSpIkSZIkSZLKLCpCjf0JMX5s4MCBpfY3b96cSy+9lJtuuonZs2eXGmpIkZCTm8/EBVms2byLtNS6XNAzg/oJ+/FXtiAP5o6DuXdDUADJrWDYw9CmT8UXLUmSJEmSJEmVKCpCjcpWp054seS4uNJ/Hd988w2PPPIImzZtolGjRvTs2ZOuXbtWZYmqZXJy8xn24DxWbNhBTChEYRAw7cOvmXp57/IHGx89A3P+Ev58+JkwZBzUbVjhNUuSJEmSJElSZav1oUZ+fj7//Gd4fYGTTz651H3efPNN3nzzzWJ9/fv3Z+LEibRu3fonz5+bm0tubm5Re9u2bQdYsWqDiQuyWLFhB4UBFAYBACs27GDigiwu79++fCc78lxY+gp0HQmHj6iEaiVJkiRJkiSpasT8/C412/XXX8+nn37KkCFDOOmkk4ptq1evHn/6059YtGgRW7ZsYcuWLUVresyePZtBgwaRk5Pzk+e/8847SUlJKfpJS0urzNtRDbFm8y5ifrRofUwoxJrNu37+4B0b4bUbIG93uB0bB+dMMtCQJEmSJEmSFPVqdahx//33c88999CpUyf+9a9/ldjetGlTbrnlFo4++mgaNmxIw4YN6du3L2+88QbHHHMMK1as4LHHHvvJa9xwww1kZ2cX/axZs6aybkc1SFpq3aIZGnsVBgFpqXV/+sBlr8KDx8I7D0Lm7ZVYoSRJkiRJkiRVvVobavztb3/jd7/7HZ07dyYzM5PU1NQyHxsXF8fFF18MwNy5c39y34SEBJKTk4v9SD/ngp4ZtG+aREwI4mJCxISgfdMkLuiZUfoBe3Jgxu/g2VGw8ztoelj4dVOSJEmSJEmSVIPUyjU1JkyYwDXXXEOXLl2YOXMmTZs2Lfc5mjRpAvCzr5+S9kf9hDimXt6biQuyWLN5F2mpdbmgZ0bpi4R/vQhe/A1sXhlu97wSBv4J6iRWbdGSJEmSJEmSVMlqXahx1113cf3113PkkUfy5ptv0rhx4/06zzvvvANA27ZtK7I8qUj9hLifXxT840kw7VIozIcGLWHYQ9C2f5XUJ0mSJEmSJElVrca9fiovL4+lS5eycuXKEttuvfVWrr/+erp168bMmTN/NtD44IMPKCwsLNE/c+ZMxo8fD8C5555bMYVL+yOtB9SpB51Ph8vnG2hIkiRJkiRJqtGiYqbGtGnTmDZtGgDr1q0DYMGCBVx44YUANG7cmHHjxgHw9ddfc+ihh5Kenk5WVlbROSZOnMif//xnYmNj6dOnD/fff3+J62RkZBSdE+Daa6/liy++oFevXrRq1QqAjz/+mFmzZgHhkKRXr14VfLfSz/j2Y2jRNfz5oHS49C1omA6hUGTrkiRJkiRJkqRKFhWhxkcffcTEiROL9a1atYpVq1YBkJ6eXhRq7Mvq1asBKCgoYMKECaXu069fv2KhxnnnncfUqVNZuHAhr776Knl5eTRr1oyzzjqLK6+8kj59+uz/TUnltXsbvPr/YPGzcO6L0P74cP9BGREtS5IkSZIkSZKqSigIgiDSRdQm27ZtIyUlhezsbJKTkyNdjqLFmvdgym9gSxaEYuCEW6HXlZGuSpIkSZIkSZLK7UC+J4+KmRpSrVWQD2/dA3PugqAAUlrDGY9Ces9IVyZJkiRJkiRJVc5QQ6qutmTBlEtgzbvh9uFnwin3QGJKRMuSJEmSJEmSpEgx1JCqq7XvhwONhORwmNH1rEhXJEmSJEmSJEkRZaghVSdBAKFQ+PPhI2DrV9DlDBcDlyRJkiRJkiQgJtIFSPqfLxfAY8fDjo3f9/W51kBDkiRJkiRJkv7HUEOKtII8mHUbPDUEvn4fMm+PdEWSJEmSJEmSVC35+ikpkjathCm/ga8XhdtHnA0n3hrZmiRJkiRJkiSpmjLUkCIhCOCjf8Or/w/27IDEFDh1PHQZHunKJEmSJEmSJKnaMtSQIuH9x+GV68Kf03vDsEegYVpka5IkSZIkSZKkas41NaRIOPwsaHQIDBoDF8ww0JAkSZIkSZKkMnCmhlQV8vfAJy/AkedAKASJyXDZPIhLiHRlkiRJkiRJkhQ1DDWkyvbdF/Dir+HbxZC3C3r8JtxvoCFJkiRJkiRJ5WKoIVWWIIAPJsJrN0DeTqh7EDRoEemqJEmSJEmSJClqGWpIlSFnE8y4Gpa+HG636QfDHobklpGtS5IkSZIkSZKimKGGVNG+nA+TL4Lt30JMHTh+DBx7BcTERLoySZIkSZIkSYpqhhpSRYuJgx0boNEhMOJxaHFEpCuSJEmSJEmSpBrBUEOqCHm7oE7d8Oe0HvDL5yCjN8TXj2xdkiRJkiRJklSD+D4c6UAtfh4mHA7rl3zf1+FEAw1JkiRJkiRJqmCGGtL+2r0NplwCUy+BnI3w7kORrkiSJEmSJEmSajRfPyXtj7WL4MWLYEsWhGKg3/XQ57pIVyVJkiRJkiRJNZqhhlQehYUwbwJk3g6F+ZCSBsMfg9bHRroySZIkSZIkSarxDDWk8vj4OZh5c/jzYcPg1AlQt2EkK5IkSZIkSZKkWsNQQyqPriPh0ynQeSgcdS6EQpGuSJIkSZIkSZJqDRcKl35K3i6YOw7ydofbMbFwziQ4+jwDDUmSJEmSJEmqYs7UkPZlw+cw+SLYsARyNsLgu8L9hhmSJEmSJEmSFBGGGtKPBQG8/zi8fhPk74b6TeGQEyJdlSRJkiRJkiTVeoYa0g/lbILpV8GyV8Lt9sfD6Q9BUtPI1iVJkiRJkiRJMtSQiqxdBM+fA9u/hZg6cMLNcMxlEOPSM5IkSZIkSZJUHRhqSHslNYE9O6HRITDicWhxRKQrkiRJkiRJkiT9gKGGarddW6DuQeHPDVvDeVOg6aEQXz+ydUmSJEmSJEmSSvC9Oqq9Pn4BJnSFL978vq9VdwMNSZIkSZIkSaqmDDVU++Ruh6mXwpTfQO42+GBipCuSJEmSJEmSJJWBr59S7fL1InjxYti8CkIx0O8P0Of3ka5KkiRJkiRJklQGhhqqHQoLYf79MOtWKMyHlDQ44x+Q3jPSlUmSJEmSJEmSyshQQ7XD6jnw3zHhz51Ph9MmfL9AuCRJkiRJkiQpKhhqqHZoNwB6XALNusDR50MoFOmKJEmSJEmSJEnlZKihmqkgD966F7pfBElNwn1D7o5sTZIkSZIkSZKkA2KooZpnS1Z4MfC1C2Hte3DOZGdmSJIkSZIkSVINYKihmuWzqTD9asjdBokpvmpKkiRJkiRJkmoQQw3VDHt2wus3wKKnwu1WPWDE49CwdUTLkiRJkiRJkiRVHEMNRb8tWfDvUbDxcyAEfa6F/jdAbJ2fPTQnN5+JC7JYs3kXaal1uaBnBvUT/GshSZIkSZIkSdWR394q+tVrBAW5kNQMhj0C7QaU6bCc3HyGPTiPFRt2EBMKURgETPvwa6Ze3ttgQ5IkSZIkSZKqoZhIFyDtl9ztEAThzwkNYNSzcOnbZQ40ACYuyGLFhh0UBpBfGFAYwIoNO5i4IKtyapYkSZIkSZIkHRBDDUWfNQvhwV7w3qPf9zXtBElNy3eazbuI+dEi4jGhEGs276qIKiVJkiRJkiRJFcxQQ9GjsBDeHg9PnATZX8HCx6Egb79Pl5Zal8K9sz32XiIISEute6CVSpIkSZIkSZIqgaGGosP29fD0GfDfsRAUQJfhcPF/y7QY+L5c0DOD9k2TiAlBXEyImBC0b5rEBT0zKqxsSZIkSZIkSVLFcTVkVX8rZsLU30LORoirC0PuhqPOhR+9Oqq86ifEMfXy3kxckMWazbtIS63LBT0zXCRckiRJkiRJkqopv71V9Zb9Nfx7JBTmQdPD4MwnoUnHCjt9/YQ4Lu/fvsLOJ0mSJEmSJEmqPIYaqt5SDoaBN0H2WjjxNqjjeheSJEmSJEmSVFsZaqj6+XQKNOkEzTqH271HH/CrpiRJkiRJkiRJ0c+FwlV97NkJ06+Gyb8K/+zZGe430JAkSZIkSZIk4UwNVRfrl4SDjI1LgRB0OhVi4yNdlSRJkiRJkiSpGjHUUGQFASx6El67AfJ3Q1IzOONRaNs/0pVJkiRJkiRJkqoZQw1Fzp4cmHYZLHkp3G5/PJz+MCQ1iWxdkiRJkiRJkqRqyVBDkROXCDmbICYOjh8Lx14BMS7zIkmSJEmSJEkqnaGGIicmNvyqqe3roFW3SFcjSZIkSZIkSarmDDUUWSkHh38kSZIkSZIkSfoZvutHkiRJkiRJkiRFBWdqKCJycvOZuCCLNZt3kZZalwt6ZlA/wT+OkiRJkiRJkqR981tkVbmc3HyGPTiPFRt2EBMKURgETPvwa6Ze3ttgQ5IkSZIkSZK0T75+SlVu4oIsVmzYQWEA+YUBhQGs2LCDiQuyIl2aJEmSJEmSJKkaM9RQlVuzeRcxoVCxvphQiDWbd0WoIkmSJEmSJElSNDDUUJVLS61LYRAU6ysMAtJS60aoIkmSJEmSJElSNDDUUJW7oGcG7ZsmEROCuJgQMSFo3zSJC3pmRLo0SZIkSZIkSVI15qrMqnL1E+KYenlvJi7IYs3mXaSl1uWCnhkuEi5JkiRJkiRJ+kl+i6yIqJ8Qx+X920e6DEmSJEmSJElSFPH1U5IkSZIkSZIkKSoYakiSJEmSJEmSpKhgqCFJkiRJkiRJkqKCoYYkSZIkSZIkSYoKhhqSJEmSJEmSJCkqGGpIkiRJkiRJkqSoEBWhxuTJk7nqqqvo06cPycnJhEIhzj333P0619q1a7noooto2bIlCQkJZGRkMHr0aLZs2bLPY5YsWcJZZ51F06ZNSUxMpGPHjowZM4Zdu3bt7y1JkiRJkiRJkqRyiot0AWVx2223sXjxYpKSkmjVqhVLly7dr/OsXLmSXr16sWHDBoYOHUqnTp147733uO+++3jttdeYN28ejRo1KnbMu+++y8CBA8nLy2PEiBGkpaUxa9YsbrnlFmbOnMnMmTNJSEioiNuUJEmSJEmSJEk/ISpmaowfP57ly5ezbds2Hnroof0+z+WXX86GDRu4//77mTZtGn/5y1+YNWsW11xzDcuWLeOmm24qtn9BQQG/+tWv2LlzJ5MnT+bf//43d911F++++y7Dhw9n3rx5jB8//kBvT5IkSZIkSZIklUEoCIIg0kWUx+zZsxkwYADnnHMOTz/9dJmPW7lyJe3btycjI4OVK1cSE/N9nrN9+3ZatGhBEARs2LCB+vXrAzBr1iwGDRpE3759mTNnTrHzrVq1inbt2pGens7q1asJhUJlqmPbtm2kpKSQnZ1NcnJymeuXJEmSJEmSJKkmOJDvyaNipkZFyMzMBODEE08sFmgANGjQgN69e7Nz507eeeedov5Zs2YBcPLJJ5c4X9u2benQoQNffvklq1atqsTKJUmSJEmSJEkS1KJQY9myZQB06NCh1O2HHHIIAMuXLz+gY34sNzeXbdu2FfuRJEmSJEmSJEnlV2tCjezsbABSUlJK3b63f+vWrQd0zI/deeedpKSkFP2kpaWVt3RJkiRJkiRJkkQtCjUi5YYbbiA7O7voZ82aNZEuSZIkSZIkSZKkqBQX6QKqyt5ZFXtnX/zY3v6GDRse0DE/lpCQQEJCQnnLlSRJkiRJkiRJP1JrZmp07NgR2Pf6F1988QVQfP2M/TlGkiRJkiRJkiRVjloTagwYMACAN954g8LCwmLbtm/fzrx586hXrx7HHntsUf/AgQMBeO2110qcb9WqVSxfvpz09HTatm1biZVLkiRJkiRJkiSogaFGXl4eS5cuZeXKlcX627Vrx4knnkhWVhZ///vfi20bM2YMOTk5nHfeedSvX7+ov1+/fhx66KHMnTuX6dOnF/UXFhbyhz/8AYBLL72UUChUiXckSZIkSZIkSZIAQkEQBJEu4udMmzaNadOmAbBu3Tpef/112rZtS58+fQBo3Lgx48aNAyArK4s2bdqQnp5OVlZWsfOsXLmSXr16sWHDBoYOHcqhhx7Ku+++S2ZmJh06dGD+/Pk0atSo2DHvvvsuAwcOJC8vjxEjRtC6dWtmzpzJ+++/T+/evZk5c2a51szYtm0bKSkpZGdnk5ycvP+/FEmSJEmSJEmSotCBfE8eFaHG2LFjufnmm/e5/YcBxk+FGgBr1qzhz3/+M6+99hqbNm2iRYsWDBs2jDFjxnDQQQeVev4lS5YwZswYMjMz2b59O+np6fzyl7/k+uuvp27duuW6F0MNSZIkSZIkSVJtVuNDjZrEUEOSJEmSJEmSVJsdyPfkNW5NDUmSJEmSJEmSVDMZakiSJEmSJEmSpKhgqCFJkiRJkiRJkqKCoYYkSZIkSZIkSYoKhhqSJEmSJEmSJCkqxEW6gNomCAIgvLq7JEmSJEmSJEm1zd7vx/d+X14ehhpVbPv27QCkpaVFuBJJkiRJkiRJkiJn+/btpKSklOuYULA/UYj2W2FhId988w0NGjQgFApFupxKtW3bNtLS0lizZg3JycmRLkdSDeL4IqkyOcZIqiyOL5Iqk2OMpMpSGeNLEARs376dli1bEhNTvlUynKlRxWJiYmjVqlWky6hSycnJ/p+ppErh+CKpMjnGSKosji+SKpNjjKTKUtHjS3lnaOzlQuGSJEmSJEmSJCkqGGpIkiRJkiRJkqSoYKihSpOQkMCYMWNISEiIdCmSahjHF0mVyTFGUmVxfJFUmRxjJFWW6ja+uFC4JEmSJEmSJEmKCs7UkCRJkiRJkiRJUcFQQ5IkSZIkSZIkRQVDDUmSJEmSJEmSFBUMNSRJkiRJkiRJUlQw1JAkSZIkSZIkSVHBUEP7lJGRQSgUKvWnefPmpR4zf/58hgwZQmpqKnXr1qVr165MmDCBgoKCfV7n5Zdfpn///qSkpJCUlMQxxxzDxIkTK+u2JFWhyZMnc9VVV9GnTx+Sk5MJhUKce+65P3lMVY0jEydOpEePHiQlJZGSkkL//v15+eWX9+s+JVW98owvWVlZ+3ymCYVCjBo1ap/XKe9YUVBQwPjx4+natSt169YlNTWVIUOGMH/+/AO+Z0lVY9OmTTz22GMMGzaM9u3bU7duXVJSUjjuuON4/PHHKSwsLPU4n2EklUV5xxifYySVxx/+8AcGDRpEWlpa0d/jo446iptvvplNmzaVekw0PsOEgiAI9utI1XgZGRls3bqV0aNHl9iWlJTE73//+2J9L730EsOHDycxMZGRI0eSmprKjBkzWLZsGSNGjGDSpEklzvO3v/2Nq666ikaNGjFy5Eji4+OZPHkya9eu5brrrmPcuHGVdXuSqsCRRx7J4sWLSUpKolWrVixdupRzzjmHp59+utT9q2oc+f3vf88999xDq1atGDFiBHv27OG5555j8+bNPPDAA1x55ZUV/ruQVLHKM75kZWXRpk0bjjjiCE4//fQS27t06cKIESNK9Jd3rAiCgLPOOovJkyfTsWNHTjvtNDZv3szzzz/P7t27efHFFxk6dGiF/Q4kVY6HH36Yyy67jBYtWjBgwABat27N+vXrmTJlCtnZ2QwfPpxJkyYRCoWKjvEZRlJZlXeM8TlGUnnEx8dz9NFH07lzZ5o2bUpOTg7vvPMO77//Pi1btuSdd94hLS2taP+ofYYJpH1IT08P0tPTy7RvdnZ20KRJkyA+Pj5YuHBhUf+uXbuCnj17BkDw7LPPFjtm9erVQUJCQpCamhqsXr26qH/z5s1Bu3btAiCYP39+RdyKpAiZNWtWsHz58qCwsDDIzMwMgOCcc84pdd+qGkfmzZsXAEG7du2CzZs3FztXampqkJCQUOxckqqn8owvq1evDoDgggsuKPP592es+Pe//x0AQa9evYJdu3YV9b/33ntBfHx80KRJk2Dbtm3luk9JVW/mzJnB9OnTg4KCgmL93377bZCWlhYAweTJk4v6fYaRVB7lHWN8jpFUHj/8+/tDN954YwAEl112WVFfND/D+PopVYjJkyezceNGRo0aRffu3Yv6ExMTue222wB46KGHih3zxBNPkJuby5VXXklGRkZR/0EHHcSNN94IhP8Fg6ToNWDAAA455JBi/5JxX6pqHNnbvummmzjooIOK+jMyMrjiiivIzc3lySefLN+NSqpy5Rlf9sf+jBV7x6jbbruNxMTEov7/+7//Y+TIkWzcuJHJkydXSr2SKs7AgQM57bTTiIkp/p/LzZs359JLLwVg9uzZRf0+w0gqj/KOMfvD5xip9vrh398fOuusswD44osvivqi+RnGUEM/KTc3l6effpo77riD++67j8zMzFLfpzZr1iwATj755BLb+vbtS7169Zg/fz65ubllOmbw4MHF9pFU81XVOOLYI9Ve33zzDY888gh33HEHjzzyCB9//PE+9y3vWLF7927mz59PvXr16NOnT5mOkRR96tSpA0BcXFxRn88wkipKaWPMXj7HSDoQM2bMAKBr165FfdH8DFNylJR+YN26dZx33nnF+tq0acOTTz5Jv379ivqWLVsGQIcOHUqcIy4ujjZt2vDZZ5+xatUqDj300J89pkWLFtSvX5+1a9eyc+dO6tWrV2H3JKl6qopxJCcnh6+//pqkpCRatGhR4phDDjkEgOXLl1fYfUmqPt58803efPPNYn39+/dn4sSJtG7duqhvf8aKlStXUlBQQNu2bUv9IsLxRYp++fn5/POf/wSK/0e5zzCSKsK+xpi9fI6RVB7jxo1jx44dZGdn8/777/P222/TtWtXrr/++qJ9ovkZxpka2qdf/epXzJw5k3Xr1pGTk8Mnn3zCb3/7W7Kyshg8eDCLFy8u2jc7OxuAlJSUUs+1t3/r1q3lPmbvfpJqtqoYR/bnGpKiX7169fjTn/7EokWL2LJlC1u2bGHOnDkMGDCA2bNnM2jQIHJycor2r8zxyPFFil7XX389n376KUOGDOGkk04q6vcZRlJF2NcY43OMpP0xbtw4br75ZiZMmMDbb7/NySefzBtvvEGTJk2K9onmZxhDDe3TmDFjGDhwIM2aNaNevXp06dKFhx9+mGuvvZZdu3YxduzYSJcoSZL0s5o2bcott9zC0UcfTcOGDWnYsCF9+/bljTfe4JhjjmHFihU89thjkS5TUjV2//33c88999CpUyf+9a9/RbocSTXMT40xPsdI2h/r1q0jCALWrVvHlClTWLVqFUcddRQffPBBpEurEIYaKre9C1fNnTu3qO/nZlXs7W/YsGG5j9lXkiepZqmKcWR/riGp5oqLi+Piiy8Gqu65xvFFij5/+9vf+N3vfkfnzp3JzMwkNTW12HafYSQdiJ8bY/bF5xhJZdGsWTOGDRvGG2+8waZNmzj//POLtkXzM4yhhspt7zSlH05v7NixI1D6+8/y8/NZvXo1cXFxtG3btkzHfPvtt+Tk5NCqVSvX05BqiaoYR+rXr8/BBx/Mjh07+Pbbb0sc88UXXwClvxtSUs1U2nPN/owV7dq1IzY2llWrVpGfn1+mYyRVfxMmTOCqq66iS5cuZGZm0rx58xL7+AwjaX+VZYz5KT7HSCqr9PR0OnfuzGeffcZ3330HRPczjKGGyu2dd94BKPYHeuDAgQC89tprJfafO3cuO3fupFevXiQkJJTpmFdffbXYPpJqvqoaRxx7JP1Qac81UP6xIjExkV69erFz507eeuutMh0jqXq76667uOaaazjyyCPJzMykadOmpe7nM4yk/VHWMean+BwjqTy++eYbAGJjY4Eof4YJpFIsWbIk2LFjR4n+1atXB+3btw+A4Pbbby/qz87ODho3bhzEx8cHCxcuLOrftWtX0LNnzwAInn322WLnWrVqVZCQkBCkpqYGq1evLurfvHlz0K5duwAI5s+fX/E3JykiMjMzAyA455xzSt1eVePIvHnzAiBo165dsHnz5qL+1atXB6mpqUFCQkKxc0mq/n5ufFm0aFFQUFBQov+///1vkJCQEADBvHnzim3bn7Hi3//+dwAEvXr1Cnbt2lXU/9577wXx8fFBkyZNguzs7AO4U0lV5ZZbbgmAoFu3bsGmTZt+cl+fYSSVV3nGGJ9jJJXVsmXLgq1bt5boLygoCG688caiv+N7RfMzTCgIgqB8MYhqg7Fjx3LPPffQt29f0tPTadCgAStXruSVV15h9+7dDBkyhKlTpxIfH190zLRp0xgxYgSJiYmMGjWK1NRUpk+fzrJlyxgxYgQvvPACoVCo2HUeeOABrr76aho1asTIkSOJj49n8uTJrF27luuuu45x48ZV9a1LqkDTpk1j2rRpQHiRqtdff522bdvSp08fABo3blzs73lVjSPXXXcd9957L61atWLEiBHs2bOH559/nk2bNvHAAw9w5ZVXVt4vRVKFKM/40r9/f7744gt69epFq1atAPj444+ZNWsWALfeeit//OMfS1yjvGNFEAScddZZTJ48mU6dOnHaaaexadMmnn/+eXbv3s2LL77I0KFDK+tXIqmCTJw4kQsvvJDY2FiuuuqqUtf4y8jI4MILLyxq+wwjqazKO8b4HCOprCZMmMANN9zAcccdR5s2bWjUqBHr169nzpw5rFq1iubNmzNz5kw6d+5cdEzUPsOUKwJRrTF79uxg1KhRQceOHYOUlJQgLi4uaNy4cXD88ccHEydODAoLC0s97u233w4GDx4cNGzYMEhMTAy6dOkS3HvvvUF+fv4+rzV9+vSgb9++QVJSUlCvXr2ge/fuwVNPPVVZtyapCo0ZMyYA9vmTnp5e4piqGkeefPLJoHv37kG9evWCpKSkoG/fvsGMGTMO9JYlVZHyjC+PPfZYcMoppwTp6elB/fr1g/j4+CAtLS0466yzgrlz5/7kdco7VuTl5QX33ntv0KVLlyAxMTFo2LBhMHjw4BL/glJS9fVz4wsQ9OvXr8RxPsNIKovyjjE+x0gqq08++SS44oorgiOOOCJo1KhREBsbGyQnJwfdu3cPxowZs8+ZYdH4DONMDUmSJEmSJEmSFBVcKFySJEmSJEmSJEUFQw1JkiRJkiRJkhQVDDUkSZIkSZIkSVJUMNSQJEmSJEmSJElRwVBDkiRJkiRJkiRFBUMNSZIkSZIkSZIUFQw1JEmSJEmSJElSVDDUkCRJkiRJkiRJUcFQQ5IkSZIkSZIkRQVDDUmSJEmSJEmSFBUMNSRJkiQpyv3973+na9euJCcnk5ycTM+ePXnllVciXZYkSZJU4UJBEASRLkKSJEmStP9eeukl4uPjOeSQQygsLGTixIn89a9/ZdGiRXTt2jXS5UmSJEkVxpkakiRJUi2QlZVFKBTiwgsvrFHXqo5mz55NKBQq+unUqVOlX3Po0KEMHjyY9u3b06FDB26//XYaNGjAggULKvQ63333XbF7C4VCFXp+SZIk6efERboASZIkSWX34y+R4+PjSU5OJi0tjaOPPprhw4dz4oknEhsbG6EKtVe/fv3o378/jRs3rtLrFhQUMGnSJHbs2EGvXr1K3Wf9+vW0bNmSyy+/nAceeKDM565Xrx5jxowB4KmnnuLLL7+skJolSZKksjLUkCRJkqLQ3i+WCwoK2Lp1K5999hn/+te/ePzxx+nevTvPPPMMHTp0KNr/4IMP5vPPPyclJSVSJdc6/fv3Z+zYsVV2vU8++YSePXuye/dukpKSmDp1Kocffnip+7700ksUFhZyxhlnlOsa9erVK7qn2bNnG2pIkiSpyhlqSJIkSVGotC/L169fz1VXXcWkSZM4/vjjef/992natCkAderUqZLXIClyOnbsyEcffUR2djaTJ0/mggsuYPbs2XTp0qXEvlOnTqVRo0b07ds3ApVKkiRJ+881NSRJkqQaolmzZjz33HP079+fNWvWcMcddxRt29c6F9OnT2fQoEG0aNGChIQEWrZsSb9+/XjwwQdLvcZ7773HyJEjOfjgg0lISKBFixaceOKJvPDCC6Xun5WVxahRo2jcuDGJiYl0796dl19+udR9n3rqKYYPH07btm2pW7cuycnJ9O7dm6effrrU8+69n+XLlzNy5EiaNm1KTEwMs2fPBiAIAu677z46d+5MYmIiBx98MFdeeSXZ2dlkZGSQkZFRah3vvvsuI0aMoHnz5sTHx5OWlsZvf/tbvvnmm1L33x+ZmZmEQiF+//vf88EHH3D66aeTmppKSkoKw4YNY926dQAsWbKEs88+m6ZNm5KSksKpp57KV199Veo54+Pjad++Pd26dePOO+/kyCOPZPz48SX227ZtG7NmzeK0004r8Zqyt956izPOOIN27dqRmJhI06ZN6dGjBzfccEOF3bskSZJ0IAw1JEmSpBokJiaGP/7xjwA8++yzBEGwz30fffRRhg4dypIlSzjttNO47rrrGDJkCLt27eLJJ58ssf8//vEPevXqxbRp0+jVqxfXXXcdp5xyChs2bCg1BPnyyy/p0aMHWVlZnHfeeYwcOZJPP/2UoUOHkpmZWWL/yy67jC+//JK+ffsyevRoRo0axZdffsl5553Hn/70p1LvYeXKlRxzzDFkZWVxzjnncMkll5CcnAzAFVdcwejRo8nOzuaSSy7hl7/8JW+88QYnnHACeXl5pZ7viSeeoHfv3rz66qsMGDCA0aNH0717dx577DG6d+++z0ChvD744AMAli9fznHHHUdsbCy//vWvad26NdOmTeOiiy5ixowZ9OjRgx07dnDBBRfQoUMHXnnlFc4///wyXaOwsJDc3NwS/S+//DJ79uxh2LBhxfrvuOMO+vbty6JFixg0aBDXXnstp512Grm5ubz++usHftOSJElSRQgkSZIkRQ0g+LnH+N27dwdxcXEBEKxatSoIgiBYvXp1AAQXXHBB0X5HH310EB8fH6xfv77EOTZu3Fis/dlnnwVxcXHBQQcdFHz66acl9l+zZk3R573XAoKxY8cW2++1114LgGDw4MElzrFixYoSfbm5ucHAgQODuLi4YO3ataVe44Ybbihx3Ny5cwMg6NChQ7Bly5Zi5+vTp08ABOnp6cWOWbZsWVCnTp2gXbt2xa4VBEHw3//+N4iJiQlOP/30Etf6sczMzAAIxowZs899zj777AAImjVrFixevLiof9u2bUHDhg2D2NjYoHnz5sH8+fOL1Z6WlhaEQqFg165dxc73hz/8IZg7d26wevXq4OOPPw6uv/76IBQKBf/5z39KXHvEiBFB/fr1i51j3bp1QWxsbHDccccFubm5JY758Z+HIAiCfv36/eyfRUmSJKmiOVNDkiRJqmESEhJo1KgRABs3bvzJfePi4qhTp06J/saNGxdrP/TQQ+Tn5/OnP/2Jww47rMT+rVq1KtGXnp5eNGtkr5NOOonWrVvz3nvvldi/Xbt2Jfri4+O54ooryM/PZ+bMmSW2N2vWrGjR9B+aOHEiADfddBMNGzYsdr4777yzxP4Qvse8vDzuu+8+Dj744GLbBg0axC9+8QtmzJjB9u3bSz2+PPbO1Hjqqafo2rVrUX+DBg3IyMigoKCAu+++m549exarvWPHjgRBQE5OTrHzrVu3jnPPPZeOHTsyaNAgFi5cyKuvvsrgwYOL7bd7925ee+01Bg8eTGJiYlH/0qVLKSgooEOHDsTHx5eo98d/HiRJkqRIcaFwSZIkqQYK/vfaqVAotM99zjnnHK677jo6d+7MqFGj6NevH71796ZJkyYl9n3nnXcASnxJ/lOOPPLIEms2AKSlpbFgwYIS/V999RV33XUXM2fO5KuvvmLXrl3Ftn/99dcljjniiCNISEgo0f/hhx8CcNxxx5XYduyxxxIXV/I/hfbWNGfOHBYuXFhi+4YNGygoKGD58uV069atxPayysnJYfny5bRt25aTTz65xPYvv/yS1NRURo4cWeq2Bg0aFIVWez311FNluvabb77Jjh07Srx66rDDDiMlJYUnnniCjRs3cs4553DiiSdy0EEHlf3GJEmSpCpgqCFJkiTVMLt372bz5s0ApQYUe1177bU0btyYBx98kPvvv58JEyYQCoXo168fd999N927dy/ad+vWrQAlZjD8lB/OkPihuLg4CgsLi/WtWrWKHj16sGXLFvr06cOJJ55ISkoKsbGxZGVlMXHixFLXh2jevHmp18jOzgbCMzl+LDY2tkQoALBp0yYA7r777p+8rx07dvzk9p+zePFiCgsLOf7440tsy8rKYsuWLZxxxhklZtDs2LGDFStWlBrUlNWUKVOIj4/nlFNOKdbfuHFj3n77bW6++Wb+85//MGPGDGJjYznhhBO4/fbbOfroo/f7mpIkSVJFMtSQJEmSapi3336b/Px8mjVrRkZGxk/ue/7553P++eezdetW5s+fz9SpU3niiSc46aSTWLp0aVEosjeg+Prrr+nUqVOF13zvvfeyadMmnnzySS688MJi25599tmi10n92L5mouxdLHz9+vW0bdu22LaCggI2bdpUIqBJSUkBwoHI3uMrw95XT5U222PRokX73Pbhhx8SBMF+BwwFBQXMmDGDgQMHFt3rD3Xp0oVJkyaxZ88e5s6dy6OPPsqkSZNYuHAhX3/9dakzYiRJkqSq5poakiRJUg1SWFjI7bffDsDZZ59d5uMaNmzIkCFD+Mc//sGFF17I5s2bmTt3btH2Y489FoBXX321Ygv+nxUrVgAwfPjwEtvmzJlT7vMdddRRQDjg+bF33nmH/Pz8Ev177/Gtt94q9/XKY2+o8cOZMHvtDTVK27b3lVr7G2rMnTuXTZs2lXj11I/Fx8dz/PHH88ILL3DcccexadMm1q9fv1/XlCRJkiqaoYYkSZJUQ2zYsIFRo0Yxe/ZsWrduzY033viT+2dmZhatvfHj8wDUq1evqO+yyy4jLi6OW2+9lSVLlpQ4Zu3atQdU+94ZJbNnzy7W//rrr/PYY4+V+3znn38+ALfffnvRq6gA9uzZs8/fy5VXXkmdOnW45pprWL58eYnte/bsqZDA44MPPiA+Pp4uXbqU2PZTMzX2hiH7G2pMnTqVmJgYhg4dWqz/ww8/ZOXKlSX2X7FiBZ9++imtW7cudSF4SZIkKRJ8/ZQkSZIUhcaOHQuEZ2Zs3bqVzz77jLfffps9e/bQo0cPnnnmGRo3bvyT5xg2bBhJSUkce+yxZGRkEAQBb731FgsXLqRbt27F1nzo3LkzDz74IJdeeilHHXUUQ4cO5ZBDDmHTpk0sXLiQ5ORkMjMz9/t+Lr/8cp588knOPPNMRowYQcuWLfn000957bXXOOuss3j++efLdb5+/fpxySWX8Oijj3LYYYcxfPhw6tSpw4wZM0hJSaFly5bExBT/N16dOnXiiSee4KKLLuKwww7j5JNPpkOHDuTl5fHVV1/x1ltv0aRJE5YuXbrf95mbm8uSJUvo2rUr8fHxJbYvWrSI9PT0Utf8+OCDD6hbty6HHnpoua8bBAHTpk2jZ8+eJdYZeeCBB3jqqac49thj6d69O0lJSaxatYrp06cD8MQTT5T4XUmSJEmRYqghSZIkRaGbb74ZCL8qqEGDBqSnp3P++eczfPhwTjzxxDJ9Cf2Xv/yF119/nQ8++ID//Oc/JCYmkp6ezl133cVll11WYqHq3/zmN3Tp0oVx48Yxe/Zspk2bRuPGjenatSsXX3zxAd1P165dyczM5I9//COvvPIK+fn5HHHEEUyZMoWGDRuWO9QAeOihh+jUqROPPPIIDz/8MI0aNWLYsGHccccdtGrVinbt2pU45txzz+WII47gnnvuITMzkzfeeIP69evTsmVLRowYwciRIw/oPj/99FPy8vJKnYnx5ZdfsmnTJvr161diW25uLp9//jndunUjNja23Nd9//33WbNmDaNHjy6xbejQoeTl5fHee+/x1FNPsXv3blq2bMnZZ5/NH/7wBw455JByX0+SJEmqLKGgtPnmkiRJklRDffHFF3To0IFRo0bx7LPPVvj5Z8+ezYABAxgzZkzRjJpIu/HGG7nzzjtZtWoVbdq0qZBz9u/fnzlz5pT6CjNJkiSpsjiHWJIkSVKNtG7dOgoLC4v17dy5s2i2ws8tmH2gbr75ZkKhEJ06darU65TF1KlTOeKIIw440Pjuu+8IhUKEQqH9WsBdkiRJOlC+fkqSJElSjTRhwgSeffZZ+vfvT4sWLVi3bh0zZ85k7dq1DB48mDPPPLNSrpuRkcGYMWOK2j+3tklV+PzzzyvkPPXq1St2b5IkSVJV8/VTkiRJkmqkmTNnMm7cOD766CM2b95MXFwcHTp04Oyzz2b06NEl1gyRJEmSVP0ZakiSJEmSJEmSpKjgmhqSJEmSJEmSJCkqGGpIkiRJkiRJkqSoYKghSZIkSZIkSZKigqGGJEmSJEmSJEmKCoYakiRJkiRJkiQpKhhqSJIkSZIkSZKkqGCoIUmSJEmSJEmSooKhhiRJkiRJkiRJigqGGpIkSZIkSZIkKSoYakiSJEmSJEmSpKhgqCFJkiRJkiRJkqLC/w8lInNrlPlYPAAAAABJRU5ErkJggg==",
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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -224,7 +226,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -263,7 +265,7 @@
     },
     {
      "data": {
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       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -300,7 +302,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
diff --git a/examples/tidal_example.ipynb b/examples/tidal_example.ipynb
index c062ac554..66d341f3a 100644
--- a/examples/tidal_example.ipynb
+++ b/examples/tidal_example.ipynb
@@ -136,7 +136,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -163,7 +163,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -194,9 +194,17 @@
    "execution_count": 7,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\sterl\\codes\\mhkit-python\\mhkit\\tidal\\graphics.py:290: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.\n",
+      "  ax.set_yticklabels([f\"{y:.1f} $m/s$\" for y in ax.get_yticks()])\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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",
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ZWVddfzNptVpCQkIYN24cx44d47fffuPzzz/nrbfeuup+l/476du3b7H1ffv25cUXX2Tbtm3lXmYhhLhREoiFEKIcabVaBgwYwP79+5k0aRJr1qyhT58+1h+MCQkJJe537tw5wPaHZdHAUkajscR90tLSrviDvLzs2LGDefPm0a1bN5YuXWozMrDZbObjjz++qee/VGJiYrEBh4xGI8nJySXWnF7Jpe9FeHh4sfUlvRcVoXbt2vzyyy9ERESUOIBTWbi7u3PhwgUMBkOxeV9Lumbfffcd0dHRjB8/vtho3Js3b2b69Ok3VJ727dsDls/T5TXv5e1mv5brVfSa09PTy/R5rSytWrXit99+u6YQ6+TkRFBQEHFxcSX+n+Tp6QlAbm5ueRdTCCFumEy7JIQQN0HRqMtq4dQrrq6uhIeHc/bsWY4fP15s+7Vr1wLQtGlT67KiH5FxcXHFtj9x4kSJU8EUzfNZ1trKKzlx4gQAvXr1KjZNzrZt2yr0B+66deuKLduwYQMmk4kmTZrYLNdoNFe8BkXbRkVFFVt34sQJzpw5Q1hY2E2/2VCSyz8316tJkyaYzWbWr19fbN369esxmUw2n7Wi9/mhhx4qtn1J172swsLC6NatG3l5eaXWkAI3VBt9s1/L9Sqaiui///6rtDKURVFz72udcqtbt26AZWqxyxUtu7wbgBBC3AokEAshxHWYPXs2K1euLPHHYkJCAjNnzgSgY8eO1uXDhw9HVVVef/11m7CWnJzMe++9Z92mSO3atXFzc2PBggU2zVtzc3N58cUXSyxX0dQmsbGxN/DqLiqa5/jy8JiUlMTzzz9fLue4Vu+9955Nn8y8vDxrU86i+ZeLeHt7l3gjAS5e40mTJnH+/HnrcpPJxGuvvYbZbGbEiBHlXXzAMv3QZ599VuLNDFVVef/99wHbz831KHqNb731Fjk5OdblOTk5jBkzBsDmNV7pfd69ezeTJ0++obIU+eyzz3Bzc2Py5MlMmTKlxJYPsbGxDBo0yNrf9npUxGu5Hi+88AI6nY5XXnmlxOnaCgoKbpmwnJqaap3KqqR+6CV5/vnn0Wg0fPjhhzb/rvLy8hg7diwAgwcPLveyCiHEjZIm00IIcR22bt3K9OnT8fPzo3379taaj+joaJYsWUJubi69e/emf//+1n1ee+01li5dyoIFC2jUqBH33XcfOTk5zJkzh6SkJN544w1r01Kw9E9+6aWXeO+992jSpAl9+/bFaDSycuVKAgICrANUXapNmzY4OTkxbdo0UlJS8PPzA2DUqFHX1Uy1RYsWtGvXjrlz59K2bVvat29PYmIiS5cupVatWiWW4WapU6cO9erVs5mH+OTJk9x///08+uijNtt27dqVP/74gwcffJCmTZui0+no2LEjHTt2pG3btrzxxht8/PHH1K9fn/79++Ps7MzSpUs5cOAA7du35/XXX78pryE9PZ2XXnqJ119/nXbt2lG/fn1cXV1JSkpizZo1nDp1Cl9fX6ZMmXJD5xkyZAgLFizgr7/+ol69evTp0wdFUZg/fz7R0dEMHDiQRx55xLr9Y489xieffMLLL7/M2rVrqVGjBsePH2fx4sX069ePP//880ZfOnXq1GH58uX079+f1157jenTp9O1a1cCAgLIzs5m7969bNy4EUVRePPNN6/7PBXxWq5H7dq1+eGHHxg+fDj16tXj3nvvpWbNmhgMBmJjY/nvv/+oUqUKR44cueFzffjhh8yaNavEdS+++KJN64D58+cTExMDXBxUa9GiRaSkpNCiRQueffbZazpns2bNGD9+POPHj6d+/fr06tULBwcHli9fzvHjx63/7oQQ4pZTuYNcCyHE7Sk2Nlb94osv1D59+qg1a9ZUXV1dVZ1Op/r5+ak9e/ZUf/nlF9VkMhXbLzc3V33//ffVevXqqQ4ODqqLi4varl079ffffy/xPGazWZ08ebJavXp1VafTqUFBQerrr7+uZmdnlzjtkqqq6tKlS9XWrVurzs7O1mlUiqbGKZp2qaRpWaKjo1VAffzxx22Wp6SkqCNHjlRDQkJUvV6vVq9eXX3rrbeuWIarTf10tamkrnT+oqlk8vLy1LFjx6qhoaGqvb29GhYWpk6YMEHNy8srdqzExER18ODBqq+vr6rRaEo85+zZs9V27dqpLi4uql6vV+vWratOmjRJzc3NLXa8K11rVb36Nb1cXl6eOm/ePHXkyJFqkyZNVF9fX9XOzk51c3NTmzZtqo4dO1ZNSkoqth9XmUbqStMfmUwm9csvv1SbNWumOjo6qo6OjmrTpk3VL774osTP5sGDB9UHH3xQrVKliurk5KQ2bdpUnTlz5hXfl7JMu3SpzMxM9dNPP1UjIyPVKlWq2Lz+MWPGqKdOnbLZ/mpTD13ps1aer+VKn9nr/Uzs27dPffzxx9Xg4GDV3t5e9fT0VOvVq6c+/fTTxaYlut5pl672mDdvns1rvvzh6uqqtmjRQv34449L/LdQmn/++Uft0KGD6urqavPvqqR/p0IIcStQVPUGOyoJIYQQN1FkZCTr1q274X61QgghhBCXkz7EQgghhBBCCCHuShKIhRBCCCGEEELclSQQCyGEEEIIIYS4K0kfYiGEEEIIIYQQdyWpIRZCCCGEEEIIcVeSQCyEEEIIIYQQ4q4kgVgIIYQQQgghxF1JArEQQgghhBBCiLuSBGIhhBBCCCGEEHclCcRCCCGEEEIIIe5KEoiFEEIIIYQQQtyVJBALIYQQQgghhLgrSSAWQgghhBBCCHFXkkAshBBCCCGEEOKuJIFYCCGEEEIIIcRdSQKxEEIIIYQQQoi7kgRiIYQQQgghhBB3JQnEQgghhBBCCCHuShKIhRBCiNuUoigoikJISAh5eXklbhMaGoqiKBiNxgounRBCCHHrk0AshBBC3OZiY2OZNm1aZRdDCCGEuO0oqqqqlV0IIYQQQpSdoih4enqiKAomk4kTJ07g4+Njs01oaCinT5/GYDBgZ2dXSSUVQgghbk1SQyyEEELcxpycnPjf//5Heno6EydOrOziCCGEELcVCcRCCCHEbe75558nPDycb775huPHj1d2cYQQQojbhgRiIYQQ4jan0+n48MMPMRgMvPnmm5VdHCGEEOK2IYFYCCGEuAP079+fNm3aMG/ePDZs2FDZxRFCCCFuCxKIhRBCiDvElClTAHjttdcquSRCCCHE7UECsRBCCHGHaNOmDf3792fr1q38+eeflV0cIYQQ4pYngVgIIYS4g0yePBmdTsdbb71FQUFBZRdHCCGEuKVJIBZCCCHuIBERETz33HNER0fz+eefV3ZxhBBCiFuaBGIhhBDiBkyYMAFFUYiKirrmfSIjI1EU5aaVady4cXh4ePD++++TlZV1084jhBBC3O4kEAshhBCXUBSl1EdZwm9l8PLy4u233yY1NZWUlJTKLo4QQghxy7Kr7AIIIYQQt6Lx48dfcV1oaGjFFeQ6vfjii3z11VfExMRUdlGEEEKIW5YEYiGEEKIEEyZMqOwilEpV1Suu0+v1REdHV2BphBBCiNuPNJkWQgghyslPP/1EkyZNcHR0xNfXl+HDh5OQkHDF7fPz83nnnXcICwtDr9cTHh7OxIkTZXRoIYQQooJIIBZCCCHKwdSpU3n22Wdp1KgRL7/8MrVq1eLHH3+kbdu2nD9/vsR9BgwYwA8//MCDDz7ICy+8gKIoTJgwgYceeuiqtb9CCCGEKB/SZFoIIYQowZWaTDs4ODBmzJhiy5cuXcrWrVtp0qSJddkrr7zCtGnTGDNmDN9//32xfQ4fPszBgwfx9PQE4P3336dz584sXryYX3/9lUcffbR8XowQQgghSqSocgtaCCGEsCptOiR3d3fS0tKszydMmMDEiRMZPnx4sdCbnp5OSEgI+fn5pKWlodfrAcu0S+vWrePnn38uFnqjoqLo3LkzkZGRrF27tnxelBBCCCFKJE2mhRBCiBKoqlri49IwfKlOnToVW+bu7k7jxo3Jy8vj8OHD17RP+/bt0Wq17N69+4ZfgxBCCCGuTgKxEEIIUQ6qVq1a4nI/Pz/AUlt8LfvY2dnh4+NDRkZG+RZQCCGEEMVIIBZCCCHKQWJiYonLi0aZdnd3v6Z9jEYjycnJuLm5lW8BhRBCCFGMBGIhhBCiHKxbt67YsvT0dPbs2YODgwN16tS5pn02bNiAyWSyGZxLCCGEEDeHBGIhhBCiHPzyyy/F+v1OmDCB9PR0Bg8ebB1Q61Lvvfceqamp1ud5eXm89dZbADzxxBM3t8BCCCGEkGmXhBBCiJJcadolgD59+tC4cWObZT179qRdu3YMGDAAf39/NmzYwIYNGwgNDeXDDz8s8Th16tShXr169O/fH51Ox4IFCzh58iT333+/TLkkhBBCVAAJxEIIIUQJJk6ceMV1oaGhxQLxK6+8Qt++fZk2bRp//vknLi4uDBs2jA8++ABfX98Sj/PXX3/x3nvv8dtvvxEfH0+1atWYMGECY8aMKXX6JyGEEELcOJmHWAghhBBCCCHEXUlqiIUQQogKlp2dTUpKCpmZmWRmZpKVlWXzZ2nLcnNzMZvN1ofJZLL+PT4+HoBq1aqh0WhsHlqtFq1Wi4uLC66urtY/L/371Za5ubnh4+ODnZ38fBBCiKvJy8ujoKCgUs5tb2+Pg4NDpZz7diQ1xEIIIUQ5UFWVzMxM4uPjOXfunPVR0vPMzEwURbnuYOro6IhWqy0x8B46dIjo6Gi6d++ORqMpFpgNBgPZ2dnXFcTz8vLQaDT4+vri7+9v8wgICLB57ufnh729fWW/LUIIUeHy8vIIC3EhIclUKef38/MjOjpaQvE1kkAshBBCXANVVUlMTOTYsWPWR0xMjE3YzcnJwdHREX9/f6pUqYKjoyMNGzYsMTh6enqi0ZT/ZA8Gg4F///2X++67D51OV67HLigoIDEx8YpBv+jvSUlJmM1mfHx8bF53REQENWvWpGbNmtSoUQMnJ6dyLZ8QQtwKMjIycHd35/TOUNxcK3ZSn4xMMyHNYkhPT5f57K+RtHkSQgghLpGens7x48dtgm/RIzMzk6CgIGrVqkXNmjVp0aKFTc1oQEAAbm5uKIpCRkYG//33H/fff39lv6RyY29vT1BQEEFBQVfdzmg0kpSUZBOYz5w5w6FDh5g/fz7Hjh0jIyODwMBAa0C+9BEaGlruYV4IISqai6uCi2vFDpBoRgZkLCsJxEIIIe5KiYmJ7Ny5kwMHDtiE3sTERLy9va3hrH79+vTr14+aNWsSERFxzbWaer0eo9GI0Wi86/rc2tnZERAQQEBAQInrVVUlKSnJ5rqvX7+e7777jhMnTmA2m6levbr1PahVqxZNmjShQYMG0gRQCCFEubq7vqGFEELclRISEti5c6fN4+zZs9SoUYNGjRpRq1YtOnbsaG3K6+3tfcPntLe3R1EU8vPz77pAXBpFUahatSpVq1alQ4cONutMJhOxsbE2Yfmvv/5izJgxZGZmUr9+fZo1a0azZs1o2rQpjRo1kpAshLglmVQzpgrunGpSzRV7wjuAfEMLIYS4o5w7d65Y+D137hw1a9akWbNmdOrUidGjR9OkSZOb2r9KURQcHBzIy8vD2dn5pp3nTqPVagkLCyMsLIwePXpYl6uqSkxMjPU9nTdvHu+88w7p6enUq1fPGpKbNWtGo0aNcHR0rMRXIYQQ4nYhgVgIIcRtKycnh02bNrFhwwZrUEpISKBWrVo0a9aMzp0789prr9GkSRNcXV0rvHx6vZ68vLwKP++dSFEUa1Du378/YAnJsbGx1vd+0aJFTJgwgdTUVOrWrUuzZs1o2bIlnTp1ok6dOiiK9K0TQghhSwKxEEKI20ZRAI6KiiIqKopt27bh5+dHx44d6datG2+++SaNGzfGxcWlsosKgIODA/n5+ZVdjDuWoiiEhIQQEhJCv379AEtIjouLs4bkf/75h1dffRVXV1c6depEZGQkkZGREpCFEDedGRUzFdtmuqLPdyeQQCyEEOKWlZOTw+bNm60BeOvWrVStWpXOnTszYsQIfvnlF0JDQ2/ZYFPUZFpUHEVRCA4OJjg4mL59+wKW6aK2b99OVFQU8+bN47XXXsPV1dUajiMjI6ldu/Yt+zkSQghx88g8xEIIIW4ZVwvARcElLCzstgkuR44cIScnh6ZNm1bYOW/mPMR3ivz8fGtAjoqKYuPGjbi5uUlAFkKUi6J5iOOPBlbKPMQBtc7IPMRlIIFYCCFEpTp69CgLFy5k8eLFbN68GV9fX2sA7ty5820VgC8XExPDuXPnaNOmTYWdUwJx2V0akNeuXcumTZtwc3PjnnvuoVevXvTo0QN3d/fKLqYQ4jYhgfj2Ik2mhRBCVCij0cjGjRtZtGgRCxcuJDY2lq5duzJ48GB++OEHqlevftsG4MvJoFq3B71eT/v27Wnfvj3vvPMO+fn5bN26laVLl/Luu+8ydOhQOnXqRK9evXjwwQcJDQ2t7CILIYQoJxKIhRBC3HQZGRksW7aMRYsWsWTJEuzs7HjggQf4+OOP6dat2y0zCFZ5k0G1bk96vZ6OHTvSsWNHJk+ezMmTJ1m0aBHz589n9OjR1KlTh169etGrVy+aN2+ORlOxNUBCiNuDSVUxVXBj3Io+351AArEQQoibIiYmhkWLFrFo0SKioqKoWbMmDz74IEuWLKFly5ZotdrKLuJNVxSIzWazhKbbWHh4OC+//DIvv/wyaWlpLFu2jIULF9K9e3ccHR154IEH6NWrF127dsXJyamyiyuEEKIMJBALIYQoN/v27WPOnDksXLiQQ4cO0bFjRx588EG+/vprwsPDK7t4FU6v1wOWPqqOjo6VXBpRHjw8PBg0aBCDBg3CYDCwYcMGFi5cyMsvv0x8fDz33HMPvXv3pl+/fnh6elZ2cYUQlUimXbo9yO1qIYQQNyQ2NpaPPvqIBg0a0KZNG44fP85bb73F+fPnWb16NS+//PJdGYYBNBoN9vb20mz6DqXT6ejcuTNTp07lxIkT7Ny5k3bt2vHNN9/g5+dHv379+Oeff6QfuRBC3MIkEAshhCiz1NRUZs6cSWRkJOHh4URFRTFmzBgSExP5448/GDRoEB4eHpVdzFuCDKx1d1AUhbp16/Lmm2+ybds2Dhw4QMOGDXnzzTfx8/PjqaeeIioqCrPZXNlFFUJUEDMqpgp+SA1x2UkgFkIIcU3y8vKYO3cu/fr1w8/Pj5kzZ9KvXz/OnDnD0qVLeeSRR+7YwbFuhAysdXeqUaMGEyZM4Pjx4yxbtgwHBwcefvhhQkNDefPNN9m/f39lF1EIIQQSiIUQQlyF2WwmKiqKp556Cj8/P9544w0aNGjA/v372bZtGy+++CJVq1at7GLe0qSG+O6mKAqtW7fm888/Jz4+nhkzZhAXF0erVq1o2LAhH3/8MXFxcZVdTCGEuGvJoFpCCCGKOXToED///DO///47ubm5DBw4kGXLltGqVas7Zo7giuLg4CCBWACWPsf33Xcf9913H5mZmcyfP5/ffvuNsWPH0r59e4YOHcqAAQNwdXWt7KIKIcqBDKp1e5AaYiGEEIBlJOTff/+djh070rRpU06fPs3XX39NfHw8X3zxBa1bt5YwfB2up8n0haQMMtNyblKJQFVVzsenkpcjTbkri6urK48++ijLli0jLi6O3r178+WXXxIQEMCzzz7L7t27K7uIQghxV5BALIQQd7njx4/z+uuvExgYyMSJE+nbty9nz55l9uzZ3H///eh0usou4m2tLDXEZrOZ/3vpFx5p+g6DGr7Folnry708+bkFvPnw5zzWcjwDG77NttUHy/0comz8/Px4+eWX2bVrF2vXrsVkMtG+fXtatWrFjz/+SE7Ozbs5IoS4eUyqWikPUTYSiIUQ4i5kNBqZO3cu99xzD/Xr1ycuLo45c+Zw5MgRXnnlFby9vSu7iHeMsvQh3rvxOKv/2Q6A2awyY9w/ZGfmlmt5Vv61lf1bTwBQkG9g2mu/l+vxxY1p3rw5M2fOJD4+nscff5ypU6cSEBDAiy++yJEjRyq7eEIIcceRQCyEEHeRpKQkPvjgA8LCwnj11Vfp2rUrcXFx/PHHH0RGRkqT6JugqMm0eg137S9vwmw2qxgKjOVanrycgovvswr5uYZyPb4oH+7u7jz33HPs3buXpUuXkpaWRuPGjbnnnntYsGABJpOpsosohBB3BAnEQghxF9i+fTuPP/44wcHBrF27li+//JITJ04wZswYfH19K7t4dzS9Xo/ZbMZgKD14Nu1Ym/B61azPewxug4d3+Q6w1KVfczx8Lh5z8Es9yvX4onwpikKbNm34+eefiY2NpXPnzrzwwguEh4fz8ccfk5KSUtlFFEJcgbmSHqJsFPVablkLIYS47RiNRubMmcO0adM4dOgQjz/+OM8//zx16tSp7KLdVVRVZcmSJXTs2BE3N7dSt8/PLWDPxmM4Oulp0CaizLX2BoOBf//9l/vuu++K/b8zU7PZt/kEPgEe1GocUqbji8pnNBpZsGABn3/+OVu3bmXIkCGMHj2aevXqVXbRhBBARkYG7u7uHDlcFVfXiq1/zMw0U7tOIunp6df0nSOkhlgIIe44eXl5zJgxg1q1avH2228zdOhQzp49yxdffCFhuBIoilKmkab1jva06lafhm1r3LQm7K6ezrS7r5GE4duUnZ0dDz30EFFRUWzbtg2NRkOzZs3o06cPW7dureziCSEKmVAr5SHKRgKxEELcITIzM/nkk08ICwvjyy+/5N133+X48eOMGjVK7hJXsrIMrCVEWTRo0ICZM2dy8uRJIiIi6Nq1K126dGHlypXX1G9dCCHudhKIhRDiNnf+/Hn+97//ERwczNy5c/nmm2/Yu3cvjzzyCHZ2dpVdPMH1zUUsRFlUq1aN//u//yM2NpbIyEgGDRpEy5YtmTt3Lmaz9CoUQogrkUAshBC3qbi4OF566SVCQ0PZtm0b8+bNY9OmTfTq1QuNRv57v5VIDbGoKF5eXowbN47Tp08zZMgQXnzxRerVq8esWbOuaWA3IUT5MamV8xBlI7+YhBDiNnPkyBGeeOIJatSoQXx8POvXr2f58uUybdItzMHBgby8POZ8tYqnI99n3GMzOB+fWtnFEncwFxcXXnnlFU6ePMlrr73GBx98QHh4OJ999hk5OTmVXTwhhLhlSCAWQojbxM6dO+nfvz+NGzdGo9Gwd+9e5syZQ7NmzSq7aHeMnKw8ju2NJTOtfAODXq8n8WwyP3ywkLgTiexcd4QPnv2xXM8hREn0ej0jRozg8OHDTJkyhVmzZhESEsL7779PWlpaZRdPiDuaTLt0e5BALIQQt7iDBw/Su3dvOnbsSFBQEMePH+f777+nVq1alV20O8qZk4k80f49XnrwUx5rM5HDu2LK7dhFfYg1GksNvtlkJuZIfLkdX4jSaLVaHn74YXbu3Mmvv/7KypUrrcE4Ozu7sosnhBCVRgKxEELcok6fPs2wYcNo3rw5ISEhREdHM3XqVIKCgiq7aHekv75aTVa6pWY4P7eAn//v33I7toODAzoHDaoKGq0GRVFo0UXmjBUVT1EUevToQVRUFAsWLGDhwoVERETw9ddfSx9jIcqZGQVTBT/MSNepspJALIQQt5jk5GRGjx5NnTp1MJlMHDx4kM8++wxfX9/KLtodzWy+ZCQSFVRz+Y1MotfrMatm3v31Ge55uCVDX+3Jq1MfKbfjC3E9IiMj2bJlC19++SXTp0+nbt26/PnnnzIqtRDiriKBWAghbhHZ2dlMmjSJ8PBwjh49yubNm/nll1+oXr16ZRftrvDws11wcnEAwF5vx9BX7i23Y+v1ehRFoW6LUF7+vyEMefle9I725XZ8Ia6Xoij069ePAwcOMGbMGF599VVatGjBypUrK7toQghRIWSCSiGEqGQGg4GZM2fy7rvvEhYWxqJFi+jYsWNlF+uuE1LLnx/++x+nj54jMNwXDx/Xcju2oijWqZecnJzK7bjlJTMtmw3/7sPByZ4O9zfGTqet7CKJCmZnZ8eIESMYMmQIX3zxBQMHDqRp06Z8+OGHNG/evLKLJ8RtyaxaHhV9TlE2UkMshBCVxGw2M3v2bOrUqcMXX3zBjBkz2LRpk4ThSuTq4UT9VuHlGoaL3KpzEWdn5jHqvil8NuZPPn7xFyY98wOqWvIvqrycfFLPZ1xxvbj9OTo68vrrr3Pq1ClatGhBp06dGDBgAMeOHavsogkhxE0hgVgIISqYqqosX76c5s2b88Ybb/D222+zb98++vTpI/MI38GKRpq+1ezZeIzEMxesz7euOkhqUkax7f5btJuH673JkMZjmfjEtxgNpoospqhgHh4eTJ48mePHj+Pp6UmjRo145plniI+X0dGFuFYVPaBW0UOUjQRiIYSoQEePHuXee+9lyJAhPPLIIxw7dozhw4djZyc9WO50Dg4Ot2QNsYe3i81zO50Wx8K+1EXMZjNTX/0NY4ElBG9deYD1i3ZVWBlF5QkICOCbb75hz549pKamUqNGDd57771b8rMshBDXQwKxEEJUgKysLMaMGUOTJk2oXbs2J0+e5NVXX8XR0bGyi3bXi48+z86ow2Sm5dzU89xok+nc7HyO7Y0t93LWbR7GwOe7odFqcHCy57Wpj+DorLfZRjWrFOTZTsmTl33r1XaLm6dWrVr89ddfrF69mgULFlC/fn3+/bf8piYTQojKIlUSQghxE6mqyt9//83o0aMJDQ1l8+bNNGrUqLKLJQpFzd/Bx6N+RjWruHu7MH3Ja1QN8r4p53JwcCA9Pf2q26iqitFotD5MJhNGo5HTJ87x8/8tQWunoHOwo9vDzQkI9bHuU/SnqqpER0cDcPDgQbRaLYqiWJviK4qCnZ1dscf9w1vToU8DvHzccXJxxGQyodForPtp7bT0H9mVP7+wjDzsG+hF+/ub3JTrJG5trVu3ZuvWrcycOZOhQ4fSsWNHpk2bRmhoaGUXTYhbTmU0YZYm02UngVgIIW6SI0eOMGrUKPbv388nn3zC0KFDpY/wLebnT5ZY5xvOTMthyS8buHdIWyY98yNnTyXRunsDXv30Eez11/91aTKZyMvLo6CggMzMTE6ePEl+fj55eXnWR0FBgTUAF9FoNNbAmhB7gTpt/THkmzDkm9i//Ti+gR7WsFv00Gg06HQ6DAaDNQwXBeWiR25urk3oLsgvICUpHY0WdHo7NNri4Vmv11OnS1XeaPYwhnwzoTWqkWvIwpxmwMHBwTqtlLg7aLVann32Wfr3789bb71FvXr1GDNmDK+//joODg6lH0AIIW4hEoiFEKKcZWVl8d577/H555/z9NNP8/fff+Pu7l7ZxRIl0OnsUBRQVUBVsdPZMfW12Zw+loDZZOa/xbup0TCI/s90ueIxDAYDWVlZ1kdOTo5N2DUajSiKYg2qycnJODg44OTkhJeXFw4ODtjb26PT6awBVKvVotFc7NX0RPv3SIhNsT53dNYzesKTJZanZs2a/Pvvv9SuXRudTlfqNfh+8kLmztyE2ayiaBTqNA3hg9+etQZmg8FwMcC7WF5T8oXznImPIy8vD4PBgKIo2Nvb4+DggIODA46Ojjg7O+Pi4oKLiwtOTk42r0fcGXx8fJg5cyZPPvkkzz//PD/99BPTp0/n/vvvr+yiCXFLMKsKZrVibxZW9PnuBBKIhRCinKiqypw5cxg9ejRhYWFs2bKFhg0bVnaxxFU8++5DTHziW/LzDASEVaH3iE6snrsDs8kMgKLRcP5sKiaTiezsbGvovfTvBQUF2Nvb4+LiYg2BPj4+1nBYFHhzc3NZtWoVLVu2LHNt6sMju/L5W39Zn/d8pE25XYO8nAJQFEBFNavkZOaj1+vR6/Wl7guWGvDLa7xzc3O5cOECsbGxZGdno6oqTk5O1oB8aVh2cHC462qX//77b9atW8eePXvYu3cvmZmZPPLII/z666/XtH9KSgrz5s1jyZIl7N+/n7Nnz2Jvb0+DBg144okneOKJJyr0BkSrVq3YunUr3333HY8++igdOnRg2rRpODk5ERAQwHPPPcfnn39eYeURQoiykEAshBDl4PDhw4waNYoDBw5I8+jbSJOOtflt9/ukJKYTEFoFrZ2Ge4e2ZOPK3fgGu+JVzYXgeo4sXrwYrVZrE+h8fHysf7e3ty/1XHq9HlVVyc/PL3Oz0vseaYt/sDcbl+6jYZsIOj5Yfv137x/ajtVzd5CbnY+iKAx8vluZ9tdqtTg5OeHk5FTi+qJm2pfeTEhISLDWpmu1WpydnXF1dcXd3R0PDw/c3d2v6ZreriZNmsTevXtxcXEhMDCQI0eOlGn/OXPmMHLkSPz9/encuTPBwcEkJiYyd+5cnnzySZYuXcqcOXMq9P8grVbLM888w0MPPcTbb79N/fr16dq1K2azmX79+lVYOYS4lUgf4tuDBGIhhLgBRc2jP/vsM5555hn++ecfaR59m1BVlezsbNIy0sgsSGfrtpOkpaXhEmzm3uFNyMswERQaQHjtYJydnW+4JlOr1aLT6a4rEAM06VCLJh1qXff5ryS0lj8z17zF4V0xBIX7ElLTv1yPryiKNTD7+vrarDOZTOTk5JCVlUVGRgapqalER0eTm5uLk5MTHh4e1oDs4eFxx4TkqVOnEhgYSEREBOvWraNz585l2r9mzZosXLiQ+++/36Ym+IMPPqBly5b8888/zJ07l4ceeqi8i14qHx8fvv32W5588knuueceNBoNmZmZFV4OIYS4VhKIhRDiOq1evZoRI0YQFBTE1q1bpXn0LcwaftPSSEtLIz09nbS0NMxmM25ubnh4eBAYGEi9evVwc3O7ac1Ni6ZeUo1a0pIzCQr3RWunvSnnulZGg4l1i3YTfzoZZ1fHaw7EMUfiWfjjeuz1Oh5+rhvefmW/EaTVanF1dcXV1RV//4vnzc/Pt75HqampxMTEkJOTYw3JRQHZ09PzmvpJ32rKGoAv16VLyX3a/fz8ePbZZxk7dixRUVFlCsRr166lS5cuvPrqqwwZMoR3332X9evXYzKZ6NKlC19//TV+fn4cOnSISZMmsWrVKvLz8+nQoQNfffUVwcHBNserXbs2ubm5tGrVimHDhtGjRw8+//xzfHx8+O+//5g6dSp79+7l7NmzuLm5ERoaSteuXZk8efINXRshhCgrCcRCCFFGmZmZvPHGG/z666989NFHPPvsszJg0C2mKAAnJydbHwaDwSb81q9fH1dX13J/71RVxWQ0Y6crHnQdHBzYu/UYn726CJPJTHi9anw8eyROLuUzMq/ZbOZszPky7fPV+H9Y9scWFI2GJb9t4qPfn6Nh64ir7pN6PoNX+0wjL7cAgK2rDjBz3Tslvubrodfr8fX1talRLigosLmhcfr0aXJycnB3d8fHxwcfHx+8vb1vy4Bcnopev51d2X7i7dq1C4Bjx47Rvn17evbsyYgRI1i2bBnz588nPz+fkSNHMnjwYLp06cLjjz9OVFQUS5Ys4bHHHiMqKsrmeIsXL8ZgMDBmzBhatmzJyJEjqVevHt26deP3338nODiYHj164OPjQ2JiIjt27GD58uUSiMUdxYQGExX7+8BU+ibiMhKIhRCiDNasWcOIESMIDQ1l7969VK9evbKLJLhyAPby8sLHx4fQ0FA8PT3Ram9ubezx/XFMfOp7UhLSadO9PmM+f9xmyiYHBwdWrd6KqXDQrlOHzrLy7+30Htbhhs9dkG9g7JM/cHR/HCMmtmD1gl3c279VqfttWr4fVQXVZEaj1bB97eFSA/GxvbHkZOVZnyfEppB05gIBYVVu+HVcib29fbGQnJeXR3JyMikpKRw8eJCsrKy7OiAbjUZ+/vlnAO69994y7VsUiLdt22YzIOC4ceMIDg5mxYoV7N69m5UrV9KmjWVQt4KCAiIiIli/fj15eXk2XQHmzZuHs7Mz3bt3x8HBgblz5zJjxgyee+45qlSpwpYtW2xaBQAkJydf92sXQojrJYFYCCGuQVZWFm+++SY///wzH374ISNHjpRa4Up0tQDs7e19UwJw7IlE3nvuJ86dTqF1t3q88elg7PW2YWvKq7+TmpQBwOaVB/j3t430Gd7Jul6v1+PgdOlXr4LJaC6X8v23bD8HdsSg01te83ef/Ms9fVug1V79cxpcw4+DO05hNqmYTWaCIqqWeq6g8KpotIplDmdFwclZj9d1NJm+UQ4ODgQGBhIYGAhAbm4uKSkpJCcnc+DAAbKzs++qgDxmzBgOHDjAfffdR48ePcq0b1EgnjVrlk33D1dXV0JDQ9mzZw+ffPKJNQyD5SZFrVq1iIuLIzs72xqI8/LyWLZsGT179rQuUxSFunXrAuDo6EiTJk2YMWMGffr0sR7Px8fnul63ELcqtRKmXVJl2qUyk0AshBCliIqKYvjw4QQHB0utcCUymUykpKSQkJBAQkIC+fn5NzUA7/7vKD9PWYpWq/DEmAf57qMlxMckYzarbFqxn4W/BNP/yUibfdJSsjCbVQA0Gg1pKVk26x0cHKjTPIioP48BUCXAnW4PNb/mMiUnpGNnp8HDx7XYOqPBtqGcyWQunGD56h5+tgunXjpHXm4BzTvVvqbyBIRV4Z1vR/Db1KXoHex5enw/HBwrf8ArR0fHUgOyt7c3VatWxc/PD1fX4tfxdvXZZ58xZcoUateuzS+//FKmfbOzszl27BjVq1cvsWb59OnTeHl5MXDgwBLXubq64u3tbV22cuVKsrKy6Nu3r8229erVw93dndjYWJo0acLQoUPp2bMnM2bMsNlfCCEqklRvCCHEFWRlZfHCCy/w4IMPMnr0aNasWSNhuIIVFBQQFxfH9u3bWbZsGbt370ZVVRo1asR9991Hu3btqF27Nj4+PuUahlMS0hn/xEyO7j7NoZ0xvPPoDM6fS7OGXUWjISUho9h+vR6/2PRZp9PSuU8zm/V6vR4XDwdcPJxAo5CSlMnxg2dKLY+qqnz+zt882vY9BrecyO+fryy2Tft7GxAccbE58eBnu5Q6YJfRYOL/XvuD3Ox8TEYzW9ccIuZoQqnlSYxL4adP/uXkwXg0dtqb2lT6RhQF5MaNG9OtWze6d+9OYGAgKSkpREVFsWrVKg4cOEBycjJmc/nU1FeGL774gpdeeom6deuydu1avLy8yrT/3r17MZvNdOtWfMqtmJgYUlNTiYyMLFa7npWVxYkTJ2jcuLHN8rlz52Jvb8/9999vs9zHx4cNGzbQv39/jh49SnZ2Nn///TcBAQF8+umnZSqzEEKUF6khFkKIEqxbt44nnniCoKAg9uzZQ3h4eGUX6a6RmZlJYmIiCQkJXLhwATc3N/z9/alRowbu7u4VMrfq2egkDAVGyxMV8nIK6DqgHkt+34yiKGgUhchexecCHjzqHmo0CCIhLoVmnWoTEGLbBNTBwYHkpFSyMnIBSy3uJ6Nn88e2CcWOtW7hLnauO0xYnQBqNQ3j39+3WNf9MnU53R9uic8lzZSdXRz4bM4LHNwVTdz5w/R9vD0Ap48lkH4hi9pNQm36MwNkpGaTcSH74gLV0jS8ep2Aq16fGRPmEXciEYBDO04xe/pynh7f96r7AGRn5nJ8Xxx+Qd74BVd8jaCjoyOhoaGEhoZiNBo5f/48CQkJbN++HVVVrTXHvr6+t03T6mnTpvHKK69Qv359Vq9eXWxqq2tR1Fy6WbNmxdbt3LnziuuKblA1bdrUusxkMrFo0SK6dOlS4hR09evXZ86cORQUFLB+/Xq++eYb/v77b1577TW2b9/Ol19+WeZAL8StSuYhvj1IIBZCiEtkZ2czZswYfvzxRz744ANeeOEF6St8k6mqyoULF0hISODcuXPk5uZSpUoVAgMDadasGY6OjhVeprA61XDxcCIn0zJwlIe3CyPG3E+zDjU5G5NMs461CKtVfHoiRVFo0bnOFY/r4OCAVmf7YyU9NafYdlHzd/LRCz+h0Wowm81E9i1sxly0q2oZROtyegcdDVqEEffvYQD++no1P368BLDMN/zpPy/i6Ky3bu/h40JYbX9OH0sARUFnr6Ves7ArX5hCFxLTMRcODIZqGXW6NOfjU3npgSmkJmWgaBTe+PwxInsXD1kVxc7ODn9/f/z9/VFVldTUVBISEjh69Cg7d+7Ex8cHPz8//P39K+UzeC0++ugjxowZQ+PGjVm5cuV198EtCsTNmxdvLl8UiEtat3v3bgCbQLx+/XpSUlKKNZe+nL29Pd26daNbt2506NCBDRs2kJycTL169fjmm2/o1avXdb0WIYQoKwnEQghRaNu2bQwZMoSAgAD27NlDRMTVR9oVNyY9PZ0zZ85w5swZzGYzfn5+1KtXjypVqpR5yphrkZacSVZGLtXCqpRay+zq4cSUf15k3swoNFoN/Z/tgqOTnjb31LfZLi+3gNcGfsmpI+dw93Rm8q/PEFrD74rH1ev12Ok06PRaDAVFTXRVzGazzY2XHWsPodEo1tB5Ym8sWp0Wk9HST1ir0+J0SbAticlo4ucpS63PY46eY8PSvdzTv6V1mUajYfKvz/L3t2vJycrn/kfaUCXA46rHBbj/0XYc2xtbWHroPrB1qfss+WUj6SmZln3MKj99tLhSA/GlFEXBy8sLLy8v6tatS3Z2tvUGzYEDB/Dy8iIoKIiAgIBKqTk+efIkBoOB8PBw6/nfe+89xo0bR7NmzVixYsUN1aru2rULe3t76tevX2zd1WqIi4L0pYF43rx5aDQaevfubbPt7t27cXNzK9ba5sSJExw4cIDg4GCWLVvGb7/9xuOPP07v3r354osvcHFxue7XJURlM6kaTGoFT7tU+tAR4jISiIUQdz2z2czUqVMZN24cEydOZPTo0VIrfJPk5uZaQ3B2djb+/v40btyYKlWq3NRr/u/vm/ninb9RVZUm7Wsy8fsn0dlf/SswuIYfL308yGbZ+fg0pr71F/ExyUT2asKZ6POcPBQPWAbUGvvYt/y2edwVj6nT6TCbVZzc7ElPLpq2SCErIxc3D2frdqF1AlDnFg7OpVXwC63CmbPpUBjkTSYzsSeSShxcy0pR0Gg11imeALRaDSkJ6Zw+lkBobX+8fN1w93JmxJgHrnotLtd9YGv8gn04degM9VuGE9EgqNR9tFqNJT1bilbq6NeVydnZmfDwcMLDw8nLy+Ps2bPExMSwb98+qlatSmBgIFWrVr3ufuvz589n/vz5ACQkWPpsb968mWHDhgGWvrb/93//Z92+a9eunD59mujoaEJDQ/npp58YN24cWq2WDh068NlnnxU7R2hoqPV4V5Ofn8+hQ4do2LAh9vbFB0bbuXMnISEhJQ56tWvXLhwdHalTx9IqQlVV5s+fT5s2baha1Xa08s8//5xZs2bRunVrmjdvjouLC6dOnWLhwoUA/PDDD2i1Wh577DG6du3Ko48+SvPmzfnzzz9p1KhRqa9DCCGulwRiIcRdLTk5mWHDhnHw4EHWrFlDq1alz9sqysZgMBAfH09cXBwXLlygSpUqRERE4O/vf1NqggGy0nNY9fc2FEUhsnczvp4wF7VwxOXdG46xacV+Oj1QvA/w5fJyC1j193YK8g106dOMj1/9nUO7YjCbVP78eg1evm42218+qvTlFEXBTqOzCcQajYLewTaI9H0yktSkDLatOUhEvSCGvfUgz/eaSm5WPqoK9g52BNeoiqqq5GTn4+SsL1brrdVqeHZ8X+uNgPotq+Pl68bwjpMoyDOgd7Tnoz+fp1bjkFKvQ0katomgYZtrb0Xx4BMdiFqwk/jo89jp7Hj23Yeu67wVzcHBwRqOs7KyOHPmDAcPHmTPnj0EBAQQGBiIt7d3mfq279mzh59++slm2alTpzh16hQAISEhNoH4ctHR0YClv+60adNK3KZTp07XFIgPHDiAwWAosQb49OnTpKSk0KlTp2Lr8vPzOXz4MM2aNbPeGNixYwdxcXG8/PLLxbbv3bs3BoOBbdu2MWvWLPLy8ggICGDIkCG8+eab1KhRw7pttWrVWLlyJe+//z5t27ZlypQpPPPMMxUyfoAQ5cmMgrmCxzA2I1XEZaWo6jXMySCEEHeg//77j8GDB9OqVSu+//57PDw8KrtIdwyTyURiYiJnzpwhMTERNzc3AgMDqVatmnVe0pulIN/IC/d+zJmTlkGfgmv4ERuTbJkzt9BrU4bQtd/VpxdSVZXXH/6cgzuiURQFbz938gvMZKZb+vxqtBpCa/lxqrCGGEBjp2Xe3veKzU98qfXr13Nw4zkW/rATjVbDS5Meonv/FqW+rpOHzvLrZysxGU0Mfq4rXlXdeXvE98SfTiE43Jf3vx+OT1V3DAYD//77L/fddx86nY4L5zPITMshKNyX8U/MZOe6I6hmFUWj0O7ehrz08SAObD+Fj58HEfWqlVqOG2EoMHLmZBI+fu64ejqXvsMtqqjP8ZkzZzh79ixarZZq1aoRFBSEm5tb6Qe4Q7399ttMnjyZU6dOERZWel/0axEVFcWQIUNo164dM2fOlP+nxW0hIyMDd3d3luyrjrNr+c2AcC2yM03c3/AU6enpd/X/R2UhNcRCiLuOyWRi8uTJTJ48mU8++YSRI0dKzUM5SU9PJyYmhrNnz6LT6QgKCqJu3boV2g/w5IE46wjIYBll+d5H2rLsz62AZXCptj0alHqcpDOpHNxhqYlTVZXkc2k0jazD7o3HUTQaVLNKh54NOXX4nHUfs8lMckJ6sdGlL+Xg4ED3Ac0Z/PT96B10NoNcXa7onrWiKITXrcb4GcOs6z54+XfiYy8AEHvqPD9NW8mrk/sXO4ZXFTe8qlh+FOns7VCwtFxWsDS9frbnFFKSLINiPf32A/R9omOp1+Z66eztCCtlBOvbwaV9juvXr09SUhJnzpxh/fr1ODs7ExISQlBQ0G0zUnV5mTdvHo0aNSq3MAwQGRnJ3r17efzxx2natCl//vknLVqUfgNJCCGulQRiIcRdJSEhgaFDhxIbG8uGDRto0qT0ZrPi6kwmk7WPZUZGBgEBAbRu3RpPT89KudHg5euGoigXw6RGYchLPXjgsfZkXMimXouwq9bgFnHxcMJOp8VoMFmXPfZyD5p3rM252BTadK9PUHVf/vhqjWWKJkXBq4orVfw9ih0rMS6Fv79ahclootmDYeTl5eHhffWbBP98u5Zf/u9f7HRaXvjg4WIDUB07eAZQC/sVqxzafZpjB87wz4/rqNvRhYICY7FA9uirPTmw9SSZaTm4ejrjH+bLlvXHrOt/+3zVdQXi7Ixctq09jLObI8071bqr+uBrNBr8/Pzw8/Ozdg84ffo0hw4dIjAwkJCQEDw9PSu7mBXi8OHDN+W4VapUYfHixUyZMoXIyEgmTZrEyy+/LDcyxS1Ppl26PUiTaSHEXWPlypUMHTqUe+65h6+//hpX16sMSCRKlZWVRUxMDLGxsej1ekJDQwkKCipxYJ6KtvyPzXz77nwUReGZCf245+GWpe9Ugk3L9zP9rT8pyDMw9JV7eeipzsW2Obo3lrk//oder2Pw813xv2x+3fzcAka0m0hqYS1sq94RdB/amtZtrtxfPfpIPM91/9j6XKvV8Puu93C7pJnxwHaTbKZscnbRk51nQGev8OT/mvPvDyf5ZsHoYsfOy8knIe4CfsHeLJ+znRmTFlirjD29Xfl98/+u+foAZGfmMerBqZyLTQHgnv4tGP3xwDId41qdO53MzPfmk56cSe8Rnej4YNPSd6okaWlpnD59mri4OFxcXAgLC6NatWo3rd/83WLLli0MGjSIBg0aMGvWrBIH+xKishU1mV64L7xSmkz3anhSmkyXgfyvLIS44xmNRsaPH8/06dP5/PPPGTZsmNQsXCdVVUlMTCQ6Oprk5GT8/f1p1aoVXl5et9Q17TGoDY3a1uSHDxay+u9tOLs60PbehmU+TtseDYo1rz5x4AzvPf8zqecz6dqnKaMmPcRb0x654jHio8+Tci7N+jw1MZu0Cxms+Gc7+7dFU6thEPcNbmVTq3qhMDxbbvQrmExmMlKzbQJxQYHJOuo0QE6eAVQVFMtx4s+mkpdTgIOT7Q0KByc9oYVzKLfrXp+fpi4nNysPFIXHR/coy+UBYNuaQ9YwDLDy7+08804vnN3Kd+5eVVV555GvSIi7gNlk5tDOaKoGeV/3oGA3m4eHBx4eHtStW5ezZ89y6tQpDh48SHBwMGFhYTg73759qCtT69at2b17NyNGjKBx48bMnj2b9u3bV3axhChR5Uy7dH11nWfOnGHcuHEsW7aMlJQU/P396dOnD+PHj7+mVi5RUVF07lz8pvHlYmNjCQoqfWaCiiSBWAhxR4uLi2PIkCGkpqaydetW6tWrV9lFui0VFBQQGxtLdHQ0ZrOZ0NBQmjRpctMHyLpeqqoy9pGvSIhLwWxS2bf5BJ/9+xoR9QNv+Nj/G/G9dTTpZX9tI7hG1as2M/YN9MLRWU9ebgGoKvnZBnJz8/j27b/RaDWsmreTrIxcBo3sYt2nduNg7PR21ubaWns7XD2dbI6raJTCAGwJxQqgKsrFkKwo2DvYfs3Hn05m2pi/SIpP494BrUhPzyE/t8AyRZOisH3dUXqUsTb9+P4ztgsU0OlL/3lx8uAZTh2Kp27zMKqFVSl1+/zcAuJjki8uUOHkgTO3bCAuotPpCA0NJSQkhAsXLnDq1CnWrFlDlSpVqF69OlWqlD4vtrDl6enJP//8w5dffkmPHj0YO3YsY8aMuaua6gtRnk6ePEnbtm1JSkqid+/e1K5dm23btjF9+nSWLVvGxo0bS22NERoayvjx40tct3//fubOnUv9+vVvuTAMEoiFEHewlStXMmjQIPr168f06dNxcnIqfSdhIycnhxMnThAbG2ut7fL397/lf3jmZOXbhCdVVTmxP47AcF/+/X0zmem5dO3bjMBrCGJgmas6JysfZ1eHYlMrbVx+gLDaAfz93Tr0jvYMe6UHQeG+1vXObo5Mmv0CP0yaj9FoYsDLXTiXFguKZRAugK1rD9kE4tTzWRiN5ovzDhvNHNsbR9P2NclMz8Xdy5nw2v7s3xFj3aeKvzsJiZmWUAw4uzmRcCaVf2ZtQFVVHhrWnvef+4nTxxIwm1R+mrKUui2rWyfoMJtVkhPTr/kaF0k6e8Hmud7BvtQ+2v8t3sPk539CVVXsdFo++vMF6ja/+kBMekd7wusHEn04HlVV0ShKqfvcShRFwdvbG29vb3Jzc4mJiWHXrl3odDoiIiIIDAy87nmN70aKovDCCy/Qtm1bBg4cyLp165g9ezZeXl6VXTQhbjvPPfccSUlJfPbZZ4waNcq6fPTo0UydOpWxY8cyY8aMqx4jNDSUCRMmlLhu8ODBADz11FPlVubyJIFYCHHHUVWVzz77jLFjx/LVV1/x2GOPVXaRbjsZGRkcP36c+Ph4/P396dChA+7u7pVdrGvm5KInpJY/cScSUM2WVsS1m4Tw3sif2L3xGIpGYcGs/5ix7DV8A67eFCz6yDneGjqD9AvZBEdUxdnNgeyMPOv6Kv7u/G/ED5hMZhQFjuw+zay1Y9DZX/yKrduiOv9X2J83NzeX8yvOoNFqMBeG3rSUbJtzevq6odPbYTCYAEtNsNFoZmiXj0lNyaJ6LT/emT6EN4Z9R/L5LJxd9NRvGU7C4r3WEJ2Vlceoh78kOysfgHXL9lOQlo3ZZInAGo1CQLA3h/fEUjT09H2Dyj4P98FLQjmA3rH0Acv+mbnWOuiZ2WRmyS8bSw23iqIw6deR/D51Gemp2dz3SFtCa9+eI1Y7OjpSp04datasydmzZzl+/DhHjhwhPDyc0NBQ6WdcBk2bNmXnzp089thjtGzZkoULF1K3bt3KLpYQQNE8xBXbAqSs5zt58iQrVqwgNDSU559/3mbdxIkT+fbbb/nll1+YMmXKdXX1SE5OZt68eTg6Ot6yv8du7Vv8QghRRvn5+Tz55JN89NFHrF69+pb9z/dWdeHCBbZu3cq6deuws7OjS5cuNG/e/LYKw2AJTx/8NpLuA1rT5t4GTPplJFWDvNm14RiqCmaTSm5OAbs3Hi/1WB+99CvpFyyBNfZEIqERfujsLTV5fsFeNO1QG6PRhKqqmM0qF85nkpKYYXMMo8HEtFd/4+E6r/POoK9QVRVHF501vCZftr2zqwP+ob6WJK8ooNUw67NVpKZmgQKnjicwfcI8kpMtz7Oz8zlarOmyQlZhGAbIzsijZuMQFEVBo9WAAl16N6VJ+5p4+7lz78CW3FPK3MwlycstsHnudJVppIq4ujtZylBYThf3a+tv7OHjynPvP8xbXw2jUbuaZS7rrUar1RIcHEyXLl1o2LAhZ8+eZcWKFRw5coSCgoLSDyAAcHNzY+7cuQwePJg2bdqwePHiyi6SELeNtWvXAtC9e/dirb9cXV1p164dOTk5bNmy5bqO/9NPP5Gfn8/DDz98y84jLrcghRB3jMTERPr160dBQQHbt2+nWrVqlV2k24Kqqpw/f57jx4+TmppKWFgYDRs2xNGxfAdFulFms5m9G4+Tl5NP04610TtefTRrr6ruvPTxIJv93TydyEzPRTVbaif9gyx9onKy8km/kEXVQE9MRjM/T1nK/m2naNCyOolnU22Om3jmAn9un0jq+UyqBnqSnJiBzl5raeIMePu64eNnewNh0Y/rWDF7E6oKR3bG0HJACM5u9mSnW0KP0WCiIN9gbWpsMpmJvWQuZVSIP3vh4gTCwKG9tgH4XFyKpS9qYc2rf4AHcUeTbLZ56KlOnOlYi/Pn0uj0YBN+n7GG3ZtPgqqy7O8d1GsWRrc+ZRu5uUGr6mxfe8T6PPLB0qcye3pcH8YO/Zrz8WkERVRl0KjuZTrnnUZRFPz9/fHz8yM5OZnjx49z4sQJQkNDCQ8Pv+X+Ld6KNBoN7733HvXr12fQoEH873//44033pD+2aJSmdFgquD6R3NhR5iMDNsbrXq9Hr2++A3Lo0ePAlCzZsk3GWvUqMGKFSs4duwYXbt2LXN5Zs6cCcAzzzxT5n0ritQQC3EXUxQFRVEICQkhLy+vxG1CQ0NRFAWj0QjAzp07URSFVq1Kblo5e/Zs63Gjo6OLrc/NzcXBwQEnJyfy8/NLOML12bVrF82bNyc0NJT169dLGL4GqqoSHx/PunXr2LlzJz4+PnTv3p169erdkj/Ap47+nbcHf8m7I77jlV6fWgaDKgONRsOEb4fjXdUdjZ0Wdx9XsrPy2LzqIP2bj2N4t495rNNkfvx4Cf/MjOLontP8MzOqWO1laG1/HJ31BIT6oLXTUrWaJ699PBAHJ3tUFTyruJKbY/vZPnc6GaXwzrvZZCYnswAn14tNi+10Wps781qthrBa/mi0F3/Mu11WDr2DbdNkrVYDeQVQ2C85MSYZrb0W7DSgVbDT29GoRXUGjOzC8+/2o36LMEsf5KIRSRWFLWsPlemaArwx9REiezWmWqgP/Z+OZOgrpY9UHRRRlVmbxvHXvg/4esUbeFaRKdDA8n9ylSpVaNu2rbVWZtWqVezZs4esrKzSDyAYOHAg69ev54svvmDo0KHk5ube9HNez3fp5fte7REVFXXTX4O48wQFBeHu7m59TJ48ucTt0tMtY0dcqSVY0fK0tLQyl2HdunUcPXqU+vXr07Zt2zLvX1GkhlgIQWxsLNOmTWPMmDGlbtukSRM8PT3ZuXMnGRkZxea4W716NYqioKoqa9asYcSIETbrN27cSH5+Pvfcc0+Jdyqvx19//cXw4cOlRuAamc1mzpw5w/HjxzEajURERBASEnJL91tMPZ/Bqr+3WZ9HH45n94ajtL6nwVX2slBVlZij5zAZzVQN9CTtQjaqqpKems37L/6K1k6DasmQpCRmsGr+Lmv/VlVV8avmib29jvjTyVQL8+HF9/sXO8fKuTvJzTWAAscPxTPr0+WMmtjXur79/Y1Z/ON6S79hkxkXFydqNw0i5nAqaDQMfr4LdjrbAZUmzhzOzA8XcSEpg/sGtyHfaGL6hPmgWCqJn32jJ19OXkJOrgGNBvz83Ig7cR6N6WLZC7MxKApGk5m8PAPOrhdHBjebL45Sjaqi09mRFJ9KSlIG4XUCsNfrOHHgDAd3RNMisg4BoT7FXruLmyNvThta6vtwOY1Gg6uHDHR3JZ6enrRs2ZLMzEyOHz/O2rVr8fPzo2bNmrddF4aK1rRpU3bs2EG/fv3o2LEj8+fPr5CbpGX5Lr3clUbnBUuYFrenypx2KS4uzuY3Wnn95iqLb7/9FoCnn366ws9dFrfurx8hRIXw9PREURQ+/PBDnnzySXx8iv/gvZRGoyEyMpJ58+axbt06HnzwQZv1a9asITIykn379pUYiNesWQNwXc1uLmc2mxk/fjyfffYZf/zxBw888MANH/NOVlQjfPjwYcDSDCooKOiWHzEawF6vQ6NRLAGukKPTtX25f/HO3/z72yYAGrarYZ3KCCyjN5uMZpvt8/IMgCUnqiq06FyHASO7kp9nKFYrW+TU0QSbKZC2rTtis75h25p8PO9ltq86SGCNqnhH2HNs/04ovPa/fL6a9j0aEFz94ujUVQI8ePuzR22OU9Xfk5NH4mnQPAwPH1fsnPWoRjNmwMXTBdRLmkgXlqXoiinAns0nWDV/J6pZZegL3ajbJJiDO2MslcSKglcVV4Z1+QjVrFIt1IfI+xrw2/QVAMx4dx4Tv3+Klp3rXPV6m81m4mOScfN0tpk3+VIXEtPJzsyjWvUqt8XnrzK5urrStGlTateuzYkTJ/jvv//w8/Ojdu3auLi4VHbxbllVq1ZlzZo1jBw5khYtWjBv3rwrtmwqD2X9Lr3clUbnFeJ6ubm5Fau0KEnRDbaimuLLFS0va//fCxcu8M8//+Do6Mijjz5a+g6VSL6FhLjLOTk58b///Y/09HQmTpx4TfsUhdmicFskJiaG6OhounbtSqdOnawDNVyqvAJxZmYm/fr1Y/bs2WzevFnCcCmSkpJYt24dBw4coEaNGnTp0oWQkJDbJow4uzkyclJ/60BMPQa3oWHbGgAU5Bs5sO0UZ04mFdsv/nSyNQwD7Nt4vNgcuZeOBg3g7evOk2/3ok33Bjz5di/6P9MZKN5E2WafqrY/OnKyincHqN8qgifG9uaeAa3BrEGjVW3Wf/2+7UBAWRm5jHv2J/q1mMjYJ38gIy2Hpm0j6Pd4e2o3DGLp/J2kZ+SBBlQFomNTwMkeo6vlRkFAqA+qRgGt5aEqCtPHz2Pv1lPs2xbNO0/9yKhxvenSqwn1m4Xy0rt9WT1vp7V/dfzpFP78JupigVT47oOFV7wGAPl5Bbw+4Aue6vohQ1qMZ+2CncW2WfTTfzzScjxPd5nM2KEzMBQYSziSuJyTkxMNGzaka9eu2NnZsXbtWvbu3XvFJrrCUiP2/fff8+abb9K1a1d++eWXm3au6/kuFeJWUKtWLQCOHTtW4vrjxy2DT16pj/GVFA2mNWDAgFt2MK0iUkMshOD555/niy++4JtvvuHFF1+kRo0aV92+SxfLfKmrV6+2WV70vEuXLri7uzN37lwOHTpknQIjIyODHTt24OnpSdOmZRu451LR0dH06tWLqlWrsm3bNpl38ipSU1M5fPgwaWlp1KhRg+rVq9+2c50+8FgHOvdtjqHAiIe3pc9pTlYer/b/nJij5wB4amwv+j0Zad2npNbz/oFexBaFZwUi6gZwZG+stSvtk2Puo133BrS/rxHH9sURH5NMYHVfEs9cYMvaw1Tx96BN17o2TfNrNQzi+MGz1ue5OfmYjJaa6Lw8A84uDphMZlb9tZVzp5Op3swXR1fbQcHs7DScOZ3MsQNniagTwJLZW9i54Rhms8qeLSf57pN/Sc3OZ9umE1T198DRWV94W9syZ1JWbgE42qOzs9w0yFRV0BQ1hwa0kJ9TYL0Tnp9nYNeWk8SeTsZsUvGu6k5ps3VoSlm/Zt5ODu2MASwDg33xzj9E9mpqvVYF+Ua+mTjPWm29Z8MxNi/fT8drGIhLWDg6OtK4cWPCw8M5fPgwq1atIjw8nIiICHS60qe8utsoisJLL71EnTp1GDhwIPv372fy5Mk35f/Bsn6XijufGQ3mShpU61p17my56btixQrMZrPNjfLMzEw2btyIk5MTrVu3LtNxiwbTutWbS4MEYiEEoNPp+PDDD3n44Yd58803mTt37lW3r1OnDv7+/hw4cIDz589TpUoVwFL76+LiQosWLazNdNasWWMNxOvWrcNkMhEZGXndNZPr1q3joYce4pFHHmHKlCm3dL/XypSVlcXhw4dJTEwkLCyM5s2bY29/9VGZbwfOrrYDS61fvKcwDFsC13eTF9NrWAfs7Cw/dv2DfXjw8fYs+mkDAB0faEKDtjX4csJ8a7rLySngy4Uvc2RPLAGhPtRvHsaRPbG8MXQGhnwjGo3CC+/2Y+ZH/5Kbban5fWh4R5588z5rORq0CGPx7ItTUuj0OvZuO8Wkl34nNzufVp3r4OvlwKIf/0Oj1RDWuArN74+wbq/RKHTt05RnHvoCk9GMRqNQo5aftYm42ayyZ3csCclZoCgkJKSj02kujjqtKMXSf1p6LmrhoDyFB8HNw5GcTEuNoqOTnu+nLcdgMAMqE178hWHPd+OHT5aCqhIQ7EXX3k34ecpSwBIsnp1g6RetqmqJffWNBpN1TmMAo9Fku4GqWudBLmIymTEUGLHTaaX/fxm4urrSsmVLLly4YA3GNWrUICws7La96XUzde/enW3bttGrVy8OHDjA7Nmzy70vdlm/Sy91pSbTDg4O19UnWYhrFR4eTvfu3VmxYgVffvklo0aNsq4bP3482dnZPPPMMzZzEB85YukWVLt27RKP+d9//3H48OFbfjCtIvJLUggBQP/+/WnTpg3z5s1jw4YNtG/f/qrbd+3alV9//ZW1a9cyYMAAwDKXXYcOHbCzs6NevXr4+vqyZs0aXnjhBeDGm0vPnj2bJ598kunTp/Pkk09e1zHudHl5eRw9epTY2FiCgoLo2rXrLTlidPkqDFGKgqrC3s0naNahlnXtyAn9eODR9piMJkJr+ZOTlc+X7y6kKLWdPpHI1nVH2LDsACePnMOriivhtfwwFfY1Nqsqf3y9xma+3cW/b7YJxG3vqYejs94SmBWFggIjH73+p3W06a1rD+OgFIZbk5mcjALcvBx4+MmOHN4XR7c+zVi34gBGswoaMKGSXXi+or7TBaqKekmNcIHRbMnAhfnS1VVPZk7BxVGjTWbLAHdFV0mjYfyXj7Hinx2oZpXmHWoyecycwiCtYDKp/PvXtsLLqaBotTz0VCStu9Xn2L5YmnaoyeZVB3lnxA+YTWY6P9iE+wa35uNXfictOZMHhrZl4PNd+fvbtSQVTlX12OieNiHX3kHHIy/34NepywCoXrcaK//awsejfkant2PsjOG06lbvej8IdyUvLy/atm1LUlIShw4d4tSpU9SqVYvg4GC5wXCZGjVqsGXLFgYPHkyHDh1YtmwZAQEB5XqOsn6XFrlSM2t3d3cJxLcxk6pgUiv23+H1nO+rr76ibdu2vPjii6xevZo6deqwdetW1q5dS82aNXn//fdttq9TxzKWRNEAlJe7XQbTKnJ7dB4TQlSIKVOmAPDaa6+Vum1Rs+mikHv48GHOnTtnbXoDEBkZSVRUFGaz2Wbb6wnE06dP55lnnmHevHkShktgMBg4dOgQq1atoqCggMjISBo3bnzHh+FW3eoWqx29kGg796KiKARHVCWsdgCKoliC7WW1qgt/2Uz0sQQA0lKyOHn0nDVnaxQFJxe9pW+tAmgU7B1tm6YW5BvJzSlALQygqlrYj1i9WAZXT2fL1EuKQnZqHhqtypxfNnFwbxxTx8/j0L64i+VCIel8JpO+HcbApyOZ+PVjODjrC8t8sezPv3ovQdV9aNm+BmPf7YtZVTE5WGoH9XYaVAVLH+LCyuSAYB9Gv9+fVyc/zJm4CxevQeHjXGyKtcxnos9z/MBZwmr702NAKxxdHPj63YWYjGZUFdYs3M34J7/n/Lk0CvKNzP1+PbM+WUrSuXRQNATV8OP+R9oUe88eeeVevlrxBh/99QJt7qnH7v8s/dYM+UYmPf39FX9giStTFIWqVasSGRlJ3bp1OXbsGGvXruXcuXNyPS/j7u7OwoULadGiBW3btrXOwVqeyvJdWkRV1RIf1zPVjRBlFR4ezo4dOxg2bBhbt25lypQpnDx5kpdeeoktW7bg7e19zcdKTU3l77//vi0G0yoigVgIYdWmTRv69+/P1q1b+fPPP6+6bVGoLeo3fGn/4SKRkZGkpqaye/duzp8/z/79+6lWrdoVm9iURFVV3nrrLSZNmsTq1avp3r17WV/WHU1VVU6fPs2qVatITU2lXbt2tGjRAlfXu2NeV08fN1oX1igqCrh5OtGsk6V2OC+3gOgj8eRk2Q465O3rhnJpZ1hFsQzWZW1ZrOLi5kTVapa+6e5ezrz2ySAcXfQUbZSZnsv+7Rfn2XZ0sqdaqI9NuGzTta51vau7I/f0a27JmopCTpYBRaPg6KS1NovOzMyzBGqNBlVjqWVu0jaCjvc3IrxuAEGhPpawrVVQNZbH5u2niDmXxva9p1mweA9mV3soHHgsx15zsdOvooCdBg+vi03eDu2JLd7J+tLniuVaFTl3OqXY9c/OzLcOwgWwYs42a6COO5FE1KI9xfYBCKsdQMPWERzdc9pmudFgshlJXJSNoigEBgbStWtXQkND2bt3L5s2bSIjI6P0ne8idnZ2fPfddwwZMoR27dqxbdu20ncqg7J8lwpxqwgKCuLHH3/k3LlzFBQUcPr0aaZNm4anp2exbYtu2JTE09OT3NxccnJybvnBtIpIIBZC2Jg8eTI6nY633nqLgoKCK24XHBxMeHg4J06cIC4ujjVr1uDh4UGTJhcHxymqLV6zZg1r165FVdUy1Q4bDAaGDx/OH3/8wcaNG2nRosX1v7A7UFpaGv/99x9Hjx6lcePGtG3btsQvrlvJ9rWHGNL0HfrWep3Zn60ol2O+/cWjvPzhAIaPeYAvFo/Gq4obZ2PO80TnyTz3wFQe7fA+xw+csdnngUG206/cP6jlxX6XCgx8OpJvl7/GcxP60LxLXU6fSCInp8ASRBXLY//2U9b9FUUhol7AxUGpFMugVR98P5zRH/RnxsKXuJCQZh0l21hgpiDfhKOLpaZZo1VwdLK3CdSqouGNZ3/imYFfMeS+T9HZa1F1iiUQ2ylo7bVs32Ypg9FoZuPWE7YXRqtBvbQGGDi0P5ZP31vAnJ830KxdhO32isKTb9yHs6sDTi56Rk3si1/QxQHrwmr5YX/ZCN01GwVZa80dnOzRlDbq1mVadqtv89zDxxWtVn6a3CiNRkP16tXp2rUr7u7urFu3joMHD2IwGCq7aLcMRVH44IMPGDduHF27dmXZsmXlevxr/S4VdzYTmkp5iLKRPsRCCBsRERE899xzTJ8+nc8///yq23bt2pWTJ0+yatUqoqKi6NSpk81gWbVr18bPz481a9YQEhJi3eda5OTkMGDAAOLi4ti0aRP+/v7X/6LuMAUFBRw+fJi4uDjCw8OpUaPGbTG4WEGegfef+YGCPAOqCj9/soRGbWtQt3nYDR1XZ29HjwEtbZb98dVqMlKzAcjLzuenKUuZ9ONT1vXPvP0gASE+HNp1Gt9AL7r3a06Hexvy3f8tJTkxndMnk/hr5jp++WwlACvn7kSnt8NgMFGUepfP28WQ5y5+nk2mwibVhf16Tx1PoEmbi6EzokEQy/6wDLyl0SioRmjYMpT1y47j6e1CSE0/tm05adm4MMDu21VYg6rC+qgjYK+1hm6j2Wxbo2sCNBfv2itG08VBo1UVnb0do4f/YF0WUr0KAUFexMddAKBj93r0f6ID/Z/oULiLyuLfNrN+6V4CQ6sw/I37+HrxaKa+NYfcnAJ6PNycGe8usNa2+4f4cO/Alnw9YT4A1esEEBjmw3cfLMS7qjsPPNqu2BRXDzzWnoTYFFb9vQ3vqu6M+8523nJxY3Q6HfXr1yc4ONg6N3y9evWoVq2a9C8u9OKLL+Lr68tDDz3EN998w9ChQ8vluGX5LhVCVK5b/xeUEKLCjRs3jp9++on333//qqNBd+nShW+//ZapU6eSmppq03+4SOfOnVm4cKG1n9a1BOKUlBQefPBB7O3tWb9+fbmPBHq7UlWV2NhYDh06hIeHB5GRkbi4uFR2sa5ZTlYe+bm2NVTbVh+84UBcJCk+jYW/bEJRIDszz9p0V1UtNaiX0mo1BEdU5bspyzEaTSyavYUuDzZma9QRVBVOHk7AwcH2K9JyDMUaSFMvZNusd3S+ZBRvBc4npFOQV4C9g2V5z0fakJGazZaVBwir408VPy/2bD2GWYHklCwMR+ML+/taTqCaVBTV0jQaLE2lLwZgy5RKzm56MvIMoMK9Xeqy/L/DGF0s5a5dy4/mjaoz94+tuLg6oFVV4rMvzo98+tR5lmwZx+F9cTg42RNR258DO2PYtekEYTX9UE0mvpwwD4CD26O5cD6DRq0jcHJ1oEGr6phNqk3z5uij57h3YGtadq5DWkoWWq2G0f2mYzarmM1mju45zZjPH7O5Zoqi8NT/+vDU//qU/gaL6+bm5ka7du04e/YsBw4c4PTp0zRo0MA6G8DdbtCgQfj4+NCvXz8SExN59dVXy+W41/pdKu5cZlWDWa3gaZdk3IAyk0AshCjGy8uLt99+mzfeeOOq23Xp0gVFUdi/f7/1+eU6d+7M7NmziY6OplatWlSrVu2qx4yLi6NHjx7UqVOH3377DQcHh+t/IXeQtLQ09u3bR15eHo0bN8bPz++2q+Fx93ahTrNQDu+MsQa7ed+vY+AL91jm1C2j+Jjz/PPtWhRF4b6h7Rgz7Dsy03IAcHDUodPbkZ9rwM5ey5AXuhXb/4+ZUZhMlpGkDQVGaxgGy82Hy/uy2tlpMFwSrFVU8vMNHNwZQ/qF7GKhGyArO4/oXbGkpWbTol0NBr/YncEvWvrBr1i2FrNqtJ7nwoVsuLQGVWtpNm2lKKgaCgOygmpWSc83QuEP7eWbjmLQaymK5fviU/h06mMMHd4RgGcGfXmxCbWqgqpiMJjQO9pTNcCTXZtOMPaZWZZRrU0q9ZsGo9VqMJnMmM0qezefZOuawwBsjzpCy862YwH4B3tjr7fDL8gbvyBv/vpqlWXapcLLuHHZvpLeRlFBivoXV61alaNHj7J+/XpCQ0OpVauWzF8MdOvWjbVr19KzZ08SEhL46KOPbjjEXut3KVx52iWAPn360Lhx4xsqixDiyiQQCyFK9OKLL/LVV18RExNzxW2qVKlCgwYN2LdvHz4+PtSvX7/YNpfWGpdWO3zw4EHuvfde7r//fr788kuZS5Pbt3l0SRRF4aFnujDpmR+tywryDKQlZ5Y5EGdn5jG673RrAF63eDdZeRcDaV6ugWGje1CzQSDB4b54V7W0Mjh9PJGFP2+wNN1VLWVSVdU6gNSF5MzC5dDh3gasXbDLGlj7PNqOObP+s57DUGDk84kLWDV/FwBuno4Xw2Zh4J/x0VLWrzgIWI7/5R8jrQNbubo64+iiw6yx1DprNBpMcLEfcmH5ip6rZtVSS1y02q6wNrmoCbWqoipgKvx4qArk5OTj4mIZadzX34PoUynW8ikaDY/3/Yy01Bzs9XY0aRRkDcMA8XGpmEzmojcPZzc9+fkGSzlUlYM7Yi5ePyCifqDNe2Q0mq1hGKBKwNX7t587fZ6X+0wjIzUHD28Xps1/haqX9GEW5ePyZtSrV6+mfv360owaaNasGRs3bqRHjx4kJiby/fff3/DNgmv5LoUrT7sEEBoaKoH4NlUZfXpNSA1xWd2ev6qEEOXiatNx6PV6oqOjr7i+yN69e6+6PiIi4pqm/di0aRMPPPAAL730EuPGjbvrf5hd2jza09PztmsefSUN20Tg7u1CZlo2qgphtf3xDSx76Ik5Ek96Spb1eVZqDjjatiaw19vRpG0N6/O0lCxGD/ySvBzLADcePi44uejJyswDBRq3CqNp2wiWzNmGwWBCa6fhhxWvc/zgWUIifLF30DHv100YTSYUxTII1qoFuy0HVxQyUnPBTmPTr/e/lQetf09JymDlgl0c2HaKM6fOc8+gOnh4O1pGhVYUzICXrysphSFfq1Ew5RmtfXQVBeu0TpYFYNZgHR7TrIJJr1B0H8lsZwnUu3bHULWqO5kZ+daygiWrpqXngAYKDEZOnDpvrSHXaBVyMnNtXouLmxMpiZmWQxQOopWVnmM9pCHftjn8xmW2/zc0bHPZIF6XGTf8OzJSc6zv1YSnvuPrZaXXrInrc3kz6piYGBo1anTXjFB/JTVq1GDTpk307NmT3r17M2fOHJydna+6z418l8q0WEJUPgnEQohKt2jRIgYPHsz//d//8eyzz1Z2cSpdTk4Oe/bsISsr67ZtHn0lrh7OTF80muV/bMHeQccDj7a7rlGFA0KroNPbYSyw3AvX2WsJqRfAiYPxADg42tOhZ0ObfY4fOENO5sUpmC4kZhBQ04+cnHzMZpU5szZQxd/DEpCB5fN34eLmyKPPdeXE4Xh8qrrxyHNd+OnLNahAbp7ROoBWEY1GsdQoF75fGq3GMvhVoRXzdhJ/6jxmk5lNq45Qu7WfpRkzlh/GRWEYwGRWwU6xOb5qr8FU+FHQaSisUS7qY6xi1iqYCttMm+0VHh32DampuShA1w61UQ+eKUzWoNUqmE0Xy5ZvMNKhe322/3eMsBpVObq7cECvwlrvvFwDj4++l10bjlGjQSA16lfj41dmF9awK/Qc3PpiOVWV+Jhkm+uv0129xUdacqbN89SkG58qKDMth+jDZwms7otXVRmL4HKXN6Net24dtWrVIjw8/K7u8+rn58e6devo06cPXbt2ZfHixfj4+FR2sYQQN4kEYiFEpZo9ezZPPfUUP/30Ew899FBlF6dSFc0pfPDgQapVq0aLFi3uyL59VQO9eOy1+27oGJ5VXJn449N89vZfJJ65gNGk0qRVdXoOaEVGWg6R9zfC57IAFFTdF61Wg7lwdGZHJ3sSz6VZm0QrGoXky0LY5qjDbFx1kISzqSiKQmhtf7jYBRe0GhTFbA2ZikYDqiVkKoqCq6eTzeBbiQnp1hCam2XEyUVnCb2WPSy1vloFVQHFDIpZsVbSqhowXlIjXKBiU4OrahTMdhcH4TJr4UJaHorGUqu8bstx6xzFAFqd1lKWwsBrp9Py9pRB1vUP1H272DUfNLILg0Zaxgo4fTyB1vfUIysth/5PR9Iyso512+Rzadaa+CKhta8+Unzb7g0s8xgXatez0VW3L03s8QRe7TuNrLQcdPZ2TPz5GZq0r3VDx7xTFTWjDggIYPfu3cTHx9O0adO7urbYzc2NpUuX8uijj9KxY0fWrFmDn59fZRdL3GbMgEmt2BvaxUezEKW5e2//CSEq3a+//spTTz3F3Llz7/ownJOTw+bNmzl69CgtWrSgcePGd2QYLk81GwZxPj6tMJyqzJmxhuq1/Bj0bGeb+XOL+AV58c6Xj+Lu7WoNtDXqBlj70wK4ezrZ7GOnsyPxXBpgOUdc9HlUFMu3p0ZBsbP9GtVoLaFWxTLSp4ubpf+uWvhw83K29BfWKmSn5+PsbGed1xhFwWynsfQT1ljmGnZw1lkzr2KvoHfU4Oqqw9tLj5+vI0EBjgQHORES7ERoNSfCqznRqa7ltYcFOBEa6kxwsBNBgU74VtXj4+eAu7ceJxc7dHqNtQm2qigEBHrx908b+GH6Ck6fTELvqLsYuBWF1l3rWl+npfn5V2xdc5iDu07zxfh5FOQbresdnR2K1fxXr331AfVe+WQwj7zUnbrNw3hsdE9Gvf/wVbcvzbxv11pbBBgNJn79v39v6HiX+vvvvxk1ahQdOnTAzc0NRVFuaLqeM2fOMHz4cAICAtDr9YSGhvLyyy+TmppabmW+Fl5eXkRGRuLj48O6des4fvw4CQkJaLVaRo0aVaFluRXo9Xpmz55NixYtiIyMJD4+vrKLJIS4CaSGWAhRKWbNmsWoUaNYsGDBNc9NfCe6tFY4ICDgjq0VvhlysvIuDvpUKC0l8wpbW9g72JNW2Pc4N6eAw9tj8AuvQsLZNEDBxc0Jk8lMZlou/kFetO9ah9knk6z7azUaDJrCuYiLaok1Ckrhn95VXMjMKSAr09Jf12gyFQZcDagqLTrVIvH0BY4eOkNAhDf2ei216rrj4GiHs7MdTq46nJy02NtrsddpcHKwQ6fToNVammOD5TNTYDBTYFQxmMyYzZYRr1XArICfm2WAsk61vNCEWTKtolj2t9dp0BU+ihQUmDAUmMGskJ4dh0ExsWDuMga81IL9W2PIyShAUbS07laTvLw89Ho9Jw+dtWl+fj4+jYS4FIIjqgLg4u7ISx8N5PO352A0mOj/TGdqNw0p9T0d+kpPhr7Ss9TtrkVR32vLE0vz9fIyadIk9u7di4uLC4GBgRw5cuS6j3Xy5Enatm1LUlISvXv3pnbt2mzbto3p06ezbNkyNm7ciLe3d7mVvTRarZZ69erh7+/Prl27SE1Nxd/fn379+lVYGW4lWq2WH374gaeffppOnTqxdu1aAgMDS99RCMCMBnMF1z9W9PnuBBKIhRAV7rvvvuOVV15h0aJFREZGVnZxKk1RX+HMzEyaN29O1apVK7tItxUffw+aR9Zmx/pj1prM/5btp06zMGZ9tpLkxHTu6d2Ujj0aoKoqOVn5HNlj2y8W1MIwbHEmJpnn33qAX75azbkzqWzfeIJqIV6ciUlB0Who1r4GG6MKw09h3vIP9ORcfDqKRsE3yItzO05jZ6/Fw1uPkxs0D/fDw0uPs4sOZ9cc/Ku70qpnfUwmM6qq0rSpNxkZBrKzjaSmFxCXaKLAaKagwIyTgz0fjn8YOzs7tu87zYTPl1NQOAo0Cph0QNEgWoDJQYNOpzD2njC+33gW+5iCi/3PzSp2uZZpphQNlnCspTB8a7FXVPQa0Om1ODhqCYuoRtd+jcjNycXeUcuhY/vYc8DSDFqjaHno5UbkZBSQmZpP5oV8FJ2B7OxsnJycUBSFe/q3pHPvZphNZuwdKv4mT/+RXdmyYj+p5zPRO+gYNubBcjv21KlTCQwMJCIignXr1pU4B/u1eu6550hKSuKzzz6zqYUdPXo0U6dOZezYscyYMaM8il0mXl5edO7cmUmTJvHpp59SrVo1VFW9Y8YzKAutVsvMmTN59tlnraE4ODi4soslhCgniirD2wkhKtCMGTN44403WLJkCR06dKjs4lSKy2uF69evL7XC12nXhmOMHTbTZlnNFtU5dugsamHf4Hc+HcxP01Zw5tR5PHxcSEvMsBlpmcvCWlCYD2dOp6CaVRSNQqceDVi/fD9ms4rWToODmwNZGXloNNCoRTA6BygoyMfdyx4PbwfcvfS4uNmTl2skLSWftNR80lPzycowkJ1lIDutgJxsI3m5Jh57sS7Llp3lbGIuigoGJ+3FqZRUsLfT0LltbY6eSCAioirLth9F1WJpXm1WMdtb+hsDmFQweYBep+F/7cP4YPlJHGKMlubdljbcaPMvqVE3mdFc8gtAp4I5u6CwgzR89O0wGresbnNtDAUGDEYDn779B2djz+Psao+rlx5PXyfqtQwiOzsbRVFwcnLCxcXF+nB2dsbFxQW9Xl+hgSovJ58zJ5OoGuSNq4dT6Ttch6ioKDp37swjjzzCr7/+WqZ9T548SUREBKGhoZw8edJmIKvMzEz8/f1RVZWkpKRSRzoGWLt2LV26dOHVV19lyJAhvPvuu6xfvx6TyUSXLl34+uuv8fPz49ChQ0yaNIlVq1aRn59Phw4d+Oqrr4qFvIyMDKpUqcILL7xAz549sbe3p0mTJri6uvLff/8xdepU9u7dy9mzZ3FzcyM0NJSuXbsyefLkMl2H24XZbOaFF15g6dKlrF27ltDQ0MoukrhFZWRk4O7uzte7WuDoUrH1j7lZRkY23U56ejpubm4Veu7bldQQCyEqzBdffMHbb7/N0qVLadeuXWUXp1Lk5uayZ88eMjIy7tpa4dTzGRzeGUO16r6E1LyxQWr0jsVvJBwvCsOFwWvmJ0tJirsAQFpyliVwXqxkxc/PncTzGZjNEBJehezMPGuYVlWVfTuiUTTgU9UJ32rO1G3mj6OLFkcXBVBIS84jNUVL+oV8juxNIe1CAWkX8snLNYGq4hPgTtL5LFAgvHoVy1zHhbKzDDi422NOsTSxVrRF8/payl5gMrN83SFQ4OTZC5hdNZgLa4RVRSkMx5bnZkcsr62QyUnBpFUKK5DVi+uKasc1is3oKwaTCa1iWa8oEH7JIFgZqdlMeHYWh3fHElKjKoqiEHPsgnW9zt6OF98ahtlsJjc3l6ysLOvj7NmzZGRkkp+fh4IGnyrepCbmsn3lMcwFGoa/0YvQmlcfcOt6OTjpiWgQdFOOXR7Wrl0LQPfu3YuN6uzq6kq7du1YsWIFW7ZsuaauJbt2WebEPnbsGO3bt6dnz56MGDGCZcuWMX/+fPLz8xk5ciSDBw+mS5cuPP7440RFRbFkyRIee+wxoqKibI63ePFiCgoK6NSpE507d+bw4cOsW7eOs2fP8txzzxEYGEiPHj3w8fEhMTGRHTt2sHz58js2EGs0Gr788kteeOEFOnbsyLp16wgLC6vsYolbmEnVYFIreB7iCj7fnUACsRCiQsyYMYOxY8cyfvz4u3akzvj4ePbs2YO/vz/Nmze/K2uF404k8kqvT8nOzENRFF6bPpQufZtf9/HqNg2la5+mrJ6/CxQFFw8nFEcdGWm5lg1UlfOXT99jbS5t0aR1OCuW7EVVzcRFJ+PoYk+VACeqBDjh4++MbzVnvHwdMBrMJCfkkJtjZNf2c5xPyCEzw4De3o7cnAJrAHdwsCMv31QYQBVyDSbLHEnAydMpoFWs/Vuzc0w4O9thSaLYBHnLAqzDX2oUMNhhDbaqCqqdYg3EJf0GMhf2H0ZVUEyqZQRqxbLikjryogtjvTaqCisW7MLJ0Z6wmn6sXbCLo3vjAIg7lUT1y0aM7nBvA0sZNRqcnZ1xdna23uxJT83i8fbvYTKa8A5woU7LQBQ7E8EN3PD0c2brjo3EJwfg4eFhfTg6Ot4VTXOPHj0KQM2aNUtcX6NGDVasWMGxY8fKFIi3bdvGli1baNjQMvXYuHHjCA4OZsWKFezevZuVK1fSpk0bAAoKCoiIiGD9+vXk5eXh4HBxPu958+bh7OxM9+7d0Wq11K9fHwcHB+Li4pg2bRqPP/54sRqo5GTb6bbuNEajkQEDBnD+/Hk6d+7MunXrCAkpvX+8EOLWJYFYCHHTfffdd7zxxhssXbqUunXrsnnzZgDCw8MruWQVw2QycfDgQeLi4mjcuDHVql19tN072ZJfN1rCI5ba19+nLb9iIDaZzKhmy3RAJTlzKon4mGR6D+vAmsV7Uc0q2Rm5qLmGwmbHhSM3X1IbDGCvt8Pb141zhbXGqxbvpmqIK9VCXQkIdcW3mhNGg5nz53I4H5/D7g3nyMsyc/Z0OlX9PWjUvgbRx05aB/SyzEd8MUzm5RkpmkIJIDM7/2Ls1CgoOo2lfbOikJVntIw0XVR7a1ZRdIV5vWhRUeELl5kBNKCaQFUuBmGlAEyuKkb9xcCs0WIZFRtL82+1aBJjjYpiuCREm0Exmmxex3dTlqMaLH2Oa9UNsL4Gs0nFaDTjUcWV3Mx8mneqxSuTrzwi9OzPV5GfYwAgMSaDxJhD1nV2Og1eAc68/XVDcvKyOXr0KJmZmeh0Onx8fPDx8cHb2xtXV9c7MiCnp6cD4O5e8hzJRcvT0tKu6XhFgXjWrFnWMAyW2ubQ0FD27NnDJ598Yg3DAPb29tSqVYu4uDiys7OtgTgvL49ly5bRs2dPm5B89uxZRo8ezSeffMLGjRtp1qwZvr6+1vV38ny9BoOBzZs3Y29vz++//86LL75oDcVBQbduSwRRecwomKnY/7sq+nx3AgnEQoib6scff+SVV15hyZIl1mbSbdq0uWtCcVZWFjt27EBRFCIjI6+pH+CdzMHRHtV8sZ1u6vkMUs9n4FnFtpYpasEupr3+O4Z8I/1HdmXYmw/YBKLlf21l2muzAdDo7FC1hQ2DVVALjOBob3teVwfyC0dFfuqt+5n/axQtugcSUN0d30BncrKNxEdncnRPMttWG4mPSbPsWBgOBz0VyeI520lIyMBh3xlMJjNarYLJpGJnp8FotB3tWi1segyFdbDaS2tfL84tnJ1jws3DvnB7wKzQuG4QirOWKt5urIk6QL5Bte5r0iqFg2gpqFoVsw5rDbKqBbNesT436y3lKLpqZi6pfFYUVJ2CYjBbCqi1FNQa3BUFs/niz6r0jFyb1xd7Isk6f/PGFQeIOZFARJ2Sb/QYC0P1xVQPWjsNqqpiNql4eHhSs3YN6/trMplIS0sjOTmZc+fOcfDgQbRa7V0RkG9EdnY2x44do3r16tx7773F1p8+fRovLy8GDhxY4jpXV1eb0axXrlxJVlYWffv2tdm2Xr166PV6Ro0axSuvvEJ+fj7BwcE0bty4WLPvO0lRGNbpdLRs2RKtVssXX3zByJEjraH4br7ZKcTtTAKxEOKm+eWXXxg1ahSLFy+mY8eO1uWenp53RSg+c+YMe/fuJSQkhLp1697RPxavVd+nIlnx5xZSz1v60eZl5zP9zT+Z8MNT1m1ys/OZ8sqv1iD115eraNm1HvVaXBzgaea7861/NxuMaO11FI0RWTXIi3PnM62tojUahcbtg+nyYG0yMzMwqkl07l+ds6cyOLLjPGv+PIFfkC86Z3tSz6vUb1ad+NjdXKxaVvjn103WeXZjjiXSqFUo0WfTyM7Jx9fXzTIKtYJl/mAzF8OaammmrBbOW2yZ/0fFbG85bnauEb8AJ8vUTIBZo7LtdPzFJt0aCsM0ln0Lw7Bl1idL+FULtzNrwaQtHHkabAbcQlVQFJVLa67NqsnydwUUtXBaJ9PF8K1ccv1c3Z1wqlGV9NRsmraryar5u2ze128nLyE7M4+QGlUZObYXru6O1nWGAqNlWGtVBUVD9Tr+jJrUn2V/bMHVw4kBI7vYhNulv25i/g/r8PBxZdTkgbRu3ZrU1FSbgGxnZ4e3tzc+Pj74+vretjeaimqAi2qKL1e03MPDo9Rj7d27F7PZTLdu3Yqti4mJITU1lX79+hXrqpGVlcWJEydo3769zfK5c+dib2/P/fffb7Pcx8eHDRs2MHHiRL755hvmzJnD66+/zvbt22nSpAktW7Ystay3m5LCMFi6CHz99dc89dRT1lDs739z+sOL25P0Ib49SCAWQtwUs2fPZuTIkSxYsKDEqZXu5FBsNBo5cOAA8fHxNGvW7K7tM10Sdy8XWndvwPI/tmA2mTGbVU4fPWezTW5W3iW1ihbphXMHFzEZbdd7eTjQqW9zDu6Jw8PHBSfvNLAzEFrLneAa7tjrdcSeSmBHVAzxpzLQanSkpmRZB5ca9HQDvv96DUajmXPxqWjsNJgNlhpSRVGsYbjI4eNJFJhMmM0qZ86mYqfXYjRbJibW6Cz7FgVgVSlsu1xILWrOrYHMbBMuTnaWUKuA2U4pDLgXm1BzycBZYPt3oxbUwnxj0gCXTsOkUy+eS1XBxMVBuMygXH6D5pIqZJ3ejohwX47uP4NXFVfiTyaSl52P2awWC8MA+7dHAxBzLAFjgZG3pz1iXbdn0/HCclv6TtdpFkbtJiHUblK83+W+zSf48p2/ATgXk8y4x2cwa9N4vL29rbWXl9Ygx8fHs3//flxcXPDz88PPzw9PT8/bpva4Vq1agGUQrJIcP265dlfqY3ypoubSzZo1K7Zu586dV1y3e/fuwum/mlqXmUwmFi1aRJcuXUpszl2/fn3mzJlDQUEB69ev5/vvv8fDwwNXV1f8/PzuqCmJrhSGi2g0GmbOnMnw4cPp0qULUVFRd+VgiULcziQQCyHK3ZIlS3jyySeZO3fuVQeCuRNDcUZGBjt27ECn09G5c2ccHR1L3+ku0/qe+iz9bRMarQazyUzbexvarPf0daPVPfXZuvIAAFWDvGnUzhIIzGYzKQnp3PNwSxb9tMG6T69hHThy9Cw6FwM+YSr1I6uRm2Xm2L5kFv1yAgcHZ+IOx19ylnwCw3w4E5OCRqPw3eersEbswimNioKkvd4ONy9nks6lX9KvV7U2GdZqNRjN5sJ+wIrlOJrCZtGKYju6MwCqJZhqFHLyTTg7alHtLOtVrYpZw8XQqygoZrWwglhBNYFJD9iByYilprlwe1WHbXDWKJi1hS2oVVD/n73zjq+iTNvw9c6ckh4CAZJQElrovfemYAfRtaJiW3Xtba2fveGuva5lxbKurgUsWAAR6Qm9BgiQAgQCJJB+2sz7/TFzGgkQICHFc/mLSaa+MyecM/f7PM/9SIHq7bOkggzQ+BIzvdqMFLdt15wX/nUNxYfLEcA1Y14I2FiS1L45edkFgCQpJZ59uw/5JjgyN+0Jej2TUxM5uK8Y3awJb54YS/rvGbTt2JKENk2Dts3N3Of7Wdcl+3cfwu3SsNn9jyuqqvoEctvWKSyavRq320VZdDnLly9HCEFCQgItW7akRYsWWCz191HH2794zpw56Lpeqe3SkiVLiIiIYMiQIcc9llcQDxhQuSbfK4irWrdmzRqAIEG8cOFCCgoKKqVLH4nNZuOMM87gjDPOYOTIkWzYsIHo6GiKioro1q1bJfHY0DieGPaiKAoffvghV199NRMnTuSPP/44al14iBAh6h/191MiRIgQDZLly5dz2WWX8dFHHzFx4sTjbt9YRLGUktzcXDZs2ED79u3p0qVLKEX6KAwa350nZ/yV9N820TY1gXOvCk7VFELw6L+uY/FP63CUOxl+dm8io8MoOVzGg1e8w86MPCKiwrjk1vEcOnyYpklh2FqU0615FHt2SHZuLGTBt1lEN2tC3q5CdF2iKBVIi4LQpJm6K9idXWBEZaVEuj1gtQQOgrOm9KVNcjx2uwWL3cKrT3/vE7XuMhfYVBTFaJNktVlwubWglkbeFG6kRArFEMEYqc7CTJEur/AQEaYa9b+mGzRKgNmVBHSjxRKAJ1yC1UyZVkFa8B23so+KcZ3S6zItJZrF79RlUYyosZfhY7qwckkmCYlNGDEylYuHP4um6Yw/vw9tO7Zgd9YBQKCoCnm5xr1TVIXIqDB0TTfvBQwYGRzNvOuFv/DaQ1+Ts20fPQa144t35uMod6FaFB7/17UMHN3Ft23vYZ2wWFV0XSKlpNeQjkFiOBCX0819F77Kzs2GAO82oB0vfHU7RUWHyc/PZ/PmzaxatYr4+Hhf9LiuJqh27NiB2+2mQ4cOQSnLHTp0YMKECcyZM4e33nqL22+/3bfu8ccfp6ysjJtuuqlaKeGrV6/GZrPRo0ePSuuOFSH2CulAQTxz5kwURWHSpElB265Zs4aYmJhK79Pbt29n48aNxMTEMGbMGFatWsWiRYsYOHBgg01nr64Y9qKqKjNmzGDSpElMnjyZn3/+OciMLMSfEw0FjdOcMn2az9cYENL3iR0iRIgQp0ZGRgYjRozgySef5LbbbjuhfQ8dOsSyZcvo3LlzgxPFHo+HdevWceDAAfr16xfkuBqi5vj0lV/44s25xLYIJ3VgS7oMSkS1CrI2HCRnYwEH9nsoLXWha8bHWuvOCezKMlrAKIogzG6h4nC5T9QKb/0tZsmu3RLU8mjIqE6k/ZGJ1KXRHzhgLBLoO7wD5bokNjacPbsPkZNTgFQEAglaQFRXSkQTGy5d+kt4NYkQAkWBu65L5fUfsih16uhSotv8xlhG6BbTj0riilV8BlweQEZjCGgzDRpFYrcqPNO1Hc/O34FyQPq9rHSJ6gk4boWGxfQCExLm/HAvNpsFR7mLi0Y8gxZgFPbQPy5l25ocnA43hwvLWPrbZl+v5sQ2Tbn6zgmk/7GFth1acNG1I7HaqhaxL/39C+bPWo2uS4SANu1b4Klw4nS4ueqes5l42RC2rs1h7v/SiG0WxUU3jSMiqmpRsTF9B/dPeS1o2dtzH6Rd1yTf7yUlJeTn57Nv3z4KCwtp1qwZrVu3Jikp6ZTans2aNYtZs2YBsG/fPn799Vfat2/PyJEjAaPO9p///Kdv+5SUFHJycsjKyiIlJSXoWDt27GDYsGHs37+fSZMm0bVrV9LS0vj9999JTU1l6dKlQWZXVeF0OomOjqZXr16sXLmy0vr4+HiioqLIzs6utK5Xr15s376dkpISVFVFSklycjJt27Zl8eLFQdted911zJgxgyFDhjBgwACioqLYuXMn33//PQA//PAD48ePR9M0Nm/eTG5uboN01j9RMRxIWVkZ48aNo23btnzxxRcNPkoe4uQoLi4mNjaWf64cQXjU6Y0/VpR6uG/AYoqKiiq1RQtRNaEIcYgQIWqE3bt3M3HiRP72t7+dsBiGhhspLi8vJy0tDavVypgxY0IRgVqioqICEe5kyr39aNI8nOyNBaz8OZeMZbt9qctSCAZM6EXRoXLOuqg/UXGRPPfgV0agVBEMHpnKHz+tR+rSEMhRNspLnYA3sBvcA3j5okyfuZV3nU9AA+u27MUZ4JiNxYjGSoRRg+zSjeMKgVtKn5A1WioJI+1ZmGnTERZKXC6k8BpweQeFUUds5F+jK9LfsxjDSMv3SS5BKZY03a1BVwg7BK7AmyjMKLSJagE8xjlUq8BqtrdyuT2GGA7o1/zaI99QUeLAarPQfUCK/xqkpMeAdow5tzdjzu193NfRZrd6GyMjhGDXjv3gdiMlvPrAF3Tpm0znPsbX8YhtGhW8QEB0k4igRdHR0URHR9OxY0ccDgd79uwhKyuL9evXk5CQQOvWrWnZsuUJZ3OsXbuWjz/+OGjZzp072blzJwDJyclBgvhYdOjQgZUrV/LYY4/xyy+/8NNPP5GYmMidd97J448/Tlxc3HGPsXHjRtxud5UR4JycHAoKChg9enSldU6nk4yMDPr37+8TbitXrmTXrl3cddddlbafNGkSbreb9PR0ZsyYgcPhICkpiSuuuIIHHniATp06AUa0tGfPnsTHx7N69WqKioro2rVrg6jtPhUxDBAZGcns2bMZMWIEd9xxB2+++WaDuO4QtYMuBbo8va//6T5fYyAkiEOECHHKFBYWMnHiRCZOnMhTTz110sdpaKK4oKCA9PR0WrVqRY8ePUIp0jWM2+0mLy+P3bt3U1BQQFKnWH75ZAdb0vbidmlMvXMim5bs8m2vCLjriUnEJ/oFRGKrOHZs3Ue33m1omdQEVRGsXJxJbFwETVtEszZtp39/i0KgvsUr+swQrWF8JUxhLHFquunSLHxu0n4HZ0D1u03rwmtoZWxrsau4TLFZ6vAQEa6iF3tbNPlTpH0X5v3TUoVPWOuAbpE+Uy10sAiJtcIUzJohmL21wcIDwuIfoI4wtLUAjy59Y83PO+yfGDC/l5c4EBiO0evS/fdMKAoxcdVPib30lnGs+GMLB/IOExZhp/xQCYGzDPt2FZLcuXouvW06tuS6hy/g4+k/IlTBTU9MIT6xyVG3DwsLo0OHDnTo0IGSkhJ2797Nxo0bWbNmDa1ataJ169Y0bdq0WuLliSee4IknnqjWOIEqI7NB19KmDR999FG1j3ck/fv352gJf8nJyUddZ7fbcbvdQctmzpwJUGX98KRJkyqlUR+LxMRERo0aRVpaGiUlJfTr1++UIvO1zamKYS/x8fH8+uuvDBs2jISEBP7v//6vhkcaIkSImiQkiEOECHFKlJeXc8EFF9CxY0feeeedU54JbyiiODs7m40bN9KjR49KKZAhTh4pJfv37yc3N5d9+/YRHR1N69at6devH+Hh4fTp1Y/NK7NoldKcdl2T0HWdz1+fa+4L0+/6Dy9+cStul4eyUieduiWxecMu7rnx39jtVs6/aAC//biWosNl5GYdIKF1HIcLy2gWH037Xq1ZOHdz8IC8ghd8Ytg4mXcdZgskqnCC9rc9kiroqmIoUxRcSN/2JQ7TadpiHFcKs4bYmwYN/l7DAtOAy6x9tskg52hnlPB1bMLiF9YSjHZQ3nWKMNos6X6hVFHhQvPoOCuC4sr+6/GXRKMohvGYRNKsZfVT8lokxfHhvAfYn3eIJvHR/P3i18nashcBRMdF0m1ASrWPBfCXv53BhTeORQhQLdUXL9HR0XTt2pUuXbpQWFjI7t27SUtLw2Kx0Lp1a5KTkxts7eupMnPmTHr37k27du1q5HjR0dGMGjWKlStXsmjRIgYPHlwv721NiWEvycnJ/Prrr4wcOZKWLVvy17/+tYZGGiJEiJomVEMcIkSIk8bj8TBlyhQOHTrEnDlzatSwpr7WFOu6zsaNG9mzZw8DBw4kPj6+rofUKHA4HOTm5pKdnY2u67Rt25bWrVtXWf+k6zrff7KEjSuysIdZmP9tcM3k39+8htee+R5HhZvUnq3YmmG0dRJekepw+bSrqirMXvs0YAjCq859mZJiBwDtOrUgK3O/77jSbJXkM9aKsRl6VRE+gRw0HaRLn8O0I0YFi/DVAhMgVM8d0oLCCg8Ldxw2dlMDos3eGmJTEDujMdoqKeCSEr2Z7muzhA5S0QmzKLyY2Inn5+3AU4LvOAKJGhAMFOUaFo8xDlWApchY2advMqV7DpG1Ld+4Dx1akL1pj+/a+gzvSP6ew+TvOcSwM7pz/4uXYDtKzfCRbFyRxY6MPHoNak+7LomUFpXz46dLcDncTLxsCC1bNz3+QWoJXdfJz89n165d5OfnEx8fT0pKykmlVIeojK7rvrrigQMH0rx587oeko+aFsOBLFq0iLPPPptPP/30uK7dIRoP3hriF1aMJuw01xA7Sj08OPCPUA3xCRASxCFChDgppJTccMMNpKWlsWjRomrVuZ0o9U0Uu1wuVqxYgcvlYvDgwURERBx/pxBHRUrJwYMHyc7OZt++fTRr1oyUlBQSEhKOKUC+/fAP3n/+R3+Jq66DNEyabGE2Ilo149DBEqQZbUU94lhOd5Bwnb3mKV908XBhKXdc+wH78oq8g/RtKwEs5rGEwBlpAZvib6skQWpGxDapeTT5+0p8kWNnEzVI5ErAEyZAEZzROQ6rTWF2RoFxGFPwBkZlfUOP8UeIXejIeOkTxBIdrJIwReHFpqn8/UAmUcv81y484O26JHRQyzRUU3ALt46tzG85LcpdKB5j4/Hn9mbgoHYsmL2Orn3a0rlvMgt+XkfzhFgunjaSsHDbUV+rQOZ8vYJXHvoKMCLMz3/yV3oNrvt/11XhcDjIyckhJyfHZzKVnJwcaqNWA+Tk5LBhwwa6detGu3bt6ry+tjbFsJfvvvuOK6+8kp9++olRo0bV+PFD1D9CgrhhEUqZDhEixEnx6KOPMm/ePJYuXVorYhjqV/p0cXExaWlpxMbGMnjw4Hrd27S+4/F42LVrF1lZWTidTtq2bcu4ceOOmUaZl3OQd5+YyeGCErzmx97p3ITkeEoPlRIWYefO5//Ccw9/61snhCAswobD4QYpSenQguyMPF+qcKuUeBRVYc6s1axevh17hN0QwwFp0lI3DKaaNI3kUJHfpRo9sG7YqCsmzBCgeYfLwCpQvGMFdAs+8exRQNqMn4s9GskxNn+dsMBwjpaGE7a3HllitloyxbIFgUvVQdX9oWkVv+pVJZqqoUqLUeOsGmnTSJCqxBJQIy0DXLQFGJMIHkMgL/h1A39/ZgpjL+hLxrpc7rnqPcOhW4cNK7PJzy2kqLCMsy8ZyI1/P+eoAueHz5YG/T7n6xX1VhCHhYXRuXNnUlNT2b9/P1lZWWRmZpKQkED79u2rXWscojLJyclER0eTnp5OcXExvXr1qrMI/OkQw2DUXr/66qtccMEFLFq0iJ49e9bKeULUP3SpoMvT+/d9us/XGAg90YUIEeKEeeONN/jXv/7F4sWLa72dRn0Qxfv27WPVqlV06NCBzp07hx6ET5KysjJ27txJbm4ukZGRdOzYkVatWlXrIfSxae+xN7cAXTPEqbCopmAUjDi7F9PuPZtZHy1k7jer6NM/maULtyGBmOhwnn5rKutWZlFe5qJ7n7Yc2HuIN54y2sTsySlg2tkvkb/XiAjrqoAjJjssNgu6LikqqggWyhazptjXIinAeAtABd1bK+yRyHD/Q4puxZf6XOLWiLarhmM0+EyzMNtC+X7HEMO+vsMIsGqmeZcRcRcqCGGeRzGUtM9wVIDuDXBLQdCfseq/DqmDcPmjxV73aYDVS7cjBL7WVuvSdxq9nYGZHy+hU/dWjD2vj2/7QwdKKC91kJQST1zzaF/tMUBssyNcoushQghatmxJy5YtKSsrIzs7m7S0NMLDw2nfvj2tW7cOtdU5CZo2bcro0aNJS0tjyZIlDBo0CLvdflrHcLrEsJcbbriB/Px8Jk6cyNKlS0PeEyFC1CNCgjhEiBAnxJdffsnDDz/MvHnz6NKly2k5Z12JYiklmZmZbNu2jX79+pGUlHT8nUJUoqioiMzMTPbu3UtiYiJDhw4lLi6u0sRCyeEyfvnvcqQumXDZEJqYgsnt8rAn64B/QynpO6wjZaVOuvdvx1V3TuCLN+fx2etzEEIgdckZlw3mj/lbKC6u4IG/zuCsKQOY9WUaAKoZDfWePchVWT8iRxnwBPTjRTVFsACcGoT5P0alABRTFHujr4pRRyyE2fLITHfG4k+NLtE0ouyqT+hKs2bYZ8gFvk9rqQQYbgFYdBTVFN26QAjpF+iKjlRUYyjS75aNlEhpdFzySgBFkz4DLtVqGm6ZYfY7HjnPd40pqS19glZRhDFBEcDKhdsYfY4R8Zv9n6W89dhMpJT0G5nKLY9fyJO5BezeeYCu/ZK57JZxle51fSYyMpLu3bvTuXNn9uzZw44dO8jIyKBDhw6kpKTUa/fk+kh4eDgjRoxg7dq1/PHHHwwePJjY2NjTcu7TLYa9PPzww+zbt4+JEyeyZMmSkAdFiBD1hJAgDhEiRLVZsmQJ1113HV9//TWDBw8+rec+3aJY13XWrVvHgQMHGDly5Gl7UGtMFBQUkJmZycGDB2nTps0x06LdLg/3Xvgau3ceAAE//3cZ78x9gLBwG1abha79Uti6NgcwonY3PHQ+7br4JyhWLdpqpAJLQxwuWbAVl9MDAhwOt08MA2iaBIvqSwkORJF+Y+cjEYrAZrfiNPdTPBIdafR8DIzm+mqK/U5bQhP+qDAgFWFEhAUUeaoQxF7naGkKYPN3qRrpzl5BrFglQgnoo4xENVOmFYuOMMcidXNcujkkAZoizOEIqDDvhRBoujQ1v7FvRYDrdO+B7ek5oB07tuTRMqkJWRn7gu7Ryt83cW67pfQZ0ZkN6Vm+dj+rF20ja0se7/96Px63hsVaOwKkoszJ87d8xOqFW0jpksT/fXBDjRt1WSwWkpOTadu2Lfn5+Wzbto3MzEzatWtH+/btT3uksyFjsVjo378/mZmZLFq0iEGDBtGiRYtaPWddiWEw3rteffVVLr/8ci644AJ+//330N9LI0dDoAXbLZ6Wc4Y4MUJJ5iFChKgWubm5TJkyhX/84x+cffbZdTIGryjeunUrO3bsqLXzeDwe0tPTOXToUEgMnyBSSvLz81m8eDHLly8nJiaGM844g969ex+zRjhn61527diPlBKpS/blFrB9w27f+if/fQOTpo2iVYcWRDWJ4P1nvuPwwRLf+tRebQ3xJwAhKK9wB7VF4kj/SCX4gUExfxeKICys8lyxxW6kHutSN4SvAhZVoLt1pBUjfdpMO5a+L2F8NyPKUhXoQqCrAl0xaop1KxTpHqyKgt2q+MSvt/WSVA0TLaNtkyGGZcA5jOxoaUSG0VAtOopZuKyoOroiTZGO0XPJgq+XsdfAGkC3K8Z4vcsDbtdn7/3BA9M+4O1nvueRW2awYVU25WUu04Fb+jeWkuL9RjPltYu2oh0RPXabadi1JYYB/vfWXFYuyEDz6GRl5PHWw/+rtXMJIUhISGDkyJEMGjSIw4cPM3fuXDZs2EBFRUWtnbexIYQgNTWVPn36kJ6ezq5du46/00lSl2LYi6qqfPzxx3g8Hm6++eaj9ogOESLE6SMUIQ4RIsRxKSsrY9KkSVx44YXccsstdTqW2o4Uu1wu0tLSEEIwcuTIUBpkNZFSkpeXR2ZmJhUVFXTo0IHBgwdX+/41bRGDqio+ESUUQfPEWObPXMm7j32Drkv6je3Kru1GG6Q1SzK5/9I3ef+3h9iTfYBhZ/fk16/TcZSb0UzNA9Zjf8RJIfyZxzYVp1NDSnCUuYKdqQVmz11we3Skz2kaU1z606ilYghW3zm8cVYhDDGrGmfUFVPoKuBA4tJ0osNUKpx6cI9jjogQSyAwQqx4sNl0I0Ksgy4FqnedqqMJHSEUo94ab42zsT7M1/pJGunrvkkCGTRbfuhACUW7D7FhZRb6ERMJfqtvUxj73cwq3e+eg9sf/cUIQErJgbzDRESFERV7Yq7OB/ceNtLmkeiazoG8wye0/8kghCA+Pp74+HgOHz5MZmYm8+bNo1WrVnTq1Ino6OhaH0NjoHXr1thsNp+Tf02/t9cHMewlPDycWbNmMWDAAF577TXuuuuuOhtLiNolZKrVMAgJ4hAhQhwTKSXXXnstMTExvP766/XCUKq2RHFFRQXLli0jMjKSAQMGhMxyqoGUkt27d7N161Z0Xadjx460bdv2hF24m7aM5f7XpvLeU7PQdcn1D5+P1W7lpbs/8xk4LfphDQS40e7eeYAnbp5B2vwM4AgN5jpCEAdGYXzGWPhCpE6HxxS1gkrRZMw0al9aMr5ItFQMIemr9xX+9Gljs4B1Kr5orVSMiK9X6Ba7NSLDVKTb7R+ur8A3wFhLyKAaYrtNQ1V141IEaLqCRRhnVxXdOIfX6TqglZPvXglzlIHRXEUJcpxWzHV6QF9lL/2GdWTn5jzCwm3k78ivdN+8WG0qsU2PniHgxePWeOrGD1mxIANVVbj7H5cxfsrA4+7nZdxFA/ntmxUIxaglP+uKodXetyZo0qQJAwcOpKSkhO3bt7NgwQISEhLo0qVLSBhXgxYtWjBs2DCWL1+Ow+GgW7duNfKZU5/EsJekpCRmzZrFuHHj6Nq1KxMnTqzrIYUI8aclJIhDhAhxTJ599lnS09NZsWIFNlv1+o2eDmpaFJeUlLB06VJatmxZp21AGgre1OiMjAzcbjedO3emTZs2p3TfRl/Qj9EX9PP9vmPTbp8YNk9aaR+vGD5ytaLJYAHndbpSFCOwa1XxuANqiL1i2LutrhviW0BSm6bs2XvYcLcWwjDH8qZYu3S0cDWgFzABrZikPyIrDGGsm0ZaEn8/YYBij4dom8UQ3WbasjdM640mo2CkZyv+CLFF1VFV6bt+RdGxoIAGFkXHJfzmXMI3FiOa7AsiSBBmBNy7iWq3oCiCESNTWThrjXFPFYElzILLadZQC8Ftj00iqU0zMlZlcc+Fr1V6fbzc8ND52OzHzxZY/NM6ViwwXlNN03n94a8YM6k/6pG9pI9C3xGdeeW7e1i3dBvturZi4Lhu1dqvpomOjqZv37506dKFbdu2sWDBAlq3bk2XLl1CvYyPQ1xcHCNHjmTZsmU4nU769OlzSu8r9VEMexk0aBDvvvsul112GWlpaaSmptb1kELUMBqnv6a3sjtGiOMREsQhQoQ4KjNnzmT69OksWbKE5s2b1/VwKlFToriwsJDly5fTrl07unTpUi+i4PWZwsJCNm/eTElJCampqaSkpNTKQ2ZyaiLtu7UiK2MPIEhIboYlzOpLmz7vquH8+EV68E6Br51HN8yzvEjo3CMJTdfp2TeZ1Yu3kbvjAEIImic1Yb/ZekkAUpe+T8g9uwuRqhJck2ymSKuaxKP6HZq99cU+9SmkLwXa6zItlYDtzAhxiWa0XtKs/rH6xGtQ7bA09/FHge2qZrhYS/DoAlVaDUGsSiwBjZCljk9Ie4/rXSc0DAEuQXp0NE1H02Dxom3c9NC5rF6ynciYMH6fs8l37SmpCSS1aQYYpmhBBAgYoUBBfnGVr7GUkh8+XkTa3I0kpyaS1D74fcbj1ozX4gT+vDr3TaZz3+Tq71CLhIeH07t3bzp06MCWLVv47bffaNeuHZ06dapXE4z1jaioKJ8oTk9PZ8CAASfV+70+i2EvU6dOZcOGDVxwwQUsX76cJk2a1PWQQoT40xESxCFChKiSDRs2cM011/Dxxx/Tq1evuh7OUTlVUbxv3z5WrlxJt27daN++ejWOf1aKi4vJyMjgwIEDdOzY8YRqhE8Gi1Xlxa/vYN5XaeiazhkXDyIiJpwNaTv4z2tz+OPHdYRFWHGUG2nGtjArkVE2igrLAYnNquI4IqisKoKtm/ayPWMv9jArrdvHszurgP15h1FUxddSqGlCDIWFZf4dg9yj/SnMijclOqD+lsC2SDKgbldIo8+xt9+vaohuCRRrHqLsZqRZBp9DVyVYZYBA9keI7RY3FjNCrGLUs9oQ4AaLIpFKQP52gHO1L1psotlAcZsrA0LtbrfGx2//hu7W6NSrje9QSMja5neY7j6wPb2HdWTd0u3GqawqmtmuSuoQGRNW5Ws87+sVvPN/3wCwdvE2Rp7fl6R28eRlHQTg0r+Nr1UTrtNFVFQUAwYM4PDhw2zevJm5c+fSqVMn2rdvf1JC789AWFgYI0aMIC0tjaVLlzJkyJATmkRoCGLYy3PPPcfGjRu5/PLL+fHHH+v1WEOEaIyE3oVDhAhRiYMHD3LBBRdw3333MWXKlLoeznE5WVGcm5vL+vXr6du3L61atarNITZoysvL2bJlC3v27CE5OZkzzjiDsLCqBU5NoXk05n29goN7DzPinN4kd070rZv50UI2rsjy/d6mcwLjLujLeVcOpazEyedvzWP98h0IRXCw1InT6U8g2xzgXO10uNmVU+D7Xdd0bnvoPNqltmTW1ytY+NtmAF99Lpg1wlIG1PRipkMbhbkSESQ0pQgQtorw9R4GM/KrGL8XSY1Eq82Xxix1DPMsAIs09jNNu4zexuYqVceq+Ot/JYo3eIzVohn51t6L8CIIMv5CmC7ZbuNasCj++mopKS9zIYANK7PNNHLjHkQ38dcEqxaVZz+7hU0rdrI76wBvPPotgQw9swdVkbEqy2empuuSrWtyeHvOA6xfvp3YplF0qSeR3pqiSZMmDBs2jAMHDrB582Z27txJ586dSU5ODpVpVIHVamXo0KGsWrWKRYsWMXToUCIiIo67X0MSw2A4T3/++ecMGTKEBx98kH/84x91PaQQNUTIVKthEBLEIUKECMLtdnPxxRczYMAAHn300boeTrU5UVGcmZnJtm3bGDx4cL1MB68PuN1utm7dSnZ2NomJicfsI1zTvP7Q/5jzZRqKIvjy7Xm8+dN9tO2UAMDWNblB2x7cfYjLbhlP5sbdlJc6ObiviP17i9A1Hd2qgs0aEPWswiXZG/cUgibxkfTom0xKh5Y+QQzGJtIXBRb+tGkC6obNVd50aOk9tDAit4oHPKYABjMN2gywF0sPnW3hRjQYc5tA4eyNCotgQWxVdBQhfV5gNsVNuHk9QgS4ZwU6V3sj0AF3RLoCfhfCf42Bd6ySYPNHktcu2cbrD/4PR4WLMZP6cSS2KlpZAXTt346f/7PUOI8icDjd/KXXw3Tq1Yb/+9d1Ve7TGGjevDmjRo1i7969bN68mR07dtCtWzcSExNDJRtHoKoqAwcOZP369T5RHBMTc9TtG5oY9hIbG8v333/PoEGD6NmzJ1dffXVdDylEiD8NQZ9Qubm5HDx4sK7GEiKEj/j4eNq2bVvXw/hTcuedd1JUVMTs2bMbXMSiOqJYSsnWrVvJyspi+PDhoXqtKvA6R2/atIno6Og66cX8+8xVgNnuyK2xfM5GnyBOTk1g/XJ/H+rmibF8MH0233y4EADVoqCbzsgWM5qr+1rlBlos46uHBUOQrVicyVcfLwmKHPv2C9jFa36FADwa0mrxR5EDaoONfY3IsHAAcf7orLRgRJYVoxdxtKr6BLLXGdrXyskizbphCar0jUERZg0xflOtMHOs0RYH++URqtabMq1gpHmbwtgiArYxzcaEEEhNr9p5W0CzFjF4PB48bp0nr/8AZ4UbKSUz319Al4Ht2WJOXJx12WBatmpKVZxx8UAqyhykzd3IoYJSsrfuRUrYtj6Xf7/wA/e9fGWV+zUGhBAkJSWRkJBAbm4u69atIycnh549exIVFVXXw6tXCCHo1asXdrudJUuWMGzYsCrfkxqqGPbSqVMn/ve//zF58mRSU1MZMmRIXQ8pxCmiSQXtNEdsT/f5GgM+QZybm0vXrl0pLy+vy/GECAFAREQEGRkZIVF8mnnnnXf4+uuvWbly5WmLBNY0xxLFUkoyMjLIzc1l+PDhx4wy/FkpKipiw4YNlJWV0bNnT5KSkuokYhWf1IT83AJDEOuSFm38guqxd6dx10VvsHvHfuKaR3PvS1dw50Vv+NZrHt0XldU1nchIG6XFDnOlDDbaAkMYmudZnbaTA/tNAyhV+MWzJsGKP0Val2A11llcEmcEZu2wAEUiva2YJL62SRYBToGvxlgK07hLSIpxE2OxGJFgILARsFR1sHgLf6UR+TWPYVc0VF+EWGJRJBZTvNotGqgekJYg12oESLfv6MaiAFvS5PbNce4rRSAp3FXovwe6jjXchtulYQ+zsmtzHud3f5SIKLu//zPGPdi5OQ+AgWO7cutTRy+7EEJwwbRRXDBtFLec9aJPd+ua5MDew0fdrzGhKAopKSkkJSWRkZHBggULaN++PampqaH64gCEEHTp0gVFUVi6dClDhw4NmtBs6GLYy5lnnslzzz3HhRdeyMqVK0PlPCFCnAZ877QHDx6kvLyczz77jK5du9blmEL8ycnIyGDq1KkcPHgwJIhPIwsWLOC+++7j119/bfD3vSpRLKVk8+bN7Nq1i+HDh4d6gh6B2+1my5YtZGdn0759+1o3zDoej7wzjem3f8rBfYeZeOkQRp3Xx7cuMiac9+f+Hc2joVpUKsqcKIrwGWIBdOmTzJb1u0AISosqglOHjyAqxu4TzD4xDEZU1NwvsI7Yh7lOdWKKXmGmTAujdDfwlN6IseqvB0b11gRDsdCIUFQsKri9YVvd3M8qwSL9gW1VGhFeDJdpi0/oSsJVJ2Gm8rUID9IqzdrggPELEF6tYEZ/fceW0L1Ha/7+wdm8/Ni3zNl1yH/vBMQ2jUBVVcoOFFNuOkuXlzoJi4vCWWSYkElFxWWuW/F7Bkt/3cCoc/2vX1UU7i8ie8veoGX9RnY+5j6NDZvNRu/evUlOTmb9+vXs3r2bHj16hNKojyA1NRUhhE8Ux8XFNRox7OWOO+5g/fr1TJ48mYULF4ZadYUIUctUmnrs2rUr/fpVrv8JESJE42Xfvn1cdtllvPLKK4wYMaKuh1MjBIpiKSUOh4M9e/YwYsSIUDpiAEemR48ZM6ZOJwvcLg9fv/MbOdv2cemtZzD+ooFH3Va1qBwuKCX99wzOnNKfX79ZCRIGj+3K/n1F/g09OphOxQKw2S04nWabICkpLXH4tw1MqdYxosQYwVUNv+gV5mpUv1j2tlvSvWI3UBB7o8IKfoMsYaQ/o0hK0dGkJMqicEh6/GLYFNFC0X2th4TQEaa3lapIXzmziosw1UOYmS4XaXEaRcteMRzQd9gnys0sbJ/cEtCnlzEhtie3MHgSQBEc3F/iu0+BWdaxzaI456YxlJc5+d+/FhgRdJPiQLfuo1BSVFFpWdd+KcfdrzHSpEkTRo4c6Uujzs7OplevXqH3rQA6deqEoigsW7aMgQMHkpGR0WjEMBjR8LfffpsxY8Zw99138+6779b1kEKcJBKBfpr7EMvTfL7GQCgXJ0SIPzmapjF16lTGjx/PjTfeWNfDqVHi4uIYMmQIS5YsQVVVRo8e3WBTwWuDoqIi1q9fT3l5eZ2mRwfy/tOz+PHjxSDgj+9Xo1rUKk2aAA4dLOHW81/h0MFSAM6Y0p/LbhlPUnIzbpn0mm87ISXSo/lSpZ2ljkr9iStxxH2QgPDo6HbVv071il3hF5xeEXyEk7MeIIjxbRtcD1wiPcTYFAo16ROqhrmWjrDohqeVL23azM7G3/EpwuIiXPVg1Y2PdruioVjc6Joa3AoK707+Vk4+cQz8MGsVi+ZtprC4HF8oOqDWOuh3c5/zrxzGRdePAqCizMX3nywBIKpJBNsz8nj/uR+YfO1Imic2qeJmQ5sOLeg1pCPrlxttm9p3TaJL35Qqt/0zIIQgOTmZxMREtmzZEkqjroIOHTqg6zrLli2jSZMmjUYMe7Hb7XzxxRf06dOHcePGcckll9T1kEKEaLSE3lVDhPiT88ILL5CTk8PMmTPrXAzVNFJK8vLysFqtaJrGvn37TrhPcWNE0zS2bt3Kjh07aN++PUOHDq03D9krF2QYBlYSFFVh3ZJtRxXES+ds9IlhgN9mrubOZy5GCMHEiwbw3guzg7ZXBEFp1cdDANKj0ywploMHSk1VbKZQC6O/sFQVM6rsjyx7Xaa9AlIHs1YYI7Xa6xKtm/uZ25boHmKsFgQOo5ZWYjpKSyxWHaEYYzeC2AGp3OZ3myJRhcQiDEcuq+KPS/gMwMydhCaNMepgcUrfcoCMDXsQEqSuowh8bZyO5Nq7J7JlXS6jzurF2Av6+pbf/Phk+o3szL5dBfznzXnM/WYlIFn86wben3M/NnvlVPwta3KwWFU69GjN8Ak9ufCG0djs9eNvsi6x2Wz06tWLtm3b+tKo+/TpQ4sWLep6aHWO2+1m7969REZGUlJSQnFxMXFxcXU9rBolOTmZjz76iGuuuYb+/fuHPr8aICFTrYZB6I6FCPEnZtGiRTz33HN8+eWXja6m1lsz7E2THjZsmE8E/pk5dOgQf/zxB/v372f06NF079693ohhgM6926KYacq6ptOhR+ujbhsTFxztl1KSudHoM3zBlUOJiQvuV6r7raarcE0W/i+CA8QHD5Sa+xO0jTT7D0tVoLikUfZrOkx7I8RSBSzGd10FifSZbPkiwMIQxsXSQ4xqAVUHVUd4U6UVHVXVsFo0bBYPFlVHEcYX+HWurkO06iDa6gTArnjQpLWSGIaAoLiCP03c/NJ0iS6lkR6uHvGYYB4rNjac/771G7t3HiAlNeGIWykYPL4byZ0TKTlcbrS/0iT79xxiT9YBjuTQgRIevvId1izZxs7Ne/jmgwVoWvUnLv4MeNOoO3XqRHp6OuvWrcPtdh9/x0ZKYM3wmDFj6NKlC8uWLePw4cN1PbQaZ/LkyUybNo1LL70Up9NZ18MJEaJREhLEIUL8STl48CCXX34506dPb3S+AVJKtmzZ4jPQioqK8tUU/1lFsaZpbN68mSVLltCqVStGjRpVL122b3vuEs64eBAde7Zh6r1nc87UYUfddtiEHrTrkhi07KFr3gOM+uKE1k39Qk/iE8Jm1vExiYoJRyiCxLZ+d2sV4xjGfwEtnATYis1ewaq3pZEhjnVTGOuK0WYJtzdaKxGKCsIUvqqkGA+xqur7XVg1FJuOatGwWtzYrR7sVg2b1Y2qaKiKHjRmIQQRiosoYdRER3jdvkSAQBcBYtzEEx5oaR2QRg2+9lX+vsvGV1F+MU6Hm7zsg0y/979V3sOk5GaoFsV8CQT2MCvNkypH8HZtz8fpcCNNp++y4gr25hy/BeS+3AL+NvFFzu9wL09e/wGOCtdx92nICCFo164dY8eOpbS0lN9//50DBypPMDR2qjLQ6tChA6mpqSxdupSioqLjH6SB8eKLLyKl5MEHH6zroYQ4QXQp6uQrxIlRf8ICIUKEOG1IKZk2bRqDBg3i1ltvrevh1Dhbt24lJyfHJ4a9VKdPcWPk0KFDrFmzBkVR6q0Q9hIZE87d/7yiWtuqqkKT+GCjIWeFG03TUVWF4RN6sG3jHsBMLQ40zPJo2KLDcLnMfkMBIlEKYbgkC8G+vMNB4WJVCDw+wSh90VfhFZrmsbyGW76fLabZlguI8qdJCwVfKnQJbmJVC0L3pjp73aQ17BYdi9lv2KJAhZQc+REepToJUzwIzUhJjlacRjTam1PtRQToYW+UOgAlYLuuPVpRsK8Yh9tNWYnTt1xYVHBr6PrR2yO1SIrj0beu4ZOXf0G1KNz40PlExVR2y03unEB4pB1nhQsERMVE0Kpd8yqPGcjrD31Jzra96Jok7bdNfPOv+Vx511nH3a+hExkZybBhw8jKyiItLY02bdrQrVu3OnWFP10cy026Y8eO6LrO0qVLG11bPbvdzpdffkm/fv0YO3YsF1xwQV0PKUSIRsUJR4izs7MRQnDWWTX7obNgwQKEEDzxxBM1etxTweFwcM899zBq1CiSkpIICwsjISGB4cOH89FHH1VKV5oxYwZCiGN+jR8/vtrn13WdN998k379+hEREUFMTAyjRo3i+++/r7Ttnj17ePXVV5kwYQJt27bFZrORkJDARRddRFpaWpXHLy4u5oYbbiA+Pp527drx8ssvn9gNCtFgeeWVV9i4cSMffvhho6sb3r59O1lZWQwbNqzKNPA/U6S4oUSFq4vm0cjcsIv8XYW+ZaPO7hW0TXSTCFQzzffsvwwKPoDu17YRkXbsgbWsgaJRShwOj9EDWZOg6b6oqe7RfNsJtwhIjRZBUdjAqCwBEWKLqgSlSQvF/70YNzHCgsWio6r+NGmrRcNu8WBXdewWN3aLTpjFjaroQYHucNUVmNFtOE+rmrGNOOJLwb/8GNz1fxfw/re3ERZm8x/Y1POK6eZ15pQBR91/yPhuvD37Ht747i56Dal6Aiq2aRQvfnkbI87pw+jz+/GPr24nPNJ+7IEBB/IOo5up1UIIDuYdPu4+jQUhBO3bt2fs2LGUlJSwYMGCRh8trk5rpdTUVNq3b8/SpUspKzu+u3lDomPHjrz33ntce+215Obm1vVwQoRoVIQixMegtLSUd955h0GDBnHuuefSvHlzDh06xM8//8x1113HF198wc8//4yiGA9fffr04fHHH6/yWF9//TWbNm1i4sSJ1Tq3lJJLLrmEb775hg4dOnD99dfjdDr57rvvmDRpEm+88Qa33Xabb/s33niD6dOn06FDByZMmEDz5s3JzMxk1qxZzJo1i88//5xLL7006Bw33HADc+fO5aqrruLgwYPcf//9REVF8de//vUk71iIhkB6ejqPPvoo8+fPb3QGJLt27WLr1q3HjQ78GSLFhw8fZvXq1SiKwsiRI4mNja3rIZ0w2zfu5sW7PqMwv4gJlwxi86octq7NAeCmxyYz+brRnHXJYAoPlPDzl2nENYvmkTemAjBrxmJ+/To96HgCCA+3UVHhoqLMiXR5QFGCtwgQxUIRZnQ1QGV6JNiMKLIKaAK/zbMpqn1pyYEu0ypGiyUIMtISQqIohmt0KS5iFAsKZm2wkKiKjhACm+oizGLsr0sNXSroHptPEEugyB1Bh7ADxChG1FuROh7NUik6bKz07xekqt3Sdz0W4JYr3/PfD/Ddh2vuOhPd4aFlqzjGnNc76D57+0MHkrs9nxkv/khFuYu/3DSuUo/hjj1a89CbV3MinH3FUN5/+jvzdZKMPYYwb6xERkYyfPjwoGhxffMFqAlOpM9wamqqb/sRI0YQFhZ2Gkdau1x22WXMnz+fyy+/nAULFvwpsgIaOhoK2mmuUD3d52sMNK53zBqmadOmFBUVYbPZgpZ7PB7OPPNM5syZw88//8y5554LGIK4T58+lY7jcrl48803sVgsXHPNNdU69zfffMM333zD8OHDmTt3rq8p+3PPPceAAQO47777OO+880hJSQFg0KBBLFiwgNGjRwcdZ9GiRYwfP55bbrmFyZMnY7cbs+7l5eV88803zJ492xftb9WqFTNmzAgJ4kbM4cOHufTSS3nyyScZMmRIXQ+nRsnPz2fdunUMGjSIJk2aHHf7xiqKpZRkZmaybds2OnXq5OvXWZ9xOdzs31NI86Q47OH+99tn/zaD/bsL0XXJzA8XBu3zwXPfc95VI7BYVa649QyuuPUM37rlv23mX8+amTSqAMV4eFYUQUR0GOXlZq2pLoPzpMyUam/2tGpT8Xj8dboSQK8isupNixbSNNoyt/NGiIXpLK0GR4+9kWEhTEEsXMRgwWoxBK0QOhazTjjMomFXJELo6FJBoOPWdKQUaEgUATZcNLWUgWbcwwTLYb9gPiISLHT/NQzu3pYuzZrx849rKXd7fPu4pfQ/JAhhVE6bK3v0a0f33m2Cjpm/u5DHbvg3udvz6dY/hbEX9GXjiizadGjBjx8vpLiwDClhw/Lt3PDQBURGhzHsrF5EVpFGXR2m3DiWVu1akLN1L31GpJLau2219929ezePPfYYv/zyCwUFBSQmJjJ58mQef/zxE5oonD17Nq+99hqbN2/2Had///7cc889DB069GQu64TxRotbtmzJmjVr+P333xkwYABxcXHk5+eTlJTE3/72N954443TMp6a5kTEMBj3o3v37jidTpYvX87w4cMblXB89dVXGTx4MI8//jjPPfdcXQ8nRIhGQa09JblcLt544w0mTpxImzZtsNvttGjRgilTprBmzZqgbZ944gnGjh0LwJNPPhmUYpydnV1bQzwuiqJUEsMAFouFCy+8EDDSM4/HrFmzKCgo4LzzzqNly5bVOvd3330HwMMPP+wTwwDx8fHcfffdOJ1OPvroI9/yKVOmVBLDACNHjmTs2LEcOnSIDRs2+JZLKZFSBj0oq6qKruuVjhGicSCl5MYbb6Rr167ce++9dT2cGqWwsJAVK1accDuSxpY+7XA4WLZsGbm5uYwYMYLOnTvXezG8Z+d+rh32JDeOfparBz9O9pY8ls/bxL+enkW+KYZPlKwte33pvGiGqHv09Sv51w93MXh0F/+Gge93gVFgk0piWBDQC9j4LnRp1AarAukJ6Ees+l2mpWqkSxumW2bdsSJ9EWJvHXGJcBMprIaGV8CmatgsupEirWrYVA92RcOueghTDVMtzRyYLgVtwguJEA7iFCNVNMLiwmZ1+V2mzfRthMR2yH+dW3P34/ZolJe7gqLAR94Pb7F0eKSdbr0qu3+/+8z37M7aD0DG6hzeenwmC39ax39en8PhwnIjBV1KNI/Ov574lpfv/Zy7LngZR/nJO+cOPqM7l9x6xgmJ4R07dtC/f38++ugjBg0axN1330379u157bXXGDp0KAUFBdU6zgMPPMB5553H6tWrOeuss7jzzjvp168f3333HcOHD+ezzz472cs6KbzR4pSUFJYsWcKOHTv47rvv0HWdKVOmnNax1BQnKoa9CCHo27cvNpuN9PR0NE2r5ZGePiIiIvjyyy954403mDNnTl0PJ8RxCJlqNQxqLUJcWFjIXXfdxciRIznnnHOIi4tj586dfP/99/z8888sXLiQgQMHAjBmzBiys7P5+OOPGT16NGPGjPEdpzqRntONruv88ssvAPTo0eO423/wwQeAkaJcXfbt2wdAu3btKq3zLps/fz5PPvnkcY/lnRkNTKGKjIzknHPO4fLLL2fq1KkUFhby3//+l1deeaXaYwzRsHj33XdZunQpa9eurfci6UQoKSkhLS2Nrl270rr10Vv0HI3GEinev38/q1evJj4+noEDBzaYiMh/X/uVQwdLACg+VM6zt8xgd/ZBVFXxdUZSVIFqUWmV0pzsrXsBuO7B87FYVTSPxsYVWaiqQrcBKSiKQp9hHfn0tTkoitEruP/ITpSXOrj5glcNkWsx/v6FN+Bp9hQWAmJiwnE6PXTsmsDGDXsCsomDioyNtGIhEB6Q4UaqtbUcXJGGEBbCbLnkFdCKNH73Wlx7a5IlqEIikJTiREUQiUq5cGNVPdhMIy274iTMIgP6DgsUxYgWa2Y6syIlkYoLiddUqwKXx3zfD4poC7QwCYYZNYeLK5g1ey3YFNB0giSHYp4wYF6iotyFy+nBHhb8N1aQ76/pleaLJ80JDdWqIj3SMPrW/BMNu3fsZ/2y7Qwa373Kv4/a4G9/+xv79+/n9ddf5/bbb/ctv+eee3jllVd45JFHePfdd495jH379vHPf/6Tli1bsn79+qCJuN9//51x48bx2GOPMXXq1Fq7jqoQQtCpUyeaNWvGypUrKS8vp23btowaNeq0jqMmOFkx7EVRFAYNGsSSJUtYvXo1AwYMaDSeGd26deP111/nqquuYu3atSQmJh5/pxAhQhyVWhPEcXFx5Obm0qpVq6DlmzZtYsiQITz88MPMnTsXwCeAP/74Y8aMGXPCxlqvvvrqCfWemzx5cpWpzUfD5XLx3HPPIaWkoKCA3377jS1btnDttdce1yQrJyeH3377jdatW5+QEVl8fDwAWVlZdO3aNWhdVlYWANu2bTvucXJzc5k3bx6JiYn07NkzaN2MGTO48847+fTTT4mKiuKZZ54JqksO0XhYu3Yt9913Hz/99BPNmx/fvbWhUFFRwbJly0hOTj4lIduQRbGu674Id8+ePWnbtm2DeuhzOt1BLYF379yPUBU0UzS1aN2UMy8ayMjz+tAqpTnbN+4mJi6CpJTmaJrO/133IWsWG++FI8/tTcdebVj08wZ6De1IbFwErZLjOf+qYUwd87xPrHGE27S0WnyiuFVKPFdeP4plC7eyYcOegHZDAWgSLMYyEVA3bNHBqfhbMSHMvsQB5lrSJ4b9YxEYKdO6kFTgId4qOKB4sCkerBYdBQi3aISrHhSM9GyBgkXoeKRAmJ5fYTiJFB50GUYBEC49WBUPTr1yplPQIu99EQLCBDjMa9ADrkX3p0vbw6yVxLDHrVF8uCLAeCs4sh8eYcfjFMQnNmHP9vygsuWYpsH9pI3dJeuWZnL4QAn9R3chOq7yNifDjh07mDNnDikpKZUc9p988knee+89Pv30U1566SUiI49+zpycHHRdZ/DgwZWyUsaOHUt0dPQJmVx5RfS9997LFVdcwVNPPcXChQvRNI1x48bxzjvvkJCQwObNm3nmmWeYN28eTqeTkSNH8vbbb9O2bXCEvGnTpvTv35+FCxfywgsvcPjwYZo1awYYpVSvvPIK69atY8+ePcTExJCSksL48eN5/vnnqz3m2uRUxbAXi8XCkCFDWLRoERs2bKBnz54N6v3xWEybNo358+czdepU5syZc9L3KESIELUoiO12eyUxDNC9e3fGjh3Lr7/+itvtrpEoxquvvkpOTk61t09JSTlhQRwYiRVCcN9991Xrg+Ojjz5C13WmTZt2Qm9WZ599Nl988QUvvPAC48aN85lCFBQU8OqrrwIcdxLA7XZz1VVX4XQ6mT59eqXzx8fH85///KfaYwrRMHE6nVx55ZXcf//9VabVN1S8D0zNmzevNGl0MjREUVxRUcGqVatwuVwN0kF6zaKtRMdGGL8EpOl6xZKiCnoO7sDUu/2TiV36Jvt+3ro21yeGARb9tJ5Fczb59h0wqjNX3z2RokNlfjdiAtoQCWFEMAMekDev380jd35uOlVLzL5HRk2wxdzPJcEeUFMsJFLxi2Bv3bAekBqNMFOmFUNYelv66g5QwozUaRsWKnDTTo+hOXbipEqMx4odC9EuHZtUsUgLim5FlVakZjeWoaIg4NCZBH4S7t30D542W4x7dIlT13HqOi5N4nLpeDrquDw6LpeGXi6pcGiUlrmpOOymvMxN+SEnDpeRWS6E8PUobpsSX+m13LZhF/m7A/KwvdublBaVgUdnd2Y+510zgl8/X4bm0bjk1jPp0jel0vE+fOY7vvnXfACaJcTy5q9/p0kzwzX+QN4hMlZlk5yaQHLnE4uM/f777wBMmDChUqZMdHQ0w4cPZ86cOSxfvvyYE96dOnXypeMePHjQN4kNsHDhQkpKSpg8eXK1x7V69WrAmOgeMWIEZ599Ntdffz2//PILs2bNwul0csstt3D55Zczbtw4rrnmGhYsWMDs2bO5+uqrWbBgQaVj/vrrrzz77LPMmjWLZcuWkZqaytdff80jjzxC27ZtmThxIvHx8eTn57Ny5Up+/fXXeiGIa0oMe7Hb7QwbNoyFCxdit9vp3Lnz8XdqAAghePvtt+nfvz+vvPIK9913X10PKUQV6Cjop9nk6nSfrzFQq6Zaa9eu5cUXX2Tx4sXs27evUpuigwcP1kiaR23XGUdFRSGlRNd18vLy+OGHH3j44YdZtmwZP/3001EfQnVd56OPPkIIwXXXXXdC57ziiiuYMWMGv//+Oz179uSss87C7XYza9YsXx3ysdJevSJ84cKF3HjjjVx11VUndP4QjYcnn3ySsLAwHnnkkboeSo2haRppaWlERETQu3fvGpvxb0iieN++faxZs4aEhASGDBnS4Fxl53+zgn/c+SlCEcGOzppOtwEdyMspILV3G/766KSjHsNmP+KahUBRBbom0TXJdrMHcWxcJNGx4ZQUVRjbaTqoxr6GQA4WxYARoTafwyX4UqTByCLWpJE2rfgMsowvSYCBlRJgqqVJLIqkuc1GC4uVFjYbzVUbzRQLTVCJxoodFYlkiLs1RWoFFUoFLtWJW3FSbi3GobqQiguP8OBEY1t5HC40NEVDCsktCb+TbC9Gdzdhy5aH6djtMf721ZU4ZBgWi8CmKNgUhTBVEF4qiPYo2FRBuEcQpipER1po1TKcyHCV8DCViAgLUkoqyj1UlGmUFLuoKNMYc0Zv9u/fT1RUFOHh4QghiGkSUfkFChTF5oSEogji4qOZufVFdF1itVX+u9U8GjM/WOD7vWBfEUt+Wse5V41g+4Zd3DflNZwON0IIHnpnGiPP7XPUv5Ej2bp1K2A4EVdFp06dmDNnDtu2bTumIG7atCnTp0/nnnvuoVu3bkyePJlmzZqxY8cOvv/+e84880z+9a9/VXtcXkGcnp7O8uXL6dXLaCf22GOP0bZtW+bMmcOaNWuYO3euz6zL5XLRsWNHFi5ciMPhqOSmPHPmTCIjI5k4cSIVFRWkpaXhdruZMGECP/zwQyWPlIMHD1Z7vLVFTYthLxEREQwdOpTFixcTFhZGcnLy8XdqAERHR/PRRx8xYcIEzjvvPLp06XL8nUKECFGJWnuCWrp0KePGjQOMmdhOnToRFRWFEIJZs2axbt06nM6TN9KoCxRFoXXr1txyyy3Ex8dzySWX8OyzzzJ9+vQqt583bx65ubmMHz++ylrgY2GxWPj555954YUX+Pzzz3nvvfeIjY3lwgsv5L777iM1NfWo5kG6rnPdddfx+eefM3Xq1OPWQoVovKxYsYJXX32VtLS0BlNTejx0XWflypVIKRkwYECN10PXd1Gs6zoZGRlkZ2fTq1cv2rRpc/ydanEsM174kbn/W06LVk2577WraNOxesaBc78y+qN7a0wjosOw2qxMuHQw0x44r1qTHB26t+KcK4by0+fGazXq3F4s/GWjIbKBfsM7+cZ54wPn8vJDXwUL3wBHacBvM+mtmdX8qc7+2l9hCOLA/URAqyUBdosgKdJOy0gbLSKtNLdZaW61Eme14tA19usu9usuDuguMt1luCJLKRMuiqWbi9VEiqylrAvPIkx1Y1M8KEKnVdghwlUPqln47NQFBR6jzlqXoEkV1VJCrL0EzeoBINZWTJEmqdA00wbbj7pfElkKSElYoY7ivRiP8bPUJbYKjcgICxFRFjp1bsHwUR1pnhCBw1HB+vXrKS83jLJ0F8Q1i+Pi2weyfmk2B/eUMeLMPiybu4niQ2W0SIxl17Z9KKqClJL+Y7qiWlSOJnUUVSEswkZ5icO3LDLaMJf84eNFuN3G9Ukp+d9b805IEBcVFRn35ihtyLzLq1OGddddd5GSksJ1113H+++/71vesWNHpk2bdkIGf15BPGPGDJ8YBkPwpKSksHbtWv7xj38EOVfbbDY6d+7Mrl27KCsrCxLEDoeDX375hbPPPpuwsDDfV2lpKddddx3FxcVBUW2g0u+nm9oSw15iY2MZPHgwy5cvx2azNZq62+HDh3PzzTdz7bXXsnjx4lDqdD1DkwLtNJtcne7zNQZqTRA/++yzOJ1OFi1axIgRI4LWLV++nHXr1tXYuWq7hrgqJkyYAFBlmpKXkzHTCsRut/P4449X6m3sPeeAAZV7Luq6zrXXXssnn3zC5ZdfzowZMxqVgVKI6uNwOJg2bRqPPPJIpfrxhoqUkvXr11NWVsaIESNqLSpaX0Wxy+Vi5cqVOBwORo8eTVRUVJ2O54/vVvPV2/MAKD5UxnM3/5t35j1UrX1btGqKoiromo6iCkZf0I/bnw/ula5pOvO/W83BfUWMmNiTNh2CBYYQgtufuYjzrxrOI9e+z8LZ61CtKj0Gtqf7gBQu+esYcjL38ch1H1KQX+yLWPrSpr25y24NwrzmU2bqs6+uNnjcPhMuYexrsym0aGYnoVk4ibFhJDSx0SzCSrFbY0+Fk3zNxeqyUg643ewLK6dE8QQYYxn/ixZlxmkVcFgcRGLFpmpYFZfhJi0gTDHEsQVDeAshjH7FwjDYcmqC5lYjamo1lb0ihOHSXcWzkcXju4noFlC8CVy6/xo9QGmJm9ISN/v3ZLF0fhaDh3fiiquG0zLOyozpP7BtUzZNWkQS0zKSJs3D6TY0gfikSGx2lRvHjiI2NpaoyGhWzcskP7eI0Rf0o/NxHKGFEPzt6Yt5/YEvcDk9DD+nNyPP6wNAeFSYeeek0UYryn7MY9UmL774Ig8//DB33HEHt912GwkJCWzZsoWHHnqIK6+80pcldzzKysrYtm0b7du3r9JrJCcnh6ZNm3LppZdWuS46OtpXH+xl7ty5lJaW+jpigGECOnnyZNavX+8TTePHj68X/ehrWwx7iY+Pp1+/fqxatYqhQ4dWum8NlWeeeYY+ffrw8ssvc//999f1cEKEaHDUmiDesWMHTZs2rSSGy8vLfTOhgXjf/E7GGr+2a4irIi8vD+CoUbeCggK+++47mjZtGvSBVBN4634vu+yyoOWBYvjSSy/l008/Dc0U/ol58sknCQ8P54EHHqjrodQYO3bsID8/n1GjRlXZEq0mqW+i2OumHR0dzciRI+tFxD8v+wCKYoguXZPsy61euxqAax8+n705B9myOptuA9sx7e/nAVBWXMFP/1mKy+Fmz65Cfv9+DYoi+OKtebz5/d2VRDHABy/8QOH+YgA0t8b2jbs494ohvP/CbNYt307B/pJK+3Ts1IJmrZtyYF8RY87uxfvvLQjeQPhdqKU5qSiBmCY2WreJJKltBEkJEcTF2Cguc5NX4iKv2MmavSVk252UaoZYdUdLUzyDHu4xDqqYnlqmKha+L4lDcZCgxWIVHsIUN3ZVQwWsigeb0FFNcatIgapIn7DXpMIhdwQpNje6WfccLiyYTl6VUM1WzEiJcPs3CQqg+2qA/QdIW5LJip/WIzQdNA00jcP5FbClyLezUOCcKwbSe1oyRUVF7MnbTXgrFymtwjnk3MOmTQ7i4+Np2rRplX/HH7/4I1+8brSTOfOSQeRu389fxz7HlL+O5ZK/ncHqP7awa3s+0XGR3PTEibUT8kaAvZHiI/EuP16HiwULFvDAAw9w4YUX8vLLL/uW9+vXj5kzZ5KamspLL73EzTffTPv27Y95rHXr1qHrOmeccUalddnZ2Rw6dIgpU6ZUulelpaVs37690nMWwLfffovNZuPcc8/1LYuPj2fx4sU8+eSTPPfcc9x555088sgj5OTk8PTTT9OvX79jjrO2OF1i2EtSUhJOp5P09HRGjRp1TPO0hkJ4eDgfffQRZ555Juedd16N+GqEqBnqog3SyZ6vpvqzg5H18s9//pOFCxdy4MABmjRpQpcuXbj++uu5+uqrT2p8tUmtCeLk5GS2bdvGpk2b6N7daKegaRr33Xdflc6LTZs2BWDXrl0nfK7aqiHevHkzKSkpREQE10aVl5dzzz33AHDOOedUue+nn36Ky+Vi6tSp2O3HnsHesWMHbrebDh06BH3gFRcXV6pP/vrrr/n3v//NwIEDg/oKetOkP/nkE/7yl7/w2WefhcTwn5j09HRee+010tPTG1xt6dHIz89ny5YtDB8+PKg3d21SX0Txvn37WLVqFe3bt6dLly71xiV1yISe/Pe1X81IpM7I8/pWe98mzaJ58es7gpZpms4Dl73Fzs17DGNj829X1yVut8ayuRtp02FcpWMd2BcsbsrLnDx3Z9WGgVabyqNvTKXPsE7YbP7jf/jBH+g+ty2zrZIuiWlio1XbKFq1iaR16wgiIq3sy68gd085GTkHyC90UubQKG+h+KLL5S3NVGsMwy1vyrXRc1gGtTKSZvhWMV2nnWoFEZ7mWFUdm6JhEx4UAXahYxMuvyBGQRFOEHaEBE1308yiYxEq3sZJFgSqqoF27MkT4csbl0gzjRwpfQ7aBCeW+39WVUMU+w5irtUhN7MgyPlYSkne7nwW/rySffbDxLTcjcvlpEmTJjRr1swnkPdmF/jEMMDc/6UjVAWpS958+CvadW3FO/Me5PCBEmKaRlZZg3wsvIZKR+vSkJmZCRy9xtjLjz/+CBiO0kcSERHBoEGDmDlzJmvWrDmuIPYGCfr3719p3apVq466bs2aNUgpKwlZTdP44YcfGDduXKXU8B49evDVV1/hcrlYsGABAwcOpFWrVkyZMoWtW7ce93mlpjndYthLu3btKCkpIT09nZEjRzaKz8lhw4Zxyy23cO2117JkyZLQM2CIE2LHjh0MGzaM/fv3M2nSJLp06eJ7lvzll19YsmRJtTMq3nzzTe68807i4uI499xzadWqFYWFhWzcuJGffvqpcQniDRs2MG3atCrXdenShdtvv505c+YwYsQILrnkEsLCwliwYAF79uxhzJgxlVKNu3TpQlJSEl988QV2u53WrVsb6XC3337UWp/a5n//+x8vv/wyI0aMICUlhZiYGPbs2cPPP/9MQUEBI0eO5O67765y3w8//BCoXrr0+PHjycnJISsri5SUFN/ywYMH06ZNG7p27UpYWBjp6eksWLCA9u3b89VXXwW92T311FN8/PHHREVFkZqayjPPPFPpPDWRKh6i/uNNlX700Uer1Se7IVBSUsLKlSvp3bv3aU/vq0tRLKVk+/btbN26lb59+1bp3F+XdOjempe/u4dFP66hZeumnHXFsFM6Xv6uQnZs3A149ZnfbEvqkuZJlV/7NYu3Ed8yhtzMfN8yoQjkETWzXsHmdml065/iE8NgGD21SYknJ/sgVptC23ZRpLSPoVXbSCKjLOzbV8GeXeXMnZNH3oEK3LohfD0RCijC5xjtF8GB5w0w3VIkQtV9xtVSx+zLJBGmIHarFYTrdixCwyo82BSJIiQqLmxCR/EJYp1wNBxIw9hLShJUJyreomdQhYqoKjwsJSLg/nh7MHs39U0LmMK9aVwEhw6UGisd7qB9A48ZKIonXBRc0uNxazx+1YfszT4IQhDbNJI3fr6bcmcZBw8eZP369VRUVGC3hjPwvPbkbDjI/txiQPjqzAF2bd9HtwHtaJZwcs8FXgE7Z84cdF0PKikqKSlhyZIlREREMGTIkGMex+uBcrTWSt7l1clk8QriqsqgvIK4qnVr1qwBqCSIFy5cSEFBwTGz02w2GxMmTMDj8fDyyy/zyCOPkJ2dfVodmOtKDHvp0aMHy5YtY/Xq1QwcOLDeTDSeCk8//TR9+/blpZde4u9//3tdDydEA6Im+rOD8d56xx13cOaZZ/L1118THR0dtP5Ig+X6wkkL4ry8PD7++OMq140ePZoHH3yQr7/+mueee47PPvuMiIgIxo0bx8yZM3nqqacq7aOqKt9++y0PPPAA//3vfykpMVLcpk6dWmeC+LzzziMvL4+lS5eybNkySktLiY2NpVevXlx22WVcd911Vc4qpqens3HjRgYNGnRKtZuXXnop3377LcuXL8ftdtOuXTseffRR7r///kqRY2+UvLS0lGeffbbK49VEqniI+s8TTzxBZGRko/kwdLvdpKWlkZKSUmcGUnUhijVNY+3atRw8eJARI0YcN4Wzrkjt3ZbU49SEVpcm8VHYwqy4vb2JPR6aJzen6FAZEy8exOjzegdtv+indTx326coqmI4S5lqUdcqu0YH8t0nS7jyVn966r69Bxk6KoneA6No1TaSw4dcZG8vZv5Pu9i3pxyXIkBRkEh0q+JzlA4sBva5SwsMAyuLabSlAaq3jtcQw76hKSClHiRaXUo5YXoYNqFhVSSq0FGFjlXoqEL6BLEAWtoK2e1KRAOQFnRhRREeNN/hFSwWHaqoRFIDxh7oMuEVxoZINoR/s5YxvP3hDXz+9nx+/u/yqm+qAFQFAYy7oC9nTA6OaO7JOsCenV7xKDl0oITcbQfpOyLV9++6vLycAwcOsHvbAXqMbo3HrZO39TA71uazZ9thBIJeQztVff5q0qFDByZMmMCcOXN46623gh78Hn/8ccrKyrjpppuC0miryuIaOXIkb775Ju+99x433XRT0GTVzz//zJIlSwgLC2PYsONPEq1evRqbzVblBOaxIsReIX2kIJ45cyaKojBpkt+hfc2aNcTExFR678rOzub555/nL3/5C4mJiURHR5OUlHTcMZ8qdS2GwTBKHThwIH/88Qdbt25tFA7N3tTpM844g/PPPz+UOl0PkFJBl8rxN6zhc54INdWfHeD+++8nPDyczz//vJIYhqOXmtY1JyyIU1JSjBYV1eCiiy7ioosuqrR8xowZzJgxo9LywYMHH9Ok6nQzYMCAKmdlj8egQYOqfY/g6CnfTzzxBE888US1jnG0exriz0VaWhpvvPFGo0mVllKycuVKoqKi6NatW52O5XSK4oqKCtLT0xFCMHr06ErtVBoqGauy+Prd37DZrVx591m07hDsSB0RFcZj71/Hm498jdPh5qp7zubsK/yuui6nB6tN+CI5f/y4FiFA144IWeo6KP4UZrvditPhn5We++0q3K5yiksPk9AxBqsdHI5ScnaWsGBuHsWHXXjtmwWAXTXFrvmQITCO7ZJIuzCFrwSLMKKqFSBjCHCm9v4MQugo3lZMEqQuzOCqca4KpQKLtGBDQRE6NsVIk7YKiUXoPn8sRUCi/TB5ngSEFGhSZ7czmqZWQzgawxSUu21Vmmr5rsOXFk1wVnTAPlk7DxLfIoaY2HBfzTiKQFUEukcgbFakoiLNiH5MXOUWTM1axmK1W3C7PGZ7Z0FCm6bBr39EBMnJyVzzt9asXLAZt+6gSzeVDn13gSqJjW6CizIcjqhT+jfx9ttvM2zYMO644w5+++03unbtSlpaGr///jupqamVJpWryuK6+OKLOeOMM5g3bx5du3blwgsvJCEhgYyMDH788UeklLzwwgvHTTF0Op1s3ryZXr16VRlNXrVqFcnJyVUeZ/Xq1YSHhweJHikls2bNYujQob4WjQBvvPEGM2bMYMiQIQwYMICoqCh27tzJ999/DxgT8F27dmX16tWUlJSQmppaaxHT+iCGvdhsNgYPHsyiRYuIjo6ud1k4J8PQoUO59dZbmTZtGkuWLGkUzwIhapea6s++ceNG1q9fz+TJk2natCm///47q1atQghBnz59GDt2bL01+g39KwkRopHgTZX+v//7P1/dfkNn06ZNlJeXM2rUqHqRznY6RPHhw4dZvnw5LVq0oHfv3o2mDuzg3sM8eOmbuF2Gy/K6JduYsewJbGHBs8X9R3flo8X/F7TM5XTz3G2fkvbbZuKaR/PE+9eR2qsNLVs3RQjhE2LC7BUspaRd5wQiYiPYtmE3TrcHhKBJ8zBS+8XTuX8LLDY3BzOc/DE7i9zcMpwOHQlYrAqPPjWF5x/7xuhUJITfWdpMIZbm36Kqg1uAVASqAzzhGBFXDTzez3wPEAYoEtygREhfvTACpEXgdEls5m0o0zQ0NCJ1G5rUzDRpiZA6wuyd5P2XEKEa6ySSOLuLbeVx9Io6HHzjRdWmWoEEGm4TOG4Tm934G5x01TAW/bSevNwCIxDu8piiHtMlzKjH3pNT2VwtukkEj39wPe88/i2aR+eav59LYnLVbX4sVpUhZ/qzq/r270tJSQn79u0jJyeHdevWER8fT+vWrUlMTDzhiEOHDh1YuXKlzzzmp59+IjExkTvvvLPa5jGKovDTTz/x1ltv8cUXXzBz5kzKy8tp2rQp55xzDnfccYevG8Wx2LhxI263u8oIcE5ODgUFBYwePbrSOqfTSUZGBv379w96j1i5ciW7du3irrvuCtp+0qRJuN1u0tPTmTFjBg6Hg6SkJK644goeeOABOnUyIu+RkZGkpaVRXFxMv379avz9pz6JYS8xMTH079+flStXEhkZWW+zcU6Ep556ypc63ZiMNRsiGgLtqLOStXdOMLyIArHb7VX6BNRUf/YVK1YA0KJFC8aMGcPChQuD1vfs2ZNvv/2Wjh07Vv9iThMhQRwiRCPh8ccfJyYmhvvuu6+uh1Ij5Obmkpuby6hRo+pVik1tiuL9+/ezYsUKUlNT6dixY72YBKgpdm7eg8uM0krg0IES9u8prBQlrorZ/1lG+vzNABwuKOWl+7/gX7/ez5V3nEle9kHWLs0kJi6SlC6JhEfaadO+OZOvHcn1Z/0Ti13QZVACnfrEE9cygr07S1g7fy+bluxC1yRaXKQRTVaMuKrm1hkxugtnnd+Xn74zajRx60ir6s91DkiX9vYptrjBLbxmXObAvTbVihkl9thQRLnPVVpKUBVp+FJJYxgHnZG41AoiZRilio5FSITQ0TEOHRzM9WDUH4OCZGD0/irungZH7fjrHad5TGmmScvgVk0jRxuppHHx0bw7+252Zx3kvotfo9ypmbtL4zLNNlojJhqpv8vnbuS/r/2KLczKjf83mf6ju/LBgkeOPZaqhicEMTExxMTEkJqaSkVFBXv27GHnzp2sW7eOxMREWrduTYsWLaodfWjTpg0fffRRtbY9WhaX1WrlrrvuqiQ+T4T+/fsfNaMsOTn5qOvsdnuVtXgzZ84EqFQ/PGnSpKAU6qMRGxvL6NGjSUtLY9myZQwePLjG3n/roxj2kpCQQGpqqs95uqFn5YSFhTFjxgzGjRvH+eefX+cZViHqhiPLzB5//PEqM09rqj/7/v3GZ9CHH35Iq1atmD17NiNGjCA/P5+nnnqKzz77jHPPPZcNGzbUeqeQEyUkiEOEaAQsX76cN998kxUrVjSK9KjCwkLWr1/PoEGD6rzXblXUhijevXs3a9eupXfv3nVWK12btOuahNVmwePWEAKi4yKrNMmqiqLCMsMoS5NIXVJUaBg8RUSF8X/vXsMt57xE7vb97N97GNWi8O4v93CwcD8jL2xLYvsY9maVsHFZPtkZh3A7NDPlVwWPG1waRAQ8mCuQvjSTKZcOZrYpiIVX+PrCqIYbs9e/SgqzfNmsJbYI8AiJVEAVCprQfG7NQhiO0sJrtgXoejhCVACSCj0St1qBXbNTKjwoppGWRA+KMQggSvUaaoFDt9DU6qx076KsDg65bMGp0VQRNDbH59VfgeeafKE/emm1WWjXOYGULklsXp1jXLNH4+q7JlJa4qB7/xSGndGdvOyDPH3jh4ZxlRA8cuXbfLriKcLCT/0hKDw8nI4dO9KxY0eKi4vZvXs369evR9M0kpKSaNOmDXFxcY1qQqm6zJw5k969e9OuXbuTPobdbmfYsGGsXLmSxYsXM2TIkFN29q/PYthLp06dKC4uZsWKFQwfPrzepnZWl8GDB3Pbbbcxbdo0li5d2iieDUKcGLt27QryHKptF3ldN2aDNU3jiy++YOhQo+QpJiaGTz75hC1btrBy5Uq++eYbLr/88lody4nSsP+1hwgRAqfTybXXXstjjz3WKGaBvfWzXbt2pUWLyj1n6wteUbx161Z27NhxSsfasWMHa9euZeDAgY1SDAM0T4rj2c//xoCxXRk6sRfTv7odezXF0bjJ/YLa61itFhZ8v4Y3H/2KG8e/QO52Y1a6WatIhk1qR/qqZezcuZPiAg+fT1/Djx9ksHXVAZzlHkMMgxluFYjScgyBa6Q+SyH47JPF3Hj9h0irglQEiimsZIDolYowvjBaK0nfckMI+wy2vNFXUwALIVEUw1VaERJVkWapszSHJAxBrNuxmC7RCpKqjJ19AWsBERYnO8qDJ48kUOw2DVB8oeXKite47oDxKgLc/jPO/GYlh8xJCICM1dlsXpNr3D8hiE+K47JbxnHjA+cy7AyjXCN7a55R2y2NNOrSogoKzfZYm1ZmMfs/S9m1w+8MfrLExMTQrVs3zjzzTJ9/x/Lly/n999/ZsWMHLpfr+AdpRGRkZLB27dpTPo7FYmHQoEHExsayaNEin9HpydAQxDDgq3PUNI1169adkBdMfeXJJ5+ktLSUl156qa6H8qdFl/5exKfvyzi3N7PG+3U0QVxT/dm96xMSEnxi2IsQwpelkp6eXp1bd1oJTReFCNHAefnllwkLC+Pee++t66GcMpqmkZaWRsuWLY/bu7M+cKqRYiklGRkZ5OTkMHz48NPeUup003NIR3oOOfHaobYdW/LWD3dzx+TXqChzcjC/iOl3/QfQURVB5wEt6TYsiaYJEWxfc4CMhYU88tpkDmQKFhZvP/qBVYHiAY/TDeF2X73wFm8LJyEMt2i3RLp1pF31GXX5hKNu1AEbIV/8ec3eiKwIiMYKQwh7Wyz5EG7fMiHAbSnDroWjYEXFhQDcBAd5JQSJ5EirxKGJoId4HQ1F0fFohgAR3hMc+aBvmoRJT0CqtEWAqSXnzF7HvB/Wceu9E5l08SB2ZOT5dpUSDu4rwu3yYLP7U2s3r8wKcvqOiYugReum/PLFcl576H/GKawqL355K137plT58pwIQgiaNWtGs2bN6NmzJ3v27CEnJ4eMjAySkpJo165do//3VdMoikLfvn3JyMjwRYpP9B42FDHsxWKxMHjwYP744w927txZZ73na4qwsDA++OADJkyYwOWXXx7UGzxECC811Z/de5yjCWfv+0dFRcXJDLNWCQniECEaMLm5uTz77LPMmTOnUaRDbdiwAUVR6NWrV4NJdzxZUazrOuvWrePAgQOMGDGiyvYEIfyER4dRXupPCY5qYqfbsAS6Dk2gotTNpqX7yFy1H5dD468Pnw/ABVcN49uPF1NUWAaAalHQPAEyUlXBoyFcHmSEWTN4ZF4yAhSJokk0Uwx7o70I01DKjLAKF0i7PxXaF3UFn+mUV4wG/nn72y6ZlcFqBWGeGJSghr/h6LLC13bJ2NKf5CUEtLCX4kHDYzpi6ehYVQ2XFlADeoSTtPAYwlpgmmjrR2wY0LP4rZd+Zdns9bTvnGC0upJGZNwaFc6V46cz9pzeWAU4K9ws+mE1gbTv1gqLVeW7GX6TFV2XzPkyrUYEcSCqqtK2bVvatm1LUVER2dnZLFmyhJiYGNq3b09SUlKDT4c9XQgh6NatG3a7nSVLljBo0KBqZ+40NDHsJTw8nEGDBrF06VLi4uJo2rTp8XeqxwwbNoyLL76Ye+65h6+//rquh/OnQ6+Dtksner6a6s8+ZMgQIiMjyc7OpqysrFKLpo0bNwKcUklHbRH6RAgRogFzzz338Je//KVavS7rO7t37yYvL48BAwY0mAcnLyeaPu3xeEhPT+fw4cOMHDkyJIarQZNmUbTp2ILmbaI585ouXP7IAOJahvPbJxn87/kVRFhjiGvRBBSF916YzbO3f8rNZ79E+eEyIiLtldozGaXApjh1+y2Vpff/gSnGAoQmQUik6o8QeyO20ky5tlQY6dMoQCn+SLIzwFX6iBbJQoDVYpxTMWuR3ZZybFq4T/waWDGss8wvqaAFiFtNh2KPio5uWlyBLiVuqVQh8r3fDedrVMNBW/cuF6B4b0mgv5aUrEnP4ttPljLx0iGMvaAv4XFRuNwaJUUVfP/f5cz8eDE/f5lGcbEj6PULizBS9cLM18J7vJimx+5pearExsbSu3dvJk6cSKtWrdiyZYvPLbUqU6oQVdOhQwf69OlDeno6u3btOu72DVUMe2natCldunRh5cqVjSLtfvr06cybN485c+bU9VBC1EO8/dmzs7N56623gtZ5+7NfddVVQQJ3y5YtbNmyJWjbiIgIrr/+ehwOB48++mhQxtKGDRuYMWMGFouFiy++uHYv6CQ4YUGcnZ2NEIKzzjqrNsbToPnss8+46aabGDBgAHa7HSHEMXsD79mzh1dffZUJEybQtm1bbDYbCQkJXHTRRaSlpZ3y9ifK9OnTEWZN2PLly2vk3MXFxdxwww3Ex8fTrl07Xn755VMeZwiDOXPmMG/ePKZPn17XQzllSktLWbduHX379iUionIP04ZAdUWxy+Vi6dKleDweRowYccpmNY0FKSXffbSQuye9wj/v/sxnnOVdV1hYyNRHhnLhXb1p1rwJnZK7k9qhG527d+C2Zy7mslvPJH/3Id8+i3/dSH7eYdwujfIyp9EeyPvZLCCueTQdUluClChujSaRNqRi9jgOiqKaKlUxDKek6SQtAV01+ulKxTDdUnwmWxIrAanVAYFed3nwob3fRcDvHrUcqyc8IHJsLpfeejTDO1oL+AhXBUjFhUfq6OaFuqTmC3JX3WtYIO3+n33RbylRvTXERyRqeI+3aW0O9/7jMsrKXb62VJiXqms6Pittk1Hn9yUrI48dm/b4XofmreL4y81Hb+FRk1itVjp06MD48ePp27cv+/fvZ86cOWzevBmns7IZWYjKtG7dmkGDBrF+/Xq2bz96KUJDF8NeOnToQExMDGvXrm3w9cQtW7bk6aef5vbbbw/9vZ9mjIZ5p//rRHn77bdp0aIFd9xxB5MnT+ahhx5i3LhxvPLKK1X2Z+/atWtQD3QvTz/9NH369OHVV19l6NCh3HvvvUydOpXBgwfjcDj45z//WS9LERp+jmU94tFHHyUnJ4f4+HgSExPJyck55vZvvPEG06dP983MNG/enMzMTGbNmsWsWbP4/PPPufTSS096+xNh48aNPP7440RGRlJWVnbKY/Vyww03MHfuXK666ioOHjzI/fffT1RUFH/9619PapwhDJxOJ7fffjvPPPNMvTaeqg6aprFy5Uratm1LYmJiXQ/nlDhe+rTT6WTp0qVERETUWSTc7fLwwTOzWL1wC136pnDL0xcTEVX3LUYWz17Lu499A8C2dbkUFZTy1Mc3kZ+fT2ZmJsXFxbRv357BQwb5jEF6DkjlnCuM7Ig/Zq+rfFBvOPaIsOzz/76ePkM64nFrrFuaCQIycwr44L0F5n5HHMd0lhaaRLcpQTXE0tvmV8FXgywVglKlVVQ0jBZFLkcYEbGOYDHsD8EC4FbLsWqVJ4bc0mK4TQtDFLsDBLGiQIy1FJeU6N5zoWO1OHDoNl9A2Hsa39yAAroCQgdhDlpIyZFlzkEzChKim0Sy9PeMyvcpwLRLtShobo3hZ/di1Hl9+PCFH/G4Nf8hdUlUzOmdEBJC0LJlS1q2bElBQQGZmZnMnTuXtm3b0rFjxwY7IXe6aNGiBcOGDWP58uVomuarGfTSWMQwGH8r/fr14/fffycrK6tB+Foci1tuuYUPP/yQV155hQcffLCuhxOinlET/dnBMPJatGgRzz//PF999RVvvvkm4eHhjBgxgvvuu69a/dnrgpAgrkE++OADOnXqRHJyMi+88AIPPfTQMbcfNGgQCxYsYPTo0UHLFy1axPjx47nllluYPHmy7+HvRLevLm63m2uuuYY+ffrQqVMnPvvss1MeK0B5eTnffPMNs2fP9mUUtGrVihkzZoQE8Sny8ssvEx4ezs0331zXQzllNm/ejBCC7t271/VQaoSjiWKvGI6KiqJ///51VsP4v7fm8sNHi5BSkrfzADa7ldtfOLmJtJpk2/pdvj62UkqcWikLFizA6XTSoUMHhgwZEtQP9cDew8z5Xxr2MCtnXzGMhKSq+ycCoEsUm2I4TEt46rZPeeWLv5HcsSWderfhlrP+wcGCMkhs4t+nUm4zCE0HRUUK0IXw9SBGx9eGSXowjLjMNk3S3NePFfCnE3tPo2kSr3ZwigosehiKVIKMtNxSRTdlq5QCTQZHASp0gQMNqZuCWEo8uvExL/GWMcsgwW/UQJuRb49xMqlWEV0QAsyWGqpV5ZaHz2X+T+uD7xHQLjURNJ3Lbh3P4LFdcZS7aBJvlATsyy1A6v4a6timddtSzWvCVVRURGZmJr/99htJSUl06tQpqFVJiGDi4uIYPnw4S5YsQUpJ586dDXf0RiSGvdhsNgYMGMCyZcto2rTpcV126zMWi4W33nqLiRMncsUVV4QMtkJU4kT6sx8rayIqKopnn322UlS5PlPrT2QfffQRgwcPJioqiqioKAYPHlxlGvGCBQsQQvDEE0+wcuVKzjzzTKKjo4mNjeXCCy8kOzu7tod6ypxxxhkkJydXe/spU6ZUEpgAI0eOZOzYsRw6dIgNGzac9PbV5dlnn2XTpk38+9//PuoH2MmcW0qJlDLowV9VVV+fshAnh9dI66233mrwRlp79+4lNzeXAQMGNCqTmyPTpx0OB0uWLCE6OrpOxTBAVkYeXkWk65Idm3bX2VgC6TW0I7qm075Pcy59ZBADz2tPSkoKZ555Jp06dQoSwyVF5dx5/st8/tqv/Hv6j9w8YTp3XfAKBNSEWqyqry0QYEQizc/vijIXT9/2KQCv3P8FhfnFCI/mU6fGPkemTht1tdKMDAuB/xNUF75+xBY3PqHsE8NV6EtfINVc55L4WjGViwokEosWjiaNNGQJeFDwSO+XwC2D369n53fFpRtlwd5junT7EWMQ/jRwYYprYQp6M4wsvOnSRzzwpHZrxd1PX8iHs++mQ5ck3E5PUN63alF467s7eeenexl9bh/CIuw+MXy4oJTlv20Kuv4pN1b+TKkLYmNjGTBgAGPHjkVVVf744w9WrVpVZbZUCIOYmBiGDx9OdnY2GRkZuFyuRieGvTRr1oxOnTqxcuXKBl93Pnz4cC6++OJG0ZWioaCZk5en+yvEiVGrT9N33HEHb7zxBq1ateL6668H4JtvvuHaa69lzZo1vPbaa5X2WbFiBS+++CJjx47lpptuYs2aNcyaNYsNGzawceNGwsLqPrXvdOB9+Kuu4DnR7b2sXr2aZ599lqeeeuqke9ge7dyRkZGcc845XH755UydOpXCwkL++9//8sorr5zUeUIY3HPPPVx88cUMHz68rodySpSXl7NmzRr69OlTyYmwMeAVxUuXLiUzM5PmzZvTt2/fOhf+A8d2Y8lP63zR2MFn9KjT8Xhp17MFt755Fg6HA2ehwjnnTSAyuup02s0rszh00N8X9eDew8YPHg00nT6jOnPX9MsoyC9i5aJttO3Qgn89/2PQMYoOlSGlJH+PUXcclFLsJciMyhTKwjTR8vYgVgSKAh4h0RWB7hXEAqNtkTcD14MRHMYQg8F/BhIhVAQehACnpuBRK7B4wtEsAuELVqvoUkVIHYmCO8C4WpdQICMpl6Cayws1Kx6zljkoK1sPEOvSiHQLKc2eymbk23cD/Ddlwvl9GX9+X777fBm7sg6wZeMe370BSO3R+qh/32XFFb7osJfYuJqLEEsp+e2bFezctJu+I7swcNyJf55FRUXRp08fUlNT2bJlC/Pnzyc5OZnOnTufcObVnwGvKF6yZAm5ubnExMQ0OjHsJTU1lYKCAtatW0f//v0bTBeEqpg+fTqdO3dm7ty5nHnmmXU9nBAh6gW1JogXLlzIG2+8QdeuXVm2bJmv6fMTTzzBkCFDeP3117n44osZOXJk0H4//fQTX3zxRVA96tVXX82nn37KrFmzuOyyy6p1/ldffZXDhw9Xe7yTJ0+mT58+1d6+NsnNzWXevHkkJibSs2fPGt/ei9Pp5Oqrr6ZPnz78/e9/r5WxzpgxgzvvvJNPP/2UqKgonnnmGW677baTOlcIv5HW1q1b63oop4Su66xcuZJWrVrRqlWruh5OrREeHo7VasXhcBAbG1vnYhhgwmVDUK0q65dmktq7LedcVbcTK0VFRWzevJnCwkJSu3aiffv2x53Ya9na3walUltdKdmUtoMX7/uCnVv20nd4J/5yw2i2btjNrE+W+DYbOKoLd136NlnZhRBmB6cLXG5k+FGEj/kALN0SGaYgVYLqhXUAFazC0L4IjB8Cc579QwwYsxEVFsL/t1GsheNRK7BqEThREOgoEiQCKQUSFQ3w6Ap+jSloFV5MgR6FxUyTdmDDZvHg8tjwZkuDUS/s1bqKNxgujdbDAFi84W1JoJW13W7hXy/O5scv0xCKQNdBqKqZCSTo2qtN1fcOSEqJp9uAdkZ/YiA5NYFuA1KOuv2J8uWbc/l4+o+oqsLM9xfw6PvXM/zs3id1rIiICPr160fHjh3JyMhg7ty5dOzYkQ4dOgRlKoQw+tza7XZKS0uJiYmpF+9xtYG3nnjBggXk5uaeUDZgfcNrsHXbbbexfv360GRPLdMQ2i6FqEVB/PHHHwOGAPaKYTCiJo8//jhXXnklM2bMqCSIR40aVcmc6brrruPTTz9lxYoVJySIj2dqFUhKSkq9EMRut5urrroKp9PJ9OnTjzvTeqLbB/LYY4+RmZnJqlWrTmpGtzrnjo+P5z//+c8JHztEZbxGWk8//TQtW7as6+GcEhkZGWiaRo8e9SM6WRt4a4abNWtGu3btWL58OUKIOndXFEJwxsWDOOPiQXU6jrKyMrZs2cLevXtJSUmhf//+2Gy2au2b0jmR25/9C5++8jO2MCudurViyc/rfevdOmxZm4uuS5b/tpkv31vATQ+dR7MWMaQv3EKfQR0oL3fye4ARlzU6grMn9uC7hZnGgqMEgKwVGs5I1e/ILAJ6EnvTqM2fLULBbbhVGbbQppbSPKDYvKeRprGW3/Eqt7wZbrUciyccHYEmFV9vYx1w6yo64NFVQyCbYrnEY0eXAqcpiHUpfDXECPxGWd4SZmEMSxWGuZbfgAyj3ZIkKGo+fHRnPn31V+N8mkRRFVokxeJwuOk1oB1X3TL2qK/Zl2//5hPDqb3a8OynN2EPq97rXR3++M7oe6xpOooiWPrzupMWxF5iYmIYPHgwBQUFbN68maysLFJTU0lJSWmUUdATxVszHBYWRr9+/Vi2bBmqqlbpPNsY8F5neno6cXFxDbrO/JZbbuGDDz4IGWyFCGFSa4J4zZo1AIwZM6bSurFmA+i1a9dWWte/f/9Ky1q3bg1wQhHf2qg5fuKJJyotu+uuu2rMZEHXdaZNm8bChQu58cYbueqqq2p0+0CWLVvGP//5T5544omTEiWncu4QJ8crr7xCeHg4t9xyS10P5ZTIz88nKyuL0aNHN9qHSqfTyZIlS4iJifGlSR/LffrPhMfjYevWrezcuZPWrVszbty4ajv76rruaw13zpXDOOdKw2G6YF8RqxZuwVFm9gsVpoEWhpPxuuVGG6yLrx/FBVcN49k7/8Pa5TuCjLPcLg/T7jmHdbv+Q3b2wconlxIpBEIjqIWRz1jLm2rsLT32CmQTgYqUGghwlluxWI1aRK8YNuPPpna24zYjxKCiSX9vYU0quHUFDypuafQkRhjmWC6pUi5tmJ7SFOthKEJHF0qQsBWB/+yE71LwDUiXQfMB3iB3VHQ4TZtHc/BACUijvdJfrhnBuZcce3Kl+FAZH7/0k+/3bet3sX3jHvoM63TM/U6ENh1bkpu5zzRlg1bta859v1mzZowYMYL8/HyfMO7Zs2eDn5g8Faoy0PKmTyuKUsl9urHQokULnxvvqFGjGqyPh9dg66yzzuLKK6+kTZujZ3eECPFnoNb+JRcXF6MoCs2bN6+0rmXLlgghKC4urrSuqhk37xuOpmmV1p1OnnzyyUrLpk2bViOCWNd1rrvuOj7//HOmTp3Ku+++W6PbB+LxeLjmmmvo1avXSc0Mnsq5Q5wcubm5PPPMM/z6668N9gMYwOFwsHr1anr16kV0dHRdD6dW8PYZjo6Opl+/fr4UwuO1ZGrsSCnJy8tj48aNREREMGrUqKDsoePx1bvz+eTln7FYVW57+mLGTxngW9csIZYb/28ybzz4P2OB5gHV5hN7W9bl8vP/0jn7kkE8fP2HbFplZg8F9j6SkooyF60Tm1QtiDHcl4VihE8lAkUYflpSmHnSdkM0o4G04C/BVY7oJ+y0IKUHIQzjQa/nVaD3lcfibb0kkCh4THNoKQVuKXBjwaUpSOlESjs6YLd6cEgrbmlEXp3SgsdvL41XrcsjBLEUZhq1F2+atNdoS0osFoW83AJ2bM1HYpiK2cNtnHFBn6rvlZR89tpc5s1cSfOkJpXqsz1uT5X7nSx/e/Ziyksd7Ni4mwFju3JxDfc3FkKQkJBAy5Ytyc7OZtWqVTRr1oyePXv+6Vo1Hc1NOjo6mmHDhrFkyRKEEKSmptbxSGuHzp07U1BQwMaNG+tFZuHJMmLECC666CLuuecevvrqq7oeTqNFR6CfZpOrk+lD/Gen1p6sY2Ji0HWdAwcOVOqTun//fqSUtZpuUhs1xLXVmF3Xda699lo++eQTLr/8cmbMmHHMOpwT3f5ISktLycw00gKPlqI4dOhQAGbOnMnkyZNr7NwhTo7777+/wRtpSSlZv3498fHxjbbdg6ZppKWlERERUaWb9J9VFJeUlLBhwwaKi4vp3r07rVu3PiFTmqyMPP493TDF8rg1Xv77fwmPDmPh7HVEN4lg6h0T6NgjIMLh0cAmzTxgg/+89RtnXzKIzE17gg9uvq9PuXYED057n727CiEhulI7IaPXsCEQlQodd5QKqvClSSsCNDNKrJSBFoYhRHUJKoawNFscISxG/a0Q3kN6Wx37FLFLrSDMFWcOURhO01LgAXSpUq5b0HQrupmjrQOKCoVaBOG6IAo45InCYvHg8agBFxFwHjM92hfRNlcrniM+64RA0yWP3vkZHt1wBNOlpMLp4aeZaxh/Ti9iYoMN0L799yI+f2seAPvzDhMWHY6jpAIAq93Cy/d/wVV3n8XZlw898uU+KZo0i+aZz2o/e0YIQbt27UhKSiIjI4P58+fTqVMnOnbs2GgzXgI5XmulmJgYnyi22WykpKTUzUBrEUVRfP2Jk5KSKj3jNiRefPFFOnfuzG+//cb48TU7iRQiREOi1gRx3759WbNmDQsWLOCSSy4JWrdgwQKAWp1Zayg1xIEC89JLL+XTTz895ofqiW5fFXa73ef6fSQLFy4kMzOTCy64gObNmwd9mNXEuUOcOOnp6fz4449s3769rodySuzZs4eCggLGjRtX10OpFbxGYcAxWys1JlGcm7mPfz0xk9Kicqb8dSyjL+gXtN6bHp2VlUVycjIDBw48KVOiQEdpAF2TPHPrJ6aWlSz5dQP/Wfp/XHn3WXzx+hwsNpXw+GgOF/rb5pSVOrhp8ms43ZqvdzBAXPNopn90PVvW7uLbXYsMPerWwB5gCe3DiAxbKiTu2AB9aYpi73eLMFynfcZavnlHU/VKw/REETLQswpd+js5udUKLFqEr+USCDyoaNJov+TWbbh1r3mKYXBV4Aynic2BW7cRBegoaJol6NQAigOf8BaKMNPB/duoTi34uqVE1yV7cgsN5S8BVQFV4Z1XfuWrz5by9md/pUmc3y1+wY9rgl4zR7nLN0Hhdno4dKCE1x/+iq79UkjpnEhDw26306dPH9q2bcv69evZtWsXPXr0ICEhoa6HVmtUt89wbGwsgwYNYvny5djtdhITG97rezwiIiLo3r07a9euZezYsQ3WbK1ly5Y88sgj/P3vf2fFihWhAEctIBGnPWIrQxHiE6bWBPE111zDv//9b5588knOOussXzS4qKjIl3p8zTXX1NbpG0TfYm/q8SeffMJf/vIXPvvss+OK4RPZ3suOHTtwu90+h8zw8HA++OCDKredNm0amZmZPPTQQwwZMuSUzx3i1JBS8uCDD3LXXXc16IcKh8PBhg0b6NWrV6N0tJRSsm7dOsrKyhgxYsRx09obgyjWNJ2Hr3ibQ/uL0XXJC7d9TOsOLejQ3fB8yMvLY8OGDURERDBy5MgTSo8+km4D2pHQthn7cguMBUIE6DXBoYOlbEjbwdR7zubyOyYgFEH6gi08eeunvmMIq8qurAO+/VEACYcKynj/H79w5gV9/dt6dKT3z9SMFAtdGuLUDOcaEWOjdjcw1zkoM877bOmNyOqYts5gtGMXCGHD6M9EUFqxU63A6gn3HViX4JaGq7RLV3FqFty6gi5V44FLgi4sVOgqYXpADbEi8egBWdPmWIxvASnj3g2E0YLpyPH47Kx95mH+Cz14oIRFv2Vw/sX+NPbW7Zqz3duW6RjZVfm7CxukIPbStGlTRo8eTU5ODqtXr6ZZs2b06tWL8PCqW4Y1VKorhr3Ex8fTv39/Vq1axZAhQ4iPjz9NIz19JCcnk5eXx6ZNmxp06vRtt93Ga6+9xldffVXJ1DZEiD8LJy2IN2zYwLRp06pc16VLFx588EFuv/123njjDXr06MFFF12ElJJvvvmG3bt3c8cddzBq1KiTPX295IMPPmDx4sWAcX+8y7wR8REjRnDDDTf4tn/qqaf4+OOPiYqKIjU1lWeeeabSMQNTuU90ey/jx48nJyeHrKysk05fOtlzhzg15s6dy7p165g5c2ZdD+WkCUyVbqwtlrZs2cKBAwcYOXJktZ2SG7ooLiuuoGBfkX+BhOwte2nTqQXr169n//799OjRgzZt2pxyz86wcBt3PPsXHr7K9CvwCTa/0KooN0SlajEe1IeM68YT71zDC/f+F3z2ihQAAQAASURBVGeFm/JSR6U0aC9r03byyMuX0X9kKisXb0O6NDiyNbYwy2aEYhxGGPFiFIEUpuWVNPOfA4IsKgqaGY4WAe2WdF0EOEsbuKX/Q9kpHFi0cHQdM0qs4NHBI1V0acGpW/DoAl1azNbChrt0mRaOSxpHcekWnxlXUCmxVxR7PcWOiAYr7soCVgT2EK7iPkZG2Tm4r4hvP/wDj1tj8tXD2bI2l327Cs0LrnzM6CYRdOufUml5Q0MIQUpKComJiWzatIn58+fTo0cP2rZte8y//Z8+W8JPny6mactYbn32L7Rs0+w0jrr6nKgY9pKYmEiPHj1IT09nxIgRDdqVuSqEEPTp06fBp06Hh4fzxBNP8OijjzJlypQGG+2ur+iyDmqIT/P5GgMnLYjz8vJ8rZWOZPTo0Tz44IO8/vrr9O3bl3feeYf33nsPgO7du/PUU09x7bXXnuyp6y2LFy+udE+WLFnCkiX+3peBgtgbxS4tLeXZZ5+t8piBqdwnun1NUpfn/rOi6zoPPvggDz300ClF1+qaxp4qvXPnTrKyshg5cuQJR4UasiiObhJBSpdEcjPzAYmqqiR0jGH+/PnExcUxbtw4wsLCaux8XfomE9c8mqLCMkPPCb/GSkxuRu8hHSvtExsXYaTqgqEqVQGK4m8v5DGEaniEjamjnqe0xAF2FYuu4wlwoPYj/dFgj0TaDXGsgxEx9QBhZgTVg/EJe4RblpDGeo9DICIEIiAMa5hmGaetUB0oqChaOLrqRGIIZl1acEuBU1PRpMB7BF2CU7fg1FUkhmCp0KyGydcRl+BNF/fXDweLc1WDFi1jaBYfRW7WQcoOVxjZ1IYltu9AQlGQumT42C4MHdmZW875JwfMSZLfv1/DB/P+jpTw0FXvkr1ln7mbP9I8YHQXopscOfPQcLHb7fTr14/8/HzWrl1LXl4effr0qfJ9Yc3irbzx4JcAZG3ZyxPXvsc78x463UM+Licrhr2kpKT4WtCNGjWq0RmQeVOn16xZw7hx4xqsmJw2bRovvfQSH374ITfffHNdDydEiNOOkKZT1OrVq33pLf369TvefiFC1Bqhv0WDL774gvvvv5/MzMwaFRanE4fDwfz58+nTpw9JSUl1PZwaZ8+ePaxZs4Zhw4bRtGnTkz7OoUOHWLZsGZ07d25QovjwwRK+eGMO5eUV9JnQBoe7jJ49e56waVZ12ZtbwLfvL0AimTRtJLnb9+N0uBkyvhsRUZX/jRQeKOGqMS+gG/nJSIuKVAPCt7okOtJGTHQY+3YVoksJVhWEwB0TZvwcgEfTkdEWUARuVeBsYTF6+ap+V2l3JKCCS5EQY4hf3aaD91CKKaotDqwxRg/i2AiHT2eGW1wIAf3jshm2+1I2J/1MufUwOlDhVqggAo8u2FcRaxhYmxMDuoTE8CLibA7sUmXYrkv5Lekbfl7UHV2zggRbsZn+XQ52B0G9hoVDx14BSElkniPoutElwhNgQ+1NmTYH/cjTU0hp24ybz3kpYBvBqHN7c/ENo2nfNYlFs9fx8cs/G2nvUqIogik3jOH6h86v9uvfkHC5XGzcuJG9e/dWGS3+5l/z+fCZWf75ASGYnftqrfy7OVlOVQx78WYJebNoGlvZjJSSZcuWER4eTt++fY+/Qz3l22+/5dZbb2X79u1ERjaeiaq6ori4mNjYWC6adw3WyJrruV4d3GUuvjnjY4qKihpdZkZtEaqeDxGiHuJyuXj00Ud58sknG6wY9tbVNm/evFGK4QMHDrBmzRoGDhx4SmIY/JHirVu3smPHjhoaYe3TJD6aSTcPo9v4eCJjwhg3bly1UqSzt+7loSve5o7zXmLhD2uOuW0giW2bcevTF3Hb0xfTpkNLhk/sybhJ/aoUwwBNm0cz9rzelVeY7YRUm4V/fX8XmltD6tJXMysBtdxVKZXYErC/optbei/V14/YEMGq/3BwZMdACXgUdE2gayL4NOb3Ck3gUitQPZHoKOgoSCHw6ApuXeD0KOhSmGnTAk1XcOkqFZqNMs14+HLrAUlgZpmwkBjR66D7ARZX5SFKX620KX4V/5cMGOv336ykSfNoI109YJvFv27knkvfISdzH/1GphLXLMq3V+v2zbnor2MrvzaNBJvNRr9+/ejfvz8ZGRmkpaVRUVHhW997WCfj70gxvvqM6NQoxTAYYr9Xr17ExsayfPlyPJ6abblV13hTp/Py8sjPz6/r4Zw0F154IcnJybz22mt1PZRGhS6VOvkKcWKE7liIEPWQDz74AJvNxtVXX13XQzlp9uzZQ2FhIb169arrodQ4RUVFpKen06tXL1q2bFkjx2xootjtdrNq1SrWrFlD9+7dGTRoULUmbzRN55Er32H90kwy1+/ihds+YefmPcfd70gW/riWmyZM57bzXmLTyqyjbjfhIr/Rk5B6UO9hTdeZ9d/lXP43I53fZ8SsKAgpUMrcASm+pig0ha7iqyPGn4KM32RL8ZpOieD1/p9tSCmQUqAFmDpLs4XS7tImuFQHVi3c18dSkwpuKfBICx5dwaMraNL/vdxtw6kruHVDvDg1CxLFL9ohQKUfgTdf2yvkvQJfEcG7BKRZe3fLzzvMo9d9iKZ73cOML12X6LpO2u9b+Pf0H9m6LgekcW/6juhMk2ZRRxlM4yEhIYFx48Zhs9mYP38+u3fvBqBjzzY8999bGTtlIBfdPJ5H3qu680NdUJNi2IsQgn79+mGxWFixYoUva6OxEOg67Xa763o4J4UQghdeeIHp06dTUFBQ18MJEeK0EhLEIULUM0pLS3nqqad47rnnjutWXF9xOBysX7+e3r17N7r0OIfDwfLly+nUqVON91NuKKL48OHD/PHHHzgcDsaOHXtCxlklh8spNJ2pwcgkmPHi7Go9IB8uKOH5m//NjaOe5vnbPyE3M58dm/bw98veoqiwtMp9eg1qz0MvX86AkamcNWUAVrvq/+QT8L9PljD2/D7+HTTdF0E2Ir4B16UIqPD4BaYuTfGLXyi6CBaggIISIES9dtQgdYHUBWUOJVCPIqWgXA/DqTiweCLQpYJHKigoeHQLmlTRAU0KNDNCrEuBW1dxaDacmiFgHLrFqDAONNOC4Ii1ORzFLMy2lHn8aj9wGwWkVxwHTCogBPt3FbB9c16V91/XJYltmrI3pwDd7DGl65L83YVVbt8Y8UaL+/Xrx/r161m7di0ej4c+w1O579WpXPfwBURG1w9X6toQw15UVWXQoEFUVFSwcePGGjtufSE5OZno6OgGfW1jxoxh6NChPP/883U9lEaD9/35dH+FODEa5tN2iBCNmFdffZX27dszadKkuh7KSeFNlW7RokWjS5XWNI0VK1YQHx9Pp06dauUc9dloS0pJVlYWmzdvplOnTqSmpp5wmqeUOs0SYoMcqlfM38yC71Yz7sIBx9gT/nnnp6xZuNUQ01b/x5eu6Xz26q8AaB6dKTeMpnV7v+PrqLN7MepsI1PhYFE56YszzSCmYa71w1fp2OwWXE4PRmPgwEhn0OCx6OBGIoVALZJ4zG4yhlD0R5l9kePA1ks+zOiqJkABj9uOLiuM9si60eLXKa04VQcWLQKPVJDS0LGaruKWoOkuhAjzjVAz2zG5NAuKNESMW1fQdREcpZb+cmbfYAXEhFmp2F+OrUwLvmwBUdHheCrcOB1uU/0fgafyZEZEpB2LzcKEiwYw+tzeVJRUsH75dhRVQdd0Rp7bp/JxGjmJiYnExsayatUqFi5cyIABA+pVfV9timEvVquVwYMHs3DhQmJiYk6680V9RAhB3759mT9/PklJSTWWPXS6ef755xk+fDh33HFHjU/6hghRXwkJ4hAh6hEHDx7kH//4Bz/88EO9qic7EfLy8igsLGx0rtJeYxhd1+nTp0+tvj71URS73W7WrFnDoUOHTrqv6OGCEm6b+CKF+cX+FGSMFNq9OQePu/+ODbvQNVN8HeEC/eNnS3yic+7X6Xy85DGaNo+udIxzpgwwBbE/yvnffy/mrEsH8f0nSw1RKiVSVZCa7j+PmdMshJkWbRVYPeDBXK8BVuND1QWGEHYAVWUFe/W2brRsQqpouiGOpTR6CguhUqE6iHU1QTNNszRU3FIxttUVNG8LKIzAtq4avYoVs37MpatGMbO3ZNO8dSocIZIlJWUuRISK7tBRtIB7KyXderRi9bIdleYHvAi35ptgUIRR6/3/7J1neBzl1Ybvd3ZXvdiyJNuyJKu5997lApiOgUDokBBaQugQAuQD0kghgVBDCBBCSQIhFBPAGHdwky3JtmzJlmxLsmWrS1bXlpn3+zEzu6tiLLmt1tn7uvaytNPO7K6188w55zlPvHITSamemxLnXzOL/rGR7N52gDHTUpm2YFT3OzvDCQsLY86cOezevZt169Yxfvz4kzKa7EQ5HWLYJDw8nGnTprFp0yYiIyMZMKBvjps6HkJDQxk7dizbtm3jrLPO8ssqr0mTJnHppZfy5JNP8sYbb/g6nAABTguBkukAAfoQTz31FHPnzvXbGd0ul4udO3cyZsyYM65Uev/+/VRWVp7yi0WTvlQ+XVdXx5o1a9A0jQULFhyXGAbYuCxPF8MmUqJYFIQimHH22GNuP3XhaIRhRITaKSvplYF1OVXef3lFt/uIjAjpMk7JYXdx208vYsmNc/R9uzRD3JkWzl4iHNzzntzJXzN92wmrt/OUSsfSZQloQhfFUiANoatqRvZYCtqEnSA1DFVaUKUFKRWcml4+jaIbbLlUgUvVTbVUTcGuWmlX9Ytwl7SCKnStak6Mkt3rWgEgBK5go3xbmA/BorPH6v3BZim5+zWXoGrupyL7hzN1wSgOHajjtgue5ZVff4r0cgybec5YvvfQBf+zYthEURRGjx7N9OnTyc/PJycnx6d9p6dTDJvExsYyZswYsrKyaG1tPeXHO50kJycTFhZGYWGhr0M5bn75y1/yz3/+k/z8fF+H4vdoCJ88AvSOgCAOEKCPUFpayp///GeeeuopX4dy3OzZs4ewsDCSkpJ8HcpJpaqqioKCAqZPn97rWcMngq9FsZSSffv2sWHDBlJTU5kxY8a33ujYs62Un133Mj+96kV2bCzqsjyyn9cMUimxBVm47Jb5PPPRvWSMTTxmPHf99mque+B85l86hZ+9ejMhoUef+SmUrhcE27P28+CNf+1S4jtldjr/fmMdG9ftRhWAy1C3QoDTe9yQkdVudXkEoyEULebhBOA0fxR6FlaCvmM6CHfpcKtOw1hLoKoCzRDJrYqDIC0E1XANdRnGWi6pYBEKUipo0oJmiOVWuxVNCqRxMaRqIKTimT0sOx6/czwAQkqPq7QQBAVbsbd1sqAWMHJMAuFBVqwtdv1oikJTQxtZa3a7V/vknQ0U76mgtKiCd5/7ki/+uRHV1c2dg15SVlbGzTffTEJCAsHBwaSkpHDvvfdSX19/XPtbuXIll112GYMGDSI4OJiEhATOPfdcPv/88xOO9duIj49nwYIFtLe3s3btWhobGzssr6ysxGKxcNddd52yGHwhhk1SU1NJSEhg8+bNZ5TztOmqvX//fpqamnwdznGRnp7OD37wAx599FFfhxIgwGnB/2o5AgQ4Q3nyySf5zne+w4QJ3YyJ8QOamprYv38/mZmZPi//O5k0NzezdetWJkyYcMLjlY4HX5VPq6rK9u3bqa6u7tGc5ZamNh69+iXaWu0gIX/rft7c8AQxA6Pd68w6bzxnXTGNlR9sISQsiIdfuomZ54zrcUxBITauu+989++lRZW8/ewyAKw2Cy6nLrZswVYuvHa2e72GumYeuOolyg7Wg8UCTsPW2aJwwXemEBMTzpsvGBlli4IQEikl0qJnV6X351lKgjVoN4SjtVHi8n5pJFjsoAah9xQbrtMC3URL/0G6+3elJvSKa00BIdEQWPXxxjQLO8FqCC6jjFozMsTSaFg2K5ul1MW00xlEmCqxGPe621uCdcGNvn4XQWz+rOF29LI48ThLS3A4VZ754xf6DQav7PKjP7+M1Utz+e+/NlNX5bnoNzPN5mtVtr+KPz7wD1wOF5omyc8u5oE/XNuDd7t79u3bx+zZs6mqqmLJkiWMHDmSrKwsnnvuOZYtW8b69et7VYL7k5/8hKeffprExEQuueQSYmNjqa6uJjs7mzVr1nDBBRccd6w9ISQkhNmzZ7Nnzx6+/vprJk+ezODBgwH45JNP0DSNyy+//JQc25di2GTcuHFs3LiRnJwcpk2bdsZ8d0RHR5OcnExeXh6zZs3yy/P62c9+RkZGBhs2bGD27NnH3iBAt/jC5CpgqtV7AoI4QIA+wL59+/jHP/5BQUGBr0M5Lsz+2pSUFKKjo4+9gZ/gdDrZvHkzQ4cO9WnW+3SL4vb2drKysgCYP39+j8YpVRyopbW53f270+7i4N7KDoLYYlF48E838OOnvostyKrPrT0Brr17MaOnplJ1qJ7J84ZTuqeCirI6ps4fycBEj0r9xQ/f5FBxjW4IZTFEm0tDqhpZG/dTXdUINivCpeoXrkKCywW2YIQQXmW/ptGWxNqi4oqwYHEIXGafsQZY9B5ddx5UBRRQEGhIXcyak5ywIDUVKcClKoBECBuadKIALcKJTdqQriA0RUWVAk1TDIdpvcTavM7WNIFTVWhuD8YapGe1Gw5Hewli/bjiaBliKbC2uXSRbhqOod8YEMbPGOJbEXD3LW/QeKgeoRlPmGOjpHSL5uDQICoO1OK0u9yv4epPcjoI4qpDdXzyxjoALv3BfOIS+n/re/6jH/2Iqqoqnn/++Q6Z0/vvv59nn32Wxx57jFdeeeVb92Hy17/+laeffpqbbrqJV199laCgoA7LT1cZsxCCkSNHEhUVRXZ2ttuw7qOPPmLAgAGnpIWmL4hh0MvHp06dyrp16ygsLGTEiBE+ieNUMGrUKFauXEl5eblfGkwOGjSIu+++m1/84hcsW7bM1+EECHBK6SKI/fWCPMCZw//iZ/Dpp5/myiuvJC0tzdehHBeHDx+mqamJ6dOn+zqUk4aUkq1btxIWFsbo0aN9Hc5pE8X19fVkZWURFxfHhAkTenyhPCQ1jn6xETTWtwAQEhpMyqjuLwJDwk5ef/nE2R6379hB/bpdp7SoUv9B8zLJAmSQVRfDoGdFLQrCHLukepX2qno22UQIgcUpcQr0UmlTVDrxsnA2t0X/pjWEsJ4p9lrPcJpWnQpCaCjCgpQuNKBZc6GiYVNDsYtWXBqosh1N6qOYVNWCEOb4KoFNUbHZJE5N/2qPSG6iKS9GV7iacSOgyxxiCZo+dkkLtiDsLoRXM5VA6KObvIzFNAlHGtogPBilqd3IChv7FsL8h/RRgxmcFOMWw4oiiBvseY/aWuzcv+RP1Nc0AZK1S3N4be1jR/187Nu3j+XLl5OSksKdd97ZYdnPf/5zXn31Vd5++23++Mc/Eh4e3u0+TOx2O4899hjJycndimHQHZF7yurVq1m0aBEPPPAA1157Lb/4xS9Yt24dqqqyaNEi/vznPzNo0CDy8/P51a9+xYoVK7Db7cybN4+XX36Z5ORkEhISCA8PZ/PmzdTV1fHNN99wxRVXdPk/+PXXX/Pss8+yfft2Dh065HZrPuuss3o0LqeviGGT4OBgZsyYwddff01kZKRfisfusNlsjB49mry8POLj4/3SYOvee+8lJSWFnJwcJk+e7Otw/JJAhtg/cP/vjI2NJSwsjOuvv96X8QQIAOhOnMdr3ONvlJeX8/e//50tW7b4OpTjwjTSGj16dK8uIPs6u3fvprW1tU+VgJ9qUXzw4EG2b9/OyJEjSU9P79V5h4QF8/SH9/LeC8vRVI3v3HEW0THdWSyfflKGDWJXdokhRjU9U2z2A3u7VSu6s7RAz+iqAFIiNNFF6CqqaRUNOCUEeZUke40zUjTDg8t0de6sPQyxqtkVNAu6ODaSri4stCt2bGooLqUdTSrYELRqek+xphkGY+j9wqG2jmpXF7YaSAvCCXSaqGRisUuPYZiivw7m6yI16dbQnYVvB3My80dj5aAgK3c9eSlDMwbynVsX8MU/NxEzMIqH/+S5xijZfZjaygb377UVDRTklDJp7vBuotRFJ8DixYtROo1/ioyMZM6cOSxfvpxNmzZx1llndbsPk6+++orq6mruvfdeFEXhs88+Y+fOnYSEhDB9+nRmzZr1rdt3JicnB4DCwkLmzp3L+eefzw9+8AOWLVvGxx9/jN1u54c//CHXXHMNixYt4qabbmLNmjV89tln3HjjjaxZswbQS23nz5/PZ599xs9//vMu/8efeuopt5A/99xziY2NpbKykq1bt/Lll18eUxD3NTFsEhUVxeTJk8nOziYyMpLIyK4O8f5IUlISpaWlFBYW9okbq70lPj6eW265hd/+9re8//77vg4nQIBThlsQJycnU1BQQE3NsUdfBAhwqomNjf2fmX/37LPPcs455zB27LFddvsiZ6KRVnV1Nfv27SMzM7PPifxTIYqllOTn51NSUsK0adOOe35mYlo8Dzzb926q/vyvN3PdnF/pc3Q1Te8jBoRLRQYb76/hoCwVBaHpWWLR6kCGBXn1/XYjJ4XA0g6aMXLJbQ3kAIJBEQKpSl1oquj9xEIgjXSxbrwlwSHQgvX+YM0Qye0uhTbFTrAWiiYb3T3DmlEu7dKEW19rml427Z0ClhJdzAMo3WSITaMt4dnAfRPEvFmgGCXkpgg21pPG6+cWye7XQ1/dYXcRHBKEEIJbHr2EWx69BNCzwk/e/Fdy1u0mOWMgFquCqmruuP7vhj/z0HM3MP+SrtmoPXv2ADB8ePeCediwYSxfvpzCwsJjCmLzBmRISAiTJk1i586dHZZnZmbywQcfEBcX9637MTEFcVZWFps2bWL8eH3u9eOPP05ycjLLly8nNzeXr776yi22HQ4HGRkZrFu3jvb2dndrQnBwMO+99x6DBg1i1KhR1NXVERMTQ2VlJY8//jhz585l5cqVXbLax7p+66ti2GTw4MGkpqaydetWMjMz+1x8x4NpsPX111+TlJTkl0L/wQcfZPjw4RQWFh71/16AAP5Oh/qN5OTk/xkREiBAX6C+vp4///nPfPXVV74O5bg4E4202tvbyc7OZuzYsURFRfk6nG45maJYVVWys7NpbGwkMzPTLy/YTNpb7bz73Jcc3FvFrMVjWfzdGQghCI8K5fp7FvP67z4zHJc1vQS6Uwm1brQl3PN5FaeK5lQhxKaXOZsNuMI02jIcph367GJ3H7ECwgHS0CvCLFl2os8FBrdplTm/FxmE5nIiFImmKaBImhxWWoSDYFcILpsxkklRUA0naSkteJs2N7cJgoK8+ribFTBmEh/NUEvg1SPtLXqN8zRHTJn9w+bTCIgdFI1oavMy1fIcQFEE4ZFde8/fe/ErslbuQmqS4t3lDBufRG1FAzXlRwBQXRrP/eRfZF48qcvflIYGPZt8NJ8C8/kjR450u9ybqqoqQG9XGT16NF9//TUTJ06kuLiYBx98kOXLl3PllVe6M7fHwhTEb775plsMg565TklJYdu2bTz99NMdMs9BQUGMGDGCgwcP0tLS4hbE7e3tfP7555x33nmMGDGCDRs2MGnSJPbu3YuqqgwfPrzbEu9vq6rq62LYZNSoUdTW1rJz506/NZjsjL8bbCUnJ3P11Vfz+9//ntdee83X4fgdgZJp/8D/GhoCBDiDeOmll5g6dSozZ870dSi9RkpJXl4eQ4cOPWOMtKSU5OTkEBsby9ChQ30dzrdyMkSxaRqmaRqZmZndXmT7Ey889m9Wf5KD1CSbV+4iNDyYzIsmATB9wUhe//3nerJX1cCquBO/prA17J71nWkSRRGoRu+wd3ZVE+gO000qzn4KFqFXTQN6ijgIbNKYvmSOPDIrfE2n6e4crjTdtEsPQdDuCqJZOAhTw4hwRhGiBhOshqC4IgmXQQQpQVjQ5yUrEiyKIMihkS77AXBVUAbtSVbdhEuTCDu4NElLm4tmu0pTq0pLu4vmRgdqi4bLZUyBMsLprn/Y82JAdXUjllaXnk3vIKrhzv9bQpT3mC2DmvIj7qprTdVw2l1cfdc5vPjov93rOOxONE1isZy6izrNmC1ttVpZunQpKSkpgO56/NFHHzFixAjWrl3Lxo0bj1k+3dLSQmFhIWlpaZx33nldlpeWlhITE8NVV13V7bLIyMgOzthfffUVzc3NXHbZZaSlpREWFsbWrVvdf2vfeOMNqqurue6661i8eDH9+3+7EZm/iGHwmGytWbOG2NhYhgwZ4uuQTgr+brD18MMPM2nSJJ588kkSE489Ii9AAH8jIIgDBPARLS0tPPfcc7z77ru+DuW4OHz4MI2NjUybNs3XoZw0ioqKaG1t9ZvxHyciitva2ti0aROhoaFMnTrVLw1fOrNj4159LjC6o/XOrP1uQZycMZAxU1L0XmLD+AkAhxOCrCAEU2ak8Z1rZhIbF0njkRZ2ZZeSt6ecLVuKOx7I2NjmkDjNsmJVglXoZdHePcMKCBdIG1ikQHULR71k2oogwRbMYGsQUVYL0RYr/aQgWlqJdNmIJggFwUj7EFoVB004qJcqjdJJhWpHExJVSD3pHeREUTTsmspoOYA9lnramsNQpIJFCoLsYBMKUTYLcRFBRA62EBlsJSLYgqII2u0qLa0u/dGi0tLqpKXJSXODk+qqNhobnB2Fsaafw8SZ6ezctA9VlW6tHxbZvTHWwkunsOrDrSiKQNMki6+aQeZFk/j3n1dRebAWgGvuXozFonTZ1rzxZmaKO2M+369fv26Xe2OuM2nSJLcYNgkLC+Pcc8/l9ddfJysr65iCePv27Wiaxtlnn91lWUlJCfX19Vx++eVd2i+am5vZu3cvc+fO7fD8hx9+SFBQEBdeeCGgu/3Onj2bTZs2sXTpUl544QU+//xzPv30UywWC+eccw6//vWvuzU98icxbBIWFsbEiRPJzc2lX79+xzRI8wdMg62dO3f6pcHWqFGjuOCCC3jmmWd45plnfB2OXxHIEPsH/vU/MkCAM4jXXnuN5ORkzjnnHF+H0mtUVWXXrl1nlJFWbW2t2xDHn87peERxc3MzGzduZMCAAUycOLGLQdGpRkrJvl2HQErSxyaetJsPo6em8s3n29E0iapqjJzUMcv/qzd+wEdvfkNdZQPZWfs5XFavJ24dLqQQ7M0/xC8ffp/zlkzCZrUQHhnCDT/I7CiIpXQLXglukynRDjIcrBJcpmY0e4aNn62KICk4iCEhwSSGBpEYHMxgazBtUqPcZeeIy0mDprLP0UprkIMm4WRskJVENZpPInagaQKXpnCkLQxNCtqcIZ4ssxREBLdiVSQ7RS2jXQPYJmqoq43WTbUcENwEQgVF1c9DceibhtS7iBAKESFWIiJshIdZCA+zEhFmJWFwGNEjbAwYEIzTqVFV2U51ZRtVlW1UlTbR4lCJiA5Dqp5xSwAv//ITpCpZePFENE2jtrKR6P7hTFkwij98eA/b1xeSNmYIM87WvRNeXv4Tdmwoon9cFCM6vW8m5kiewsLCbpcXFRUBR+8x7m5fRxPPZta1ra3tmPsyy6WnTJnSZVl2dvZRl+Xm5iKl7CBkVVXl008/ZdGiRR0qb2JiYpg3bx4bN27kscce4+233+abb77h1Vdf5d///jdbtmzh0KFDBAd7bkT4oxg2SUhIoKamhq1btzJv3rzT/jfqVGAabBUVFTFq1Chfh9NrHnnkERYuXMhjjz3Wq1nfAQL4AwFBHCCAD3A4HPzhD3/g2Wef9YtMZGf2799PUFDQGWOk5XA42Lp1K6NGjepRdqmv0RtRXF9fz6ZNm0hOTmb06NGn/fMnpeSZ+99lxQe6qdH8Sybz8Is39jgOR7uTPz74Tzat2ElSejyPvvw9EobqvZN3/+YqovqHU1pUwZxzx7Pw0o4iJCQ0iGt+uIinH36Pw8U1YNMFggSwKjQ26OLn439t1sWdBlu+KeKBRy7kn29toLGpjabGNn1Wr7vfWM8MW116ObI5k9dmEQwODmJI/2ASw4MZEhZEfEgQbapGmd3OAXsbX7paOOhsp14zrLhMb6ngdkJsGkJK4oSNYcSjahY0qc8f1jQF1Wh/NucMS9kxeWuemLvUWwOhdVzsXuaEdlWjvdVOTb3D+81C2DUU9FONHRBEfHwo8fGhTJ4WS+wFibicGtXlrcw4P4nqsmZqDrXQUNtOU10rT//kPZLT4/jTA/9g784ywiJC+PmbtzF2Rjqjp6Z2iCUsIoSZi8d963u/cOFCAJYvX46maR1EUlNTE+vXrycsLKxHLShnnXUWQgjy8/O77Atwm2ylpqZ2t3kHTEE8derULstMQdzdstzcXIAOgnjdunXU1tZy2WWXdVk/MjLSLYq3b9/OwoULOfvss5k3bx7ffPMNlZWVbh8YfxbDJmPGjOHrr78mPz/fb00nvRFCMGbMGDZs2EBqamqP5rv3JaZNm8bMmTN54YUXePLJJ30dToAAJ5WAIA4QwAe8++67hIWFdXvR09dxOp0UFRUxdepUvxTznTH7hvv16+e3c6ChZ6K4qqqKLVu2uMcq+YIDhRVuMQywdmkOV/zwLDLG9qwv7ZM31/H1Z7lICcW7y3nu4ff43b/0mbThkSHc+csrjrqtvd3Bv15ayaql2/QnTAHbeYQQ6KXXEnZtP8CuvAOoITZjXaOlVpN6H3KrioxWsAoY3C+EEYPCSY8PJT4iiBanyqE2B4da7eyqb+FQu51a4UJaAQFqlDEHyWtMk3AJaFOQUXrvbhMq4VoQTk0XbC5N4FL1wVCggrQawhicTgtWq5GWRp9NjKZ0cpL2+tcUyN5CWcHTM6zp5lhIUFU9O1xV0Y6QdQBYHRox8SHEx4cQPziMCfMGM2BQGC6XRmVJE6UF9Xz6ztfszz8E6A7TLz72Pq+seKRH73Vn0tPTWbx4McuXL+ell17irrvuci974oknaGlp4fbbb+9QYrtv3z6cTifp6ekdKj+GDh3KxRdfzNKlS3nuuee477773MuWL1/Ol19+Sb9+/brtCe5MTk4OQUFB3Yq2b8sQm0LaWxB/9NFHKIrCkiVLOqybm5tLVFQU6enpzJ07l82bN7Nx40bi4uLYuXMnycnJ7t7OM0EMA1gsFqZOncratWuJjY1l0KBBvg7phImJiSE+Pp49e/b4pWnYo48+yhVXXMGDDz5IRETfGKvX19H/1J7mG8+n9WhnBgFBHCDAaUZVVX73u9/x8MMP++WFSlFREdHR0T0eR9LX2b9/P42NjSxYsMDvBf63ieLy8nKys7OZMGGCTzP7Qun6GivdPHc0qg8fQRjzgjVVo/JQXbfraZrGqo+yObi3kmkLRzF2ejq/v/ddNnyZB8H6OCCpaWAxvga7pFdxG2FJiS6GwcuRGqw2hdFJ4aSMjCJ9cDgIKKxqYU3hEQ462mm0qzjD8JRXG2JTWLztmun4swqIYKTWihRwRHMRhg1NVdBAd6BGQTNHJoExPkm4TaE9mteYgaQJvbJaGlliI3MspHstPQXc+aJNCCSa516B99xmKdGQ1FS0UXuwmd0ALhWLgP7xISSmRTFiykDikkK4fMhUSnbUUJJXQ1uLgxPh5ZdfZvbs2dx9992sXLmSUaNGsXnzZlavXs3w4cP59a9/3WH9s846i9LSUoqLi7v0Cr/00kvk5uZy//3389lnnzFp0iSKi4v5+OOPsVgsvPbaa8c0DLTb7eTn5zN+/PhuTemys7MZOnRotyWmOTk5hIaGustnpZR8/PHHzJo1q8vosxdeeIE333yTmTNnMnXqVKKiokhISMDhcGCz2XjjjTdQFOWMEcMmERERTJgwgZycHBYuXEhoaKivQzphRo0axZo1a0hPT/c7Ublw4UKGDRvGq6++yv333+/rcAIEOGkEBHGAAKeZjz/+mJaWFq6/vu/Naz0WbW1t7N+/nzlz5vi9eARobGykoKCAWbNm+b3Dskl3ovjw4cPk5OQwZcoUBg8e7NP4kjIGct61s1j2Dz2+c747ndRRPXddnXvhRP77zjdubXbud2d0u97bzyzjXy9+hWJR+Pcrq/jNuz9k88pd+kKnC2mzgjAcpIXQM77ewlxAVHSou4zaFIIRkTZS0yJJSY8kMTmcxgYnhYea+c/6ckoa2pE2PYXsDNPXFypg0fcnNd13y2U6TUs87tMmLomwKGiaQFGgQVMRCEK0YJpwokqBpglDv1vweHQJNNUCaHpm2AzZNPlq8xLD3iOYPFXeXRDGSCgpJYoQ+pgqc8eamdL2jJEKC7HS3uqktryN2AExXHrlhbQ1tfDCv//BoPQoLl44ieCQILZv387AgQOJi4vrtWBLT09n69atPP744yxbtozPP/+cwYMHc8899/DEE08c03HZm8TERLKzs/nFL37B0qVLWbduHVFRUVx88cU88sgjTJ8+/Zj72LlzJ06ns9sMcGlpKbW1tcyfP7/LMrvdTkFBAVOmTHG/Blu3buXgwYPce++9XdZfsmQJTqeTrKws3nzzTdrb20lMTOShhx7irbfeYu7cuWecGDZJTEykurqa3Nxcvxxb1JnIyEiSkpLYvXt3t6X0fRkhBI888gh33nknd955Z4ee9QDdEzDV8g+ElN3dFg8QIMCpQErJtGnTuP7667u96OnrbN++Hbvd3qMLxb6Opml8/fXXxMbGMmbMGF+Hc9Kpr69n48aNDBo0iMOHDzN16tQ+U3IopaRsXxWaJkkeNrDHF7hOh4v/u+kvbN+4F4CzLp/KA3+8ttvtb5rzC6oO1QP6Rdysc8eyO/cAdVWNegxWK9isYFGQZtm0WS4MoEnd6dgi6B8XTMqYGFLTo4iJDaH8cCvFexvZd6CF+hYXjjCBDLWgAa5QAIErHFAELgtI45rRPZkpHFDAGePqKogbwCIsyIRWhAIhwe08JibyjmU35aIVl6rQ0mIFrGguBaRAagJcCrYQO6FRDkJQ+JmczM/JpTU3CjSBpRlsdl0g6yZfEqtdgioJaZRdksNICSpYnLoIVlx6k7LQdDEsXJohrCXCqRE/OJqakpoOu3j6ndsYOzmF+upGdm3Zz6ChA4iKC6aiooLKykrsdjtxcXEMGjSIgQMH+l1P5cnm0Ucf5Te/+Q379+/vUe8y6H/HzDniFouF4ODgM0oMmzidTlatWsWIESO6ZPr9kba2NlauXMncuXP9zrdC0zTGjRvHfffdxy233OLrcPosjY2NREdHs+izO7CGn94bB64WO6sufIWGhgaioqJO67H9Ff+37QsQwI9YuXIlJSUl3Hrrrb4Opdc0Nzdz4MABv3TH7I69e/ficrkYOXKkr0M5JfTv35+MjAwOHjxIYmJinxHDoAvUpIyBDB0+qFfZns0rd7nFMMDqT3Jw2J3drjskNc5T3SwlG77MY/a5pmmTANNESZNeZdDG+CRVEhpuZfysOK6+YyRLbhhO/35B5Gyt4fVXdvPR+8Vsy66hvq5d31W7vrmCPmJJSGMIsab/biphs0xZaPoyXN0EHqIvk5qC1AQuVaEJJ6FqCC7VYpRMC6RUvGyu9WNoLiuatKBKwyzMqYBLP55weZlqec0MFma2uvPbIAzDMDptIwRCEWaXMhiCuaWpHe/EM8D+/MMA9I+LYu4FE8kYk0R8fDzjx4/n7LPPJjMzk379+lFaWsry5cvZtGkT5eXl7hnB/2t89NFHTJgwocdiGPS5vePHj8dut9Pc3MyECRPOODEM+tiiSZMmsWvXLlpbW30dzgkTGhpKamoq+fn5vg6l1yiKwsMPP8zvfve7/9n/qwHOPAKCOECA08hzzz3HHXfc4ZdzFQsKCkhKSiIyMtLXoZwwjY2NFBYWMmnSpDPy4hH0OdGFhYWMHTuWw4cPs2/fPl+HdEI0HWll6d/WdXhOarLb1l+Ae393dYf3VhECq81C8jDjxoCqeQlho7xZQHJGFOddlcaN948jMTWKjSsP88Zzu1j+8QEK8+uxtxkq1hCMUggs4BaPwhCZVoe+X4vZ1Gv28JqOz0YZcxdsIFSBdIHUBKpmoVG6CJdBqJqCqilIBFIDU8UK9G2QeJVTexm5mFrWPL6+EEUzqsbdPcKdHt4GW177UawK0qWB09NfnJAUY2ynz3iWAmYuGt39m4N+UyQqKooRI0aQmZnJOeecQ//+/cnLy2P58uXk5+fT0tJy1O3PRAoKCti2bVuvtnE6nWzevJl+/foxcOBANm/ejN1uPzUB+pj4+HiGDBnCtm3bOBOKG4cNG8aRI0eorq72dSi95pprrqGlpYUvvvjC16H0ecyS6dP9CNA7AoI4QIDTxP79+1m+fDl33HGHr0PpNfX19VRWVrpnd/ozmqaRm5tLamoqMTExvg7nlFBeXk5OTg5Tp04lPT2dWbNmsWfPHr8Wxc/95J/s3Lyvg/nV9fedR0ho973f8UP6M3LyUBSL/jWnaZIhKXGcc+V0XfxqGtgd4HQxblIiUzIHcf09Y1h46VDqa9v55/M7+fTdvewrOIKmakbPb0c3aot7CDEIpyGIVX2Z4ja9EnqG1hx7JAGHsVkbdGnedbflKkgNVKegSbqI0IJwqQqqJpCqAM1QrWaWWNOztlIKI4uMfp6GCDb8s3Sk0DPUptiVsntXUndGGfd5SylRXRoEW5FW02gMWo60eLL9QoCq8ewj/6a9tWcmWqGhoYwYMYJzzjmHSZMm0dzczMqVK9mwYQPl5eVnhAA62Xj3DM+YMYMpU6YQERHBxo0bcThOzLzMGyklR2qacDnVk7bP42XMmDE0NzdTWlrq61BOmKCgIDIyMsjPz/e7z7fNZuOOO+7gxRdf9HUoAQKcFAKCOECA08TLL7/MkiVL3KMx/ImCggJSU1PPCIfPM71Uurq6muzsbCZPnuwukzaNtvxZFBdtP4BUNVA1FCmZf9EEMi+cyGfvrGfnlv3dbvPgH68jY2wikf3DuOiGOZx/zUwuvHYW4VF6r2rsoDAWXJnOrIviSRgazjdflvHWn3ayedVhGhuden8seARhp4tW73vwiktfZpHo2WIjCyzAcIHGk2F16suCZCdfS+/dtwFSQaoKDZqLCGxIKZCaoqd1pTBmEBuiWAVN7Rii5nVct7mWaayl6L3ASr3aYTcdzlDDPX7JfcKKJwONxeLOJttbHZ7npW64tX3TPj7++zfdvjdHQwjBwIEDmT59OosXLyY2NpYdO3awYsUK9wilAN2PVlIUhSlTphAWFsbGjRtxubqrye8dTUdauefCP3DNxMe4bvLP2LPNt0LUZrMxceLEM6Z0Oi0tjfb2dsrLy30dSq+57bbbWLVqFYWFhb4OpU8TyBD7BwFBHCDAaaC1tZXXX3+dH//4x74OpddUVVVx5MgRhg0b5utQThizVHry5MlnZKn0kSNHyMrKYvz48SQkdHRu9ndRPHnBKL13VRFoqkbqqCH88Lzf8+LPPuChK1/g839s6LC+lJJ3n1tGYW4pLfWtDB02CIvVQkhYEOffOJlL7hzPpfdMQAM++PMuPn2riOLdRwwtZ2SCDdMoNDOD6iWKzXWcKlIBRcWYeSQ9pcmmGPXWcBpYuyhPA+9ZwU7z+AoNmkokNlSXgqYqoBpi2BS4ThBSIGwSTROoqr4jTSp65skrHne7sAThkoggr6y39zWUlFiMILteKMgO68+aP4Il183uuIqmZ9UrD9Xx1I/e5P7L/sSX723q5qSPTkhICMOHD+ecc85h9OjRHDp0iC+//JIdO3bQ3Nzcq32dSXybm7Qpiq1WK1lZWSfc4/nRX1ezb2cZAE0Nrbz82L9PaH8nA7N0Ojc31+8yq52xWq2MGDGC/Px8v+vHHTRoEFdccQUvv/yyr0MJEOCECQjiAAFOA++++y7JycnMmzfP16H0Cikl+fn5DBs2zO/HEmmaRk5ODqmpqb0azeIvNDc3s2nTJkaMGEFycnK36/izKP7hL77DdfedR+bFk/jJCzdSdagezcv06cNXV3dYf9v6Ir56PwvQy6X//Ph/2L+vhFWrVjFkdBh15e2888strPuomPqqNo+BlKkYVQ2casc+Y8VY6FU2rZhiVzHNqYQnC2uURAvVKJn2NnNW8WSOvRHgxAXN6GXRqoUGl4sobLrRliqMTDGelK7RPyyRxsglz9glMAS6Kj3HNhyksQg0m9exvWNRDbOxoyQaJJCY2J8gRWHzV/l8+NZ6rDaL/jq6VPdmBZv3sf6LHRRkl/Cnh/5F7je9zyYpisKQIUPIzMxkzpw5OJ1OVq9eTXZ29v9cn3FPRitZLBamT5+Ow+E4YdHY2tTuKZfXJC1N7ce9r5PJmDFjaGlpoaSkxNehnDDm3+sDBw74OJLe8+Mf/5i//e1v/9M3qI5FIEPsHwQEcYAApxgpJS+++CI//vGP/W5+YmVlJe3t7b1yPe2rFBUVoWnaGVkq3d7ezsaNG0lMTCQjI+Nb1/VXURwUbOPSHyxg1rnj6RcbSWT/cKRXRuVQSQ3/+atHFNu9elcThvfj0vsnk70lm2V/y+XLv+zG4gzD3qaC0+UpjTYcptMzBiIcqj5iyFgmhWfmrjcWwD1TWPUy1gKsLvRMa+fNTJF8tExxKFg1qzsZ3aiqRAqrLoY1IzvsMh6qPhpKGKOXNNWTIZaaoj/vLpmWxsPLcbrT7GV9QwkW4V7X/Zz3ioqgYm81zlYHUpM01LXoPaYC3cFbCIKCbRworNB7sA2KdpzYRX///v2ZMmUKZ511FkIIVq1axY4dO2hv7xtC7VTSmznDNpuNmTNnUl9fz65du477mOddO4ugYE9p/3fvPPu493UyMUun8/Pz/b50WlEURo0aRWFhod9liWfOnMmwYcN4++23fR1KgAAnhPXYqwQIEOBE+Oabbzhw4ADXXXedr0PpFVJKCgsLSU9Px2r17z8VLS0tFBUVMXv27DOuVNrpdLJp0yZiYmJ6PE/ZFMUbN24EID09/VSG2Gva2xy8+dtPKdp+gCkLRnHVXYtpbWrnrguepvJgHQDnXzeb1JEJ7C847N7utV8vZeGSycTERzMpczgTMlNJHBtF/NBISrbV89+Xt+NoV1EsCmFGH7FAIJ0qhFiMfl9J8e5yFEXoLtaqUW+savrcYnC7UnsCViHUiuIAzWroY02iGKZWevrWK9tqCmiJngmO7vQCBIHSYswYBhpUlShhBdNBWhWYg4/cglUD2i3IEE9s0gWKUVqtCI/hl3s7KcEpofO+vH8213M/L92nrtpd3d9VN87P3mqns+IfN/Pbb9j0lLCwMCZPnkxGRgYFBQWsWLGC9PR0MjIysNlsx96Bn9EbMWwSEhLCrFmz+PrrrwkJCTnmzbLuSBmZwKtrHmNX1n4S0+PJGJd0POGfEszS6R07djBz5kxfh3NCJCQkUFBQQFlZ2VErfPoiQgh+/OMf8/TTT3PHHXf43U3/AAFMAhniAAFOMX/+85/5/ve/T1hYmK9D6RW1tbU0NzeTkpLi61BOCCkleXl5JCUlnXGu0qqqkpWVRXBwMJMmTerVxUhfzhS//quP+fRv68jfWszbf/ycpW+sZdPyPLcYBvjiHxv43k8u7LJte6uD5uZm8nbuYM5VaYwYl0pG0hjsDRacdj37oqka7c0OomMjDKXY8XWTEv7vhesYkhJLfGwEo8YlEhfjNSrNe30psdk1lCYnwqW5M8ruZLKKp4zaC+EEJNgaRGfNaKSd0bfR9AyxTSiESIteHq12ep+NjK90gOYSaC5TECsoUhflmqvr+khhOGWbQXmtoxrZYU26zbTcfdVAsM2iJ5cV3FcSiiJQLAqKlKCq4HR2eK3iEvoxanIKJ5OoqChmzJjBrFmzqKmpcZtvqarvHZFPFscjhk3Cw8OZOXMme/bsOe6S3LiE/iy4dEqfEsMmo0ePpq6ujoqKCl+HckIIIRg2bBhFRUV+1xd91VVXUV5eztdff+3rUPokUgqfPAL0joAgDhDgFFJdXc2HH37I7bff7utQek1RURGpqal+n22pqKigrq6OUaNG+TqUk4qUkpycHFwuF9OmTUNRev/nvK+K4oKtxe7+YCEEe7aVEhYZ0mGdoGAb42akM3LSUPdzmZdMoLrhMKtXr8ZisXD22Wczb9FsBg6JZdGlU/EeLlRaUktDfasxf1gQP9iTpr3oqul8s3o3R9ocDM6I5/+evop/LnuQ714z0zNnF3Pikb69FBJh6ShuhZQoxjgmutFnQoIVCx126JWwNccj2VWJXWpEazZwCYSquMWy21hLAxkswGnRy6gBNAVVqiB192vhfkgUVf+322tvDRQXXmZbXqZbxs+ONgfS4pVZVuCa2xfwx7duRbbboZPDsaIIJsw6dcZ8AwYMYO7cuUyaNIkDBw6wcuVKDhw44HfiojMnIoZN+vXrx/Tp09mxYweVlZWnIErfERQUxOjRo8nLy/P7myCJiYm4XC6/c5wODQ3le9/7Hn/5y198HUqAAMeNf9dBBgjQx3nzzTeZNWuW383vPXLkCLW1tUyePNnXoZwQqqqyc+dORo8e7femYJ3Zs2cPR44cITMz84RK2k9G+XT14XqefeAfHNpfxcLLpnLjTy48LoFuMnHuCPbnH0ZKidQk42ZmMHPxODIvnsS6T3Ox2BTu/+O1hIQF87t//Zita3fR0FpLm6uBb5blojjCOf+88ezLP8RjN71KS2M7g5Ji+L+/fJ+//nIpFQdraWm2ew4oJVfeOIeElFhcLo1P/rWZ7M36DYLtW0u47TsvEh1kISKhv0fvGmXBEl1YaiEWY/6u7NCXK1yGRjbHHhmeVxbpbjnuiLeg1tD3JaFRdRGFlUrN5TbEcq+vgWqagknhEcRSuF2vZYd9C/3mgBCIzunpLkF5K3TjdykRDhWsFqTThQDCwoOYPDsDhGDehRP5+rNtCCGwBVsZnDSAEROTue3xy7o54ZOHEIJBgwYxcOBAysrKKCgoYO/evYwePZqBAwf6XTnnyRDDJnFxcUycOJGtW7eSmZlJZGTkSYzUtwwdOpTS0lKKior82iPCYrGQnp5OUVERgwcP9qvP6+23386ECRP405/+RFxcnK/D6VNoCLSjuROewmMG6B0BQRwgwClC0zReffVVfvWrX/k6lF5TVFTE0KFDCQ4O9nUoJ0RRURFBQUEMHTr02Cv7EYcPH2bfvn3MmzfvpLxHJyqKf3/XW+RvLUZTNd578SsSUuNYfNXx9/Td9PBFhEYEs2/nISbOHc4F18/BYXfSeETP6NqCbET2C0dKSVV1BQ3qYWprm1n57i4q9jeAgPxNBygpqsTl0muVqw4fYfvG/VQcrNUPYmYOFQUEvP38VzTWt0KIDentGA00N7fT7FCRDe0QYvUIXlOTOvVMsBYEONG/WQ3BbNXAhcQqBK5O1dHC1Joaer2W19glVaq6y7RxnEZNJUqxgdpuPO9lWS2FLp4dIK1e+1HNHmLd2EuYMbtNxLq5bBJGd7L0DB/2jllpdaJoXo7VxmvU2uLggRteBQlzzh7N7U9cyuYV+QxJjeXKO85iYOLR2xVam9tZ+Z8tKIrCwsumEBYRctR1e4IQgqSkJBISEigpKSE3N5eoqCgmTpxIeHj4sXfQBziZYtgkMTGRxsZGNm/eTGZm5hlzk1AIwfjx41m/fj1JSUl+8x53R0pKCoWFhdTU1PiVsBwxYgSzZs3izTff5KGHHvJ1OAEC9JpAyXSAAKeIVatW0dDQwGWXndqsyMmmubmZioqKPme01FtaWlrYu3cv48eP96s77ceioaGBnJwcJk+eTFRU1Enb74mUTx8o8jgJKxaFg0UnVpZpC7Jy3X3n8/jrt3DJ9zMRQrD8vc1sW6+P7Glvc/DyE++TlZXFzp07aTio8e4vN+hiGN0oa++uMt312Ktk1mF3YrEYX3tmf6shfJsa2sCiePpkvT8zQoBVMYcZdR1FJMDWoh/LCu6srBvDmEpqeMY6KebzeHqCvTK4aiierLKZIVYsoHn6f90PU7tKRe9NNquVXR4x647H2J+lszGYsUwx4zf/9SqXttksjBw5WL8f4OVY7ca4UbBhRT4fvr6OHRuL+OKfm7jv0j/R0thGdzjsTr43++e8/LMPePHR9/nenF/w1QdZqK4TL381M25nn302ERERrF69muLi4j5fRn0qxLDJqFGjiIyMZOvWrX7naPxt9O/fn8TERPLy8nwdyglhtVpJS0ujsLD348l8zR133MGrr756Rn2uAvzvEBDEAQKcIv7yl79w8803+91d+KKiIoYMGeJ3JmCdycvLIzEx8YyaOWy329m8eTPDhw9n8ODBJ33/xyuKZ583HiF0MaxpGtPOGn3SY2ttaneLs4zJ8Sy+bRQWi4XB0an8/ZfLPXOE0furkRK8LsxCQm0suWkutz9xmTtLChJp9gR3nrlr7gPPuoA+m7gT4eFBXg7S3oZbXvsyjbW8j2H2ADvpKHABggEHXoJYJUqxGgLa6AXWvOYbC6G7SKuKPo7J2L9pruIe8eTuUxZer0MnDEHvHs0kdBXvdKrsLa5BM4W6KjvebPLaV7UxJ1pTNeprmijMO9j1OMB/3/qGpnpjbI4iaDrSyjMP/pOnfvxWt+sfDzabjQkTJjBjxgyKiorYsGFDnx3VcyrFMOifk8mTJ9Pe3n5C45j6IqNGjTojDLbS0tKor6+nvr7e16H0issuu4yGhgZWrVrl61D6FIE5xP5BQBAHCHAKqKysZOnSpdx6662+DqVXtLW1UVZWxrBhp8785nRgGmmNHn3yhZmv0DSNrKwsYmJiTun7czyi+M5ff5dbH7+MC6+fw1P/vJPxp8A8adHlU4kfEsXiW8Yy5zvDUNoimDp1KvWVzfoKpgDWJEu+N1fPgioCVJXFV0zh/Otm84s732Lz2j1MzhypZzO9MxlmZtjMjhqLBLBo8RjOXjwWAItKB/EN0C8uUjeYcnVvoKW0offsHiXpqdTRdeSRpJMgdhFlsQD6zGGh4jbTEpqhb+3CLZJBH7NkxWOk5TbV8k7gdMgSS/3cTHdp00FawZ1idlkE0qZ0FPudOO+KqViDLO5dKxbB4OQB3Z67NF9Ls0Td2GjDsh00HWnp/gU7TuLi4li4cCERERGsWrWqz2WLT7UYNrHZbMyYMYODBw9SWlp6So7hC4KDg88Ig62goCBSUlIoKirydSi9IigoiJtvvjlgrhXALwn0EAcIcAr45z//yaxZs/yu7Hjfvn0MHDjQrw1XVFUlLy/vjDLSklKyY8cOVFVl4sSJp7wEvLc9xVabhctuXXjK4pFSYtdauPLRGQiXlcRBqYyfoYvuSZkj6BcbQUNtC1JKpi4Yye1PXM6E2cPZvqGI4ROSQVF4+qH3AKg4WEdMXJQxekhzO0W7celmUcLM6EqNXdvKWHDeOKKiQ2lsaENpd6GFedzXDx2qB0VgaXOhBln0kmWLx4zK5hKoAhRp6GyjbFoYSegQl4VWTfWUU5tZYJcwyqwlTarKiGCrvooKUhGesA3RbHUJXA4QNiMr7DBLsWUHAdulnFt49uF2r+6cMRdGs7OZ7RaiGzEsGDY2gR8/cSlzzx7Nq7/4GFXVuOnBCxiU1L0gPvvK6fzjT8tobXV0eD44xEZw6Mn3MDCzxYMHD2bbtm0cPnyYSZMm+bwi5nSJYZPw8HCmTZvG5s2biYiIYMCA7t8ff2Po0KGUlJT4vcFWeno6K1asoKmpya++j2+++WbGjRtHfX39GVWddSL4YgxSYOxS7wlkiAMEOAW8++673HDDDb4Oo1c4HA5KSkr8Pju8d+/eM85Iq6SkhIqKCmbMmHFCjtK9oa+MZLLb7WzZsoW8vDwmTZrIBUsWu8UwQHRMBC98/hA3P3oJd//uKv7vtVsQQjBr8TjuePJyFl02lQP7qtw3EaSE2qpGltw8n2HjkvR5uSZCgGHC5S32qsqP8P6bX9PSoPfBHu1Sw8weW0HP3JrJT2NfiqFK3ZXLphAFo2waT9bXKrAJxSh1FjS4VKIs+nsvVONhZodVEA59O4sqsBhC2KIJXb8aMQt3b7Bxfhru34VbKBvRSam7fHuFaL4mwuVJMXvanvUMe9GuwxzcX82UzJG8+PmD/ObdHzH7vPFHecX09++u311tmJsJ9+Ph524gKPjUfdbj4+NZuHAh4eHhPu8tPt1i2CQuLo4xY8awZcsW2tq67/H2N4QQTJgwgb179/r1OYWGhpKUlOR3WeLhw4czYcIE/vOf//g6lAABekVAEAcIcJLZs2cPeXl5fOc73/F1KL1i//79xMTE+PVdXYfDwd69exkzZswZY6TV0NDArl27mDp1KqGhoaf12L4Sxa3N7VSW1VFWVsaqVasQQrBo0SISEhK6XT92cD+uuGMR5187u1sRNTVzRBexs/w/WyncdRhUDRxOcKksPHeMPs6o275aUB0uT4a1u3UUBWGUYQsvselt1CU9ieMOGWEhjFJoKTB8nlHRDLEKTS7dVEvPEIsuoliRQi+HduFWqcLp1Tts9iubP5v/P0yPMDPjK0GYo5e8SphDQmxccsEkIoWCMMucTRFLx9cjMjqUffmHuH72L7lx7q+4ffHT1FU1dn29DOxtji7PTZ5/6kfV2Ww2Jk6cyLRp03zWW+wrMWySkpJCXFwcOTk5fap8/ETo378/gwYNYvfu3b4O5YTIyMjg0KFDfbbf/Whcd911vPPOO74Oo88Q6CH2DwKCOECAk8y7777LRRddRL9+/XwdSo9RVZWSkhIyMjJ8HcoJUVhYSExMDLGxsb4O5aTgdDrZsmULw4YN89k59UYUN9Q1U1pYjqoev8vohmU7uGH647zz6gdsWp/F6JGjmTZt2gmNlxo7NZVRk4Z2qIxuM0p03ZlTTWXzxn1gMwS1t6mWgd5brGdO3f3C0EE4CqdpxGUs04wfTUMtFx4hjNe/3WgRTTF6foEmVSXMYsGGQFFBUYXuTq0KhCpA8QhkxeXZtSnKFSOTrKheX/yd2odRQWgSRe1qMtbW5uSzpbm0NLV59RXTQVgDzFo0igHxUbz6q6U0H9Ev5MsP1PL+K0c32pm2aDRR/cPdu5p/8SSCQ05fu0PnbPHBg90bgJ1sfC2GwZNRbWtr80tn46MxcuRIysrKaGw8+o2Yvk5ERAQDBw6kuLjY16H0iquvvpr169dz4MABX4cSIECPCQjiAAFOIlJK3n33Xa677jpfh9IrDh8+jM1m86u5h51pbW2lpKTkjDHSMvuGQ0NDGT58uE9j6YkoXrs0h+sm/Yw7Fv2G+y7+I20t9uM61uu/+5iL7ppASLiNd5/cQN7XJ0ecPPKnaxk2NhFhUfTxSubDyHIGR4XR2maMYvLOfBoIAbhURJsDhEBxeI108hLOil0X0vpoJE8ZsrVR/13pzuvHFMr6qp5sbrDn9xZVRZWSCMUomzaEr+IyxC662HWbbWGUUXs5S3c4I9Nl2zymlPp+DNftDlcHijlwSqIFeYk2U3Grnh7l2IHRALS12NHcr4ukraVrFtgkJi6Klz5/gFt/toSHnrmWh5659qjrnirMbPHUqVPJy8tj586dpzRj2hfEsInVanVnyWtqanwWx8kkIiKC5ORkCgoKfB3KCZGWlkZpaalfmYQNHDiQs846i3/+85++DiVAgB4TEMQBApxENm3aRF1dHRdccIGvQ+kV+/fvJzU11a/LjHfv3s3gwYOJjo72dSgnhQMHDlBdXc2UKVP6xPtyLFH88mP/dmeGi/IO8tX7m3t9jOrqahbfOpqy3XV89vJ27C0uHO3OE44dIG5wP3720o0dE7FmVlcRhPfTTZU8/b0CoXjKgU03ZM90pa7viVkdjQTFLD82trMaR7aYK5o7Mx7WFjxC2FhH97HSRzFJKWhSVaIUC1L19P+6y7KdgAqKEyyGuLY4DUfp7nSd13xh6SWa3Zlxt+uz19kJod9E6HDSZvm0/sjZvJfGI61c9cOz3K+RLcjKxTfM7iYID7GD+nHZzfNZdNlULFbficOBAweSmZlJZWUlmzZtwuk8OZ8/b/qSGDaJjo5mzJgxbN26Fbv9+G5m9TVGjBhBdXU1dXV1vg7luBkwYAAhISGUlZX5OpRecf311/Puu+/6Oow+gWmqdbofAXpHQBAHCHASeeedd/jud797QuWdp5v6+nqamppISkrydSjHTWNjI4cOHfJrV1FvGhsbycvLY/LkyYSEhPg6HDffJopdXvN5BQKX09V586MipaS4uJjNmzdjdUay4cO9SE0SMzCKhZdNOe5487cW8+6flvHIDa+wZNRPeeS6VzonfvV4FYWhqXFddKOwWDqWTStCLyU2e2idXuZSpjpVDGMur75hd8+u9wE6xWFtFZ6xSNI05BIden+bVN1YSwr0EU7mw6U7WCtS7xtWjGSsIr0O1Z0o9upnFuDJiJpO0t2hdVqmap4rCSE4VFrL319awZzzxvGXLx/isZdu5MX/3k9ddSO7c/URP9XlR3jtqaX89VefUHmw74mViIgIMjMzEUKwdu1ampqaTtq++6IYNklJSWHAgAFnTD9xSEgI6enp5Ofn++35CCFIS0vrcyPCjsWll17Kvn372LFjh69DCRCgRwTGLgUIcJJwOp289957fPjhh74OpVfs37+f5ORkbDbbsVfuoxQUFDB06FDCw8N9HcoJ43K52Lp1K2lpacTHx/s6nC4cbSTT9356ES//7AMABibFcNYV03u0P03TyMvLo7y8nFmzZjFgwAAmTB5NTfkRxs3MIDzq243EWpvbyf16D5H9whk3M92dldyyOp/Hv/cqCI97cXlpDbEJ/aita0FTJTabglPVGDikH/f84lJuuvA5/aLT2IeZ8dYFpZe6bHNAiA0hFaTNYqpJvdQaEA6JtEm9x1cBXCCt6OJVoas4FSCkIX5NcWmWObs8YrvRpc8ilkIvlXYnmqWXwZaCZ86wWSatGvFJr4yvNOIz9yKNQ2tSNxbrjKqPXHI7TCsKqFq3jtuFO/VsVlJ6PAMGRnHvpX/i4N5KAC6/dQFf/3cbtYbJ1uqPs/nr6kcJj+w7N37AM6s3Pz+fr7/+mqlTp57w/8e+LIZBF18TJ05kzZo17N271+8nDoBuTFVSUkJVVRUDBw70dTjHRWJiIrt27aKurs5vxmNFRERw6aWX8s477/D73//e1+H4FOkDk6tAhrj3BARxgAAniS+//JLw8HDmzp3r61B6jN1u5/DhwyxYsMDXoRw3tbW1VFdXc/bZZ/s6lJPCrl27sNlsfTrb3Z0ovvh7mUyYM5zaigZGTUkhJOzYVRLmSCWXy0VmZqZ7FmzGuCQyxh27YqGlsY1bF/ya+mo9gzd2ehpP/+ceAFZ9uNXTD2ugaRJ7q4MPNj9BU0MbbzyzjLwtxYycnMKqj3KRdicEGzeGpEQoooNANkuJFQk4XBBsRdWknhU2XZqFwCLBJSUKur5UhEBFQquAaIw5xZ1ORsHd+6sfC4/RlfFN3eTSS6YJEog20x3a2M5MyJuiG10kS83dAtwxKy2ErqQlKJrHQExonUYtmZu1OnXRLYQxLkrqL4vXeZuBV5fV8/k/NzJ78Tiy1xa4xTDAh39d0+G062uaKC44xNjpfW9muxCCMWPGEBUVRVZWFiNHjiQ9Pb3bcvlj0dfFsInNZmPq1KmsX7+e2NhYv546APr5DBs2jPz8fOLj4/tE+0lvsVqtDB06lOLiYr8RxKCXTd9666389re/RVECBakB+jaBT2iAACeJd955h2uvvdav/vAfOHCAmJgYIiMjfR3KcSGlJD8/n4yMjD5VWny8VFVVcfDgQSZPntznP0fdlU8nDxvEpHkjeiSGGxoaWLt2LcHBwcydO9cthr3Ztr6Qd/+0jOy13Y9PWf1JtlsMA+zM2s/unBJAH8Wku0J3dLxefMU0gkNtvPzLj1nzRR61Nc2s+mwHH769HuF06UJXSiwWBewuvQfYnR02hK9iaFergnBpxpxhXRi7DakcxsxfDfcc4mDDaMtizhw2MUqX3XOFO5ROG5ljFRqdLqKtVrcoV5zGw2WYPhvbul2m3aXXR0F2Er4aukEWeNVZ6/3TFovFq+/Y6wZAZzQ4Un6EF/7vQ354wR+pr/EqNza2E4qeqRYCLFaFQcl9+yI/KSmJOXPmsG/fPnJzc3ttcOQvYtikf//+DBs27LjOtS+SmpqK0+n0uz5cb1JSUigvL6e9vd3XofSYc845B4fDwdq1a30dSoAeUlZWxs0330xCQgLBwcGkpKRw7733Ul9f3+N9LFiwQB8jeJRHX/0MBzLEAQKcBBobG1m6dClbtmzxdSg9RkpJSUkJY8aM8XUox01lZSXNzc3MnDnT16GcME6nk23btjFmzBi/Kf0+Wvn0sTh8+DA5OTkMGzaM4cOHd5u1Wbs0h9/e+Xe37rr3D9dw7lUd32fT6MobUyBffddiSvaUs319EQlpcUw/awxpo4eQeeEEfnPnW2xaVQBWz1dgU5NdF5oOJzicenWx1bDC6pAlpkMPrSKlLo419KyvMAy5zFVVibQonuXo4lU19wW4V3YAQXiVNRuLjVLnJpdGYrCxE3N/ZjpXw2205b7VbWoZY5neZOzJdpsGYO5juFQUCS7TOMt48YUq9ffIKjzHQyA16ZlhDNDuAPM54EhdM+88t9yzL/O0JAxOHkBQsI3v/eRCYgf1o6/Tv39/MjMzycrKYv369UyfPr1HN+H8TQybDBs2jPLycvbs2eP3zv0Wi4WRI0eye/duhgwZ0udvNnZHREQEMTExHDhwwOdTB3qK1Wrl6quv5t1332XhwoW+DsdnmF6Fp/uYvWXfvn3Mnj2bqqoqlixZwsiRI8nKyuK5555j2bJlrF+/vlcVCk888US3z1utfVN69s2oAgTwMz766COGDx/uV+KyuroaVVUZNGiQr0M5LqSU7Nmzh2HDhvl1/7PJrl27CA8PJyUlxdeh9IreiGIpJYWFhRQVFTFlyhQGDx581HVXfbjF2Eb//YWfvk/6mEQyxia61znv2lm8/ccvaKpvASCqfzjjZ+t9j+GRIfzy77d32e/h0hq++WI7BHX9zET2j6C5rtkI1uMYLY3fPScCiiI6VDjrFc+mS5U+dklKiVsemtubQrZzvzBgaRZo/WQHQSyEXvaMovcQR1qtHndpFY+YNrPBRh8xGDOHDQdqL+3rjkMYTthICU7pKb+GDjcBzAx3x3Sy9JiLASGhNuytndyJJdjbOo5banc1UXRkI9nN1TQ0HuGLPX/m0kuX8OSTT55Qee4777zDDTfcAMBf//pXbrnlluPe19EIDQ1l7ty5bNu2jbVr1zJjxoyjzpuvrKwkISGBJUuW8NBDD/mVGAZQFIXJkyezbt06Bg8e7Pel00lJSRQVFXHw4EGGDh3q63COi9TUVHbt2sWwYcP8pvT7uuuuY/Hixbz44otnRBXXmcyPfvQjqqqqeP7557nrrrvcz99///08++yzPPbYY7zyyis93t+TTz55CqI8dfjfbbIAAfog7777Ltdff72vw+gVxcXFJCcn++XdcoCamhpaWlr8TkB2R1VVFYcOHWLSpEl+c6HjTU/mFEspycvLo6SkhMzMzG8VwwDxQ2I6/K6qKq888Z8Oz9lsVt7a/CQ3P3Ix3//pRfzlW8yZdmzay0PffYGn73tHf8Lh7HLbfuiwgR3KgqVTJSY+kqmZIz2rejfYtuvl1e6ybFW6K42F4SptQS+5Fk70f9s67cNEYMwQFrrZlSqMkUueUupmh0qU1eIRwN4l1qoxh9gw4nKjSSNr3el4ive/xkJFoCnoplpen0Nhuml3ilkIgbQoSIvChJnpPPrC9YycNNQzrkrTvESzpNV5hA2H/8Gh5l3MnDWDGaPPorlS5fnnn2fyxCnU1tZyPBw8eJAf//jHREREHNf2vcFisTB58mTS0tJYv379UWP+8MMP0TSNRYsW+Z0YNomKimL48OHk5OT4fem0EIKMjAyKior8yq3Zm0GDBqGqKtXV1b4OpcdMnz6d+Ph4PvvsM1+H4jM0hE8evWHfvn0sX76clJQU7rzzzg7Lfv7znxMeHs7bb79NS0vLyXxp+hT+eSUcIEAfory8nNWrV3PNNdf4OpQe09bWRmVlpV+LycLCQtLS0vps+U1PMUulR48e3W0frb/wbaJYSsn27duprKxk7ty5REVFHXN/Nz54AQMGRXvtBFoau/YehYQGceWPzua7d55DvwEeQVRxoJZHr/8z913+HF9/to3/u+lVdm0pZs+2A1gsin65YLe7RXFUdCg/+f13Pf2xgBZio6bFwZYtxSjhQYCefQ0KtjJwSD9j1rAElyF6NXN0kZFZ1UAYP5tyyOo0trHTJetsUcxtjIcpdg3h2+RUibBaUDRPNtgcvSQMvdKlZ9gMx/vb3lsQmMnfTolsN0LoL4er05Bkh6oHrAiwKGxav5fXn/mS3dsOGHONpSfLLBQQFvLrVuPQWjl76pXcdsVDhNcMY3rc5aRETKTkQDGPPfZYl/f3WEgp+f73v8+AAQO44447er398SCEYNiwYYwePZpNmzZRU1PTYbnT6eTNN98kOjqa2267zS/FsElGRgZWq5Xdu7vv4/cnkpKSUFWV8vJyX4dyXCiKQnJyMsXFxb4OpccIIbj++ut55513fB1KgG9h9erVACxevLhLkiQyMpI5c+bQ2trKpk2berzP9957j9/+9rc888wzfPHFF31+vnlAEAcIcIJ8/PHHzJ49myFDhvg6lB5z4MAB4uLi/FaA1dfXU19fT1pamq9DOWH8tVS6O7oTxVJKcnNzqampYe7cuT3uj47sH87P37yN4FBdiCLguz86q0fbOuwu7jj39+SuL2J3bilP/fgtHHanXsKsSVRV03WaqkFrG7S2MSA6mBWfbkMas4alEBBk9Zg9SwiPi2T0xBR++/rN/OCec93iWRECXJq+P++GMUMou+cVa7rjNJohYGXHh7f4NcUwGGZXGjQ5VCxCEKEoejbaZZRFG5nhbjPP0ih57i5h4D1T2fzHIjruQzPib3OgtDkRdpf+MDLP3pnkcmOUEuAeQQUCoSi0Oo9Q01ZCqDUKS/UQ/vXSSve2GVEzsQjbcWUgnn/+eVatWsXf/va34+69X716NUIIHnzwQXJycrj00kuJiYkhOjqayy67jIqKCgDy8/O59tpriY+PJzo6mrvuuouEhAQ2bdpEVVUVoIvhFStWkJuby6WXXkpQUFCHY3399ddcfvnlpKenExISQnx8PNOnT+eRRx45rthPNYqiMGnSJPbv3887z3/KXec/zQs//Rftncvj/QBFUcjIyKCwsNBvs8QpKSlUVlbS1tbm61B6zFVXXcUXX3xxRmcXvw0phU8eoPvbeD+OJkr37NkDcNT+dHMEW2FhYY/P++qrr+aRRx7hgQce4IILLiA5OZkPPvjgW7dJS0s74cfzzz/f4xi9CQjiAAFOkKVLl3LJJZf4OoweI6XkwIEDfttHBVBUVERKSkqXi01/w99LpbvDWxTv3buX7Oxsjhw5wty5cwkN/faZwp1JH5PIq6sf4eEXb+LlL3/Cwsum9mi7g/sqsbc7OzxnC7aiWBQUi0L0gAiSRiSAzYaw2UARVNa38taLK8CqgFVBmrrQzBgLQVOznbycEn77k/fZ+o1+AYFTn8+raFI305Kesmm3JNT0J9zuzxrYunGUBjzzhL2zw8Zy1SVpdalE2az6CGGhZ6XNbdymWiZGltYtvr2Xyk7rGc3SwqoY23nWUYRA2CxG1lp6jMU6f2a9Tc4MwWELsiKlpLb9AAADQoYihKC2uhHFpmdOrUoQIzPG9DoDUVBQwE9/+lPuueceMjMze7xdZ3JycgD9Ym/u3LlYLBZ+8IMfkJyczMcff8zNN9/Mp59+yvTp02lubuamm25i+PDhfPbZZ9x3331MnDiRrKwsysrK2LhxI1lZWTidTi6//PIOx3nqqafIzMwkOzubs846i/vvv5+LL74Yu93Ol19+edzxn2qioqJwHrHiDGpif0EZy/6xkVd//pGvwzouhg4dSltbm1+VHXsTFhZGbGysXzlmjxw5kuTkZL766itfh/I/R1JSEtHR0e7Hb37zm27Xa2hoACA6Orrb5ebzR44cOeYxlyxZwqeffkpZWRltbW3s3r2bRx55hCNHjnDVVVexbNmyo25bUlJCfX29fvP6OB6lpaU9irE7/LvWMEAAH9Pc3MyqVauO+46UL6ivr8fpdDJw4EBfh3JcNDU1UVlZ6fdzh1VVZceOHYwaNcpvM/VHo3///syYMYP169cTHBzMggULCA4+9iim7ogfEtOln/hYDErquv41dy2m4kAtiiIYOTWNPz36gUfQ2Wy0tniZPykCoYmuWSQhwKpQdfgIyz/O1ddTNaTeKey2j5YW0NWlopdCC4FbnkuJ0i7QQvEYWHkbYzm9tKsplM0Er9DLpiOtFioVPbsrXBz91rYQesm2lHpptcWsnzYywxI9i23uQtGXu2/OmMLYeE0wM+dCeIYbe49fanfo+/bqqw6LDKaxvo0WZz0A4TbDnEnqZdr3/P4qYgf1528fuNhVtI3CwkLOOuvYlQAul4sbbriB5ORknnrqqWOu/22YgjgrK4tNmzYxfvx4AB5//HGSk5NZvnw5ubm5fPXVV8yaNQsAh8NBRkYG69atIzY2lgkTJpCdnU10dDR5eXmEh4ezePFi9zEqKyt5/PHHmTt3LitXruxyM69z2XVfo3BzJfGjrUxYlEzu8lIKt5X6OqTjwmq1kpaWRlFREfHx8b4O57hISkpi79697qydP3DJJZfw6aefcumll/o6lP8pDh482KFF6Xi/h3vDfffd1+H3ESNG8NRTT5GQkMBdd93FI488wnnnnfet2z/++OPHdewT8cQJZIgDBDgBli9fTlpaml99MR08eJCEhAS/7WsrKioiKSmp19nGvsa+ffuwWCxnRKl0Z6SUFBcXExYWhsvl6nU2w+VUyVm3m52b9x1XaWN4ZCh3PHEZFqv+FTdr8ViuvvNs7vv91dzz26vYtqGoc8Bdd2IRHtMsswdXAYIsHtdpl6FonZrHkdltAS08/cXGMUzXaouqZ1pFO11Lph10nUcMuniWuiCOsliQQXo8QpOezGznIgNNzw67Y+pUou2O1/hBdlqGEHqvMIBL9azc3Y0CTaLQVSQ31DWz+IqpjJqqt5RYFV0IKhbBoKQYWprsJA8f5HZr7und/V/84hfk5uby5ptvnvDfAlMQv/nmm24xDHrvXEpKCqqq8vTTT7vFMEBQUBAjRoxASsmRI0coLi4mOjqapqYmKioqOP/88zu46u7evRtVVRk+fHi3lS2xsbEndA6nmgmzhvP1e3uYfF4KkTHBTJ4/ytchHTepqakcOXKEuro6X4dyXAwaNIiWlhZ3Vs8fuPjii/nvf/+L1mku/P8CmhQ+eYBe3eH9OJogNjPAR/tMmc8fzVW/J9xyyy1YrVa2bdtGU1PTsTc4zQQyxAECnABLly7l4osv9nUYPUbTNA4fPsy0adN8Hcpx0drayqFDh/x+pmFrayuFhYXMmjXrlLp8Syn59M2vWfdpDonpA/nBY0uI7Hdqs9Fmz3BDQwPz5s2jtbW1V3OKXU6Vn171Aruy9gNw1nem8eBzN/Q6jiXfm8eS781DStmhHL38QA1rPskGxbghZKjF6P5hNNS3enYgBKHBVuxCoHkLTdNISvMoSqFJXUOq0pjVKz0ZVLOnWPP8LoQueK1OiStIeISs9JQ+dxC3ZqZYgyaHMXrJTPSiL5NCdi1hNmu3zUXeM4jdi4XH/Eoz4jfjBixmH7TqdU6gZ4Atns+usHcsUTdRhKCm4ghhEbo4vOCaWQQ3JVK2v4ry/dW8/uul/OuF5SQucnS7fXds3ryZp556igceeKCDSD0eWlpa3AZ93WUtSktLiYmJ4aqrrup2WWRkJIWFhe45w5999hk//vGPu6w7ZswYoqOjeeONN6iurnaPo/GXcUbnXTsLKSXVNYe4+pH5LLnyfF+HdNwEBQWRkpJCUVERM2bM8HU4vcZmszFo0CDKysqOWuLa15gzZw5Op5OsrCxmzpx57A0CnFZGjBgBHL1HuKhIv4l8IjOwQ0JCiIyMpL6+npaWFiIjI7uss2XLFhISEo77GCeyfSBDHCDAcaKqKp999plf9Q9XVVVhsVh6NVy9L7F3714GDRp0WsarnEp27txJQkLCKX8f1n2ay5//7wN2Ze3nq/c28cf7Tq3Tp+kmXVdXx5w5cwgODu7RSCZv8jbvdYthgJX/2ULVoePP5HiLYdWl8vS97+rCz+VyZzEF0FDV9Y51xtgh/O2jruLGjdLRgEqoxi8q7n0rxvEVc+yS2edrzAju/LBi9AR37iU2Hk1Oz+glKYRemezu6aVrllgKhEt6hLkRl+kEfVTDLfc5adDuRAGi+oUartJ66XhCYn8uunwqaUP6IRyeWU+h4XoWQgiBpkmy1+0he/VeAJb9ZwMX3ziXipIaIw5Jc0Mbewv0EtzG6nay1+1GdXU/5sflcnHjjTcyfPhwfvnLXx498B6yfft2NE3rtgXD7GdbsGBBl1nnzc3N7N27l9TUVLcYtlgsfPTRR7z22mtERER0GMkUGxvLN998wxVXXMHKlSu5+uqriYuL4/zzz3dnqPsyQgguuH4OV918CcFRgtq64xuR1VdIT0+nqqqKxsbGY6/cB0lMTKSsrMxvzMGsVisXXHABS5cu9XUopx3zz+7pfvQGM8mwfPnyLln8pqYm1q9fT1hY2AndzNizZw/19fVERkYetSJmypQpxxzJ+G2cyPYBQRwgwHGyadMmpJQnnKE4nZSVlTFkyBC/NHCy2+0cOHDAr8rTu6Oqqorq6mpGjx59yo9VuK0Ui5HF0zTJ7pySU3YsKSU7d+6kurqaOXPmdCgX7Y0oDg6xdXnOajs5xUybvtpJwdZizxWD5lXqLOloCgUUbDvAoeIaLrx0svu56KhQpEV45vqqqr4fl+aePdzli1X1jF0CdCHqMrPB0l0OrdtZGw7U5kPzekjdaToyyGIYdYFA6qcjpJ7sFSCNAIQTFE1fq6volUYZt3D387oz3+br41SxtDpASjRF0NDY7u4lBsHhQ0f44pMc5iweqx/POMaPnljCmGlpeOWhCQ/S+7rrm6v540/eIyI6tMPfoeo6fRTOynfy+Nn1r/DzW17vtryyubmZwsJCCgoKCAkJQQjhfvz85z8H4NZbb0UIwb333ttl+86YYnTKlCldlmVnZx912ZYtW5BSMnLkSLcYVlWVTz/9FJvNxtixY9m0aVOHstyxY8fy73//m/r6er766isuv/xyli1bxuLFi/v8SBKTkJAQRo4cyY4dO/x6NnFISAjJycnuzJe/ER8fj6Zpxz272xeYfcQB+h7p6eksXryYkpISXnrppQ7LnnjiCVpaWrjhhhs6OPnv3r27yzi24uLiblsRqqur+f73vw/o7tN9cVxm34soQAA/YenSpVx44YV+04vrdDqpqKg4ITdWX1JaWkr//v1PqIfF13gbaXkLxlPFuJkZfPjqahB6pnLC7FN3M6GoqIjDhw8f1U3aFMXHKp8eNSWVxVfNZPl7utvwTQ9fREz8secWfxtSSnK+3kP2OuPLW5MdzaBMXBqRsRE0NeojTTRN8sf/+w8Ohwva7BAaTENjG4QGIZ0qwu7UW3OFQKgq0qYYpcR6BtcsMRZSj0FqIBS9V1fRQLoMEy0rum40s8FOjFSx18MQy812lcj+VqJsFvqFWYmMEERZrYhggVAEFkUwabBeQTE1PQq1TaKpErVdo6XNRXObSnObC3trZzGjl0SbhwIQFoEabINg/VJBulSEamSVDVRVY/M3hfzitZspyisjY+wQyvZVsWtride+BQOCkwCobSuh+Ugrv3rzVv5w3zs01bdy7rVT+ckf/opFWIkO0u/ub1mVT8nuctJGdxxnFxwczA9+8INu3+ecnBxyc3OZO3cuI0aM6NHNSlMQT53a1cHcFMSdlzmdTj766CMAzj//fPd3wLp166itreWyyy4jJSUFTdPYtGkT8+fP73AhGRQUxNlnn83ZZ5/NvHnz+Oabb6isrCQ5OfmY8fYFUlNTOXDgAPv27TuhEkpfk56ezurVqxkzZsxp+Xt8MlEUhSFDhlBWVtbn+89Nzj33XK6//nr2799/RoxMPNN4+eWXmT17NnfffTcrV65k1KhRbN68mdWrVzN8+HB+/etfd1h/1CjdR8C7SmHt2rXccccdzJ07l7S0NGJiYjhw4ACff/45DQ0NTJ06ld///vfHHWN1dTUvvfQSu3btAvSbjD/60Y+Ii4s77n2aBARxgADHyaeffnpSSvZOF+Xl5YSHh3dwHPQXNE2juLi4g+GNP3K6jbRmLh7HQ8/dwDefbychJZbr7j81fX8VFRXukTXfNgv2aKJYSsm+nWU4nSrDJyRz3x+v5foHzsdqs9A/7sQ/r2/+/jPe//NKABSrgubSRyV95wcLyN1ayv6CwwBERYeSPiqBbVn73V/y9TXNAAirRXdYNrFZkO1OhJHaFUJBanpOVNrQM7oWr4pqCYpdotlwZ5E1zTDR8k4pG6XRkRYLCVHBDI4Ion+IlcggK5FBFvoHWwmxKvx05lCaHSpNdhdtrSpORaJK0KRkYKRespwYE4ri1I8VbFWICLUQHmolyKbgchkCuUWlpcVJS4uLI3V2Kqvaqam1o5rl38FelwlWC2gu3IZhxutRtO0Au3NKGTMlhV/c+ro+8sri1aMtJWG2fgwIHUptWykRw48wae5w3t3yCwDuv/9+WttaSQofi1XxVAhUVB/CsbuJ9PR0d8lyaGgor732Wrfv85NPPklubi433XQTt9xyyzE/F6AL4qCgIMaOHdtlWXcZYqfTycaNG92VDt5i+aOPPkJRFJYsWQLoMzVbW1tZu3YtGRkZXcTj3r172blzJ8nJySQmJvYo3r6AoiiMHz+ejRs3+rXBYUREBLGxsZSUlDBy5Ehfh9NrEhMT2bhxI+PGjfOLG/PR0dHMnz+fTz/9lHvuucfX4Zw2vOcCn85j9pb09HS2bt3K448/zrJly/j8888ZPHgw99xzD0888USP/A6mTJnC1VdfTXZ2Nrm5uTQ2NhIZGcm4ceP47ne/y+23337c4zI3bNjA+eefT3NzM7GxsbS3t/Of//yHZ599lmXLlp1wb3pAEAcIcBzs3buXffv2dRir0dcpKyvzq4subyoqKhBCMGjQIF+Hcty0t7efFiOtziz6zjQWfefUmag1NjaSnZ3NpEmTepS9N0Xxhg0b+PJfm/j6g3xCwoKpOKCX/k1dOIon/3YbcQknz2zoo9fXun+WEs69ZibnfncGdTVNrPg0Vy97VhSaGtrY9k2RnuntjHc2uUNmWYDdCaFBeh9tiPFlr+lzEd0l2YBFE2hGmbJEL5lW7JLwCAuDo0NIiAomISqIwVHBRARbqGl1crjZTl27i0NNdprsKhZF8J0RcfxqfTFSA5waIY0Ce7RxFAGfFFTz2IJUPsyrRNSoCBX3DGSAIKsgwqYQGWIlItRKeJiFyCALqamRzJgRR0iwhdo6O9WVbVSXt1Fd2UZNVTsul3SbbndwnG53sndnGSs/2Iyj3eHpVzbWOf+amSz+7nQ2rJ3FfT+7lfe/eI36sw4ycfJ4Vny5mty8rQwbNow7v/swy97aCsA1dy/m2pu+S2lpKcXFxb2+iSSlpGxfFcGhtqOO7bLb7eTn5zN+/PhuL9Kys7MZOnSou9ffFMM2m42DBw8SGhraIUvy8ccfM2vWrA4j7UaPHs3GjRvZsGEDq1evZsqUKURERLB//353P+Ubb7xxWv8mnAwGDBjA4MGDyc/P77ak3F9IS0sjNzeX4cOH+9170L9/f4KDg6msrDwhI6LTySWXXMInn3zyPyWI/YmkpCT+9re/9Wjd7vrXx40bx5tvvnmSo9K5++67mTx5Mm+99RZJSXrF0dq1a7nyyiu577773Dfaj5eAIA4Q4Dj49NNPWbhwYbcueX2RtrY2ampqmDRpkq9DOS72799PamqqX/Y+mxQWFhIXF+e3hmbd4XA4yMrKIi0tjSFDhhx7A4P+/ftTlttCbHowSWP7kbfGM5Zp6+oCCrYWM3bGsR2pe0p4VCgNtU1Ga6xkzLQ0mhrb+NUdb3oymSZS6uOUrJ0ujlUNHC6wGWlfuwuEIGZAOHWVjUiXhrAZI5mcGiLYgsQjCtEMY2uHJCjCQtqgMIYlhJMSH0p4iIWaFieHG+wU17bxzf4GDrfZscuuDckhNoUgi4JNETjMHlt3jTMdeoWlEHpvtGr0SBvLnE5JfYuTRul0i1fFoenrAJGRNuLjQ4iPCSYlLZJps+IJCbVQW91OSVEDJYWNVB9udfdhC6uF3TvLkHaH+16BkBrTF43hgutmM23hKIQQFGwtZlL0d9jLRtat/YaVq1cQpIQxNHwCC1Iu584nruGme5eAEPQbEMHjLx7Pu61XlPzq9jfY8MUOAK6//zyuu69rdcTOnTtxOp3dCrrS0lJqa2uZP3++8Zp5xPCECRMoKChgypQp7szc1q1bOXjwYJe+ZUVRiI+PJzQ0lKamJv72t7/R3t5OQkIC1157LQ8//LDf+iKMGjWKlStXkpGR4Tdux52Jj4/HarVy+PBhv7thLIRwm2v5iyC++OKLuf/++2loaPDbz0xv8ZcMcV/h888/54ILLujy/Pbt21m2bJlbDAPMnz+f6667jldeeeWEjxsQxAECHAdLly7lyiuv9HUYPebQoUMMGDDAL0vbGhsbqa+vZ/r06b4O5bhpaWmhtLTUfXF9JqBpGlu2bCEyMvK4yg33ba+k4otKLvrxRIAOolixnNxMzUPPXsevf/gmrc3tzD1/AguXTOGvv/4EYVE63uX2Hj/UCSEEwqmiOVUIsUGoDQnUNrQhFIXgEBt2VQNVw6JYcAn0UUvGqURFWElLjSA1JYLkQWHUNTkpOtzCx5sqOdDSjhMMwypDo1q8ZxrjFr12p4ZT04gMslKrOsHqEbtSeP0LICRSFSia5vWkfp4Ws4faaBqWQne5lkBTk5OmJifF+UfcN6EiIm0MHhJKalokl1ybjsupUVp4hJIdNRwuctJY38r0+SPI+lIXoZH9wvjRL75D/BBPpv+/76wn1BrJuAGLURSB5lLd/dwHC2opyC5m3MwM9/olJSW9ep+ffPJJnnzySbatL+SRX3qMYd55ZhkX3jiPfgM6utNPmTLlqC69Q4cOdS/zFsOmgZbT2XHMlNlTfNlll3XZ15IlS2hqaqJ///489NBDfie8jkZYWBipqakUFBT47SgdIQSpqakUFxf75fuSmJjI6tWrcTgcx12KejpJSUlh1KhRLFu2rNtRZgECXHTRRdxwww386U9/6lCmHRcXx4YNGzjrrLPcz2maxubNmwM9xAEC+IL6+nq++eYb/v73v/s6lB5TVlZGamqqr8M4LoqLixkyZIhffNkfjYKCAhITE/2yf/to7Nq1C7vdzvTp048rc5950URe+tkHfP7yDi74kd4bnremjPmXTGbk5KEnHN/hkho+eWMtQhFcfusC3t/+axztTvdIoCGpcUjVyLBaFIzZRSAlMbERTMwczqr/7vAYWhnZXmGzIL3m8cpgK9KhEhIWRKjNQn1di54ZDrEQGxvM8GGRpKdEENMvmEMVrRQdaGHZthrqWzw1zKqQCJswMsogDFWsWECqGDHoqlUqhrFWkIXaVoe+vpR6f7N5H8EtiDsZh0mvf71nJQsQbcaxvUuhvTZpbnRQ1GBn7856FAUSBoeSmhLBnItTCIsK4lBRA4MTBnPtnQupKW9kzPQ0Pv7bNyx7bxOxA/vx0DPXMGBQNBUHa9FUaYx9wjPLWAjefnYZv/vXnSdcCaK6urpTy24cq3tCd2K4Oz766CMmTJhw1L+zkZGRTJ06lS1bthAeHu4384ePxbBhw1ixYgU1NTV+Y+7UmaSkJAoKCvwyaxkREUFUVBSHDx8+bd4UJ8oll1zC0qVL/2cEsSYF4jRnbDU/zhAvX76c2267jTFjxvDSSy+5bzLedtttPPHEE3zzzTdMmjQJu93OsmXL2LNnD7/61a9O+LgBQRwgQC/54osvGDt2rN84gjY3N9PY2Og3JVXeuFwuysrK/Gq0VWeOHDlCRUVFh7ua/k5paSllZWVkZmZ2mc/aUy68cS5RMRHsyS1hYHQimVeFcNWPz2Hy9AknLIhaGtu4/9JnaTrSCsD6z7fz17WPERoejKZpPPvwe6z4YAuKRaFfbASDh8axK6cUU/7VVzcxbY4hiKUhlDWIjAmjsc3uKYM2CbXp7tNCEBSkkDFhAKMmDWDAgBD2lTSTlVtHycEW7E4NFEF7pOLOBoOemdVU9NlMZmmzBOmSujjWm3b159BnEUcGWTyC16scGrx+FqAgEHg5QxuxexuECaELbX1mE559KrjFuEToM4nRzcBstnA2fFrChk9L6B8fytAxMYyZNpTCg/qM7ZyN+bz3sm5k1lTfyq9+9HeeePX7/PyW1zhUXMOU+SOJiAxmzafb3HHkbd5HQ11Ll0xub5kwexgTZg9j+wZ9pM6SmzOPy5ytp2IY9Jtex2LgwIGMHDmSrKws5s+f73fOxt0RHBxMRkYG+fn5zJs3zy/bWoKCgkhISKCkpIQJEyb4Opxek5iYyKFDh/xGEF988cWcd955OJ3O4/7+CHDmcvbZZ5OXl8fDDz/MFVdcwRVXXMFLL73EE088Qb9+/fjDH/7AV199BcCQIUN47rnnuOuuu074uAFBHCBAL/nss8+46KKLfB1Gj6moqCA2NtYvv3gOHTpEWFiYX2dTCgoKSElJ8cty9e6ora0lLy+PmTNnfquj9LEQQpB58SQyL9b72uvr69m4cSP790cedSRTW4udF376Hjs372PM9DTu/t3V7oyvN/t2HaKhttn9e035Ecr2VpIxLomslfms+GALAJqqUVfbSl1taefgeO+VVeB06T3GhiYdNTGZzd8UgdWrN1gIsCr07xfM+GlxDB8fQ32tnV159eSXNOsi2LsEXEqESyKDOwpSRZWoqgCLcGekFQDNy6naoNmuEhHkEWfdjRg2j6VosqPJlTlj2DwpU08LPOOojG0779Q8js2qUJZX5t6+vqoNl6OeX708390e0Ogs46qfTmHX+nL2ZFVScbCOxLR4/rrqUTRNQ1EU8jbv8whiAUHBNsK6eT97i9Vm4dfv/pA92w4QFGIjfUzP+9tNeiOGe0N6ejoNDQ1kZWUxZ84cv3AHPhbp6ekUFxdTUVHB4MGDfR3OcZGSksLGjRsZM2ZMn5yR+m0MGjSIXbt2+Y3AnDZtGkFBQWzcuNFvx0AGOLWEh4fz4osvctVVV3HLLbcwatQonn/+ee69917uvfdempqaAE6qj49/WeoFCOBjpJSsXr2as88+29eh9JjKykq/dWcuKSkhJSXFL7MOoM/Mq6ur8+tZnd60traSlZXFmDFjTnp5pOk+vWfPHvdIm868/YfPWPtJNtWH61m3NIe//+6/3a6XkBKL1WZxlwAHhdiIT9Sdhs0ZwwAoilv4uT9iigJCULq3GqFJcDjB7kDaHbha20FqoHrE4tD0SC65Jo0rbxmJYlX4+K29/Pu1PezMrcVhVw0R6hWcBoqzmx5lDV2QGoJVSM9z7nHERqxNDheRQd9y0e4+F8PNunMK2ftXoT8hzLHEphD2FsRCgCK48/7zGJkcg1bZoI+u0l9gEIKm+ha++NdmoqKiGDduHGOHTWbbqjKGT4vn+iems+DqETQ2NhkvsX7pMW5GOtfevRirzUJkdBiPvHgjQSEn54LeYrUwemoqGWMTe/3341SJYdA/jxMnTkRKyfbt24/aw+xPWK1Whg8fTn5+Ptpxlqb7mv79+xMeHs6hQ4d8HUqvCQ8PJzIykqqqKl+H0iMURWHRokWsWbPG16GcFqT0zeNMYN68eWzfvp2bbrqJG2+8kSVLllBRUUFkZORJN7UNCOIAAXpBYWEhdXV1zJgxw9eh9AiHw0Ftba1fCuIjR47Q1NTkl0YnoN88KSgoYNiwYX7d/2zicrnIysoiISHhlPWjHyqqo2B1Ndtz88jdugNV1WhpbHOLhgNFlWiG4ZWmSQ4UVXS7n9jB/Xj89VtIHz2EjLGJ/OLvtxPVX89mzzxr9FFHOiWkxKJYvMSTEB4xalHI3bhfny/sVEnNiOSaHwxnwXmJHCpt4e8v5bPqvwepqtBLp0W7C0uTvVsxpti7u2KRKO0SoRli2HigogtwDd2kS4Nmh1EybcTo+raLH7NX1/2z9y9eMXmvay7yzhZLyZqvdlFdUqPH0/nqS8Bff72UW+b/mree+YIHrnqZPdnVfPh8Hp+/XgCKZPXqVWRnZ9Pa2uo+7g33n8/Swqd5f9uvmXl211nAp5tTKYZNLBYLM2bMoLq6+qg3f/yNlJQUNE3j4MGDvg7luBBCMHToUIqLi30dynExcOBAKiq6/3vYF1mwYAGrV6/2dRgB/ICQkBD+8Ic/sH79evbu3cvo0aN7PBqqNwQEcYAAvWDNmjXMmjXLb3q/qqqqiIyMJCwszNeh9JoDBw4wZMgQvygB647q6mpaWlpIS0vzdSgnhby8PKxWK+PGjTsl+684UMujV7/Eyvdy+ORP2ezdW8STP3yeK0Y/zGPXvoy9zcGsc/VjW4wSZPP3zjgdLvbuOEhSxkAuvXUBE2Z7xtpE9gvnpc8f4KIbZncQpVJKQsOD0MzsryL0Umez3NkQRoOHhvOdW0aw4LxE8nJqePvPu8neWEV7m5FiNecOC4HF2f3tegsYohLPQwgsqvGzhluUmplioeliGSlpchgl08a2Wuf7LV4l0/ooJb22Wc8yC0+uWHg9OmwvEV5ZcIQAVbKnsIJxszL018TIDJvrI8HRaudQcTX/fHkVDrvHNKyytIkV7xay7C8F7NtZxsqVK8nLy8Nut7tfq77A6RDDJiEhIUyfPp3du3dTV1d3yo5zulAUhZEjR1JUVOS3WeLExESamppobGz0dSi9ZtCgQVRWVvrNa79gwQI2btxIe3u7r0M55ehfAeI0P3x91idGZWUlf/jDH7jrrrv4wx/+QHl5OdOnT2fbtm386Ec/4vbbb+e88847qTfg/KtRIkAAH7NmzRoWLlzo6zB6TEVFhV9mhzVN49ChQ0ydOtXXoRw3RUVFpKWl+V0/WndUVFRw+PBhFi1a5C53PdkU7TiA06GLqKqSBj59PoeL7pqEvc3JtjV7+Pzd9Vz6gwWERYSQv3U/o6aksvCy7j8fr/78Iz57ez0IWPNJDlarhcmZI/jrLz+mZHc5s84dR/qYRP1KRTXKmhWFfbsOu8uD3SgKSBgwKIwZ5yUxcGgE2zZUsn1TFU6hePpyhfAyxTK2V0G0uZCh1o5lylJ2qKTWPbMM0SrRVbDpEmqaW5kru6DJ3rFkWrrXN+LwcpkW0uz91bPLGE7Wno2lJ+OLIZZVzZi3bOxP1RAInE6VrfmH0RTFczddCIYMHcChonJ9u6OJWykpySvnbzsruOqehcTFtbBixQoyMjJIT0/3+f+T0ymGTfr378/IkSPJyclhwYIFPn8NTpQhQ4ZQUFDglzN9AWw2G4MHD6asrIzRo0f7Opxe0b9/fxRFoa6uzi/cvocPH05MTAybN28+o8YRBjhxcnNzWbRoEQ0NDe7nnnrqKVasWMHkyZP51a9+xRVXXMH3v/99xo4dy29/+1t++MMfnvBxAxniAAF6iJSSNWvWsGDBAl+H0iM0TfPb/uHq6moURfGLL/buqKuro76+3m9HXXnjcDjYtm0bY8eOPaXGYOljErFYFd1gShHUHGzmvy/kMvWCVMYtSqKpvhUhBAsvm8qdv/4uiy6fdtTM4tY1BUgpkZpEsShsW1/IC4+8z4oPsijcfoC///4zWo60Ygu20H2K1ENk/2AWXTOMy340hvrqNv759HayV5bhckldAEJHgy131lUgFaPkunOcQkC71vHIUurfyJqXGMbIEKueh6UdmuwqEcGeDLEws8rufXn26Z0oEKZ7dOdYjGywUA1xjJfI9z4/JE1N7RDasWrj9scuITw8CKEIsHmJOikJjwhm4MBIaLe7n37/xbWEEcvMmTOpqqpixYoV7N+/v9fZLXubg/Y2R6+26Q5fiGGT9PR0goKC2L1792k75qlCURQyMjIoKiry297oxMREysrK/C5+IYRflU0LIViwYMH/TB9xgJ7zwAMPYLFYWLNmDa2traxduxaLxcKDDz7oXmfixIls3bqVhx56iPvuu++kJKoCgjhAgB5SWFjIkSNHmD59uq9D6RG1tbVYLBb69evn61B6TVlZGYmJvTfD6SsUFRWRmpp6RvQO79y5k+jo6FM+ZiwhNY5fvHUHE+cOZ+bicVx+20KqDzTx3xdymXJeGmPn9zzjNGx8krsXWFM10scMYc+2Unc5tMWikLNuN84WLzHl/VmTEsUimHpOIlfdPwFNk/zr9zls/KSY9sZ2sLs8o4m6cTIxq6A1Reif4W4uroPb6fq8BYS9Yym1XubspbNdust0uM2CxUgKu0K9RjN1QHj0r/Tar3e5M+hO1EZ2WEiJRHpNYDJrt3Fnw4XVc+lw9mWTmTxvGOljk7qepoDv/CCTsy6d7NneMPp69IZXqDnYzNy5cxk3bjwlJSWsWrWKmpqaLq9Vd7z/8gouH/kTvjPyJ7z34lc92qY7fCmGQRcGkyZNoqSkhNra2tN67FPB0KFDsdvtXQyeDhdX89nb37B9faGPIusZ8fHxqKrql2XsgwYNoqKiwm/E/P+KID795dL6w1/JycnhxhtvJDMzk5CQEObNm8cNN9xATk5Oh/UsFgs/+9nPyMnJOSml9/5dnxMgwGnE3/qHzXJpfxOVLpeL8vJy5s2b5+tQjovGxkaqqqr8cp5lZyoqKigvL2fRokWn5XM0OXMkkzNHun+fuXgc5QdqyJiYQEFRHvv2RRx1JJM3d//2KoJDgiguOMSsc8dx/nWz2Z9/iC/e3QiAqmokDx9Ezro9+lBdi1EebJSDDxgczsKrMgD46OWd1B5q8TKPEihWBc2lIYIUpCr1UUmgZ4XNILxHHDk1MHt+vV9GzWtbczOnAO/JQ5JO45Cg1e5Ck5IIq4VGuwpWo0vYMH72Ls1WjH89z3V6sTr1C2vCGP3k9X5L1TNyCePniXOGEWqFoq37+N3db7Nj0179GObrKSA0LJiFl0wmLqEfKz7YQlX5Efc+VVXjXy+vZPjYIfzj2WVYbRZu++2FbNq0ieTkZEaPHn3UEuLDJTX87Tefun9/83f/Ze6FExiSGt/t+kfD12LYJDIykpEjR5Kbm+v3pdMWi4W0tDQKCwsZOHAgAPvzD3HvxX/EafSV3/bEZVx2a99sPVIUhYSEBA4ePMiAAQN8HU6viIuLo62tjebm5pPuwHsqWLBgAXfffTft7e1+c10V4NQTExPTxe390KFDxMTEdLv+6NGjWb9+/Qkf13//6gYIcJrxp3JpKSUVFRWnzADpVFJeXk5YWBhRUVG+DuW4KCoqIjk52e+/4E9XqfS3MXZGOmNn6AK4X2wEGzfqgvZYojgiOowHnr2uw3O3P/kdYuKjOFBYyfSzxzDvwonszi5hd24pSMms8yawdWMRE+YnMGF+AtvWHCJ3Tbnuaq0YZcVmf7GU4FTBqhhjmrovtlKEQJOgqBqaVNxtvpgzgFUJiiFGjecVq0SVXsrZ7AE23LWDgDZN0OJQiQy20tju0t2nJQiLVx8w6K7VoMdtziPujBCGcZdpJkbHbLnR22ymf4WqodldbNu0D1wuaLNTWlSprxsUhFR0IzKhKFx00zwGJekXMY++8n0evOJ5XE7V2K2gtbmdt37/GQAup8pL933CaxseZfv27ZQWH2TSpIkkJnedIdziPTrLoLmh63PfRl8Rwybp6emUl5dTUFDgl3+3vUlNTaWoqIja2loGDBjAin9vRnV5yuE/em1NnxXEAElJSWzatIlx48b5/HPRG6xWK3Fxce6xNH2dYcOG/U/0EZuFOaf7mP7Ktddey1NPPUVUVBTTpk0jOzubDz74gJ/+9KdH3eZkeJsEBHGAAD3AnD98Mhr3TwdNTU3Y7Xbi4uJ8HUqvKSsrIykpye8y2wAtLS1u8yl/Jy8vj379+p3yUumeYs4p7qko7kxQsJXr7ju/w3NP/+ceCrcfICI6lHZHGxmzI2lrc/DxS3nUVbW7S4AlgNXizvgKjHbfVgdEhnr6bc1/vT+7LokIUvR+Y9Od2dypQ+rfwp1bjN09wJ5/hepZT5ijl2wW0IThPg1SER2y0NIqjOSw14l0vr6XEuHQPMfUj9Axw90hNq9ssfdFiM2mH8Vw5ZZS8v7r6xgyNIZzvzONEROS+eMHd/PIDa/Q2tROeEQIc88dx451nt5ZVdX44OU1fPbOBsbNT8ThsFNZmcKESeM7ZE3TxgxhzPQ0dmXtB2DMtDQyxva8pL6viWHwlE6vWbOGhIQEv8tOemOz2dyieMCAAUQPiEAaN1wURdBvQISPI/x2+vfvj81mo6qqisGDB/s6nF4xaNAgDh48yLBhw469so8x+4hXr159RgviAL3jiSeeoLW1lVdffZXXX3+d0NBQ7r77bp588slTetyAIA4QoAfs2bOHhoYGv+kfrqioIC4urk9c6PWG9vZ2qqur/bbceO/evQwePJjw8HBfh3JCVFRUUFFRcdpKpXtKZ1GclpbG33/3Xz5+fS2R/cN56LnrGT+r5xeCVpuFkZOHkr+rgD179rA76zA5K0qNUV+6IJTeok94CUU8uVhpZnqF8JQMe+Pt4OzuyRUE28ERIpHmqCaMLK5T07PPhhW1eyYxuMVxc7suiIWmL7O4JC6Lvl+3F5b58OpH1rPUsqNoNzPSUoLo/DdDeEy3TUduDN2tSRRF6H5eQug3DRQj+20c8/n/+5BpmSOIiYti+IRk3lr/OIdLqklIicNiEXz82hoOF1cDMH5WBsv+sQkk5K0p48CuWq59NJj6hjomTZrkFokWi8JT/7iTzV/lATDjnHFYrD37W9cXxbBJRETEGVM6nZ6ezldffUVDQwNLbp7P9g1F5K7bQ//4KO79w7W+Du9bEUKQmJjIwYMH/U4QDxw4kB07dmC32wkODj72Bj5mwYIF/OMf//B1GKcUX/T0+nMPsc1m45lnnuGZZ56hurqa2NjY03Id4r9/bQMEOI34Y//w0KFDfR1GrzH7RPxxbrLT6eTgwYPMnTvX16GcEGap9Lhx43xWKv1teIvig0WVvPfSCgAcFQ388tbXeW/HUz0un2pvbycrK4vW5jY+ejaX2kPNAKhOB9hsnhnE3goTuPCqGRTnH6axvoWqpnbaHarev+sufzYzxUYmWZPg9BLNxjoCwGWUOguhZ3uRurGWBTqkqA1RLKVESEGzXSUyyKofUuru02oQ7mpsgKBGr+yu5h1Xp4sLmwWcmruau+NYJq+fnVoHZ+3Lb5pDfeURhqTH8/ZLq43eYzyvlZSoDheHS2uJidNbIMIjQxg2Lsm9y+f++wDrPs0lKMTG3AsncO3kx2lr0R2pG6rbCFfiSUmNYePGjQwfPpxhw4YhhCAo2Mq8iyYd9b3tjr4shk3OlNLp4OBgEhMT2b9/P5MmTeKpf9yJo92JLdjap26yHY3ExETWrFmD0+k0bpD5B6GhoURHR1NZWdlnqnu+jUAfcYBv43RWOQZcpgME6AH+1D/sdDo5cuQI8fG9M5jpC5ju0v7IgQMHiI6O9ktXb2/MUumkpKRjr+wjTFHc0F7DuAX650VKSXNDm9u451jU19ezdu1awsPDmTlzNi1HnAjDFdpqjg7ydo/2Enr/fT+LXbmllO2vpr26qYNIdLtKdzDPMiqVNbwsqPVssHBqCE0YmV79OQVPVli4d+Up18YuaW53ERlslHGroFlBcUiwSzBeAuEyzkHDY5zlXY5tOk8LPR5pOkF7j1vyGt2kgNvgKyTUxq2PXcK9f7iWj97e1LEc3HztNI2IyBDe/N1/ue/y58hand/lfWhrsVNf1UhdRQMuh8p9f7gGmzFjecbZY5h/0WTS09OZO3cuJSUl5OTkoKpqj95jb/xBDIOndLq0tNTvXafT0tI4dOgQDofu5h4UYvMLMQy60VlkZCSHDx/2dSi9Jj4+nurqal+H0SPMPuJNmzb5OpQA/+MEBHGAAMfA3+YP19XVERYW1ieze99Ga2srDQ0NJCQk+DqUXiOlpLi42O/nDpeXl1NZWcmECRP6/IVr//79GT9mElMvTGP8Il28z7lgAsGhxx51tW75ZlZ+tZo1/y5g09JSovtH8NRbdzB+Rjpjp6dx7++uBCRoUhepJt4viTF6SAGUFruXeDaWe21nOj0LTfOMUDLLpNul3gOsSY8AFgLh8JrJqwHSNL8Cq0vS3OYiIsiCUPWxSdL4NlcMkywAR6RwbwdeyV73D8I9g9hd8mwuFMaKnbLF7n23OSneU87Xn++gpam9yzkjBJmLx9LW1MaurcXszi3l57e+TvkBj8hrbmjlnov+yD/+tIw3f/9ffnLF88w+bzzv5z3FP7J/yROv34LVpgvXfv36kZmZSWtrK9988w1tbT030fIXMWwSERHBqFGjyM3NxeXq2Q2evkhUVBT9+vWjtLTU16EcF4mJiX4piAcMGEBtba1fjF/6n5hHLH308ENGjx7Nyy+/7JPtAyXTAQIcA3/rH66pqfFLQ5aKigoGDBjgl7N7q6qqcLlcfinmTVRVJS8vjzFjxvjNzZTUYUlomkZQsJXMiyZx7qUL3MsKckp446mlqC6N6+47jynzRyKlJD8/n8r6Mla8vZsD+fVso4xD+6sJjwzm2nvOZfzMDD77x0bD1VnqvVimgDKzqaCPWFKEx50Z9AZdrxbaDr26mgRVGGXVZs8wBAlwSOnlDW2URrs6mld7C3OhQkubSkSwBcUQy/IoV0ASEKYztmYc1XtV0Wlt86DuPmkDVdOfU/SfpYT//nMT0+aN8Jyf1wipYItCv/5haKpH2GuqpDj/EIOT9b9PBdkl1Fc1upcXFxymvLSGxLR4Qrq5sRESEsLs2bPZsWMHa9euZfr06UcdxWHib2LYxMyu7t27l5EjRx57Ay/sbQ7Wf74dKSVzLpzY7Wt5ukhLS2PXrl1kZGT0+ZtsnRk0aBAFBQV+VzYdExNDe3s7ra2tfuFn8b/QRxygZ+zevbvHs+hP9vYBQRwgwDFYv34906ZN85v+lpqaGtLS0nwdRq8x5yb7IyUlJQwdOvSkWP/7iuLiYmw2m1/0nXmTPmIoMfFRbNy4kdIDJaSnp9PS1M5j171Me6sDJDz5/Vf567pHKT20j8bGRj58dhtHqjwZxtwNRYBk04pd/HnZQ0T28/SwCyH0TIvESPV6XdTbLEi7S3dedqrIYKVjSbTN6/OggSKkMcaJDm7TwqFBkILb2Eoa3ladza/M/ajQ0qKXTAuXp+xZuh20OvUpg7v8uYskUSW49P5haQpno2xaqJonSWx3gcsoVTbGUH3+7kYO769ixIQk9mw/iHRq+sqqhkNKaioaurxfuev3MPu88QAMTIpx3z0QAqxBVvrHfvu4GIvFwsSJE9m/fz8bNmxg/PjxR/3M+qsYBv1zN3bsWDZs2EBKSkqPv39Ul8pPr36R3dklAHzyt3U88/F97kz76WbQoEHk5eVRVVXlnkvsL0RERBAWFkZ1dbVf3ey0Wq3079+fmpoavxDE8+bN45577sHlcvm1kdxR8YGpFn5sqnUi1QInctPtDPzkBQhwcsnOzmbq1Km+DqNHmP3DsbGxvg6lVzidTmpqavzSXbqtrY3Kykq/NsBxOp0UFhYyZcoUv8viQFf3acURQluz3b08JMJKzrYt9IuJZv78+bwfls0R2oyMp+kcrc/C/frz7bz9/FcdD6Bq+nqaAGvnGUlGttfhQtos7lFEAvRRM4ZFs4KhW10SaZEdem6tTlAVvezZdITWjZ89jtaAIU4liipoaXURHmJFaBiZX2N1ox8Y9Eyyvp00SqM1I4vtJZgdukmXpnQjvoUAqRkiXOvokm38m7ulBCXIStr4RIrzypAu1S2qp2SOYNPyPP11MM6lbL+nt1F1aSRmDKL8QC0RkcHc/8frCI86dnWCEIL09HQiIyPZunUrTU1NjB49usNn15/FsElMTAxxcXEUFhYyfvz4Hm1TXHDYLYYBirYfYG/eQUZOTjk1QR4DRVEYOnQoJSUlfieIQRf0FRUVfiWIAWJjY6mpqfELc83hw4djsVjIz8/v8ec8wJnLmjVrfFJCHxDEAQIcg+zsbO6++25fh9Ej6urqCA8P95uSV5OqqioiIiL84m52Z0pLSxk4cKBfOmOb7N27l6ioKL80YjPxFsXpaekMGBhNfU0TkQNCuPiuicTGxTF12mQURaGuvA5cLvDORgi9NDr760L3zFQANA1hsejPWZWOAhWhZ4ldqt4X3OZAhntl8lSPIDZ/Vyx6ObTmnqWkj0mytEnUEMXIlgrdGEvx6uGVnkMKTdLW5MKiCMKsCu3tegkzRoLaLFQQprO09NqB+bPUlylS71/WM9R0EMvuBLWm6f3FTpfnvKTUn7MqaMC+wkoItoFLRRgBvPH0FyxYMoXVH23V9yEEo6foffaqS+WxG16hoa4ZTZM0NrQTO7hfD99tnfj4eDIzM9m4cSNOp9Pd+34miGGTUaNGsXbtWtLT03v099G7uuHbnjudDB06lBUrVtDW1uZ3302DBg0iKytLd3f3o5uFsbGx5Obm+kXcFouFSZMmkZ2dHRDE/+OsXr36hPeRkpJyXNsFBHGAAN+C0+lk+/btTJkyxdeh9Iiamhq/yw6D/5ZLa5pGaWmpX2a2Tdrb29m3bx+zZ8/u8xdOx8JbFD/wyhVs+mw38aNsDB40mGnTPdnvwUNjKd59GM27Z9ZYVl/b7Nmh8Vy/mHAaGlrRpNSzxN4lz6ALQ5eKkKKrkY3WjTGVELrrs0VxjycSYMwz1le2SGn4XXn6jb33oTkkdodKpM2KvcWhj3hygaboGV8Aazvu7LG7d9jM1pqxqF7GYZ3ff7Pv2OwDVhRwqoSFB9N2pAVpsXTdJsgKTj013dpsZ+/OMk9PNdB8pBWAxvpW6muavA4lKS2qIG30EHpDREQEc+fOZf369eTm5jJ27Fg2bdp0Rohh0I2phgwZwu7du3v0PTQwaQC3P3k5r/3qY5DwvUcuZkiab290hYaGEh8fT2lpaa/7oX2N2aNeV1fnV94c/fv396s+4ilTppCdnc33v/99X4dy0jH9Fk/3Mf2R+fPn++zY/tvwFiDAaaCgoACbzcbw4cN9HUqP8EdDLU3TqKys9EtBXFNTg5TSL0sBTfbs2UN8fPwxzYn8BVMUH648SPLUcEaNHcGseTM6iP2fvnQTaaOGEBrS9Z5wc2NbFxOihtomrBaLkeU0nvQWkQKkEAhNg1ZPqbYAd2bUM5IJw3HayMxq0jOOyakZu9aFsZ5Elm5nZ/d+jX20tKlEhFj07LQmEaqG4jDGL5lhem9opnzNKzRvIyzvTLZpqOV9fEMUK1aFtpZ2pNWil1N3EdF0uBqztzr0GIyscWlRBQDRA8JJGTEIxSJQLAKLReG5n77H5WMe5vN/bujyvnwboaGhzJ07l/r6elasWIHVaj0jxLDJyJEjOXz4MA0NXXuyu+PSWxbwcdEf+XjvH7nijrNOcXQ9Izk5mYMHD/qF87E3QggGDhxIRUWFr0PpFd59xP7AlClTyMnJ8XUYAf6HCQjiAAG+hezsbCZNmuQXZkn+2j9cV1eHoij079/f16H0moMHD5KYmOi3mdXm5mYOHDjAqFGjfB3KScVm0+edappGUFBQl/cnMX0gL3zxEL9//y69DNirV7exvpX2didBIR5XWQk47Q5dxHZ3u18Io5zaEKZGxlcXxHi2Mdp4MTylhSlKjeppoeKVUTbLmqFj2bTxi4o+einU4h4PJaQ+gkkxe4rdJ2AcAy9XbInbkEt2znh36l32fvU0VUNinK/V0qnHWeqZcov+93L4+CTGz0r37FsIiveUA3pv6W/+cSdLvpfJlMyRqKqGvc1JW4uDFx75N/t2lfH33/+XF376Hnu2HXtsj8ViwWKxuN9zf/ib3VPCwsJITU0lP7/rHOejYbVZfGak1R0DBw7E6XRSX1/v61B6jdlH/P/snXeYFFXWxn+3qtPkAMMMQ845IyggwSxB0XXVNSfUT901rLtrDqtu3jWuOeccAANKEhUJApJzGsLkPD3Tqep+f1RVh5lBQGaYKbff52mY7rp17+nqVG+957zHbrDqiO2AESNG8OOPP9q6zdiBIE1TraN9i+Pw8Mv5xYgjjmbAihUrbJMubdf64YKCArKzs21HKkOhEPn5+XTs2LGlQ/nZ2LRpEx07diQl5aedfe0Er9fLd999R9euXRk7dixbtmxh27ZtbF6dx5ol2wgFtfBYt8dpKLiBYMwcqqoweHQPFFM19SS4TCVXN9KB5YGJsVCEMc56CCK9fq37ugzX4QohDGIsIZq+SCHCvLh+X0kpDALtrQuR5FEjD1rLRocV7m8sDaFagqIbxNmIVSIb++yZ+4RjV4RRRy2JGJEB6DoulwL+IARCCIdKYmoiT3/+ex5+73q69MqJYdT+usixTm+TzNV3T6dTj4YZFv/43Wu8+8RXfPHW9/zh7EfZu6OoYYwmrJpht9vNCSecQGVlJT/++KPt1MifQu/evSkrK7MNwakPRVHo0KEDe/bsaelQDhvt2rWjtraWmpqagw9uRbAIsR0+B3369EFRFDZt2tTSocTxP4o4IY4jjp+AnQhxvH746KKgoIDExETS0tJaOpSfhYqKCgoKCmxX0/dTqKurY/HixeTm5tKvXz8yMzPp1K47P65cwwsPv8+fLnia2y56mmDAUCFyu7blzCsnGARW18McT9N0JkwdysBjDAMoX20gvIbQdURQN9KbY/y1hOEyLQSEjPkijszEjBWW0U1Ihh2hw9bSUoaF4AOexhpZ2nhrNZITHIbaLEzdWY8aF6UGY2nWYXMugwwrmoy0W7JgpVZrEsVSxaNJc3R6tTBTu4m0vPDW+Ni5Od8gQd3aAYpZqy049bxjGzydAcfEtolTVYW8zfnoukTXdIKBEGu/39booahvoJWYmMiYMWMoKSlhzZo1tiADhwKXy0XPnj3ZsGGDbZ9Tx44d2b9/P7quH3xwK4LD4aBt27a2U4kzMjIIBALU1ta2dCgHhdVObcWKFS0dStNDipa5xXFYiBPiOOI4AEKhkO0MtexWP1xTU0NdXR1ZWVktHcphY+/evbZOl96wYQPdunWzXUZBY9B1nYrSapYsWUJWVhYDBw5ECMGuTfu5/dfPMPO/axh5amcGjc9l/fKdvP7Ilyya/SNn9v4Dn7y4iE69cnj8k5uYMHUoPfrncvmtp3Pi9OGsX7HrgO4kAgy1OKiFa4RRzJulpIbHWjW7elg9lpgcsV4qNSEdNEMhDs8ZrRKHOa40Wi8lOGIeD5tXE2nhZKVUG88lKhXbqmFutH7YUJRjSL0gog5H3fxef4Na4sVfrgPg/ecWhj8jQlEoLaxEC2kxY487ZSC/+d0pJKV6aNs+nb++eR1ZuelhhR6gc++GF80O5CadkJDA2LFjKSgoYPv27Y29fLZEjx49qK2tJT8/v6VD+VnIzMxEVVWKig6s9rdW5OTkUFhY2NJhHBbsWEf8iyTEcdgCcZfpOOI4ADZu3IiiKLYw1AqFQrasHy4pKSEzMxOHw15fRX6/n6KiItu2iCguLqa8vNw2/bV/Cvt3lXDHBU8y+OT2pGclMfDsoWECtnz+BrSQTnFeNbOfXsfUawcC8O7T8w3n45BBTvdsK+TdJ+dxx1OXxcydmp5IeUlNhPxBg9raBnRZmI+apFMgTS4ZqaPVhTAbEwuTvJppy1KiBEE6pGmsRb11RXhdhJEynZyghsmqwPC5Cvcllmbnpug5zPUwY5LRJNdSgnUZQ7INZVnHF6hke+EiSmp2ENDqcDuSaZfam57pY3A5PDGx7t1mkB6/LwhIAlodhd6trHj1I/79+h/whWpwqA769u3Ptf93NZffdDmX3HJ6+DA+8Nr/8cQd71JZWsP0qyY2UJEP1lopMTGRUaNG8d1335GSknLIxneFhYXk5uZy3XXX8fjjjx/SPkcLDoeDPn36sHHjRnJycmxXJy2EoGPHjuzZs8d2WUFZWVmsW7cOTdNsZdbWpk0bSktLbdGPeMSIETzzzDMtHUYc/6Ow11loHHEcRaxYsYKhQ4fa4sevsrISt9ttO7XPrmne+/btIyMjw5a9h6WUbNy4kV69euFyuQ6+QyvHi3+ZSceBKWR1TuGj/6xk9w817NtZTG2Nj5zObQySp2kU76mJIcVrF+2PmWf3lojqVlZYSUlBJb//5/ncdeWLsSpxPcMpgyzWSymur6xGkeHIg6JeOybjMYWIWBwzHxF3aWsub51mKMQisqaAsHGWo04nXLErIzXL0ff1aLIf/Rw03agvxiD1tXWlLNv+MgGtlqzU3iR52lJZu5+80uWUVm9ndKeLcDkSzPpojbbZKbzx+Fe0aZ/Ojk37KfBuYUPpXNxqEpnujrRP701VTSU/rlzNVVddxeeff857770Xjq9Ln/b884MbaQyH2mc4IyODIUOG8MMPPzB+/PhDqpX/5JNP0HWds88++6BjWwJdunRh+/bt7NmzxxYkpz46duzI119/TTAYxOl0HnyHVoKkpCRcLhfl5eW2+s1KT0+3TUaBZaxlt4sOB0O87ZI9ECfEccRxANipfriiooL09PSWDuOwIKWkpKTkZzdRb0ns3buXzp07t3QYPwtlZWVUV1czZsyYlg6lSeBM1ukzqjMfP7yCuuoAKxZtQtcMhbW8uDpMYF0OGpLi+XnheUafNACAb2b/yN9/+yqappPV0WxF1ajplDSVWrP3cBTRFEIgNQ1Uh1FjSz0lWSfioGWlE0sZmcc03CJaARTC4N1R03i9QZIS1MiDpjrtqDUkYqFFnYlJidAxWHVYvQbpqE/wTWjmOCGQus7G/V8Q0Grpm3sqnduNDs+5ed+X7C5ZypbSbxjQ5kSDjAuoqvLxxhNzEUKgOh0kqWkMzzqDrIRuEVLuEfROH8uSgrf44IMP+PDDD/nVr37VMJYoHCoZttCpUyeqqqpYunQpEyZMOCgJ++ijj2jTpg3jx4//yXEtBUVR6NWrF9u3b6dz5862K9lITU0lJSWF/fv324rQCyHCJlV2I8TV1dWEQqFWn4ll+Vls2rSJAQMGtHA0cbQGDBkyhGuvvZaLLrqo2c0/7ZVvE0ccRxF2IsSVlZW2M3eqqakhGAzart1SXV0dFRUVtG/fvqVD+VnYsWMHXbp0afUnR4eCyspK+k1ox4LXN1K234uigBbSGzUdcjodCN1Mn35qLSNP68LZvzuOLn1yOPf6E7n0D1MAePbPH6GZPXeL95VFLu9bbNS8eZI9TD5/NNfcNgUFEWuiBQ1JtBZVOwwNVWWMk24BpmlXY5f4Y026vHUabpeK0xFrcqUJPXYXCUI3lGOhE3adRmI4TcvYJYSmo1g1xEBtoJzSmh14nGl0antMzFo9ssejKk7yK9cRUnRQFVAUNq/egzRNsUJBjdOnnU67xO5mL+bIgm5HEt0yhwGwcOFC3n96HpeMuoffTf4nS79ax+8m/5Nz+v+Jx29/B1+dL4YML1q0CCEEt956KytXrmT69OlkZmaSlpbGWWedFWOCtHXrVv7xj3+QkZHB1KlTycuLXAyxUFVVxfz585k2bVoDov3NN99w9tln06NHDzweD+3atWPUqFHcfvvtjbxOzYuOHTvi9/ttUxtaHx06dLCNahmNNm3a2O6Yezwe3G43VVVVLR3KQfGLNdaq7wFxtG6/AGzYsIEbbriB3NxcZsyYwQ8//NBsa8UJcRxxNIJQKMSPP/5oG0JsR4W4tLQ0bLJiJxQUFJCZmYnb7W7pUA4bdXV1FBQU0K1bt5YO5Yjh9/tZunQpffr24Q//vJxb/n0BT311GwNH9zCIZRTxUhRBjwEdDNVW1yjeVUn5Np0O/VP4w9O/5vI/TUN1qOTnlRIK1SOTliOuZfAkBCgKaW2S6DWkM2dcdBwPv3ENKSmemPjCplvmPgIRrlkOzx2zQ4RUK5pECR0gz85i1ALqAjqaJknyOBpMBVH8PYbwxqrG1vgork/Mg0jKq3YB0DalB6Je3apDcZGe2BFNBqn074+tt47i6e1y00lIcjd47ooqGHvqUADKCqt44cFPKN5fwfZ1e3nw6hfYvn4f3qo65r63hDmfzYtRhleuXAnAli1bGDduHKqqcuWVV9K5c2c+/vhjrrjiCmbNmsXo0aOZO3cu3bt359prr+XTTz/lkksuaXBoZ8+eTSAQ4Kyzzop5/C9/+Qvjx49nxYoVnHjiidxyyy1MmzYNv9/PnDlzGszT3FBVlS5durBjx46jvnZTICcnh+LiYtv1nG3bti3l5eVomnbwwa0EQgjS0tKoqKho6VAOCXFjrTiisXfvXh544AGysrJ44YUXGD16NCNHjuS5557D6/U26Vr2lwjiiKMZsHnzZoQQtmhJEwqFqK6uth0htlvqmQW7tokCQx1u164dSUlJLR3KEUFKyfLly8nIyKB3794IIejWrwMAdzx5Gfdc9gz5u0tom52OO9FJr8GdufKOM/h+zhqWzl1P5945/Pr/TsJbW83333/Pym+38MF/l1JZZv7AOiyjKpPR6bqR/hyFovwKHrn7Q/L3luHxuKgprzXV0Xqp0ybpDPf6Dc+J5XhV/8mhCIEizVriaCVZRMZYd7x1IZISVSpqonspWzFEjbcMtWLmaGQs9beD11cKQKI7s+EYKUl0ZVLKTry+Mtp6YtNgM9olM+6Ugcx6aVFMeGkZifz2r+fRZ2gnJp1spCf37jSIJRjKra5LdN0gHi6PypQbhhGoC8WkSVuEeNmyZSxZsgS3lsb7T8/j18f1IC/vNr788ktWrVrFV199xXHHHUdNTQ1paWls3ryZjz/+GJ/Ph8cTuZDx0UcfkZSUxCmnnBJ+rLCwkHvuuYdx48Yxb968BnX3LaUYduvWjblz5+L1em33eU5OTiYhIYHi4mJbZdrYuY7YToT4ueeea+kw4mglyM7O5o477uCOO+5gzpw5PPvss8yaNYtrr72WW2+9lQsvvJCrr76aoUOHHvFacYU4jjgawfr16+nfv78t1MuqqircbnfMiV1rh1U/bKeTCjAuPpSUlNiSEIdCIXbv3k2PHj1aOpQjxvbt2/H5fAwbNqxBDeXbj3/JjvX78Fb5yNteyODjenHDQ78mIcnNCWcfw+1PXsaFN52Gy220JNErk5AuL50HRJUcKEosGYaY1GkppSHmAgtn/4ivLmDw4JBmOjRHsctACGnW1aKKiLkVhGuFpZRIXTcy3aya4lC9PseWmqtDdI9Jb51Gkif2e0qadyUyHI/Q9Jh5QERirU+Gg3qUuCsI6X4AHA5PbAq5boxzqobyG9J9kTnM5xH0hWjfsR6RFoJXl9zH2NMG87d/PcS6deuYPHkyV15/CU63A0VVEEKQ3TEzTIb9tSEG9BsU851sEeKXX36Zjtld+NO5j/Pt56tZ8sVGlIAHTdP45z//yXHHHQcYRGz48OGcf/75ZGZmxigMPp+PL774gtNPPz3mu3TTpk1omkbv3r0bNaFrqe+whIQE2rdvb0uVWAhBTk6O7fr6RtcR2wl2IsSDBw9m/fr1tu213RikFC1y+6Xh1FNP5YMPPmDPnj088MADtG3blmeeeYYRI0Zw7LHH8vLLL+Pz+Q4+0QEQJ8RxxNEItmzZQp8+fVo6jENCRUUFaWlptjJXseqH7aZqFxUVkZiYSHJyckuHctjYu3cvCQkJtutVXR/V1dVs2rSJYcOGNVoHvWtzPrrpsix1yba1e35yvq0/FjL7mfWMPKUzg47PjWwQUX/oMuK6bJldmbfyijpOPns4SckeI1Vbjz2RM1yfdVBEVO1wZJvU9Ej/YiXiPC00ieLVY2cyVWMhpXmDmvq9iKOUbakYBFxEq92IhgQ4+uRTyrDfl1F7HFWQJqPGhuuqzeNTH+a4mspaFn+5FsWhhNs7jZjQF5fbyWOPPca///1v+vbty2uvvUbH7u14eOYtTL9yApfdNpVH5/yeq/55Mpnt0jnptEn0HxFpveT1etmyZQvdu3fntNNOY+fG/fjrAlgtn73+CtLT0jnvvPNiwsrJyWHt2rX87ne/IzMzQtS/+uorampqGqRLDxgwgLS0NF588UXOOOMM3nnnHcrLyxs+3xZA9+7dycvLIxgMHnxwK4PV19duxMeOdcTRxlqtHb169aKiooLS0tKWDiWOVors7Gxuv/12/vOf/5Cbm4uUkmXLlnHllVfSqVMnHnnkkZ81b5wQxxFHI9iyZYst+g+DPeuHrf7DdlDgo2HndOldu3bRrVs3W104qQ8pJatWraJr164HJPbpbWIvVqxfvpO8rQdWogYf28PsU7w2QootomemP3sSXbg8DlMVjU1z9tcFmTH1Uby+IMlpjbQ9E6JxfxNriujaZIvIYtT9qpZ6Gz2B9ZExH6utNXsRW5ASYZ33WoZZloEWVk2xQahRRAMyHFaNwzXYEodqKKYhzWeSft2Yw1KBpaEgOxVP7JwmaV63fCc6CqgOhNPBqb85jieeeIIbb7yR/v37s2DBgjA57TGgIzPuOYuzrp7I6tWraJuTwVnnTaHf8Ni699WrV6PrOieddBIA7bu0NZV9qNWqCUo/QweNbOAqXVNTw8MPPxwmkxY+/PBDXC4XU6ZMiRnftm1bvv32W8455xzmzZvH+eefT1ZWFqeffnpYoW4pZGZmkpSUxL59+1o0jp+DzMxMdF1vNRcXDhV2rCP2eDy4XC5bGGslJyeTm5vLli1bWjqUpkXcUKtJsG/fPu6//366dOnC2WefTUFBAWeccQYff/wxd999N6qq8vvf/5677777sOeOE+I44mgEcULcvCgtLbVdurSUksLCQlsS4oqKCmpqaujQoUNLh3JE2L59O4FA4IC1/Wu+38aiWatiSJ6m6Xz84tfs3lLQqBp1wvThTJg6hOK9Ncx+ag0jT+nEoPGR45SUlkCb9mmEAgdWV3RdooV0qivrIqTShBQYJNFMvY7pA9wYzLZLRmq0bDg2XAts1RBrhqmWLsO3mPbG4SCjUp2teaINsOo/HrVzkse4+FDrr+e6baLWb5CaJHdmRAG3XLd1CSJyqiElXH/lrfz2t79l4MCBLFiwoMFn6lBaK1lk1DI+TE63eoILqgKFAPTq1vB9smrVKrxeLzt27GDdunX88+ZXmNr1Rt5+413GHDu2Ubf+gQMH8t5771FeXs5XX33F2WefzRdffMEpp5yC3+9vMP5ookuXLuzevbtFY/g5UBSF7Oxs26VNW3XEZWVlLR3KIUMIYau06d69e//yCHEcPxtSSj777DPOPPNMunXrxv33308wGOSOO+5gx44dfPzxx5xxxhncd999bN26lREjRvDCCy8c9jpxQhxHHPUgpWTz5s22IMR2NdQqKyuLSVe0Aywlw25togB2795Nhw4dDtqD9VCwdU0ed134JLed9wRrFm9tgugODQdLlQbYuTFKKTMJm67rfP7G91x70t+459Jn0EKGsuOtquP+K5/nnIG38/XMlaBpFO+pZvbT6wxSfLxh9lNX62ffzhJ0LYoI6nqDtYGwqio0PaLMmvWwaA2JJrqE+m7SUoJqqrA6KL56SlQ9JcBbGyI5UY1xidbVqLksdTgm3Rkj91o2snZINiC9mSldASit2WGELiPjQ8E6Kmr3ogonaQm5EWVZCFIzEiNKu/ncd5QvZe3+rxg6dCgLFiygXbt2MU/vUPsMW4R45MiRAKRmJDHhDKN9U1WgCICpZ5/aYL9Vq1YB0LVrVwJVkNBOo8i7B1+wFrXypy/SuVwuTjrpJN59913GjRtHaWkphYWFP7lPc6Njx45UV1dTWVnZonH8HNi1jrhNmza2IsQAaWlptnmP9OrVK06I4wDggQceoFu3bkybNo1Zs2YxZswY3n777XAdcadOnWLGp6SkMG3atJ/1vRwnxHHEUQ+lpaVUVFTQq1evlg7loKiqqsLlctnKUMvv91NXV2c7El9QUEC7du1QFHt9bYZCIfbu3UuXLl2OeK7aGh+3nfcEq77ZzJrFW7nr4qcoLWj+k6xDSZUGGHxcT1SLgNYnn8APCzexfMFGAF7+x6csm7ee2uooEw4pKc6rCqdPjz+7t0GEoyAAAsEoIimNX1LVqP+VliGXZWIV7S5tGWNBWEVWIEJOrXECdCEgpKFo0tiuSRQdFIt0m+O94RpiKx4R+WUXwuw5bBJi3Vor6rlY5NpUl9WoWK01Et2ZtEnuTl2ggryS5ZF075DOtsJv0GSQ3LQBOBSnqURDgGpOv3QQuoj0Z95WtpgtpYto37YL8+bNa5AlcqhkGAxC7HK5GDhwYPixPzx6MXc9ewUZ3Y39xh5/XKP7AQwfPpzibX4yc5PpNioNEKTTMWbsqlWr2L59e4M5tm3bxrp16+jcuTMdO3ZssP1owul00qFDB3bt2tWicfwctGvXjpqaGmpra1s6lMOCncilhbhC3HKIm2r9fNx7771UVFRw3XXXsW7dOhYuXMi55557wIviYGQNNdZW72CIt12KI4562LJlC+3btyclJaWlQzkorHRpO9WFVlRUkJSU1CRq5dFEaWlpk5DKo419+/aRmJjYJMp2YV5pDIEM+kPkbSugTU7DNNOmxLZt2wgEAvTr1+8nx3Xr14G/vHU9X727lNQ2SZxw9khuOO1fMWM0k6ju31kcNt+KThPO7phBn8FdCJR46DE8k/Li9qz9NlbFErpEBoLGPp6o97FiKsGYhllBzSDKDodxP6SDQ4mkKkenK0elFRvbJIoGuiYbv3KtS4SAWm+IpEQHUf2UCDPikB61r0QQtZ6U0Y9YC4M0iXy0CK5L+nU8nWXbXmbT/jmUVe8gyd2Wytp9lNXmkejKpFfWBMJ9m3WdZfvfZv5vn2Z812tIdKaxr3It28q+QwiFcy+czmOPPRaJTEpKCytxJzo559dnH5QM+/1+NmzYwODBg2Ocn1VVYexpg9l10Ta6dOnS6MWTlStXkpCQQL9+/XAGU3j4jleYfvEp/LBqGWddenLM2Mcff5yXX36ZY489lpEjR5KcnMyOHTuYOXMmAC+++GKruEDWpUsXvv/+ewYMGPCTJ4qtDU6nk/T0dEpKSujcuXNLh3PISE9PZ+fOnS0dxmHBMtbSNK3Ve3f07t2bV199taXDiKMV4Omnn+bCCy88rNZykydPZvLkyYe9ln2+OeOI4yjBTvXDVVVVpKamtnQYhwU71jyHQiHKy8vD9Yp2Ql5eHl26dGmSiybtu2WRnpVCVZkXpMSd6KJbv9yD73gE8Pv9bN68mWOPPfaQTuQGH9eTwcf1DN8/7/qTeOe/cwHI7pjJyAlGXenokwey8pvNxiAzD/iSWydz1pUT8CS6WThzJS/+82OmXjcYEKz9Zr9JYKMIb2N1wIpAYiqsOhDQkaq5j6KYYmlsj2J0EasQm0EJBKpPQ090xJJnS9FVoKYmSKJHxalCYoIThyrIzjBIYptkB7VAbU0wth2ULsGpxNYLg6Fqx+REm9B1kpzpHNv9crYVLaKkZjvF1dtxO5LpnDGSnm3H4VI9YRMuNI1QKDatvDZUaYav8+ijjzb20tGza2/Sq3szfOgIXnvsc7av38vw4/sw/aqJCCHYvaWAbz/9kdLafILBYKOfx927d1NaWsqECRMabPP7/WzcuJERI0agqip9hnXh1EuH882y+fzhzhu54LrTYsafeeaZBINBli1bFm7pkZubywUXXMCf/vSnVpNFlJGRQUJCAvv377cVsQTDtbm0tNRWcaenp1NXV4ff78ftdrd0OIeEhIQEHA6HLUqsevfuzdatW9F1vVVccDpitITR1S/EWKtv376Ulpb+JCHes2cPO3fuZPz48Ue0VpwQxxFHPdiJEHu93gY1FK0dlZWVtqvDLSsrw+PxkJiYePDBrQh1dXWUl5czatSoJpnPk+DiXx/cyDtPfIUW0jj7mhNIb9O8mRSbN28mKyvrZ5uwpbVJMvrlKoLCvBI+efFrzr3+ZIYc17Ne/Sz0HtwZT6JxgtuuQ4ZRU/zkGqZeNxghoHRviKvvmMqP32/joxcWGaQ2qIHTIOpCEWbPYaOaV2ISzKAGLodBgHUMldiCECiAHpLgiCLGqoIe1FEkCJ+OdCsIRdAmw01OppvsNm7aZLpJTnYghODGS3uj6RItJHG5jPl/dV53nE4FKSW13hC1NSGqKwOUFNZSVOijsLCOWp8WNr8Kt4ySUUqxESQgSXCmMqjDFPNkqxGHavM44nAwsdu14eeHlPRqM47+7Scwc91fYl6f3Zv38cVncwnUhpjz7BoWf7aG/zjf4JvZq5FS8sOCjThdDoaO683vpv6bUCCErkv+dcsb/P7fFzR4vbt06XLAVj5ut7tBi6Llaxbzxhtv8Nxzz1FVVRVjqnXmmWdy5plnNjpXa4IQgo4dO5Kfn28rYgmGa/OaNWtaOozDgtPpJCkpicrKygY18K0VQgiSk5Opqalp9YS4e/fuBAIB9u7da7v3cxxNi0mTJnHvvfdyzz33HHDMq6++yj333HPEzu9xQhxHHPWwZcsWjj322JYO45BQU1Nju564FRUVdOvW7eADWxFKSkps54oNUFhYSEZGRpOqGB26t+OW/1zYZPP9FLxeL7t3725U7TtUrPzaUIGlSfZWLtrEudefTPuuWWR3zKQ4vwKAxBQPvQZHLi71H9GNK26byjtPzuPb93Zw0mX9GDCgPz169ODL938wWvwAQtORms70S8bw+azV+H1RhEtVIKQbKda6YbAFmOquiFGZFV2iO9RYJdipkJbgoHO/NLr1TCU3JwFdQlGxj+IiHxs3V1LjDTH5lFxmzt3Pnn11gMStCG6Y0YcX/rsRLaiTlOQgOclBYrKD1DQXWTmJ9OyfQUYbN96aEHt2VLNzayV7dtYQCugN3KjDGnIMEZaxsVr/i3pEOQonnz0y5n4wGGRr3iaDDD+3Fs1MN9+2bl/4uAhFsH75Dnx1QYL+UPjxhR+vaJQQHy4++ugj2rdvT7du3di4caNtvvvrIycnhy1bthAKhWyVNp2ZmUldXR21tbW2uuCYlpZGRUWFbQgxGA7ZXq+3pcM4KFwuF926dWPLli2/EEJs2R0e7TXtj0PpUy6jWgAeCezzrRlHHEcJW7Zs+VkF+UcboVAIn893WLUVLQ27GmqVlJTQtWvXlg7jsGHnvskAmzZtokOHDkdUFtBjQAdWLNxo/Ggqgp4DDdLrcjv414c38uGzCwgFNc64/HhSM2I/S7++9kR+fe2JgOEy/v333wMQCmqYvY8M4yrgij9OYdnSnezfY7rPWj/QTpMgBkOgumJTlBuQTeOxlFQX/Qem07NnKukZLvbledm2o5r53xZSXh5ABHTjfMd0b67xhnCaBNsoIbaMvIyla6oCeCsDUQS2xAjNpdAuJ4EuPVMZM6k9ydNd7N1ZzZZ15excX2YQ1GhTMbURszCMzAGhafiq62KfnwUhGD2pL9fdOz38kGWglZScyIaFRca0QuBwO+k3oiv5u0uQ5oWEviO6kdE2JXLxQBG0bZ/ecJ2fgY0bDZM1v9/PV199RWlp6U8at7VWpKSk4Ha7KSkpsdVn3s51xHbroWwpxHaAZaxl9RmPI44DYffu3U3i+RMnxHHEEQVd19m2bZstUqZrampwOp0xpjKtHZWVlbYz1AqFQlRUVNhOIQ6FQhQXFzNgwICWDuVnobKykv3793PiiSce0TwX3HwadbV+1izexsDR3bn41ojZRtv26Vx971nh+766AKUFlbTrkIHTFfl5rK6o5W+/fYf8fYVMvaaO2mC5yYcNUjxyfB/+eOEz7N9RBNEqL0QcmwGp6WZ6tYyt8TIF186dEhkyIovOXZLYvbOGZUuKydtWRag2RCjJGU6vtvaxCKLXGyLZ4zDIq2pkPANoCQ6o9ceS5Kg64qBfZ99uL/t2eVk8N5/0TDfd+6RyzPgcxp/akY2rSli3pIiaCn9kX8s4LOrK/fV3TSMUCLFjUz6L566nrKQmUusMtMtNZ/xpg5j92reMnNCPdh3Tw27SI4aP4IEt74aPVSio0b1/B9LbpLBt7R6GT+jLtEvHIYRg8+o8vnjre9pkp/HHxy4+vDfCQeB2u+nZsycbNmxg3LhxtjIqBCMl1mpjZCdCDEbatB3riO3m7J2cnGybNle/NKfpOA4df/7zn2PuL1y4sNFxmqaRl5fH22+/zbhx44543TghjiOOKOzbtw+/30/37t1bOpSDwuv1kpSUZKsTt4qKipgaPTvArvXDxcXFJCQk2C6l3sLGjRvp1q3bER93l9vJ//35nIOO27pmD3dc+CQ1lXVkd8zkn+//lqxco9b99Ue/ZM0So/3O7KfXMfUao9XP2m/yAUlCgpOV3+xpXB2FsKO00HSks55JjJR07ZnG6Ik5JCY5WLu6nAVz9+OtCYVJpxI2ZYlNU1ZCIJFG66Ukh6EIqyJco6w7FYRuqtDRqWdmynZsHFBR5mfl98Ws/KaADl2TGXRMFhfcOJAtq0v5YcF+gxjLhsl4H7y4iN2bCyI9h63njCTJo3LSmUP4502vA5CY9hnX/uMkklOTGDVqFIqikJqZRFWZN0zwsztmcvaMSQ0O44y7zmTGXc1X09ujRw927txJQUEB7du3b7Z1mgs5OTmsWLGiyVIIjxbatm3L6tWrWzqMw0JaWhq1tbUEAgHbXJS2FGI7vD969+7N7NmzWzqMpkHcVOuwcN9994X/FkKwcOHCA5JigA4dOvC3v/3tiNeNE+I44ojCli1b6Natmy1+4OxaP2w3Qy271g9bSlFrP/FpDCUlJZSWljJs2LAmnXfL6jzmvr+M9DYpnDVjIglJkdrq5x/6JNxSqji/gnf+O5cbHvo1AGuXbQ/XxxbvrWH2MyYplrB20T7WLttuGmZJCITA/RMZECE9bMKV0yGR4yblkp7pYsXiItatKiVk1RlHvW46EsUfRPc4Y52izXTt2poQSQkqUondD8VQi4UuDSdro2g56v/oVk2xCvL+nTXs31pBWqabY07swPm/G8j6ZUWsXLCfQG0ovER2h4wIGY6ex4S3so7Zr34HgMujcuoVA6go9nLCSZPCruF3Pn05f7v+FarKvUy+cAxjTht84OPXjHA6nfTp04eNGzfa8rPTpk0bNE2z3fesHeuIXS4XiYmJtqojTkpKIhQKEQgEWr07dlwh/t/FggULACMD6oQTTuCyyy7j0ksvbTBOVVXatGlDnz59msSNPE6I44gjClu2bGk1rTQOBjsS4srKStvV4paXl9OhQ4eWDuOwIKWksLCQkSNHHnxwK8TGjRvp2bPnIZ207dlWyEfPLUB1KPzqmhPJ6dx4/Wfe1gJ+f9Yj6LpESsm6Zdv5y5vXhbcHfEH0sIoqCfgj5lhqdLqxEIb79NNrmXrtQNA01n+bj3SoBhXUpSnmSnMmIsRTCNDB41EYf2onuvRI4cdlxcx+r4Sg33TIFKZ6G63oKgJFB+ELGfOpAmkRUCGorQmS2zGpoSogRFR7YjOFOWq/qAAjY0I6wjK0AqrK/Mx7dzttshM59tROXPiHISz5LI+Ny4tBSgr3lkWpwvVaOYUMB+uq8lrcCQ5Ov2YQ/togalJqTAutQaN78MYPf24VylXXrl3Zvn07e/futZ2Dv6IoZGdnU1BQYCtC7HA4SE1NpaKiwjaEGIy0aTsRYofDgcfjoaamxhaEeOfOnbZS4A+IuEJ8WIg20bz00kuZPn36ERlrHip+AQ2+4oij6ZCXl2cbwmY3QqxpGrW1tbbqmyylpLKy0lZp3lJKSkvL0HWdzMzMZl9v9+Z8Nq3chabpBx98CCgvL6eqquqQyhaqK2q5ZfrDzHl7CZ+9vphbz3oEf12g0bE/freFUFBD13SkLln1zWbTHMvAb353SsxV5pS0BHTdeE4jxvcBPfb5Fe+uZPaTaxh5eleOOb0b3fu0N+qHVQX8wYhTczQZBnoMSOc3V/dFUeCNZzez/LuiCBmuBymEcV5jEU4r/VkHoUlEyFClvTVBkhIdqH4tlkhruvkjX689kl7vbMm6G+0QrcXOVVpYy6cvb2buW9sYcUIHplzeh+S0xk9UBRJ8fgiFQAhcHpXTrx1MoC5E0ZYgUy9qvN6rpckwGKSyZ8+ebN++/ZAcTlsbrDpiu8Eil3ZCSkqKbUyqLNjFWMu6CL1///4WjiSOlsRLL73EGWeccVTWihPiOOKIwv79+21ROyalDNcQ2wVerxeHw9Hqr0xHo7a2llAoZBsSv3HFTi4acTdP3PM6JXnecKuh5sKLf/mEa0/8Kzef8R/u/M1/Ywjmz8WOHTvo3LnzIRmv7Vi/l5qKWnRNR9d0Sgsr2bezuNGxnXpmh/9WFEF2x0wczohKOerEAZz0q5EgJXpQ48NnFjDr5W/YuHIXg4/tyaQzhhkkMRQy/lcUigt8zH5mPf3HtSepXYRMCiGMMRaEwOVROPmcrkyY0plv5+zliw93U+sNhbeHyaipMEurU4dVF6zpxrwShJTGDRA61FaHSEx0oIYkak0IETTIu6PCvDhgjcXaF8MYSzdVY4u8W+Oi3a+t+BQFVEHelkrefXgNNZUBfn3zYHoPb9vARExGOVG7PCqTrx1MoDbI3Fc28sPCzfz3ng/CFxtaIzp16kRtbS2lpaUtHcpho127dlRXV1NbW9vSoRwWrDZGdoJdyGU0kpOTbdF6SVVVsrOz44Q4jqOGOCGOI44o5Ofnk5ub29JhHBSBQIBgMGgrhbimpsaWJmCpqbHpna0Z//zda5SXVNN5YFuWfbaZr95d2mxrVZV7ee/JeeH7qxdvZeWiTUc0Z11dHfv37z/kPtUdurfD4VQNPqkIPIku2nVoPFV02Lg+XHPfWbTv0pa+w7ty/8tXNxizfd3eGFL70fMLuWX6I9x54VOUFVZx6nlRPWpNNbl4n5fZz6xj5EmdGDQu4u4rdCBoqKxpbdz86qq+uDwO3n56I9vWV5hk1CS/1v+KElFvLZIsBKgKGkQUYisFzyTGdd4QSckOJBJFlzh8BhkXmowZG61aG8SX2H7DIS1iwBXSG6jbxvMW+H0aX7+/nblvbuO4KV0YO60Liln77HI7jFRpiCHDc15cT8BUwr98bxkXj76Pa0/+G2u+3/oTr3DLwOFw0KVLF3bs2NHSoRw2XC4Xbdq0sZ1KbCnEdlLl7UIuo5GUlGQbEp+bm0t+fn5Lh3HkkKJlbjaEoig4HI5w/biiKKiqetBbU/Rej9cQxxFHFPLz822hENfU1ODxeJrkS+BowW4p3mAQYjv1TC4vriI5w016diJ7N5VRXlzdbGs19nOr1HcuPkzs2rWLrKysQ36ftG2fzn0vX81r//oM1aFy+e3TSE47cA3i9CsnMv3KieH7NVV1LJ27nqRUD+uWbGP7hv1hpVZKSeHeSJ/R1Yu3cuKvRjLnzcVGWrSMENfivTVmTfEgEAprv4uQkU7dUzj5vB5sWFnC0vn7sbTXcBsmVQmTa8waWqlL43Ew64kBjwPpMwirMP+RpvO0tyaIw6Hg9qj4g0QIrEOAnwYKbnQqtBAC/MFw3XB4m1XHHG1WYu2rGDHu2VzOh0+s47RL+3D5Xcfyxj+W4Kv24U5wIfVQDBnWQrFEp6yoivKiau69/DneXPFAjMFZa0C3bt2YN2+erYyeLFhp03bolmAhNTWVUChEXV2dbY53UlISgUDAVnWuycnJ5OXltXQYh4T27dv/MghxHIeM8ePHI4QIfwdY948G7HM2HUccRwF2SZm2I7m0Y8yVlZW2eD9YmHrp8WzeuIX8reUoQmX8tKZ1aY5GSkYSF91yOq//53MAjpnUn2HH9/nZ8+m6Tl5eHkOHDj2s/UZM6MeICf0Oe72aqjpumPJvwxQKDKJnEV0pOXvGRD58dmHMPrldsxh98kCWzl1njLMuSEmJoqn8+FUBI0/pAFKydnEhA0a347jJnfn6k91s2VAeNrmSltlVdKp0TNo0GA2VAMyxDgVEyNhmGVFLkIogGJQEAzqJyU5qq4MEE8zKYVU0rGW2nqu1HoBTRfqDDdOkGzsPCRNmBSkl1eV+Pn5yHSec24OzbhjM7GfW4a8NMe36odRVBxolwyBAUZFIfLUByourSEjKOsRX7uggMTGRdu3asXv3bvr1O/z3V0siJyeH9evXEwwGbdPzXVVV2xlrOZ1O3G43NTU1R8WvoSlgqdqtwcDuYPilEGLrK/hor2lH1G+v9FPtlpoacUIcRxwm/H4/ZWVltiBAdlQtvF6vbdw4wUhjraiosNXJ8BV3nMEXs78iUC158qszD+i43FS48JbTOeFXx+CrDdClT84RtT4oLCxECHHU3iPLF2yIkGFokB48+LheJCS6eeOROQCc+KuR9B/ZjaFjexmEWJdh06hLfn86aW1SePzeD9m7q5SpVw8gt3squd1Tmf3CJgryaowzlMQoFUk5sGqrAFoDlVigqyqOoIbUrRZHxjYJeL1BPClOgkIiXEaKvz/NibO4DqEAVtp/lLIdWdBUqXXdmPZASr9utlWKdq4GggGdOa9uZszUrky/YTC11cEDk2ER/R4RoEJ1ZaTetarci98XJKt9euMxHEV06dKF1atXN1lbj6OFpKQkEhMTKSkpscXvmQWrjtgOZUsWrDpiuxDixMREdF3H5/ORkJDQ0uH8JNq3b8+ePXtaOow4/kcQJ8RxxGGioKAARVFsQdr8fj8ej6elwzgsWDXEdoHdDLUsCKfO8aeNOmonaO27NE2P5t27d9O5c+dmVy2klHz5zlLe/u9X0Q8a/wvTekoI3ntmAf969wZOu+A4QgGNmqo6Vny9CV9tABQ1TCjdLoWZL39DRXmtYbSVV82mZYUMHp/L6m/yKcyrCRta6SHNcKJWwPRirhccYTIqghLpiCKfAqRbQQZCxkPm+rouwQm13hCJ6S6oDUX2calIVaBoEqlbbZ0EWMpxTCq1+f+ByHC05NCY/CDhhy/z6D64DRnZicx/awuaJiMEWOqx60Vh7gfLydtSQFlhFa/841OklEw8czh/eOziFiWi1m9BUVEROTk5BxndupCRkWG7DJf09HTbKYJ2qyNWFAWXy4Xf72/1hDg3N5elS5vPB+OoId52yRaIE+I44jCRn59Pdna2LQyUfD6frYiaVWdlp5TpqqoqUlJSbPF+sODz+fD7/bZ6b4BhplVUVMTgwYObdZ2AP8Rt5z/BxhW7jAeEqEcARfi/9T/sYt3ynQw8phvvPPEVL//jUwBS2yTH1PzqQqGq3Bs2rup3bA59R2Uz/+2tjJveHW9lgLXf5htGViHd7B+sIBVi05ZFlOpr1RJLGUmxBlMxjlWTCengceCtDZGU6DC3RZ4HioIMGSQ60nJYhNcJq9NafYJcb4y1W7RzufW4rhsGWlf2oyzfS3lRHZOv6s/Hj67GW2k6XYvGiK3x/Ga/uCh2PmDhJytZs3Q7f3z0YoaMaZne8Iqi0KVLF3bv3m07Qpyenk5xceOO660VaWlpbN68uaXDOCwkJSXZzh3b4/Hg8/laOoyD4peSMh3HoePPf/7zz9pPCMHdd999RGvHCXEccZiwS/0wGMTHTgpxTU0NbrfbNvVsYM+a54qKClJSUmxltgZGdkZmZmazlQFIKfH7gnwze1WEDBsbDCIIhmpaT8HcunYP/Ud04fWHvwg/VlXmjaQfC4GmGQZcSMjtkcqYM7vz6bNrKdheRUW+1zDaQrL22wKj/jeoIVQFqeuRGmSIJaqWC3RIR3erUfXGoLsUFE1iMVyhGc+v1hsi2W2lRZtz+jSUaPKsKpELABbR1fVwuyVr7fB2C9Etkkx1O0yMpTTI8BX9CNSFWP55PsV7y3F5VE69oj+f/HetoRSbrtqeRBe+2gCpGYn0HNiBlfM3HOBVg7KCSu6/8jneXvUQLk/LfHd06NCBrVu3EgqFbPW5Sk9PZ+vW1ufg/VNITk7G7/fbqvY5OTmZvXv3tnQYhwW32x0nxHE0ir1793LPPffwxRdfUFpaSvv27Zk+fTr33nsvGRkZP2vORYsWMWnSJHRd58477+TBBx884Nj77rvvZ60RJ8RxxNGEsIvDNNiPEHu9XtuRSzsS4srKStLS0lo6jMNGQUFBsylwW9fu4e5LnqGy3Is7ofGTbHeCkwlnDGfr+v3s3JQfJoPLF2zg+6/WEhLCSHXW9TCxA0AIcjpmUJJfhjvZycmX9ue7D7dRsK0ChELxnhpmP7ueqVcPAARrvytA6BLdH0JxO9AV3VCbowgvJrkGgxDjVAxzLAABoRQnrqpgWNkVgPRpeKuDZOYk4KjRUJKM8c46PWwWJsEgw9GE17oFtYYGWtF1zfVTna3Ubl3icithMjzn1c0kpSTgSnSyZn4B48/vzsTzejHvzS3GeE3y+MybyGibQlJqApqmc07fP+Kri/RLrr9WXY2fmspaMj0t875OTk4mISGB4uJi2/w+gKG2+v1+6urqWn1qrAWXy4XL5aKmpuZnn3wfbdjJpMqCnRTi4uJiW10gaRQt0QbpZ6y3fft2xowZQ1FREWeeeSZ9+/Zl2bJlPProo3zxxRd89913tGlzeL4k1dXVXHrppSQmJh5Su68FCxYcdtxNhTghjiMOE3bpQSyltF0NsdfrtVX9MBgxH+6Xf0ujoqKCrKzW5dZ7MIRCIUpKShg0aFCzzP/wH9+mstyo8fPXBcPKarhWV8A1957F3A9/YOeGvZE6Yqmz6tstkfRoIUBVcbgVQv6goc4K6D2oA+f830Tyy3aydVUxm1aUGPuYXLJ4TzWzn9/A1Bn9AcKkWGoSIXSkKsEZrRRHiGi4BZMjKq1aCIIeFYffVG0VgdAk3toQnRIdKJLwNhGd6myhvqO1JKYmOQaNqcVR87lcCpOv6GuQ4de3oAV1qkqqQUCgLsiXr2zi7BuHMOyEDqyav884Hvsr6NjdqM1VVYU/PH4Jf7/hFQL+ENmdMjn9wjG88/hXBkmW0GdYFzLatVwJgBAi3MbIToTY4XCQnJxMZWWlbQgxREyq7EKIExMT0TTNFiZVFjweD36/v6XDOCiys7MRQlBQUECnTp1aOpxfPK677jqKiop47LHH+O1vfxt+/JZbbuHhhx/mzjvv5Omnnz6sOW+88UYqKyu5/fbbufPOOw86fsKECYcdd1PBPraJccTRzLBLyrT1Q+Z2t66+nT+Furo6WxF4sKdCbLe+yWAYFiUmJjbbsa4ordeLuV7dsJTwxF3vs+GHnZEHpJUiHEUehQBFIaQTTnV2OlWyO7Zh+45t+Lwhvp+9y9hNUYh2NSneXcXs59Yz8qSODBqbY6qyIYTl6hwFiSkomHeUkBVD1BinEo4HIdDdKl6fRmKiAylluGETgVBs/NFPOroXSP0xUXPHBhcJ1uVUmGKR4dc2owV1I2CHahwfh0pddYAvXtrI8JM60a5zMultk1FUwWdvLGbnpv0AjD5pABnZaSgOleL9Fbzx8Bxu+tf5nHDWSC685TQeeuP/Wlx5y8nJobCw0EiNtxHS09NtV9+alJRkK5MqVVVtk4JswS4KsdPpJCsry/Zp00K2zO1wsH37dr788ku6du3K9ddfH7Pt/vvvJykpiddee+2wPpuffPIJL730Eo899pgtxKY4IY4jDhN2SZn2+Xw4nU7bmT3ZiRAHg0H8fr+tCHFdXR1+v992KdPNmS4NcPZVkw46RtcPcPZgGmU1UEgVQdtObWjfNYvF81eQ3S2RBe9sjfDo2MlBSop3VTH7WYMUDx6bbZywBDUUXYZrdI20Zoyew6rhQq1aMURDVSIZcSbB93oNU62QR8GfahB2EQgd4DlF7atpjZtpWX9b5NgiyJqOy6Ew5fI+BHwac97YihY0a4SjHaGFAFWhdG81hdt8nHHNEC743UncdsGTPH7ne9ww5V+s+nYzZcXVFO4pQ9d0dF0SDIT4+/WvMu+D5Xz66rfURLVkailkZmai6zrl5eUtHcphIT09ncrKypYO47BgKcR2QpwQNx/idcRHB1aq8imnnNLA2T8lJYWxY8dSW1vLkiVLDmm+oqIiZsyYwfTp07nooouaPN7mQJwQxxGHCTsRYjuRS7Bfm6iamppwPZtdUFlZSXJysq2Mf6SUFBYWNishPufqSfzh4Qvp3r8DWbkZDWtlwSR9akw7pT+/fFWEMIZTmK3AoaSgkvx9pUw4vw/ff7SNmjKf9aSMfaLnNvcJK8UndmLwmHYIHfCHIGCuUa9FL4oxn/AZLYtk1E2P5q1BjdraEB6PikhVwbxWVtcpOUKmw8Teug/oEhF1McBQp801rH2ioSi4Ep1MuaIP/lpDGQ4FdUPRVpVYldmqhZaQmZJFanoSeXt2Rhtp89mb35PRNoXMdqkoioiYVpvrVpZ5mfXKt428YEcXiqKQnZ1NQUFBS4dyWLD6+toJdiTEdiKYYK9444T4yFBVVRVzO1CqvOXu3rt370a39+plOP1v2bLlkNadMWMGuq4fdoq1oig4HI7wOoqioKrqQW9Ncd5jnzOnOOJoZsQJcfPBbjHb0QTMjunSluLW3D2TTzhrJCecNZI57yzhkT+9E35cUQTJ6YlUVUSdHAqFPkM6MnJCf667/2yevv8jdF3n1HNGseLbbZQUVhpsTpcce0YPygtq2bjYOGFTPQ40zZSJGyitxp/FeTXMfm49U2cYRltrFheihDR0ISDBEWmxJDCcr0MSNagR1NUYwqwlOlBqjLRr4deoqwyg65Ikj4O6gEnIVdCFMI2h610J0DFUXdNpWpo10kCYCEuirh9IicutMuWS3vjrQsx5cyualc6tKCANt+2YVXQdIeDVf39OenYi59w6gnZdUinaXYUAUjOScDhV/vb29bz411nUef3s31VM8X5LiZWoB+qLfJSRk5PD5s2b6d+/f0uHcshIS0vD5/PZ6vvXIsR2M6myQ02uBbfbjd/vt8Uxzs3NZf/+/S0dxpEhOivnaK4JDWqv77333kadnK1MkgNlmFmPH8oFthdffJGZM2fyzjvvkJ2dfegxA+PHj0cIEe44Yd0/GogT4jjiwFCqysrKaNu2bUuHclDYTW3Vdd12MdfU1NjOBMyOhlpW72/rB89bVcdbj39JaWEVJ/1qJCMm9GvS9SZNH8l3X6xh+YKNqA6FiVOGcsaVE7hx+mMx40759WhK8itZ9vVmcrq1Y+LUoVx04ym8+9Q8Xv6X0YIpIyeR3iPa8c7ffgBFcM7Vk2jfNYvH73q/Yf2tpc6ajxXviSbFsHpxIYqioAd1pEuNtGCSEukQEJSo/hBaYpTTqkOgO0DVFGSSgp7gxFsXIikhihBb6dbRjtHhmMxtimLUxjrqpTub262T5jAZ9oWY86ZhoNWAZAtB9z45HDO+D8V7yijaV8a6ZdsBqCis5cd5eRx7RndmPv4jQhGcfZVhoNKpZzb3vnAVACsWbuT+q54n6A+RlZvJGVe0nMlKNNq1a8eKFStsZRDodDpJTk6moqLCNn2Uk5KSbGlSZRfFFYx47WLO2aZNG9uVKrQm7Nmzh9TUiClhc3vP7Nq1i5tuuolf//rXnHvuuYe9/8KFC3/yfnMiTojjiAODZIZCIVuogj6fz1aGWnY1AWuunrjNhYqKCnr27NnSYRwWSkpK6N69e/j+Q//3Equ/M3qnfv3JSv7z8Y30Hda1ydZzuR3c+NfzmDHhAXx1QeZ/uJy8rQVMPGMYC2euMgZJydP3fcDn73Zkx6Z8dE3nzSfmUl1Ry6yXF4EwyO7oKV3ZuKSA6ooAOBzMnbmK5LREXIluAv56tbuNXOGOIcVCsHpxAUowhEx2I92O2P0cCkKrZ4IFaAlOVG8Iaaqo3jqNZI9KSVTZaMzKFifW6ym5VtunaOgRAu9yiigybCrDujTbOMXu5qvxs+XH3eR2acvpvzk2TIgBVs/fw4CxuXTum0He+lL2bCuiQ7d2MfuPmNiP15f/meL95XTqmYPL3TpOU5xOJ5mZmZSUlNiGEEOkjtguhNgyqbJTuyi3222rWm1VVXE6nbYgxMnJyRQVFbV0GEeGFmy7lJqaGkOIDwRLAT7Q+9h6/GBZaFdccQUJCQk8+eSThxFs60C8hjiOOCBcs5SSktLCkRwcdkp/AyNel8vVwKihNcOOx9huhlq6rlNVVRXzA7t68TZ0XYZNrtYu2R6zT0VpDU/e8wF/vf4VoyXSz8Dapduoq/EjNR2pS7auzuNXMyZAKGTcNI2gP0TetkL0cPozzH59cZgg5nRNIbdnGivm7Q0/VlFSw75dJREyHO3cHO3eHIXivV6jpviEDgw5LttwlQ5qDdVcRRj1xg0eN6fVJWg63toQSQn1zPZ0QJOgGfXCwiLDMT1/o9XsqG1C4PKoTLm0TywZVhRwqkZKt9mlCgUIaezfWcSq77by6VtL+PKDHxgypmf4uQc1wcp5exl9Zg+EgDbZjb9fUzOS6DGgY6shwxbsaFJlxzpiOyqudooX7BNzSkqK7WrK7Yg+ffoAB64R3rrVuFB9oBpjCytXrqSoqIisrCyEEOHb5ZdfDsBDDz2EEILp06cfdozBYJA1a9bwzTffsGbNGoLB4GHP8VNoXb82ccTRQqiurkZRFFuQoEAgYCu11Q5XoevDboS4pqaGhIQEnE7nwQe3ElifueisjO79ctmxcR+6ZqTq9hjQMWafey59hu3r9yGl5NvPfuSJz/9At76H186hQ7dIWrlQBInJHjp0zSK9bTJVZV50KREIhhzbgx8WbQEkUpdI0y0aYNSUrqxZuA9fTewPsox2qz5QqjKmaVSiBwQUFvgMpfhqUyleXozwhZAJ9V5LXUfx6eiJjsj8gC4kSkBDCQlqq4OkuCKE2FXii73qHUOCIxCA1PRIOyopzJphhakX9zIMtF7fjBatcljPzawftlK8o+dfu3wHby+9h1f/M4fZb3wPwPrF+Qwen8u5N0+g12B79RZNS0tj586dLR3GYSE1NZXdu3e3dBiHBbvV5NqFXEbD5XIRCARaOoyDIiUlherq6oMPbM1owRriQ8WkSUY3hi+//BJd12MEjOrqar777jsSExM59thjf3KeSy65hNrahp0Btm7dyqJFixg6dCgjRoxg2LBhhxxbaWkpt912G2+++WbM58zj8XDBBRfw17/+tUnKHeOEOI44MAhFSkpKqzeYAAiFQrZyErYbuQT7paXb8RhXVFSQlpYW85m75/krefreDynOr+C03xzH8PF9wtsCviBb1+wJ35fA+uU7DpsQd+mbS79jerBt3R7S2yZz2+OXkpDk5i9vXMczf/6I2iofv77uREafNIAPXljE3A+Ws3+nmbInBG07JpPVKYUvXtzY+AJR6moMzPs6QHJC5DGXoLDQz6xn1zPtarOmeFkxIadi9PQF0HSkIlBCOnpQB2fkZCWU5MRh9iKurdVISXLgrDRqiN3lAUJWTGCmQVvx1AtPYijJUiJNZXjqxb3w12nMeXu7YZytRindMWSYhmRbgFAUbj77CfbtLA6TbV2T/LhwHwOOa8/Hzy9g4vSRpLdt/Zk5YCjEVVVVDU4YWzPsSNbs2MbILiZVFhwOB6FQI23ZWhmSk5PtT4htgB49enDKKafw5Zdf8t///pff/va34W333nsvXq+Xa665JqZcZNOmTQD07ds3/Nhjj8X6cVh4+eWXWbRoEVOmTOHBBx885LgKCwsZO3YsO3bsIC0tjVGjRpGTk0NBQQE//vgjL7zwAgsWLOC77747bAOv+rDPWXUccTQjqqurbZEuDQYhtlsPYjuRS13XCQQCtiKYdiXE9euRsnIzuPu5Kxsd73Q76Ni9Hft3lxipzAJ6DjQU5IA/yNP3fsAP8zfSa0gnbvrHb0jJaLzO87pT/s6+HcUAFO+vpKbaOPHu1i+Xv711fczY31x3Ij36tufeK58PE76Bx3dgy/ICArUBbn34Qv5169uRHerV+Db4u7HHFAEeB0XFfmY+u54zrh6ABFavLEU6dKRDQToUs15XoGg6ujPKtEsQbsHk9YbIyvKgBsONhiPrBEOg6QbJdkS+P6Q1zCK5EtwuhSlhMryNUEgP9yKOSbeOVr8DmrHNjBMp0STs210SOTZmzFuWFjDq9C58/OR83n96AU999acDvl6tCVY2Q01NzSHV5bUGeDweQqGQrS6k2o3EW79vdsqGsgshjqdMHz08+eSTjBkzht/97nfMmzePfv36sXTpUhYsWEDv3r156KGHYsb362eYXspGsqCaCnfccQc7duzgpptu4r777ov53q2qquLee+/l0Ucf5c477+T5558/orXscYkzjjiaGTU1NbYw1AL7KcR+v99WhNiOJmB2JMSVlZWHVfMshOCBV6/h2JMH0n9kN/70+CVhw633n5rHF298T/H+cpZ8uY6n7/3ggPNYZNjCzJcW/eS6Q8f2MoikBJdHpcewLNZ9u5/RJw2grqbeSbvZjgkrvbr+iUJ0j16IVWmdKsUVIWa+tIljTuzAkOFtUEJ6g32FLiNzm/W+useBCGpGDXGSg2CSQXh1xYxF04zFHA5Q6/3sK0TMsVQFV4LDbK2kMeed7YRC0jAScxg9mqUVi0VwdR18QUQDhThKRQYjVk2HYJBgbYCtywvoN7YDpYWVrPh600++Bq0FQgjS09NtVZPrdDpRFMVWBNNuhDjapMousAsh/kUoxLKFboeJHj168MMPP3DZZZexdOlS/v3vf7N9+3ZuvPFGlixZQps2bX7W0z8SzJ49m+OPP57//Oc/DS5Cpqam8vDDDzN27FhmzZp1xGvZ56w6jjiaEXZSiDVNsxUhDoVCtiJrlqJtl5RIMEi8Xd6/0Lih1qEgp3Mb7n72igaP79lWaBbCgq7prFu2g4uPuQeAGfecxfhpkXol1aGgRRHN3K4/XXt0z2XPGn9ISc+hWZTuq0GRTvy1Af5rtViyUofD6cNRE9RTiBUh0ANBcDlj+hNbBLK42M/MFzcz7QojXfzHtRXIqI97OLVZDe+IEAYRr6kzCLF0GNv8OUk4t1Ua/YWViHotBQYJlkTqhgGXSzD1QrNm+K2thDTMdkwi5jmZFcMQ0gyCHt07uT6siwPRirKAjd/tZ/rvR/Dde1vIaGcPtRUiJlWdO3du6VAOCUKIcN9Zu1z0tVsNMdiHYFpQVdUW8f4iaohthE6dOvHSSy8d0tjDUYYvu+wyLrvsssOOp7q6mnHjxv3kmOOPP56VK1ce9tz1YZ8zvjjiaEZUV1fb4mRB13VbEmI7xWu3FG+wn0JspcA11Wdu9MkDkbpENdXP4v0VlBRUUlJQyd+uf4Vdm/PDY3//8IU4nIaC2r1/LlffO/2A8372xmJWf78trKB2G5LF9lXFlBZU8ON3phunrofdqQWNmFZpDV2jFV0i/EFDMY2GEKAoFJcFmPXqFkae2IEhg9LDtb3WGEWTCAlCRhyjpUPB69dI9KgI85dd96gNhAJpKsExZBwMA62LjDTpL97ZTkgKgwxb46LPFnQ93JYpZv5o4itl5NhIHfQQho22cQGhvKgOb7mfc2+awODjWme7MH9dgM2rdlNWGHGWtptCDPZTXO1WQwz2I8QOhwNN01o6jIPiF5EybROFuDWib9++5Ofn/+SY/Pz8sEv2kcA+Z6lxxNGMsEy1WjusHzA7EUy7EWI71YFZsBshttKlm8qAZuKZI3C6HPz47WYSkz28++S88DYpJQ/OeIHnvr4TIQSTzhzBpDNHHNK8bz42J/y306OS2yudb947QLsnIUhOTWDEhH4s/HR1zOMWmXUnOPH7QiAxyHMghNTNFkYQSS8Giov8zHppM2dc3gcUwapNVeF5REgiFQxiq+uIgIYQhsu0oggSzPkUn9b4MY42/rLcpC/saZDht7ehWcpx/X2lBE03ib/5mENBarqRMi0B3TzJ1jTjFq0MR5Fqh8vJiDGDCAQCCCEIBTVKCirIbJfWKtotlRdXcfMZD1O4pxTVoXD7k5czdvIQ0tLSqKqqspWBkt0Iph1NquyiuFpwOBx4vd6WDuOgSE5Oxu/3EwwGbdVFIY6mwY033sh1113HTTfdxODBgxts//HHH3n33Xd56qmnjnitlv/ViSOOVgC7KMTWD66dCKbdCLEdf3jtpmrX1taSmJjYpHOOPX0IY08fgr8uwJfvLKWiJJJmt29HEVXlXtIyD+8z7kl0h/lcp76ZVBbVUlVS14AoHjOpHz0HdeStx+fy9ayVCEU1WjBZZlUYf/rLa8DpMFKYJUaqc8i8mK8qDXK2ivd5mWWmTwtg5boKUAUCgcOnowsN6VARimrUEgd0fH6NJJcxkavEZ6S1WeTXNCNDRNyhXS7FSJOu0/jirW1ommxYZwyGIiyl+XQOQLIbg64DAqfbQTAqVd3hVGjfvj3Lli0jP6+EP53/JMX7y0lvm8Lf3rqOLr1yfvK1aW58+uq3FO8rA0DTdJ5/8GPGTh5CYmIimqbZqv2d3RRi6/s3FArZ5rvYjgqxHeK1hIqamhoyMjJaOJo4mhuLFsV6enTr1o2TTz6ZUaNGcckllzB+/Hiys7MpLCzk66+/5rXXXuP000+na9euR7y2fc5S44ijGWEXhdhymLbLVXOwZ82z3eK1W512c6rw7gQXf3j8Yu78zZOAUUOZkOLhpjMfRtclPQd1onPPbM687Hg8SW6e/8tMNvywkyFjenHFn6bidBmvvRbSGHv6YD54ZgGaptNlYFt2rSsx2auphgpBVk4a9zx7BRcf92fA9JFCMmh0dzas3GU4NEuQQhpkWEoIBg1SrBrEVGhmn2PMNGUAc7/ivTXMemET067si3Sq/Li6LMJHVSWWmgqB1xsK9yLWUtwo5X4jXl2CIhASZEgDReByq5E06fd2oAkF1OhiYfNvXRIWha3vHt0kt9ZwxUyvVswrCEEzrRrjabXvlsXeHUXoZvp3t77tyczMBOCjl+dRaqYlV5XV8PLfZ3Pv81f97PdAkyPqkDgcDhwOh63MAj0ej63STq3v3zghbj7YJd7ExESEEFRXV9uXEEth1qoc5TVtiIkTJzZ6fiul5Pnnn+eFF16IeQzgk08+YebMmUdcAmCfs7444mhG2MVUy25kDewXs93aWvn9foQQuFyuI5pH03TmvreU4n3ljD19CN36d2iS+BZ+vILl89fTtW8uZ82YhMOp4vP5mtWxcvjxfbnl4Qt5/6l5uD1Otm/Kp3aPQSSL9lew5Kt1fPPZakZM6MNnb3yPlLBzcwF7thfxwEszEELwj9+9xqLZPxoTSklOt1S+eafAJIkRt+ji/eXcc8kzZLZLparci65LdE1nzeKtBuklMjw6TTmcfuxQjdRQXUf6A+ByYBUBW7tZSvHUK/ogVcHqVWXm+sSItVIBb51GUqLxeZNOM53ZmixqXpdTZcqFvfD7TDKsRdU/R6u9mpm2Kuo9Hk2WVSW2xzEg3C7DhDqggaqQt7UwZv/1y3dSXlxNmzZt2JNQGd4mJdTVBlg2bz3lxVWMOnEAGVlH33Rr8sXj+Oq9ZRTtLUNVFa6888zwNktxtVPrpdLS0pYO45AhhLBlCnI83qaHoigkJSXFjbX+R3DPPfe0mOBjn7PUOOJoRtTU1LSIpfzhwm7kEuwXs93UVqt++Eh/RJ686z0+e+07FFXwzhNf8dhnf6Bbv9wjmvPrmSv5+w2voCgCKX+gvLiKq+89+6ikeJ/869Gc/OvRrF26nT+e94T5qHGMdF2yb2cxnkRXhKNJWLFoC889OJOLbj6VRZ+uDhNal0uQlpVI8Z5GTsqkZNWiTdz8yEW8+fhXFO8rR+oy1tPkQK+NlBAMGVHpuvEaBjWkM6p+1/y/KL+WWa9tZdrFvUDC6pVlKEEd3R118UaCty5EYoL5eVNMxdZygjbncrkUplzSC39diC/e22mQ4RiYrtJmunWDWs5oMzBViaRiRz1f4xgoZHVOp3hfeeS5RJFip9tBWloaPYe0x+F0EPAHUVRBUpKbe01377Q2yTw5549kZqc1fgybCZntUnlmwR3s3LCPrNwM2rZPD2+zWwqy3eIF+xA2C3YxqbJgp+Nrd2MtwwDx6K9pR9x3330ttnbcZTqOVokVK1YghGD06NGNbn/rrbcQQiCEYOfOnQ2219XV4fF4SExMPKT2DXGFuHkgpbRdzHZL8W4qcjn/g+UA6JqhcC7+YvVB9jg4Vn69CUVV0HWJlLB8/gag+UzAdm3az7K566iprA0/1nNgR9rkpCGUWFLqcKocM6mfcSdKuf34ha9ZsWhTRN0F2nZJo7rMhz8AOJ0N++2qKlIKho/tbTg+W/13w22YZGxbJiu9OIqkoqqxCm0UcZRSglOh2CTFo8a0Y+iQDERAQ60OoPiCCL+GYvYiTvSYplp1QWNKNaLwutwWGdb44t3GyDCRFGErPl1GbsEo12wr3uhjG60cYzh+N9hm7rv4izWkp6ejK0Gem387dz19Oc989SeWfrUuPKyqrIZvP19NRUk129fvJRg4eifxngQX/UZ0iyHDYD+Cabd4wZ4mVfF4mwdH2nrJOlf8qdvChQubLuA4bAn7nPXF8T+FYcOGkZGRwYoVK6iqqmqQmjZv3jyEqVzMnz+fK6+8Mmb7d999h9/v5+STTz4ksmCXejC7pfPquo6U0lYE024EvqnIZbsOmezdXmik/OqS7E5HnjHRvX8HdFNNVFRBz4GdkFI2Sw3xp69+yxO3vwNARrtUHvvsD7Rtn05CkptHPr6J/971Pkvmrg+PDwVCOBwqvYd2ZsuPeWHyJ0MhVn+31RhkEr6sTskU74lSKBTFcFC2ehADj9z2rllXG2umBTTo9xue2zK8stylNc1QiqVilipbym5kvuKCOma9vo1pF/VEAGuWFaMHNIRTRQahtjxAbkfDsMxZWksoSpV1uVWmXBxpraRpmPJFVF1wIATR7/9oMi+tOOqlTktzX2u71GMvBkQ/Z6v+Gnjk9nd5+stb8Xq9ZLRLZuxpg5FSkpjqoaaiNrxs/u4SLjrmHrSQTsce7fj3RzeRmpHU2NvgqMBurs1ut5tgMIimabb5/bCj4mqn3sl2uuDQVBd07r333gNuawpTpgOiJdog2VQhbknY56wvjv8pKIrCxIkT+eijj/j666+ZNm1azPb58+czceJE1qxZ0yghnj9/PgAnnnjiIa1nlxMFXddtEaeFuCt28yMYDB5x/TDA7U9dxt+uf4WifWWc/OvRnHD2yCOec+plx1NeUs2SL9fSfUAH/u/P5xAMBtF1vckJ8Wv/+jT8d2VJNV+9s4Tf3HQaAG1z0nG4Gr6mX7y9hGfm/omLR99PbY0/TGjHnDqIeZ+sIuAzYk1vl0hZQW2D/aPbJAFR9cEYRDP6pMQii9FEWRFAVF2xqkZqi83CX6kqxpioGt3i/RH3aaRkzfISZNDYx19aR3J3w01b6BGl2eWqR4ZDJhHWASIGWEIIw+ArSq0Oc+AGTtLmcwyGGm63XK0VNZJObfUmFpHdK0tqcblcYRdZIQR/euwS/nLdy9RW+5g0fQTfzP4RzXSo3rezmM/fWMx5N5zc8PU4SvB4PJSXl7fY+ocL6/shGAza5vfDTgom2C9eVVXRdf3gA1sBFEVpklhbMh03jp8PKSXvv/8+c+bMYd++fY1eeBJCMG/evEb2PnTY56wvjv85nHjiiXz00UfMnz8/hhDv2rWLnTt3cuWVV5KRkcGCBQsa7Hu4hNguRNNOfRmB8I+YUp84tGLYjRA31Xuia99cnp53exNEFIGqKlz2p6lc9qep4ceqqqpQVbXJj7HL7Yzxq3J5Yt1pO/XMNtOGY+Mr2lvO47Nv4YUHP6G6wstZMyYxfHxfHv7gtzx03avs3VlEUqqT4rxIyt5FN53KsScP4IapD8cGIUyCK01JILyWBF3EpGGHibOlHEcrxuG5MPv7SmRdwKjXVRWEplOy3xupKRaCNUuLQEJtVYCEJEd4WXSJy+1gymV98Ps05ry9LUKGLYSNtwxy3ui7KYbsmvctbmule+tRUoiUIFSTANdTw83vhexOmfQe3InSpXtiFKARE/vx7tq/EgyE8CS4uHDk3TFLa6GWPZG3Wwqy9f0gG1zQaL2wI8G0k6JtZdjZAXYi73E0Lfx+P5MnT2bhwoXhc53o9611vynOgexzlhrH/xxOOOEEgAZXfaz7J5xwApMmTSI/P58NGzaEt1dVVfHDDz+QkZHB8OHDD2ktXddtQ9rsRIitLy47xWyXbAELdrtI0lz9W2/423nhlklJ6Yks+GQlHz2/MPwePP+6kzj5V6Nwe5xhQleYV8oNp/+TsqJq7n7+Kv7x/o0cd+pgALr2aU/PgR1AShLT3NRW+kDXOGZCHy688RR69O/A1XedEQlAQNsc04dAiCgH6CiCqOkGGdRMRTbcuqieshomkOGpUYRA0SVKUDOUXyEo3lPDrNe2MfL4bAYf0xa8PmrL60hMNi8GaBKXR2XK5b3x+0JhMizC8Zgp2gc6qNFxKWbqtmrGpyqgCqRi3iD8f8zziK6XBoQiaNchg04929EuN501S7c3SjBVVcGTYCibV95xRrgGvE37dE674LgDRXxU4HK5bJUea0dCbDeCaTfY6T3RVApxHPbD3//+dxYsWMBdd91FSUkJUkruu+8+9u/fz5tvvkmnTp04//zzCQQCR7yWfWSQOP7n0K9fP9q3b8+6desoLi4mKysLMNTf5ORkjjnmmHBt8fz58+nfvz8AX3/9NZqmMXHixEMmuXYhxHb48YqG3cga2C9mu7x3LeiWm3ITY9SJA3hz9V+486Kn2bpmD9vX72P7esMheNzkIbg8Tm75128466oJ3H7BU1SWGoqvLiXPPPAxZ1x2PCdMH8HWNXv49M3vyemYSWKy2yDEqS68VQGQktXfbQGgYE8ZvQZ34q+vX81dlz2LFtQpya88eKCx9tMQ0sL9iA+kEscgSgYXQlCyt4bZL29m2mV9EJrOppXFOM0+xM5EB6f+xnCTrq8Miwa1vY2sVz8eVcSsH65vDqeI10sJt64KRD03qUmKTNfpPdsKWbd0B7e/9KtGCaaUkqVz11NSUMk9z19FUoqHXoM74UlsWb8HRVFs911sN1JhJwUT7BevndDcKdMej4fbbrvtiOePo+nx3nvvMXz4cO6///6Yx3Nycjj//PMZNWoUQ4cO5ZFHHuH3v//9Ea0VJ8RxtGqceOKJvP766yxYsIBzzz0XgAULFnD88cfjcDgYMGAA7dq1Y/78+dxwww3A4adLg31Ihd3Imt3itSPsdoyllM32WUtKSWDPtsIoIy+F7ev3Mm7yEMCoP71p+qME/MEwmZMobFu/n//84R3mf7SCHxdvC8+XkOQGIUhIclJXHQAhSEzx8MnL3/DMQ7MMwzinSgMhqzHF13rOQomkHGum+VRIMxXYeoSyvpuz9ZiVnmzVFO+LqikGQiEdh0PhtAt74fOGmPPWVoMM10d9IhxNyuuPqf+8YuYx/wmbatUbZz0Wrmk2jbdMElG4p4LUjOQG037wzHxeeGgmQhEIAX9/97ctTobBnuTHbjHH421eRCvErf33o6kIcX1SZSEtLa1ZCbGAo9926egu12zYvn07M2bMCN8XQhAMBsP3u3fvzpQpU3j55ZePmBC3fgYQx/80rLRpi+Ru3LiR/Px8Jk2aFB4zceJEFi5cGP7C/CUT4jiaH3Y4QYhGPN5YDDu+j0GgFIGu6QwZ0yu8bcWiTQYZhobkE2LIMECd12/wVEWE61Yry7w8+5dZ4ZPfUEBr6OgZdm2WJKUmRNKGVTXiGC0EONTGSbCmGSQ5pEXUWGubtQQgNAkhHXSTFL+0mZEndEALGrEG6jSDDAej6pnrtXSKhhRm6rP5v9VC6oAmqfXnqd9mKhp6FLmPfs5CsH39Pr77/Eeeuvt9Ar7Iyc7nbyw2ptMNEr3goxWNxn20YTfyA/aMOY7mg51+M5qKEEvr+6zeraKi4siDjKNZ4HQ6Yww4U1JSKC4ujhnTpUsXduzYccRrxRXiOFo1LFJr1Q1H1w9bmDhxIu+++y6rVq2ic+fOrF27lg4dOtC3b99DXkfXdTRNi7ny1BoRCoWQUrb6OC1YcdolXjB+NO3wXrAQCoVQFMU28Tb3e+Kmf5xLx55ZFO8tY8zpQxgwqlt4rZwuGTjdZn24RQ4U475QBA6nStAfa+STnGz8GKsOJbKvKlCFEpnHKKAlpl7YREKyi0BIi1WJoxEy1VPLXCsclxKbnmw5UtdTkmXUvhXFdcx9ZxuTLzWU4q8/3oGiChT1AEpw1EmxbOwEWdcbxNFglHWiao3RgJDE6XbQJjudpBQ3vho/+3eWGMZi4fUjx8JbWYfqlCx6ZzWarnHNfWcDkNM5k9KiCnTduIjStn1qq3ifa5qGruutIpZDhRCCQCBgm5itHvZ2iVfXdVu9JyzDskAg0Oo9M4QQ8Xry/1F07NiRffv2he/37t2b77//PmbMqlWryMzMPOK1hIxfMoyjlaNnz55s376dvLw8brzxRhYsWEBpaWlY0d20aRP9+vXjH//4B126dOG8887jkksu4ZVXXjnkNSZOnMiwYcOYOHFiMz2LOOKII4444ogjjjgOB3fffTe33347v/nNb37W/i1lIFZVVUVaWhpd/vYQShO3GTwYdJ+P3bfdSWVlZdhrx464+uqrmTt3blgB/stf/sJdd93FFVdcwVlnncXChQv5z3/+wwUXXMBrr712RGvFFeI4Wj1OPPFEtm/fzty5c1m4cCETJkyISW/u27cvOTk5zJ8/ny5duoT3ORwoisKgQYOYPHlyk8be1Ni1axclJSWMHHnkPWKPBrxeL9988w2nnXZaS4dyyFi4cCGDBg2iTZs2LR3KIWHDhg0IIejXr19Lh3JIKCoqYtOmTYwfP77FYpBS8vHzC/nizcVktEvl+gfPpVOv7PD2Oq8fp8vBtSf9jfLSaq78x3jeeuB7aquDgODuZy4nrW0Kz/1lJkV7y+ncK4c92wqoKKkJ1y9HY8bdZ4AUfPnRCnZvLYxNX9b12HZM1uPRaq6ljqhq7PaoEzynR+XUy/rh92uUFngZdnwH/HUhVn6Tz7oVJcYgs72ShBh1WJhp0Y0q2NZYyzG+fl1zY2nTujTSuaO3m/+lJLuorqyLmXP4SR1JTHby7QeRlPVHPrrRaJXVClFeXs6qVatiMpVaO+bMmcOYMWNISUlp6VAOCatWrSItLY3u3bu3dCiHhL1797Jv3z5Gjx7d0qEcEvx+P/PmzeO0005r9eVif//731t9jHE0Dy644AL27NnDrl276Nq1KzfddBOffPIJL774Ii+99BJSSnr27Mnf/va3I14rTojjaPU44YQTePbZZ3n44YcpLy+PqR+2MGnSJGbOnMnmzZuBn0eIhRA4nc6DD25BOBwOW8Rpwel0IqW0TbxgXE1WVbXVxZy/u4TnH/yYqjIvZ1w+nuOnDgOM9iR2OsZWnM0drxbSKN5fQUZWCm6zfU80zr3uFM697pTGY0w3Yjv9grG88o9P8deFUFWFYG2IlIxEXnhwFgF/kBPOGsmp/z6Oy49/EK0+EY4iivk7Srn67ulMu2gcv53+KNvW74uMkRKURkilEpWSHTRNwAhFUpijiLQrwcEpl/bFV6cx5/Ut9BhkXMz57M2tnH5BLzQdo08xQDjMqHpkM+1ZEpW6ba0d3QbKSpkWEVOscJ/h6H1CZmsoa2zYTAsq/SHjWEURYpfbQW11kKA/khaphVrve1pRFBRFabXxNQYpJS6XyzYxt9bv4QPBbu8JK2Xa5XK1+npi23u8HNCEoZnX/AVg4sSJMZmbiYmJfPfdd3zyySds27aNrl27Mm3aNBITE494rTghjqPV44QTTkAIwdq1a8P362PSpEm89dZb7Ny5kz59+tChQ4fDWiPe+L150Np/aA+E1lZJIqXkrgufpGBPGbqus27Zdh7r3IZegzsjhLDVe/domPuUFVXxx3MeY9+OIpLTEnjwjevoM7TLYc9z/g0n06lHO3bs30RimpuqYh/V5bVUV9QB8MYjc6gs9zYkw/VI4pKv1rFh6XZ6DOrImJMHsH3Dvghf1s2exA5T/bWcp6N7FEeTY12PUZBdHpXJV/XH7zPIsBaSVJYbLYxKCuuY9dpWpl3cCwSsWVoMjihlWSdmLkHsfaLMtcLbo+432kdZN0y+pOkmLaLfm5qGJokl3UDbDikEaiL3R53Qnx4DDu87/GjCbkZ2YL+Y7RZvHM0HXdebpM75QG2XAKZPn87QoUOPeI04mh8Oh4Nf/epXTT9vk88YRxxNjKysLAYNGsSaNWto27YtAwcObDAmWjU+XHUY7NOj0Y5OoXaLtzVeHPHVBti/qyTmsW1r94YJsZ2OsdPpbHbjmQ+enkf+buN4eat9PHPvh/znk5t/1lxjTx+C/Laaof8YxKfPLefrWatithfvLSO3a1v27y6JOCmbnYWs/OT8XcXkS9i8MR+EwOVxEvD6Yvv5+kJRPX2jTLCcjljCaY1X1QgZrgsx581tRma1EFSV+wDwJDgoLqhj1uvbmHZRTwBWLysxCLaUCBVAIDUdpJkOXY+EhElyY2gsbVpi9FVGCbtUC11GXSQwnvMlN53KaeePJhTU2Lh1Dd26dWP8iaORUjJwVI9WrQgFg0EcDvucPlluuq35mNaH3VRBuxF46zfDDjE31XvhQG2XALp27dp8hDiuEDcZqqqqqKysJC0trclro+3zbRPH/zRWr16NlJLi4uJGv8B79uwZ/tH/73//e9jzxwlx88D6EbPDsbXgcDjC6WStBZ5EF937d0BRFYQiUFWF/iO7AfZ571pwu90EAoFmjdlXFwj/LaXEV+c/ovkSEhJwJzroOahThMSan8N+x3TnqjvOIC0jMdJrF0xiqxou1opqEFsTQX+QPsO74vaY6ZW6NEikqhpKsaWgKiI2bVlVweEAhyNMhgN1Iea8sslotSSMM6+Q2XM4LdMDuk5xfi2zXt/GyPHtGXxcNtKpIl0quhBIAdKhIFURdpqWAuO+am4Ho465PjG3EO1YXU/9Ndy1ReT5mRg5oS91Xj87N+dTW1tHQkICg4/tyZDjeqGqrfvUxOfzxbQCae2wE/mxEAqFbHXRwW4EHuzzfjjSY3ugdkvRt8suu6zpAo6jSREIBHjooYfo0aMHGRkZdO3alYyMDHr06MFDDz1EIBA4+CSHAPt828QRRzPCLqTCbumx1glNKBTC5WpYx9kaoapqqyPEQggefOP/eOPhL6gu93L6RWPp0qc9YMRrp5YUbrcbMEhFU9T9NIZplx7Pgg9/oM7rRwjBb3536hHNl5iYiNfrpc+w2LTr5FQP895fxp5tRQ13ik7xU6JaMmH01d23o4hHZ93M6sVbeeq+j80tIjLOOldt5KQ1hgy/utkgwJ6o9kgm0jNd7N0OYJDimW/v4IzfdAcBq38oAaeC1KWhDCsCiQ5aVB2xEKAKUCRSERDUEOixCnZ9g68DXa8TwnhOTgcXXD2ekoIKbvrVqyiq4IqHjmPf9lLatm17gJ1bF+xGiK3vh9beXicaoVDIdvHajcD/rxDiOOyL6upqTjzxRFasWIEQgs75mfonAAEAAElEQVSdO5OTk0NBQQG7d+/mnnvuYebMmcybN4/k5OQjWss+n9444mhGOJ3OJrvK1JxwOBy2Ij/WCY2dCHFrVIgBMrJSueEv5zZ43O124/P5WiCinwdFUcIxNxch7to3l+cX3cWmVbvp1DP7sNyKpZQs/mIN29btZfXirWxfv4/jpvRm5OTu+PK9MWNrKuuoqTrAsa+ffqxLg1xa+5Z7ufaUf6I6FCDKuCqaFFsXvxQlXEfcgAxrEjzOSJ2xomB6RpPaxmM4SEtj3aKCOma+u4szzjWyC1avLAMhkZoeJsXoDdOmDZUXcBgE2opNBEMm8bdUc2IJdWPHQQjefHwuHXrmoEtJVocU/HUh7r7iFZ794lZyOhn9JD96fiGzXvmGjHap3Pi38+jcK6fx49wC8Pv9JCQktHQYhwyfz4eqqrYibHYjmPF4mw92K1GoDyHN5J2jvOYvAffeey8//PADZ599Nv/85z/p1q1beNvOnTu59dZb+eijj7j33nv597//fURrxS+5xBEHkJycTE1NzcEHtjBaK1k7EIQQtovZbhcdPB6PrQgxGDH7/UeWxnwwZGanMea0wYfduufd/87lwWte4u0nvmLjil0EfEFWLtxOdXU1bz0+B6EIFFWgKIKczj/RmqtBerHZPikUMv4XCggFLVQv9Tj6/3rzuBIcTJ4xIJwmHQpJcKoR5+loxRZIy3THxqRJivJrmfnuTo4Zm83gEW2QCkinYijAikC6VKSqhNOnY6AIcCjGmk4HUlUM0h4IgT+ECGrG/ZD5PHXdqB+OTjPXDdIfDBgtrNp2SKJ4bw3BQIgv3l0KwMpFm3n2zx+Tn1fGhpW7uPnsRwn4G/8O8VbXxaTIHw3YTSG24rWLIgiGqm0nEmQnggn2ire6uto27cLiaFq89957DB06lPfffz+GDAN069aN999/nyFDhvDuu+8e8VpxQhxHHBiEuLq6uqXDOCjsRi6hdaYg/xTsdoztSohbQ8xlRVWsXLSJssLK8GNz3lli/BHFU6tLffjrgjhMDjT6pIFMv3ICD7xyDYp6AJKh6wYpDIWMFkVgklaDCIchaKCghvOlLZIrREQZrjVrhkMysm9UTbMRu1lD3MYDQlilxShSQlCnqKCOT97dyagx7RgywkhTlg4F3WESYYGhBouo+Rqoxph1wQq4HOBQkYoIe4kJBELTjeMQds6OtJDqM9iox87qmEzJ3hpAhpfI21YQSbFGUFvt460nvoxZXkrJM/d+wDn9/sQ5ff/Ip6992/jr0Azw+Xzh1H87wG7xgr0IG8RTvJsTtifEsoVuvwCUlJRw6qkHLnkSQnDqqadSWlp6xGvFCXEccQApKSlxhbiZYLeY7Ravx+NB07Rmd25uSrSGNO/NP+7mynF/5s4LnuTysX9m/fIdAGR3zERpxNSpeHc1Od3TkNKoUZ5xz1nUev3o2kHOPARRhDLKfMoifIoSa6KlKoaxlnWyKgROj8rkGQPw14aY88pGgwxbBDOaDEtppjYbJ+ZpGe4Iadal0R/YVKqLCuv45L2djDrOJMWm+ZVUo5RmVTENt6IJfCPp1EIYqrHDHG8ZcelRcYU0Qxk3j8k3n64BXSe7awpFe2pAh92bC1m/fKdxkaHehYIdG/bHLLtxxU4+fuFrADRN58k736O6PDalvbng9/ttpRDbLV4ppa0IG9iLYIJ94pVSUlNTc8T1oXHYE127dqWiouInx1RWVtK1a9cjXitOiOOIA4MQ20khtpPTtN0Ipt3idTqdKIrS7CnITYnWoBC//9Q8/D7jIkIwEOKdxw0F8vLbp+F0qSAlLo+TKRePJSs3g7yNZXQZ2JbM7FR6mz2Nczq3we1xhlNRVYdC515RKdox3LG+ClwP9ZViRQGnE2eqhynXDDL6DL+51UiTlmaVsNthKMCYLZMaqddNznQbY1VhEGVLMQ7psaR4uJn+rQj0+t8viuFALRsY20QU7BiXacspW0pDHbZ6J9dDWtsEUjM97N1YClLy/VfruPW8//LU/TONAVHPaeAx3WP2ramsi7mv6/KopE7rum47gmm3FG+rZMUOhM1CPMW7eeD3+wmFQvZWiOP42ZgxYwbvvvsue/fubXR7Xl4e77zzDjNmzDjiteKEOI44sFcNMWCrGle7EUy7xSuEaBUE83DQkvH66wI8/+AnrFu2PfyYAFSnoarOe385Qb9BlEOBEKUFFTz88U3Ulmnk9EgjFAyyZXUeAKkZSfz51WvoM6wzfYZ2ZtyUoeRtK4ykEzdW2xtWdA9+UcvlUZlyZT+jz/AbWw1l2FKTXY6GZNQineb9qgo/6W08sSZXplGXAoiARlF+LZ+8X08pdiroaj2yqxq3cNQhPULx66dsgzHWYbZaqv9UzftdBrRh39YKQsFGBlhp5RJ6DujAOddMihkxZEwvuvbNDd8fO3kIbdunH/hgNhGs962dUpDtRoit7187EDYLdiGYFjRNs0WKtyVU2FohjqdMHzLy8vJibmeffTbHH388w4cP5y9/+QvffvstW7du5dtvv+Whhx5i5MiRTJgwgbPOOuuI17bPpzeOOJoRdlGIo12b7fLjazeTKrfbbSu1FVpHCvLhIDU1lc2bN7fI2s/++WM+f3NxxC0ZSEpN4JLfTwagsrQmzO10TWfJV+vZsvpflBVXM7ygI+26pfCvm1/n9WX3I4Rg8LE9efjjm1m7dDt/PO+JxtVfaNASCU2LpEVHpzyb41wJDiZf1R9/ncac17eEs42Nkx1Jg36/WNnZkTUqy3ykt/GwZ0fku01IkCE9kq4thVFT/N5Ozvy16T69ohQU0KVE6DJK2zZSuqVu1vtqWsTdOrodnFn7i9NhxOtQjPRpSyk2jbS7Dsxk7+bKyKEJP//IMfu/+85iysVjGxhCuRNcPPTmdfz3jnfxVtVx2gXHHRXTqKqqKpKTk23VBsbn89GuXbuWDuOQ4ff7cTgctjrGdvpNBvvEW1NTg6qqtrqgE8fPR9euXRv9HpdScvfddzf6+CeffMKsWbOOWMho/Z+GOOI4CrCLQqyqKkIIWymYdlNc7UYuoXWkIB8O0tLS8Pv9LaJcrV+2PUyGFUUw8oQB3PbEpSQkGYrfqecfyzefroq5wF5eXA0Sdq8todvgLLb9sI77r3iOu5+7EtWs192/qzh2oej05cYIrKJGyG29dGlXotM00Aoy57XNaNJUmnUdpwJBXcQSb2stIRD+EKhG9FWlftLbuI11LJ6pmn+EdHAqBjfVjJZMn7y/kzPPsVoylYJDIKVEBjSUxoy/dJ0we9WJ7fVRj9hGCLihJme2T6Z9j3TOPPd0vuz5A+88Na/eKyVJSU9kykVjUBup6QZ45NY3WbFwIwBrvt/Gfz65mb7DuzY6tqlQUVFBenp6s67R1LCbQmy3eME+BNOCXeK1DLXs5JBeH/G2S4eOSy65pMVe69b/aYgjjqMAuyjEYD/F1W6E2GoJJKW0zY/w0Whj1JRwOBwkJydTUVFBTs7R7S876Nie5G0rROoSXZeMPnFAmAwDDDu+D49/9gceu+0dtqzZg9SlwWdVwZZlBfz69mPwJDlZOnc9S+euZ8xpgwEYOrY3nkQX/rqgodLWf+9EKaMxqDfMaSnDtSG+fHUzekADzM+7pqO7HIaBVfQaUWtJp8NI1wYqyvx065NucFcpwWEQYMwWS+YeCARSk7GkWMLqVaZzp0tFBs0UaYvES4k008yNDGcBwVBk3hhluR4UhX7HticjPYPcTu3Iap9K2IlbElaYqytq2bBiF4NG92h0mlXfbEY3L24IRbBm8dajQoizsrKadY2mht0Ipt3iBfsQTAuhUAin09nSYRwU1dXV9k6XjuOw8PLLL7fY2vbJR4kjjmaEXdougf0IpsvlIhA4un1CjwQejwdd123l2mw3hRggPT39oO6RzYEZd0/nnGtPYMTEvlx971mcfuGYBmO69+/A5bdNi+mAdPEtp1NZXEvhzir6HNseIKY3bnbHTIaO6x1JWY6uq9V00EI4HCpgpg6LCHG0coadHpUpV/WPGGhZ3Zqsm0M1CKCkIRm2CLISUY+rKoKktXEbPYY9TiPdWVEMTmu5W5tjjRRoKMo3jbbGmEZbpvIso+uHdd10wjZvVt9iK8U1FNV/OPpYhJVs6NQvjVUL8tBCGh88tzBy8KMPOlCwt+yAr2XXfrlhR3CpS7r173DAsU0FuynEoVAITdNsRTDtRoillASDQVsQTAt2IfA1NTX2N9SSomVucRwWWv+nIY44jgLs0nYJ7EeI3W63bS42gHF8HQ4HPp8Pl8vV0uEcEtxuN8XFxQcf2IqQnp5OSUnJUV/X5XFyxe1nNLrt+zlree0/n+F0Ophxz3QenfV71i7ZRrd+uQwd2xtvtY8Vi9Yw5qyeVO0PcuzJA8L7vv6fz1ny5brGF1UMshgKmp/baJMtq2Y40Wm0VvJphoGWBigKUuiGg7SqRtKeJQbJdqgNzayi0q8ry+pISXOhJDjMvsVm6rJuTSIi8SGMdTQZJsXhmuKVpYbTtEsgAxqqrHctXTGkb6mYDtZOFekLGvPVr50GOvfNxOlWWblwBxun7iY/L6qHpLmPogo8iW6Gje3V4HBKKfns9cWkt00ht1sWDofC5IvGMnJSP5bNXc8Lf/kEb1Udv7r2BM66alKD/X8ufD4ffr+ftLS0JpuzueHz+VAUxVZkzW4u3lZGkZ1itgshjivEcVjYu3cvq1atoqKigrS0NIYPH07Hjh2bbP7W/2mII46jgLhC3Hywo3pp1RGnpqa2dCiHBDse47S0NLZt29bSYYRRkFfKQ9e8iG6qt3dd9BR/eet6Bo7qQY+BhvJ45R1nMGpJf3bkb+Tmx87Bk2ikWi9fuIE3HpkTETbD6i0RBTga4Wxlo7bYleCIJcOh+m2PDDVXWnM3kiodnk9GCGhtTYhgQCc9w01piT+KiBNlzlW/x7BhpFW0v7YhKZYSnCq61FD0+kRcgKoiNc14eg7ViDek1es+JRgyIZcNi/Pp3rc9noSGF50GHtONngM7Mu2ScbTNSW+wfc7bS3jizveMrk/AWVdN5NTzj+P2859g9Xdbw+Oeve8jeg3qzMADpFwfLioqKkhOTrYFkbDg8/lwu922Kf8AI+bMzMyWDuOQ4fP5cDqdtnBttmAnQmx7hTiOI8Lu3bu55ppr+OqrrxpsO/nkk3n66aebpA9x6/80xBHHUUBKSgqBQIBAINDqVUGn0xlP521mJCQk2CrmxMREamtr0XXdNs6saWlp+Hy+VpMeuW9HEZpm1bFK/HVBfn/WowAce/JA7n7uCvJ3l/C3618hu2cyVad4UXU33ft34su3lpj7YVk903toZ/oN60pqeiIfvbiImqq68NxAuE7W5VGZPGMAgejWShZ50TRE/dfT3CajjbikWa+rm+7RUdVQFaU+0jNclJb4ozyuREMybcVmxiWkpGhfY6QYgxSHNBQtKiU6vL+ITQl3OpCBYJgUd+yZSpsOScx5cT3X3/cregzowPQrJ/DxC18bA3TJuqXbydtcwHnXn8w3s39k8Zw1bFq5i6qyGo47ZRDBkI6iCHTd6Me86tstdOzeLoYMh1/XnUVNSojtlC4N4PV6SUxMbOkwDgut5TvhUGE3RRsgGAzaghD/MlKmOfptkGxqqlUfBQUFjBs3jn379tG1a1fGjx9P+/btyc/P55tvvuHLL79k3Lhx/PDDD0fsR9L6Pw1xxHEUYH3h1tTUtPor03ZzQbajSZXdjnFycjJCCKqrq22Tzul0OklKSmoRY63G0GtIZ5LTE6mt9oWNmiws+WodG1fs4vM3F1NRWk15STVDTuzEp+8t5Lf3Xkxymkk4pMGIXR4Hj35yCwDLF26kprqR95KUuDwKk68eSMBntFbSQ1GEMqSZxDUy3lJzJRjp0haEQIY0Q42OFqmEoKLUT3q6G0l1uBjZaoMsrHmjP5eK2SLJGErRXm+EFEtYs6LUVIMVdKmjhLRYR2lp1jgrUY9ZypmuMXpKV1Z9lUfQF2LgqO4IIbjm7umgywgpllBV7uWNh79g9qvfRmLTdeZ/uJxjThxomIRhOIX3HdYFX11DUzlPoouh4/o0PPY/E5WVlbRt27bJ5jsasCOJtxshtlu8YJ+Y4ynT/9t44IEH2LdvH3//+9+55ZZbYrIwNE3j4Ycf5o9//CMPPvggTzzxxBGtZQ8pIY44mhmJiYlhQtHaYTfF1ePxIKW0lbFWQkICdXV1LR3GIUMI0WImVUeCzMxMSktLDz7wKCA1I4mHP76JY07oH3E8joKiCIL+EEjDwGnZrJ3k9EkiFAox9dJxOJ1mSyFd57I/TA3vt3jOWsP4KaZtkYIrwcm064eiKKrRZ9hMkxZW799olRVir/hbCm/0zaGC2xmbUi0llaU+0jNdkV/7sPEW4TFYBljRqd6W2RZQvLuame9aRluZkbEOFV1VkJoMk+gYtq1EBS4lvYZnk5jqZufqMpLTEvnL9a+wde0eAIoaMc/au6PIcK+Oet5CUWiTncpFN51Gn2FdmHzRWK6+ZzoTzxxBm5zIxaABo3vw6Ke3kt2xaS5w6rpOaWkpGRkZTTLf0UJlZaWtCLGU0jZkzYKVlm4XSClto2r/ElKmrbZLR/v2S8Cnn37KKaecwh/+8IcGJQmqqnLrrbdyyimnMHv27CNeK64QxxEHBqFITU2loqKCLl26tHQ4PwmPx2ML4m5BVdWwSZVdThqSkpLYv39/S4dxWEhLS6OysrKlwzgs5OTksHHjRgYMGHDwwUcBHXtk0659OoqiGCqxSQrHTx1Kn2FdOPvqSSydux5/XYA9G8pISUlmy5Yt9B/Qn6fn3c7q77bSqWc2A0f3oKSggrce+5Kta/KMumQLQjHSpK8dhL8uRO/eA9FCa41tVupx8AAeAdEpyfWNrcJjMPsDG39XlPjo2MOshY8mwZhp14ow65yVSK2vWUtsuVkLVaHIJMVnnGumT/9gXshwqAaBD2pR+0bHC6gKDgeMOq0Ty+bkUVXph1AIb2Ut91z+LG8su59hx/dh8Zy14V0Hj+nFoNE9ImnQ5nOXuuT4acMYNq4PF958Wni8J9HNU3NvZ83irWRmp9JvRLfGj8/PRFlZGYqi2IoQ67pOZWWlbbJGAGprawFsleZtNwJvteizQ8x2e//G0bQoKCjgwgsv/MkxI0aMYOHChUe8VpwQxxGHiZycHAoKChgyZEhLh/KT8Hg8tlIvIaJq2+WHLTk52Tau4xbS09PZuXNnS4dxWGjXrh0rVqygpqam1aTFdejRziCw0rhQ1m9EV27776UIIegztAsvfnMXOzbsp0ufHNzJKosWLaJ9+/bkds0it6vRn1bTdG4777/k7y4xUnsVESbZrgQHk68ZSKA2xJyXNlAz2QnBIKgOQEIgRO8hndi1vYiAX2tIMHUdoQikrhvpzfVTnqPTl8FImW7jgZAEZz3HZ0sJVk1SLK39ZQNFWggo2lnJzLd3cMb5JilebriES4dqtJsKavXSzkS4rvq4KV2oKvOzeXmhsUlV0TWNipIa6rx+ivLLEaqCUOD404dww0O/piCvlOL95fz47RbS26bQf2RXjjt1MANHNV4TnJKeyNjJzfP9XVBQQHZ2tm3KPsBQ14QQreazdSioqakhKSnJNl4IYBBiO6XSWx0U7HCM8/Pz6d27d0uHEUcLIS0tjd27d//kmLy8vCY5t4wT4jjiMGEV6rd2WDW5doLd0ryTkpKoq6tD0zTbOIemp6dTVVVlK2Mth8NB27ZtKSgooGfPni0dDgBTLxnH/l3FfPfZarr0bs8t/7kwhgRlZqeRmR358e3VqxerVq1iwoQJ4fdKRXE1+3ZGtcHSJbrUDWU4igzrOiz8ZIUxJmR+pkMaPfq2Z8v6/bEO0GD09kUiFdVoaaTrsc7TRlFwDEGuKPeTkOggwSnwBbRwPNKtxqZXKyA1GVGJZezfKAoiqFO0q5qZb+3gjN90Bwmrl5vP0+Ew2kQFQkZslou1hA49Uuk1rC3vPbKGsDW0eaFg8Kge7Ny4n/eemm8spcG3X6xl+bz11Fb7yGyXyj8/vDF8saGlUFBQ0GoyGQ4VVv2wnUh8a7o4dqiwS/qxBTtla+Xn59O+ffuWDuPIEDfV+tkYN24c77//Ptdddx1jxoxpsH3p0qW89957TJky5YjXssdZUxxxHAXk5ubaghC73e6wSZVdYDcS7/F4UFUVr9fb0qEcMqyTSLsp21ZmRksg4Avy4l9nctdFT/HR8wuRUqI6VP7vz+fw+g8P8NCb18XUpTaGXr16IYTgk7e/5LkHPmbbur2ktUkmMcVjEkKDoBpp0oMJeIPMeWEdWlBDaiZBRYAw64wVhc/fXR7ZN/qmachwDXCkTjhChqmnFkMwoOOtDhoqsQTdoaB7HEghjLmI2k/TIKiBz3Cxl5bYbM3vVhFIindVM/ON7Rwzth1DjmkbOflSFKSqIi1jLgkut8qkX3VnyWd5VJf5zJ7FBvoM6cJ9L1xFZVns50wLadR5je+LitIa3n78y0N+TZsD1dXV1NXVkZXVsqT8cGHHdFOv10tSUlJLh3FYsFvKtJ3izc/PJzc3t6XDiKOFcOeddwIwYcIELr74Yl588UU+//xzXnrpJS699FKOP/54AO64444jXitOiOOIw0T79u1tUTdqmVTZjWDaKc3bSjO0E7kUQpCWlmY7Y62cnBzKyspaxHTt2T9/xPtPz2fF15t49v6P+PzN7w97DkVRWPrxTjRHLT/+sIFbpj9C/u4Srrn3rPAYl1sxyHBdiDkvrkfTTQJs2T5DrDlWdEslCKdKIwQ4o5TdaFIcto2GqD9ASqP1Uhs3umq4Q6OIGGIaFjCEEm6rFCHYUaQYwKUigJK8Gma+tpVjxmYz+JiodFFVgENBOlWEgJN/04Oywlo2LCtsYATmSnSTkORmwDHdUJTYmCPX+yQhS9luIRQUFJCVlWWLNjXRsKPDtN0UYssEzC6KK9hH0ZZS/mIU4qNuqGUfveQnMXz4cN5//31SU1N54403mDFjBlOnTuWqq67itddeIzU1lXfffZcRI0Yc8Vr2+naPI45mRPv27fn++8M/IT7aUFUVp9Npq6u8iYmJLaYC/lzYjRADYafpzp07t3Qoh4yEhARSU1MpLCykU6dOR3XttUu2I013ZEURbFi+g8kXNkzL+iloms7XH69h97a2nHBJPz55ZCUrvt7E9CsnULK/gpkvL+SUGQNJTE5g3qxNBhm2YNXriij2KYhNlY6p+W2oDgshzGyRWGU4Mp+gosR0mlZFzLBwCybrb6cC6Mb0MWSbSJxSIqVEAUryqpn5+jbOuMhId1+zLCpNXBWMPqULKRkePvrvWtO4K2pBIdi+YR9gKPV6UIsy/TJeD12TuD0uzr5m0kFfh+ZEYWEhHTt2bNEYDhe6rlNVVWU7Quz1em1FiH0+H1LKuAlYM6C0tJRgMGh/QhzHEWHq1Knk5eXxySefsHLlynDmy7Bhw5g+fXqTZZTECXEccZiwS8o02C8FOTk52Vbpx2DUEdst5vT0dHbt2tXSYRw2rLTpo02IB47uzp7thUhdouuSfiO6NhizaNZK3nxkDm6Pk2v//KsG7sWqqpDdMZO89aX8+FUep109mPZdjFY/p5w/irTukoQED6U7NPbuKDGV4WhEGVhFq74xQ2SkD3F9gmyR4vrjMVOehaCiPEB2p6SGfYqt9cN1xALpEAgtkuptbZPRNcVOFRnSEAhK9tQw69WtTLukF0jJWpMU9x7chn4j2vLhsxvx+/VIz+OoQLv3M1IhM9ul0a5jBiX5lUgpcThVrrnvLDat2s2ICX3pOfDovi+i4ff7KSsraxIF4mjCuphnJ3KpaRq1tbW2Spm2qwmYHVoZ5efnk5KSYqv3QxxNiyuuuIJBgwZx8803c8EFF3DBBRc021pxQhxHHCbsYqoFRh2xnUyqLEJsJ8On5ORkSkpKWjqMw4LVekmaJMkuyMnJYdu2bYRCoaOalnr1PWfhSXSzY/0+Rkzsx+SLxsZs37OtkL9d/wpSlwhFcPdFT/HGygdxJ7hixt37wlU88oe3yVtbyYgTe+FTy9i5eQ/zvvyaumo/c55fh06U4hpWX/WYOmN0HRRHQ+doXQfVdHLWpVHsZI1vQLCjOKeigAoVZX76DMkEnViiG6XWhmNTFaSum8ZYUcqzqUQLIYz1HSpSCwKCkn1eZr22lWkX90IA5SV1TJjSiS/e3k5lqR9cDsNsS9Mj6wC7tuQz49R/cM5VE/n7O7/ljYe/wFfrp9eQzjxx53sIIZj73jJqa/wxyv2ct5fw2r8/w+V2cP1D5zJiQt9DfMUPHwUFBaSlpZGQkNBsazQHKioqSEtLs9X3gNfrRVVVW6iXFixCbCfYRSH+RaRLQ9xU6wjw5ptvcvPNNx+VteKEOI44TFg1xHYgE3ZzbY42qbLDlWmA1NRUqqqqbPF+sGAd2+rqalJTU1s4mkNHWloaSUlJ7Nu376j2AXcnuJhx9/QDbt9rqscAUpd4q32UF1eT07lNzLhufXN5dNYtgKFyLV26lB/Xr8RXG2TOc+vQNIPQGjCIpsOpEvLrEdJpksT+QztRsK+CsqKqiFOzRV4daqT2N0opltFz1DPVAqgoD5Ce4UbB4NPR9cMxaduWGqwCmjBq0eqPszKoAeFygCaRQPFeL7Ne2cKZl/dBVQXzPtzJ3u1R/dKdKhJpqM8mairqqKmo45Hb3+O2Ry/i9w8b/Sb/et3LhuptHvsv314SJsS7txTwyB/fCrdz+vNVz/PWqgdJTG6eE/zdu3cf9cyFpoAd64erqqpISUmxzfct2K/mGezjMr1///64odb/OLp27UpRUdFRWcseUk0ccRwF5Obm4vf7bWFKZDdCLIQgKSnJVjW5KSkp6Lpuq7RpuxprCSHo0qXLQfsNHm30Hd6VxBQPiqqgqIJOPbPJyk3/yX10XScYNFya3QkOVKdlnhWBogpGTugLmm4STKMFEapCRUkN404b1GBeCWbKNFE3Gavyioiaa/xv3KoqAyiqIDnVGTtpoyna0lCdFYto1xtvTWy5VFs1zVKSkKCiKKDrkqSURtZyOZFqI8o08NlbEf+Gtu3Tw0dMURXadcwIbyvcUxpRP6RRf1xR0jzfK1VVVVRVVcUJ8VGCHWO2W82zZcgZV4iPImQL3X4BuOCCC/j8888pLy9v9rXihDiOOExYtSp2SJtOSEiwlWsz2K+OWFEUUlNTbUcu27RpQ3Fx8cEHtjJ07NiRyspKqqqqWjqUMDKyUvnPJzcz5eJxnD3jBP7x/u9QHQfuSx0MBvn+++9xuVxMGD8BdMH0m4eT2tZDt945DDimO063A12TLJm7nuQ2yZHaYJMc7t9dwsyXvzHSoaMNrcJ/Ry2oR535xIyJrTPWdUlVRYCMTDeE9Njt4b+tuYyUaGnVJUe1fRL165ej2j31G5nFqb/pyfwPdvDJ8xsZOTGXwaOzYuMC00W7HgmXkjVLtvHxS4sAuODGUxl2fB+cLge9BnfimnvPDg/tN6Ib6W2TUVSBUATd+3cgu5NRs71/ZzHPPfAxr/xjNhWl1Rwpdu3aRW5uLk6n8+CDWxECgQAVFRW0adPm4INbEexIiO2WMm2ZgMUJcRx2wO23387IkSOZNGkSs2fPprCwsNnWiqdMxxFHFKw64v79+7d0KD+J5ORkduzY0dJhHBbsphBDxLXZTg6zOTk5LFmyxFb12gAul4vc3Fx27drF4MGDWzqcMLr0bs91D55z0HEWGXY6nYwaNYqlczew+bsy0jsqnH3rSL56aQPl26sJ+kPhfWqqfDz66a38/tePEwoeoLWQSR6FLiNc2FKHaUQVjv5bCCzTrIoyP+mZbvbsrkGPrkOuv49xx3SkFsiAFjHEsuaLikEIGHt6R3oNzOTTN7aRv60SBMx6aTPTLu8DEtYsMU9iwjFjuk4TIcuazjMPfMyEqcNY9Nlqtm8ppG3ntgwZ14fP31zMcacOoseAjqSkJ/Lo7N/zxVtLcLkdTL1kHKqqUFlWw03T/o232gdS8t1nq3lq7m0/eQHjpxAKhdi7dy+jR4/+Wfu3JIqKikhJSbGV87GUksrKSgYNapgd0Vqh6zq1tbW2Uoi9Xi+JiYmo6s/7XBxN5Ofn2/LzVx/hVkhHec1fAqwLN1JKzjzzzAOOE0IQCoUOuP1QECfEccQRBbv0Ik5KSqK2ttZWpCc5OZm8vLyWDuOwkJaWxr59+1o6jMNCRkYGiqJQVlZG27ZtD75DK0L37t357rvv6Nevn61UufpkePn8jTxwzYtGJvFyKN3v5bQZA1kyawcV35pZEiYR/OyN7zjrygm89/T8RmY2a3br9xyun+pcX/Gtl+ospQwT4gbqbDTJNfeXFmlVBdKhoGoGeZV6lNO0lHgSVE45uyuJSU4+eG4jVeUBhGKkRBfv80ZIMbBmqVkHpmmGQZgeBClAl4gokv3hi1/z/jMLAagoqeHdp+YjpMbbj3/JIzNvoeegTrTrkMklt06OeRqbV+2muqI2fH/PtkIK8krp0L1dI8f14NizZw+JiYlkZmb+rP1bEgUFBeTk5LR0GIcFy3TRLh4TYMQshLCV4ZqdFO14DXEcxx9//FHzFIgT4jjiiIJdWi8lJiYihLDV1Wm79vVdv369rYy1hBBkZ2dTUFBgO0KckZFBamoqeXl59OjRo6XDOSRYZFhqkJKQRSigsXzBBhRVQTddlTctLaCisJaTLx9A536ZLHpnM97KAOiSOW8vZcTEvrTrkE7RvorwvEJREArolnCs1CPA1vtRl+CIVXqt96rE4JxIKK8I0K1XKrpDQMioWZZgpjybKjRGqjSRh8CloPk0VIQhO5hG0d16pzJxcify93j54r0tBH2hiAJspnIX7/Uy68XNTLuij5EW/V0BAoFUAI8r0tfY6w+T4vefnh/rnC0EUgo0Ce8/u4DbHr+k0dcht2vbcE9mIQTuBCeZ2WmH8Ao2hJSSHTt20KtXL9t87i3ouk5hYSFjxhxeP+2WRkVFBampqba5wAuR+mE7vUfsZAIWT5mOY+HChUdtLft888QRx1GAXVov2dGkKjk5Gb/fHzYc+jnQNJ2Pn1/If255g4Ufr2jC6BpHamqq7Yy1INLX147o3r07O3fubGjo1AphkeGq0lr+deVH/OGsx7ju5L+R3alNmAwLRZDVPp1Tzh7Dso/34vMGOfe2UfQ5JtuYRBGs/GYLRXvLo5RdgZSgh2Jrh0X9Ol4LBzpUJomWDoXSqiAZmW50VUF3KqZJV5SztABdFUjVMMuSQiAV8+Yya5wVBXeCyslndOKEaZ1ZPHcfc97fSTCgE2HQMpxuHVaKX9jEyBM7MHhsjknmiVWqE90RIg4oikBpxE170ac/GinRjaBjj2z++Pgl5HbLokufHO5/+RoSkn6ek25xcTGBQIAOHTr8rP1bEqWlpaiqarta3Hj98NGBXUzApJRxQhwHpaWllJWVHZW14oQ4jjii0KFDB/bs2dPSYRwSkpKSbEXUXC4XLpfriEj86//+jGfu+5B5Hyzn7ze8wvwPlzdhhA2hKAopKSlUVlY26zpNjaysLOrq6qiuPnJjoaON3NxcNE1r9RemotOk33zwG0IBQ8rNzytFajrn/t+JdOyexZhTB/HY7N+TnJbI5hV5LHhtI/Nf28Coad2ZesNQsjqlxJL/aPXXSo1WBDjVCDnU9QMHVj9lWgikgIrqIKmpTlSXCk4FGaU4S2E4XCMw3a6JVaQdClKFgcPbcMHVfXC6VN56eiNb1tZz/hQCqSiGSVhUi6hoUjzouOxInJEdweMOm3jpmsaEaUPpO6wzVg20oRTD5h8P7EQ+cfqI/2fvvOPrquv///ycu7J3kzSjGc1o05km3S0d7B9TEUEQVFRwC4oDUYaKoCIKXxBRFJUhovj1y56ddO+dpG1GM5o0eyd3nM/vj3NX0rRN0iQ3J5zn43Hb5N4z3vfckfM67/f79ebPG3/C0x/cw+wl2Wc+Rufg2LFjpKen66LPsj+ecmk9ZS0BWltbdSeI29vbdSEu/dFLzE1NTfT09Bgl0x9T/u///o+srCzi4+OZNGkSOTk5vPbaa6O6T0MQGxj4kZ2dzbFjxwIdxqDQYwlyRETEeYnLre8dBEB1qSiKYNe6IyMV2hmJjo4eE8v/kcRsNjNp0iRdZokVRSEnJ4cjR46gnk34BZD+PcOynx+WyaTwhR9cyZ/W/IgfP/0FomLDKNl3AsWk/cmtONjIh38uITo6iqu/NZeLP59H5CS/PsQBRO1pY5U8x0b4reP50S8WKQSqImjvcuF0SiKiLFrWFy0rLPtMheo/tknbZ05OBLfcls3s/BjWflDDG/8qp6vT5WfyJfus7nWnNinexxqqOnjj2SMUXpzK7EUJfhH67cts0sQ0IF0qX7rnyj7HVQhBzpyBRyDZex20NLSfd2VBfX09LS0tuinZ90dKqcv+YSmlLjPEra2tREYOryw/EHhMwPSQ1T569CgJCQlEREQEOpTzxxi7NCS2bt3KddddR2lpKVJKpJQcO3aM6667jq1bt47afg1BbGDgR05ODkePHh23J+L+6FEQe1ybh8vUGckoJneJp5SkTxv9q8exsbE0NDSM+n5GGj2XTaelpSGlHJfVGv3FsMlk4o4HPukVuymZ8Vx646LT1svNT9PKqIUm7ApWTOPGL16NvS4Em83KDfcs4MJbpzMpNRyk+/tHamc2iqVvplIAOF14G3oHNMpyi10FEBIptD7iqGgrUtEeUxWBKtx9xmjLqIp2U0yQNy2Sm29IZ/nieLbvbuTFvx/n+PF2Xwm1//76l0H3j1cI6ivbeeNPhyi8MIXZC+PBJX2O2VKCyQRWCyiCqLgwZhRm8uV7ryYiOoTo+HB+8scvEBZxunPyno+KuXH2j/jM3Hv54af/h55u+5levrMipeTw4cNkZWVhtVqHtY1A0t7eTk9Pj+68A1pbWxFC6Er8uFwu2tradCXiu7q6EELown28pKSEnJycQIdhEAAee+wxVFXlvvvuo66ujtraWn784x/jcrl47LHHRm2/hqmWgYEfmZmZ9PT0UFNTM+5H7ehVEB89enTY63/lp9fhcqkcO1DJ/NV5fOLLq0YwuoGJjY1l165dOBwOXTkfJyQksH//fnp7e7HZhtdLGSgURWH69OkcPHiQlJSUcVO6OpAYBq1Ud9aiLBrrWkmfNhmrre/7ZPuHh/jjA/8BKQmLDOH/fXYpN995GYqicNM3LgO0fkRn51rSZ8bRVNvJkS0nKd1bj73HReHyXLavL+qzTWFS8PN89ntA+GVsNUct4QIENLfaiY62Ics7ECbhFsuKdhXeBSgQG2djRk4kedkRdHe72HugmUPFrbhc0l1RrZVUuwDTAPvqQ/9srVCor+7ijWcPc+WX8oiOC+dUpYOjB6twOlxama+UYLEQGq2VdebMnkJMXDi1Jxp57x9bmbMoi5CwvjNUH7/7H/R09QJwYOsx3nlxM9d+aeVZX8uBqKmpoaenh8zMzCGvOx6ora0lPj4es1lfp3YNDQ3Exsbqqsy7ra0Ns9msC3HpwdPzrIfjbAjijy9btmxh+fLlPPDAA977fvrTn7J+/Xo2b948avvV17emgcEoY7PZSE9Pp6SkZNwL4tDQUHp6enA6nbo5AYqKiqKtrW3Y46LCIkP4wZOfG4XIzkxwcDChoaE0NjbqqhQxODiYiIgI6urqmDJlSqDDGTJJSUkcPXrU6/YbaM4khj3EJkYSmzhw+eT//PCf3jnDna3dRMWEYrGa2LX+CA0nWylcOZ3YxEiuveFyvrDi5yRODWPG0iSWfyqbUxXtnChpIzzKRntLrzujqmrZVNAEpKpqKV0/Uy7vY2gi1gk0tduJjrIizQLp0gy/hIDkhBCmTgljanoY4aFmjpV38Oaak1RWdyJUv15jmwl6XZr4DTJBj9PrkaXt1i2yXaovFpPbP9ozXklAfVUnb/zpEFd+eQbN9SeYlBBBXXWLr9xZwj9+v4a4hAge/8E/vc9l64eH+N33X+YrD3ySmHgtm9jV0UNne7efLhd0dQxsvHU2VFXlyJEj5Obm6ub7tD91dXWkpg5cUj6eaWhoYNKkSYEOY0h4yqX1IC49dHZ26qJcGjRBXFhYGOgwRgRjDvHQOHXqFDfddNNp9y9cuHBUS6b1+a1vYDCK5OTkUFJSwurVqwMdylmx2WyYzWY6Ozt108cUEhKC2WzWXalZXFwcDQ0NuhLEoLmm19bW6lIQCyHIy8tj586dpKWlBbSE9Vxi+Fz09jr7aNXeHgd/+/Vb/PPJ9wEIjwrhybe/R3xyNPc+9Xnuvv5/OLy5hrCYIGYuSSU5K5zCS5LpaLZTX91BQ2UHp0520VjXTU+3O/2roplYSakJUuiTtZUCmtsdpKeEkpsVTkJMEAlxQSTE2nCpktITHWzYVk95TRdOl/Saewn/uccCXGYwOfx6hk0C6RowV903Q2xSkKqKcIdWX93FG388xJW3z2Dn+yeorVRPW/WfT31w2iY3vr6H7e8f5KGXvsaHr27n7Re3eMvVQbtodtH1C4by8gB4Z6Tr8bMC0NvbS3NzM/Pnzw90KENCVVUaGxuZNm1aoEMZEnrsedbTyKWSkpIBRZHB6FJVVcV9993HO++8Q2NjI5MnT+baa6/l/vvvJzo6elDb+PWvf83atWs5fPgwDQ0NKIpCWloaF198Md/5znfOmWxyOBwDXrgJDQ3F6XQO63kNBkMQGxj0Iycnh+Li4kCHcU6EEN6yab0IYiEEkZGRujuZiIuL043Zmj+JiYkcPXoUl8s1bsqOh0J8fLy3zH7GjBkBiWGoYrizvYftHx5k8zv7CY8K5eY7L+Oz37mMp3/yKgDRceFcfP1Cbrvg5951Otq62fD6Hj71ldVMn5fO46/dxY61R3A6VV56QhPN1jArCWnhJGVGET8ljBlLJxMebcPlVOlsd9DV4aCrw4nTqXrF6UWXJmOxKISEmgkNtxASbEZKyfyZMdQ19FBc1s66XQ2cau7VRK1ToihopdhCmzusCqG1B3uCNZm0/mUBLqsZk8OluVJ7RLQqvT3DfZASzCakw6VlL9wjmTyiGFXlwKY6r6nWsktmcKKkxre+p5xaCOx2J3988H8p2aeJWNWlYrKYuevRz1CwYhpRceFDeo2dTidFRUXMnj1bV3Nw/amrqyMyMpKgoKBzLzyO8PQP6+VvmIeWlpZxUbkyFDo6OsZ95RtoF0mOHj06sUqmdZCxPX78OEuWLOHUqVNcc801TJs2je3bt/P444/zzjvvsGnTJmJjY8+5nWeeeYawsDBWrFhBQkICDoeDPXv28Nvf/pY///nPrFu3jvz8/DF4RkPDEMQGBv3IycnhrbfeCnQYg0KvfcR6G2Ok1z7iiIgIrFYrp06d0u08x+nTp7Np0yYyMzMJDg4+9wojyFDF8PYPD/Hz2/+Mwz2CSSiCg9uP88yH9zBzwVROVTczY34m4VEhWG1m7D3aTG6pSsKjfL2IWTNSyJqRwpE9FV5BbO+wU3mokcojTZpoFAJzkImQyCBCom2ERFgJCTNjMimYrZoYbajvobvLSUenk1aXxC4kt30yg1feraLHrrrnEOPL9ErpFsO4BShIk0S4K6BFn35hwCxwqS5MLoGUKsItiqVfb3GfDLOqaplip+rtF66v6uCNZw5y5R0zQMKBjTUgJRv/u5Mp05JOLw1HIoVCw6m+I8VcTheLLp5JaMTQ3yOlpaUEBwfr9jMCWv+zHuNvbGzUXf+wHg21QD8Z4pqaGrq7u3Xp9K5nvva1r3Hq1CmeeOIJvvnNb3rv/853vsNvf/tb7r33Xv7whz+cczsHDx4c8MLcn/70J26//Xbuvffec55j//Wvf2XdunV97isvLwcYsHpTCMGHH354ztjOhiGIDQz6kZOTw+9+97tAhzEowsPDaWtrC3QYQyIqKkp32Va99hELIZgyZQoVFRW6PFkGbezV5MmTOXDgAPPnzx+zE+fhlEk/+aN/ecUwaEK38mgdHS1dZOYlk5mXDEBnWzddbd2+mcNSkjo1/rTtTZs7hRVXzWX9G3sBSE6Po7qi0Rdjr0prYzctbQ6fSJUSs01h/ook9u5pwm7XSpGdJnBGmOnscRIVY+NkQw/CbfLsTf+aBLikz6xLAGYBvb70hnD2c+A3m8Dp1GYVqy6EcI9aUhRNwPYZIeUu4TYp5MxIYlZhBuVFNexad4Q3nt7PlV+dDQIOrKsCASdK6337EcKdPdYEe3NjJ9awIOzufuGLrl8wLDHc2dlJSUkJixYt0pUo86e7u5tTp04xZ86cQIcyZPTYP9ze3o7JZNKVoZbdbqenp0cXgrikpIT09HTdmUGekUCMQRri/o4fP857771Heno6X//61/s89uCDD/LHP/6R559/nt/85jfn7EM/U5XKpz/9aW6//fZBGauWl5d7BXB/+gtlYES+uw1BbGDQj5ycHMrKyrDb7eN+9EZkZKS3900vREZGnpexVqCYNGmSLvuIp0yZQklJCd3d3WOeYR0pZs6cyZo1a6iurh6Tkr/h9gx7Mr4ehID45BhCI/se966OHm0EE3jF4pr/3UnUpHDefGEzFquZa2+7AKdTZdemo94y4uryRrxnOl4xfXoc3kyu35mYopiQZoWmNgfRERZONvZoGWGTX0bYhXbz1khrAtZlBpNTInpdIEEVwj2zUYtBKkIrgzYrWuk0vvJmiV+WGN82C5bnMn1OKq89vxksFupP9vDGHw5w5VdmaZniDdW+53j6M0RKicOh8r3HP0tYZAiFq6af6+U5DSkle/fuJTU1VXejivypqKggPj5ed59vvfcP6+kCSmtrK8HBwboQmYbD9Nizdu1aAC655JLTzsvCw8NZunQp7733Hlu3buXCCy8c1j5ef/11AGbPnj2oWMYaQxAbGPQjNTUVs9lMWVkZubm5gQ7nrERFRdHZ2amrUt7Q0FAURdFdydmkSZM4cuQIM2fODHQoQyIkJIRJkyZx4sSJcf9+PhM2m405c+awd+9e4uLiRrVP8nwMtD773ct56t5/AWC2mJi7NJuv/vRTp51gxE2OYs6SbPZtdl8pF4I3n9/E2tf30NNlByn56O19XHPbSjpau30rCryjh88087fPY55SZXCPWIKmNgcxkdosYtBGKEnhywg7Hdo4JW/m2JMldklUm2/+sHRJTE4AiWozo/Q4EYqfwZZ/DFJqcftlxP/x9BomxUfgcDi9y9XXdPPGHw9z5e15WqZ4Q03f5yN9PysmQUJKDJveO0RbSxcOp8rSS2ed5dU5nbKyMjo7O1mwYOgmXOMFKSUnTpxg1qyhPffxQFNTEyaTSZf9w3r62wX6itkQxGOPxzfnTMc9Ozub9957j5KSkkEL4meffZaqqio6Ojo4cOAAH3zwAWlpaTzyyCNnXW/FihVDC36EMASxgUE/FEUhOzubkpKScS8gbDYbwcHBtLS06KbsTAhBdHQ0zc3NuvkDDZrB065du3TTh+VPeno6Bw4cICcnR1dZDX+SkpKorq5m3759LFiwYFSex/m6SV956zJmLsjkVHUzeYUZhEUOXFIphODSm5awb0vf1oGudt+4oOqyeg7vKvU96N+/637uAvxGFQ2QTfV7TLrLo5s67CTG2LT+Ydz9vt7krUDaQNqlW0C7FbEicQJmj7B3Z5ZVp9TmEgtQzQomp6qJZ4fqy1wr7hJq3AGoPtOv1ubO0zLc9VUdPlGM4MD6ai0WKZmUGEFweDD2HifxydHUlDew9cNDqC7JoR1l/M/rdzLVXZZ+Ljo7Ozl8+DALFy7UzcXEgTh16hSqqpKQkBDoUIZMbW0tCQkJuvtOam5u1p1g84yJ0gMlJSVcdtllgQ5jxAjk2KX+LXU2m23AKgGPr8uZ3iOe+1taWgYdw7PPPsu2bdu8v8+fP5+XXnqJrKysQW9jLNFPvaKBwRjiGb2kByIjI3VpUtXQ0BDoMIaE2WwmLi6O2traQIcyZBISElBVlVOnTgU6lPNi9uzZNDU1UV1dPeLbPl8x7CF9WhILLpxxRjHsv1x/+ggDKVn36g4Sk6N8wlaVfY2whLaOcLhO21a/DSNVTZQ2ttuJDrdqxdTurLFEyxJL0Eqo1X7iWgiw9BXOCIF3RLGUYFF8+/Jf1X8bLpdWPu2+2e1+IzT8RjTVV7bzxjOHKLx0CrMuSPLONa4/2cqcRVN5bv2P+MkfPk9DbSuqyz1WSkqOHRzc+0JKyZ49e0hNTdXNhcQzUV5eTlpamq7aT0B7DWpra3XXgmK322lra9Ndib2RIf54kpqaSmRkpPf28MMPj9m+t27dipSShoYG3nvvPQAKCgp49913xyyGoaCvb1ADgzFCT4I4KipqSFftxgOeub5SjvFl0/MkMTFRl4JYURSmTJlyRpMKveApnd6/fz89PT3nXmGQjJQYHgoZ0ybzue9foblCW0x84Z4r+cETt2gPuscXKYpg5RWzWX7ZDC2hqyiaiZVbkArP/F3FZ6jVp0zaD+HO0DZ1OIgJt3j7hqV7fam4xbCCWyH3+2yavOrX959FwQXe/ameONziTHidofHNR1bd2WNVe44AX/ju5ZjMfqcjQmii+Kl9FF6WxqyVvqzv63/fRE+3ndDwINKyE1BMAqEITCaF6fPSBnXsy8rK6O7uDtgor5HCY6alx9nJHR0ddHd36+6CRENDA+Hh4broxfXgcDjo7OzUhSB2OByUlpZOLEEsA3QDKisraW1t9d7uueeeAUP0ZIDPlFzx3D+c91BsbCwXX3wx7733HsHBwdxyyy10d3efe8UxxhDEBgYD4CmZ1gN6FMTR0dG4XC7a29vPvfA4IjExkaamJux2e6BDGTIZGRmcOnWKzs7OQIcCwJvPf8TtKx/i+596ghNHB3+RISkpifj4ePbt2zciF1QCIYY93PjNS3i97DFeO/4brrvjQqRLJTktFoF0TymSvPPP7Wx8Yx/hEcGa8PV7ztKlIt2uzX0k8EDHRYLigpZOBxaTIDzE5Km59mV0hZY1dlkHODVQhNa+7GfkJYTWg+zdhdWkCWRPfbZbAMclRCDsTi1G1SeShdB6rZdcnIfL373aLezrqzp548l9FF6axqwVmii22syYzSaEEPzi+Tu47NMLWXbZLB76++1MyTp32bCnVHru3LmYzfruGisrKyM+Pv6crq/jkdraWiZNmqS716ChoUGX2WG9GGqVlZVhNptJTU0NdCgTgoiIiD63M70HPO2BZzrv9ThDn8+FiqioKBYvXkx9fT2HDh0a9nZGC319ExkYjBHTpk3j8OHDSCnHfX9TZGSk7oy1FEUhJiaGxsZGIiIiAh3OoAkODiYiIoK6ujrd/cEODg4mMTGRsrKygBuDHdh6jCfveQWA6rJT3HfrH3hu8/2D/qzNmjWLtWvXUlZWRmZm5rDjCKQY9uB5zk/88GXee3krQoDJbGLOkmzaWrs4fkgrA25t7tQyryazV/BK0DLGnm3h7imWfe7RflIBp8SlQkunk5hwG+3dXUjVb1EtIFQLKL3SzxhaE+KqVaDYtfSD8KyguHuJPfOLbWbNmQtt5jAOJ801LRQuz2XPlmO4XJLQMBtJabEoJoVbvnUxTz/4v30Mt7yYFOqrO/jw7yVceKvW/776siWYLdpzjomP4JsPfWrQx9rlcrFz506mTJmiu8xkf1wuFxUVFRQWFgY6lGFRW1uru+9Q0OYmj3dvkf7oqX/48OHD5OTkBOS7+OPMqlWrAHjvvfdOmwDS3t7Opk2bCAkJYdGiRee1H0+703i8EGZkiA0MBmDOnDk0NDSMSq/iSBMUFERQUJDRRzxG6LVsGiAzM5OKigqcTue5Fx5FKopPen9WXZK6yiYc9sHHZLPZWLBgAYcPH6a+vv7cKwzAaIvhuqomnvj+yzz67ec5drDyrMu6XCofvKKZj0gJLqeL+RfOwGKzoPYTiH0E4wDl0QJ8PcX+vbyK8C7a1O4gOtyiSWXP2CX/5c34SqT9t2/y/Sw9vcBCuB2r3cErApfnQqIQYDLhckl2bjqmjWuymOjqcXLp9QuYFBfGmv/sYPfGYt/6fZ6MALOZE8VNvPH0AQoumcLubfuw92rvFXuPg8d/+E8+t+ynPPyNv9HZduYyPM+IJSGE7kulAaqqqrDZbLrLVgL09vbS3NysOyOw3t5e2traiI2NDXQoQ0JP/cO7du1i3rx5gQ5jRPGYao31bShMnTqVSy65hPLycp566qk+j91///10dnZyyy239KlGKSoqoqioqM+yJ06coK6ubsB9PPPMM+zYsYPU1NRx6Yo//iS6gcE4IDQ0lGnTprF79+4xmXt6vnjKpvV0chQXF0dpaakusvD+JCYmcvz4cd3NUQaIiYkhNDSUEydOnFdm9XyZszQHs8WEqmqiasaCqVhtQ6tuiImJYdasWezYsYMVK1YMqWx0tMWww+7ke5/8HY21rYBk09v7+PNH9xETf3o1REdrF6/8/gPMVrMm9NyO0DHx4UyaHNXXhbn/+22gj42/qPRfV6D1CqM5TXv6iN1e00jPxgSgaKXTwt+ry5MlFmjO0qr0llxjEqguVbtfSgixoPY4EA4VoQivkNc0tBbUUw/+L9LucHuDCaSq+hyp+5mLIaC+soM3fr+fK782m51bdrNgaQHfv/Epived0Ay3qlsIDgvizkduGPA1OXbsGA0NDaxYsUL32ScpJaWlpWRmZurqu9NDXV0dkZGRupub3NjYqLv+YdAEsV6y8bt27eLyyy8PdBgfS37/+9+zZMkSvvWtb/Hhhx8yffp0tm3bxtq1a8nJyeGhhx7qs/z06drsd//Wpd27d3P99dezePFisrKySEhIoLGxka1bt3LgwAHCwsJ4/vnnx+V3sL7O5gwMxpCCggJ27doV6DAGRVRUlO4yxHrtI46MjMRisejSsVkIwdSpU70XIgJFalYCj/7nTi77zGJu+OYl3P/cl4e1nbS0NFJTU9m2bRsOh2NQ64xFmXRdZRP11c2oLhXVJenp7OXYgYGzxA/c9iyvPrMWhzurq5gE/++zS3A4XKx/fY+2kEdoem79UVXfYy7VJ5LcxlkAqnA7rUhJU7uD2HCLb6awpK+4FuASwuvg7LtfIM3CK1Ld9tRa+bSCJpIlWq+z2YQ0C+19ZnKbd/n3PyPAYkGqEqmqRMWEIuD0iwZ+WfD6yg7eeHIvTR2n+N+X3tPEsGd7UlJedJKBqK2tpbi4mIULF47qDOuxoqGhge7ubl1crB2IkydP6s5dGvTZP6wnQy0pJbt27aKgoCDQoYwsATTVGgpTp05l586dfP7zn2fbtm385je/4fjx43z7299m69atg6qMmDdvHt/+9rfp7e3lzTff5NFHH+Uf//gHQgi++93vcvjw4SHNGc7MzOSJJ5446zJPPfXUiFzgNzLEBgZnoKCggPfffz/QYQyKyMhIqqqqAh3GkPD0ETc0NOiqj1gIQUpKClVVVbo8qUtKSuLQoUPU1tYyefLkgMWRm59Gbn7aeW9nxowZbN26ld27d59zPvFY9QzHJUURFhVCV1s3UkoUk0JazunvFXuvk0M7SvvcJ1XJOy9toaq0HqG4xyV5sqZOJ5jNPvFr6ndNW0qkSUENsWq/C18PsTQpSHfGt7HDwXyP0zR+AteLQJolOP16ej3iWdHErOIW1wJtF8KjkN33SUCYTdrqLhWhACp9t+d2yo6KCeWvH/2Ep37yKu+9sg0U0+nZbyGwWRSu/cJF5Galc6BnL7NXJLN/fbX3gsGSS08vw2tvb2fXrl3k5+frQhQMhmPHjpGenj4u+/DORW9vL3V1dQH3MRgOeu0fDgoK0kVWu7q6moaGBubOnRvoUD62pKam8txzzw1q2YEuqk+ZMoVHH310xOIpLy8/p2lsS0sLFRUV570vI0NsYHAG9JYh7ujoGHSWbLwQFxdHY2NjoMMYMikpKdTW1urueAOYTCZycnI4cuSI7sZeDYSiKBQWFtLe3n5aP5M/Y2mgFRRs5eF/foM5S3PIm5/JA3+9g4TU06+uW6wmrSwafEZZqkR1qezfcgzpUvte6ZcSi2dOsISCpdl9MqhSUcB6ukiS/hljVdDU4SAqxKJNaxKAy7d/bywmkGa8o5G8mBRUi3af8GYjfILa6yRtUrT9mhT3bGKhPZ9+hEaFEJsYya/ufEETw579u/drsZj42gOf4JXdP+Xbv76RvzzyBt+6/Am2/KecgkvTmL1Sy5IuvGgG139ldZ9t2+12tm3bRmZmJsnJyUwEGhoaaGpqIisrK9ChDIuamhqio6N154xt9A+PPrt372b69OmEhJx9hrvu0EmGWK+0t7djtVrPezv6u7xoYDBGzJ07l7q6OmpqakhKSgp0OGclKCiIkJAQmpubiY+PD3Q4gyYuLo7jx4/rro84IiKC0NBQTp48qcsZoOnp6Rw/fpwTJ06Qlnb+WdpAY7VaWbhwIRs2bCA8PPy0UtJAuElnzUzlFy9/46zLCCGYkpNI/ckW7Q7PzN4+mV9fPXNQiI2eHqf3sxIeE+YrpxbitLFMvmwsSKnlcIUKbd1OXFISFWqhqcOBomjJW7/AtP0qmpju/9mUFhPYXVpkUvqEsVuoC09zsqdvWSgIRWo7V9U+Ir6zpYtjjR0cO+BX4SJVbWVV4rSrPPfI68xfOY0nfvQvXG5RfXDLCTLzkll4ZSZLLp7JlTdc1CdOVVXZuXMn4eHhpKVm0NHaRVikvk+0pZQcOnSIrKysETkBDARVVVW6LPXWa/9wU1MTMTExgQ5jUExEQy2DoXPixIk+v7e0tJx2H2hO+ydOnODVV18dkZJpI0NsYHAGwsLCyM3N1U2WOC4uTneuzVFRUUgpaW5uDnQoQ8ZTNq1HFEVh+vTpFBcX43K5zr2CDggPD6ewsJC9e/f2cQEfD6OVzoYtyNJ3vNFAN1Xy0798mYiYMEATRkIR1FU2+dYDbe6vtkCf+6XE/ddeIF1aWXNzh7uP2NNA7K+IPdlqIVAVBhDZEqefqBUShL/JlrfXWcsMa6loAOmbQ+x0IRwOX9b4tP5o7XepSro7e9m9oRino+97dUpGMqtWr8AS5aS8vMx7v6qq7Nq1SyvPPdLD9TN+yPUzfsgf7n9V11URJ0+epLu7m6lTpwY6lGHR2dlJc3OzLrP1tbW1unPFllLqqu95QvYPGwyZ9PR0MjIyyMjIAODxxx/3/u5/y8rKYvXq1Rw/fpwvf3l4PiT+GILYwOAs6KlsWo9jjBRFISEhQZdjjFJSUmhoaKCnpyfQoQyL5ORkrFYrZWVl515YJyQkJDBv3jx27tzJqVOnxr0YBvj01y/CFqxl+4KCrVxxy9LTlrn+axcx74IcouLCNNdmNNEnXC4SU6K8YlIIAQ6nL9PsRoCWqVW09lzQRi/FhFk8j4L0n9GkiV1VAcyeqmip3dyPyyDpNe3SRLZnP+5teHWnr48Zk8ntOA24VEJCbSgesSyEN8v8/d/dTGSs77kCTE6P46ZvXeL7PS2WC66cS3R0NIsXL6a4uNhbbbJnzx7a29uZlTeXP/zkP6hu0f1/f15P0e7yc7wi4xNVVTly5Ai5ubm67B0GLTuckJCgu+y2lJK6ujrdeUa0tbWhqqpuZhBPVEGsh7FL44lbb72VW2+9lVtuuQWA2bNne+/zv33hC1/g7rvv5p133uHOO+887/3q81vVwGCMKCgoYM2aNYEOY1DExcWxd+9eHA4HFsvQRtgEksTERIqLi8nLywt0KEMiODiY2NhYqqurdZmxEUIwffp0du3aRVpamq7eM2cjKSkJl8vF9u3bCQ4OJiQkZNyKYYDcuWk899FPqCipJS0nkai4cGYuzOLff1pLWEQwd9x3LZPT4vjmNY9TVuy+cKQIUBQO768Cm88tGrRyZe/JkLts2Z0n9opiJDS224kJsyKFZpTlr1t9rli415EI6Z3R5M5Qe1yy8IlxT/m2Kn0xeZyuFQFmBemQ2kOK4MLrCpEulaLdFZw4WovqVPnMty5h1bWFTE6bxGPfe4m2pk4+8cWV5C/NIX9pDvNXTqepvo1ZC6YSHKqVr3pE8ebNm6mursZut7Ns2TLaGrtOywh3tevzAlZlZSVSSt22OEgpqaqqYtq0aYEOZcg0NWmVGNHR0QGOZGg0NDQQGxuri/GANTU11NXVGYZaBvz1r3/1/vz3v/+dT3ziE9x3332jvl9DEBsYnIWCggJ+/etfBzqMQRESEkJwcDBNTU26Ku2Kj49n165ddHZ26s5oJSUlhbKyMl0KYtCOfWRkJEePHtXdBYmzkZiYSFFREZ2dncycOXPcimEPkbFhRMaEUl/TTERMKCuvmcfKa3y9dH/8xes+MQy+ecT9XabdeCWgXxmydM8g9iSEmzqcpMQFa4lh9+xh3zpatlZ4e4kF0uUzpQZA1eSwSQImAS5PqbTfMt54BdJiRjqcKCYBLk1cr762kNzZ2nzUxrpWSotOkpGrOZ9Py0/jjx/cc9pzy5o5cP9pVFQUcXFx1NbWkpubS1BQEB9t3U9IpOb2jZRMnZHMrEX6M6NyuVwUFRUxc+ZMXYibgWhtbaWnp0d3WVbwlUvr7djrrVx62rRphIWFBTqUkScQJlc6zhD7o6qnGzGOFvr6dBsYjDH5+fnU1tZy8uTA8y3HG3rsI7ZYLN4TWb2RlJREe3s7bW1tgQ5lWHiyxKWlpbot/e6Pp0w6LCyM2bNns2PHDurq6gId1ln5w32v8tWLHuFb/+9Rfnrbn7zGUQD2Hgfvv7qzX3+tmwEEsWpSkFb3BQCX6jsXUzxJW00BN7bbiQ2zeLPIKqAqoCoCVfTRvZpo9t+VxzVakd4FpWcGsfd/bTay8B9ybDZp6wHWYAtp2dqFu6K9J/jiRb/ivi/9hdsu+iUHdwytjF9VVXbv3k17ezsLFy6ktLSUrRt28pu7XqKrvQehKIRFh/LLV7+NNUh/lRClpaXYbLZxb+54Nk6cOMHkyZPH/cWpgaitrdWdkJdS0tjYqCtBPBHLpQ30gyGIDQzOQnh4ODk5OezevTvQoQwKvY4xSkxM1KUgtlgsJCcnU15eHuhQhk1MTAzx8fEUFxcHOpTzpn/PcHp6Ovn5+ezYsWPcXtSqr2nmtec2eH/f9sEhDrtnE0sp+ekdf6GjpbPvSv7GVX5CWQrAZvaZaZlN7nJlxZP0xd0FTFOHg7BgMxazJoA1R2l3+bS/ZhGaOpaeswXh+1/aTO4f/cy1PA97+tg8s4f7GYX19jh57tdvA/DvZ9fhsDsBcDpc/POZwbepeAy0WltbWbZsGYmJiSxevJi6phpmuUcySSnpaO1GdY5dtmGkcDgc3goOPTnx++N0OqmsrCQ9PT3QoQyZjo4Ourq6mDRpUqBDGRJG/7DBRKGpqYlHH32UT3/601x88cWsXr36tNuFF1543vsxSqYNDM6Bx1jriiuuCHQo5yQ2NpY9e/boso/44MGDuosbNEfELVu2kJeXp1uzm+nTp7Nu3TqmTp2q25K1MxloJScnoygKu3btYvr06WRmZo57YeGJr7Oth13rizQRaTL5xK9LRbGasVgUQmPCaWroQErVr2934Oenenp5BXTbVbp6XcSEWTjZYdfcoP38tZxI7QTBU1KNpm0Vz33ukUxOBSxortfS7kII4etZ7udcLdyxSbMJ4XSxe/NRACwW3+dGAO3NnXx55UMoiuCLP76GBatnDPh8ent72bFjB06nk6VLl3pH4kRHR5OXO4vey+0IRXBwXRWZecmERQYP7gUYRxw9epTIyEhdjdPrT3V1NSEhIbrrwQWoq6sjLi5Od3+X9NQ/DJog/v73vx/oMEYHo2R62BQVFbFy5Urq6+vPOiFgJP6m6+OTYmAQQPTkNO3fR6wnQkJCCA8PH/elrQMRHR1NSEgI1dXVgQ5l2ISHh5OamkpRUVGgQxkW53KTnjx5MkuXLuXo0aPs3bt3XI2ampQUzXV3rPb+vuTyOeTN18ZNBIdaCQ0P0h7w76UyKaiqxN7twOV0IVXV7dx8etbYmy0Gr4j1lEA3djiIibD6xLAnO6xw+tnBQGXTUoLFL/OraHOJ8cTjF4fA/bvFpIlyk0JGrlaGevM3LyIiWvMPUEwKxXtPUFXZzInjp/jZl/5Mc/3pLQmtra1s2LABm81GQX4hD3/l71yddTd3Xv0Y9SdbyJ6eQe7UmSy8aio3/XAlD7341XF/IaQ/3d3dlJaW6r6/v7y8nPT0dN0df9DnuCXQV//wyZMnqa2tJT8/P9ChGIwz7r77bk6dOsUPfvADSktLcTgcqKp62m0k/qYbgtjA4BwsWLCALVu26GZ+pR77iEHLEutREAshSE9Pp6ysTDfvkYHIzc2ltrZWdzOhBztaKTo6mhUrVtDW1samTZsC3jMtpWTT2/v4x+PvsvyqfP604cf8/v0f8uM/3ubN6pjMJu774xdJSI4mJNRKRGSIz1ALrV23pclXTu0dAzyA8FCc0juP2JOwaOq0ExemZb6kp2TaXR4tzXhHKXm31//QmrWMs/dUxOMy7e4lFlIiVBWTInziWEqwmpDA1DxtHm1KZjx/XXcPX/rhFTjtTl+G22zG6XBxqrrve7KmpoaNGzcyZcoUCgsLefUP69ix9jAOu5OjB6p4+r5XAZizYBqrVq8geoqF+ib9tWQUFxeTkJCgy8yqh5aWFtrb20lJGdgMbTxjt9tpbGw0+odHGU+FlV6rk86FMXZp+GzcuJErrriCX/ziF6Snp4+qB4EhiA0MzsH8+fPp6OjgyJEjgQ5lUOi1jzghIYG6uroxdRUcKVJSUujs7NSdmPQnODiYrKws9u7dq5vXYKhzhoODg1m2bBmhoaGsX7+elpaWsQl0AF556gN+fvtfeOGxt7nrmt/SfKqNjOlJp2XRZi/O4q8f/YRXDzxMeFTIaT3DErxGVS6zohlt+W/DPTvYZJcI6e4hdmeAGzscxIT6jLV8olgi3SOKvXsbKLunaPdLEz4LahWE6jHTgqnZCagOly9z7dmOReGvj73LVdN/yB9+9n9YbWZtJJLn+bmXMwVZSXM7T0spKS4uZs+ePcybN4/c3FyEENTXtHhDUl0qp6p8FTL95xTrhYaGBqqqqnSfHS4tLSU1NVV3JccAp06dIjw8nJCQkECHMiT01j+8bt06Vq5cGegwDMYhUsox+w40BLGBwTmw2WwsWbKEdevWBTqUQREbG0tLSwsOhyPQoQyJ6OhoTCYT9fX1gQ5lyFgsFlJTUykrG5o77ngjJycHgJKSkgBHcm6GKoY9mEwm5s2bR2ZmJh999FHASt3ffXkLAKqqzfBd/9rAxn11lY3UlGv9U1fdutQ741cVgNVvBrEQYHYfA492lW7x654FLMxu0esWvg0dDmLDrNqifv3DnhJq1zkrXN3l115zrX6tay7J8aJat/O0X4YYQGinH06Hi//720dsevcAq68t8GWZ3ZgtZn7xled48It/5N03P+RYyXEWFC7s47i86hMFmtZWtIAvuWFRnyj1JoqdTid79uxh+vTpuhtF509vby/V1dVkZGQEOpRhUV1dzeTJkwMdxpDRW//whBfEMkC3CUBBQcGYGX7q49NiYBBgVq1apRtBHBISQkhIiO6yxEIIUlJSqKqqCnQowyIjI4OampqAl+KeD4qikJ+fz7FjxwKaPT0XwxXDHoQQZGdnU1hYyN69ezl48OCY9xUnpMRqM3nRRhZNSjq9LPbhr/+Nzy/9GV+84CEe/OKzXH3rUmJSYnwmW/2cm4G+M4A9J0UCTUB7HnffmjocvtFL4JtJLLS5xU7hJ5TRdKrq/UW737tdd6U0ZneGWsUbk9ZX7OlvRvsZ/MQxNNa1kZwex01fv6jPMo5eB8eLKkiZF0JZURXP3v0hd1/zJM31bXR39vL2i5s5VdXEz56/g1u+ezkPPvdlrv788tOOpZ5E8eHDhwkODiYzMzPQoZwXFRUVxMTEEBEREehQhozdbufUqVO6LPWur6/XTbl0Q0MDBw8eZMWKFYEOxWAcct999/HWW2+Nyfm3Pi1RDQzGmJUrV/L4448jpdSFMYin/FhvvU8pKSls3LgRp9OpO8fm8PBw4uLiKCsrY/r06YEOZ9hERUWRlZXFnj17WLFixbjLMpyvGPYnMTGRCy64gN27d7Nu3TrmzZs3Zv2a3/rlDTx0x1+oPH6KhRfO4BNfWqn1y1Y1EZMQQfmRGja8vse7/LYPDnFweymtzX4jmPxLkOUAaQEFkFo5teIZuORXBt3U5cBqEoTZFFodap+RSgCEAJ24xS1gEkghwYlPIZsE9Kqa6FWET/gq+FymPbOIvWoacDiRqqq5UgsovCAXgFvuupTkjElsfGsvFquZju4G5l2WzsF1Vex4sxTVqWLvbubtFzez5d0DHDtQCUD6tMk8/ubdWG1nLs31iOItW7Ts/NSpU8/2EgWEhoYGTpw4wcqVK3Xxt+ZMqKpKWVkZs2bNCnQow6KmpobIyEjd9bU6nU7q6+t1U2q/YcMGZsyYobuxVgZjQ2VlJddccw2XXHIJn/nMZygoKCAqKmrAZW+99dbz2pe+zjgNDAKEp4/48OHDzJgx8AiQ8URiYiK7d+9m9uzZujqpioyMJCQkhJMnT5KamhrocIZMVlYWO3bsICsrS5c9cx5ycnI4efIkxcXF40rcj6QY9hAeHs7y5cs5duwYmzZtIiMjg2nTpo2qeQfA5LQ4nnzHN2akub6Nuz/5ODVl9QSH2bj6ttMzJuXFJ3G5Z+kKl4o0KX1FMWf+rHv6jD3GWgiBE2jtdhIbaqG1pVdb0Hv9w1durSJRPNsW4AJMbnWtVThLnGaB2SndPc2e2cS+UmkhJbhcfTPZVgtSVZEule4uuzfW1dfOo3B1Fjt37qLyuIU3/mcvdWWt3m1JCS2N7V4xDFBedJJjByrJKzx7VnU8i2L/Umm9CbH+VFZWYjabdVlyDFr8es0OBwcHEx4eHuhQBsXatWsndrk0gTG5miimWp///OcR7okGzz//PM8///xp57SeRJUhiA0MxgCr1crSpUtZt26dLgRxbGwsTqeTlpYW3TmUpqamUlVVpUtBHBcXR2hoKBUVFWRlZQU6nGGjKArz5s1j48aNTJ48+YxXZMeS0RDDHhRFIScnh8TERPbs2TPm2WKAV/+whtoTWptDT5edbe8fQDEpqC5NAJvMCnOW+N5TApAOp9Y37BHF/VzONRHsK0vGdPqc4sZOB7GhVspbe7XyZ+9DAhTp7T/27VhoathzkiclapAJkwpOhxOzO1Msne6ssdd5Wvo27klmu0u/TQJiE7SyWlVVOXbsGCUlJUxOSGZ/8UnMwkr2rFSOH6pEdUlSsuK5/KalvPHXj3zO7gKi4gYnAsarKJ4opdJSSo4dO0ZWVpauLsh66Orqorm5mQULFgQ6lCHjGROll+O+bt06HnjggUCHYTBOee6558ZsX4YgNjAYJCtXrmTdunV8/etfD3Qo50RRFBISEqitrdWdIE5OTubIkSP09PQQFBQU6HCGhBCCnJwc9u/fT0ZGxqhnGUeTyMjIcVM6PZpi2J+IiIg+2eLMzExyc3PH5HW09zq9P0tVIlXJb/97J3/95RvU17ZysqKBr1z8K2YtzeHA7hOAO+vqyZoCOFxaD68/wpurRarQp6paeASxxXu3v24FrTra7F/+jOdxbYyTAFBAFQLFZkK4s8TCI75d0lde7flfKH12cs3nlhMdF05bWxt79uzB6XSyYP5C7vnUH6itaAAhCIsM5pm192LvcTAlOxGzxcQ3H7mBZx74D1JKvvTja0hKH3zZ5XgTxfX19Zw4cYJVq1bpRsyciZMnT+J0OnV5UROgqqqK+Ph4bDZboEMZElJK6urqKCgoCHQog6K+vp5Dhw5xwQUXBDqU0SUQJlcTJEP8uc99bsz2Nb6awwwMxjEeQayXWbOePmK9ERISQkxMTMDcf8+XxMRELBYLlZWV5154nONxnR4rl8eBGCsx7MGTLb7gggs4deoUa9eupaamZtQ/91d9fjnBodoJuFAEN991OTlzpnD3726mquwULpeKlJIDHxVjQoLDeZq4FaLPDz4B6nluLtXtkiW9erSx022s5bcd301ol8396++8Rl3+os1v9JInI+w1+3LvSwWcLhSHyzuSCSB+ciTXf3k5hw4dYsOGDcTGxtJRaeHJe/6PmopGVFWiulTamjqpq2xkSnYirz6zhke+8TfMVhP/Kf4V/z36KFd9fugn1ePFaMvpdLJ3717y8vJ07SoNmigrKSkhKytr3PkPDAYppW7LpZubm1FVldjY2ECHMiiM/mGD8YSRITYwGCTz58+nq6tLN33ECQkJ7Nmzh66uLt3NUUxJSaGioiLgWZvh4HEwLi4uJi0tTdfZnkCXTo+1GPYnIiKCCy64gIqKCvbv38/Ro0fJy8sbtZO31KwEnt3wY4r3VpCcGU9KZjwAnW09p13tdzk0R2xp8ntvKQJpMWnGVh5U1TveCLS+3v6jkRo7HMxPi9DMtlyA/yEW4LKC2SH6rCSEQJWqtit31lmaBAgFp+LCrPqbayngcnHtrUs4uOUYALfedRmdnb10dvaQNi2MLds+IioqimXLlvHey7t57ldvaSOUzGat79jlQiiCyWlxPPfI6/zvs+tBwPrX9iCE4KJPDb+0dTxkig8dOkRwcLBuxxP5U19fT3d3N2lpaYEOZVi0trbS3d2tO0NKgLq6OuLj43VzIWLCj1sy0BWGIDYwGCR66yO2Wq3ExMRQW1uru560pKQkDhw4QEdHhy7NZTxl3zU1NSQnJwc6nPPCUzq9e/duVqxYMWai9HzEsL3XwStPvk/lsToWXDiDC4cpmBRFISMjg9TUVEpLS9m+fTsxMTFMnz59VC4ORMWFs/CimX3uS86cxNyl2ezddBQAS3gIDqeKVAR4jNs8Gdf+J8KK8CWIBZi7VJxBAulXrtzU6SA6xIKCRBV+wte/3dhtEN3nFVAEuCRS1RystQUlWBWtTFpVvVlkKQQ5s1L4yg+vBLQ+4RMnTlBcXEtTcw/z58/3XmjYvVGbgS3dY5cUi4ngMCtLLp1N3ORodq47omXrJSiKYN/mo+cliCGwori+vp7KysoJUSoNcPToUTIzM3U3JcBDVVUVkydP1mX8tbW1ZGdnBzqMQbNu3ToefPDBQIcx+hgl04MmMzMTIQQffPABGRkZgz53FUKcd5WPPi4jGRiMEzxl03ohMTFRl2XTVquVhIQE3c4kVhSFrKwsSkpKdFNifzZycnJQFIWioqIx2d/5Zob/cP+rvPT4u2x8Yy+P3vkCG9/Yc+6VzoLZbCYnJ4eLL76YiIgIPvroI3bu3ElnZ+e5Vx4inW3dvPqndbzyhw9paWhHURR++tzt3PPkrcRMicPhdpmWlgFO2F1qn19lH3ErNNNoe9/3Y2u3EwlEhVi0E4J+b1fpOUvwTxJ7xir5lWYLQCpuQy3oc3YREmZj//ZSmhs7qKmpYc2aNRw7doxZs2ZxwQUX9Mm6Z89K6VONraqSzi4n7/97O49++3ly5kxBcWfBVSmZOmNkSlsDUT5tt9snTKk0QFNTE83NzbrNdEspdWvo2NXVRXt7OwkJCYEOZVB8bPqHDYaEqqqoqtrndynlOW/+6wwX/V0CMzAIICtXruS3v/2tbuYRJyYmcuTIERwOh+7GAKWmpnLgwAFyc3N1caz7k5aWRklJCbW1tbodPeJBURQKCgrYsGEDkZGRo9pfNxJl0rvXFXkzjCaTwt5NJSy/Mv+8Y7NarcyYMYPMzEyKiopYs2YNKSkpZGZmEhkZed7bdzld3P3pJ6koqQUBb76wmf95/Tu89cImaioaaW3u6rtCn5FLmsmW7P+4p4RalbgUgQzqN7ICLUscG2Khqcvdlyz8HtQSwZhMgNNtpOXJeEgJJhAOjyAWCCkRSJ96lpKeHgfHjpbzv/9+nbjEcEyOUKLC40hISDzts33LXZeya2MxZUdOep6VtjMJm97exysHHsZkVjh2sJoFq/O46vPLh3OoB2QsM8WqqrJz504iIiJ0KyD7U1xcTEZGBlarNdChDItTp04B2rQAvVFbW0tsbKxu/s5v2LCBmTNn6vJYD5U+5v1juE89Ul5eftbfRxNDEBsYDIHCwkJd9RGHhYUREhJCfX09SUlJgQ5nSCQkJLBv3z5OnTqlm6ve/phMJnJzczl8+DAJCQm66es6E+Hh4RQWFrJjxw7CwsJGpWR4pHqGc+amUX+yGdUlcblUsmaObMYnODiY/Px8srKyOH78OBs3biQqKorMzEwmT5487As4lcdPUV7sFoISTlU38/0bnqSiyH1fkBVMJk1r9hPDgFs7+iSx8OsnluCreZbuf9zrN3baiQuxcpRuPyHr26hqlZjsaMurnrnC2mNe8esW36oKwqQQYbMgVRczCuKYURCHo9fFgS11FG3dj6vXBS6VuUuyuPrWpcxenE1ouOYob7VZuPWuy3jw9ufcZlxaL7KiCOJTYgiNCObOX31mWMd3MIyVKD506BA9PT0sX75clxf8+lNfX09TU5NuHI4Hory8nLS0NF1+V9fW1uqq79noHzYYb+jvU29gEEA8fcRr1qwJdCiDJjExkdra2kCHMWQURSEtLW1MrxCONGlpaV7X0olAQkICubm5bNu2jZ6enhHd9kgaaH3rlzdw4acWkDNnCp/7wZVc+plFIxipj/DwcObOncsll1xCQkICBw4c4P3336eoqIiurq5zb6Af0ZPCUUx+f5YFPjEM0GPXEr7+FSr+Jfmekmn3fcLlG8uEovVZeUWz/yziDiexoVpmyWMK7X+TCiDR5hQrIPqNb/IWq0nABKmZ4Sy9ZDK3fnsGk1NDWfdGBS89dYiDW+pwOnwO1Hs3H+Ont/+Fr132K9qafeXniy6awQ9+dzPLLpvNjMIMIqOCSJ+WxH3PfnHIx3Q4jHb5dEVFBZWVlSxcuFA3Gb2zIaXk8OHDZGdn6zY73N3dTV1dnS7NwBwOBw0NDboSxGvXrmXFihWBDmNskAG6TUDa29uprKykra1txLdtZIgNDIbIZZddxltvvcU3v/nNQIcyKBITE9m+fTuqquruyndaWhoffPAB3d3dBAcHBzqcIaMoCtOmTePQoUOkpKToei6xh6ysLNra2ti+fTtLly4dkec00m7SYZEhfOc3N593XIPFarWSnZ3N1KlTOXXqFOXl5ZSUlDBp0iRSU1NJSEgYlPDZ9sEhVKfLK1avvnUZr/1lQ59lUlJjOHGsDmlSEJ4+YlVFCkUTvL0OMLn3JfoW63kSw8i+rtGNnQ5mxYT1LZX2xwL0gGfwsFDdC3mWM0niQqxkpYcxIzcSq0XhyIEmXn6miNamXu82pZRa1tolfdlftEz4hjf2cuUtS727XHl1PiuvPv8y9+EyWpnixsZGDhw4wMKFCydE3zBoc4e7u7t1Z97oT0VFBfHx8bqbyABaqXdYWJhu3k/l5eUUFxdz4YUXBjoUAx3gdDp59NFHefbZZykrK/Pen5GRwZe+9CXuvvvuETHB09fZsYHBOOCqq65izZo1tLe3BzqUQRETE4MQgoaGhkCHMmRCQkKIj4+noqIi0KEMm+TkZGw2W58vcj0jhGDu3LlIKdm3b995m4YFcrTSSKMoComJiSxatIiLL76YmJgYjh49yttvv82mTZs4fvz4WY243nl5q/aD1HygW+rbueY2n+mMKcjKidJ6MCkoQmsPttlM2kxip0tbT5VaZhi0jLEikEIgpBiwmU0CjV12YkMsvvHE/hkGz6Yk7m1pdykI0hKDWV0Qx5evy+CmT6SRGB/Exi2n+MsfS9i05iStjX5VBCYFgi3a5rwzin3BOOwO1vx3FyX7Twx4bGorG3n3lW0c3FF6lldgZBnpTHFXVxc7duyYULNXVVXlyJEj5Obm6tKZGbTnUFFRQXp6eqBDGRbV1dW68ql4/fXXueCCC8Z8jJ+B/rDb7VxyySXce++9lJeXk5qayoIFC0hNTaW8vJx7772Xiy66CLvdft770ue3l4FBAMnOziYzM5P33nuP6667LtDhnBMhBCkpKVRWVhIfHx/ocIZMeno6+/bt8zod6w0hBHl5eezcuZO0tLQJUSJpMplYsGAB69evp7S0dNjZs4kkhvsTHBxMbm4uubm5dHV1UVtbS21tLYcOHSIsLIzExEQSExOJjo72lj/HTY5CMSmoLhUhIDYxksIV0+hs66a9vYdt64oBbYyRVBRwqfR2ucBsAqcmfq3BVqLitVFlikNFmlUwK+71+jlFu2noshMZZMZqFthdUquB7vNRE6gmSaiiMDU+hJz4EKYmheB0So5XdbJ2Wz2VZR24HCpCBZNLRZgUpMPVV38LoQljVPccJ22eU15hBn979C16ux0A3HHftVx7m6+csqyohrs++bj38dt+cCXXf2X1iLxO52KkMsVOp5Pt27czefJk3QqvgaisrERKqctSYw91dXUIIXTpVWG326mrqyMvLy/QoQya119/nauvvjrQYYwZwmNEOMb7nAg89thjrFu3jiuvvJLf/OY3fcaKHT9+nO9+97u8/vrrPPbYY/zwhz88r30ZgtjAYBhcffXVvP7667oQxAApKSl89NFHOJ1O3V3F9xhSnTx5UrczfSdNmkRkZCRHjx7V1YnL2QgODmbhwoVs2rSJ8PDwIV9smchiuD8hISFkZmaSmZmJw+Hg1KlT1NbWsnWrlhGOjIwkKiqKT35tMW0trRzYWsGshVOZmpfEvTc/rYlkCVj9S6Hp+7PNDCYFh6rS2NgBgAwya6LTuyzglAirez6xO2nc6VTpdriICbZQ26FdaTcrgsQwK5PDbSSFW0kKsxEfbqW+zc7Rk53s+qCa2oZed/k0mFxSO/FzaT3CQoDJakJ19BuHYTNDl90b0C//+Q02v7OPI7vcFRRS8uxDr3HhJ+cTHqWVr37w7x3Ye53eTTz36ze54Mq5JKTEDP9FGQLnK4qllOzZswez2cysWbMmhIkWgMvloqioiJkzZ+ryYqWH0tJS0tPTdfm61NTUEBERQVhYWKBDGRStra2sW7eOP/zhD4EOxUAHvPTSS8ycOZP//ve/p33HTJ06lf/85z/MnTuXF1988bwFsX6/wQwMAsjVV1/NG2+8gcvlCnQogyIyMpLg4GBdmmsJIcjIyNB1ybEnS1xaWkp3d3egwxkxoqOjmTNnDjt37qSjo2PQ632cxHB/LBYLycnJFBQUcNlll7F48WKSkpJwOBzU1tew5IZ0vv4/F3LV1+dQUVVGVkECscmhhIabsdrcx0nK02/u+b8uswncF72kcuYTfIGWRbCaBDGhFtp7nSycEsE1MybxtUXJ3LsinZvnJDJ9UgiddhfrS5t56r0Knl1byfqiJmobe31NyVKiuCSKp+RaARTBE3/78sDzP9ymXopJISN3MrYgq3dMFmjjp3asOez9PSwqpM/jUpW88fymYb4Cw+N8yqdLSkpobm5m/vz5uhaO/SktLcVms+lugoE/bW1tNDU16TbDXVVVNapj8Eaad999l5ycHF33mw8Zw1Rr2Bw7dozLL7/8jN+biqJw+eWXj0hLi75SRQYG44RFixYhhGDLli0sW7Ys0OGcE0/ZtN7+eHqYMmUKRUVFtLa2jsi810AQHR1NYmIihw8f1vVokv6kpqbS1tbGtm3buOCCC85ZEv5xFsP9URSF6OhooqOjvfe5XC7a29tpaWmh+lgjs1enEh4TRHC4FSklJsVMZ7uD+po2OtsddLbZ6e5w4AJcQkFVFDznDrm5kThN2vglq0UhJMJCcJiZsBAz4UEmwmxmbBYFpypR3aONDp7qpLisk5pWO609Tp+eVSWWHrSMtHQv7y4FFHbVr0RP+2HGjCS+dcPvAU+ZNJoQVt1mXEJw9S1LCY8K4erPLeNfT3+I/ybConzmRtd8fjn//P0H3pJpIaCuuokvXfBzXC6Vz33/SlZeM2+kX57TGE6muKamhqNHj7J8+XJsNttohzhmdHd3U1JSwsKFC3WZWfVQVlbm9XnQG11dXTQ1NVFYWBjoUAbNx61c2uD8sFqt57zY3tnZOSKtaBPnUqWBwRhiMpm44ooreP311wMdyqBJSUnh1KlT9Pb2BjqUIWO1WklJSdF1lhhgxowZnDx5UpcGZ2cjLy+PkJAQduzYgaqqZ1zOEMPnxmQyERUVRXp6Old/+jIaily8+shutr1SxcLCJSxbvpTIsHjKjjTT2WYnJMxCSlYkGTlRZM+IZvrMKPJma6XEOTkR5GSEMzUtjMT4YISAU4297DvRxtsHGnjuo2p+/XYZP32vlA0VLVS19fJBeTOHGrto6XFqItd9E4DLY8qlCJw2tzZWtey0yyJwoT0mJRw4UIULgUR4ZwkD0OMRtYLYxEheeep9PrfoQfdmtSbnSz+ziMKV07zHJCQsiG/94tN9ft/85j5qyuupPdHIr771d2pPNI7+i8PQMsUtLS3s2bOHefPm6fZC3pk4dOgQCQkJxMXFBTqUYeNwOKisrNRttrKqqopJkyYRFBQU6FAGhdPp5M033+Sqq64KdCgGOmH27Nn8+9//pr6+fsDHGxoa+Pe//82cOXPOe19GhtjAYJhcddVV/PjHP+aXv/xloEMZFKGhoURHR1NdXa3LE4CMjAw2btzI9OnTdXk1H3xGS/v372flypUTpnxSCEFhYSGbNm1i586dFBYWnvbcDDE8dIKCrdzz+8+fdv+qS+az7r9H2LexEnuPQ6taNgnUECtYzFisCnd8O4/X36iiCwlmzdHZaRFgEtjD6NuHbIWGbge5cSFeJ2rp9r7yZn6lAEUiPdc7rEIbxaQ9qG3PAthxrwjSYkaoEunyyyBbTaiqxORU2buphF3vH/DFIQR/3nAvSemaA3PxnnI+/Nc2oidFsH1jsVeAd7Z1g6p6ywKlKqmtbCRxSuz5HvJBMZhMcVtbG1u2bCE3N1fXJcUD0dDQQF1dHatXj42x2WhRUVFBRESELt2OpZRUVVX1MRka72zevNn7/f+xY4KUMI813/jGN7jxxhtZsGABP/7xj1m1ahWTJ0+mtraWdevW8fOf/5z6+nqeeOKJ896XIYgNDIbJJZdcwk033cTRo0d180fJ4zatR0EcGRlJbGwspaWlTJ8+PdDhDJupU6dy4sQJysrKRmy26XjAYrGwZMkSNm3axO7duykoKPCWUhpieGT5wy9e58D2UqQq3eN8JQRZ3dncfmdeCl7xK5Duh/3EsARh1wRxXIil73oSpL8oFv4rCe1/RYK/lYIEhILA5a5BE77HhQSTCUwSl9nE7k1HwWLWxkahxe5wm2dVFNdw9zWPoaoSKSVKsM3bR6yYFEIigulq7QIBkbHhZM+ectpxklKOWjnv2URxe3s7mzdvJjMzk6ysrFHZf6BQVZX9+/eTm5ury9nwHlwuF8ePH2f27NmBDmVYtLW10dXVpatxS6+99hpXXHGF8f1vMGg+/elPs3fvXh555BFuv/320x6XUvL973+fT3/60wOsPTQmRnrCwCAAhIeHs3r1al2VTScnJ9PS0jIkA6TxRHZ2NqWlpTgcjkCHMmwURWH27NkUFRXR09Nz7hV0hNVqZfHixbS2trJnzx6klIYYHgX2bjnuE4eKZ76wQLjc6VuPKHbf7/ldOE/fFkK7Mt7QbSfYbCLEpiAVd4bYfzGJN3uszRAGd/Gzn7zW5hQjtASuNxbpqbv2xYoicCkCxeK7Lj9jQSap2dromz0binE6XKguFalKXL0OEJoYlqrku4/dzI3fuoTrv3oRj7/+HULDfWWjZUdquG3ZT7ky/S5+8ZW/YO8dne+LgcqnOzs72bx5M1OmTCEnJ2dU9htISku1OdB6vKjqT2VlJRaLhcTExECHMiyqqqqYPHmyrqZGfFz7hz1jl8b6NlH4xS9+webNm7ntttvIz88nMzOT/Px8brvtNjZt2sQjjzwyIvvRzyfJwGAcctVVV/HKK6/wne98J9ChDAqr1UpCQgLV1dXk5uYGOpwhExsbS0REBOXl5brJyg/EpEmTSEhI4NChQxPKYAsgKCjImyneu3cvbW1tWK1WQwyPIHn5adSfbEFVJaoqtbJkt9P0aRliKbWZxYCQEsUuNbXrcaB2L+6QkpZeJ3EhFjrben2XywXaaKX+J1hC4AqTWNq0FmGFfstILXuNlAiT8M5J7rOMxcyCJVksWT0dk1lh2f+b6y21T832CRVFESQkR3Pll1ZRe6KRpZfNZs6SbBZdNGPA4/PYd16g7kQjqirZ+OZephdm8IkvrRrUsR0q/pliu91OZWUlycnJTJ8+XddmUwPR3d1NcXExCxYs0HW7h5SSY8eOkZubq8vXyFMuPXfu3ECHMmiKi4upqKjg4osvDnQoBjpk0aJFLFq0aFT3od9vNAODccBVV13Fpk2baGpqCnQog8ZTNi37nzjrACEE2dnZHD9+XDcjr87ERDXYAq1XesGCBVRVVdHb28v8+fMNMTyCfOOBa8lfkuV1bRYI6La7M7D9TvBVtHSBexSS9rifMBW+W2O3g7hgq/d+1ZMVBq+5Vp9vDRNuQaGJcuF1kZYIs+ITxZ7MsN/oJE1FK+TkpzGtIIPVn5yPNchXsl2wcjq3P3Adk9PjmD4/kwf+/lU++aWVfO2n1zFnydkvhjXWtqF6M+gKTXVtgzuwwyQ6Opr8/HxKSkoIDg5mxowZuhRa5+Lw4cMkJCQwadKkQIdyXtTU1KCqqm7n2jc0NKCqqq5eh9dee40LL7yQ0NDQQIcy9gRi5JL+Tu8CjiGIDQzOg9TUVGbNmsXbb78d6FAGTWJiIr29vbS0tAQ6lGGRkJCAzWbjxIkTgQ7lvAgODmbatGns27dP9+K+Pw6Hg7179xIdHY0QgoMHD+ryAsx4JSTURkr6pD4nPYpLIrr7OchLqZU+u8WZBJzB7t7f/npNan3EsSEWpEnz0JL+CV3Ztzga8JVjWxTtcZdEePZnEkiXx/VK+gSx5+bmb//zAXdc8ms++M/O057nJ+5YzV+2PMij//0OU3IGX9p6xS1L3eEJFEWwYpRHMnV0dHDgwAGSkpJoa2vzlhVPJE6dOkVtbS0zZgycldcLUkpKSkrIysrSbZa7qqqK5ORkXcX/cS2XNtAP+vk0GRiMU6666ipee+21QIcxaEwmE0lJSboVlJ4s8bFjx8464kcPZGZmYjabKSoqCnQoI4Z/z/DixYtZtmwZDQ0N7N69W/ev13giwm9OL4C0mJAWK7j8jrEnM+uPoind/skEcwfU99qJC7ZoQtjTRyzA5ckUS3cW2A/tUo5EUftlg4VAWjzzh9HUtdPlXV+glXCjCFQpef637573MfFw012Xcd+fv8QXf3wNT777fbJmpo7YtvvT3t7ORx99RFJSEoWFhSxZsmRQI5n0hOcC14wZM3RtpAV4Rw+mpaUFOpRh4XA4qKmpITV19N7TI01jYyObN2/myiuvDHQoBjrAZDIN+RYUFMSUKVO48cYb2b59+7D2awhiA4Pz5Nprr+Wtt96iq6sr0KEMmvT0dKqqqnA6B3LZGf94xphUV1cHOJLzQ1EU8vPzKSsr01XZ/ZkYyEArODiYZcuW0drayq5duwxRPAAtDe3s33yUlsb2Qa/T0+XLBku0EUfSJMDk+7MuZd8aZ7em9Y1O8kNRoaHHQVyQxT1rGFSreyWPbHbRtyRbCOwhWhn2QK+q9O9Tds8Y7iOqPe7XZgWrbeQsTYQQLL50NtfdsZq0nNFz4W1ra2PTpk2kpaV5y6SHMqdYLxw6dIiwsDDdikh/SkpKyMzM1G0LR3V1NaGhoboaFfXf//6XefPm6bZE/XwxTLWGhpRyyDe73U5VVRWvvPIKy5cvZ8OGDUPeryGIDQzOk7lz55KamqqrLHFUVBShoaFUVVUFOpRhoSgKWVlZHD16VPeluBEREeTk5LBnzx5dl06fzU06KCiIpUuX0tnZyY4dO3T9PEea4j0VfGHJg/zg0//DF5b8lJJ9g6vciIh29+K5Twi0MmXtT7rvEzFAH6twlz6LfjcEDd12YoMsPs3reQlNYuBtAdjQDLZsHvHrJ8LNiu/zKSWYFS3d7HGeVlWQEpPFzNce/OSgnvd4obm5mU2bNpGRkXGagdZEEsWnTp2iurqauXPn6r4vurGxkba2NjIyMgIdyrCQUlJeXk56erquXosXXniBm2++OdBhGOgEVVWHfHM6ndTU1PDkk0/icrn42c9+NuT9GoLYwOA8EUJw880388ILLwQ6lEEjhCA9PZ3y8nLdCsopU6Zgt9upq6sLdCjnTVZWlq5LpwczWslms7FkyRLsdjsfffQR3d3dAYh0/GDvcfDib9/mZ7f/mZ5ubSxQb7edfz753mnLFu+t4LblP+MT077Pnx/6P6SUXH7DAqZO07KfJpOfu7TELWDxOUS77/aUQA9kuCKAZrt2oSLKk631LG/CN3apn5O0Vz2bFXd/sHtZCSgKLk9sfq7THuMtpMRiM/O7f3+D/KX6cY2vrq5m06ZN5OTknNGtfyKIYofDwZ49e5gxYwYhISHnXmGcc/ToUTIyMrBYLOdeeBziGZmop0xrZWUlH330ETfccEOgQwkchqnWqKMoComJiXzta1/j+uuvZ8eOHUPfxijEZWDwseOmm27ivffeo76+PtChDJrk5GQ6Ojp0a65lMpnIysqiqKhIt6Leg55Lp4cyZ9hqtbJkyRLCw8NZv3697p7rSPLUT/7Ni799l8baVu99Am3Obn9+fMsfOFneQE9nL//+wxo2v7OfoBAbq67JZ8GqaXzhu5dhtZrA4e7o9cseCb9aZunu79WmM0mfcZaQSDRD6qZeB5M8ZdMe/EYw9aF/lmqgUr1gq69f2LMtVWq9zqokNy+Z537zDkcGmRkPJFJKjhw5wt69eyksLGTq1KlnXV7vovjgwYOEh4dPiFLppqYmGhoadD0/uby8nNTUVF0J+n/84x9ceOGFup33bKA/srOzaWsb+mQBQxAbGIwAGRkZLFiwgFdeeSXQoQwai8VCamoq5eXlgQ5l2GRkZNDb20tNTU2gQzlvIiIiyM3N1VXp9FDEsAeTyUR+fj7Z2dls3ryZysrKMYh0/LFr3ZHTLuSERARz07cv63NfV2cPHS1+/gRSsmdjMS8/vYZnH3mTHeuK+POv3sbR68TkUqHXoWWCwde3iyZ8UTQzLU+PMJ6fPf87/fqI3fepnk14YvXMJPYLXRWa2Jb4lUP7AsblXzbtaR+WYDGbOLSznN2bj/Hdm56hqqyeg9tLeezul/jzL16jo3V4vgwVxSfZ9sFB2po7h7X+QDgcDrZv3051dTXLly8f9Am+XkVxXV0dNTU1E6JU2nMhY+rUqQQFBQU6nGHhcDiorq4mPT090KEMCaNc2mCs+elPfzosrxJDEBsYjBCf/exnefHFFwMdxpBIT0+nuroau90e6FCGhclkYtq0aRw5cmRCmDVNnToVs9nM4cOHAx3KORmOGPYghGDq1KksWLCAAwcOcOjQId1n+YdK9qzUPtngm++6jL9tfYCM6Ul9lnPZXX7l0Nr/U7IT2PjOft9d7hJpFRAWE/iNY/GWKQvt8T5lz94ZxNr6Sk8/Yy0BLrOW9pW4xxlLfGOTVO3msmm/OoNMvm17hHF/t2k0t2mQOB2+Cz9SSl546kN+eOOTfPjqDv7zx7U88MVnh3JIAXjvn1v5ysWP8MAX/sSXVz5EXWXjkLfRn87OTjZu3IjT6eSCCy4gIiJiSOvrTRTb7Xavq/REKJU+deoUra2tZGVlBTqUYVNZWUlERASRkZGBDmXQ7N+/n+PHj/OJT3wi0KEEFMNUSx8YgtjAYIS4/vrr2bVrly5OeDxERkYSGRmp2xFMoM2CFkJQUVER6FDOG0VRKCgo4MSJE5w8eTLQ4ZyR8xHD/sTHx3PBBRdQW1vL1q1bcTgcIxzp+OWu39zEiqvymTojmc99/wpu/s7lhISdnr0Kjw5l5bUFXjE8KSmaCz+1gNZGd/ZT8cveWUyaAHWrUu/oJNyrK7gdnxmg/Fl7vE+GGMCsmW5JP00rAOEesyRwZ59BE74DXdiw+J1quEct9RkP5aanoweXU0V1SVRVcmh7KQ770JzwX3jsba8o72jt5u2Xtgxp/f40NDSwYcMGJk2axOLFi7FarcPajl5EsZSS3bt3ExUVNSFKpaWUHD58mJycHF2VGvsjpaS0tFR3ZmAvvvgi1157LWFhYYEOxcDgnBiC2MBghIiNjeWyyy7TXZY4MzOTsrIy3WboFEVh+vTpFBcX63aMlD9hYWHMmTOHPXv2jEvjqZESwx7CwsK44IILEEKwYcMGOjo6RijS8U1EdCjf/59befKd73PjNy85a1nq9564hfuf+zLfe+IWnv7gh4SGBxMa4RbPbodob9YW/MqkhVesIv3EsfD97505LEDphvr+gtjkHiHsryXUvopaNbv3491X31ikYnLf5zbX8mSZXap32cSUaK66abF3NcWkkJQeh8U6tHFM1iCrt7VZSoktaPgiqKysjK1bt5KXl8esWbNQlPM7ZdKDKC4tLaWtrY38/Hzdl0qDZoDmcDh0Jyb9qa+vx+l0escN6gFVVXnppZeMcmkwTLV0giGIDQxGEE/ZtJ7E5eTJk1FVVdduzZMnTyYkJGTcnmQOlZSUFJKSkti5c+e4KgUfaTHswWKxsHDhQhITE9mwYQOnTp0ake1OFBRFYdHFs1j9yfmERgQDcONXVuMdLAyAhF6HJjQ9QsYzYslzgiSkr/paxa9kWruZ0DLE0TYL5v5iyJMYFX4u0iogBUIIXCZAEagmd2226u4XlhKhqr5yafxilh5zLZVLri2g4IJcvv3LG8iYnsTcpdn89G93eHdfdqSG7177W764/Ge8+fxHZzxWX//5p7AGacGm507mys8tH+xh9qKqKvv27aOoqIjFixePaKZ0PIvi5uZmjhw5QmFh4bAz4eMJVVU5cuQIubm5up07DNpFirS0NF09hw0bNtDb28vFF18c6FAMDAbF0C69GhgYnJUrr7ySL37xi+zcuZP58+cHOpxBoSgK6enplJWV6dYJUghBXl4e27ZtIyMjY0KczM2cOZMNGzZQVFREXl5eoMMZNTHsQQjBjBkziIiIYPv27UyfPp3MzMwJkaUaDS76RAEZuZMpOVDJtg3FbN1QrOljVUV4SqbRsru+0mb3Tx5TLf9D676vw+Gix6USF2Shtruvt4B3cSkRsu/rolqBblBtCia7ExSBYhKoPU5MHb14AxJ+I6K8K0smTdZ6Iy/7zGIu+8ziPg9LKfnJLU/TfKoNVZU8ec8rpOVOZuaC012e85fn8o89P6e5vp2E1BhMA7h2n43e3l527NiB0+lkxYoVo9JD6xHFW7Zo5dzncqseCxwOBzt37iQ3N5eYmJhAhzMilJeXYzKZmDJlSqBDGTadnZ3U19czZ86cQIcyJF544QVuuOEG3ZapG3z8MDLEBgYjSHBwMJ/61Kd0VzadlpZGQ0ODrstV4+LiiImJoaSkJNChjAhms5nCwkJKS0sDnjEdbTHsT2pqKkuXLuXYsWNs27ZtXJaNBxKnw8X7/9rOv/+4lrKSk4RHhXDNLUuZVZCuzQI2KQj3PGFpEd7L3ibwU8b0MdbyCGfp7jHu30fsqZCW7gZir1GXH8Ldu+y0Kd4e4x/9+GoEAjU8GNVqPm0dxSQwmRUuvX4+81dO459Pr+H5371LdVkDVaWnsPe45zP3OGisbUX1KwuvPHrmipbgUBtJ6XGnieGt7x3g9z/+F+/+Y8uAlRe1tbWsXbsWm83GsmXLRtVQajxliqWU7N27l7CwMF0bT/njdDopKSlh+vTpur6o5rlQHRwcHOhQBk1PTw///ve/+exnPxvoUMYHRsm0LjAyxAYGI8zNN9/MTTfdxKOPPorZrI+PWFBQEElJSRw/flx3V6L9ycvLY+PGjWRmZk4Id9SIiAhmzZrF7t27WblyZUBGhoylGPYQHR3NqlWrOHDgAGvXrmXWrFmkpKTo+sR2pPjlt5/no7f2gdmMNLkdpd3GWgmZcdTWt/tcnXH3C0ttDrD0H7fkADyaV/j6irXRS/Y+o5e0xwQORWJ1eauvvY97cAmByQwuNAH+0P3/1WITAhliRXW4MDl9ztK33X05131xBS6Xyrc/8TilRScRwD+e+gDZYycyNoxfvvJN0nISmblwKod3lILQRPScJdlDOm4b39jDL+74CyaTgsul0ljbyk13aSOu7HY7Bw4coK6ujpkzZ3qN+kab8ZIpLi8vp6mpiZUrV06Yz9jx48cJCQnRbdUTaN+9FRUVLFq0KNChDIk333yTuLg4FixYEOhQDAwGjZEhNjAYYVauXInZbObDDz8MdChDIjs7m8rKSnp6egIdyrCJjIxk8uTJFBUVBTqUEWPKlClMmjQpIP3EgRDDHqxWKwUFBeTn53Po0CG2b9+u6/fmSNDT1auJYQDPa+HnMl1b3QKA06b9aZeKQCqKW7QKn3gVnsplicRPKCugOKCh20GczeLLNLhNt1zea0yelDG+XmJPE7IQOCL93id+AksGW7wu04pJUF5cC0BdVRPHD9cgVc1dWnPEFrQ3d/L3X78JwAN/vZ0bv3Up/++zS3jsv3eRlDFpSMdu67sHUBSBy+1uvfGNPdoxq61lzZo1OBwOVq1axZQpU8ZUFAY6U9zc3MyhQ4coKCjAZrON+f5Hg97eXo4dO0ZeXp6uBX55eTkRERG6K2F/8cUX+exnP6vrYz+SGGOX9IEhiA0MRhiTycRNN93ECy+8EOhQhkRERARxcXEBL987X6ZPn05NTQ1NTU2BDmVEEEIwZ84cHA4HBw4cGLP9BlIM+zN58mRWr16N2WxmzZo1VFZW6sq0biSx2CyEhgf1M9PyQ3H3Dpt8plrg7h2W7kyve6aw94+/Wwx7yqZRtZLpScF9naYRgLmvthZux2jPz56Nqp55xJ5ZxJ6bWXF7awlUl2T2Qi0jGhUbhi3I4tPOUnoMqb1l06HhwVz86YXMWpRNUMjQPQJSpsZ7D5liUsiYkcTu3bvZtWsXeXl5LFy4MGBlqYESxT09PWzfvp1p06YRFxc3ZvsdbQ4dOkRcXJyun5PL5eL48eNkZ2frSlg2NTXx5ptvGu7SBrrDEMQGBqPAZz/7Wf7zn//Q1tYW6FCGRE5ODuXl5bqeBxsSEkJ2djb79++fMMLJbDazcOFCampqKCsrG/X9jRcx7MHIFmuYTAr3Pv15wqNCfZlXv6qBpMnR7vu0//xHKml10/SdQazgnluML0MsoL673+glz/om8A4i9uAVvH53e2Pr9/kTAtU9K/mzd17CRZ8soKzoJAe3l/K933yGxNRYrDaz9pwEmMwK13/tIgAO7Sjl9lW/4OGv/ZU7LnyE7WsODenYXffVC7n0psXEJUVx0U35zLo8gd7eXlavXj3mWeGBGGtR7HK52L59O5MmTRoXpl4jRVNTEzU1NcyaNSvQoZwXJ06cwGazkZCQEOhQhsQ///lP5s6dS3b20FoaJjRGD7EuMASxgcEoMGfOHPLy8nRnrhUTE0NkZOSYiK7RJCsrC6fTSXl5eaBDGTFCQkJYsGABhw4doqGhYdT2M97EsD+ebLHJZPrYZovzl+Xyq39+w3eHyT0HSYGTpfXgcOE9G/KIX89feuFxmtbQzpu0dT3CWSpahjjEbCLErPiZb/Utue63EW+Jnuf1kEq/Zdz/S7e5VmtTJ288v5mv/b/fcP+X/sLTD/wvF3+yAHtXD0iJMCnkLchk9mLN5Om1v2zA5e4/VlWVV/+wZkjHzWqz8LWHPsVdz3yCnOWTyMubzqJFi8aVWdFYiWKPiRZof6sCfTFgpJBSsn//frKzs3XtIaGqKseOHdNddlhKyTPPPMPtt98e6FAMDIaMIYgNDEaJr3zlKzzzzDO6O2HPzs7m+PHjOJ3OQIcybEwmE7NmzeLIkSP09vYGOpwRIzY2llmzZrFjxw46OztHfPvjWQx7sFqtFBYWMnfu3I9ttjg9J5HPfHW117DKk5EVikB02b0GWd5EgRCoLr+kgUcouwBF0G+CEnaXpM3u9GWJpTfprP3v93uf0cKqLx5HkPu9o7qj8JRQm7Vy6tdf2MLfHnvbu8/m+na2rzmCorj7n1VJbaWv7SE4zOZ9nooiCA4bmsFcXV0da9as8WaF09LSxqXYGAtRfPz4cRoaGsbtZ3y4lJWV4XQ6de+UXV1dDUBSUlKAIxka27dvp6ysjBtvvDHQoRgYDBlDEBsYjBI33ngjZWVlbNu2LdChDIn4+HiCg4M5ceJEoEM5LxISEoiNjeXw4cOBDmVESUtLIzk5mW3bto1oabsexLA/SUlJrFq1ypstPnbsGC6X69wrThBuvfMSrr51Sd87pdR6ef0zuUIghUB4hKmfBhQKvn5dT3m1/+glm89p2oNnBJN2f99eZX956Qwz+R73vyYoQHUbgZksJj9BLQmPDkF1qQj348sun+Nd7ea7LiM+WSsJj46L4Is/uvqcxwigo6ODHTt2sHPnTqZNmzbussIDMZqiuK6ujqKiIhYsWBAQ1/rRore3lyNHjjBr1qxx/911NqSUHD16lKysLO/FIb3whz/8gVtuuYXQ0NBAhzKuEO7v5bG+GQwNfX3aDAx0RGhoKLfccgvPPPNMoEMZEkIIsrOzOXbs2Ji7Go80s2bNorq6esIYbHmYOXMmNpuN3bt3j0gFgt7EsAebzUZhYSGFhYVUVVXx4YcfUlFRobuqjOEye36m7xfPcxYC6wBPXwDC5ddiJkC6tBJq2Ufk4jXW8s8Qe7btCvPbqN8+cYtrryg2Cbf5tPD1Gbs3L4MtBAVb+MpPrsFi0UbTJWdMYseaQwhFIFXJzIWZ3HbPVd5dTUqK5tkNP+b57Q/y1y33k5p19t7K7u5u9u7dy9q1a7FYLOM6KzwQoyGK29vb2blzJ3PnziU6OnpEtjleOHz4MJMmTdJdz21/6urqsNvtTJkyJdChDInm5mZefvll7rjjjkCHYnAeVFVVcdttt5GUlITNZiM9PZ0777yT5ubmQa3f2dnJiy++yE033cS0adMIDQ0lPDycwsJCfvOb32C320f5GQwfQxAbGIwid9xxB//85z8H/WUyXkhKSkJRFKqqqgIdynkxEQ22ABRFYf78+bS3t3Pw4MHzem56FcP+xMfHs2LFCvLy8igpKWHt2rWcPHlyQr3mA5GQFOXNpqIomM0mMnMSWLUiz7eQW+wKwNro6mOqJRX6CmHPGYH0M9by07wALjPeUUzgFtRoArvPqA8hcAR7FpIIVbqz1IDJRE+XnRf+5wOeXfND/rLuHgqWZ2NSFKR7mZqyBkymvqcoJpNC3OQozJYzv0cdDgeHDx/mww8/xOFwsHLlSubOnTvus8IDMZKiuKenh61bt5KRkUFKSsoIRTg+aGpqorq6mpkzZwY6lPNCSklJSQlTp07V3ffw3//+d+bNm6d7M7NRQSemWsePH6egoIDnnnuOBQsWcNddd5GZmcnjjz/O4sWLaWxsPOc2Nm7cyGc/+1neffddZs6cyTe/+U1uuukmqqurufvuu1m1atW4bXEyBLGBwSgya9Ys8vPzef755wMdypDwZImPHj2qe1ExEQ22QOulXbx4MdXV1Rw7dmxY25gIYtiDEIKUlBQuvPBC0tPT2bdvHxs3bhxVA7JAs3VdkZbxdKtVh0ul9EQTG17dA/iMsnCoWmbW7NdzLIS751fblvSMFXY/1NjtYFKwNt7IK3SlBLNv/9K/ZFoI+hesu0Lc7yeTx54anwM2UFPRyE++8Ed+9/2XURTFOyNYMSmk5SQO6Vi4XC6OHj3K+++/T3NzM0uXLmX+/PmEh4cPaTvjjZEQxZ7PeUxMDNOnTx/hCAPLRDHSAmhsbKSjo4P09PRAhzIkPGZaX/nKVwIdisF58LWvfY1Tp07xxBNP8N///pdHHnmENWvWcNddd1FcXMy99957zm0kJibywgsvcPLkSf7973/zyCOP8Mwzz1BSUsK8efPYvHkzTz311Bg8m6FjCGIDg1HmK1/5Ck8//bTuhGVqaipOp5OTJ08GOpTzYqIabIFWlr9o0SJKSkqG3PM9kcSwP4qikJmZyUUXXURCQgLbtm1j69attLa2Bjq0EScxORrVk3WVUusXNil9/7ILibRoWVzV0nd9xc+Q2re89l9Dt4PYIHPfecUep2nhXq9fqbX0fwtJCRZfhliq+Iy1/L4LTxw7xYFtx/m/v31EYtokgsKCmLkgk+/+5qZBHQNVVamoqOCDDz6gurqawsJClixZMqFKgs9HFHvGKwUFBZGfn6+bkvHBMlGMtACOHj1KRkYGFovl3AuPIzZs2EBtbS2f+tSnAh2KwTA5fvw47733Hunp6Xz961/v89iDDz5IaGgozz///DnNPOfOncvNN9+M1dp3Vnx4eDjf/e53AVi3bt2Ixj5SGILYwGCUuf7662loaODDDz8MdChDQlEUsrKyJkSWOCEhgbi4OPbv3x/oUEacqKgoFixYwP79+6mtrR3UOhNVDPtjNpvJzc3loosuIjQ0lA0bNrBr165RceceS8qO1LD53f20NnVQd9KvFcNP6Lhs7tfTI2JNAhSBqV+lmudV7yN63TT3OFCEINLqTgn7fQWosu+yXiyefmFtYwJwemJTJagqigCls8c92liCoiClRHVJaqua6O1x0tbSQ2xC5FmPg5SSmpoa1q5dy9GjR5k5cyYrVqwgPj5+wok+GJ4ollKye/dunE4n8+fP151J07no6uriyJEjzJ49W/ffYS0tLTQ2NpKZmXnuhccZTz75JF/60pd02ZYwFggZmNtQWLt2LQCXXHLJad8T4eHhLF26lK6uLrZu3Trs4+C50GM2m8+xZGCYWN+OBgbjkKCgIL785S/z5JNPBjqUIZOWlkZXVxd1dXWBDuW8mT17NvX19dTU1AQ6lBFn0qRJ5Ofns3PnznMaiH0cxLA/NpuNWbNmceGFFyKEYM2aNezdu5e2trZAhzZk3n5pC1+79Ff87Mt/4dZlD/HPP23ou4AApMRlOf1PuwqY1X7Lnkk3CnCpmij2GmvhG+HkCPJbtV+WWEjhd5dADXHHYhIo3Q7SJ0chnKovSyylb3yU1MYtlRef5MSxgb9zVFWlurqaDRs2sH//fjIzM1m9ejXJyckTUgj7MxRRLKXkwIEDtLW1sWjRonF7EjpcPLOUk5KSiI+PD3Q4501RURFpaWnYbLZAhzIkKisree211/jqV78a6FAMzoPi4mIAcnJyBnw8OzsbgJKSkmHv4y9/+QsAl1122bC3MZoYgtjAYAz46le/yttvv627Plaz2UxOTg6HDx/WfZY4KCiI2bNns2/fvglXOg2QnJxMXl4eW7dupb29fcBlPm5i2J+QkBDmzZvHihUrkFKyfv16Nm/eTG1trW7e2y/+7h3tB4sZu1PF0ePoKyzP8jSk/z/C/07OuF5Dj4O44NNHL7k8bbn+845Fv814zLhcfTcePSmcaz63DLPFRER0CAXLc5i/4vS+1lf/tK7P62K32ykpKeH999/n8OHDpKSkcNFFF5GRkTHhMp9nY7CiuKSkhJMnT7J48WLdiazBUFFRQXt7u+6NtEDrHW5sbDyjGBnPPPPMM1x22WVkZGQEOpTxSwBNtdra2vrcznTu42kpiowcuDLHc39LS8twjgBPPvkk77zzDnPnzuW2224b1jZGm4l1ydDAYJySmprKlVdeydNPP80vf/nLQIczJNLT0yktLaWyslJ3oyD6k5ycTE1NDfv372f+/PmBDmfEyczMpLe3ly1btrBs2bI+JjMfZzHsT0REBPn5+eTl5VFeXs7evXsxm81kZGQwZcqUcd2/FxRsRQiBVBQto+pUwer3OtodYFZwmf3Uq8fLSgGzv+uVv0717wn2oGh9xP4ZYv/1TtPQHhdqITFJ73BhVJsJehze7PXezcdYeflsXjv0iz4Z3VuX/Zz6mhbv7x/8Zxfvv7KNzJnxfOrOZXR0txAdHc3s2bNJTEyc8Nngs+ERxVu2bAFg6tSpfR4vKyvj+PHjp30HTBS6uro4dOgQ8+fPH9ef18EgpeTw4cNkZWXp7sJFb28vf/zjH3nxxRcDHYrBGUhNTe3z+/33388DDzwwpjH85z//4c477yQxMZFXX3113H5mPz6XVQ0MAsw3v/lNnn32Wbq7uwMdypAwmUxMmzaNoqIiXK7+PrL6QgjB7NmzaWhooLq6OtDhjArTpk0jMTGRTZs20dXVBRhieCBsNhu5ublccsklTJs2jaqqKt5991327NlDU1PTuMwaf/2h67EFW8AzH1zxCU9PPy5S+O73GlnhaypT6WNq5cXvLim0W707Qyz9SqK9DKhHBS6bu95a1TLW0qZoJdTSbTAtJe/97y52fnSU+lqf0dl1X17h/dlsVcgumMQ135rLqltz2L/lKMuXL2fp0qVMnjz5Yy2GPZwpU1xRUcHhw4dZtGgRERERAYxwdPCUSicnJ0+IUuna2lo6OztPu6ihB1555RViYmK46KKLAh3KuCaQPcSVlZW0trZ6b/fcc8+AMXoywGcyn/TcHxUVNaTn/t///pcbb7yR+Ph41q1bN6575A1BbGAwRqxYsYKkpCT+8Y9/BDqUIZOSkoLFYtFdyfdABAUFMWvWLPbv3z8hS6eFEMyaNYv4+Hg2b95MW1ubIYbPgqIopKSksGLFCpYtW4aiKGzZsoW1a9dSWlqK3W4PdIhe8pfl8I89P+dP73+fC6/NJzjcL6Mk3EJYSkSvduFK9Lv5hLBfzbSnr9gjnv1WbOj1yxD7u0vTbzP+v5sFQgivCAaBPUQBkwLhQUgBh/ad4Cdf/zu3XfEYB3eXA3DB/5tL/JRwll83lVseWMjsFUkc21XH33+8mbUvHDljKd/Hmf6iuKKiggMHDrBo0SJiYmICHd6oUFFRQUdHBzNmzAh0KOeNlJIjR46Qk5Ojyx7vJ598km984xvGBapxTERERJ/bmaoQcnNzgTP3CB89ehQ4c4/xQPzrX//i+uuvJyEhgfXr13v3MV4xBLGBwRghhOAb3/gGv/vd78Zl9ulsCCHIy8ujpKQEh8MR6HDOm+TkZGJjYyek6zT4MuHR0dGsX78eRVEMMTwIoqKimDNnDpdeeilZWVlUV1fz7rvvsmPHDiorK8eFOA4KtvLeq7v48H93093hu6CjKILp+WkAWNv7xSklwoHbuMrvu0f4Gn99s4Y9j2k9xFE2M2Yh+iSaBQL7QFljD/7iWpWowWbfsn79vg67kz8//ibFxcXsO7SL6+7KJyI2lG3/V8n/PraXQxtrsHc7+X+fXTqEI/TxwiOKjxw5wv79+1m0aBGxsbGBDmtU8JRKz507d9yWXQ6FyspKXC6X7uYOA2zZsoUjR45w6623BjoUgxFg1apVALz33nuoqtrnsfb2djZt2kRISAiLFi0a1PZefPFFPvOZz5CUlMT69eu9plzjGf1dkjIw0DG33nor9913H2+99RZXXHFFoMMZEvHx8YSHh3Ps2DGmTz/dBEdPCCGYM2cOa9asobq6muTk5ECHNOI4nU46OjqwWq10d3djt9uNsRiDxGw2M2XKFKZMmUJbWxs1NTUcP36cPXv2EBMTQ2JiIomJiYSFhY15bHa7k38/u177xal6M8O5s1K5+Sur+fE3ngeL+8KHxJ0BFihmwIUmSlX3ekJoc5FMuB2i3au5RW6bw4VDlcQEmznVpfUBq4AiwWUDPGOcFHwzhtH+FxLfPvwQZkFyejhp06JJnxZFcKiZpqZmMjIySEpKwnKNJnRqyuvZ+v5B4pNjWHr57JE9iBMMj2O6EILW1lbi4uICHNHII6Vkz549E6ZU2uVyUVRURF5eni5N4R5++GG++tWvTsiy/BHHz+RqTPc5BKZOncoll1zCe++9x1NPPcU3v/lN72P3338/nZ2d3HHHHYSGhnrvLyoqArQ2LX/+9re/cdttt5GWlsbatWtJS0sb/vMYQwxBbGAwhgQHB3PXXXfx8MMP604Qe7LEmzdvJiMjg6CgoECHdF7YbDZmz57N/v37iY2N1f3z8cfTM2y1Wlm6dCkHDx7ko48+YunSpRPSZGc08ZSaTZs2je7ubmpra6mtreXIkSOEhIR4xXFMTMyYlA6aFIHFasLe69SSsy6V2EmRLLswj4IlWdz7qxv45z83A55eMr/eYUA4JVgU7/mS4vKJXLVfHzH4nKZPdWuVIZ7NCRvg33EgvP9gtwpsnkISKQmyKGTPiCJjagRTMsNx2F1UFLWw8fVyqkvbeOIfi0lLm9zneSalT+KTX9ayFi6Xisvpwmo7/ZSl4WQLb72wCZNZ4YpblxEVG37aMhOZ8vJyDh48yKJFizCZTGc02tI75eXldHZ2smDBgkCHMiKUlZVhtVp1eTH24MGDvP/++/zxj38MdCgGI8jvf/97lixZwre+9S0+/PBDpk+fzrZt21i7di05OTk89NBDfZb3JEb8Kx7Xrl3LbbfdhqqqrFq1iueee+60/URFRXHnnXeO6nMZDkLqrXbTwEDntLa2MmXKFN544w2WL18e6HCGzLZt2wgODmb27ImRtdm5cycOh4NFixZNiF6ogQy0pJTs37+furo6li5d2ucqr8HwcDgc1NfXU1tb653TnZCQQGJiIpMmTRrVks41/7ebx+75Fy5PFtbNd3/xKS66dh52u523336b3718HLtDBZfE3K0ihKAzTkFaFE3wmrRSaJe7cMBlAhRwmQF3+J/JjKemq5f1Na1a1tcFZrvWe2zr57/ijUSVJDoUpqaHMXVKGMmJwTTU91B+tI3yo600Hm32M/6SxMaF8+L6gc1eNry+h8fufoneHgfX3nYBt9/3Ce/ntKujh9tXPkTzKS1DOjl9Ek9/8EMs1o/Htf7S0lKOHDnSp0y6ubmZLVu2kJubO2FEcVtbGxs2bGDhwoVMmjQp0OGcNw6Hg/fff5+CggISEhICHc6Q+exnP0t4eDhPP/10oEMZ17S1tREZGUnBDQ9hso7tBXeXvYdd/7yX1tbWIWXxKysrue+++3jnnXdobGxk8uTJfOITn+D+++8nOjq6z7Ke72F/GfnXv/6VL3zhC2fdR1pa2rj0ozEEsYFBAPjRj37E3r17eeuttwIdypBpa2tj/fr1rF69ekIIK4fDwbp160hPT9dFn8vZOJubtJSSQ4cOUVVVxaJFi4bsFmlwZqSUNDU1ebPHnZ2dREREEBkZSVRUFFFRUURERIxoD3dnRw+fWvBT/yBIy0nkc9++mLf/s52Ci6Npt8fxx79+BC53VbMQdE5SwOzOEJsAIXDaAEUbzYQApw2kWxBfPDmaSKuZV0vrNcGrgrkbQGJr0ZYJtZmYHG1jcqSNxCgbk6OshNhMVFZ3cbyig9KydrpaHVg6ndoKrV0objGvtRUL3jzYN/vQ1dHD63//iL8/+jaq0+du/4uXvkb+Ms3YZd+mEn54w5N91vv9ez8gI09/WbehIKWkuLiY0tLSAQ20JpIodjqdbNiwgcmTJ+u+VcfDkSNHaGpqYsmSJbq7CFtaWkpeXh6HDx8e147B4wE9CuKPMx+Py6gGBuOMO++8k/T0dPbu3cvcuXMDHc6QiIiIICUlhaKiIgoKCgIdznljsVgoLCxk06ZNxMbG6tad9VyjlYQQzJw5E5vNxqZNm1iwYMGEyLaMB4QQxMbGEhsby4wZM+jq6qKlpYWWlhZOnjxJUVERDoeD8PBwoqKivEI5MjJy2CI5NKzfCZYQuJwuHrzzRSxWhYKLC/nbP7YiFK3BV7q0a9/CBdLdM9ynv9e/z82JliEW0GB3MDUi2DumONxqIjXERnKYleRpNiZH2QgPMtHY4aC2pZcTDd1sP9ZMc0knLoevp9iTjRYANgui1+nddXK6r+d176YS3n5xM/u3l9La1IFUpZZNdtdztzV1eJdNSI1FKAKpSoQAk9lE7OSoYR1PvSClZN++fdTV1bFs2bIBT3bPNadYTxw8eBCLxTLuHWoHS09PD8ePH9elGAb49a9/zXXXXWeIYYMJhyGIDQwCQHx8PLfddhuPPPIIL7/8cqDDGTK5ubmsWbOG5ubm08po9Eh0dDTTp09n586drFy5EqvVGuiQhsRQ5gxnZ2djs9nYtm0b+fn5uuxhG++EhIQQEhJCUlISoImY7u5ur0iuq6ujuLjYK5IjIyOJjIwkKCiIoKAgbDYbQUFB5xzFkj0ziaMHa7y/z12SReXLjaimfifa7nFMSIm5R+J0Z3+llD5RLEGoIEwQLhRCQsxEWEzE2SwkB1v5fE4CySE2wiwmGroc1LT1UtrQzdajLdS19WJ3anOHPXs22QQ2u/T1FnvUsCo1069uO6FRIWTnJXPPYzfy/r+3s/Gtfexcc0jrU/YYDXlFgyQ+JZqCFT4Dl8QpsXz/iVt57pHXMZkV7njgk0RE679q5Uy4XC527dpFe3s7F1xwwVlN8iaCKK6qqqKmpoZVq1bp0nhqIIqLi4mPj9flhdfa2lr++te/sn379kCHoi90YKplYAhiA4OA8b3vfY/c3FyOHj2qu1LdkJAQpk6dyoEDB1i+fLkur3T3JzMzk/r6evbs2cOCBQt085yGIoY9TJkyBavVys6dO+nt7TWu9o8yQogziuTW1lZaWlpobGykp6fHe5NSYjabTxPJ/r8/8PRNvPj7NZyqbmFySjSnqusJDjVDkPYeCAkyYbUoKAooUqIIgQxVCI61ERZkIizUTFiQmdBgE+E2E2E2M2FWE2ZF0O100e500eF0YTUplHf2sP5kCyc77bhapVby7JRYPcZaHl2taj+6rKLPSZmQ0i2KBZhg9oJMFq6azpU3L2bd/+3mt99/2T0Fyj3s2OvepW34pjsv5drbLiAssq8p3MprC1h5rf4rVc6Fw+Fg27ZtqKrK8uXLB3XRTs+iuKOjg3379lFQUDBh3PGbm5uprKz0jrjRG7/97W+56KKLmDVrVqBDMTAYcYweYgODAPK5z30Oq9XKn/70p0CHMmScTidr1qwhNzdXN7b658Jut7N27VqysrJ0cfI4HDHsT1NTE1u3biUjI4Np06bp5iLAREdKid1up7e3t49I9tw893e0d+FyuVAUgcl85gyaqkrtJsHpUunoVenocdLucNHR46LN4aTN4aK910m73UWH3UV7lC/d+5MZU/hraR3Vbb3aXR1gcYJQJZaevskI4XKLXxVC69xW00Ig7C5MvU7vaCfR0YvicJGSOYnQYDPF+074bK6lJosVs4nw6BBu/talXHnLx3cWcXd3N1u3biU4OJjCwsJzVg70R289xS6Xi40bNxIXF8fMmTMDHc6IIKVkw4YNJCQknDamRg+0tLQwZcoU3nnnHZYsWRLocHSBt4f40w9htoxtD7HT0cOuV4we4qFgZIgNDALID37wA+bNm8cDDzygu9JVs9nMrFmz2Lt3L5MnT9ZdmfFAWK1WCgsL2bJlC7GxsePaeOp8xTBATEwMy5cvZ8uWLfT09DB79uwRNX4yGB5CCGw2Gzab7YwnM61Nndy48MF+K4Jis2AJNfOFe+bx++dK6LG75y2pgCJwCbDHmEG4ZwkLoblKu/W09Db6+v5v6HUwKchCdVuvJn6tIBz4Erl+y0sFhKoNNRbgnoXsQkiJajWh9Lq0HLBZgAOqSuu1uciKCaTLOyN59sKp3PmrG0mcEnvex1PPtLW1sXXrVuLi4pg7d+6wSof1lik+dOiQd8zfROHEiRPY7XbdVYN5eOqpp8jPzzfEsMGEZWI0ZRgY6JS8vDwuv/xyHnvssUCHMiwSExOJioriyJEjgQ5lxIiNjSUnJ8c7jmk8MhJi2EN4eDjLly+ntbWVzZs309vbe+6VDAKOYhJ+c458qC6Jq1d1/4wmSF1+60ktsysk3tnEeJbD7bXVr+etvtdBnM2CKtwLWPD1C/uL5z7iWOA0C99CQrtJgaaizQOMpVL8VLngYy+GT548ycaNG5kyZQr5+fnn1UfrEcXFxcUcP358BKMcWWpqaqisrKSwsHDC9A3b7XYOHz7MzJkzdXnBsauri9/97nfcc8/Ao9EMzoHbv2HMbwZDYmJ82xgY6Jh77rmHZ555hsbGxkCHMmSEEMyaNYvKykpaWloCHc6IkZ2dTUhICHv37mW8dZWMpBj2EBwczLJlywgODmb9+vW0traeeyWDgBIeGcLn7rrM+3t8UhTxkyP7LKO4JMLll6nFLXidElwSPEZYal8RLPq95evdGWIvwrd8n0U9fcRusdwbbfIZykjtASkESAGK5jp9phO3I7vLvT/XlNfzjyff562XNuN0uAZcfiIhpaSkpIRdu3aRn58/Yu0M410Ud3Z2eicvTISRfh6OHDlCdHQ0iYmJgQ5lWDz77LOkpqZy6aWXBjoUA4NRwyiZNjAIMAsWLGDhwoU8+eST3H///YEOZ8iEhYUxdepU9u/fP2EMtoQQFBQUsH79eo4ePUpOTk6gQwJGRwx7MJvNFBQUcPToUTZu3Gg4UOuAG7+6mguvnUdPl52UzEkIIfjo/UO8+rcNgCZahar9IIU7hSuEJoAF2hxiD7Jvwln0gHRrkga7g/zoMO0SujurrAligUT2md7kMZT2jFry9gW7HxTuOBQByWmxhEUEUbKvEovVhKPLV50QEq4ZKdVVNfGNK35Db7cdVZXs3XSUHz31ufM+duMVp9PJ3r17aWpqYvny5URGRp57pSEwXsunPaZhqampE+p7p6WlhcrKSlauXKnLv412u51HH32URx99VJfxGxgMFkMQGxiMA+655x5uuOEGvvvd7xIWFhbocIZMdnY2lZWVnDhxYsIYbNlsNhYsWMBHH31EREREwK/uj6YY9iCEICcnh4iICO94l9zcXONEaBwzqd/c3WUXz6Bg6VTee/9d7Q7/kmb36yjc448Up0A1uaud/cudJVjbJL2hWslzfa+DOKulb4W2J+urcPqIDxPg0sSwiqcUTWriHMjKTeR7D1xLxtR4ALo7e3E6Xdxz89McP1iFyazwrV9cD8COtYfp7vQJ5Y1v7sXx25uxWCfe6Ut3dzfbtm3DZDKxYsUKbDbbqOxnvIliKSW7d+8mKCiIGTNmBDSWkURKyf79+5k6daou/64DvPTSSwQFBXHdddcFOhTdIuTpVTdjsU+DoWGUTBsYjAMuvPBCcnJyeOKJJwIdyrAwm83MnDmTw4cPY7fbAx3OiBEVFUV+fj67du2ira0tYHGMhRj2JzExkeXLl1NZWcmOHTtwOp2juj+DkaWzvUf7QVV9JcnukmXvz4DZrt0npF9ZtftmVn0LNtodmBVBhM39vhO+9uMBT7y85dcCR7DQXKcR3hPDXoeTF/+0jl1bjwHgcqn85u6XqatqYc7SHP687l4WX6KNdolP9s05F4ogKi4Mp8PJprf3sXdTybhraRguTU1NrF+/nqioKJYuXTpqYtjDeCqfLioqor29fUL1DYNmpNXT06NbIy273c7PfvYz7rnnHl32PhsYDIWJ881jYKBjhBA8/PDD/PKXv9RlLzHA5MmTJ5zBFkBycjKZmZls27YtIGJ/rMWwh4iICFasWIHD4TD6inWCy+niZ1//O7dd9CsAQmxWr/hVPHOApbt/WLpVrdN3v0ew+vccg7Z4s93Zp4/YadLMuU7DL2ssAFeoAgiESwWXJtBPlNWz8YPD3PvNFzlefJK//+Ztdqw9QkdbNwe2lfLvP67zbm7+qjxu+PpFBIfZSEyJ4QeP38K3r3yMn9/+F+658Sme/NG/RvAIjj1SSo4dO8bmzZvJyclhzpw5YyYKx4Morq6uprS0lIULF06ISQUePEZas2bNGvKYrPHCn/70J2w2G7fcckugQ9E3MkA3gyFhCGIDg3HCypUrWbx4MQ8//HCgQxkWE9VgC2DatGlERkayY8cOVFU99wojRKDEsAer1cqSJUtISUlh48aNlJWVTZiM3ERk4zsH2Pz+Ie/vSl0r2VMTfFlcodlECxfebLEwCb/ssDz9ZMr9s8dp2nOfGiH9N+lD+K0mJSjCPUrJd59HSEsp+eW9r/LRe4e8GWdVldRUNPg2JwSf/94V/OfgI/xlw49pa+qk8lid9/G3XthEZ1v3kI/VeMBut7Nt2zZKS0tZsmQJmZmZY96eEEhR3NLSwp49eygoKCA8PHxM9z3aHDlyhKioqIC32gyXjo4Ofvazn/HQQw/pVtAbGAwFQxAbGIwjHn74YX7/+99z4sSJQIcyLPwNtiaScBJCMG/ePOx2OwcPHhyTfQZaDHsQQpCbm8uiRYsoLi4e1+OoPu50dfQdmdVjMlG6v0pzg1bxiWC3KBUu2U8E9xNjfh/hhl4Hk2xWn0eW6Sx9agKkkN5tCommnL1ZZeEdDVJReormpk4wmcCknZKsuHLuGZ9jcFjfUmKTWcFs0V85Z2NjI2vXrkVRFFauXElMTEzAYgmEKO7p6WHbtm3k5ubqVjSeiebmZiorK5k1a5Zu/Rd+97vfkZGRwbXXXhvoUAwMxgRDEBsYjCPy8/O59tprefDBBwMdyrDJzs7GbrcHvC9tpDGbzSxcuJDq6mrKy8tHdV/jRQz7ExcXx6pVq3A6naxbt47m5uZAh2Tgx8EdpQipEpcQ4btTCBSXBIcLxb+82V3OrJy1tE6An772zxD7C2Gvnhb9Essm936kwOlx7RLCPQO5706lWzOYrGbu/9MXuOT6BZw4VseLj7/Lu69sw+XyVWUUrJjG6k8WasubFL758A3YgvVTaiulpLi4mC1btpCdnc38+fPHRanwWIpil8vF9u3biYuLIysra1T3Nda4XC727NlDdna2bo20Ghoa+PWvf80jjzyiW0E/nhBqYG4GQ8OogzAwGGf87Gc/Y+bMmXz3u98lLy8v0OEMGbPZTH5+Plu2bCEhIWFClcKFhIQwf/58tm7dSlhYGHFxcSO+j/Eohj3YbDYWLVrEsWPH2LRpE9OnTw9ImadBX15+6n3+9uu3AIiICeX2e67AhVZ2LE0CoYg+8361EmmBlBLhyQqr7gf8XkpLFzhsgIT6HgfLJ1ncltT4ss4m8M1dkprudQ8iFqq2nCPUhLnD5XOdVv1nPAmkoiBcKiaTwsHtpbz78jZ2rj+C6nShqpLDO8u461c3AqAoCt97/Ba+/JNrsQZZCAkLGunDOWr09PSwe/duurq6WLZsGVFRUYEOqQ9j4T4tpWTfvn1IKZk7d+6E++4oLi5GURTdGmkB/OIXv2Dp0qWsWLEi0KEYGIwZRobYwGCcMXXqVL74xS9y7733BjqUYRMbG0t6ejp79uyZUKXToGVKZ86cyfbt20fceXo8i2EPQgiys7NZsmQJx48fZ8uWLXR1dQU6rI81r/z+Q+/P7S1ddJ2tp1ZKTfyqfZ2lRb8eYiElpna8Qrqh106M1YzZ76xBBfoMIfb+rI1r8hh0SZtJW9j/q0B1m3up7j5jwN7r5D9/3sC2NYdxOlVUd1Z7zf/uPO1pRMWF60oM19TUsHbtWqxWKytWrBh3YtjDaGeKS0pKqK+vH7ffb+dDc3MzpaWlzJs3T7du2RUVFTz99NO69TIZlximWrpAn59YA4MJzk9+8hPef/9975V6PTJt2rQJWToNkJ6eTkZGBlu2bKG7e2QMffQghv2JiYlh1apVBAcHs3btWioqKibcxQ+9EBxm82pRqUqCQ919tlKiunt1td8BhDdz20ekqtrFDm+SWAgUj6iV0GZ34ZSSGJvFWyPtOlONmbYBHO55x0LxSz6btPnE3v1KiVAUr+GWFEIL1x2roojTZi3rid7eXnbs2MG+ffuYNWsWBQUFWCyWc68YQEZLFJeXl3P8+HEWL15McHDwiG13POBfKh0REXHuFcYpDzzwANdddx1z5swJdCgGBmOKIYgNDMYhCQkJfOc73+GHP/yhbkWGp3TaM2NyojFt2jQSEhLYsmXLeY9j0psY9mCxWMjPz6ewsJCioiK2bt06YhcIDAbPd371GW8f7bzlOaz+RAEAP/nVp5kxd4pb/PrmDQt8QlcbweTekJRIKb0JBsV7v/Z7g6eP2L2AK5Q+pdhI/1JowObuF5ZowhzAkznzi8UXVL9ss6IwOS2WH/3+cyNwlMaempoa1qxZg5SSVatWkZKSopsS4ZEWxTU1NRw8eJCFCxfqWjCeiYlQKn3o0CFefvllfvaznwU6lAmFd5zdGN8MhoYhiA0Mxil33303hw8f5u233w50KMNmIpdOCyGYPXs2oaGhbNu2DafTOazt6FUM+5OQkMDq1asJCgpizZo1RrZ4jClYMY1/7nmIf+z6GT//+1ewus2v/vPXjzi+v/L0EySPy7SUbgMWicnTE6wIr0CVgHcekoSGHgeTbBZtHUlf8Svwlj57BbYApNCMtYJ8fcxSeoSyv5j2rNNXMD780tfJmpl6XsdnrOnt7WXnzp3s3buXWbNmMX/+fIKC9FPe7WGkRHFDQwO7d++moKCA2NjYEYxwfDARSqUBfvSjH/HlL3+ZjIyMQIdiYDDm6PeTa2AwwYmIiODee+/lnnvuGdPZtyPN9OnTsdvtHDt2LNChjDiKolBYWIgQgp07dw75dZoIYtiDkS0OLFabmajYsD4ZyKIDJ3B22sGlunuHpTYTGLz9wlr5tFv9+r/9pNR+///s3Xd4lFXax/HvMyW9994LCT0EElooIigqKKJYVsH+KqzY17Wh4uq6K2tZey8sdkVURBQINQm9k0IIaZCEJKRN2sw8z/vHZCYJPRAyKedzXSPJlGfOjMlkfnOfcx8jlqnV5Y16vO20LdObW65nbsZl1nIoc+MuqWU9smyvsex1bFlprJLw8HYmMMCt/YZPahWSBPaONri4O3bK89NVzGuFjUYjEydO7FFV4VO50FBcXV1NRkYGAwcOxN/f/yKM0LqMRiPbt28nJiamR1e+N27cyOrVq3nqqaesPRRBsAoRiAWhG7v33nuprq5myZIl1h7KeVOr1SQkJJCVldUrp06r1WqSkpJoaGiwdE89F70pDLfVtlq8atUqcnJyevQHOj2ZbDRVYtVNBlRg2nfYvC+wbDpJBsUyhRpordq2hDhJ3zrFuryx/ZRpSU37MGy+nXnOtYRlyrQkmSrEUsuUbIBBQ0P5YsXD+Pq5omqpLksqFbb2Nji7O5I0sT8lBRUX6dnpXDqdjvT0dHbu3MmAAQMYMWJEj6wKn8r5hmKdTkdaWhpRUVGEhoZexBFaT2ZmJhqNpkdvH6UoCo8//jgPP/wwPj4+1h5O72OeDdPVJ6FDRCAWhG7M1taW559/nqeffvqC16lak4eHB2FhYWzfvr1XhiOtVktycjLHjh3jwIEDZ71+bw3DZuZqcXJyMkVFRaxZs4aysjJrD6tPktSmbY1oMprW77Y0tDKFXAW1vu0a4pNvr2nCUiE+1qDHy05ruT2A8VQrBVr2IEaS0GtaQ7Flw2K1Cgwyu9NzuXncP9m/qxClJRD7BLjRb1AQtcd1rFu+i/lXv86R/PJOfU46k9FoJDMzkzVr1mBvb8+kSZN6fFX4VDoaipuamkhLSyMgIICYmJguGGHXq6ysJC8vj6FDh/boqdLLly8nKyuLhx9+2NpDEQSr6bm/wYLQR9x88804Ozvz5ptvWnsoFyQuLg6DwdAru04D2NvbM3LkSPLz88/4GHt7GG7Ly8uLcePGERYWxpYtW9iyZYuYRt2FHnlhJlNmJJqqtuYKbUsnaXP4VenNFQVa5zq3OakbJCS9aa1xRb0eFxsNdirJEm6NNpyysZb5ELKtZKpEy6CoW0KiZNn9mOrjOpoa9SgKePq78c7PD7Br00HTDG+jTFOjnu3rsy2H/+njtdwz8SWevPkdjloxKCuKwtGjR1m9ejWlpaWMHj2awYMHY2NjY7UxXWznGooNBgPp6em4uroycODAXvfhALR2le7pU6X1ej2PPfYYTz31FM7OztYeTq8kmmr1DCIQC0I3p1aref3113n22Wc5cuSItYdz3tRqNUOHDiUrK4vq6mprD+eicHZ2JikpiczMTPLz80+6vC+FYTOVSkVkZCSXXHIJKpVKTKPuQg26JqqP1bVOoZNPCK6KgtwSVk3TqZWTOj2be2xhgIZmGZ3eiJdDm8Zatubjtd7mxEqzJJvuW24JxFLzqRvQVZTVoNGq8fR1sUyhBvAPNTVi2rJmP+8u+IGCnBJ2bsxi4V0fne9Tc0Hq6urIyMhg586dxMTEkJKSgru7u1XG0tXOForNYVij0ZCQkNArwzDAgQMHevxUaYD//ve/gGl5liD0ZSIQC0IPMGHCBK644goeffRRaw/lgnh4eBAVFcXWrVvPuytzd+fh4UFSUhJ79uyhoKDAcn5fDMNt2dnZMWzYMEaOHGmZRn3kyBHRjfoienvhMtLXHGit4OqNliBsriKoZSzbMZ1Sy75I5opveYMeL/uWfXTN+Vlu+cZcZaYle8stt225D9lGDUYj2KiRJdrsj9y67u2XJZuIHxGBh58rtnZafALcyN5ViNEoczjzqCVgyUaFguySTn2+zqa5uZl9+/aRmpqKvb09l1xyCaGhob029J3O6UKx0WgkIyMDgKSkpF77GldSUkJ+fj7Dhg3r0VOljxw5wrPPPstbb73V7ffGFoSLTWPtAQiCcG5eeeUV4uLiWLt2LePGjbP2cM5bbGwsFRUV7N69m4SEBGsP56Lw8vIiOTmZ9PR0JEnCz8+vT4fhtjw9PRk3bhz5+fns3r2bgwcPEh8fj5eXl7WH1jsZZSS9ATRqtE0KBhsNigpLJVctgb5lqrSqHmTnNlVi8xRqS9tocyC2AUXXrjkXqvaVZXPrLEnVMotaNnWVtlzLRgP1zSd9LP/+i7+2fiPLlB2p4vP/rKAgp4TD+4tNQ2m5r4Rx/TrtaToTo9HIoUOHyMnJwc3NjTFjxuDm5tYl991dmUNxWloaAGFhYWRkZCDLMiNHjkSj6Z1vLxsaGtixYweDBw/GycnJ2sO5II8++ihXXnkl48ePt/ZQerc2HxR26X0KHdI7X7EEoRcKDAzkmWeeYd68eWzfvr3HfqIrSRIJCQmkpqZSUFBASEiItYd0UXh5eZGUlERGRgZZWVk4Ojr2+TBsplKpCA8PJzg4mNzcXDIyMvDw8CA+Ph5XV1drD6/XULXZF1iSZYwteyJJMiiSYgmxNlUG9M4avN3tKZVam/cpLWVhyQhKy49teYMePweblkZbiml5snyK918t54OE3l7CVndC51Pz9kyn2ovYch1TGpckidSftoMsI6kkHJ3tmDZnLDP/75ILfIbOTJZlCgsLyczMxNbWlsTERNGFtw1zKN60aROHDx9Gq9X26jAsyzLbtm3Dz8+PoKAgaw/ngqxdu5aff/6ZzMxMaw9FELqFnjvXQxD6oPnz52M0Gnt8gy17e3sSEhLYvXt3r9yKyczNzQ17e3vq6+sJDAwUYfgEGo2G2NhYJk2ahLOzM+vXr2fbtm3odDprD61XcHCwsTTSQgZJrbJsoSS1BFoUBZUM910zkuXv32e5rQKWzliSsaXC29JYy8te29IoS2rdsunExlpt3l3INi1BXC+32cO4pfu1rJy1gtJ2Wr0iK9RVN3DT/CnY2msvypR7RVEs+wnn5OQwYMAAxo0bJ8LwKbi4uODs7IxOp8PPz6/HflB7LrKysmhubmbgwIHWHsoF0ev1zJ07l2eeeYaAgABrD6fXE021egYRiAWhB9Fqtbz55pssWLCAo0ePWns4F8TX15fw8HC2bt2K0Wi09nA6nXnNsIODAyNGjGD37t3t1hQLrWxtbRkwYAATJ05EkiRWr17Nrl27qK+vt/bQejRdbaMpcBpMDcykNo3MJMxBV0EyKvz4yw7ueOjz9gcwz282v8mSoVzXbFlD3LZb9Ukst1VQNBKSQUZSQNGqWwOwuet0S6iVpNbp0ABBLc20JAnTllEqFSqVRHxiOP977XemRz3CdQP+zqYVu8/j2TmZoiiUlJSwbt06du/eTUREBBMnTiQwMLDPrRM+F23XDI8cOZKDBw/22l0EysrKyM3NJTExscdXwN98800URWH+/PnWHoogdBs9+7daEPqgiRMnMnXqVB599FEWL15s7eFckLi4OCoqKti7dy+DBw+29nA6zakaaJmnTyuKQmhoqLWH2C05ODiQkJBAVFQU2dnZrFq1ioCAAKKjo3v01ibWopi7SusNaLW2uHm5cKSxdUq01LJ9kkENZQ0NlOU1gLcac2tpc5XYMmVagspGA1q1hIuNGl2jERQJRWWu8iqWICwBRgXULWepzP2zpDYhWKOGZgNIEpOuSSBpQhxarYaG+iZCIn3471PfW64K4OrlzPgrBzN0bAzPzvkAAIO+gZfnfcbXe17Czv78tjySZZkjR46Qk5NDU1MTkZGRhIeH9/jgczEZDIZ2a4bN06XNa4ojIyOtPMLO09jYyPbt2xk4cGCPfx06evQoCxYs4KeffurV1fxuxdw0sKvvU+gQ8WovCD3QokWL6NevH+vWrSMlJcXawzlvKpWKxMREUlNT8fLyIjAw0NpDumCn6ybt7e1NcnKyJRSHhYVZd6DdmIuLC4mJidTV1XHw4EHWrl2Lj48P0dHReHh4WHt4PY4kSRia9JTnV4CXI6jbTw6T7dWtjbRUYN5H2BKILZ2kwWhUqG404OWopb7eCArIalDJtM45M78XM3evViRkRTFdrChte3QB4ObhiGww8o+5pgr18PH9WPDebQRHepO5I99y0Lrqemb+30QObM1rN/7mJgMNdY0dDsRGo5GCggIOHjwIQFRUFCEhIWJpw1no9fp2lWHzBwcnNtrqDaFYURS2bduGt7d3r+h38eijj3LFFVcwYcIEaw9FELoVEYgFoQcyN9iaO3duj26wBaaq4NChQ9m+fTtubm44Ojpae0jn7WxbK5m7T2dkZNDU1ERMTIyYinkGTk5ODBkyhNjYWHJzc9m0aRPu7u5ER0fj7e0tnrtzdWLTaHPlWGozJ5q2X7Z0mla1NNaiZU1ay9cV9Xo8HbQUKo2tNzUAbV+GFEwBuWU1hMFWhU29EUmtQlHJSEYFjVaFvbMdVRV1rP5ph+WmW1Iz2Zp6gOAo33aVDqPByKH9RxgyJhYvfzfKS6pAgeET43Hzcj7np0Ov13P48GFyc3OxsbEhLi6OgICAHr2FTldpbGwkPT0dGxsbRowYcVIVvbeF4uzsbBoaGhgxYkSPf71Zu3YtP/30k2ikJQinICliE0hB6JGam5sZMmQId999Nw888IC1h3PBdu/eTWVlJWPHju2RFZqO7DNcU1PDpk2bCAgIYODAgT3+jVZXaW5uJi8vj0OHDmFvb094eDhBQUE98uflYtLr9SxfvpzsDfVkrM6kscmA0Wgq8eq9HFu2RaJlarREsx1gawo29S4SioPp+TRqsDTWamtqjCdGo8Kf+ypNVWA9qAygaGm3dlgygqYZU75uNuJY1jJd2yijajSYDq03tNsf2czJ1YG6mgagZe2zoqDRqvlw9d/xCXSnqryWdb/swN7RjvHTE9DanP3zfZ1OR15eHvn5+bi4uBAdHY2vr6/4/TtHOp2OtLQ03NzcSEhIOOMHCMePHyctLY3Y2NgeG4orKipIS0vrFdts6fV6EhISmD17No888oi1h9Mn1NTU4OrqysjLn0ejtevS+zboG0n77Rmqq6t7/DT/riI+DhWEHsrGxqbXNNgC6N+/PwD79++38kg6riNhGExTglNSUigrK+u1TcUuBhsbG2JjY7n00ksJCwsjNzeX33//nX379okGXKfwwIsz+W7bc1x/d5t9yw2tjbXMFWJNM5Zuz5rm9sc4MQyjQIVOj5ej1tJoS5JATWvjLUlu7XJq6niqgKbl7YZiqj5bcrM5VEmSZTxhsf7U1zVa7lJtq2XomBhe+OxufALdAXD1dGLanBQuvW7EGcOwoiiUlpaSnp7O6tWraWxsJDk5mTFjxuDn5yfC8Dmqqqpi/fr1+Pr6MmzYsLNW082V4qysrB7ZaKupqYmtW7cSHx/f48MwmBppGY1G0UhLEE5DTJkWhB5s4sSJXH755Tz22GN88cUX1h7OBVGr1SQmJrJu3Trc3NwIDg629pDOSUfDsJmDgwNjx44lPT2d9PR0RowY0aOnvncljUZDWFgYoaGhVFRUcOjQIf7880+8vb0JCwvD19dXTH9t4y/zJuHh5Uz2vmIGj4igTpY5mFPK8t9M3ZlNAdYUDCVFabMGuM1BzIuKZVMgTgp2QTEqmH/ajeb/qMzXlZAUBVkBlSKd1OTFMgNbJVm+llQSb3x/P7sycvno5eWW+/X0ceEfn99jup2i8OELP7Hs0/U4uznw2Bu3MGR0zEmPubGxkYKCAvLz8zEajYSFhTF48GDs7e0v5Knsk44dO8bmzZuJiYkhKirqnD9E6KnTp2VZZuvWrXh4eBAeHm7t4VwwcyOtpUuXir8x1nCWbeUu2n0KHSICsSD0cIsWLSIuLo4///yTSZMmWXs4F8TJyYnExEQ2b96Mo6Njt2+gdL5h2MzW1pbRo0ezZcsWNm7cSHJyMnZ2XTu1qieTJAkvLy+8vLxoaGigoKCA3btNIS8kJISgoCCcnc99bWlvJMsyKpWKK28aedJlqakHqNc1I5ubaCmgamx7Y1pKvy3ft0yfrtTpcbfXolJjWjuM6camZtOtbaYlRTLtE9zSXMsggUY2X11pDVaSKTArChwrreaKG0ey8fe9HNiRj62dlvtfuNYypC2r9/PDB6kAVJXX8sI9n/DN7n+gUqmQZZmysjIKCwspKSnB09OT/v374+fnJz4gOU/FxcXs2LGDQYMGnVdTqZ4Yivfs2UNzczNJSUm9YgbB/PnzmTp1KhMnTrT2UASh2xKBWBB6uMDAQP71r39xxx13sGfPnh6/XsTHx4e4uDg2b97MuHHjum1F50LDsJlGoyEpKYkdO3awfv16Ro4ciZOTUyePtvezt7cnNjaWmJgYSktLKSgoIDU1FRcXF4KCgggMDOxzHza88eR3rF66Aw8fF556ezaxg9rPumhs0AOmzGtoCa0aIy3VXMlU8T1F4+aaegOKouDmoKW6Rt+mW1eb5lvmKdPmiKyA3k2LprzJ9L1WBfqWdCy1VqXLiquwc7Bh0df3UVFaQ11NA3XVDTQ16rG101JRWmMZh6KArqaBY2XllJQe5ciRI6jVagIDA5kwYYL4PbpAeXl57Nu3j8TERPz8/M77OD0pFOfl5XHkyBHGjRvXK7bd+vbbb1mzZg379u2z9lAEoVvr+b/tgiBwzz338N133/Hoo4/y3nvvWXs4FywiIoLq6mo2b97MmDFjul3TpM4Kw2YqlYqEhAT27dvHhg0bSE5O7hXr1qxBkiT8/Pzw8/NDr9dz5MgRioqK2LdvH15eXgQHB+Pn59cnpg6u/XUXsqxQUVbDvx5cwker/tbucku3aYWWTYNb1va2bKGkAuSWNb+W67XMfq6sN+DpqKW6yhSIJcy5tk07avO+xOYl8qo2S5LbVt40GlQGI5JKYsjIyJaLJbaszeSNp78HBQLCvHj1m7mMmBiPs5sDaluIGubLoHHBbNu+lYCAAIYPH46np2evqOpZk6IoZGZmkpeXx6hRozplpk5PCMXl5eXs27ePkSNH4uDgYO3hXLCysjLuu+8+3nrrLXx8fKw9nD5LMn9Q2MX3KXSMCMSC0AtIksRHH33EwIEDmTlzJpdeeqm1h3RBJEli8ODBbNy4kZ07d5KQkNBt3uR2dhg2kySJAQMGYGdnx4YNG0hISCAgIKBTjt1XabVaQkNDCQ0NpaGhgaKiIg4ePMiuXbvw9fXF398fHx8fbGw6tn9tT6FSmX5nFFnheHntSZc7ONhQX9/SRUvBFFxbpk+bQ65sTsBSm82JWxpreTppybNUglvehMktX7TsXSy1OV67/Z9Urb/Pdk62TLy0P5fOSCQ0urUS+dHLv1oqx0fzK1j182b6Jwdy96JLqNPVoVHsGDK8P/7+/t3uQ7Oeymg0smPHDiorKxkzZkynzjjqzqFYp9OxZcsWBgwYgKenp7WH0ynmzp3L+PHjuf766609FEHo9kQgFoReIjQ0lH//+9/ccccd7N27t8dPnVar1YwYMYK1a9dy8OBBoqOjrT2kixaG24qKisLR0ZHt27dTU1NDbGxst/kwoCezt7cnOjqa6OhoampqKC4uJicnh+3bt+Pp6WmpKvfkfbBPpLXV0NxoQFFg6o3JJ10eFOxOdlYpAJJRQdGApQRs1qbg27bZVkVdM54tnaYtl8ktTblUtN5IpmVLpZalwrQUhxUJWZJQAY2NBi6/cSTR8e0/AFJrVQREuRIW70FovDs4VXP8uAPRMVEEBAT0iSp/V2poaCAjIwO1Wk1KSspFWWLQHUOxXq8nIyODwMBAwsLCrD2cTvHNN9+Qmpoqpkp3B7JiOnX1fQodIgKxIPQid999N9999x2PPPII77//vrWHc8Hs7OxISkpiw4YNODs7X9A6tgvVFWHYzN/fn7Fjx5KRkUFNTQ0JCQm9Yj1bd+Hi4oKLiwtxcXHU19dTUlJCSUkJ+/btw8nJCV9fX/z8/HB3d+/RzZj+881ctqZm4xPozqjJA066vLCg0vK1Si9jbPmZllqmTAOtCbbN+ytJgYo6PYOCnFuvoyimztJy6xJkFMWyFVPLLZG1KtTNMid2nT6YeYSoOH+am5s5duwYJSUl3Ph4AvW1TeQfqGTj0kMcPVTD5+uews1TrA3ubJWVlWzevBlfX18GDRp0UV/fulMoVhSF7du3Y2try4ABJ/+O9ERlZWXMnTuXt99+W0yVFoRzJN5hCUIvIkkSH374oWXq9OTJk609pAvm5ubG0KFD2bZtGykpKVbpGtyVYdjMvFfxli1bWL9+PUlJSb1iXVt34+DgQEREBBEREej1eo4dO8bRo0fZvHkzRqMRT09PSydrV1fXHhWQ/YI9ueb2lNNebmuroaGlsVbrVGkFmmSwa/kZN4ddqbVMbFSgsk6Pp6MWuWXrJQnJMs3achtAartOWQG9vQZ1U7PpDtUqbG0k/EOd2LNnD0eP5eDhY4+hSeLQrjL2ZxyhtLCuXRivKK0WgbiTmbuzx8XFERER0SUzUrpLKM7MzKSmpoZx48b1qN/t01EUhfvuu48JEyZw3XXXWXs4Aohtl3oIEYgFoZc5ceq0q6urtYd0wQIDA6mpqSEjI4OUlJQuXfNpjTBsZmtry6hRo9izZw9r165lxIgRvWZ9W3ek1WoJCAggICAARVGoqamhvLyc8vJycnJykGW5RwdkxRxSWwLPuAlx/PTDNpAklDZrem0bodkGU7rVA7Zt1v4ioUKhsq4ZF3sNWq2E0tx+42LTWuKWr42KZY9jFAV7JzVhPi4EhjoRGOKEp48dx8sbOZJXy7ZVxRw9XENDrR6am8Eog1qN1LJXsYubA64eTuxYn0VBTglDRscQGut/EZ+x3k1RFPbt20dBQQEjRozo8mqitUNxcXExhw4dYuzYsb2mj8DXX3/NunXrxFRpQeggEYgFoRcyT51++OGH+fDDD609nE7Rr18/ampq2Lp1K8nJyV0SRKwZhs1UKhWDBw/GxcWFtLQ0BgwY0GvWuXVnkiTh6uqKq6srkZGRpwzIRqPRch03Nzfc3NxwdnbuliF5yVur+Ord1djYannwxZmMnjyAhGHhpkCsKEhGWtYQY6r0ypiKuAYw2tBmGyWQJahvkmloNuLhoqWivLm1CtIShiUFHDQqfPzs8PWwxdfLDl8vW9xcbKg81khxfh1bN5ZwJLeGJl3LZsZ6Q+uaZJXKFIiR0drYom82UFtVz31XLKL2mGnrJY1Wzb++/StxCWGnfMwHtuXx3bursLHV8peHLicwQkwfNdPr9WzdupX6+npSUlKstkWVtUJxVVUVO3bsIDExscf32zArLS1l3rx5vPvuu3h7e1t7OILQo4hALAi90IlTpy+77DJrD+mCSZJEQkICGzZsYOfOnQwdOvSiTu3rDmG4rfDwcJydndmyZQs1NTUMGDCgWwav3upUAbmuro7q6mqqqqooLCxk7969yLKMi4sLbm5uuLq64uzsjJOTEzY2NlZrjpa5q4Av3vgDJNDrjbz04Jd8u/kZkkdGM2pMDJs2ZKNSIDzUi9z8cjSNMgYn08+7ythS7G3bWKvl64o6PZ5ONhw/1oyrkxZPRy0+rjb4etri52mHq7OWqppmysqbKD3WyJ79VZQda0A+0tC6/ZLeYFmubAnBkgRqDZLRiEqtprnJYHkstdUNLWuaFWRZ4c/vNp8yEB87cpzHZ72JvtmAJMHuTTl8krYAG1vRiKu2tpbNmzfj6OhISkqK1ZuTdXUo1ul0pKenExsba9W+FJ1JURTuvfdeLrnkEmbOnGnt4QhtWDrwd/F9Ch0jArEg9FKhoaG88sor3HXXXb1m6rRWqyU5OZn169dz4MAB4uPjL8r9dLcwbObl5UVKSgqbN29mw4YNJCYminXFViJJEs7Ozjg7OxMUFASY3pTqdDqqqqqorq6muLiYuro6Ghsb0Wg0ODk54eTkhKOjo+VrBwcHtFrtRQ3LOfuK271DMhqNlJdWExzhw8jR0aRvykGWFaJDvHjxhetQq1Vc8df3TPsGt1R9VZKEs60aDwctXjZavBy0uNhruGyoN3YjfJEVheO1eiqPN1Na3siuA1WUlzXS2CibErXUOm1abatC09TSactSCW652PyFSmLOI1OJHxbO47e8h9y2a6paAoOCoih4eJ+6undofzHNjXrLMSvLajhWfLzPV4kLCwvZvXs3YWFhxMfHd5sO9l0VipuamkhLSyMwMJCoqKiLch/W8NVXX7FhwwYxVVoQzpMIxILQi91111189913PPTQQ3z00UfWHk6nsLe3Z9SoUaxfvx5bW9tOf+PUXcOwmbmqs3fvXlJTUxkyZIjYr7ibkCTJEnTNIRnAYDCg0+moq6ujrq4OnU5HWVkZdXV16PV6VCoVdnZ22NraYmdnd9JJo9GcdOrI7AB3L+c2TbFMZNlIdXUdH3/wJx6etmhtVBw6lM++PQfw83fmiuE+ONmpcbLX4OigxtHG9HtQ1WCgQqensqaZ0qomNGqJFRvLqK3XoxgV1AZau0vrldYc3ibQtl2vjEoy7X984tcSJIzrR3T/IIaP60fGmgOtN9FokA3NDBkdw4y7J5zyMYfHBaC10WDQG5EkcPFwxDvA/Zyfs97GYDCYGpcdPcqwYcO6ZWX0YodivV5Peno6bm5uDBgwoNt8GHChSkpKmDdvHu+//76YKt0dKcpJXfW75D6FDpEURTxrgtCbFRQUMGDAAL766iumTp1q7eF0muPHj7Nx40aGDBnSLnxciO4ehk9UXFzMzp07CQ4Opn///t1+vMLJ9Ho9jY2NNDU10djYaDm1/V6v12MwGDAajZbbqVQqSzhWq9VIktTuDX51dTUuLi5IkoTRYKQ4vxy1VoXWRoXWVo2qJZQajQr6ZiN6vUxDg5HGZpmaGj3Vikxdo4G6BiOVaiN1TUbqmo3ILfsKa5qgX4AjI6PdWPxroelOW0KwSlFMexIbFdO+xJbNh023Vdc0ozYXhRUF9MbWadP61sc47+lpXHlDEt+8u5pPF61AURRUKomI+ACuv3Mc376zCgdnO+5+5hoi4gNPem53p+Xw7Vt/YmOn5dbHriA0pm824DL3XrCxsWHYsGHY29tbe0hndPz4cdLS0oiNje20UCzLMunp6QAkJSX1mtdKRVGYMWMGtra2fPXVV9YejtBGTU0Nrq6ujL7kWTSazt/T+0wMhkY2rnrW8ndAODtRIRaEXi4kJIQ33niDOXPmsGvXLvz9e8ebQnd3d0aMGMHmzZuxsbG54A6pPS0Mg6n7tpubG1u3bmX9+vUkJiZarTmOcH60Wi1arfacthNTFMUSjA0GQ7uT+XLzdXbs2EFUVBQajQZJkggK0LE5NRujWk3atgJys0rRN8voVZJpCjKg2KotU5YbXdQoahVI0OTSeh3TFU2nyjo9Hk5a9IBlFaqstG6/REswlhXs7LU0NjWbKsAaFTS3JGJJApWESjJNyza0CcQN9c0ATJs9hl0ZuWxfn427twuz7pnIi//3sSkgSxJP3PQ2i7c8j0bb/nd20MhoBo2M7sD/jd5FURQKCgrYs2cPkZGRxMbG9oi+A51dKTbvNdzc3Mzo0aN7xGv7uXr33XfZvHkzu3btsvZQBKFHE4FYEPqA2bNns2rVKm6++Wb++OOPXvOGwMfHh8GDB7NlyxZGjRqFu/v5TYnsiWHYzNHRkbFjx7J//37Wrl3L4MGDO61iLnQvkiRZAvSZ6PV6duzYgZ+fn+W6fn4QGx9JU6OeTz566cQDo1KBoc1+w+a1w8iKaesliZZmVlgW+h6v02Nno8bJSU1TrdF0GxVIRvNtQa2XURSF5vrm1iqwjRpFL1sazcT2DyAqxp8hiWG89MjXlmF99J8VTJo2BHcvZ/7xyV00N+nR2mhY/8tOlJYp2LKiUF1RR3VFHZ5+Pb9PQmfR6/Xs3r2bY8eOWWVLpQvVWaFYURT27t1LVVUVY8eOtXoDsc60c+dOHnnkEZYvX46Xl5e1hyOchqRYoamWmPvbYd3/o0JBEC6YJEm8/fbbFBcX8+KLL1p7OJ0qODiYfv36kZ6eTl1dXYdv35PDsJlKpWLAgAEMGzaM3bt3s3PnTkvVUBDaOWHdpCRJSIAin3A9GSRZRgI0TUprOG5ZHywroDco1NTr8XDQtgToltKwbLquubuqdIqepwpY1rldfeNI5j89zRJy215p5dJtlm9tbE3Nx/olhGJrp0WlVqFSqwiK9MHN++wV9r6iqqqKtWvX0tTUxPjx43tcGDYzh+KsrCxyc3PP6xgHDx6kuLiYkSNHYmtr28kjtJ66ujpmzZrF448/zrhx46w9HEHo8UQgFoQ+wtnZma+//pp//vOfrF271trD6VSRkZGEhISQlpZGY2PjOd+uN4Thtvz8/JgwYQK1tbWsW7eO48ePW3tIQjdja3vCxLC2bUSMrV9LLRVcSeakfy17EitQUavHw9XGUjmWME2RluQTtv5oG3YVBUVjevshofDF+6kAxA0JOWm8332y4aTzfAI9+Pf385k0czhXzR7Dy9/8FbVaRW1VPX98nc7G5TsxGk9M+L2foijk5OSwYcMGQkJCGDlyJHZ2Xbt2sbNdSCjOz88nOzubkSNH4ujoeJFG2PXMWywFBQXxxBNPWHs4wtkoVjoJHSKmTAtCHzJkyBD+/e9/c9NNN7Fz585e1ZEyPj7esqXGmDFjzmlaaW8Kw2b29vaMHj2anJwcNm7cSEREBLGxsb3m8QmdQWl9w2RQQN3y2Xib6rFGr2DQmBKuqklBclC1u7n5dLxWj4ebTWvzLMv0QNP3siyjUkBRS6bA3bKHsKRqCcwK1NQ0AOAb6I6kktpVinU1jXz//hr2ZuQSOzSU6/5vImqNmuhBwTz4yk1trtfAXy//F6UFFQCkTEvg7+/c1snPW/dVW1vLjh07aG5uZtSoUXh4eFh7SJ3mfKZPl5SUsGfPHpKSknrFloNtffbZZ/z+++/s2rVLvK4LQicRFWJB6GPuvfdeRo4cyezZs5Hl3lNFkSSJIUOGYG9vT0ZGxhmnDPfWMGymUqmIjY0lJSWFsrIy1q5dS1VVlbWHJXQTGpVpErMErWEYLA2xwBRqVUZTtVdtwBRmZSwnlWKaEl1Zp8fTWdsuJBvN4Vg23Ykk0676DG0KGIqCrqaBlct28PZLv+Ds1n5fbUdnOz584SfS/9jL5//+lSWvrzzlY9qWesAShgHWLdtOdWXHl1D0NIqicPDgQdauXYuHhwcTJkzoVWHYrCOV4oqKCrZu3UpCQkKv+tAXYP/+/cybN4/Fixf3mgaZgtAdiEAsCH2MJEl8+OGHHDhwgP/85z/WHk6nUqlUJCYmArB58+Z229SY9fYw3JaLiwspKSkEBgayYcMGDhw4cMrnROhb7B1Pt5ZSafeVJCuojKCWQW1onTatMrbuL1xZ04yHk7ZlrbBpvbCkMl/XdKakKPh4OZkOajQtQJYUU5gDU9H4v//4mV++3UxdXSMaOy0qjYqAEE9iIj1bx6TAzo1ZLHp4CTcOf4Znbv/AEnpdPNpPidXYqLG1t2l3XnOjnh8/WMPHLy0j78CRC3gGu4fa2lrWr1/P4cOHGTlyJAMGDOjVr2fnEoorKytJT09nwIABvW5/9vr6embNmsX999/P5MmTrT0c4RxJimKVk9AxIhALQh/k5ubG119/zYIFCyx7M/YWGo2GpKQkjEbjSaG4L4VhM3O1eOzYsZSWlopqsYCvn1vrN7Lcuo7Y0lm6zVrhNieV0RSQLR2oFYWKmmbcnWxMZygtp5bmWwATx8YgGWTKi6tMa4tbmnKhgKQ27Z2sAM1NemSjgiwrGAxGPvr1QT7+7SEGDI+0zOSWJAlFkli1dBtV5XVsW5fJu8/9CMDg0TFMv2McSGBjp+Xh127B7oRA/NLcz/hg4VK+f28ND0z7D8V5ZRfl+b3YTqwKjx8/Hk9Pz7PfsBc4UyiurKwkLS2N+Ph4wsLCrDPAi+iBBx7AxcWF559/3tpDEYReRwRiQeijRowYwcKFC7nhhht6XfMlrVZLcnIyer2eLVu2YDQa+2QYbsvV1fWkanFvmjIvnLvLpg81faEoSOa0qSioDK1B9sS+0NIJU57NIblaZ1qa4OKobW26JYNklJFkhdQ1WZbO1JJRQS1JqFUSzk52TJ2ewNhJ8cz/+5UnvRnJzSoB4Pr7LuGGv04mPjGca++egLd/69ZqslGhMNcUaiVJ4v+en8lPB//DD9mvMH76sHbHMxqMpP+xx5TXjTLNjXq2rc08n6fPqurq6tiwYUO7qrBG07fawZwqFB8/fpz09HTi4uIIDw+38gg735dffsl3333Hl19+2ef+f/d4spVOQoeI3ypB6MMefPBB1qxZwx133MH333/f+ua4F9BqtYwcOZJNmzaxefNmmpubsbGx6ZNh2MxcLfbz82PHjh0cPXqUwYMH95nqkmBSWV5r2moJ03Q+BVCpTJVaue1rgGyeBy0hGRRQSyhSS4VXbunBpZj2I3Z3t6G2Sm8K1iqpdRsmVcu+xgB6I0OGhxMS4c30GYkEhbT+3H3/6QaK81vXAP/+/VaW/PcPjuRXMHpyf/751VzTHsS/7mTdrztRq1UYjTIpVw5p99hs7E7dTE+lVuHt70ZFSTVyS9Mu/9Ces3er0WgkNzeX7OxsQkNDiYuL69PBqG2jrYaGBgoKCoiNjSUiIsLaQ+t0Bw8e5J577mHx4sWEhJzciV0QOktRURHPPPMMK1asoKKiAn9/f66++moWLFiAu7v72Q8A/PHHH6xYsYKdO3eyc+dOKisrGT16NBs2nLxjQHfSd19NBUFAkiQ++eQThgwZwltvvcW8efOsPaROpdVqGT58OKtXr0atVjNq1Kg+G4bbMleLDx48SFpaGv7+/vTv37/Hb9EitHf8WC0/vJ9Kg66Jq24dQ9TAYAB0dU3mZs8gK3i62ePk7oRGI5FdWGm5vSRjmkemKCBLphAMLeuCsVQhjtc04+5mQyE6ABTJFLBlWUHVpmO0pCiUFZRz5fQEtqfngqLw0asr2Z6ea9oOyjwoRWHP1jz0uiZkWWH1sp1ExQdyzewxjL1iCM/Z27BzUw6R8YFMvKZ9Jfh0JEnimY/u4tWHl3D8WA1XzUkhcXzcBT/HXaGsrIzdu3ejVqsZOXKk+ACrhbu7OwMHDmT79u34+vqeU/fpnqapqYlZs2Zxxx13MG3aNGsPRzgP1ljTez73l5uby6hRoygrK2P69On069ePzZs38/rrr7NixQo2btx4Tq89b731Fj/99BN2dnZERUVRWVl51tt0B5KiiJXXgtDXrV+/nssuu4z169eTkJBg7eF0GvM0abVajV6vx87OjuHDh4tQ3EZDQwN79+6lrKyMfv36ER4ejkolVtP0ZHq9nuXLl/P7O5nkZ5UCoNWqefKDOzh2rBZZUfjvy8sBU5VY62SLwWBKu3qb1t8NxfxjIEk02Soo9prWrZkUBallYvX4IZ5o1RJrV5eAomBvpyXUy5Wa6nrKi6ug2di6DlglWZYsazQqjM1GkE1VZdnYZj0zgN7YsjWTQmRcAG/+eP9pH/OKL9P45fMNePi4cO/z1/ao6u/p1NfXs3fvXo4dO0ZcXBxhYWHid7ON48ePk5aWRlBQEEVFRcTGxva6UHz//fezadMmNm7ciK3t6ZrhCd1RTU2N6cPnsc+g0XTth80GQyPr1j9PdXU1Li4u53SbKVOmsHLlSt544w3++te/Ws5/6KGHePXVV7nnnnt49913z3qctLQ0XFxc6NevH4WFhYSHh/eICrEIxIIgAPDPf/6Td955hy1btuDj42Pt4VywE9cMy7Lcp9cQn01ZWRl79uxBpVIxcOBAvLx6fqDoq8yB+KNH16FvNDWVU7RqcLQ3fa2SLNstKYBi2zrN2KiVQKMy9ciSsEx5NqjB6Khp7TzSsqUSwKBIF+LDnPhxaWFLhRdUDcbW6rDeiEqWTXemOmFZhiybKtHQ2pTLzGBEajmGRqPm573/OOXj3Z2Ww9+ufxMAlVoiKMKH91Y/0ZGnrFsxGo0cPHiQnJwcAgICiI+PF7M3TmAOw+YQfOL3vcHnn3/O/Pnz2bp1a695TH1JTwrEubm5REVFERYWRm5ubrsP3mpra/H390dRFMrKynB0dDzDkdo7fPhwjwnE4qNGQRAA+Nvf/sbIkSOZOXMmzc3N1h7OBTlVAy3zmmKDwUBGRobYfugEPj4+TJgwgaCgINLT09m2bRuNjY3WHpZwAdq1BLBvU106sVWAubmWZJpa3HYvYqml47RG3zL1T1Ys3aJVsoJKgaqaZtxdbCz7GEvmMN1ybNSm6dbmztQnsXS5br3MP8jdtEWTYnognj6nf1OXu6/Y8rVsVCjIKcVo7JldZUpLS1mzZg1Hjx5l1KhRJCQkiDB8gsrKSjZt2kS/fv0sQbEj+xT3BOnp6dx777188803Igz3dIqVTh2wZs0aACZPnnzSLBRnZ2dGjx5NfX19r9uVpC0RiAVBAExvhD/++GN0Oh3z5s2jp04eOVM3aXP3aaPRSHp6OgaDwYoj7X5UKhXR0dFccsklyLLMqlWryM3NFd2oe6h2v8Jt03Gb8yWFlqnJpu2SNFp1y37C7Q8iYeoSLRlbQrKx9ViV1XpcnLSgkUxr11qqxCjKCan8hAG0DcItP2O+AW4gKxzNr8QrwA2VWsLLx4XHX73xtI9z0MgoVGoJlcp0GpgchVrds97e1NfXs3nzZrZu3UpERAQpKSl4eHhYe1jdTkVFhWVrpRMbaPWWUFxcXMw111zDiy++yKWXXmrt4Qh9QFZWFgAxMTGnvDw6OhqA7OzsLhtTVxNNtQRBsHBwcGDp0qUMHz6cQYMG9bgmW+eytZK5UpyRkcHGjRtJTk4Wa7NOYG9vz/Dhwy3TqPPy8oiLiyMgIKBXdSLv7WIGh7B/y2EkScLYNpxaukebvpVUEopRRgIMzQbLdGpzJ2pLKD5x5yUZQKG+3kBTsxE3Fy3Vx5paKs6tQbpdMJfNnafb7H1svg9JorTouOW88rJa5j0znStvSD7j44zsH8SLS+byxzcZuHs7M2tezwkRTU1NZGdnc/jwYYKCgpg0aZJ4PTqNo0ePsm3bNgYOHEhoaOgpr9O2+zTQ46qrDQ0NXH311UydOpX77z/9mnmhBzlxKUhX3Semadtt2dranvL1pbq6GjA13DwV8/lVVVWdOMjupWd9hCoIwkUXHBzMjz/+yN/+9jdWrVpl7eGcs47sM6zRaEhOTsbBwYH169dTX1/fhSPtOczTqKOjo9m7dy9r166lrKysx84e6Guefu92bp4/hWlzxvL8f2/BzsG0VlitafnTb55a1/YzDkOb2QAt058lpbVCjLm7dNvzFThercfbRWuZUm3vYIuNVk1IqCc33JRseVMY3S+gZWp1y6lNF2pLZbmNLevOrSIxeFQ0j7z2F+54cjpOrg4dep6swWAwkJWVxZ9//olOpyMlJYWhQ4eKMHwa+fn5bNu2jYSEhNOGYbOeWilWFIU777wTGxsb3n77bfHho3DBgoODcXV1tZxeeuklaw+p2xIVYkEQTjJy5EjefPNNrrvuOrZs2dLtP2XvSBg2U6vVJCYmsmfPHtatW8fIkSNP++loX6ZSqQgNDSUoKIhDhw6xdetWXF1diY+PP+d9CQXrcHC24+YHLwNg95Y8Guv0SCqQm42gafM7IgPmb1WqU68/U0BqUsBOsWyP1C/Kl9yCcgxGGV2dAXc3W6AWgKYmPX+sa21sFRzmxRfvrKGmSmdplGXm6u5ARIwfyRP68c4/fm132YQrBl/gs9C9yLLM4cOHyc7OxsHBgaSkJNHA7gwURSEnJ4ecnBySk5PP+bnqiZXif/3rX6xbt46tW7eKD0aETlFYWNiuqdbpfq7M733MleITmc93c3Pr3AF2IyIQC4JwSrfddht79uxh2rRpljb63dH5hGEzSZIYOHAgtra2bNy4kREjRog3p6ehVquJjo4mLCyMnJwcNm7ciK+vL/369cPZ2dnawxPOYsMf+1CrJEuzKXOzZ0tR1tgadE9HJSuoZdArMpICB7NNWzpJskLpsQbcPG1NxwEUFHbvzKe06DgODja8tnAZsrn6fML9zLxtLNfNGQuAX6A7Lz/6LXq9gStuSGJ8LwnEiqJQXFzMgQMHUKvVDB48GD8/P1EFPANFUdizZw9HjhxhzJgxHf7AsieF4l9++YWFCxeybt06fH19rT0coROZZ9l09X0CuLi4nNN7t9jYWOD0a4RzcnKA068x7g1EIBYE4bT+9a9/sW/fPm6++WaWLl3a7bYqupAwbCZJErGxsdjZ2ZGens6wYcPw9/e/CKPtHbRaLfHx8YSHh5OdnU1qairBwcHExsZib29v7eEJp+Ef5G5pjiapJbQaNc36ls5YSpsw3Gw0bbt0iqAmKSDrZUsxWW6zLvl4dTPhwY6WfYNR4Mn7F9Ncrzdfuf2xJMnUSEtR2LYhm5mzxyBJEknj4vhh8zMX4RmwDvNWJQcOHKC5uZnY2FhCQkJEED4Lo9HI9u3bqa6uJiUlBQeH85sG3xNC8f79+7n55pv5+OOPSUhIsPZwhD5owoQJAKxcuRJZlk/admnjxo04ODiQnHzmfg49mVhDLAjCaWk0Gr766iuysrJ4+umnrT2cdjojDLcVGhrKsGHD2LZtG4cPH+6cQfZi9vb2DB48mAkTJqDX6/nzzz/ZtWsXOp3O2kMTTuGqG5OZPCMRJ1d7YuIDaW42WNbxSuYTLW8KTgxrls7RJ4TaNg2xqqqacXe3bdmSyXRZY4O+zZXN/5q+UFrCMMDOjEPkZh497dgry2p49q6PuOuSl/jfG7/3iDXsiqJw9OhR1q1bx7Zt2wgKCuKSSy4hNDRUhOGz0Ov1pKenU19fz9ixY887DJt15zXFlZWVTJs2jfvvv5/rr7/e2sMRLoa2r59deeqAyMhIJk+ezOHDh3nrrbfaXbZgwQJ0Oh233HJLuz2IMzMzyczM7JSnqDuQlJ7wl0UQBKvKzMwkOTmZd955hxtvPP32J12ls8NwWxUVFWRkZBAZGUlMTIx483qOampqyMnJ4ciRI/j7+xMdHS3WZFuJXq9n+fLlTJ06Fa1We9LliqJwx7TXKSmqRJYVFLXKModaBmRn2xNvYPoHQCO1htqWCrEigY1Gxdx7Ynnv9X3oG1vCrlFGJbd5c2bp12WqIEtGo2W98pRrE3nwuWtO+Xgev+lt9mw+hNwy3fvhV25k0rXDz/PZubhkWaa4uJicnBz0ej2RkZGEhYWh0YgJeeeisbGR9PR0bGxsGDFiRKc+b8ePHyctLY3Y2NhuUSk2GAxcdtllODs78/3335+0/6vQs9XU1ODq6sq4kU+h0XTtXuIGQyNr016gurr6nJe75ebmMmrUKMrKypg+fTpxcXFkZGSwZs0aYmJi2LRpE56enpbrS23/DrSxYcMGPvzwQwDq6ur4/vvv8fHx4fLLL7dc59NPP73AR9j5xCu0IAhn1a9fP7788kuuu+46oqOjSUxMtNpYLmYYBvD09GTMmDGkpaXR2NjIoEGDRCg+By4uLgwbNox+/fpx8OBB1q1bh7e3NzExMWI/1W5GkiRefGc2H772OzXH61HZqdmRfggkCRXtp0K3rTRIgGJUWrdrUgC16V+DXqauTo+7pw1lhfWc+TfGtO2SYj6UAjY2Grauy2TbumySJsYxZFS05dp5mUctYVilVpGfXdJZT0WnMRqN5Ofnc/DgQVQqFVFRUQQHB3e7ZSbdmU6nIy0tDXd3d4YOHdrpAbG7TZ9+6KGHKCsrY+nSpSIM92KSbDp19X12VGRkJFu3buWZZ55hxYoVLF++HH9/f+bPn8+CBQvOuYnmwYMH+eyzz9qdV1ZW1u687hiIRYVYEIRz9sorr/Dqq6+SkZFBUFBQl9//xQ7DbdXX15OWloazszMJCQmiwtNBjY2N5ObmcvjwYVxdXYmOjsbHx0d8uNAFzlYhPtGv32/hjRd/sawjNthqwEYNktSyj/CJGxADUsv55uqyojDjmlAyd1eSvasSaDnbvE4Z2lSIWzQbkACNVs2MvyTx7buplotmP3I5Rw6Xk59dgmyUObinEJVKQjbKvLj4/xg6uns0d9Hr9eTl5XHo0CFsbW2JiYkR+3Wfh+PHj1v+rvTv3/+iPn/doVL83nvv8eSTT7JlyxbCw8OtMgbh4jJXiMcnWadCnJrRsQpxXyfe4QmCcM4efvhhsrKymDJlCuvXr+/Syl9XhmEABwcHxowZw5YtW9iwYQNJSUmiaVQH2NnZ0b9/f2JiYsjLy2P79u3Y29sTFRVFQECAqIh0I0OGR5jCZkvjK19Xexz9XMnLO4aTsx21tY3tb6AoSEimmc9tpkRXHW/C3cO2ZX9jBY1WQm/u1yW3by4dPyiYyqJKSosrMRiMfP/RhnZ3seS/f2A0yJYxjb58EM7OdoycMrBbhOGGhgby8vLIy8vD1dWVoUOHig98zlNhYSG7du0iLi6OiIiIi/4cWrtSvHTpUh5++GFWrFghwrAgdBMiEAuCcM4kSeKdd95h5syZTJs2jZUrV15ww5Nz0dVh2MzW1pZRo0axe/du1q5dy4gRI8T03w7SarXExMQQERFBQUEBmZmZ7N27l7CwMMLCwrCz69pPzoWTOTrZoRgUzAt6j1XWc6xBDwrUnRCGzeEWZCQkbGxUNOtNpd+q4834+9iB0VQV1jeZbqO0VIZd3B2prtQhSbB/dyFSk97SmEs+oQt12zCsVqtw93Zh7rPX0NjQjEFvRKNVo282kJ99FC8/N9y8Tt76q7KshsOZRwiL9cfD98LXsyuKQkVFBXl5eZSUlODj40NycnK7dXXCuVMUhf3795Ofn8+IESPw8fHpsvu2Vihet24df/nLX/jf//7HmDFjuuQ+BSs7jyZXnXKfQoeIQCwIQodoNBq+/PJLJk+ezA033MAPP/xwUacTWysMm6lUKgYPHoyrqyubNm1i4MCBhIaGdukYegONRkNERATh4eGUlZVx6NAhcnJy8Pf3Jzw8HA8PD1Fds5K8nJKWqm8L88Leln+8vJ2prW2gqcmAt6cTx4qrTUuMFQW9TkalVqEoClXHGomPc22/52bL1yqVZKom0/JeTQJFrUIytplHrZJAVpBUEkPHRLN9vWnvS6NRZlBSBG8//R0/f7oeG1sN//fctfzw3iqKcstQa1T8/d3bGX15657FmTsO8/ist2hqaMbWTstLX80lbtj5VeMMBgNFRUXk5eXR0NBAaGgoEydObNdxVegYvV7Ptm3b0Ol0pKSk4OTk1OVj6OpQvGfPHqZPn85rr73G9OnTL+p9CYLQMSIQC4LQYfb29ixbtoyUlBTuuecePvzww4sSZqwdhs0kSSI8PBwnJye2bNlCTU0N/fv3F9N+z4MkSfj6+uLr60tdXR15eXlkZGRgZ2dHWFgYwcHB57TuVTg7WZb5dfFGDu0vJiGlH6MvG3TK6wWHebWrKKiajSj2WiyNrxRobjSt9z12rA7UEpKhJchKLSEWqK5oxNWjtUO1l48LQ4aHs2nNAewdbPELcONATQNyy/UlVfvXjPsXXktIpDc+QR44u9rz7ftrKcwtI/mSeJyc7Pj50/UANDcZePPJb1BawrTRKPP+gu/bBeJv3vqT5ibTtk/NzQa+eetPFnx8V4eev+rqag4fPkxRURGOjo5EREQQGBgo+glcoLq6OjIyMnBwcCAlJcWqv+9dFYoPHz7MlClTeOSRR7jzzjsvyn0I3VTr5JuuvU+hQ8SruiAI58Xd3Z0VK1YwatQonnrqKf7xj3906vG7Sxhuy9vbm3HjxpGRkUF6ejqJiYnY2NhYe1g9lpOTEwMHDiQuLo4jR45w+PBh9u/fT2BgIKGhobi7u4uq8QX4+u1VfPn6H6jUKpYvSeOJt2Yz9oohJ13Py9cVrVaFvmXqs2QwMjopkqzcMuzstBQUVrS/QZsga2+npaFRD5JETXUzKpXE3MenoMhqJk4dxKsLfqRR10RjXRMVpdXYOtjS1NCMooCrhxP15TUYmo2MurQ/468cwssP/I+MVftxcXdkwfu3ccv8yQCs+3lHuyHIxjYVbeXkKddaG01LL2tTwVutPbfXD4PBYPlZrKmpITAwkFGjRuHm5iZ+FjtBWVkZW7duJTQ0lPj4+G7xnF7sUFxeXs6UKVO49tpreeKJJzr12IIgdA4RiAVBOG+BgYH8/vvvjBkzBl9fX+6///5OOW53DMNmjo6OjB07lu3bt7Nu3TqSkpJwdj55/aJw7jQaDSEhIYSEhFiqcmlpadja2hIUFERQUJBVplT2dJtX7QNANsqoVBIZq/YRGO5NY0MzsYNDUGtaf6/0za3doBUJ1m/Iadl5SUI5cSKEJDHjhiQcHe1w93Lk9ZeXg6IgKwo1Vc2MGxdMWEQwANs2HbRMlZaA5oZmS5CtPq7jubduRW42UF/XyLLPNpCxaj8AtdX1vPLIl3y85u8ADBvXD78QT0oKTOF81GUDydlVwLHi40gqiduemNZuiDc9cBk7N2ZTU6nDydWBvzx42WmfJ1mWOXbsGEVFRRw9ehRHR0dCQ0PFbIVOpCgKhw4d4sCBAwwaNIiQkBBrD6mdixWK6+rquOKKKxgyZAivvfZat/gAQBCEk4ltlwRBuGAZGRlMmjSJDz74gBtuuOGCjtWdw3BbiqKQmZnJoUOHSExMxNfX19pD6lWMRiOlpaUUFhZSVlaGi4sLwcHBBAYGYmtre/YD9GHmbZey1lSz9uedpmqqBP0Tw9m7JQ+A/sPDqW+WycssQaWSMLapoCpqFbJj63OsqGjdl7jFmCGh7NqSR2CYJ9kHyyx7E0+dGU5QoB8/frADRZapa2imucnQesMTjjNybAzpK3YDoNaoMOqN0BKgHV3s+W7nQst166rrSf9jLw5OdiRdOoDmRj0H9xTiE+iOb/DJja0aG5opyS/HL8QTO4f2PzOKonD8+HGKioooLi5GpVJZPnxxdb3wBlxCK6PRyO7duyktLe32jQk7c0smvV7PVVddZfl9FK9bfYt526UJiU9YZdulNVtfFNsudYCoEAuCcMGSkpL49ttvufbaa/Hy8mLSpEnndZyeEobBtBY2Li4OZ2dntmzZQmxsLFFRUaIC0EnUajUBAQEEBATQ3NzMkSNHKCoqYu/evXh7exMUFIS/v79Yz3kGdz45neZGIwf3FTF4ZBQrv9lsuWzf9gJoee5kWQGDDBpTKdhGo0LjZEt9fTMAxpYGWpYwqyikrctCkhVyDxxFa6uxTFnW1eg5UJ5HRVmN6bpqqX0IbnMcSYK01ExLxdioN6JSq5BlU7X6mtvHtns8Tq4OTJo5wvK9vaMtA5OjTvv47extCOsX0O68uro6ioqKKCoqoqmpiYCAAIYPH46np6f43b0IGhsb2bJlC7IsM27cuG6/dV1nVYplWeb222+nrKyM1NRUEYYFoZsT7yQEQegUl112Ge+++y4zZsxgzZo1DBs2rEO370lhuC3zdN7NmzdTUVFBQkKCWFfcyWxsbCzbNNXX11NUVEROTg67du3C398fPz8/fHx8xPTWEzi7OfDUu7cBUFulaxeIT6zUSkaZd395gKryOmIGBFJeXsf3X2WgUkkczj/G7l2F7bfyaPlakRVCQ72wdbSlqqIODzdXDHJ9++u1vS9JsuxHLEkSinlLkpbr2DvZcsejU/EN9CBhbOfsN1xbW0tJSQlHjhyhpqYGHx8f4uPj8fX17TGvMz1RWVkZ27dvx9vbmyFDhvSY57ozQvFjjz1GWloaGzduFBW6vk5su9QjiEAsCEKnueWWWygrK2Pq1Kls3LiRqKjTV2/a6qlh2MzNzY3x48ezc+dO1qxZQ2Jiotib9CJxcHAgJiaG6OhoampqKC4uJisri23btuHl5YWfnx9+fn5dsj92T2LnYGtpMgWAXg9tfs/8gz0IjfAhNMK0F2xwiC0PPDYVgPz8cu74y3u0PYAMaFUSsqwQNzCIX7/ZjFqtYsu6RiZcHY6lnZUMWju1ZY3ynHmX8MPiNGqq6nFxc4CGZqobmy3jsLHRcPkNyWd8LE0NzWxdcwCtrYZh4+NQq9svcpZlmcrKSkpKSigpKaGhoQFvb29CQ0MJCAgQH1hdZLIsW5aTDBw4kJCQkB5Xfb+QUPzKK6/wxRdfsGnTJrGURhB6CBGIBUHoVA8//DAlJSVMmTKFDRs24O/vf8br9/QwbGZjY8Pw4cPJy8sjLS3NEtp62hvBnkKSJFxdXXF1dSU+Ph6dTkdJSQlHjx5l7969ODs7W8Kx6BBser7UahUGfWvzrBvvGUfOgRK8/Fy49+nT74u6d1fBSVuHhA8MYlRSJLEDAtm0ar9pHbJRpqq8EWc3G9RqCaPBVPm9Yc4Y/EO9KMqvoN/AIP75/mz++eg3lJVUERMXwO5jNUgtFY0p14846f4VRWFL6gGqy+sYMiaa52//kIN7CgEYddkgnvrgDgwGA2VlZZSUlFBaWookSfj5+dG/f3+8vb3F1Pou0tDQwNatW9Hr9aSkpPTo6uj5hOLPPvuMhQsXkpqaetH3NRZ6CAXTJ4hdfZ9Ch4i/EIIgdLqXX36Z8vJyJk6cSGpq6mk/Je8tYdhMkiQiIiLw8PBg69atlJeXk5CQgJ1d1zbU6IscHR2JjIwkMjISvV5PaWkpJSUlbNq0CbVajZ+fH97e3nh5efXJ9XwarZp7FlzDW09/BwoMGhnFDXMvxcb27G8DYmP8OPHjhH79A/D2cmbf1sO4uDlYZug11hswGhVcPe2oLG3Azc0e3yAP/v3Ed4DCknfBL8idsqNVyEaF3dsOc/lfRqGWFcL7+XP5DUkn3f87C37g5883AODkak9dRR1I4OHvRG3jcVLXrKO2rhonJyf8/PxITk4WW3ZZQUlJCdu3bycgIIABAwb0ig8hOhKKv/zyS+bOncuyZcsYOnRoVw1REIROILpMC4JwURiNRmbPns2OHTtYs2YNPj4+7S7vbWH4RHq9nl27dllC8YmPX+gasixTUVFBaWkpx44do6amBmdnZzw9PfHyuSD+vgAASL5JREFU8uqVAdnc1Xbq1KknrauuKKmmrqaB4CgfVKoT91NqJcsKH7+9ijV/7CU41Ivy4zry88oBcHC0Jcrbif3bCwCQVBKjLu3Pzi2H8PJxYcY98Xi4+tJYIzFweDhvv/gz637fi/nthlqtwmg0lUxUahUzbh3NnQ+delskg97ItJhHUVDw8HckIMqNgEhXAqLcUWkkSnKrmHDlSMIjQ3F0dLzg507oOFmW2b9/P/n5+QwePJigoCBrD6nTna379DfffMPtt9/ODz/8wOTJk60wQqG7sXSZTvg7GnUXd5k2NrJm+0uiy3QH9PyP7wRB6JbUajWffvopt9xyC5MmTWL16tV4eXkBvT8MA2i1WoYNG0ZBQQGbN28mMjKS2NjYM4YQofOpVCq8vb3x9vYGoLm5mfLycioqKsjOzmbr1q04OztbwrGnp2evC8htefq54ul39m2FfvtpO98s3gTAsdIaIqJ9SR4Zye4dBfSL9WPXqv2WqrEiK2TvLkRXoUNXoWP/NhcmTPWloUpm/W+7cXC0tTTSUqkl/II8KM4vR6UynTlmUv+T7l9RFGpqaigvL2fKnQPwC3dBrVFxNLeK6tImlr+zk4qiOu565hoGDIrvzKdI6ACdTsfWrVtRFIVx48b12v3Cz1Qp/v7777ntttv49ttvRRgWTiIpimVJSFfep9AxIhALgnDRaDQavvjiC2688UZLKHZ2du71YdhMkiRCQ0Nxd3dny5YtVFRUMGzYsG6/9UhvZmNjY9nOCdoH5KysLGpqanBycsLNzc1ycnV17RXTP89m37bDfPzKb8hGGYODjaUvFsChg6Xk5R5DURR2bTuMbKdF3aC33PbYkSrL1xVHdaxfuYtfP9wDmKY5xw0O4cCuAiJj/Xn6tZvYvfUwRwsqSBrfj+j4QOrr66mqqqK6upqqqiqOHz9Oc5OexloFRa9m+Xu7KS+sQ5YVvAPdeOe3J1GpVdg79t4PL7q7I0eOsGPHDoKDg+nfv3+vfi2HU4fin376iVtvvZWvvvqKqVOnWnmEgiCcr97/F14QBKvSaDQsWbKEWbNmMWnSJBYuXIiHh0evD8Ntubi4MG7cOPbs2UNqaiqDBw+2BDLBuk4MyE1NTVRWVlJdXc2xY8fIycmhqampXUg2N/Pqjts8/frFBpb/bwOT743jSF4ZoTGB53S72up6nrztQ5oaTSFXdrIFrRqUtlsmmaoOsqygstFAfXPLdkntqxHV5Q0ERbVO06urbiA2LgCtRk3BoWP8+MV6rrgxgYBIW8qrCshdsQe9Xo+zszOurq74+vry4ztb2ZdeiCwrpuqy3mAagkoiekAwji7iQyVrMRgM7Nu3j+LiYoYOHdqnXsvahuLVq1fzwAMPsHjxYq666iprD03orhSssO1S195dbyACsSAIF51Wq+Wrr77i2muv5bHHHiM1NbXPhGEzjUbD0KFDKSoqYufOnRw5coRBgwaJLWC6GVtbW/z9/dt1R29sbKSqqoqqqirKy8s5ePAgjY2NODk54eLigqOjI05OTpaTtf6f7k7L4c2/f4PWTg3E8cLdH/NB6tOWy3W1Dbz5+Dfs35bH4FHRzP3Hddjam8ZafLjcEoYBFL3RtC2T1FImVhSU1g2EUWtUyJbvwcPHmcqyWgCqyhtx9bLH2cMWN297XL3sqG4sI3iAliGTorCxM7Jtyy6CQvzQYM+A+DACglr3BK4+rmPPpoLWsSgQFO1L8cEyFKPMjnVZHNxbRNSA3rdWtbs7duwYO3fuxN7envHjx/fJ7c3c3d2pqalh/vz5fP7551xzzTXWHpIgCBdIBGJBELqEjY0N33//Pddffz2TJ0/mzz//7JN79QYFBeHl5cWuXbtYvXo1gwcPPuvWVIJ12dnZWbZwMjOH5Lq6Ourq6igoKKCuro6mpia0Wq0lHLcNy/b29mi12ovW/fhw5tF235cWVaJvNqC1Mf2p/+TFZaz7eTuyrPDnd5tx83Lm9iemAeDo3L7pi6QopiJD63/AqLTsRaygbzJy5XWJHCk8xuCRYQwZHc7WDfsxKno8/RyQMXDDo0OpqWikqrwRXY2BI7tryPi9iIqSeoaMiOKHg7spK65Ca6NhwbuzGTYmBgA7+5M/UKiq0JmSsWLah/jrt/7gyXdu68RnTzgTc1W4sLCQ+Ph4wsPD+2wX76VLl3LHHXfwxRdfcO2111p7OIIgdAIRiAVB6DI2NjZ8++233HjjjUycOJE///zT0uyoL7Gzs2PEiBEUFRWxY8cOjhw5wsCBA0W1uAcxh+QT6fV6dDqdJSjX1dVRUlJCXV0dBoMBlUqFra0tdnZ22NnZtfu67XlarRaVStWh0DF4dDRqjQqV2tS4bcCICEsYBsjPLkGWTeFWkRUKc0osl/kHe2Jjo0HfrEdrp0HrqMXR1wEHJw0OzjY4tvzr4KTB0UlrOt9RTWiCH/b2EseryhmUFI6DgwMODo5s37adXz7eT1FOFbZ2WnzCvCjIPWa5v91bDyE3NANg0Bv44KVfGPbrQwDY2mkJifKh4GCZ5fpt908GRHO6LlReXs6OHTuwt7dnwoQJfbqb9/fff8+tt97KkiVLmD799Ht3C4KFolhhyrSYM91RYtslQRC6nF6v5y9/+Qv79u1j1apVp92nuC9obGxk165dHD9+XFSLezmDwUBjYyONjY00NTVZvm57ampqQq83TV2WJAm1Wo1GozntSa1WW0KzJElUlFRz6EAxbkFaQoJDsbWzsWx3tHfzQfIyj6C1VWNjp8Y/zAuvAFcMBgMGg4G62nq0NqZpy7JRoaHegK5Wj65OT32dHl2dod3XL732F4JCfS3hdOV3W3jzmR8wGozMXjCKjb8cJHtrGUgSklaD3DLd2jztWiUbUVqqziERPrz328OW5+rLt1fxxau/oygKKrWK/sNCyd1VQH1tI06uDvzr23mE9+s7a1etwWAwsH//fgoKCvp8VRhMWyvddtttfP3111x55ZXWHo7QzZm3XZo4+G9o1F3b/M9gbGL1rpfFtksdICrEgiB0Oa1Wy//+9z9mz57N+PHjWb16dZ8NgqJa3HdoNBrL9OkzMRqNlpDa9utTnYxGI7Js2tNXURRcPB0YODKCwsJCkBT0ej2SJCFJEk0NenTVTegbDeibjDRWQWxcDEW55QRFhPDhR79yNL+SpgYDzU1GZCeH1kqDBIpKammiBSgKh7LLCQk3/d7WHNfx+pPfIhtN1z+0rxRXLzvLdWW9ATRt+gYoCi6uDlRX6lCrVcx5uP0+xNfekUJRbhlpq/bh5unENbelMHBEBMWHywmK8DlpirfQuURVuL0lS5Zw11138e2334pu0kLHyFi69XfpfQodIgKxIAhWodFo+Pzzz7n99tsZP348K1euJDQ01NrDsgpJkggODm63tnjIkCGnnJIr9H5qtRq1Wn3e+yHr9XoKCwsZMGBAu07Yf362h+0r8pGNMpIk0dAPnvjxYwwGI5IkcdvjV7Limy00N1Zz1c1JbNh0kMpjtaZMrIBGo8KoN7/TUjhaVMndU/5FaXEV4f38LGEYoKqsAb9wl5bp2wq+Qe7oZThWUgPAhCsGMW/B1eTuP4J/sAfe/m7tHoONrZaJVyew5pedHC2o5Pn7PufRV25g4vSE83pOhHPTtiocFxdHREREn64KA3zyySf89a9/5ccffxT7DAtCLyUW4QhCH2c0Gvnggw8YN24cHh4eaLVafHx8GDRoEHfeeSfLli0DYNGiRUiSxN/+9rdTHueee+5BkiTCwsJOefmaNWuQJImJEydazlOr1Xz88cdMmjSJUaNGsXfv3k5/fD2Jvb09SUlJxMfHs337drZt20ZTU5O1hyX0EjPunoiDk6myqrFR4+Ll3FpdRiHjj7189OdjLN39D+5bcDXX/mWkabp1SyLWatS07CGChMRX76yhMPcYzY16snYW4uTauhWSVm1Dv4QgrrgxmWvmjOWVL+fy9o/zefgf1/L0GzfzyD+vx9HJjkEjIk4Kw2a/f7ul3fe/Lkm7CM+KYFZeXs6aNWuorq5m/PjxREZG9ukwrCgKL730Eg8++CC//PLLWcPwuf4tBUhNTbXM3DjTSej5JEWxyknoGFEhFoQ+zGg0cuWVV7JixQrc3Ny44oorCAoKorm5mX379rFkyRIyMzOZNm0al1xyCQCrV68+5bFWrVqFJEnk5+eTm5tLZGTkSZcDTJo0qd35arWaN998Ez8/P8aOHcvPP//MmDFjLsKj7RkkSSIkJARvb292797NqlWriIuLIywsTLxBEi5IaIwfH294msOZRwiM8GHJf1eyZ/MhQEGlknB2a7+Fzq6MQ6gUxbyzEpKstHSallBQaGw2tPtUXVfbyN9euxl9k57Bo8PZvDWde5+Z3u7n9tJrhrW7j9oqHWt+2o5Gq2HiNcPadZh283LCvMuxSi3h4eNCg66JL15ZTsHBEkZOHsjUv4wWvxcXqKGhgX379lFSUiKqwi1kWebBBx/km2++Ye3atQwePPiM1+/I39K2QkNDmTNnzkV8JIIgnAsRiAWhD/vyyy9ZsWIFgwcPZu3atbi6ura7vL6+noyMDAAGDx6Mp6cnO3bsoLq6ut11CwoKyM3N5brrruPbb79l9erVJwVic5A2B+u2JEni6aefxtfXl8suu4wlS5ac9MahrzFXi0tKStizZw/5+fkMGjQIDw8Paw9N6MGc3RwYmBwFwE33T2bv5kPkZ5fg7e/OnU+0/51zcmmt+CoKyEa59RsAjQraTGDQaFSMv3IIYAoIRqORxsZG7O1bj/Pxot/48bONSJLELfdfwoovNnG0oBxFgT++28wTb83mk3/9QkVJDeOuGkrs4BAO7MgnJNKXu/5+JW8+8Q2pS7chywrbUjNxcLJjwjWJnf9E9QGyLHPo0CGysrLw9fXlkksuaff/qq9qbm5m9uzZbN26lU2bNhEeHn7W23Tkb2lbYWFhPPvss501dEEQzpMIxILQh23atAmAOXPmnPQHHMDBwYEJEyYAptA6YcIEvvvuO1JTU9ttOWEOu/fffz+rV69m9erV3HXXXZbLa2tr2bJlC66uriQmnv7N69133423tzc33XQTr732GnfeeWenPM6ezM/PD29vbw4ePMimTZsICgoiLi7uvNeXCoKZh7cL76x4lLqaBhyd7U7ayig82ofVcktnaBTT2uM2l6s1qtbO0Zi2S7Jcplbj6OjIzs1Z/O/VtchGmQnThvLth+ss1/n4ld+hsTVRZ+7I5+nZ71F46BiyUWZ3+kH+/c084oaGom5pyLUnPdeydZRarWL/1jwRiM9DeXk5u3fvBmDEiBF9cvu7U6mtrWXGjBkcP36cjRs34uPjc06368jfUqGPEdsu9QhiDbEg9GGenp4AZGdnn9P1zet/zdOfzVatWoWjoyNJSUmWrtFtrVu3DoPBwLhx41Cr1ZzJNddcw/Lly3n00Uf5xz/+gdgZzhQuYmNjmTBhAk1NTaxatYq8vDzx3AgXTJIknF0dTrmvb2OD3pR1ZRlkBbXKFELNb7aSR0ebwrCsgKzQqGtud3s7WzuWfpbKof3FHM46yieLVpw8AFVr12qA/JzS1kq0BNm7CixhGKD/iAhULbcxGmXiEs9evRNaNTQ0sHXrVjIyMggJCWH8+PEiDLcoKytjwoQJSJLEmjVrzjkMQ8f/lgqC0L2ICrEg9GEzZszg5Zdf5t1336W2tpZrrrmGYcOGnbbb8+nWEa9Zs4YxY8ag1WqZMGEC33//PXv37mXAgAHtrn+q6dKnkpKSwtq1a7nssssoKSnh9ddfP+Ub9r7G/KGDmEYtdIUxk/rzzUdrMRpkZFlh8vQEtm08yJGCCgAOHyxDrVahSKbq8djLB5Gzu5B/P7CYqvJarrxvGE7utq3FCqMM6va/x7b2NjTVNQKmbO3u7UJVRS2KrKAA8cPaB977/zkLV08nCrKPMnLyICZc3X5NsnBqbadH+/n5MXHiRDE9uo1Dhw4xZcoUhg8fzqefftrhbe86+rfU7PDhw6edMt2vXz9uuOGGDo1D6IZEhbhHEIFYEPqwoUOHsnjxYubPn8/ixYtZvHgxAB4eHqSkpHD77bdz1VVXWa4fExNDUFAQ+/bto6ysDB8fH7KysiguLub+++8HsEwLW7169XkHYoBBgwaxadMmpkyZwo033sjnn38upgm3OHEadWBgIPHx8eL5ETpVeIwft869hC/+uwoFU5X4SH6F5fLi/AoeX3Qjhw8cwdPXlctvTOa2MQupKK1GkRV2r88lMsEPSSWBomBrb4PWzobamoaWN4ng6ePCEV0TKAqSBGMuG4hRVqgsq2HydUn0G9o+UNg72vJ/z87o6qeiRzt27Bh79uwBICkpCS8vLyuPqHvZuXMnl19+ObNmzeI///nPeX342tG/pWb5+fk899xzpzzm9OnTRSAWhC4iSi6C0Mddf/31FBQU8Pvvv/P0009z5ZVXIssyS5cuZdq0acyePbvd1FzztGlzyDX/aw7C8fHx+Pr6Ws6vqKhg165d+Pn50b9//w6NLSwsjA0bNpCXl8cVV1xBTU3NBT/e3qLtNOrm5mZWrVpFbm4uRqPR2kMTeoma4zo++89K9I16DM1Gfl6S3uZS02uCq6cTcx6dylW3jkZSSVSUVKG0rPGtKm3AM8AJxSiDAo26JqZcO4whSRFExPrz8MvXMf/F67CzN6099gvx5IZ5k5m3cCbPvHc7yZM69nohtFdXV8eWLVvYvHmzZXq0CMPtpaamMn78eObPn8+rr756QTOROvq3FGDcuHEoinLK09KlSy/w0QmCcK5EhVgQBLRaLZMnT7bss2g0Gvn++++5/fbb+fzzz7nmmmu4+uqrAVOV9/PPP2fVqlXccMMNrFq1CldXVxISEizHGzduHCtXrsRoNLJmzRoURelQdbgtb29vVq9ezcyZM5kwYQLLly/H19f3gh9zb2GeRl1aWsr+/fvJzc2lX79+BAcH9/mtU4SO27c1jzf+/g262gYuvT4Jo3k9L6atl9q/oVcoLT5u+U6tVjFm6hDW/7oTlVqioboZG3sVao2E0WC63Z7Nh3jtu7+2u8/FGc9RWVqNf6gXGu3pewzU1zWiUqvabc0knKyxsZGsrCwKCgoICgoS06NP47vvvmP27Nm8+eab3HbbbZ1yzI78LRX6CDFlukcQFWJBEE6iVqu5/vrrefDBB4H2a4bbVogVRSE1NZWUlJR2zbImTJhAVVUV27dvP6/p0idycnJi2bJlxMXFMXr0aLKyss77WL2Vr68v48ePJy4ujqysLNasWUNJSYlovCWcM4PeyLN3fkjhoTIqSmv46r9/EBZt+vBJksAvyB1XNwckRTHtS4yCwdB+RsKjr/+FuS/MZNa8S3nxf/NAkXDxMoUxlVoiJPLkRkWOznY01jfz2/82kbUz/5Rj++ifv3DtoKe4dtCTLPt8Q+c+8F5Cr9dz4MAB/vzzT5qamhg/fjxDhw4VYfgEiqLw5ptvMmfOHL7++utOC8Oncqa/pYIgdB+iQiwIwmk5OzsD7atCQUFBxMTEkJ2dzbJly6ioqLCEZLO264g7IxAD2NjY8Pnnn/Pkk0+SnJzMV199xZQpUy7omL2NJEkEBwcTEBDA4cOH2bFjB05OTsTHx1u6oApCW1XltSz570rqqhsYd+UQ6qob2l1+9V9GYkTCoDcw4YohPHDD29Qc15nW/KokcvYUQ5tljlobDVfeOsby/cFCZ8ZcHs/6nzPpNySEu/5+8jrK9D/28vwdH6K0rCN+6v07GHXZIMvl2bsL+e79VABko8I7zy1l7OWDcfd27twno4cyGo3k5eWRk5ODi4sLo0aNEo32TqO5uZn777+fH3/8kT/++IORI0d2yf2e6m+p0EfIQFdP1pLPfhWhPRGIBaEP+/LLL/Hy8uKSSy45ae1USUkJH3zwAWDq+tzWxIkTyc7O5umnnwY4aX/F2NhY/P39Wbx4MVlZWURFRRESEnLB41WpVLz00ksMGDCAa6+9lueff54HH3xQTA0+gVqtJjIykpCQEHJzc0lLS8Pb25u4uDhcXFysPTyhm1AUhSdveZfD2UdBgXW/7CBqQBC5+4qRVBJ2DjYkjuuHp2/rvqrB4d6UFFUiG01NsXwD3c54Hy4uzlx6fTD3PTnrtNf56eO1lqCgKPDJSz+3C8S6mvYhHQUadE19PhArikJhYSGZmZlotVoSEhLw8fERr4encezYMWbOnEltbS1btmzplL9JZuf7t1QQhO5BBGJB6MMyMjJ4/fXX8fPzY8yYMYSHm7Y4ycvL49dff6WhoYHp06czc+bMdre75JJLePfdd9mzZw+enp4MGjTopGNPmDCBJUuWWK7fmW6++WZiYmK4+uqr2b17N++++y52dnadeh+9gVarpV+/foSHh5Odnc3atWsJDAykX79+ODg4WHt4gpXV1zZy6MCR1jNkhTGXD6S2up7KkmriE8JwdGk/3Xbugqt56aElHM4uZXhKDNfefuY3+E5OTtTV1Z3xOroTqtIlhRXtvu8/PILI/oHk7isGIOmSePxD++6MB0VRKCkp4cCBAxiNRuLj4wkMDBRB+Ax2797NtGnTSEpK4rfffuv017/z/Vt6pm2XAB544AHc3Nw6daxC15IUBamLZwZ09f31BiIQC0If9vDDDxMdHc2ff/7J7t27+f3332lsbMTT05Px48dz0003cdNNN530RmvChAlIkoSiKIwfP/6Ub8QuZiAGGD58OFu2bOGaa65hwoQJ/PDDD/j7+3f6/fQGtra2DBw4kIiICDIzM1m9ejWhoaFERUWJ9YV9mL2TLT6B7pQfrUKWFZBg+/psjhUfRzbKbFuXybfvrOKWhy633Mbbz5VF//s/Dh04ilqjwsb2zG8jHB0dqaioOON1kqcMIGdPoeV7Z3fHdpfb2Gp45Zu5bFlzAI1WzYgJcX0y/CmKwrFjx8jKykKn0xETE0NYWJjYo/0sfvjhB2bPns3jjz/OE088cVF+ds73b+mZtl0CmDNnjgjEgtAFJEUsaBAEoQdrbGzk7rvvZvXq1SxdupTExERrD6nbq66uJisri9LSUoKDg4mKisLJycnawxI6iV6vZ/ny5UydOhWtVnvG6xYdKuP9hT9RW6Xj6tvH8fVbf5KXaaoaq1QSE64exiP/udlyfVmWefH+xWz8fS8AV9yUzLznTr8vcGVlJZs3b+ayyy477XUaG5p56i/vsG/zIWzttTz13h0kTojryEPu1RRF4ejRo2RnZ9PQ0EBERASRkZFoNKKmcSayLPPCCy/wyiuv8Pnnn4vuzkKXqqmpwdXVlUkxD6FR23bpfRuMTfyZ/R+qq6vFMqlzJF5NBUHo0ezs7Pjss89YtGgR48eP58MPP+SGG244+w37MFdXV0aMGEFtbS05OTmsWbMGPz8/YmJicHV1PfsBhF4jKMKH5z+5y/L9saNVfPTiMlRqFbJRZuwVQ9pdP2tXoSUMA/y6JJ1r7xiHf8ippzA7OTnR1NSEXq8/bTi3s7fh39/dT0VJNU5uDu22Vaqva6S5yYCbZ9/7wEaWZQoLCzl48CAGg4GoqChCQ0NFED4HOp2OOXPmsHXrVjZu3MjAgQOtPSShrxLbLvUI4lVVEIQeT5IkHnnkEeLj47npppvYs2cPCxcuFFMJz8LZ2ZmEhAT69evHwYMHWb9+PZ6ensTExIiu1H3UtXeNxzfQnUMHjjBkVDSDR0Wf9TZnmoJqY2ODjY0NdXV1uLu7n/EYXv5u7c77bUkabz75LbJRZtJ1I3jw3zf0id9pg8FAfn4+ubm5qFQqoqOjCQ4O7hOPvTPk5+czffp03Nzc2LJlC15eXtYekiAI3ZwIxIIg9BpTp04lPT2dadOmsWfPHhYvXiymC50DBwcHBg0aRGxsLIcOHSI9PR0XFxeio6Px9fXtk+s1+ypJkhh7xZCTKsNmsYODsXewoaG+2XSGolB25Dh+waff5sfcWOtMgfhEDbom3moJwwB/fruZ8dMTGJbS75yP0dM0NzeTl5fHoUOHsLe3Z8CAAfj7+4vfvw7YsGEDM2bMYObMmbz++utnXTIgCBedrIDUxRVbWVSIO0p83CgIQq/Sr18/MjIyaGxsZNSoUeTm5lp7SD2Gra0tcXFxTJ48GT8/P3bu3ElqaipFRUXIstjYsLfK3JHPl2/+QcaqfWe9riIrNOia2k0DzMs8esbbODk5odPpOjQmfbMBo7H9z1xNxZm7VfdUjY2N7Nu3jz/++IPy8nKGDRvGuHHjCAgIEGG4Az788EOmTJnCc889x9tvvy3CsCAI50xUiAVB6HXc3d1Zvnw5jz76KMOHD+eTTz5h+vTp1h5Wj6HVaomOjiYiIoKCggIOHDjAgQMHCAsLIzQ0FBsbm7MfROgRdmzI4slb3gPJFHbveeZqrr593Gmvr9aoGTgign1b80ACCYmBIyLOeB+Ojo7U1NScdH5tdT0rv8lAUWDydSNwadNd2sXdkQFJkezNaP1Aa/PqfUy4pvc0zTt+/Dh5eXkUFxfj4+PDyJEj8fA4faVdOLX6+nrmz5/Pjz/+yK+//sr48eOtPSRBEHoYEYgFQeiVNBoNr776KsnJydx6663cdtttvPzyy9jadm23x55MrVYTHh5OaGgoJSUl5OXlkZWVRWBgIBEREaIBVy+wZul2JBXIRlO19/ev088YiAGeeXcOX7+7muPHapk8czgRcQEoisLhzKMYDUYi+ge2W+/q5OTEkSNH2h1D32zg4WvfoCi3DIDfvtzEO789ho1da1UvOMqH/VsOmbaEAvZvy++Ux2xNRqORI0eOkJeXR01NDSEhIYwfPx5nZ2drD61H2r9/P9dffz1ubm5s376dkJAQaw9JENoTTbV6BBGIBUHo1WbNmkViYiKzZs1i1KhRfP3110RFRVl7WD2KSqUiICCAgIAAampqOHToEOvXr8fV1ZXw8HD8/f1Rq9XWHqZwHrwC3CzvnVRqFT6BZ69QOrnYc8djV7Q7760nv+XXzzcAMOryQTz53u2WUGxeQ6woimUK8OGsoxQeLLXc/kheOQf3FRE/LNxyXv/ECH77X5ppbCqJgUlnrkR3Z/X19eTn55Ofn49GoyEiIoLg4GAxrfc8KYrCp59+yl//+lfmz5/Pc889J7pvC4Jw3sSrhyAIvV5kZCQbN27k8ccfZ9iwYbz//vvMmjXL2sPqkVxcXBgyZAjx8fEUFBSQmZnJnj17CAkJISwsDEdHx7MfROg2rrtnIof2F7N9XRahsX7c9/y1HT7G0fxySxgG2PTbbjK3HyY+0RRgHR0dMRqNNDY2Ym9vD4CHjwsqtWSpTEuShKdv+xkHE2ckUl/XSPrKvYTE+HHrI1PP92FahaIolJaWkp+fT2lpKb6+viQkJODt7S3WBl+A2tpa7r33Xv744w9++OEHJk+ebO0hCcIZWKFCjKgQd5QIxIIg9Am2tra8+uqrjB8/nttuu43Vq1fz2muvWd6gCx1jY2NDVFQUkZGRlJeXc/jwYVavXo2npydhYWH4+fmJbWJ6AHtHW5798M4LOsapwl3b89RqNQ4ODuh0Oo4V13DsaBVxQ0N5eNFN/PeJb2luMhAzMAg7B5uTjnHV7LFcNXvsBY2vqzU2NlqqwYqiEBoayqBBg8RrTSfYuXMn119/PcHBwezcuRN/f39rD0kQhF5AvFsRBKFPmT59Ojt37mTv3r0kJSVx4MABaw+pR5MkCW9vb4YPH86ll16Kl5cXe/fuZeXKlezZs4fjx4+jiPVMPYpBb+SnT9bxwcKlZO44fNbr+4V4Mu22FMv3Y68cSuzQ0HbXcXR0JG31bu6Z8m+emv0B/zflFeqO19NY14SsN5K9q5BFD/6vsx9KlzEYDBQVFZGens7KlSuprKxk4MCBXHrppfTr10+E4QukKApvv/02o0eP5tZbb2XlypUiDAs9g3kNcVefhA4RFWJBEPqckJAQUlNTWbBgASNGjODNN99k9uzZ1h5Wj2dnZ0dMTAzR0dGUl5dTWFjIpk2bsLW1JTg4mKCgIDGlugd44/Gv+eObDNRqFT99tJb//PQgMYPP3Kzo/56/litnj8WgNxLW7+S9c52cnFi3fa9lJl95aTVrf92FWq3CaJSRjTIH9xZdrId0UciybPk5P3r0KPb29gQHBzNo0CAcHBysPbxeo6qqijvvvJO0tDR+++03UlJSzn4jQRCEDhCBWBCEPkmr1fLiiy8ybtw4brnlFlatWsXbb7+Nk5OTtYfW45mrxt7e3hiNRkpKSigqKiIrKws3NzeCgoIIDAwUHb+7qQ2/7gTAaJRRqSQ2r9p31kAsSRLBUb6nvdzJyQkXLzvL94qs4OLhiNEoI6kkUCAhJbZTxn8xKYpCVVUVRUVFFBcXAxAUFMSYMWNwdXUVa4M7WUZGBjfccAPx8fHs2rULLy8vaw9JEIReSEyZFgShT5syZQq7du2iqKiIxMREdu3aZe0h9SpqtZrAwECSkpK47LLLCA4Opri4mN9//5309HSKioowGAzWHqbQRkCYlymkArKsEBjufcHHdHJywivAidZmLwrHjhznlocvR6WSUGSZI4eP0aBruuD7uhh0Oh1ZWVmsWrWKTZs2odfrGTZsGFOmTGHAgAG4ubmJMNyJZFlm0aJFTJw4kXnz5vHzzz+LMCz0TLJinZPQIaJCLAhCn+fv788ff/zBP/7xD0aNGsUzzzzDww8/LLbx6GQ2NjaEh4cTHh6OTqezVI3NzXH8/f3x8fERz7uVTbouifee/cHyvUp96s/Od6cf5MDWPPolhDF4VPQZj+nk5ISNg8o0RdpgBKCxvpk1P25FNsgAZG7L55fPN3DdvZd00iO5MDqdjpKSEoqLi6mqqsLX15e4uDj8/PzENmMXUX5+PnfeeSc5OTmsXr2apKQkaw9JEIReTrzrEARBwFTJfOaZZ5g8eTJz5szhxx9/5NNPP6Vfv37WHlqv5OjoSGxsLDExMVRXV1NcXMyBAwfYtm0bXl5e+Pn54efnJ5oRWcHWNfvbff/nd5sZNy2h3Xnrft7BS3M/Q5IkFEXhb/+9hfHTh532mPb29qhUKjz9HSkrrAEJZt13CZ++9LOl6ZqkAl1NQ+c/oHOkKArHjx/n6NGjlJaWUldXh5eXF8HBwSQnJ2NjY3P2gwjnTVEUPvjgAx555BFmzZrFd999h6ur69lvKAjdmSKbTl19n0KHiEAsCILQRnJyMjt27OCZZ55h2LBhPPvsszz00EOiInSRSJKEm5sbbm5u9O/fn9raWkpLSykuLmbPnj24uLhYwrFYo9k1vPzdLHsEq9QqvPzcTrrO8iWbACxh9of3U88YiCVJwsnJiSfevYWjuTUER/oQGR9IfW0j7z7zPQD2DrZMvr5rq4EGg4GysjJKSkooLS1FURR8fX3p168f3t7eaLXaLh1PX1VQUMCdd97JgQMH+Pbbb5kyZYq1hyQIQh8iArEgCMIJ7O3t+fe//82MGTOYM2cOP/zwA5988omoFncBZ2dnnJ2diYqKorm5mdLSUkpKSsjNzUWj0VjCsZeXl/iQ4iKZ87crKcwtJXN7Pv0SQpn9tytPuk5dVX27748WlJ/1uE5OTqhtFMZfNdRy3vTbUogZHEJJfjmDRkbj6XfxK4INDQ2UlJRQUlJCeXk59vb2+Pv7M2LECNzd3cX+2V1IURQ+/PBDHnnkEa677jq+/fZbURUWBKHLiUAsCIJwGiNHjmTnzp08/fTTDBs2jOeee44HH3xQBLEuYmNjQ3BwMMHBwZYtbkpKSti1axfNzc14eXlZTqJ63HncvJxZ9MMDZ7xO3LAwcvcVW753dD37NkNOTk7U1dWdfKyEMOISwjo6zHOm1+upqKigvLyc8vJyampq8PDwwM/PjwEDBuDs7HzR7ls4vcLCQu666y727dvH119/zWWXXWbtIQlC57PGvsBiH+IOE4FYEAThDOzt7XnllVe45ppruO222yzV4tjY7r9FTG+iUqnw8fHBx8eHgQMHUlNTw7FjxygvLycrKwsALy8vPD09RUDuAtfcOYF1P++k5rgOSSUx59GpZ72No6Mjx48fv+hjOzEAV1dX4+joiJeXF1FRUfj4+Ij1wFakKAoff/wxDz30EDNnzuTrr78WVWFBEKxKBGJBEIRzMHr0aHbu3MlTTz1FQkICzz//PA888ICoFluBJEm4urri6upKVFQUiqJQXV1tCUAiIF98AWFefJD6BFk78wkI8z6nrZmcnZ1PWSG+UGcLwJ6enqI5WzdRVFTEXXfdxZ49e/jqq6+4/PLLrT0kQbi4ZIXW7ea68j6FjhCBWBAE4Rw5ODjwn//8hxkzZrSrFsfExFh7aH1a28ZcpwvIkiTh6elpuZ6bmxu2trbWHnqP5uLuyPAJ8ed8fUdHRxobG9Hr9efdrEpRFGpra6mqqqK6uprKykqqqqpEAO7mFEXhk08+4aGHHmLGjBl8+eWXuLm5WXtYgiC0UVRUxDPPPMOKFSuoqKjA39+fq6++mgULFuDu7n7Ox6msrOT5559n6dKlHD16FE9PTy677DKef/55goKCLuIjOH8iEAuCIHTQmDFj2LVrF08++SRDhw7lmWee4cEHHxTTMLuJMwXkqqoqCgsL0el02NnZtQvIrq6u2NnZWXv4vZaNjQ1arRadTndOYUiWZerq6qiqqrIE4OrqagDLDIHIyEgRgLu5Q4cOMXfuXHbv3s2SJUuYOvXs0+sFQehaubm5jBo1irKyMqZPn06/fv3YvHkzr7/+OitWrGDjxo14enqe9TgVFRWMGjWK7OxsJk6cyA033EBmZiaffPIJv/76K2lpaURERHTBI+oYEYgFQRDOg4ODA6+++irXXnst9957L59++ilvvvkml1xyibWHJpygbUA20+v1VFdXW4JWUVERdXV1lpDs6uraLiSL6dYXzrz1Ul1d3UmB+MTwW1VVRU1NDYDl/0VoaChubm44OzuL/x89QENDAy+//DL/+te/uOWWW1iyZEmHqkyC0Cv0kKZa9913H2VlZbzxxhv89a9/tZz/0EMP8eqrr/Lkk0/y7rvvnvU4TzzxBNnZ2Tz00EMsWrTIcv4bb7zB/Pnzue+++1ixYkWHx3exSYoiWpEJgiBcCL1ez1tvvcWCBQu47LLLWLRoUbedFiScnjkkm4NyVVUVdXV1qNVqnJycLCdHR0fL191xn1q9Xs/y5cuZOnVqtxqfoihs2bIFjUaDu7s7dXV11NXVodPp0Ol0qNXqkz6MEOG3Z/r555+ZP38+Xl5evP322yQmJlp7SILQpWpqanB1dWVSwD1oVF27PMcgN/Hnkfeorq7GxcXlrNfPzc0lKiqKsLAwcnNz2209V1tbi7+/P4qiUFZWhqOj42mPU1dXh4+PDyqViqNHj7br4C/LMhEREeTn55Obm9vtqsSiQiwIgnCBtFotDzzwALNmzeKxxx4jLi6Op59+mgceeEBMo+5BtFqtZRsnM6PRiE6naxfejh07Rl1dHc3Nzdja2rYLyI6Ojjg6OmJnZ4eNjU2fC3NGo5GmpiYaGhosz1fb506WZTQaDc3NzTg6OuLn52d57kQlvufLzc1l/vz5pKen889//pPbb79d7Oss9G0KVqgQd+zqa9asAWDy5Mkn/b46OzszevRoVq5cSXp6+hlnwaWnp9PQ0MDkyZNP2s5OpVIxZcoU3n//fdasWSMCsSAIQm/l7+/PF198wbp165g3bx6ffPIJ//3vf5k0aZK1hyacJ7VajYuLyyk/ZW9ubm4X+qqqqiguLkan02EwGJAkCTs7O2xtbbGzs2t3anuera1ttw+CBoOBpqYmGhsb251OPE+v1wNgZ2dn+aDAw8ODkJAQHB0dqaqq4tChQyQnJ1v5EQmdqaGhgX/+85/8+9//Zvbs2Xz22WfntN5QEATrM+/McLoGodHR0axcuZLs7OwzBuJzOQ5Adnb2hQz3ohCBWBAEoZOlpKSwfft23nrrLa699lqmTJnCf/7zHzGNupexsbHBw8MDDw+Pky47XYCsq6ujoqLipABpa2uLRqNBrVaj0WhOe2p7uVqtRpIkS5g2f200GgFTp0+1Wo2iKJhXR8myjNFoxGAwnPZ04uV6vd4S8E8M946Ojnh4eLQ7z8bG5rRVQfNaYUVRuv2HAMLZKYpimR7t4+PDunXrxPRoQWjLimuIzX0YzGxtbU+5u0LbZoWnYj6/qqrqjHfbWcexBhGIBUEQLgKNRsP8+fPbTaN+6qmnRDfqPsIcWs+03gpapxg3NTWdNpCaw7VOp2t3mdFobBd2zV+bv9+1a9cpA/Ppwradnd0ZL+uMKeCOjo6WxyM6evdsubm53H///WRkZIjp0YLQDQUHB7f7fsGCBTz77LPWGUw3JwKxIAjCReTn58fnn3/O+vXrmTt3Lp988glvvvmmmEYtAKYp2Q4ODjg4OHTaMc1NtSZMmNCtmmqB6YMCe3t7S0dvoeepr6/nn//8J6+88gqzZ8/miy++OOUsCUEQrKuwsLDdcp9TVYehtXJrrvCeyHz+2bbL66zjWIP4KE8QBKELjB07lu3bt3Pfffdx7bXXcs0113DgwAFrD0sQupyjoyM6nc7awxA6yGg08sUXXxAfH8/vv//OunXreOedd0QYFoQzkWXrnMDS/8J8Ol0gjo2NBU6/tjcnJwc4/drgzj6ONYhALAiC0EU0Gg33338/2dnZBAUFkZCQwO23305hYaG1hyYIXca8F7HQMyiKwi+//MLQoUN55plnWLhwIWlpaWKtsCD0EhMmTABg5cqVyC1h2qy2tpaNGzfi4OBw1maIycnJ2Nvbs3HjRmpra9tdJssyK1eubHd/3YkIxIIgCF3M19eX//73v+zbt4+mpiZiY2N55JFHqKiosPbQBOGiE4G459i0aRMpKSncdttt3HnnnWRmZnLLLbeItcKCcK7MTbW6+tQBkZGRTJ48mcOHD/PWW2+1u2zBggXodDpuueWWdj0xMjMzyczMbHddJycnbrnlFnQ63Ulrld98800OHz7MlClTut2WSwCSonTwWRMEQRA61c6dO/n73/9OWloajz32GPPnzz9rMyZBOB3zGuKpU6d2uzXEAKWlpezdu/eM23cI1rVv3z6eeOIJVq9ezcMPP8zDDz980r6igiCcXk1NDa6urkzyvgONqmsbaRrkZv489hHV1dWn3DLwVHJzcxk1ahRlZWVMnz6duLg4MjIyWLNmDTExMWzatKndVmrmBosnxsiKigpGjRpFdnY2EydOZMSIERw4cICffvoJHx8fNm3aRGRkZOc92E4iPuITBEGwsiFDhvDbb7+xdOlSfvrpJ6Kionj33XctW/IIQm/i5OSETqc7aWqeYH0FBQXcdtttJCYmEhoaSm5uLs8++6wIw4LQy0VGRrJ161bmzJlDRkYGixYtIjc3l/nz55Oenn7O+4p7enqSlpbG/fffz8GDB1m0aBEZGRncdtttbNu2rVuGYRAVYkEQhG5FURR+/PFHnnjiCWRZ5oUXXmDmzJliiqJwzrp7hdi8d+0ll1wiZkJ0E+Xl5bz00ku88847zJgxg+eff75bTmsUhJ7CUiH2ut06FeLyjztUIe7rxDssQRCEbkSSJGbMmMHevXt57LHHeOihhxgxYgR//PGHtYcmCJ1CkiQcHR3FOuJuQKfT8cILLxAZGcmBAwdIS0tj8eLFIgwLgtCniEAsCILQDWk0Gu68805ycnKYNWsWs2bNYuLEiaxevfqkNTuC0NOIxlrWVVtby6JFi4iMjOTXX39l2bJlLF++nMGDB1t7aILQu8iKdU5Ch4hALAiC0I3Z29vz6KOPkpubS0pKCjNnziQ5OZmlS5eKNZhCjyUCsXVUVFSwYMECQkND+fbbb3n//ffZtGkT48aNs/bQBEEQrEYEYkEQhB7A3d2dZ599loKCAmbNmsXcuXMZOHAgn3/+uWi+JfQ4IhB3raKiIh588EFCQ0NJS0vj+++/Jy0tjWnTplm6xQqC0PkURbbKSegYEYgFQRB6ECcnJx566CEOHTrEgw8+yMKFC4mOjubNN9+koaHB2sMThHNi7jQtXFzZ2dnccccdREVFUVBQQGpqKitXrmTChAkiCAuCILQQgVgQBKEHsrW15c477yQzM5N//etffPTRR4SGhvLiiy9SVVVl7eEJwhk5OjrS0NCAwWCw9lB6pe3bt3PdddcxaNAgZFlm586dfP/99yQmJlp7aIIgCN2OCMSCIAg9mFqt5vrrr2f79u18/vnn/P7774SGhvL3v/+d0tJSaw9PEE7J1tYWjUYjqsSdSFEU1q5dy5QpUxg7diwBAQFkZ2fzySef0K9fP2sPTxD6JsUKDbVE480OE4FYEAShF5Akicsuu4y1a9eyfPly9uzZQ3h4OHPnzuXQoUPWHp4gtCNJklhH3EmMRiPLli1j9OjRXH311YwYMYLDhw/z+uuvExISYu3hCYIgdHsiEAuCIPQyo0eP5pdffiE9PZ2qqiri4uK46qqr+P3330VnaqHbEIH4whw/fpxFixYRExPDvffeyzXXXEN+fj4LFy7E29vb2sMTBAFM1VprnIQOEYFYEAShlxo0aBD/+9//OHToEAkJCcyePZu4uDjeeOMNampqrD08oY8Tgfj87N69m7vvvpugoCB++uknXnrpJQ4fPsyjjz6Ki4uLtYcnCILQ44hALAiC0MsFBgby3HPPkZ+fz4IFC/jyyy8JDAxk7ty57N+/39rDE/oo0Wn63On1er777jvGjRtHcnIyiqKwceNG1q1bx/XXX49Wq7X2EAVBEHosEYgFQRD6CFtbW2666SbS0tJYs2YNOp2OYcOGkZKSwv/+9z8aGxutPUShD3F0dKSurg5FTO87rcOHD/Pkk08SEhLCY489xlVXXUVRUREffPABQ4YMsfbwBEE4G1m2zknoEBGIBUEQ+qDExEQ+/fRTiouLmTFjBgsXLiQoKIhHHnmE7Oxsaw9P6AOcnJzQ6/U0Nzef8232bz3Efx//mv/95zca65su4uisx2AwsGzZMqZOnUpMTAwHDhzg008/5eDBgzzyyCN4eHhYe4iCIAi9isbaAxAEQRCsx8PDgwceeID58+ezdu1a3nvvPQYOHMiYMWO44447mD59Oo6OjtYeptALaTQa7OzsqKurw9bW9qzXz9tfzGPXvoGCaYuhzO2HWbj43os/0C6Sm5vLF198wUcffQTA3XffzYcffkhAQICVRyYIwnlTFKCLZ8GIWTcdJirEgiAIApIkMX78eL788ksKCwuZPHkyL7zwAr6+vtx6662sXLkSg8Fg7WEKvUxHGmvt3JiNLMvIRhlFVti2NrPHd00/duwYb731FiNHjiQuLo4dO3bw9ttvk5eXx9NPPy3CsCAIQhcQgVgQBEFox8fHh7/97W/s27eP9evX4+3tzZw5cwgKCuLBBx9k69atYt2n0Ck60lgrJNrPUvhQqSUCI7xRqXre25j6+nq+/PJLrrzySgIDA/nqq6+YM2cOR48e5aeffuKqq65CoxET+AShN1Bk2SonoWN63l8SQRAEoUtIksTQoUNZtGgRhYWFLF68mKqqKiZOnEhcXBwLFy7k0KFD1h6m0IOZG2udi2Hj47jn2RkERngzICmKZz+5+yKPrvMYDAZWrlzJrbfeiq+vLwsXLmTUqFFkZ2ezfv167rnnHjw9Pa09TEEQhD5JfAQpCIIgnJVarWbSpElMmjSJt99+m59//pnFixezcOFCEhMT+ctf/sL111+Pl5eXtYcq9CBOTk4UFBSc8/WvvnM8V985/uINqBMpisL27dtZvHgxX331FZIkceONN7Ju3TqGDBmCJEnWHqIgCIKAqBALgiAIHWRvb8/111/PsmXLOHLkCLfccgtLliwhICCAq666iiVLllBVVWXtYQo9gHnKdG+Zgq8oCvv27eOFF14gPj6eCRMmcPz4cb744gsKCwtZtGgRQ4cOFWFYEPoKRbHOSegQEYgFQRCE8+bl5cW9997Lhg0byMrKIjk5mZdffhlvb28mTpzIa6+9Rm5urrWHKXRTDg4OKIpCQ0ODtYdy3vR6PatXr+bBBx8kKiqK4cOHs2XLFp599llKSkr49NNPmTRpEmq12tpDFQRBEE5BBGJBEAShU4SHh/Pkk0+ya9cuDh48yIwZM1i+fDlxcXH079+fv//972zatAmj0WjtoQrdhEqlwsHB4ZzXEXcXVVVVfPnll9x00014e3tz8803o9PpeO211ygvL+enn35i1qxZODg4WHuogiBYk6xY5yR0iFhDLAiCIHS60NBQ5s2bx7x586ipqeH3339n2bJlXHXVVajVaq644gqmTZvGpZdeipOTk7WHK1iReeslHx8faw/ljHJzc/n5559ZtmwZ69evJz4+nmnTprFy5UoSExN7ZMdrQRAEQQRiQRAE4SJzcXHhuuuu47rrrsNgMLBp0yaWLVvG3/72N2688UYmTpzItGnTuPLKKwkKCrL2cIUu1pG9iLuS0WgkIyPDEoJzcnIYP348M2bM4JNPPiE0NNTaQxQEQRA6gQjEgiAIQpfRaDSkpKSQkpLCK6+8QlZWFj///DNLlixh3rx5xMTEMH78eMaPH8+4cePw9fW19pCFi8zJyYkjR45YexjIssyePXtYs2YNqamprFu3DoArrriCZ599lilTpuDi4mLlUQqC0KMoCtDF+wKLplodJgKxIAiCYDWxsbHExsbyyCOPUFlZybp160hNTeXFF1/khhtuIC4url1A7u7TaoWOs1aF2ByAU1NTSU1NZe3atej1esaOHcv48eN54oknSEhIQKMRb5UEQRB6M/EqLwiCIHQLHh4eXH311Vx99dUAVFRUsH79elJTU/nHP/7BrFmzREDuhRwdHfn/9u40Jq6qAeP4M1DWgkBBFss2pVAxKgVsTBsE3Jq01pYRqBFNSuoa+aAfXCqJqR9ICFqT1rikqdHaVElMjKWLFNyopVqaAJWlJEAEpAqITUrL1mGW98Mb5nUE62uLDMP8f8kNM+eeuedcJpnkuefccycmJmS1Wv/VlZhtNpva2tpUV1enb7/9Vt99953MZrMjAL/yyisEYABzym6zy26Y3xHbxfIYu/nErz4AYEEKDw+fEZCnR5DLysr08MMP65ZbblFubq6ys7N1xx13yGg08oxXN+Pv7y9vb2+NjY3N6ZTkiYkJtbS0qKGhwTECbDablZWVpdzcXO3YsUMZGRny8fGZszYBAO6HQAwAcAvh4eEymUwymUySnANyRUWFWltbFRQUpIyMDGVmZjr+JiUlEZIXMIPB4Jg2fa2BeGJiQj/++KMaGxsdW3t7u0JDQ7VmzRrl5ubq5ZdfJgADmF92m+b/HuJ5bm8RIBADANzSnwPylStX1NbW5ghEb7zxhlpaWhQYGOgIx9NbUlISj8lZQP7JfcTj4+M6e/as43tuamrSuXPnFBYW5vh+d+7cqczMTMXHx3MxBABwVQRiAMCi4Ofn5whE08xms1NIfvPNN9XS0iJ/f3+lp6crMzNTaWlpWrVqlVJSUhQWFubCM/BcswViq9Wq/v5+dXZ2qqOjQ01NTWpsbFRHR4ciIiIc33VeXp4yMzMVGxtL+AUA/GMGO3deAwA8iNlsVnt7uyMkt7a2qqurS7/99psiIiKUkpIyY1u5cqUCAgJc3fX/y9TUlL744gtt3LhxwU8PttvtGh4eVn19vc6cOSNJ6uzsVGdnp7q7u2WxWLRixQqtWrXKcQEjMzNTy5cvJ/wCWLAuXbqkkJAQ5RpMWmKY399hi31KdfbPNTIywqPi/k8EYgAAJF28eFFdXV2OQPbHbWxsTHFxcY6APD2ibDQaFRMTo+Dg4AUT0BZaILZarRoeHtYvv/wy6/93ZGREy5cvV1JSklJTU50uRBiNxgVxDgDwTxCI3QtTpgEAkBwLMK1Zs8ap3G63a3Bw0CnEff3113r33XfV19enyclJBQYGKiYmRjfddJNiYmKctj+WhYWFLZjgfL2mpqY0NDSkgYEBDQwM6Ndff3W8/uP7oaEh2Ww2hYeHa+XKlUpJSdHNN9+szZs3O0bfg4KCXH06ADD3WFTLLRCIAQC4CoPB4Ai0OTk5TvvsdrtGRkZmDYVnz55VdXW1o2x0dFR+fn6OY0VERCg4OFjBwcEKCgqa8fqvyoKCgq57QTCz2azLly9rdHTU6e/flV26dEmDg4MaGBjQ8PCwJOnGG2+cEfzT0tKc3kdHR8vPz++6+gwAwL+BQAwAwDUyGAwKDQ1VaGioUlNTr1p3dHTUKTT//vvvTuGzr6/vqsF0YmLCcSx/f395eXk5bd7e3o7XU1NT8vLyks1mc2xWq1U2m00Wi0VTU1OSJC8vrxkBfLYgHhcXp+DgYN1www2KiopyhN3IyEimNAPAX7BoSprnm1MtmprfBhcB7iEGAMANWCwWjY2N6fLlyxofH5fdbp8Rdqdf/zks/zE0e3t7O4JuQEDAopnCDQALxeTkpIxGowYHB13SfnR0tHp6euTv7++S9t0NgRgAAAAA5tDk5KTMZrNL2vb19SUM/wMEYgAAAACAR7q+VTkAAAAAAHBTBGIAAAAAgEciEAMAAAAAPBKBGAAAAADgkQjEAAAAAACPRCAGAAAAAHgkAjEAAAAAwCMRiAEAAAAAHolADACAm7Nardq3b59ycnK0bNky+fj4KDIyUrfffrueeOIJHT582NVdBABgQTLY7Xa7qzsBAACujdVq1aZNm3T8+HGFhobqgQceUGxsrMxms9rb23Xy5EllZGSovr7e1V0FAGDBWeLqDgAAgGtXWVmp48ePKy0tTSdOnFBISIjT/vHxcTU0NLiodwAALGxMmQYAwI19//33kqTi4uIZYViSAgMDdffdd893twAAcAsEYgAA3Fh4eLgkqbOz08U9AQDA/XAPMQAAbqy5uVl33nmnLBaLHn30UZlMJmVmZiohIcHVXQMAYMFjhBgAADeWnp6ugwcPKioqSgcPHlR+fr4SExMVHh4uk8mkI0eOuKxvxcXFMhgM6u3tdVkf/mz//v0yGAzav3+/q7sCAFgACMQAALi5rVu36ueff1ZNTY1effVVbdq0STabTYcOHdLmzZu1bds2zdWEMIPBcNWNoAkAcCesMg0AwCLg4+Oj9evXa/369ZL++zimzz77TNu3b9eBAwdkMpmUl5c3Z+3t3Llz1vLVq1fPWRsAAPzbCMQAACxC3t7e2rp1q1pbW1VWVqZvvvlmTgPxa6+9NmfHAgDAVZgyDQDAIhYcHCxJczZleq58+umnys7OVkhIiAICAnTbbbepvLxcV65cmbV+Y2Oj8vPzFRkZKT8/PyUkJOjZZ5/VwMDArPW7u7tVWFiosLAwLV26VOvWrdOxY8f+zVMCALghRogBAHBjlZWVioiI0L333isvL+fr3IODg9q3b58kKTs72xXdm1VpaanKy8sVERGhoqIiBQUFqbq6WqWlpaqpqVFtba18fX0d9Y8ePar8/HzZ7XYVFBQoISFBjY2Neu+991RVVaX6+noZjUZH/a6uLq1du1YXLlzQhg0btHr1anV3dysvL08bNmxwxSkDABYoAjEAAG6soaFBe/bsUXR0tLKyshzBsKenR8eOHdPExIS2bNmigoKCOW13tinTiYmJKi4uvurnfvjhB5WXlysuLk5nzpxRdHS0JKm8vFwmk0lHjx7Vrl27VFpaKkkaHR3Vtm3bZLFYVFdXp7vuustxrIqKCu3YsUNPP/20amtrHeUlJSW6cOGCdu/ereeee85RXlVVNafTxgEA7o/nEAMA4Mb6+/t1+PBhffXVVzp37pwGBgY0OTmp8PBwpaenq6ioSEVFRTNGj6+VwWD4y305OTmqq6tzvC8uLtZHH32knp4eJSYmSpKefPJJvf/++9q7d6+eeuopp893dnYqNTVVCQkJ+umnnyRJH3/8sR577DE98sgj+uSTT5zqWywWJScnq7e3V319fYqPj9f58+cVFxcno9Gorq4ueXt7O30mNzdXJ06c0Icffvi34R0AsPgxQgwAgBuLi4tTSUmJSkpK5rXda72e3tTUJEm65557ZuxLSUlRbGysenp6NDIyopCQkKvWX7JkibKzs9Xb26vm5mbFx8erublZkpSVlTUjDEv/C8QAAEgsqgUAAObRyMiIJCkmJmbW/dPlFy9evK76UVFRs9afnqINAIBEIAYAAPMoJCRE0n8X/JrN9KrR0/Wutf7Q0NCs9f/qOAAAz0QgBgAA8yY9PV2SnO41ntbd3a3z58/LaDQqNDT0b+tbLBadPHlSkpSRkeFUv76+XlardcZnZjsOAMBzEYgBAMC82b59uySprKxMw8PDjnKr1aoXXnhBNptNjz/+uKM8Ly9Py5YtU2VlpU6fPu10rN27d6unp0f33Xef4uPjJUmxsbG6//771dPTo7ffftupflVVFfcPAwCcsKgWAACYN+vWrdNLL72k119/XbfeeqsKCgq0dOlSVVdXq62tTVlZWXrxxRcd9YOCgvTBBx+osLBQOTk5KiwsVHx8vBobG1VbW6vo6Gjt3bvXqY133nlHa9eu1fPPP6/a2lqlpaWpu7tbn3/+uR588EEdOXJkvk8bALBAMUIMAADmVUVFhSorK5WcnKwDBw7orbfeks1mU1lZmb788kv5+vo61d+yZYtOnTqljRs3qqamRrt27VJHR4eeeeYZNTY2asWKFU71k5OTdfr0aeXn5+vUqVPas2eP+vv7dejQIT300EPzeaoAgAWO5xADAAAAADwSI8QAAAAAAI9EIAYAAAAAeCQCMQAAAADAIxGIAQAAAAAeiUAMAAAAAPBIBGIAAAAAgEciEAMAAAAAPBKBGAAAAADgkQjEAAAAAACPRCAGAAAAAHgkAjEAAAAAwCMRiAEAAAAAHolADAAAAADwSP8BKw82YbLKP5QAAAAASUVORK5CYII=",
       "text/plain": [
        "<Figure size 1200x800 with 2 Axes>"
       ]
@@ -238,7 +246,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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ss88KPEYvLS3tvttLk4mJCV5eXkyePJno6Gh+/vlnvvzySyZOnHjf4+7+Pendu3e+7b1792bUqFEcOHCgxGMWQojyQJJuIYQoQSYmJvTp04fjx48zffp0IiIiCA4ONnwpjYuLK/C469evA3m/vOonE9NqtQUek5SUVOiX/pISFRXF2rVr6dSpE5s3b84z47ROp+PTTz8t1fPf7caNG/kmmdJqtcTHxxfYAlyYu18LX1/ffNsLei0eh9q1a7N8+XKqV69e4KRdxVGhQgUSExPRaDT51kUu6Dn74YcfiImJITQ0NN8s73v37mX+/PmPFE/btm2BO++ne3sQlLTSvpaHpb/m5OTkYr1fjaVly5b8/PPPRUqUra2t8fDw4MqVKwX+TXJwcAAgMzOzpMMUQohyQZYME0KIUqCfzVv5b9kgOzs7fH19uXr1KmfPns23f2RkJABNmjQxlOm/qF65ciXf/ufOnStwGSP9OrjFbXUtzLlz5wDo2bNnviWeDhw48Fi/RG/fvj1f2a5du8jNzaVx48Z5ytVqdaHPgX7fbdu25dt27tw5YmNj8fHxKfUbGgW5933zsBo3boxOp2PHjh35tu3YsYPc3Nw87zX96/zCCy/k27+g5724fHx86NSpE1lZWQ9s6QUeqVW9tK/lYemX0dq5c6fRYigOfdf4oi4X16lTJ+DOsnj30pfdO2RCCCGeFpJ0CyHEQ1i5ciVhYWEFfiGNi4vj+++/B6B9+/aG8oEDB6IoCu+//36ehDA+Pp6PP/7YsI9e7dq1sbe3Z/369Xm6AmdmZjJq1KgC49Ivy3P58uVHuLr/p18H/N4E9ebNmwwfPrxEzlFUH3/8cZ4xsllZWYZur/r1yfUqVapU4M0K+P/nePr06dy6dctQnpuby3vvvYdOp2PQoEElHT5wZ+msBQsWFHjDRFEUZsyYAeR93zwM/TVOnDiRjIwMQ3lGRgYTJkwAyHONhb3OR44cYebMmY8Ui96CBQuwt7dn5syZzJkzp8AeHJcvX6Zfv36G8c8P43Fcy8MYMWIEZmZmjB07tsClBnNycspMQn779m3DMmwFzQtQkOHDh6NWq5k1a1ae36usrCwmTZoEwEsvvVTisQohRHkg3cuFEOIh7N+/n/nz5+Pm5kbbtm0NLTgxMTH8+eefZGZm0qtXL0JCQgzHvPfee2zevJn169fTsGFDunXrRkZGBqtXr+bmzZuMGzfO0A0X7owXHz16NB9//DGNGzemd+/eaLVawsLCqFKlimFSsru1bt0aa2tr5s2bR0JCAm5ubgCMHDnyobr0Nm/eHH9/f9asWUObNm1o27YtN27cYPPmzdSqVavAGEqLn58fdevWzbNO9/nz53nuued47bXX8uzbsWNHfvnlF3r06EGTJk0wMzOjffv2tG/fnjZt2jBu3Dg+/fRT6tWrR0hICDY2NmzevJl///2Xtm3b8v7775fKNSQnJzN69Gjef/99/P39qVevHnZ2dty8eZOIiAguXLiAi4sLc+bMeaTzvPzyy6xfv55ff/2VunXrEhwcjEqlYt26dcTExNC3b19eeeUVw/6vv/46n332GWPGjCEyMpIaNWpw9uxZ/vjjD55//nlWrVr1qJeOn58ff/31FyEhIbz33nvMnz+fjh07UqVKFdLT0/nnn3/YvXs3KpWK8ePHP/R5Hse1PIzatWvz448/MnDgQOrWrUvXrl2pWbMmGo2Gy5cvs3PnTpydnTl9+vQjn2vWrFksWbKkwG2jRo3K08th3bp1XLx4Efj/idQ2btxIQkICzZs3Z+jQoUU6Z9OmTQkNDSU0NJR69erRs2dPLC0t+euvvzh79qzh904IIZ5Kxp08XQghyqfLly8rCxcuVIKDg5WaNWsqdnZ2ipmZmeLm5qY8++yzyvLly5Xc3Nx8x2VmZiozZsxQ6tatq1haWiq2traKv7+/smLFigLPo9PplJkzZyrVqlVTzMzMFA8PD+X9999X0tPTC1wyTFEUZfPmzUqrVq0UGxsbwxJA+mWd9EuGFbSkUExMjAIob7zxRp7yhIQEZdiwYYqXl5diYWGhVKtWTZk4cWKhMdxv2bL7LYNW2Pn1yyBlZWUpkyZNUry9vRVzc3PFx8dHmTJlipKVlZWvrhs3bigvvfSS4uLioqjV6gLPuXLlSsXf31+xtbVVLCwslDp16ijTp09XMjMz89VX2HOtKPd/Tu+VlZWlrF27Vhk2bJjSuHFjxcXFRTE1NVXs7e2VJk2aKJMmTVJu3ryZ7zjuswRaYUt35ebmKl999ZXStGlTxcrKSrGyslKaNGmiLFy4sMD35okTJ5QePXoozs7OirW1tdKkSRPl+++/L/R1Kc6SYXdLTU1VvvjiCyUwMFBxdnbOc/0TJkxQLly4kGf/+y2bVdh7rSSvpbD37MO+J44dO6a88cYbiqenp2Jubq44ODgodevWVd566618S2o97JJh93usXbs2zzXf+7Czs1OaN2+ufPrppwX+LjzI77//rrRr106xs7PL83tV0O+pEEI8LVSK8ogDx4QQQohSFBgYyPbt2x95nLMQQgghhDHImG4hhBBCCCGEEKKUSNIthBBCCCGEEEKUEkm6hRBCCCGEEEKIUiJjuoUQQgghhBBCiFIiLd1CCCGEEEIIIUQpkaRbCCGEEEIIIYQoJZJ0CyGEEEIIIYQQpUSSbiGEEEIIIYQQopRI0i2EEEIIIYQQQpQSSbqFEEIIIYQQQohSIkm3EEIIIYQQQghRSiTpFkIIIYQQQgghSokk3UIIIYQQQgghRCmRpFsIIYQQQgghhCglknQLIYQQQgghhBClRJJuIYQQQgghhBCilEjSLYQQQgghhBBClBJJuoUQQgghhBBCiFIiSbcQQghRTqlUKlQqFV5eXmRlZRW4j7e3NyqVCq1W+5ijE0IIIQRI0i2EEEKUe5cvX2bevHnGDkMIIYQQBVApiqIYOwghhBBCFJ9KpcLBwQGVSkVubi7nzp3Dyckpzz7e3t5cunQJjUaDqampkSIVQgghnl7S0i2EEEKUY9bW1nz00UckJyczdepUY4cjhBBCiHtI0i2EEEKUc8OHD8fX15dvv/2Ws2fPGjscIYQQQtxFkm4hhBCinDMzM2PWrFloNBrGjx9v7HCEEEIIcRdJuoUQQognQEhICK1bt2bt2rXs2rXL2OEIIYQQ4j+SdAshhBBPiDlz5gDw3nvvGTkSIYQQQuhJ0i2EEEI8IVq3bk1ISAj79+9n1apVxg5HCCGEEEjSLYQQQjxRZs6ciZmZGRMnTiQnJ8fY4QghhBBPPUm6hRBCiCdI9erVefvtt4mJieHLL780djhCCCHEU0+SbiGEEOIRTJkyBZVKxbZt24p8TGBgICqVqtRimjx5MhUrVmTGjBmkpaWV2nmEEEII8WCSdAshhBB3UalUD3wUJ8E2BkdHRz744ANu375NQkKCscMRQgghnmqmxg5ACCGEKItCQ0ML3ebt7f34AnlIo0aN4uuvv+bixYvGDkUIIYR4qknSLYQQQhRgypQpxg7hgRRFKXSbhYUFMTExjzEaIYQQQhREupcLIYQQJWTp0qU0btwYKysrXFxcGDhwIHFxcYXun52dzYcffoiPjw8WFhb4+voydepUmXVcCCGEeIJI0i2EEEKUgLlz5zJ06FAaNmzImDFjqFWrFosXL6ZNmzbcunWrwGP69OnDjz/+SI8ePRgxYgQqlYopU6bwwgsv3LcVWwghhBDlh3QvF0IIIQpQWPdyS0tLJkyYkK988+bN7N+/n8aNGxvKxo4dy7x585gwYQL/+9//8h1z6tQpTpw4gYODAwAzZsygQ4cO/PHHH/z000+89tprJXMxQgghhDAalSK30oUQQgiDBy3lVaFCBZKSkgz/nzJlClOnTmXgwIH5Euvk5GS8vLzIzs4mKSkJCwsL4M6SYdu3b2fZsmX5Eutt27bRoUMHAgMDiYyMLJmLEkIIIYTRSPdyIYQQogCKohT4uDvhvltAQEC+sgoVKtCoUSOysrI4depUkY5p27YtJiYmHDly5JGvQQghhBDGJ0m3EEIIUQJcXV0LLHdzcwPutHoX5RhTU1OcnJxISUkp2QCFEEIIYRSSdAshhBAl4MaNGwWW62cvr1ChQpGO0Wq1xMfHY29vX7IBCiGEEMIoJOkWQgghSsD27dvzlSUnJ3P06FEsLS3x8/Mr0jG7du0iNzc3z4RsQgghhCi/JOkWQgghSsDy5cvzjcOeMmUKycnJvPTSS4ZJ1O728ccfc/v2bcP/s7KymDhxIgADBgwo3YCFEEII8VjIkmFCCCFEAQpbMgwgODiYRo0a5Sl79tln8ff3p0+fPlSuXJldu3axa9cuvL29mTVrVoH1+Pn5UbduXUJCQjAzM2P9+vWcP3+e5557TpYLE0IIIZ4QknQLIYQQBZg6dWqh27y9vfMl3WPHjqV3797MmzePVatWYWtrS//+/fnkk09wcXEpsJ5ff/2Vjz/+mJ9//plr167h7u7OlClTmDBhwgOXLhNCCCFE+SDrdAshhBBCCCGEEKVEWrqFEEKIxyw9PZ2EhARSU1NJTU0lLS0tz7/6n1NSUgz/6n9OTU0lMzMTnU5neOTm5qIoCjqdzjBbeuXKlVGpVKjVasPDxMQEExMTbG1tsbOzMzzs7e0NP9+9Tf+z/l97e3ucnJwwNZWvD0IIIURRSUu3EEIIUQIURSE1NZVr165x/fp1w+Pq1atcu3bNUB4XF0daWhoqlQpra+s8Dxsbmzw/29jYGJJee3t7w782NjaGBFqlUhl+VqvV/PvvvyQlJdGqVSvUajW5ublotVpDgp6Tk2NI5O9O8PWPjIwMwyM9PT3P/7Ozs1Gr1Tg5OeHm5kblypVxd3enSpUqVKlShcqVKxsebm5umJubG/tlEUIIIYxOkm4hhBCiCBRF4caNG0RHRxMdHc2ZM2eIiYkxJNM3btwgMzMTS0tLnJyccHR0xN7eHk9PTypXrkyVKlWoWrUqnp6eeHl54eLigomJSYnHqdFo2LRpE926dcPMzKxE687KyuLKlStcunSJy5cvc/XqVcO137x5k/j4eOLj40lMTESn0+Ho6GhIzqtWrUqNGjWoWbMmNWvWpEaNGlhbW5dofEIIIURZJEm3EEIIcZfk5GTOnj1rSK5Pnz7NmTNnOHfuHGlpabi5ueHl5YW3tzdeXl64u7sbkmlvb28cHR1Rq9WkpKSwc+dOnnvuuccaf2km3UWVk5PDlStXDI/Y2FhiY2OJiYkxJOxpaWlUqVKFGjVqULt2bWrVqmVIyL29vY0WuxBCCFHSZFCWEEKIp9KNGzc4dOgQ//77L2fOnOH06dOcPXuWW7duUbFiRUOLdPXq1QkKCqJu3brUr18fe3v7ItVvYWGBVqtFq9U+dWOgzc3N8fX1xdfXt8DtOp2OK1eucPz4cU6ePMmZM2fYuHEjly5d4sqVKyiKgpeXF7Vq1aJWrVrUrl2bxo0bU79+fSwtLR/z1QghhBCP5un6FiCEEOKpFBcXx6FDhzh06BAHDx7k0KFDXL9+HU9PT2rWrImvry8hISHUqVOHBg0aULly5Uc+p7m5OSqViuzs7Kcu6X4QtVqNl5cXXl5edO/ePc82rVbLmTNn+Pfffzl58iRnz55l9+7dnD59moyMDPz8/GjevDnNmjWjSZMmNGzYUBJxIYQQZZp8CxBCCPFEuX79er4E+8aNG3h5eeHn50ejRo0YPHgwbdu2pVKlSqUWh0qlwtLSkqysLGxsbErtPE8aU1NT6tatS926dfOU63Q6Tp06xe7duzl48CDLli1j4sSJpKWlUbt2bUMi3rRpUxo2bIiVlZWRrqD0jR8/nqioKKKjo4mPj8fKygovLy+Cg4MZMWJEge/rPXv2MH36dPbt20dmZiY1atRg4MCBjBw5sthzC5w8eZIpU6awbds2UlJS8PLyol+/fkyYMCHf856Tk8O0adP4+eefSUxMpEmTJsyZM4cmTZrkq/fvv//mmWeeYePGjfluxgghRHkmY7qFEEKUWxkZGezZs4ddu3YZEuybN2/i5eVFnTp1aNSoES1btqRt27Y4Ojo+9vi2b99O9erVcXd3f2znLAtjuh8XnU5HdHS04fU/fvw4p06dIjU1lVq1atG8eXNatmxJQEAAfn5+qFQqY4dcIszNzWnSpAl16tTBxcWF9PR09u3bR1RUFFWqVGHfvn14eHgY9l+/fj0vvPAClpaW9O3bF0dHRzZu3MiZM2cICQlh9erVRT73/v37CQoKQqPREBISgoeHBxEREURFReHv7094eDgWFhaG/d955x3mzp3LCy+8QNWqVVm+fDlarZbTp0/n6VGSlpZG/fr18ff356effiqZJ0oIIcoISbqFEEKUG/okOzIykoiICA4dOkSlSpVo0qQJTZo0oVWrVvj7+1OxYkVjhwrcSVCcnZ2pVq3aYzvn05R0F0Sn03H27FlDIn7kyBGOHTuGra0tAQEBBAUFERgYWK6T8KysrAK71E+aNIlPPvmEYcOG8fXXXwOQkpJC9erVSU5OZvfu3TRr1sxQR1BQEHv37mXlypX069fvgefNzc2lfv36nDp1ivXr19OzZ0/gznPep08ffv/9d2bOnMmECROAOzP+29ra0rdvX3788Ufgzo2owMBAZs+ezbhx4wx1jxgxgt9++40TJ06Uag8UIYQwBrWxAxBCCCEKk5GRQXh4OB9++CFt2rShYsWKvPrqqxw7doznn3+eI0eOcPXqVf78808+/vhjnnvuuTKTcAOG7uXi8VGr1dSqVYtBgwbxzTffsH//fm7fvs3SpUvx8PBg2bJlNG3aFBcXF1588UUWLVrEqVOnKE9tEIWNYe/Tpw8AZ8+eNZT99ttv3Lp1i379+hkSbn0d06dPB2DRokVFOu/27ds5deoU7du3NyTccOc5//TTTwH45ptvDM/lrVu3yMjIoEWLFoZ99T9funTJULZz506+/vprFi5cKAm3EOKJJGO6hRBClBkZGRns3bvX0JIdFRWFo6MjzZs3p3fv3nz33XfUqVMHtbp83DO2sLAgIyPD2GE89SwtLenWrRvdunUD7rzPIiMjCQsLY8mSJYwdOxY7OzsCAwMNLeG1a9cudy3hGzduBKBBgwaGsoiICAC6du2ab//27dtjbW3Nnj17yM7OztMtvCD3q6tatWrUrFmT6OhoLly4gK+vL05OTlhZWXHo0CHDflFRUQB4eXkBkJmZyaBBg3j++ecJCQkpzuUKUeI0Gg25ubnGDkOUcSYmJsXuSSZJtxBCCKM6c+YMGzZsYP369Rw4cAAHBweaN29OcHBwuUuy72Vpacnt27eNHYa4h7W1Nc8995xhDfW7k/DFixcbkvBnnnmG4OBgunTpQoUKFYwcdX6ff/45aWlpJCcnExUVxa5du2jQoIGhezfc+f0CqFmzZr7jTU1N8fHx4cSJE1y4cAE/P7/7nu9+dQHUqFHDsL69r68varWat956iwULFpCcnIy7uzvLly/H3t6eV155BYCPPvqIhIQEvvrqq4d6DoQoCSkpKcTHx5OdnW3sUEQ5YWFhgZOTU5GXEZWkWwghxGOl1WrZvXs3GzZsYN26dcTGxtK8eXO6du3KokWLqFu3brlNsu9lYWEh3cvLgYKS8L///puNGzfywQcf8Oqrr9KuXTuCg4Pp0aMH3t7exg34P59//jk3btww/L9r164sWbIEZ2dnQ1lycjJAoTcN9OVJSUkPPN/D1DV79mysrKxYuXIliYmJNG7cmM8//xx3d3cOHDjAvHnzDDFPmTKFb7/9llu3btGwYUMWLFiAv7//A+MS4lGkpKRw9epVbG1tcXJywszMrNz1chGPj6IoaDQakpOTuXr1KkCREm9JuoUQQpS6lJQUtmzZwvr16/nzzz9Rq9W0b9+eSZMm8fzzz5epcdglydLSUlpOyiFra2t69uxpGLd8/PhxVq1axfLly3nnnXeoWbMmwcHB9OrVi2bNmhntJlFcXBwAN27cYM+ePUyYMIHGjRvzxx9/FLgklzFYWFgwc+ZMZs6cmac8JyeHAQMG0LVrV1599VXmzZvH1KlTCQ0Nxd/fnxkzZtC1a1fOnTuHq6urkaIXT4P4+HhsbW2pWrWqJNuiSKysrLCzsyM2Npb4+HhJuoUQQhjPxYsX2bhxI+vXr2fHjh14enrSoUMHVq1aRceOHTE1ffI/gvRJt06ne2Ja759G9evXp379+kyfPp1bt27x66+/8scff7BgwQKsra3p0aMHvXr1omPHjlhbWz/2+FxdXenduzdNmjShZs2avP766/z777/A/7c+61up76UvL8qNr5Ksa9q0aVy9epWtW7cC8Nlnn9GxY0emTJkCQK1atfD29uarr75i2rRpD6xPiIeh0WjIzs7GyclJEm5RLCqVigoVKnD16lU0Gs0Dx3jLNwAhhBAl5tixY3z44YfUq1ePGjVqsHz5clq1asWhQ4c4d+4c33//PV26dHkqEm7AMDGVtHY/OZydnRk+fDibN28mPj6eb775hpycHIYNG4ajoyPdu3fnf//7n1HG8uvXpz9x4gTx8fHAneQVIDo6Ot/+Wq2WmJgYTE1Ni7Ss3f3qgv+fNb2wMd96R48eZfbs2YZu5ikpKVy7di1P67ynpydOTk6cOHHigXEJ8bD0k6Y9jcsrikenf98UZfI9SbqFEEI8ksuXLzNr1izq1KlDy5YtOXjwIMOGDePatWscOHCA6dOnU79+fWOHaRRqtRpzc3NJup9QFhYW9O7dm2XLlnH58mV27NiBn58fc+fOxdXVleDgYH7//ffHOq7/2rVrwJ3ZdQGCgoIA2LJlS759d+zYQUZGBm3atHngzOUPquvChQtER0fj5eV13wReq9UyYMAAAgMDGTx4cJ5t9/6eyHwI4nGRVm7xMIrzvpGkWwghRLHdvn2b77//nvbt2+Pr68vGjRsZOnQo169f56+//mL48OF5JnN6mslkak8HtVpNixYt+Oyzz/j33385cOAAVatWZcyYMbi6ujJ48GC2bduGTqd7pPNER0cX2L1bp9MxadIkbt68SZs2bXBwcAAgJCQEJycnfvnlF8NyXXAnof3www8BGDZsWJ66MjIyOH36NJcvX85THhAQgJ+fHzt27GDDhg15zj1+/HgAhg4det8vorNmzTL0etGzt7fH3d2dLVu2oNVqgTtrgqemplK3bt0iPS9CCFGWqRRFUYwdhBBCiLIvKyuLTZs2sWzZMjZt2kSNGjV44YUXGDRokGHNXZHfnj17cHd3f2zPkUajYdOmTXTr1k26TJYBOp2OsLAwli5dyubNm7GxseHll1/mtddee6geIPPmzWPixIm0bdsWHx8fKlWqxI0bN9i+fTsXLlzAzc2N8PBw6tSpYzhm3bp1hISEYGlpSb9+/XB0dGTDhg2cOXOGkJAQfv311zyJ8rZt2+jQoQMBAQFs27Ytz/n3799PUFAQGo2GkJAQPD09CQ8PJyoqCn9/f8LDwwttNT958iSNGzdmzpw5jBgxIs+2BQsWMHr0aFq1akXLli35+eefycrKkonURKnKysoiJiYGHx8fLC0tjR2OKGeK8/6Rlm4hhBCF0ul0bNu2jcGDB+Pq6sro0aNxd3dn//79nDhxgmnTpknC/QDS0v10U6vVdOnShRUrVhAXF8fs2bM5fvw4LVq0oG7dunz66adcuXKlyPV16tSJQYMGcevWLdasWcNnn33G77//jqOjI6GhoZw4cSJPwg0QHBzM9u3bad++Pb///jtffvklZmZmfPHFF/zyyy/F6iKpH0LSq1cvtm7dyty5c0lOTmby5MmEhYUVmnDn5uYycOBAWrZsyfDhw/NtHzlyJB9//DFXrlxh0aJF+Pj4sGXLFkm4xRPD29ubefPmldn6ilOvSqVi3bp1JX7uu2NQqVSoVKoiLWdYkvTnLelVVaSlWwghRD4nT55k6dKl/Pzzz6Snp9O1a1f69+/PM888I7NwF9OJEyfQarU0bNjwsZxPWrrLh8TERJYtW8avv/7KwYMHadOmDa+//jp9+vTBzs7O2OEJ8VQoSktljx490Gg0Bc5lsHPnTtq3b88///xDgwYN7nsub29vxowZw5gxY0oidG7duoWNjY1hxQSVSsXatWsJDg5+pHqLEmdcXBwODg5FmgviYWMYNGgQb775Jq6uriUy5r5Dhw688sor+eaSuFdcXByrVq0iNDT0gQm/tHQLIYQotuzsbFasWEHbtm1p3Lgx//zzD7NmzSIuLo6VK1fSpUsXSbgfgqzVLQri6OjImDFj2LNnD2fPnqVdu3Z89tlnVK5cmSFDhnDkyBFjhyiEAAYNGkRYWBixsbH5ti1evJhmzZo9MOEuDc7OzkZZohDAzc2t1BJuPTs7O9zc3Eok4U5MTGT37t306NHjgfu6ubkZlkcsSfLtSQghnnJnz57lvffeo0qVKkycOJGAgAAuXrzIli1bePXVV0v9g/VJZ2lpKd3LxX15e3szffp0Tp8+zYYNG4iPj6dNmzY0b96cxYsXk5GRYewQhXhqde/eHWdnZ5YsWZKnPC0tjdWrVzNo0CAAdu3aRbt27bCyssLDw4NRo0aRnp5eaL2XL1+mV69e2NraYm9vT58+fbhx40aefTZu3Ejz5s2xtLTEycmJ3r17G7bd3Q3c29sbgN69e6NSqfD29ubixYuo1eo8EyjCnXkhvLy87jupY2pqKi+99BI2Nja4u7vz1Vdf5dl+d/fyixcvolKpWLNmDR06dMDa2pqGDRuyd+9ew/6XLl2iR48eODg4YGNjQ926ddm0aVOh5y/IkiVLqFixIn/88Qe1atXC2tqakJAQMjIyWLp0Kd7e3jg4ODBq1Kh8S3j9+eefNGnSBFdXV27fvs0rr7yCs7MzVlZW1KhRg8WLFxcrlochSbcQQjyFtFota9asoWPHjtStW5fjx4/z448/EhMTw4wZM6hcubKxQ3xiyJhuURxBQUH8/vvvXLlyhR49evDJJ59QuXJlRo4cyenTp40dnhBPHVNTU15//XWWLFnC3aNyV69eTW5uLi+99BLnz5+na9euvPDCCxw7doxVq1axa9eufBMG6ul0Onr16kViYiLbt28nLCyMCxcu0LdvX8M+f/75J71796Zbt24cOXKE8PBwWrRoUWB9Bw8eBO60vF+/fp2DBw/i7e1Np06d8iWUixcvpn///vftufbZZ5/RsGFDjhw5woQJExg9ejRhYWH3fZ4mTZrEe++9x9GjR6lZsyYvvfSSYTWC4cOHk52dzY4dOzh+/DizZ8/G1tb2vvUVJCMjgwULFvDLL7+wZcsWtm3bRu/evdm0aRObNm1i+fLlfPvtt/z22295jtuwYQO9evUC4KOPPuLkyZNs3ryZU6dOsWjRIpycnIodS7EpQgghnho3btxQpk+frlSpUkWpUqWK8u677yqXL182dlhPtNTUVGXDhg1KbNpN5d/EK6V+vpycHGXdunVKTk5OqZ9LlL7c3Fxly5YtSvfu3RVzc3MlKChIWbdunaLVao0dmhDlXmZmpnLy5EklMzPzvvudOnVKAZTIyEhDWbt27ZRXX31VURRFGTRokPLWW2/lOWbnzp2KWq021O3l5aXMnTtXURRF2bp1q2JiYpLn8/fEiRMKoBw4cEBRFEVp3bq18sorrxQa0931KYqiAMratWvz7LNq1SrFwcFBycrKUhRFUQ4dOqSoVColJibmvvV27do1T1nfvn2VZ599tsBzxcTEKIDyww8/5LuWU6dOKYqiKPXr11emTJlS6DkfdG2KoiiLFy9WAOXcuXOGsiFDhijW1tZKamqqoaxLly7KkCFDDP/PyspSbG1tlX///VdRFEXp0aOHMmDAgPuef/HixUqFChUeGGdR3z+KoijS0i2EEE+BgwcP8tprr+Hh4cGGDRuYMWMGly5d4vPPP8fDw8PY4ZVruYqOuIxEDsXHsPHSP3x7cidTDm7i7R2/0nfrYl4MX45Op6PT74t5buNPZOdqjR2yKEf0s59v3LiRc+fO0bBhQ4YMGYKPjw+zZ88mISHB2CEK8cSrXbs2bdq04ccffwTg3Llz7Ny509C1/J9//mHJkiXY2toaHl26dEGn0xETE5OvvlOnTuHh4ZHn87dOnTpUrFiRU6dOAXD06FE6duz4SHEHBwdjYmLC2rVrgTtdtDt06GDojl6Y1q1b5/u/Pq7C3D2uXd9b7ubNmwCMGjWK6dOn4+/vT2hoKMeOHSvupQBgbW2Nr6+v4f+urq54e3vnaTV3dXU1nBcgIiICFxcX6tatC8CwYcP45ZdfaNSoEePGjWPPnj0PFUtxmT6WswghhHjstFotq1ev5osvvuDkyZP07NmT3bt306xZM2OHVu6kaTO4mnaba+kpXE1P5mp6CtfSU7mansy19FTi0jPR3WctkGx7N+zVpmTqckjIyqCKjf3jC148MTw8PPjiiy+YNWsWS5cu5ZtvviE0NJSXX36Zd9991/ClUghR8gYNGsTIkSP56quvWLx4Mb6+vgQEBAB3xncPGTKEUaNG5TvO09Pzoc5nZWX1SPECmJub8/rrr7N48WKef/55VqxYwfz58x+53oLcvVqGfvIz/bjxwYMH06VLF/7880+2bt3KzJkzmTNnDiNHjnzoc+jPU1DZ3ePVN2zYQM+ePQ3/f/bZZ7l06RKbNm0iLCyMjh07Mnz4cD7//PNixVJcknQLIcQTJisriyVLljB79mxycnIYPHgwW7ZsoVKlSsYOrcxL12RyITWemJQELqTc5nxyAjGpicQkJ5OuyX1wBYVI0WmxV5lyA0m6xaMzNzfnzTff5M0332Tv3r18/vnnNGnShC5dujBp0iRatmxp7BCFeOL06dOH0aNHs2LFCpYtW8awYcMMyWWTJk04efIk1atXL1Jdfn5+XLlyhStXrhhau0+ePElSUhJ16tQB7rQch4eHM2DAgCLVaWZmlm8CMbiT8NarV4+vv/4arVbL888//8C69u3bl+//fn5+RYqjMB4eHgwdOpShQ4cyceJEvv/++2In3cWlKAobN27kp59+ylPu7OzMG2+8wRtvvEG7du14//33JekWQghRNKmpqSxatIg5c+ZgZ2fH2LFjGTp0KObm5sYOrcxRFIWrGfGcTY7nXHICZ5P0/94mNafku3+nKFrs1aaQC/FZhc9mK0RxtW7dmt9//51z584xffp0OnToQIsWLZg0aRKdOnUqkeV2hBBga2tL3759mThxIikpKfTv39+wbfz48bRq1YoRI0YwePBgbGxsOHnyJGFhYSxcuDBfXZ06daJ+/fq88sorzJs3D61Wy9tvv01AQIChN1poaCgdO3bE19eXfv36odVq2bRpE+PHjy8wPm9vb8LDw/H398fCwgIHBwfgToLfqlUrxo8fz8CBA4vUgr57924+/fRTgoODCQsLY/Xq1fz5558P8azdMWbMGJ599llq1qzJ7du3iYyMfOQkvigOHTpERkYGbdu2NZRNnjyZpk2bUrduXbKzs/njjz8eSywyplsIIcq5W7du8eGHH+Lh4cFPP/3E7NmzOXPmDKNGjZKE+z+3s1PZFRfNNyd2MHzHajpu+Jr2a/7HgL/XM+PgLn49e5rDN2+VSsIN/yXdqjv3ueMzJekWJa969eosWbKECxcu0KBBA1588UWaNWvGmjVr7rs0kBCi6AYNGsTt27fp0qULVapUMZQ3aNCA7du3Ex0dTbt27WjcuDGTJ0/Os8/dVCoV69evx8HBgfbt29OpUyeqVavGqlWrDPsEBgayevVqNmzYQKNGjQgKCuLAgQOFxjZnzhzCwsLw8PCgcePG+eLOyclh4MCBRbrOd999l6ioKBo3bsz06dP54osv6NKlS5GOLUhubi7Dhw/Hz8+Prl27UrNmTb7++uuHrq+o1q9fT7du3TA1/f92ZnNzcyZOnEiDBg1o3749JiYm/PLLL6Uei0pRlPuMQhNCCFFWXblyhc8++4wffviBhg0bMmHCBHr06HHfZUCeBrlKLudTbnA8IY5/E+I4nhjHycQEMh6he/ijet7SlVwU1mfdZEKTQIbWa1Vq59JoNGzatIlu3brlG+smnh5JSUmGvw8VK1Zk4sSJvPLKK/KeEOIuWVlZxMTE4OPjg6WlpbHDKTUff/wxq1evfugJzB43b29vxowZw5gxYx6pngYNGvDhhx/Sp0+fYh23ZMkSxowZQ1JS0n33K877R7qXCyFEOXP69GlmzpzJL7/8Qvv27dm0aROBgYHGDstoUnLSOZZ41ZBk/5twk0upacYOK48URUtltQUACVkZRo5GPA0qVqzIjBkzmDRpEgsXLiQ0NJQPP/yQcePGMXjwYKytrY0dohCilKWlpXHx4kUWLlzI9OnTjR1OsYwfP54PP/yQq1evUqFChWIfn5OTwwsvvMCzzz5brONsbW3RarUlfhNGkm4hhCgnDh06xIwZM/jzzz/p1q0be/fupUmTJsYO67HL1GbxT2IsB2/EcvhWLIdu3iAlR2PssACwMjXD1dKWiqZWWGKKOleFNkehok6Fm8oMlxv2JNtmg0wgLx4Ta2trxo0bxzvvvMP//vc/5s6dy9SpUxk7diwjRoygYsWKxg5RCFFKRowYwcqVKwkODi5y1/KyYPv27Wg0dz7X7ezsHqoOc3NzQkNDi33c0aNHATAxMXmo8xZGkm4hhCjjTpw4wYQJEwgPDyckJIR///2XGjVqGDssoxi/bwMRV85zKzPbKOc3UalwtrTF0dwaW5U5pjo1miwd6WkaEpMySUrLIo4M4sjbmp1lZ0VN90qkpGeTkJ5plNjF083U1JQhQ4bw5ptvsnr1aj799FM+/fRTxo0bx9ixY7GxsTF2iEKIErZkyRKWLFli7DCKzcvLy2jnLuoM9MX1dA/8E0KIMuzSpUu8/vrrNG3aFHt7e86cOcOyZcue2oQbIC1b91gSbnO1CT62jjStWJXW9l40NfHAJ6USVjEWJJ3I4cKRJI4dvsnho3EcP32TC7G3SUrLKrS+VG0udmZ37ponpEn3cmE8arWavn37cujQIZYvX84vv/xCtWrVWLRokaFlSQghRMmSpFsIIcqY+Ph4xo4dS+3atUlMTCQqKoqff/7ZsJbn06xdFZ8Sr9PF0paGFSvTuoIXzc088E1zxvS8KdePpXPi8C2OHI3jxNmbXI9PQ6t7uLlHUzW5WJuaYKpSkZAuSbcoG3r16sWxY8eYMWMGs2fPpnbt2qxatUpmOxdCiBImSbcQQpQR6enpfPzxx1SrVo2DBw8SFhbGH3/8Qb169YwdWpnRtrL3Ix1f0dyShhUr08bei4ZKFVzi7Eg5qeHM4USOHInj+JmbXL2ZwkPm1oVK1+aSqyjYmZlwOyMTWThElBVqtZrBgwcTHR3NkCFDGDVqFE2bNiUsLMzYoQkhxBNDkm4hhDAyjUbD119/jY+PD6tXr+ann35i165dtG3b1tihlTlVbSvgbedQpH1NVCqq2TnSsqIHLSw98UmpRNZphTOHEzl8NI4zMQmkZOSUcsR3KECaJhc7UxO0Oh1JmYV3RRfCGMzNzRk3bhznz5+nc+fOhISEEBQURFRUlLFDE0KIck+SbiGEMBKdTsfKlSupVasWn3/+ObNmzeLo0aP07NnT2KGVaf6FtHZX+K8Vu/V/rdj2sdZc+yedfw7f5NjJG1yPN+4yYneP606ULuaijLK1tWX27NmcO3cOX19f2rdvT0hICNHR0cYOTQghyi1JuoUQ4jFTFIW//vqLJk2aMHbsWIYNG8bp06cZOHAgarX8WX6Qdv8l3VamZjSo6EZrOy9qZ7qSc+ZOK/aR/1qxM7O1xg30HqkaLXZmdxYNiZfJ1EQZ5+zszPfff8/x48cBaNCgAW+99RbXrl0zcmRCCFH+yLc7IYR4jM6cOUPnzp3p27cvzz77LOfOneP999/H3Nzc2KGVG63dPGmQWwWTcyZEH77NkX/iuHg9ibI+TDpVk4u9tHSLcsbX15fffvuNPXv2cPHiRapXr860adPIypIhEkIIUVSSdAshxGOQlpbG+PHjadSoEa6urpw9e5aZM2dia2tr7NDKnQoWVlhqzR56JnFjSdXeGdMNEC9JtyhnmjRpwtatW1m7di0rV66kTp06bNq0ydhhCSFEuSBJtxBClCJFUVi9ejW1atVi69athIWF8dNPP+Hs7Gzs0Mq1NtU8jR1CsaVo7h7TnWnkaIR4OF26dOH48eMMHDiQl156iZ49e3Lx4kVjhyWEEGWaJN1CCFFKTp8+TadOnRg2bBjvv/8+hw4dkhnJS0jrcph03z2mW9bqFuWZqakpH374ISdPnsTMzIw6depIl3MhhLgPU2MHIIQQT5q0tDSmTZvGl19+SUhICKtWrcLJycnYYT1RGntUwdrMjAyNxtihFNndY7oTZCI18QRwd3fn999/Z+vWrYwePZolS5bw5Zdf8txzzxk7NCFK1DPqF40dwn2F6VaXWF2xsbFMnjyZLVu2kJCQQOXKlQkODiY0NBQHh6It2QmQmJjItGnTWLduHdevX6dSpUp07dqVadOmUbVq1WLHlZmZyaxZs/jll1+4dOkS9vb2BAYGMnXqVPz8/IpUh34i2z///JOdO3dy6dIlMjMz8fLy4tlnn2XixIm4uroWO7aikJZuIYQoIYqi8Ouvv1KzZk3CwsL4+++/Wb58uSTcpcDc1IRmXu7GDqNYUrW52JiaoEJausWTpXPnzhw/fpzBgwfz8ssv06NHD2JiYowdlhCimM6fP0/Tpk1ZvHgxLVq0YOzYsVSrVo358+fTunVrEhISilRPQkICrVu3Zv78+fj6+jJ27FhatGjB4sWLadq0KRcuXChWXNnZ2TzzzDNMmzYNe3t7Ro8eTadOnVi7di3NmjVj//79Ra7n2Wef5bvvvsPZ2ZlBgwYxbNgwLC0tmT9/Pg0bNuTs2bPFiq2oJOkWQogScOrUKTp27Mjbb7/NuHHjOHToEP7+/sYO64lW3rqYp2pyMVGpsDE1kaRbPHFMTU354IMPOHnyJBYWFtStW5epU6dKl3MhypG3336bmzdvsmDBAtatW8esWbOIiIhg7NixnDlzhkmTJhWpng8++IDo6GjeeecdwsPDmTVrFuvWrWP+/PncvHmTt99+u1hxffHFF+zevZuQkBD279/P7NmzWbFiBb/99hsZGRkMHDgQnU73wHpMTEyYPn06cXFxhIWF8fnnnzN37lyOHDnCkCFDuHHjBu+8806xYisqlaKU9UVWhBCi7NJ3JV+wYAEvvvgic+fOlZbtx+TMjXh6LVpu7DCKJbS+F9+du05yrsLhD0aUyjk0Gg2bNm2iW7dumJmZlco5hHiQv//+m1GjRpGZmcnChQuly7kok7KysoiJicHHxwdLS8tC93saupefP3+e6tWr4+3tzfnz51Gr/79tNjU1lcqVK6MoCjdv3sTGxqbQetLS0nBxcUGtVnP9+nXs7OwM23Q6HdWqVePSpUucP3+eatWqPTAuRVHw9vbm8uXLXLhwAR8fnzzb27dvz86dO4mIiKBDhw4PceV3XLt2DXd3d2xtbUlNTS3SMUV9/4C0dAshxEMLDw+nTp06hIWFER4eLl3JH7Nark442VgbO4xiSdXcWTYsI0dDlkZr7HCEKDWdOnXi2LFjvPnmm7z88sv07duX+Ph4Y4clhChEZGQkcGe4yN0JN4CdnR3+/v5kZGSwb9+++9azb98+MjMz8ff3z5NwA6jVarp06ZLnfA9y/vx5Ll++TM2aNfMl3ADPPvssABEREUWqrzD6m9SmpqUz5Zkk3UIIUUypqakMHTqUnj17MnjwYKKioqQruZGUuy7m2v+fTC0+Ld3I0QhRuvRdzv/55x9u3ryJn58fa9euNXZYQogCnDlzBoCaNWsWuL1GjRoAREdHP5Z6Squ+wvz4448AdO3a9ZHqKYwk3UIIUQwRERHUq1ePqKgo9u/fz+TJkzExMTF2WE+tNr7lLOm+a9kwWatbPC28vb0JDw9n8uTJDBgwgJdeeqnIEzIJIR6P5ORkACpUqFDgdn15UlLSY6mntOoryMGDB5k6dSp2dnZMnz79oeu5H1kyTAghiiAtLY1x48axdOlSxo0bx4cffijJdhnQppqXsUMoFn33coD49Pwt3YqioNPp0Gq1eR65ubn5yrRaLfppWe7+Vz8r7MmTJzExMUGlUgGgUqlQq9WYmJhgamp634eJiUm+7oVCPAq1Ws3IkSPp0aMH/fv3x8/Pj++++47g4GBjhyaEMLIpU6bkK+vfvz/e3t6lfu7o6Gh69OiBRqPhl19+wdfXt1TOI0m3EEI8wLZt2+jfvz+VKlVi//791KtXz9ghif+42tvi6+TI+fhEY4dyX9YmauzNTDA3UeNuZU6ga0WSYq9wIDmB7OxssrKy0Gg0eRJp4IGJsUqlMjzgTmJtYmJCbm4u8P9J/N0/3y+Z1x8Hd5IkU1NTLCwssLS0xNLSMs/Pd5eV1hg48eTx9vYmIiKCL7/8kjfeeINu3bqxcOFCKlWqZOzQhHiq6VuM9S3L99KXV6xYscTrmTp1ar79AgMD8fb2LrG4ChIdHU2HDh1ITEzkl19+oWfPnsWuo6jkU1IIIQqRlpbG+PHjWbJkCe+//z4fffSRtG6XQa2reRo96bYzNcHJ0gxnCzOcLMyoZGGGvZkJdmYm2JmaYqpWkZWrI0enw0ylws3SnCytFkdHR0Piam5uniepVqvVhmS6OGrVqsWmTZuoU6dOsWcvVxQlX2t6dna24cZAVlYW8fHxZGVlGcoURcHU1NSQhFtZWWFjY4OtrS22trbY2NhIUi7yUKvVjB49mp49expavb///nt69epl7NCEeGrVqlULKHxstH796sLGVj9KPfdbTKuk4rqXfqnXhIQEVq9eXep/f+RTUAghCrB9+3beeOMNHB0d2bdvH/Xr1zd2SKIQbXw9+enA0VI/j6WJGqf/kmpnCzOcLP//ZzO1iqQcLQnZGm5la7iYlkWyRkuKJpdUjZYUbS4anYKvrSXPezrzy6WbvO7mzsvVq5d63MWhUqkwMzMrcrKuKAo5OTmGJDwzM5PMzEzS0tKIi4sjLS0NrVaLpaVlniRc/7O1tfVT0Y09ISGBtWvX8ueff3L8+HGuXr2Kubk59evXZ8CAAQwYMCDP83Dx4sUCZ+nV69u3L7/88kuxYtizZw/Tp083zCxco0YNBg4cyMiRI/PdTExJSWHChAls2LCBzMxM2rRpw7x58wrsdvnDDz8wbNgwDhw4QOPGjYsVk4+PD5GRkSxYsIDXXnuN7t27s3DhQhwdHYtVjxDi0emX29q6dSs6nS7fkmG7d+/G2tqaVq1a3beeVq1aYWVlxe7du0lNTc23ZNjWrVvznO9BfH198fT0JDo62rA81902b94MQFBQUJHqAzh+/DidOnUiOTmZNWvWPJYlDSXpFkKIu6SnpzN+/Hh+/PFH3nvvPUJDQ6V1u4xr4e2BqVqN9r9u1I9KBVSyMKOqtTnu1ha4W1ngammOrZkJaZpcbmVriM/WcD0zm2O304jP1pCQrUV7nzv1eqmaXOz/G9OdkJ5RIvEak0qlwsLCAgsLiwK365PytLQ0wyMxMZHLly+Tnp6OoihYW1tjb29PxYoVDQ9zc/PHfCWla/Xq1QwbNozKlSvToUMHPD09uXHjBmvWrGHw4MFs3ryZ1atX5+vZ0LBhwwLHPBd3iMv69et54YUXsLS0pG/fvjg6OrJx40bGjh3L7t27Wb067xq//fv3Z8OGDbz66qtYW1uzZMkSOnbsyMmTJ7G2/v9l+q5evcp7773H+PHji51w66nVasaMGUOvXr3ytHqXZjdPIUR+vr6+dO7cma1bt/LVV18xcuRIw7bQ0FDS09MZMmRInjW6T58+DUDt2rUNZba2trz22mt89913TJkyhTlz5hi2LVy4kIsXL9KlS5cirdENdz5nhg4dygcffMC4ceNYtWqV4YbA+vXr2blzJ3Xq1CEgICDPcZcvXyYjIwNPT888f7eOHj1Kp06dyMjIYP369YYlzEqbSrlfe74QQjxFDhw4QL9+/ahYsSKLFy+mYcOGxg5JFNHL/1vF4SvXin2cCnC2MLuTXFtb4G5lThVrC0xUcD0zh6sZ2VzNyCEuK4f4bA2ZuY+W2FuZqJnSwJvJ/8TQ2KsqS94IeaT6CqLRaNi0aRPdunUrdvfyx0lRFDIzM0lNTSU5OZnk5GSSkpLIyMjAysoqTxJeoUKFQhP78iAiIoL09HSee+65PK1HcXFxtGjRgitXrvDbb7/xwgsvAP/f0v3GG2+wZMmSRzp3SkoK1atXJzk5md27d9OsWTMAsrKyCAoKYu/evaxcuZJ+/foBcOPGDdzc3Jg6dSqTJ08GYOnSpfTv359Vq1bRp08fQ909evTgwoULHDlypERulOh0OubPn09oaCjBwcF8/fXX2NraPnK9QhQmKyvL0HpqaWlZ6H7PqF98jFEVX5hu9YN3KoLz58/Tpk0bbt68Sa9evfDz82P//v1ERkZSs2ZN9uzZk2f+Bf2NwnvTyYSEBNq0aUN0dDRBQUG0aNGCU6dOsX79elxcXNizZ0+xJizLzs4mKCiIPXv20KxZMzp27Mjly5dZvXo15ubmRERE0LJlyzzHBAYGsn37diIjIwkMDATg9u3bVK9encTERDp27Ejbtm0LPN+YMWOKNEa8qO8fkJZuIYRAp9PxxRdfMHnyZEaNGsWMGTOkdbucaePrWaSku4KZCT62VnhYW+BubU4VKwvU/yXYsRnZHEpMY8PVBG5k5lAy7eZ5Zebq0Oh02JmZPhEt3Y9CpVJhbW2NtbU1rq6uhvKcnBySkpIMSfilS5fyJOIVKlTA0dERBweHcjNWvLBuj25ubgwdOpRJkyaxbds2Q9Jdkn777Tdu3brF66+/bki4ASwtLZk+fTodO3Zk0aJFhqT70qVLALRo0cKwr/5n/TaAn376iU2bNrFnz54S65mgVqsZO3YsPXr04KWXXqJJkyasXr1aboAK8Zj4+voSFRXF5MmT2bJlC5s2baJy5cqMHj2a0NBQHBwcilRPpUqV2Lt3L1OnTmXdunXs3LmTSpUqMWDAAKZNm0bVqlWLFZeFhQVhYWHMmjWLlStXMnfuXOzt7QkODmbq1KnUqVOnSPUkJyeTmHhnDpjw8HDCw8ML3K9///4PNTHb/ZSPTyshhCgl8fHxvP766/zzzz+sW7eOzp07Gzsk8RDaVPNk4bZ9+cormJlQzdYKXztLqtlaUdHclGsZ2VzJyCYqIZXYjARuZpVOgl2YVE0udmYmsk53IczNzXFxccHFxcVQlpOTY0jCk5OTuXjxItnZ2Tg4OODk5ISTk1O5SsLvpu+NUFDs165d49tvvyUhIYFKlSrRunVrGjRoUKz6IyIiAOjatWu+be3bt8fa2po9e/aQnZ2NhYUFnp6eABw6dMhwTFRUFABeXneW6Ltx4wZjxoxh7Nix+VqXSkL16tXZu3cv77zzDq1bt2bOnDkMHTr0oSYWFKIklFRLcnng4eHB4sWLi7Tv/TpMOzo6Mn/+fObPn18icVlbWzNt2jSmTZtWpP23bduWr8zb2/u+MZem8vfpJIQQJWTnzp307dsXPz8/jh49irOzs7FDEg+pgXtlbC3MMdHlUs3Wimq2llSzs8LB3JSrGdlcSMtifWw8F9OyyNYZd1RVqvbOuO5LyRnodApqtSQSD2Jubo6zs7Phd1RRFDIyMoiPjyc+Pp7Dhw/nS8IdHR3LfI8VrVbLsmXLgIKT4rCwMMLCwvKUBQYGsnTpUkNy/CBnzpwBCp7Z19TUFB8fH06cOMGFCxfw8/PDzc2NXr16MXXqVM6fP4+lpaXhfPrJhoYPH46joyMff/xxsa63OExNTVmwYAEdO3Zk8ODB/P333/zvf/8r8dYnIYR4HCTpFkI8dXJzc/nkk0+YOXMmEyZM4MMPP3wqZlB+EmVnZ3Pz5k3i4+N5t3ZVLFSKIcneUEaS7HvpW7p1isLtjEwq2Vo/+CCRh0qlwsbGBhsbG7y8vApMwnNycnBwcKBSpUo4Ozvj6OhY5n7PJ0yYwL///ku3bt3yTOZjbW3NRx99RHBwsGGyoWPHjjFlyhQiIyPp2LEjR48ezTOhUWH0a9jq17q9l748KSnJULZ06VLGjRvHxo0bycrKIjAwkHnz5mFjY8Nvv/3GmjVr2L59O2q1mpEjR/Lzzz+TlpZGmzZt+Prrr4vc1bMoevXqRdOmTenbty+NGjVi9erVNG/evMTqF0KIx0GSbiHEUyUuLo6XX36Zc+fOsWXLFtq3b2/skEQxKIpiWI4qLi6O27dvY29vf6cFtJIzU7cdKHNJ9r1SNFrszO58/ManZ0jSXQIelIRHRUWh0+lwdXU1PIw9ydyCBQuYM2cOtWvXZvny5Xm2ubi45OtC2b59e7Zu3Urbtm3Zv38/P/zwA6NHjy6V2CpUqMC3337Lt99+m6c8MTGRESNG8Pbbb9OuXTvGjBnDd999x2effUbNmjV5//336dq1K9HR0Q+cVKg4qlatyo4dO/jggw8ICAhg+vTpjB07VrqbCyHKDUm6hRBPjbCwMF5++WVatGjB0aNHZS3YckKn05GYmGhItDMzM3FxccHDw4PmzZsbvtxb3UokO2K/kaN9sDRNLpUs7yR8iU/5ZGqlpaAkPCkpibi4OM6ePcvhw4epVKkSbm5uuLm5FanFuCQtXLiQ0aNHU6dOHcLDw4v8t8jU1JTBgwezf/9+duzYUaSkW9+SrW/xvpe+vCjdtkeNGoWVlRWzZs0iPT2dRYsW8dprrzFq1CgAbGxsaN++PStWrGDgwIFFuqaiMjExYfbs2QQFBTFgwADCw8NZtmxZnpmUhRCirJKkWwjxxNNqtUyePJl58+YxdepU3n333TLXzVTkpdFouHnzJnFxcdy4cQO1Wo2bmxt169bF2dm5wEmnqjk74mZvS1xKmhEiLroUbS4+pnduFDztM5g/LiqVCgcHBxwcHPDz8yMjI8NwE+fEiRPY2toaEnAHB4dSbUGdN28eY8eOpV69eoSHh+eZMK4o9OPa09PTi7R/rVq1iIqKIjo6mqZNm+bZptVqiYmJwdTU9IFr5v7555/8/PPPhIWFYWtry7Fjx8jJyaFJkyaGffT1nzhxojiXVCxdunThyJEjvPTSSzRo0IBVq1YVuuyPEEKUFZJ0CyGeaFeuXKFfv37ExcURGRlZKjPtipKRk5PDtWvXuHr1KgkJCdjZ2eHm5karVq2KnAi1rubJ2qMnH0O0Dy9Vo8X2v+7lCWmSdBuDtbU11apVo1q1anlu8Ozbt89wg8fd3R0nJ6cSTcBnz57NhAkTaNSoEWFhYTg5ORW7jn377szS/6AkWS8oKIiff/6ZLVu28NJLL+XZtmPHDjIyMmjfvv1910FPTk5myJAhDBo0iE6dOuXZlp2dbfg5KyurqJfxSFxdXfn777+ZNm0anTt3ZtKkSUycOFFupgohyiz56ySEeGKFhYXRsGFD3NzcOHLkiCTcZVBubi7Xrl1j//79/PXXX1y6dAk3Nzc6depEhw4d8PPzw9HRsciJT5tqXqUc8aNL0eRib3ZnVm1p6TY+MzMz3N3dadq0KV27dqVZs2ao1WqioqLYunUrJ06cKLRrdnF8/PHHTJgwgaZNmxIeHn7fhPvw4cPodPkXsgsPD2fu3LkAvPrqq3m2JScnc/r0aa5fv56nPCQkBCcnJ3755RfD0l9wJ0H+8MMPARg2bNh9Y3/33XcBmDNnjqHM19cXc3Nz/vjjD0PZxo0bAahbt+596ysJarWaKVOmsHnzZr799lu6dOliWH9XCCHKGmnpFkI8cRRFYf78+XzwwQfMmDGDsWPHGjskcRdFUYiPjyc2NpZr165hYWFB1apVqVu3Lra2to9Ud5tqnqiAsjyVWqomFxtTE0xUknSXNWq12rDkWL169bh16xaxsbHs3LkTa2trPDw8qFq1KlZWVsWqd+nSpUyePBkTExPatWvHggUL8u3j7e1N//79AXjnnXc4e/Ysbdq0oWrVqsCd2cv1a25//PHHtGnTJs/xa9euZcCAAbzxxhssWbLEUG5vb8/3339PSEgIgYGB9OvXD0dHRzZs2MCZM2cICQmhb9++hcauX6pr48aNeWZAt7GxYfjw4cydO5euXbtSvXp1Fi9ejIeHBy+//HKxnp9HERAQwJEjR3jhhRdo1qwZf/zxR4nOni6EECVBkm4hxBMlOzubYcOGsXHjRtatW0fnzp2NHZL4T3JyMrGxscTGxqLT6XB3d6d169YlOoa2kq01NV2dOHMjvkTqKw3p2lx0ioKtqYkk3WWYWq02zHSu1Wq5fv06sbGxnDp1CkdHRzw8PKhSpUqRZkGPiYkB7vTsmDdvXoH7BAQEGJLu1157jbVr13Lw4EE2b96MRqPB1dWVPn36MGLECNq1a1esawkODmb79u3MmDGD33//naysLKpXr84XX3zBqFGjCv39S0tL48033+SVV16he/fu+bbPnDkTnU7Hzz//zLZt22jTpg0LFy4s0ZnLi6JSpUqEh4czYsQIWrVqxYoVKwqMVwghjEWlKEpZbhAQQogiu3HjBsHBwSQnJ7NhwwaqV69u7JCeellZWVy5coUrV66QkZFB5cqVqVq1Ks7OzqU2/nLWX9tZsvdwqdRdUj6s58mSCzdwdHDg1zdfevABxaDRaNi0aRPdunUz+rJYT6KsrCyuXr1KbGwsKSkpuLq64uHhgaurq4wpLgMWLVrEu+++y+TJkxk/frwsKybuKysri5iYGHx8fB77zSJR/hXn/SOfDkI8xVQqFSqVCi8vr0InwPH29kalUqHVagE4dOgQKpWq0PHRK1euNNSrb925W2ZmJpaWllhbW+eZgOdRHT58mCZNmuDg4MCBAwck4TYiRVG4desWBw8eJCwsjFu3blGjRg26du1K06ZNSz058S8H47pT/xvXHS8TqZU7lpaW+Pr6EhAQQIcOHbC3t+f48eOEhYVx+vRpMjMzjR3iU23YsGFs3ryZefPm8fLLLz+W1+NhPkvvPfZ+j23btpX6NQghSpd0LxdCcPnyZebNm8eECRMeuG/jxo1xcHDg0KFDpKSkYG9vn2d7eHg4KpUKRVGIiIhg0KBBebbv3r2b7OxsnnnmmfvOllscv/76KwMGDODtt99m9uzZ0tpkJNnZ2Vy5coWLFy+i0Wjw9PQkKCjosa+B3MzLHXMTE3Jycx/reYsjVZuLnakJF5Il6S7PbG1tqV27NrVq1eLGjRtcunSJsLAwXF1d8fb2xsXFRVpajSAgIID9+/fTs2dP2rZty4YNG3B3dy/18xbns/ReoaGhhW7z9vZ+hKiEEGWBJN1CPOX042lnzZrF4MGDH7iEjVqtJjAwkLVr17J9+3Z69OiRZ3tERASBgYGGSX/uTbr1EwF17NjxkWPX6XRMnjyZ+fPn88033/Daa689cp2i+JKSkrhw4QJXr17F0dERPz8/3NzcMDExMUo8VuZmNPKozIGLsUY5f1GkaHKxMzMlS6slLTsHWwtzY4ckHoFKpTKs852ZmcmlS5c4cuQIJiYm+Pj44Onpibm5vMaPk5eXF3v37uXll1+madOmrF+/vlRXsCjuZ+m9pkyZUjqBCSHKBGkOEuIpZ21tzUcffURycjJTp04t0jH6hFmfQOtdvHiRmJgYOnbsSEBAAJGRkfmOLamkOzU1leDgYJYuXUp4eLgk3I+ZTqfjypUr7Nixg127dmFiYkJAQAD+/v64u7sbLeHWK+tLh6VqtIZlwxJlMrUnipWVFbVr16Zz587UrVuXuLg4tm7dypEjR0pk6TFRdNbW1qxZs4Y333yToKAgli9fXqrnKu5nqRDi6SEt3UIIhg8fzsKFC/n2228ZNWoUNWrUuO/+QUFBwJ2u5HfT/z8oKIgKFSqwZs0aTp48aVi+JSUlhaioKBwcHGjSpMlDxxsTE0P37t2xsbHh4MGDuLm5PXRdoniys7OJiYnh4sWLmJqa4uPjQ+vWrcvchF1tqnkyL2K3scMoVKomF1fLOy2fCekZeDpWNG5AosSp1WqqVKlClSpVSE5OJiYmhp07d1KhQgV8fX2pXLmydD1/DNRqNR9//DH169dn8ODBHDt2jFmzZpXKjcHifpaK8qNL08K7/5cFfx0quRs9sbGxTJ48mS1btpCQkEDlypUJDg4mNDQUBweHBx6fnp7OunXr+PPPPzl8+DBXrlxBrVZTq1YtXnrpJUaOHPlQPX8SExOZNm0a69at4/r161SqVImuXbsybdo0w9KKRdG/f3+WLl1a6PZTp05Ru3btYsf3IJJ0CyEwMzNj1qxZvPjii4wfP541a9bcd38/Pz8qV67Mv//+y61bt3B2dgbutGLb2trSvHlzw1jviIgIQ9K9fft2cnNzCQwMfOhx19u3b6d3795069aNH3/8UbpsPiaZmZmcO3eOS5cu4ejoSJMmTXB2di6zSUO9Kq5UsLQgOavkJusrSanaXENLd4JMpvbEq1ChAo0aNaJOnTpcvnyZ48ePc+rUKWrUqEHVqlVlHorHoE+fPtSsWZPevXtz/PhxVq1alWfd8ZJQ3M/SuxXWvdzS0vKhxogL8TDOnz9PmzZtuHnzJr169aJ27docOHCA+fPns2XLFnbv3k2lSpXuW8fOnTt59dVXcXR0pEOHDgQHB3P79m02bNjAe++9x5o1awgPDy/WbPEJCQm0adOG6OhogoKC6NevH6dPn2bx4sX8+eef7N27l2rVqhXrWkePHk3FihXzlRd3aEhRSdIthAAgJCSE1q1bs3btWnbt2kXbtm3vu3/Hjh356aefiIyMpE+fPgBERkbSrl07TE1NqVu3Li4uLkRERDBixAjg0buWr1y5kkGDBhEaGsr48eMfqg5RPKmpqZw7d47Y2FhcXV3x9/cv0p1uY1OrVbT08WDrqXPGDqVAqRotdmZ3PoJlre6nh7m5OdWrV8fHx4fY2Fiio6M5ffo01atXx9PTE1NT+VpWmho1asTBgwfp1asX/v7+bN26lSpVqpToOYr7WapXWJf0ChUqSNItHpu3336bmzdvsmDBAkaOHGkof+edd5g7dy6TJk3im2++uW8dbm5u/PTTT7z44ot5GkY+//xzAgMD2bNnD1999RXvvvtukeP64IMPiI6O5p133mHOnDmG8gULFjB69GjefvtttmzZUowrhTFjxjzWSQrl1qoQwkD/h+y999574L76Lub6RPrUqVNcv36dDh06GPYJDAxk27Zt6HS6PPs+TNI9b9483nzzTZYsWSIJ92OQlJTEwYMHDUvVBAYG0qJFi3KRcOu1qeZp7BAKlaLJxdbUBBWSdD+NTExM8PLyomPHjtStW5fLly8TFhZGdHQ0Go3G2OE90ZycnIiMjKRGjRq0atWKM2fOlPg5ivNZqqcoSoGPpKSkEo9PiIKcP3+erVu34u3tzfDhw/Nsmzp1KjY2Nixfvpz09PT71tOoUSNeeeWVfD0R7ezsDIl2cZbBS0tLY/ny5djY2OTrETJixAi8vLz466+/uHDhQpHrNAZJuoUQBq1btyYkJIT9+/ezatWq++6rT5z147jvHs+tFxgYyO3btzly5Ai3bt3i+PHjuLu7F2usjKIoTJgwgalTp7J+/XpDq7ooeYqiEB8fz549e9i1axeWlpZ06tSJxo0bY2dnZ+zwiq2Nb9mdTC1Vm4upWoWViZqEdFnX+WmlUqlwd3cnICCAJk2acPPmTbZu3cqJEycKXe9ZPDpzc3N+//13unbtSuvWrTlw4ECJ1l+cz1Ihygr95LedO3fON+TFzs4Of39/MjIy2Ldv30OfQz//S3F69ezbt4/MzEz8/f3zfRdRq9V06dIlT/xFtXnzZmbPns3nn3/OunXrSElJKdbxxSVJtxAij5kzZ2JmZsbEiRPJyckpdD9PT098fX05d+4cV65cISIigooVK9K4cWPDPvpW74iICCIjI1EUpVit3BqNhv79+7N8+XIiIiJKZJkxkZ+iKMTFxbFz504OHDiAg4MDzzzzDPXr18fKysrY4T00T8eKVK1o/+AdjUCjU8jK1WFvZiKzlwtUKhWurq60bduWVq1akZqaSlhYGP/88w8ZGfL+KA1qtZrvvvuOESNGEBQUVOyuqQ9S1M9SIcoKfa+PmjVrFrhdPzFgdHT0Q5/jxx9/BKBr165Gj+vtt99mwoQJvP/++/Tu3ZuqVavy1VdfFauO4pCkWwiRR/Xq1Xn77beJiYnhyy+/vO+++iT477//Ztu2bQQEBOS5O1q7dm3c3NyIiIgodtfyjIwMevXqZWh1vTuZFyXn1q1b7Nixg6NHj1K5cmWeeeYZ/Pz8sLCwMHZoJaJ1Ge5irh/XHS8TqYm7VKpUiVatWtG+fXs0Gg3h4eEcO3ZMWr5LybRp05g9ezbPP/88P/30U4nVW5zPUiHKAv2ShoVNMKgvf9ghDwsXLmTLli00atSIgQMHGi2u9u3bs2rVKi5dukRmZibnz5/n888/B+50V//uu++KHFtxyIwdQoh8Jk+ezNKlS5kxY8Z9Z9UNCgriu+++Y+7cudy+fTvPeG69Dh06sGHDBsOdyqIk3QkJCTz33HNoNBr27t1bajNJPs2SkpI4efIkt2/fpkaNGlSrVu2JnMTJ39eL1Yf/NXYYBUrR5GInLd2iEBUqVKBZs2akpKRw6tQp/v77b3x9falevXqZW6KvvBs+fDguLi4MGDCAuLi4Yo3Fvp+ifpYK8aRbs2YNY8aMwc3Njd9//z3P37CkpCTmzZuX75gxY8YUOLv4o7o34a9WrRrvvvsutWrVokePHkyaNIlBgwaV+LKCT943LCHEI3N0dOSDDz5g3Lhx990vKCgIlUrF8ePHDf+/V4cOHVi5ciUxMTHUqlULd3f3+9Z55coVnnnmGdzd3dmwYQM2NjYPfyEin7S0NE6fPk1cXBw+Pj40a9bsiV52raWPB2qVCp2iGDuUfNK0udiZmnA+VZJuUTh7e3tatmxJYmIiJ0+e5OLFi9SsWRNvb+9SWWv6afXiiy/i7OzM888/T1xcHJ9++ukjJ8pF/SyFwpcMAwgODqZRo0aPFIsQD6JvMda3LN9LX17cRHjdunX069cPFxcXIiMj8y3tlZSUVODs/f3796dixYqlFte9unfvjru7O1evXuXkyZPUr1//keq7lyTdQogCjRo1iq+//pqLFy8Wuo+zszP169fn2LFjODk5Ua9evXz73N36/aBW7hMnTtC5c2f8/f1ZsWLFE9nyaixZWVmcOXOGy5cvU7VqVTp27Fiux2sXlYO1FX5uzpy4ftPYoeRzp6XblJTMZLS5OkxNpCVMFM7R0RF/f39u3LjBqVOnOH/+PLVr18bDwwOVSmXs8J4IgYGBRERE8NxzzxEXF8fixYsfuVdBUT5LofAlwwC8vb0l6RalrlatWkDhY6PPnj0LFD62uiCrV6/m5ZdfNgw11I+/vpu3tzfKfW6Ml0ZchXF2dubq1asPnKH9Ycg3WiGeYvf7I2dhYUFMTMwD6/jnn3/uu7169er3PY/enj17eO6553j11VeZP3++dMUrIRqNhnPnznH+/HlcXFwIDAwslzORP4o21TzLZNKdqtHibm2BAiRmZOBiZ2vskEQZp1KpcHNzw9XVldjYWE6dOsW5c+eoU6cOrq6uknyXgEaNGrF79266dOlCjx49+P333x/Y4+pRPkuL8vkoxOOgbyTZunUrOp0uz/ew1NRUdu/ejbW1Na1atSpSfT///DNvvPEG7u7uBbZwF1WrVq2wsrJi9+7dpKam5vkOo9Pp2Lp1a574H1ZycjKnT59GpVLh4+PzSHUVRL7VCiGMbuPGjTzzzDO8++67fPnll5Jwl4Dc3FzOnTvH33//TUJCAm3atKFFixZPXcINZXfpsNT/upcDJMhkaqIYVCoVHh4edOzYES8vL44cOcKuXbtISEgwdmhPBG9vb/bs2UNCQgIdOnQgPj7e2CEJUep8fX3p3LkzFy9ezDeLd2hoKOnp6bz22mt5bkKdPn2a06dP56tr6dKlvP7663h6erJjx46HTrgBbG1tee2110hPT883DGPhwoVcvHiRLl265DvH+fPnOX36NBqNxlAWFxdHbGxsvnOkpaXRv39/srKy6NSpE66urg8db2GkpVsIYVQrV65k8ODBzJs3jzfffNPY4TwRbty4wfHjxzExMaFJkya4uLg81S1gTT2rYGlqSpZWa+xQ8kj9byI1QNbqFg/FxMQEX19fPD09OX/+PHv37qVy5crUrVsXS0tLY4dXrlWqVInt27cTHBxM27Zt2bZtG25ubsYOS4hS9fXXX9OmTRtGjRpFeHg4fn5+7N+/n8jISGrWrMmMGTPy7O/n5wfk7bERGRnJwIED0el0dOjQgcWLF+c7T8WKFRkzZkyR4/rkk0/Ytm0bX3zxBUePHqVFixacOnWK9evX4+LiUuBSXx07duTSpUvExMTg7e0N3LlJ0KlTJ1q3bk3NmjVxcXHh6tWrhIWFERcXR7Vq1fjhhx+KHFdxSNIthDCan376ibfeeoslS5bQp08fY4dT7mVkZHD8+HHi4+Px8/PDx8fnqU629cxNTWniWYU9Fy4bO5Q8Uv5bMgwgoRTGj4mnh5mZGbVr18bLy4t///2X8PBwateujY+Pj/QcegTW1tZs2rSJ3r170759e7Zt20aVKlWMHZYQpcbX15eoqCgmT57Mli1b2LRpE5UrV2b06NGEhobi4ODwwDouXbqETqcD/n9d7nt5eXkVK+muVKkSe/fuZerUqaxbt46dO3dSqVIlBgwYwLRp06hatWqRr2/QoEEcPHiQDRs2kJSUhLW1NbVq1WLEiBGMGjWq1HoEqhQZTCKEMIIlS5YwfPhwli1bxgsvvGDscMo1fVfys2fP4u7uTp06dZ6YdbZLyv92R/FZ2E5jh5GHtYma0AbefPhPDGM7tWNgm6YlUq9Go2HTpk1069ZNlpZ6St28eZPjx4+jUqlo0KCBLLv4iLRaLS+++CLHjh1j+/btRf6CL8q+rKwsYmJi8PHxkd4hotiK8/6R259CiMfu+++/Z/jw4axYsUIS7kd048YNIiMjuX79Om3atKFx48aScBegdTVPY4eQT0auDq1Owc5U1uoWJcvFxYUOHTrg4eHBvn37OHToEFlZWcYOq9wyNTXlt99+o3HjxrRr147Ll8tWrxkhRNknSbcQ4rFatGgRY8eOZdWqVfTq1cvY4ZRbGRkZ7N+/n0OHDuHr60tAQACOjo7GDqvM8nNzxtG67C2RlqbNxd7MhHiZSE2UMLVaTY0aNejYsSOKohAeHs758+cN3T5F8ZiYmLBq1SpatGhBu3btHrgEmBBC3E2SbiHEY7Nw4ULef/99fv31V7p3727scMql3Nxczpw5Q0REBObm5nTs2FHGbheBSqWiVRls7U79b1y3tHSL0mJlZUWzZs1o0aIFFy9eZNu2bTIb90MyMTFh5cqVtGnThrZt2xZpWU0hhABJuoUQj8miRYuYMGECU6ZMoVatWsYOp1yKj4+XruSPoE0ZTLpTNHeWDYuXpFuUMmdn53xdznNycowdVrmTm5vLkCFDaNasGQEBAVy6dMnYIQkhygFJuoUQpe777783tHAPGjSIM2fOcP78eWOHVW5otVqOHTvGvn378PHxka7kD6ksJt2p2jvLhklLt3gc7u5yrtFoiIiIIC4uzthhlRsajYa9e/diYWHB6tWrad26NQEBAVy5csXYoQkhyjhJuoUQpWrx4sWMGTOGFStW0K1bNxwcHGjdurUk3kWkb91OSUkhMDAQX19f6Ur+kKpUtMfLsaKxw8gjVZOLvZkpibJOt3iMrKysaNmyJXXq1OHw4cPS6l0E+oTbzMyMFi1aYGZmxooVKwwt3levXjV2iEKIMkySbiFEqVm+fLlhlvKePXsayiXxfjCtVsvx48fZt28f1apVw9/fH1tbW2OHVe75+3oZO4Q87ozpNiEnN5eUTJldWjw+KpUKT09POnToIK3eD3Bvwm1iYgL8/+RqDRs2JCAggOvXrxs5UiFEWSVJtxCiVKxcuZIhQ4awfPnyAmcpl8S7cAkJCWzbto2kpCRp3S5hZa2LuX5MN0CCtHYLI7i71fvQoUMcPnxYWr3vUljCrWdiYsJvv/1GnTp1CAwM5MaNG0aKVAhRlknSLYQocX/++SeDBg1i8eLF912HWxLvvPSt23v37sXHx4e2bdtK63YJa+njgUkZuoGhH9MNkJCebuRoxNNK3+odFBREdnY2kZGR0urNgxNuPRMTE9asWYOvry/PPPMMycnJjzlSIURZJ0m3EKJE7du3j759+/LFF1/Qt2/fB+4vifcd0rr9eNhZWlDP3c3YYRikarTYmJqgRlq6hfFZWVnRqlUrateubWj11mg0xg7LKIqacOuZmpqydu1a7Ozs6NmzJ1lZMlxECPH/JOkWQpSYU6dO0a1bN9577z2GDh1a5OOe5sRbp9Nx4sQJ9u7di7e3t7RuPwZlqYt5mjYXtUqFjanMYC7KBpVKhZeXl6HVOyIiglu3bhk7rMequAm3noWFBZs2bSIhIYFXXnmF3NzcUo5UCFFeSNIthCgRsbGxPPPMM/Tt25cpU6YU+/inMfHOyMhg165d3Lx5k4CAAKpXry6t249BWUq6cxVI1+Zib2ZCfJok3aLs0Ld616pVi/3793P69GkURTF2WKXuYRNuvQoVKvDXX39x6NAhRo4c+VQ8Z0KIBzM1dgBCiPIvMTGRZ555hpYtW/LVV189dD36xHvv3r0A+Pr6llSIZc61a9c4evQo7u7u1KtXr9hf7MTDa+RRGWtzMzJyyka32RSNrNUtyiaVSoW3tzcODg5ERUURHx9P06ZNsbKyMnZopeJRE249d3d3tmzZQvv27XFzc2Py5MklHKkQoryRlm4hxCPJyMige/fuuLq6snLlStTqR/uz8qS3eOfm5nLs2DGOHDlCo0aNaNiwoSTcj5mZiQnNvaoaOwyDO8uGmRIvSXeZkZCQwA8//EDv3r2pXr06VlZWVKhQgbZt2/K///0PnU5X4HF79uyhW7duODo6YmVlRYMGDZg3b95DdTM+efIkffr0wcXFBUtLS2rVqkVoaCiZmfnH/ufk5PDhhx/i4+NDhQoV6NChA4cPHy6w3r///huVSsUff/xR5FgqVKhAQEAA1tbWbNu27YmcobukEm692rVrs2HDBj7//HO+/fbbEopSCFFeSUu3EOKhabVa+vTpQ2ZmJlu3bsXc3LxE6n1SW7zT0tKIiopCpVIRGBiIjY2NsUN6arWp5sn2szHGDgO4M4O5fRHHdCuKQm5uLlqt1vCvPgFUFAVFUUhMTATg9u3bmJqaolKpUKlUqNVqTE1NDY9HvUH2JFu9ejXDhg2jcuXKdOjQAU9PT27cuMGaNWsYPHgwmzdvZvXq1XmGg6xfv54XXngBS0tL+vbti6OjIxs3bmTs2LHs3r2b1atXF/n8+/fvJygoCI1GQ0hICB4eHkRERDBt2jTCw8MJDw/HwsLCsP+ECROYO3cuL7zwAlWrVmX58uV06NCB06dPU7lyZcN+aWlpvPnmm7zyyit07969WM+JqakpTZo04fLlyxw8eBAfHx/8/PyeiPdRSSfceq1atWLFihWGmye9e/cukXrF49fytS+MHcJ97V/+TonVFRsby+TJk9myZQsJCQlUrlyZ4OBgQkNDcXBwKFIdgYGBbN++vdDtmZmZWFpaFiuuxMREpk2bxrp167h+/TqVKlWia9euTJs2japVi3cj/fjx48yaNYv9+/dz9epVHB0dqVmzJkOHDuXFF18slb9rKkUGmwghHoKiKAwaNIjt27ezZ88eXF1dS/wct2/fZu/evdSqVavcJ96xsbH8888/eHl5UadOnSfii2p5Fn0jnp6Llhs1BhVga2pC1yqO2JiquaGFof5NycrKIisrC61WW+DDcLxKlSd51ifX+pZQ/RcafTKu0+nyHH93Em5iYmL42dzcHEtLSywtLbGwsDD8bGlpaUjin3QRERGkp6fz3HPP5fldjYuLo0WLFly5coXffvvNsCRiSkoK1atXJzk5md27d9OsWTMAsrKyCAoKYu/evaxcuZJ+/fo98Ny5ubnUr1+fU6dOsX79enr27AncmXSxT58+/P7778ycOZMJEyYAd15fW1tb+vbty48//gjA9u3bCQwMZPbs2YwbN85Q94gRI/jtt984ceIElSpVeujnJzU1laioKExMTGjWrBnW1tYPXZexlVbCfbfFixczYsQINm/eTPv27Uu8fvHwsrKyiImJwcfH575J4NOSdJ8/f542bdpw8+ZNevXqRe3atTlw4ACRkZHUqlWL3bt3F+lvhz7pDg0NLXD7hx9+iKlp0dt+ExISaNOmDdHR0QQFBdG8eXNOnz7N+vXrcXFxYe/evVSrVq1IdW3cuJHnn38etVpNz5498fX1JT4+nrVr15KYmMjgwYP5/vvvi1RXUd8/IC3dQoiHNGnSJDZv3szOnTtLJeGGJ6PFW7/29vXr12natClubmVnuaqnWU1XJ5xtbbiVVrprY9ubmeBkYYazhRlOlmY4WZhhb2aKvZkJtqYmqFUqcnJ16ACLzBzS09OxtLTE1tYWMzOzPC3T9z4Ku3Gj0WjYtGkTQUFBmJmZ5dl2d0v5vQ99eXZ2NllZWdy+fdtwAyArKwudToeJiYkhGbe2tsbW1hZbW1tsbGywtbUt1peosiwoKKjAcjc3N4YOHcqkSZPYtm2bIen+7bffuHXrFq+//roh4YY7Nz6mT59Ox44dWbRoUZGS7u3bt3Pq1Cnat29vSLjhzk2STz/9lN9//51vvvmG8ePHo1KpuHXrFhkZGbRo0cKwr/7nS5cuGcp27tzJ119/za+//vpICTeAnZ0d7du3599//2Xbtm00atSIKlWqPFKdxvA4Em6AAQMGcPPmTXr06MGuXbuoX79+qZxHiEf19ttvc/PmTRYsWMDIkSMN5e+88w5z585l0qRJfPPNN0Wu72Em1i3IBx98QHR0NO+88w5z5swxlC9YsIDRo0fz9ttvs2XLliLVNWHCBLRaLdu2bSMgIMBQPn36dBo2bMgPP/zARx99hKdnyU66+mR8OgohHqsFCxawaNEi/v77b6pXr16q5yrPiXdKSgpRUVGYmZnRoUOHJ3byofKqTTVP1h879cj1mKpUuFqa4WJpfifB/i+5drIww0ytIilHS3y2hlvZGs6lZpKUoyVVk0uKJpdUrZa6FWxo61KBb89eY3jfFzAvxcRV3zpe3ORYURS0Wm2eJDwjI4O0tDTi4uJIT09Ho9EYbhjok3BbW1vs7OywtrZ+YlrI9Tcy7n4OIyIiAOjatWu+/du3b4+1tTV79uwhOzs7T7fwgtyvrmrVqlGzZk2io6O5cOECvr6+ODk5YWVlxaFDhwz7RUVFAeDl5QXc6co5aNAgnn/+eUJCQopzuYUyMTGhYcOGODk5ceTIEeLj46lbt265maPicSXceuPHjycuLo5nnnmGffv24e3tXarnE6K4zp8/z9atW/H29mb48OF5tk2dOpXvvvuO5cuXM2fOnMc6PC4tLY3ly5djY2OTL4kfMWIEX3zxBX/99RcXLlwoUmv3hQsXsLe3z5Nww52bqi1btmTjxo3cunVLkm4hhHGtWrWKiRMnsnbtWpo2bfpYzlkeE+/r169z+PBhfHx8qF27tnQnL4NaP0TSbapSUdnKHHdrC6pam+NuZYGrlTnZuTpuZOUQn63hakY2/9xOIz5bQ0K2Fu0DRnGlanOxM73zhT8hPZPKFewe+ppKi0qlwszMDDMzM+zs8senKAo5OTmkpaWRnp5OWloaiYmJXL58mbS0NExNTalYsSIVKlSgYsWKVKxYsVwm4lqtlmXLlgF5k+IzZ84AULNmzXzHmJqa4uPjw4kTJ7hw4QJ+fn73Pcf96gKoUaMG0dHRREdH4+vri1qt5q233mLBggUkJyfj7u7O8uXLsbe355VXXgHgo48+IiEh4ZFWlyiMu7s7FStW5ODBg+zZs4fmzZsXe6zm4/a4E269OXPmcOPGDZ555hn27t2Lk5PTYzmvEEURGRkJQOfOnfN9Z7Gzs8Pf35+tW7eyb98+OnbsWKQ6V61aRUxMDObm5vj5+REUFPTAG4/32rdvH5mZmXTu3Dnf549araZLly589913REZGFinprlu3LocOHWLXrl20bdvWUH7z5k0OHDhA5cqVqVOnTrFiLApJuoUQRbZ7924GDBjADz/8QOfOnR/ructL4q0oCtHR0Zw9e5YmTZqUyy6XT4vWD1ivWw1UsbbAw9oCd2sL3K3NcbU0JytXx9WMbGIzsglPTuJqZja3c7T3ret+Uv9bMgwgIT2jTCbdD6JSqbCwsMDCwiJf1+Xc3FxSUlJITk4mKSmJs2fPkpKSgqmpaZ4kXD/jd1k2YcIE/v33X7p160aXLl0M5cnJycCdWb4Loi9PSkp64Dkepq7Zs2djZWXFypUrSUxMpHHjxnz++ee4u7tz4MAB5s2bx5IlS3B2dmbKlCl8++233Lp1i4YNG7JgwQL8/f0fGNf92NjY0K5dO44ePcr27dtp2bIlFStWfKQ6S4uxEm64kyAsW7aMbt260b17d7Zv317sBESI0lKUG35bt24lOjq6yEn3vUNqXFxc+Oqrr4rV46YocQFER0cXqb65c+fSvXt3OnXqRK9evahWrRrx8fGsW7eOihUrsmLFilL5LJKkWwhRJJcvXyY4OJgJEybw8ssvGyWGsp54a7Vajhw5wu3bt2nXrl2hX5pF2eBqb0t1Z0fO3boz27c+ya5ma0k1Wyt8bC3RKQpX/kuwT11P52pGDkmah0+wC5Ki0WKmVmNlon4i1+o2MTHBwcEhz6y3ubm5pKamkpSUlCcRt7a2xsnJiUqVKhm6TZcVCxYsYM6cOdSuXZvly407Cd+9LCwsmDlzJjNnzsxTnpOTw4ABA+jatSuvvvoq8+bNY+rUqYSGhuLv78+MGTPo2rUr586de+S5OUxMTGjSpAnnzp1j165dNG7cGHd390eqs6QZM+HWMzU1Zc2aNbRs2ZIhQ4awePHictfjQzyZSvLmYa9evXjvvfdo3LgxlSpV4tKlSyxdupQ5c+bQt29f/vzzzwKH0JR2XADt2rVj79699OnTh19//dVQbmdnx4ABA0ptzgVJuoUQD5Senk6PHj3o0KEDH374oVFjKauJd2ZmJvv378fU1JSAgABpvSgHdDodQd7uVFHl5kmyY9KyOJeawV/XE4nLzKG0l/jI0Snk5OqwMzMhPu3JS7oLYmJiYmjh1tNoNCQmJhIfH8+FCxc4cuRImUnCFy5cyOjRo6lTpw7h4eE4Ojrm2a7/0qf/cngvfXlRWn9Lsq5p06Zx9epVtm7dCsBnn31Gx44dDeMia9Wqhbe3N1999RXTpk17YH0PolKpqFGjBnZ2dhw6dIiUlBRq165dJpLKspBw69na2rJhwwZatWrFvHnzGDt2rNFiEaI03PuerlWrFp988glVqlRh5MiRTJw40ZB0JyUlMW/evHx1jBkzplR6zISFhdGvXz+aNWvGsmXLqF27NnFxcSxcuJBJkybx559/sn379hKfGFSSbiHEfSmKwhtvvIGJiQnLli0rE2OTy1rinZiYyIEDB3Bzc6NBgwZl4jkSBcvKyuLGjRvExcURHx+Pr05Hlo3lY02yC5Ly37juJ7Glu6jMzMxwdXU1tLjem4QfPnwYGxsbXF1dcXNzo1KlSo/ld02fFNWrV4/w8HBcXFzy7VOrVi2ioqKIjo7ON9eFVqslJiYGU1PTIo03rFWrFlB4V8mzZ88ChXe11Dt69CizZ89m0aJFuLu7k5KSwrVr1wzjvAE8PT1xcnLixIkTD4yrONzc3GjXrh0HDhwgJSWFJk2a5JtJ/3EqSwm3nq+vLytXrqRnz57UqVMnz3AFIYyhJG/4FWbw4MGMHTuWo0ePkpqaip2dHUlJSUydOjXfvv379zfMBVJScSUmJtK3b1+sra1Zu3atYbnDatWq8cUXXxATE8O6dev46aef6N+//8NdZCHkm6EQ4r6mT5/Onj172LBhQ5maHEefeJ85c4bz588bLY5Lly6xZ88eatasScOGDSXhLmMURSElJYXo6Gh27NjB1q1buXTpEg4ODvj7+9Pxmc6svHSLXbdSuG6khBsgVaPFzsyU+Kc46b6XPgmvW7cuAQEBdOvWjXr16pGbm8uhQ4fYvHkzUVFRxMbGkpOTUyoxzJ49m7Fjx9KoUSMiIyMLTLjh/5cYK2jJmh07dpCRkUGbNm2K1APmfnVduHCB6OhovLy87pvAa7VaBgwYQGBgIIMHD86zLTs7O8//s7KyHhjTw7C3t6d9+/ZotVp27txJenrpLs9XmLKYcOt16tSJmTNn0rdv3yKPRxWitJTUDb/7sbS0NEyGpv+b4O3tjaIo+R76Gf5LMq49e/Zw+/ZtWrZsaUi479ahQweAPKtBlBRp6RZCFGrt2rXMmjWLrVu3UrVqVWOHk48xW7x1Oh0nTpzgypUrtGzZEmdn58d2bnF/Op2OhIQE4uLiiIuLIzs7G2dnZ7y8vGjRokW+m0cNq1Ym6vJVI0V7R6omF3uzp7ul+0HMzMxwc3PDzc0NRVFISkoiLi6Os2fPcvjwYSpVqmTYXhLL2Xz88cdMnjyZpk2bsnXr1nxdyu8WEhLC+PHj+eWXXxg5cqRhre6srCzDkJxhw4blOSYjI4PLly9jbW2dZ2magIAA/Pz82LFjBxs2bDCs1a3T6Rg/fjwAQ4cOvW+X7VmzZnHu3DnWrl1rKLO3t8fd3Z0tW7ag1WoxNTVl+/btpKamUrdu3WI+O0Vjbm5O69at+ffff9mxYwfNmzd/rDN2l+WEW2/06NEcO3aM7t27c+DAgTI7AZ148ukTzq1bt6LT6fI0IqSmprJ7926sra1p1arVQ5/jzJkz3L59Gzs7uyL/LWjVqhVWVlbs3r3b0Dqup9PpDMNn9PHfj/6m461btwrcri83NzcvUmzFIUm3EKJAx48f5/XXX2f+/PmPPLNtaTJG4p2Tk0NUVBRZWVkEBAQ81vUqRcEURSE+Pp7Y2FiuX7+OWq3Gzc2N+vXr4+zsfN8v262reZaJpNvO1ISE9EyjxlFeqFQqw+Rsfn5+ZGRkGG6ynDhxAltbW6pWrUrVqlULbM14kKVLlzJ58mRMTExo164dCxYsyLePt7e3ofuhvb0933//PSEhIQQGBtKvXz8cHR3ZsGEDZ86cISQkhL59++Y5/sCBA3To0IGAgAC2bdtmKDcxMWHx4sUEBQUREhJCSEgInp6ehIeHExUVhb+//33HAJ88eZKPP/6YOXPm5FsLety4cYwePZp27drRsmVLfv75Z2xtbfOtyVuS1Go1DRo0wN7enn379lG3bl18fHxK7Xx65SHh1vv2228JDAykb9++bNq0qUzHKp5cvr6+dO7cma1bt/LVV18xcuRIw7bQ0FDS09MZMmRInu88p0+fBqB27dqGspiYGCpUqJDvRuWtW7cYMGAAcGdW86KOmba1teW1117ju+++Y8qUKcyZM8ewbeHChVy8eJEuXbrk6/1z/vx5NBoNvr6+huEtrVu3xtTUlN27d7N169Y8K/FcuXKFb7/9FqDIs7MXh0pRHrCAqBDiqRMfH0/Tpk3p1atXgV82y6Lbt2+zd+9eatWqVaqJd2ZmJnv37sXa2pqmTZsadZyiuDOWKzY2ltjYWHQ6He7u7lStWhUHB4ciT9505Mo1XvrfqlKO9P4CXSviZmnG0SxYN+zVR6pLo9GwadMmunXr9lS+PzUaDXFxccTGxnLr1i0cHR2pWrUqVapUKXLrxZQpUwocY3i3e5NluLOs4owZM9i7dy9ZWVlUr16dgQMHMmrUqHyJ1LZt2wpMuvVOnjxJaGgokZGRpKam4uXlxUsvvcSECRMKnVAuNzcXf39/zM3N2b59e77fAUVRmDFjBt98841hybC5c+c+thurCQkJHDhwAE9PT+rUqVNqE6yVp4RbLz4+nubNm/PCCy/w+eefGzucp0ZWVhYxMTH4+Pjcdwhdy9e+eIxRFd/+5e+USD3nz5+nTZs23Lx5k169euHn58f+/fuJjIykZs2a7NmzJ8+ykPrf4bvTySVLljB06FDatm1LtWrVcHR05PLly2zatInk5GSaNWtGWFhYsXp1JCQk0KZNG6KjowkKCqJFixacOnWK9evX4+Liwp49e/J99/P29ubSpUvExMTkuQE5bdo0QkNDUavVdO/e3TCR2po1a0hLS6N3796sWbOmSHEV9f0DknQLIe6h0Wjo2LEjFhYWbNmypVx8WdEr7cQ7NTWVvXv34uzsLOO3jSgzM5PY2FiuXLlCRkYGlStXpmrVqjg7Oz/Ua5Kr09Fq9jek3jPW9XFq5mhLY0c71sWlsPO9tx6prqc96b5bVlYWV69eJTY2lpSUFFxdXalatSqurq7l6m/bk0T/d9TJyYlGjRqV+N/R8phw6x09epT27duzcOFCXn/9dWOH81SQpDu/K1euMHnyZLZs2UJCQgKVK1emd+/ehIaG5ln6EQpOuo8fP86cOXM4dOgQ165dIyUlBTs7O+rWrUufPn0YMmTIQ3XfTkxMZOrUqaxbt47r169TqVIlnn32WaZNm1bgEMjCkm6A9evX88033xAVFcXt27extramXr16vPbaa7z11ltF/rvx0En35cuXiY+PL9JJhChNTk5Oeca4icdn2LBhREREcODAgXK5znRpJd6JiYns27cPHx+fMrMEztNEq9Vy9epVrly5QmJiIs7OzlStWpXKlSuXyLIew1duIPyM8Sbkq2lnRXf3SiyIvsbxj0Y90vtLku6CpaWlGXpFZGdn4+7ujqenZ7F6RYiSkZmZ+X/snXd4VGX+t++ZlEnvIZX0DgmBhARSCAFEZF0rdlBA3bULrG3XXUDXrigi1lVBwdV9AQVdFYFAQksCAUJLJb2QhPQ6KTPz/pHfZIkJkDKTk0zOfV1c65458zyfGYaZ8znfRkpKCsbGxoSHh2tsNM9YNtxqtm3bxgMPPMD+/fuHVTsrMjAGY5pERH7PYD4/Pd9yxcXFPXVRIiJCY2JiQmZmpmi8R5iPPvqI//znPyQnJ49Jww3aqfGurKzk+PHjBAUFDWjkj4jmaGxspLCwkJKSkp6GU+Hh4Rq/OIrydhPUdDd1KTA30KNLqaS+TY61iTDzqHUZMzMzAgIC8Pf3p66ujpKSkp5SEQ8PD1xdXcWbFCOEsbExMTExpKamcvToUWbMmDHsxkW6YLgB7rjjDs6cOcPNN9/MyZMncXFxEVqSiIiIBugx3dXV1bS2trJ161YCAwOF1CQyzsnMzGTx4sVUV1eLpnsESUxM5JlnnmHbtm094xnGKpo03sXFxZw5c4apU6eKFz8jhFKppLy8nIKCAurr63FxcWHmzJlajUjO9Bq57xqpRIKVsRFWRkaYGRgik+hhKpFgoq9HhPEE6htaRdOtRSQSCTY2NtjY2DBp0iTKysooLCzk/PnzuLq64unpOWZvOo4lDAwMmDlzJmlpaRw6dIioqKgr1qpfC10x3Gpeeuklzp07x0033cThw4eH/L6IiIiMHvrk8wQGBjJt2jQhtIiIiAhERUUFd955J3//+9/5wx/+ILQcjTBc461Sqbhw4QI5OTniSLARoq2tjaKiIgoLC9HX18fT05PIyEitjO74PV52NjhZmHOxsUlja5rLDHEyN8fSQIZ+l4SOpk7qL7VSW9OMQtFKDa3UXHb+9bd5UZFfQ219K55OtldcV0Rz6Ovr4+7ujru7O/X19RQWFnLo0CGsrKzw9PTEyclJ7N2gRfT09IiIiOD06dMcPHiQmTNnYmFhMag1dM1wQ3fH92+++YbIyEhWrFjR01FZRERk7CKODBMRGecoFAruvfdewsPDeeGFF4SWo1GGarxVKhXnz5+ntLSU6OhocW6qlqmvr+fChQtcvHgRe3t7pk6dyoQJE0a8zjbK240dp84P+nkGelImWlpiKzNGvwPkjR3UVrZQX9dKJe1UDnCdlnYFZkZ61DWKZV5CYGVlRWhoKEFBQRQXF5ORkcG5c+fw9PTE09NTTD3XEhKJhClTppCdnc3hw4eZMWPGVWeiX44uGm41JiYmbNu2jYiICObOncudd94ptCQREZFhIJpuEZFxzuuvv86FCxdIT0/XyYjOYI23Uqnk1KlT1NbWEhMTg5mZ2UjIHHeoVCpqamrIzc2lpqYGNzc34uPjBX2/Z3pd23SbGBow0dISK30ZkjYljdVtVBY1UNNV2ytqPRSa5QrMjPSpbRBNt5AYGhri4+ODt7c3lZWVXLhwgdzcXDw9PfHy8hKbLWkBiURCQEAAMpmMo0ePEh4ejqOj41Wfo8uGW01AQADr16/noYceIiwsTKvjMEVERLSLaLpFRMYxhw4d4rXXXuPXX38dcGRhLDJQ461QKDh27Bjt7e3ExsaKF9daQKVSUVlZSU5ODk1NTXh5eTFt2jRkMpnQ0ojyckMCXD5H09TQAC9rG8yU+jRVtlKWXUeF8hIVWti/Wd6FqZEetWKke1QgkUhwdHTE0dGx5wbRvn37mDhxIr6+vpiYmAgtUefw9PREJpORlpbGlClTmDhxYr/njQfDrWb58uXs37+fO+64g+Tk5FHxXSkiIjJ4RNMtIjJOqa6u5q677uK5554jLi5OaDla51rGu6uri2PHjtHV1UV0dLSYSqphlEolZWVl5Obm0tHRgbe3NzNnzhxV77ONqQlTXJ1QdCowUxrQXNVGaXYtxUptWOy+NLcpMDMW08tHI7a2ttja2tLQ0EBubi4JCQk4Ozvj6+s76Bpkkavj7OyMvr4+x44dQ6VS9WmoOp4Mt5rPPvuMsLAwnn/+edavXy+0HBERkSEgmm4RkXGISqXi/vvvJyAggNWrVwstZ8S4kvHu6uoiNTUVlUo16ozgWEepVFJUVMSFCxcA8PHxwc3NbdReKEebOrP1lzRB9m5p7xLTy0c5lpaWhIeH09zczIULF0hKSmLChAn4+flhbW0ttDydYcKECURGRvZ8L7u7uwPj03BDd333d999R3R0NHPmzOGmm24SWpKIiMggGXQBZ2FhIRKJhAULFmhUSGJiIhKJhLVr12p03eEgl8tZtWoVs2bNwtnZGSMjIxwdHYmOjmbTpk10dnb2On/z5s1IJJKr/pk7d+6A91cqlWzcuJFp06ZhYmKChYUFs2bN4scff+xzbllZGevXr2f+/Pm4ublhaGiIo6Mjt99+O6mpqf2u39jYyEMPPYSdnR2enp68++67g3uDRMYs7777Lunp6Xz77bc6Wcd9NdTGOzs7m7y8PLq6ukhJSQFgxowZouHWECqVipKSEhISEigoKCAgIIC5c+fi6ek5qi+Up08Wbkxhc5sCU7GR2pjAzMyM0NBQ5s2bh6mpKUeOHOHYsWM0NWmu+/14x97enhkzZnDu3DkKCwvHreFWM2XKFN58800eeOABiouLhZYjIiIySMRI91Vobm7m448/JiIigj/84Q/Y29tTV1fHr7/+yvLly/nuu+/49ddfe0xLaGgoa9as6Xet7du3c/78ea6//voB7a1SqbjzzjvZsWMH3t7ePPjgg7S3t7Nr1y5uvvlmPvjgA5544ome8z/44APefPNNvL29mT9/Pvb29uTm5rJz50527tzJv//9b+66665eezz00EPs3buXJUuWUF1dzbPPPouZmRl/+tOfhviOiYwFjh07xt///nd27tyJg4OD0HIEQW28jx49Sn5+PiYmJkRGRqKvL34lDheVSkVVVRUZGRl0dnYSEBDAxIkTR7wT+VAJ9XfB0ECPjk7FiO/d00hNg2PLRLSLsbExkydPxsfHh+zsbBITE5k4cSL+/v7ibGUNYGdnx4wZM0hJSSEnJwdzc/NxabjVPP744xw4cIC77rqLgwcPijeJRUTGEOIV5lWwsbGhoaGhz4zYrq4urrvuOvbs2cOvv/7aM9c4NDSU0NDQPut0dHSwceNG9PX1eeCBBwa0944dO9ixYwfR0dHs3bu358f7tddeIzw8nGeeeYYbb7wRDw8PACIiIkhMTOxTm3vo0CHmzp3Lo48+yi233NLTgKO1tZUdO3bw888/92QtuLi4sHnzZtF06zD19fXccccdPPXUUwO+AaSrmJubY2JiQlNTEx4eHqLh1gC1tbVkZGTQ1NSEn58fHh4eY+7i2MjQgBBfZ9IySkZ87xZ5lzgybIxiZGTElClT8Pb2Jisri4SEBDw9PfH19R2ROfO6jIWFBcbGxjQ3N+Pj4zPmvlM0zZdffsm0adNYvXo1r7/+utByREREBojW8ko7Ojr44IMPuP7665k4cSIymYwJEyZw2223cerUqV7nrl27lvj4eABeeumlXunYhYWF2pJ4TaRSab8/lvr6+tx6660APXWKV2Pnzp3U1NRw4403DjiyuGvXLgD+9re/9bpbbmdnx8qVK2lvb2fTpk09x2+77bZ+m2HFxsYSHx9PXV0dZ8+e7TmuUqlQqVS9Uov19PRQKpUD0icy9lCpVDz44IO4urry2muvCS1HUNQp5YaGhkRFRZGbm0teXp7QssYsTU1NpKamcvToUWxtbZk3bx7e3t5j9uJ4+iR3QfZtliswlenRJu9E3tF57SeIjDrMzMwIDw8nJiaGhoYG9u3bR05ODl1dXUJLG5OoU8qNjY2ZOXMmmZmZFBUVCS1LUCwsLPj3v//Nhg0b2LNnj9ByREREBojWQju1tbWsWLGC2NhYFi5ciLW1Nfn5+fz444/8+uuvHDx4kOnTpwMwe/ZsCgsL+eqrr4iLi2P27Nk961hZWWlL4pBRKpXs3r0bgMmTJ1/z/M8//xzoTuceKBUV3d1yPT09+zymPrZ//35eeumla66lTj+6PJJnamrKwoULueeee1i8eDG1tbV8++23vPfeewPWKDK2+Pjjjzl06BAnT54cs2ZIE6ibpkkkkp6U8sHM8Rb5H3K5nMzMTEpLS3F3d2fevHk6MWYtcrIbH28b+X2b5QqkUgkmMj1qG1pxtrcceREiGsHKyoqoqCguXbpERkZGT18DNze3MVNqITT91XDPmDGj57ta3VxtPBIREcHatWu57777OHPmDE5OTkJLEhERuQZaM93W1tYUFxfj4uLS6/j58+eZMWMGf/vb39i7dy9Aj8n+6quvmD179qCbqa1fv576+voBn3/LLbf0mwZ+JTo6OnjttddQqVTU1NSQkJBAVlYWy5Ytu2ZjtKKiIhISEnB1dR1U8zk7OzsACgoKCAwM7PVYQUEBADk5Oddcp7i4mH379uHk5ERwcHCvxzZv3szTTz/Nli1bMDMz45VXXulVJy6iO6Snp/PMM8/w7bff4urqKrQcwVDP4VapVMyYMaPnRtRA53iLdKNUKikoKCArKwt7e3vmzJmDqamp0LI0hr+HAxZmRjQ2y0d0X4VSRVvH/5qpiaZ77GNvb8+sWbMoLy/n/PnzFBUVERISMioDCqOJKzVNs7W17anxlkqlV5zjPR74y1/+QlJSEvfeey/79u0b1zfTRUTGAloz3TKZrI/hBpg0aRLx8fH89ttvdHZ2aqQJxPr16weVbuTh4TFo0315RFkikfDMM88MqJZm06ZNKJVKli5dOqgvxBtuuIHvvvuON954gzlz5vREj2pqanpmNF7rRkNnZydLliyhvb2dN998s8/+dnZ2fPPNNwPWJDI2aW9v55577mH58uXcfPPNQssRDKVSyfHjx1EoFL0MtxrReA+Mmpoazpw5g0KhYPr06UyYMEFoSRpHKpUQHjiR/cdzR3zvFrni/+q620Z8bxHtIJFIcHFxwcHBgdzcXA4fPszEiRMJDAwU67374Vpdyu3s7HrGienp6eHs7CyQUmGRSqV8/fXXTJ06lXfffZdnn31WaEk6TcDa0Z0JmrV2pcbWKi0tZfXq1ezevZuamhqcnJy45ZZbWLNmzaBGIx4+fJi3336b06dPU1FRwYQJE5g8eTJPPfXUoKdg7d27l927d5Oenk56ejq1tbVER0dz+PDhwb48oDsj++WXX2bnzp1cvHgRW1tbFixYwMsvv6y14JRWOwelp6fz1ltvcfjwYSoqKvqM2KqurtZISoy2677NzMxQqVQolUrKy8v56aef+Nvf/kZycjK//PILFhYW/T5PqVSyadMmJBIJy5cvH9Se9957L5s3b+bAgQMEBwezYMECOjs7e3WcvtqoJ7XRP3jwIA8//DBLliwZ1P4iuoM6c2TdunXCChEQlUpFeno6bW1txMTEXPFmn2i8r4xcLicjI4Py8nL8/PzGdM32QIiY7C6I6W6Wq2d1t4z43iLaRV9fn8DAQCZOnMjZs2dJSEggKChITDm/jIGOBbO3tyc8PJy0tDQMDAywt7cfYaWjAxsbGz7++GMWLVrEH//4RwICAoSWJDLGycvLIyoqiqqqKm6++WYCAgI4duwY77//Prt37+bIkSPY2tpec52PP/6Yxx57DFNTU2699VZcXV0pLS3l+++/59dff+WVV17hxRdfHLCuDz/8kF27dmFkZISPjw+1tbVDfo01NTVERUWRk5PDnDlzuPvuu8nKymLTpk38/PPPJCcn4+XlNeT1r4TWGqkdPXqUGTNm8P333xMaGsqTTz7J6tWrWbNmDVOmTAG6I3BjCalUiqurK48++iifffYZR44c4dVXX73i+fv27aO4uJg5c+b0W5t9NfT19fn1119Zu3YtUqmUzz77jO+//56bb76Z7du3A1wxwqRUKlm+fDn//ve/Wbx4MZ988smg9hbRHY4fP8769ev58ssvezrXj0cyMjKoqalh5syZ18yu+f0c7/GOUqkkLy+PhIQEFAoFc+fOxc/PT6cNN0CEQPO6m+UKzIz1qBUj3TqLmZkZM2bMIDQ0lOzsbA4dOjSoEjldZbBzuB0dHQkJCeHYsWPj+v1buHAhd955Jw888AAKxciPOhTRLR577DGqqqrYsGEDO3fu5I033mD//v2sXLmS7OzsARnlzs5O/vrXv2JkZMSJEyfYsmULr7/+Olu2bCEtLQ2ZTMarr746KB/4/PPPc+7cOZqbm/npp5+G8xL529/+Rk5ODqtWrSIhIYE33niDnTt38v7771NVVcVjjz02rPWvhNYi3eo389ChQ8TExPR6LCUlhdOnT2tsL23XdPfH/PnzAUhMTLziOUNpoHY5MpmMNWvW9Jn9rd4zPDy8z3OUSiXLli3j66+/5p577mHz5s1XjYiL6C5yuZz777+fRx55hJkzZwotRzAuXLhAcXExsbGxA27yJUa8u6mtreX06dM6nUp+JVwmWOEywZKyqoYR3Vfdwby2UYx06zISiQQnJyfs7e17pZwHBQWNy9nLgzXcatzc3GhvbyclJYXY2Fid6i0xGDZu3MikSZNYt24dzz33nNByRMYoeXl57NmzBw8PDx5//PFej7300kt89tlnbNmyhXXr1l3131ptbS0NDQ2EhITg7+/f67HAwED8/Pw4e/Yszc3NAw4Iaeo6trm5mS1btmBqatqnh9gTTzzBu+++y2+//UZ+fr7Go91aM915eXnY2Nj0Mdytra2cPHmyz/nqL9ih3KXTdk13f5SXlwNc8cexpqaGXbt2YWNj0zNeTFOo67DvvvvuXscvN9x33XUXW7Zs0flolMiVUX+ZvPnmm8IKEZCSkhKys7OJiorCzMxsUM8dz8a7q6urZzTPeEglvxIRk935Yf+ZEd2zua0LF1sjcVb3OOHylPMzZ86wf/9+QkNDBzxeVBcYquFW4+PjQ3t7O8nJycTExOjEBIXBYmZmxqeffsptt93GH//4xz4NeEVEBsKBAweA7sDi7wN25ubmREdHs2fPHlJSUq7aSHrChAnY29uTk5NDbm4uvr6+PY+pj4WGhg4oTV3TpKSk0NbWxvz58zE3N+/1mFQq5frrr+ezzz7jwIEDGjfdWguBuru7U1dXx/nz53uOKRQKnnnmGS5dutTnfBsbG6D7InmwFBYW9sydHsifpUuXDmjdjIwMWlv7Xvi0trayatUqoDutpz+2bNlCR0cHixcvvuZdnLy8PLKysvrUvDc2NvY5d/v27Xz55ZdMnz6d2267ree4OqX866+/5o477mDr1q3j8iJZpBt1/c2mTZvGbaOeqqoqTp8+zfTp0wfV+ONyxmOqeU1NDYmJidTV1REXFzcuUsmvxPRJI59iLjZSG5+YmZkxc+ZMAgICSEtL4+TJk32uCXSR4Rpu6M4amDRpEtbW1qSkpIyL960/FixYIKaZiwyL7OxsAPz8/Pp9XG2erzU9SSKR8OGHH6JUKgkLC+OBBx7gr3/9K/fffz9hYWFMmjSJbdsEmMuJ5l7jUBhypPvs2bNXNK8BAQE8+eST7Nmzh5iYGO68806MjIxITEykrKyM2bNn90nLDggIwNnZme+++w6ZTIarqysSiYQnn3wSS0thxqb8v//3/3j33XeJiYnBw8MDCwsLysrK+PXXX6mpqSE2NpaVK/vvFvjFF18AA0stnzt3LkVFRRQUFODh4dFzPDIysqfDqZGREceOHSMxMREvLy+2bdvW68fp5Zdf5quvvsLMzAw/Pz9eeeWVPvtoIq1eZPSjTit/9NFHmTFjhtByBKGuro5jx44RGho67JTo8RLxVigUZGZmUlhYSEBAAN7e3uO+uVN40ESkEglKlWrE9myWKzAVG6mNSyQSCe7u7kyYMIH09HSdj3prwnCrkUgkTJ06lZSUFI4dO8aMGTPG5c3CjRs3MnnyZN555x2ef/55oeWIjDEaGrrLqa7ku9THB1LSe8cdd+Ds7Mw999zD119/3XPcwcGBZcuWaaVR2UDQ5GscLEM23eXl5Xz11Vf9PhYXF8cLL7zA9u3bee2119i6dSsmJibMmTOHH374gZdffrnPc/T09Pj+++95/vnn+fbbb2lqagJg8eLFgpnuG2+8kfLyco4ePUpycjLNzc1YWloSEhLC3XffzfLly/uMHYLuKOO5c+eIiIjoMxt7MNx11118//33PXduPT09+fvf/86zzz7bp2O6uoN7c3PzFZu7aSKtXmT0s2bNGqRSKW+88YbQUgShqamJlJQUAgMDNTb2QdeNd21tLSdPnsTQ0JC4uLg+KVfjFUszY/w9JpBZUDlie3Z3LxcbqY1njI2NmTFjBsXFxaSlpeHs7MzkyZN1qtZbk4ZbjVQqJSIigiNHjnDq1CnCwsLG3Y1DdZr5rbfeyk033SSmmYsIxtatW3n44Ye57bbb+Mc//oG7uztFRUX885//5IknniApKYn/9//+H9BtcNXjkC9nxYoVWFlZjaxwLTJo0+3h4YFqgHf9b7/9dm6//fY+xzdv3szmzZv7HI+MjLxqY7KRJjw8vN9mZdciIiJiwO8RXHnk2dq1a/sU+V+JK72nIuOL1NRUPvjgAw4cODAu08rb2tpITk7G3d1d48ZYF423QqEgKyuLgoIC/P398fHxGXcXqdciYrL7iJruFrkCQ30pbW1ylEoVUqn49zEeUUe97e3te6LeU6dO1Ylmhtow3Gr09fWZMWMGhw4d4uzZswQHB4+777Trr7+eu+++m/vvv5/k5OR+g0MiIv2hDnKqo8G/R338WkY4JyeH5cuXExISwpYtW3rqwwMCAtiyZQvZ2dls27aNxMREZs+eTX19PS+99FKfdZYuXapx062p1zgUxLbWIiI6gjqt/LHHHiMyMlJoOSNOZ2cnKSkp2Nvba+3uvi7VeDc0NJCYmEhNTQ1xcXH4+vqOu4vTgTDSo8PknUq6FEpMZHrUN4nR7vGOiYkJM2fOxN/fn+PHj/dMExiraNNwq5HJZERFRVFeXk5ubq7G1x8LfPDBB1RXV/POO+8ILUVkDKHuNH6lemb1v6cr1UOr2bNnD52dncTFxfVpyCaVSpk1axYAJ06cAP4X0P39n8tLbjWFpl7jUBBNt4iIjrB69WoMDAx47bXXhJYy4qhUKk6cOIGRkRFTpkzRqnkc68ZbpVJRUFDAoUOHcHV1JSYmRkwnvwohvs4YGY5spKi5p5ma2MFcpDvq7eHhQXx8PA0NDRw8eLCnBG8sMRKGW436ZkVubm7PtJnxhKmpKZ9++ikvv/wyGRkZQssRGSPEx8cD3aZZqVT2eqypqYkjR45gYmJyzX5B6vnb/TXOvvy4EBmZM2bMwNjYmCNHjvT5HlUqlezZswf433uhSUTTLSKiA6SkpLBx40a+/PLLcZlWnpGRQXNzM+Hh4SMyl36sGu/Ozk6OHz9OTk4OM2bMwN/ff0Ter7GMoYE+of4uI7pnt+nWp1Y03SKXYWJiQkxMDA4ODiQlJVFcXCy0pAEzkoZbjaWlJVOnTuXUqVNXTCXVZebPn88999zD/fffT1dXl9ByRMYA3t7ezJ8/n8LCQj788MNej61Zs4aWlhaWLFnSa0Z3VlYWWVlZvc6NjY0FuicunTnTe+xmeno627dvRyKRMGfOHC29kitrMzMzY8mSJbS0tPQp4d24cSOFhYVcf/31Wmn0JhZ6iIiMcdrb23nggQd4/PHHiYiIEFrOiFNSUkJRURGxsbEj2mhorNV419XVkZaWhpmZGbNnz77mKEOR/zF9kjspZ4tGbL8WeRemRnqi6Rbpg1QqJSgoCFtbW06ePEl1dTUhISGjum5XCMOtxtnZmaamJlJTU4mLixt333sbNmwgODiYd955hxdeeEFoOSJjgI8++oioqCieeuopEhISCAwMJDU1lQMHDuDn59enWbO6nO/yXlYREREsW7aMTZs2MX36dG699Vbc3d0pLCxk586ddHR0sGLFCiZNmjRgXYcPH+bzzz8HuptGQ3cq+OWTtH7f26o/bQCvvfYaiYmJvPvuu6SnpxMREUFmZia7du1iwoQJfW44aIrR+y0tIiIyINatWwdwxa71ukxdXR2nT58mIiJCkBTpsWC8VSoVeXl5ZGVliaPAhkjEZPcR3a+5TYGZsR61DaLpFukfBwcHZs+ezcmTJ0lKSiI8PFywSS9XQ0jDrcbPz4/GxkaOHz9OVFTUuMruMTU15aOPPuL222/n3nvvxc1tZHtUiIw9vL29SUtLY/Xq1ezevZtffvkFJycnnn76adasWYO1tfWA1vniiy+YNWsWmzdv5rfffqOpqQkLCwtiYmJ4+OGHufvuuwel68KFC32mZlVVVfU6NtCG0ra2tiQnJ/PSSy+xc+dODh06hK2tLcuWLePll1/W2OSb3yNR/Z/9P3nyJGFhYZw4cYJp06ZpZTMRkYEgfhYHTnFxMYGBgezYsYMFCxYILWdEaWtr4+DBg3h7e+Pj4yOolrq6OpKTk/H39x9Vxru9vZ1Tp07R2NhIeHg4NjY2Qksak6hUKm544tMRq7GODrTGxswAG0cPHrszZtDP7+zs5JdffmHhwoU6NWZKpC8qlYqcnBxyc3OZNGkSHh4eo+am2mgw3Gq6uro4fPgw1tbWTJkyRTAdQnHTTTehr6/P999/L7SUUYdcLqegoABPT0+MjIyEliMyxhjM52f83O4TEdFBVqxYwbx588ad4VYoFBw7dgx7e/tRYXJHY413XV0diYmJSKVSZs+eLRruYSCRSJg+aeKI7dfc1p1eLjZSE7kWEokEf39/ZsyYQXZ2NidOnBgV9bujyXBD9yixiIgIysvLKSgoEFSLEHzwwQfs3bu3p0mUiIjIyDNo011YWIhEIhl3F/kDYevWrfz5z38mPDwcmUyGRCK5aqpDWVkZ69evZ/78+bi5uWFoaIijoyO33347qampwz5/sLz55ptIJBIkEgkpKSka2buxsZGHHnoIOzs7PD09effdd4etU6SbPXv2sHfvXjZu3Ci0lBFFpVKRnp6ORCLReqfywTCajHdJSQlHjhzB29ub6dOnj8vmeppmJFPM1d3LxfRykYFiZ2dHfHw8crmcw4cP09Ym3Li50Wa41ZiYmBAREcH58+ev2FVZV3F3d2fVqlU8/vjjPZ2lRURERhYx0q1B/v73v/PZZ59RVFSEk5PTNc//4IMPWLlyJfn5+cyfP5+//OUvxMTEsGvXLqKiovjPf/4zrPMHw7lz51izZk2vjoSa2Puhhx5ix44d3HvvvcycOZNnn32Wzz77bMg6Rbppb2/n8ccf5y9/+QsTJ45cBG40kJeXR3V19ai6mFMjtPFWqVScP3+eM2fOMH36dHx8fEbNTYmxTsSkkTTdXZgZ6YuR7kGyfft2nnzySWJjY7GwsEAikbB48eJ+z1UHEK70Z7D1hgBHjx5l4cKF2NjYYGxsTEhICOvXr+93rnZjYyOPPfYYrq6u2Nra8sc//vGK3xmff/45BgYGnDp16qr7q+dTW1lZkZSURG1t7aBfw3AZrYZbja2tLcHBwaSlpdHS0iK0nBHlxRdfRCKRiMEPERGBEBupaZDPP/8cX19f3N3deeONN/jrX/961fMjIiJITEwkLi6u1/FDhw4xd+5cHn30UW655ZaebpuDPX+gdHZ28sADDxAaGoqvry9bt24dtlaA1tZWduzYwc8//9yTGeHi4sLmzZv505/+NCiNIr1Zt24dEomEv/3tb0JLGVEqKyvJysoiOjp61NZeCdVcrbOzkxMnTtDc3MysWbPE2dsaxsHWHHcna4ou1ml9rxa5AhOZHvVNoukeDK+88gqnT5/GzMwMV1fXPqNi+mPKlCnccsstfY5Pnjx5UHvv2rWL22+/HSMjI+666y5sbGz46aefWLlyJUeOHGHbtm29zl+6dCk//vgjixcvxsTEhM2bNzN37lwyMjIwMTHpOa+srIxnnnmG559/nqlTp15Th1QqZcqUKVhYWHD06FGCg4Nxdx+ZG0aj3XCrcXd3p7GxkdTU1BGfeiEkhoaGrF+/njvuuIP77rtPbKomIjLCaD3SvWnTJiIjIzEzM8PMzIzIyMh+U64TExORSCSsXbuWtLQ0rrvuOszNzbG0tOTWW2+lsLBQ21KHzbx58wb143bbbbf1MbHQPd8uPj6euro6zp49O+TzB8qrr77K+fPn+fLLL6/4IzmUvVUqFSqVqlenUD09PZRK5aA1ivyP4uJiXn31VdavXz+u0oabm5tJS0sjNDR0wN0zhWKkI97Nzc0cPHgQlUpFXFycaLi1xEilmLe0K1CpVGIa6CB57733yMnJobGxkY8//nhAzwkNDWXt2rV9/ixatGjA+zY2NvLwww+jp6dHYmIiX3zxBW+//Tbp6enMnDmT7du389133/WcX1lZyQ8//MDq1avZvHkzH330ER9//DFFRUX897//7bX2I488gouLC6tXrx6wHolEgpeXF5GRkZw/f56zZ89q/Xd3rBhuNZMmTcLIyIhTp071GSekyyxcuJC5c+eycuVKoaWIiIw7tGq6n3rqKZYvX05ZWRkPPvggDz74IGVlZSxbtoynn3663+ccP36cWbNmYWho2FMfvXPnTubNm4dcLtem3FGF+s7rQGdvDvZ8NSdPnuTVV19lzZo1BAUFDU7kNfY2NTVl4cKF3HPPPTz99NMsWbKEd955h/vuu29I+4h0s2LFCubOncvChQuFljJiKBQK0tLScHNz09ooB00zUsa7qqqKgwcP4uDgQGRk5LiJ2gjB9EkjExlSqbqNt4EetMo7RmRPXSA+Ph5fX98RL6nYvn07ly5d4u677yY8PLznuJGREa+88gpAr5sARUXdM98jIiJ6jqn/W/0YdPeJ+eWXX/jyyy+HdIPV3t6euLg4Ll26REpKCh0d2vksjTXDDd0ZAeHh4dTV1Y27xmoffPBBT08YERGRkUNr6eUHDx7kgw8+IDAwkOTk5J75kWvXrmXGjBls2LCBRYsWERsb2+t5v/zyC9999x133XVXz7H777+fLVu2sHPnzgHXWa1fv576+voB673lllsIDQ0d8PnapLi4mH379uHk5ERwcLDGz1fT3t7O/fffT2hoKM8995xWtG7evJmnn36aLVu2YGZmxiuvvMITTzwxpL1E/tc87dy5c0JLGVHOnz+PRCJh0qRJQksZFNpONc/PzycjI4OQkBAxVXAECAuaiJ6eFIVC+9k6LZc1UzMxGj8ZLSNNeXk5n376KTU1Ndja2jJz5kxCQkIGtcb+/fsB+m0wO2vWLExMTDh69Cjt7e3IZLKef6snTpzoeU5aWhpAT7ZcZWUlK1asYOXKlURGRg759ZmamhIbG8vJkyc5ePAgkZGRGs2EGYuGW42hoSHh4eEkJydjY2ODlZWV0JJGBHVTtccee4xz584NuiRRRERkaGjNdKuHla9du7bHcEP3ReiaNWu477772Lx5cx/TPWvWrF6GG2D58uVs2bKF48ePD8p0X37H+Fp4eHiMCtPd2dnJkiVLaG9v580337zmD9hgz7+c1atXk5uby4kTJ4b0QzmQve3s7Pjmm28GvbZIX9TN01atWjViNXqjgfLyckpKSpg9e3avUoWxgjaMt0qlIiMjg+LiYqKiosRxYCOEmbGMIE8Hzl64qPW9ujuYdzdTc3Ww0vp+45W9e/f2ifjNnj2br776asA3srKzswHw8/Pr85i+vj6enp6cP3+e/Px8AgMDcXR05Oabb+all14iLy8PIyOjnv3+8Ic/APD4449jY2PDP//5z2G+QnoMcUZGBocPHyYyMlIj3xlj2XCrsbW1xc/Pj7S0NOLi4sZNptCLL77Iv//9b959991r9h8SERHRDFoz3eoum7Nnz+7zWHx8PADp6el9HgsLC+tzTJ1OOpjItTZqwNeuXdvn2IoVKzR2d1SpVLJ06VIOHjzIww8/zJIlSzR6/uUkJyfzzjvvsHbt2kE3jBnu3iJD491330UikfDiiy8KLWXEaG1tJT09ndDQ0Ct21h8LaNJ4K5VK0tPTqampITY2FjMzM03JFBkAEZPdR8R0t7R1YWthQNWlaspNQS6X097ejlwuRy6X09HR0dM3Q6lU9vpvdSnW/v37kUqlSKXSXp259fT0kMlkGBkZYWRkhEwmw9jYuOeYoaGhzne9NzEx4R//+Ae33HILXl5eAJw5c4a1a9dy4MAB5s6dS3p6+oC+dxoaGgB6BRguR3388muYr776iueee46ffvoJuVzO7NmzWb9+Paampmzfvp3vv/+epKQkpFIpTz75JN988w3Nzc1ERUXx0UcfDbocTJ0pZGxszNGjR5k+fToODg6DWuNydMFwq/H19aW6uprTp08TFham8599+F9TtTvvvJPFixePuykoIiJCoDXT3djYiFQqxd7evs9jDg4OSCQSGhsb+zxmYWHR55i6Vri/sRsjyUsvvdTn2NKlSzViupVKJcuXL+ff//43ixcv5pNPPtHo+ZfT1dXFAw88QEhICC+88ILWtYoMn+LiYl555RW2bds2bpqnKZVK0tLScHFxwcXFRWg5w0YTxrurq4u0tDTa2tqIjY0dtR3cdZnpk934YmfKsNaQSMDO3BBbcwPMjPUxM9LDzEgfM2O9nv82kemhUqloqi4mp6O2xyAbGxtjZWWFoaFhj5m+3FRLpVIaGho4c+YMoaGhPc0rLzflamPe3t5OU1MTly5d6jHzXV1dSCSSXqb8938sLS3H/GdvwoQJvPzyy72OzZo1iz179hATE0Nqaiqff/75FfvPDBdLS0s+/fRTPv30017Ha2treeKJJ3jssceIjY1lxYoVfPbZZ7z99tv4+fnx7LPPsmDBAnJycob0d+Dl5YVMJuP48eNDLkvRJcMN3Tckpk2bRmJiIkVFRXh4eAgtaUT4wx/+wJw5c1ixYgU7duwQWo6IiM6jNdNtYWGBUqnk0qVLTJgwoddjVVVVqFSqfg22ptBGTbe2OlwqlUqWLVvG119/zT333MPmzZuvmkY72PN/T3NzM7m5uQBXNHAzZ84E4Icffug1TmW4e4sMjb/85S/jrnlaZmYmCoViSJkYo5XhGO+Ojg5SUlKQSqXExMSMmzTI0UawtxMmRga0yjsHdL5EAnYWhjhZyXC07v7jYGmICqhu7KCpTUGLvIu6lk5Kqttolitolnfh7WCMs60xtk5e3DJ7xqA0mpubc+bMGWxsbAb9Oenq6uoVUVeb85aWFmpqamhtbaWlpQUjIyOsrKywtLTs+V9jY+NB7TUa0dfX56GHHiI1NZWDBw8OyHSrI9nqiPfvUR8fyA36p556CmNjY9544w1aWlr4+OOPWbJkCU899RTQXaM9a9Ys/v3vf7N8+fIBvqreuLi4YGBgwPHjx+no6MDHx2fAz9U1w63GyMiIsLAwUlNTsbGx0er16Whi48aNBAcHk5CQwNy5c4WWIyKi02jNdE+dOpVTp06RmJjInXfe2euxxMREAK3WUI+Vmu7LTexdd93Fli1brvojNtjz+0Mmk/Hggw/2+9jBgwfJzc3lpptuwt7evtcdX03sLTJ4jh07xs8//8z58+eFljJiVFZWUlBQQFxcnM59xoZivFtbW0lOTsbc3JywsDCde0/GEvr6ekwLmMjh9Pw+j0n/z2A7Wst6TPYEK0OUSqisb6eivp0TeQ1U1LVT29TJ1W7jWpsa4OeiR23jyM7q1tfXR19f/6pp1Z2dnTQ2NlJfX099fT1lZWU0Nzcjk8mwsrLqZcaNjIzGXLquOkOvpaVlQOf7+/uTlpZGTk5OnxK5rq4uCgoK0NfX70ljvxI///wz33zzDXv37sXMzIwzZ87Q0dHBtGnTes5Rrz/c34MJEyYQHR1NSkoKcrmcSZMmXfPvSVcNtxp7e3u8vb05fvw4cXFxg54GMxZxd3fn0Ucf5ZlnnuHEiRNiEEVERIto7RvlgQce4Msvv+Sll15iwYIFPXcNGxoaetK0H3jgAW1tPybmeqvTtL/++mvuuOMOtm7dek3DPZjz1eTl5dHZ2Ym3tzcGBgYYGxvz+eef93vu0qVLyc3N5a9//SszZvwvujLUvUWGh0ql4rnnnmPJkiV4enoKLWdEaGtr4+TJk0yZMkVn500Pxng3NjaSnJyMo6MjISEhY87A6CLTJ7txOD0fC2N9PB2Mcfq/CPYES0MU/2ewL9a1k3ahgYr6dmqaBhYVvxx1I7XSmpE13QPBwMAAW1tbbG1te451dXXR0NDQY8TLy8tpamrqMeKWlpbY2NhgZ2c36n87UlK6yweuZZLVzJkzh2+++Ybdu3dzzz339Hrs4MGDtLa2MmvWrKt2iW5oaODPf/4zDz74IPPmzev12OXz2jU5OtXKyoqYmBiSk5Npb29n6tSpVzRdum641fj7+1NTU8PZs2eZOnWq0HJGhH/84x9s3ryZbdu29WlkLCIiojmGbLrPnj3L0qVL+30sICCAF154gSeffJIPPviAyZMnc/vtt6NSqdixYwelpaU89dRTzJo1a6jbj0o+//xzDh8+DHS/P+pj6sh+TEwMDz30UM/5L7/8Ml999RVmZmb4+fn1zPO8nMvT3gd7vpq5c+dSVFREQUHBkGuVhrq3yPDYu3cv6enpbN++XWgpI4JSqeTEiRM4ODjofGOXgRjv2tpaUlJS8Pb2xs/PTzTcAqNSqaivr8fdGh6c54q9hSFltXLKa9s5lvt/EezmwRvs/miWd2H6fyPDxgL6+vpXNeINDQ2cPn2ajo4O7O3tcXR0xMHBQbDa8JMnTxIaGtrHZCYkJPDee+8BsHjx4l6PNTQ0cPHiRSwtLXFycuo5vmjRIp5//nm+++47nnzyyZ5Z3XK5nL///e8APProo1fV85e//AWAdevW9Rzz9vbG0NCQ//73v6xcuRKAn376CUBj4xPNzMyIjY0lJSWF1NTUfg31eDHc0D2/OywsjMTEREpKSnT+dwi6PwOrVq3ib3/7G7fddptYuiQioiWGbLrLy8t7xoL9nri4OF544QU2bNjA1KlT+fjjj/nss8+A7h+Kl19+mWXLlg1161HL4cOH+7wnR44c4ciRIz3//3LTrY7GNzc38+qrr/a75uVp74M9X5MIufd4RalU8txzz/HII49gZ2cntJwRIScnh/b29l5ZFrrM1Yx3TU0NKSkpBAYGDjjiJqJ5FAoFly5doqKigoqKChQKBQ4ODpwvbSM9rxx5p3ZmdrfIFehJJbS0tmll/ZHg90ZcpVLR1NRERUUFRUVFnD59GisrKxwdHXF0dMTc3HxYN5Z27tzJzp07AaioqAC6J3WoAwR2dna88847AKxatYrc3FyioqJ6JqScOXOmZ+b2P//5T6Kionqt/8MPP7Bs2TIeeOABNm/e3HPcwsKCf/3rXyxatIjZs2dz9913Y2Njw48//kh2djaLFi26agRx3759fPHFF/z000+9OqCbmpry+OOP895777FgwQJ8fHzYtGkTEydO5N577x3y+/R7jIyMelLNU1JSiIyM7EmtHk+GW42xsTFTp04lLS0Na2vrcTEhYuXKlXzyySd88cUXPPLII0LLGZV4fP2G0BKuSuH9g2+M3B/bt28nKSmJ9PR0Tp8+TVNTE/fddx9bt24d9FqlpaWsXr2a3bt3U1NTg5OTE7fccgtr1qzB2tp6UGvt3buX3bt3k56eTnp6OrW1tURHR/cEOwdKS0sLO3fu5Oeff+bkyZOUlJQglUrx9/fnnnvu4cknn9Raw2KJ6v+6g508eZKwsDBOnDjRq35IRGSkET+L3Xz33XesWLGCvLy8MT0ua6DU19dz+PBhYmNjrzh6R1epq6sjOTkZf39/vL29qa6uJjU1lUmTJo2bTrqjCblcTmVlJRUVFVy6dAmZTNZjDG1tbZFKpbz06W5+OZyhVR1/udmT71Mv8d3bfxrU8zo7O/nll19YuHDhqI5aDeR9Hgxr167td8qIGnd3954byF988QU//PAD586do7q6ms7OThwcHJg5cyZPPPEEsbGxfZ6/efPmfk23miNHjvDqq6+SnJyMXC7Hx8eH5cuX89RTT13RrDY3NxMcHEx0dHS/F7Xt7e08//zzfPPNNzQ1NREVFcXGjRsHPTJsIHR1dfWk1s+YMQOVSjXuDPflnDlzhoaGBmJiYsZFltHnn3/Oiy++SH5+/ri45lAjl8spKCjA09Pzqpk348V0h4aGcvr0aczMzHB1dSUrK2tIpjsvL4+oqCiqqqq4+eabCQgI4NixYxw4cAB/f3+OHDnSKzPqWtxyyy3s2rULIyMjfHx8OHfu3JBM9+7du7nhhhuwsbEhPj4eHx8f6urq+PHHH6moqCAqKoqEhIQBZ2EN9PMDoukWGYWIn8XuTtUBAQE8+uijPPvss0LL0TpKpZKkpCScnJwICAgQWo4gqI23i4sLJSUlBAcH4+7uLrSsccHlEdiKigrq6+uvGYH99UgGaz/ZrVVdf75+InvSq9n65p/R1xu4AR0rpvtyrpRR4OjoyIQJE8bNqESh6erqIjU1FYVCgUqlwtDQcFwabuh+LxITE/H09BzSiMexhlKpZMqUKdx99928+OKLQssZMUTT3ZsDBw7g6uqKj48PSUlJxMfHD8l0X3/99ezZs4cNGzbw5JNP9hxftWoV7733Hn/+858HNXI4OTkZCwsLAgICKCkpwdPTc0imOz09nfPnz3PHHXf0+l1pampi9uzZnDx5knfeeaen5OdaDMZ0i20KRURGIepGd9qaETvayM7ORiKR4OfnJ7QUwbC2tiYgIIDCwkIcHR1Fwz0CtLe3c+HCBfbv38/Bgwepq6vD3d2d+fPnM2vWLPz8/LCwsOg3yhUxSft/P81yBaYyPeqbxkZd93DQ09PD0dGR0NBQrr/+eqKiojA1NeXChQvs3r2b1NRUKisrtTa6U6QbfX19wsLCaGpqoqWlhWnTpo1Lww3d70VoaCiZmZk0NzcLLUfrSKVSXnnlFd58801qamqEliMiEPHx8fj6+g4ruyMvL489e/bg4eHB448/3uuxl156CVNTU7Zs2TLgCRHQPcp40qRJw/4+Cg0N5b777utzI9fc3LzHaKt7cWka0XSLiIwympubWbt2LX//+9/HRXSnvr6evLy8q3bOHQ9UV1eTkZGBn58fVVVV5OXlCS1JJ1GpVFRVVXH8+HH27NlDZWUl/v7+3HDDDURGRuLu7j6gtDJbK1O8XQeeGjcUWuRdmBnrj5lmappCIpFgbW1NYGAgs2fPZu7cuVhYWJCens7evXvJysqitXV8vScjRWdnJ8eOHcPa2hpLS0uOHz9OV1eX0LIEw87ODnd3d06dOjUubvjcfPPNTJ48mddee01oKSJjmAMHDgAwf/78Ptd15ubmREdH09ra2lPOMlpQZ4Zpa1zg+L3CFREZpbz33ns4OjpecTqALqFQKDh58iS+vr7jro77cmpqakhNTSU4OJjAwEBmzpxJdna2aLw1iFwuJycnh3379nHy5ElMTEyIj48nOjoaV1fXId09n67laHf32LCRn9U92jA1NSUwMJDrrruOkJAQGhoa2LdvH8nJyZSXl6NUaqeZ3Xjj8qZpkZGRREZGIpFISE1NHdfGOzAwkPb29nHzffzGG2/w0UcfUVxcLLQUkTFKdnY2wBWzF319fYHu5rmjiS+//BKABQsWaGV90XSLiIwiqqurefvtt3nllVfGRdQ3JycHqVTa8wU8HlGPBZs0aVJPSrm6q7lovIdPXV0dJ06cYO/evdTU1DBp0iTmz5/PpEmTht2VOGKym4ZU9k9zW7fprhvnpluNVCrF0dGRyMhIrrvuOmxsbDh37hz79u0jNzeXjo4OoSWOWfrrUq6vr09kZCQqlYpjx46hUCiElikI+vr6TJ06laysLJqamoSWo3VmzZrF7NmzWb16tdBSRMYoDQ0NAFcMpqiP19fXj5Ska7Jx40Z2795NaGgoy5cv18oe2omfi4iIDIlXX32VkJAQbrrpJqGlaJ26ujry8vKIjY0dFzcY+qOxsZGUlBQCAgL6dCkfyBxvkf5RKpVcvHiRvLw8GhsbcXNzIz4+XuOjf6YFTMRAX4/OLu2YkWZ5F2ZGJuMuvXwgGBsb4+/vj5+fHxUVFeTn55OdnY2rqyteXl5YWFgILXHMcLWxYGrjnZyczIkTJ5g+ffq46OT9e2xtbXvSzGNjY3X+PXjrrbeYPn06zz33nFY65YuIXE59fT3r16/vc3zFihVYWVlpff/vv/+eFStW4OjoyI4dO7TWgFQ03SIio4SioiI+/vhj9u3bJ7QUraNQKDh16tS4Titva2sjOTn5qp1xReM9ODo6OigsLKSgoACpVIqXlxczZ87U2g+osZEBk70dOZVdppX1m+UKTMVI91WRSCQ4OTnh5OREQ0MD+fn5JCUlYWtri5eXFw4ODjpvkIbDQOZwGxgYMGPGDA4dOsSZM2cICQkZl+9pYGAgiYmJ5OXl4ePjI7QcrRIcHMytt97KCy+8wI8//ii0HJExhvq6Th3x/j3q42pDXV9f3++4x6VLl2rddO/cuZO7776bCRMmcODAAby8vLS2l2i6RURGCatXr2bu3LnExMQILUXrZGdnj+u08o6ODpKTk3FwcLjmiDTReF+brq4u8vLyuHDhAlZWVoSEhODo6DgixiBisrvWTHeLXIGZkT6lNaLpHgiWlpZMnTqVoKAgCgsLSU9Px8jIiKCgIOzt7celUbwaAzHcagwNDZk5cyaHDh1CJpONy9GO6jRz9Xe3ubm50JK0yuuvv05QUBBHjx4lKipKaDkiYwh/f3/gyjXbubm5wP9qvj08PARpVLht2zbuvfdeHB0d2b9/v9avScdnTqeIyCgjLy+Pb7/9ljfffFNoKVqnrq6O/Px8pk2bNi7TytVzcE1NTQccMRJrvPtHqVSSn5/Pvn37qKysJDIykujoaJycnEbMYE3XYl13s7wLmYF0XIwM0yQymQx/f3+uu+46XF1dSUtL4+jRo9TV1QktbdQwGMOtxsTEhJkzZ5Kfn09BQcEIqBx92Nra4uHhMS66mXt4eLB48WLWrl0rtBSRMUZ8fDwAe/bs6dPosqmpiSNHjmBiYsKMGTOEkAfAN998wz333IOzszNJSUkjEgTqE+nOzMzU+qYiIldjPH4G33rrLa677jomT54stBStolQqSU9Px9fXd1zWXCqVStLS0pBIJISHhw/qpoMY8f4fKpWKsrIyMjMz0dPTY8qUKSMW2f49QV6OmJvIaGpt1/jabR1KFEoVcrlc42uPB/T09PDx8cHd3Z3c3FyOHDmCg4MDgYGBGq/vH0sMxXCrsbCw6KnxlslkODs7a1Hp6CQgIIDExEQKCgq0moo6GvjHP/6Bn58fJ0+eZNq0aULLERlldHZ2kpeXh4GBQa9rEm9vb+bPn8+ePXv48MMPefLJJ3seW7NmDS0tLfz5z3/G1NRUq/qysrIA+mTmfPXVVyxfvhx3d3cOHDjQ08RW2/SYbjs7O0xMTFi8ePGIbCwicjVMTEyws7MTWsaIcPHiRb766isSExOFlqJ1CgsLUSgUOl8P1x8qlYrTp0/T1tZGTEzMkEZUjXfjrVKpuHTpEhkZGXR0dODv74+bm5ugacN6UilhQRNJTLuglfWb5V10dGje0I8nDAwMCAoKwtPTk5ycHA4cOMDEiRPx9/fH2NhYaHkjynAMtxpbW1vCwsI4ceIEhoaG4+a3Wo2+vj7BwcGkpaXh4uKCTCYTWpLWmDhxIrfddhuvvfYa27dvF1qOyAiwc+dOdu7cCUBFRQUAycnJPWNs7ezseOeddwAoKysjMDAQd3d3CgsLe63z0UcfERUVxVNPPUVCQgKBgYGkpqZy4MAB/Pz8ePXVVwel6/Dhw3z++ecANDc3A91p6peP1928eXOv5wQGBgL0yko5cOAAy5cvR6lUEh8fz6ZNm/rsZWVlxYoVKwalbyD0mG43NzcyMzOprq7W+CYiIoPFzs4ONzftjuMZLbz77rtERkYKmmYzEsjlcjIzM5k+ffqQLvTGOpmZmVy6dInY2NhhNfYar8a7traWzMxMGhoa8PPzw9PTc9R8jqZPctOa6W6RK1Apx++MZE1ibGzMlClT8Pb2JjMzk4SEBDw9PfH19cXQ0FBoeVpHE4ZbjZOTE8HBwaSmphITEzPuGmI6ODhgb2/P+fPndT4CvHr1akJCQsjJybni3GUR3SE9PZ2vvvqq17H8/Hzy8/MBcHd37zHdV8Pb25u0tDRWr17N7t27+eWXX3BycuLpp59mzZo1WFtbD0rXhQsX+uiqqqrqdez3prs/ioqKelLe1XO5f4+7u7tWTLdEpetFKSIio5i6ujomTpzIjh07uP7664WWo1VOnjxJV1cXERERQksZcQoLC8nMzCQmJkZjzXfq6upITk7G399fp413U1MTmZmZVFVV4e3tjY+Pj9a6kQ+V4oo67ni2791yTbAoypHCylbeeHYx5qZGA3pOZ2cnv/zyCwsXLhx179Vooq6ujoyMDBoaGvD19cXLy2vU3MjRNJo03JeTk5NDfn4+cXFx4y5roLW1lf379xMVFYWNjY3QcrTKTTfdhL29PV988YXQUjSOXC6noKAAT09PjIwG9h0rIqJmMJ+f8dfFSERkFLFx40YCAgJ03nDX1tZSXl6u8zXr/VFdXc25c+eIiIjQaLdbXW+u1tbWRnp6OomJichkMubNm0dgYOCoNJFujtY42WmnR0FzWxemxvrUNbZpZf3xjLW1NdHR0YSHh1NWVsa+fft6RUF0BW0ZbgBfX18mTJjAsWPHUCi0M69+tGJiYoKvry9nzpzR+aZqq1evZuvWrZSWlgotRURkzCKabhERgWhpaWH9+vU899xzQkvRKkqlkjNnzuDn54eJiYnQckaU1tZWjh8/zuTJk7G1tdX4+rpovFUqFXl5eSQkJNDV1UV8fDxTpkwZ9RGI6ZO0Uw7T0q7AzEiPWnFWt9aYMGECcXFxTJ48mdzcXA4ePHjF+bJjDW0abuiekz5lyhQkEgnp6ek6bz5/j4+PD11dXTrfzT08PJzo6GjWrVsntBQRkTGLaLpFRATi888/Z8KECSxatEhoKVqlsLCQrq4unU6B7g/1aDAXFxc8PDy0to8uGe+WlhaOHDlCfn4+M2bMIDw8fMx0mY6YrJ3up81t3bO6axtbtLK+SDcSiQQXFxfmzJmDg4MDhw4dIisra0xHvbVtuNXo6ekRERFBdXU1Fy5op7fBaEVPT4/g4GCysrJob9fthocvvvgin332GTU1NUJLEREZk4imW0READo6OnjrrbdYuXKlTs+qbm9vJzMzk5CQEJ2tlewPlUrFyZMnMTQ0HJGU+rFuvFUqFfn5+Rw4cAALCwvi4+PHXEfk6ZPc0EYT9WZ5F2ZGetQ1iOnlI4FUKiUwMJCYmBjKy8vHbNR7pAy3GiMjIyIjI8nOzqayslKre402HBwcsLOzIyMjQ2gpWmXu3LlMnjyZDRs2CC1FRGRMortX+yIio5hvvvkGfX19li9fLrQUrXL+/Hns7e2ZMGGC0FJGlOzsbBoaGpg+ffqI3VQZq8ZbHd3Oy8tjxowZhISEoK+vf+0njjKszI3xc9P857xZrk4vFyPdI4mVlRVxcXE9Ue/s7OwxE/UeacOtxsrKitDQUNLS0mhqahqRPUcLkydPpqysjNraWqGlaJXnn3+e999/v2dkk4iIyMARTbeIyAijUCh4/fXXeeKJJ8akuRgo47V5Wnl5OXl5eURGRo74GKKxZLx1Ibr9e7RR190s78JEpkedWNM94ujp6REYGEh0dDRlZWUcPHiQxsZGoWVdFaEMtxpXV1c8PT1JTU2lo6NjRPcWkvHSVO2WW27B1dWVTz/9VGgpIiJjDtF0i4iMMDt37qSxsZEnn3xSaClaQ6VScebMGXx9fcdV87SGhgZOnjzJtGnTsLDQTjfrazEWjHdLSwtHjx7tuTkxVqPbv0cbdd0tcgUSiYSmFtF0C4W1tTVxcXFMmDCBgwcPjtqot9CGW01gYCDm5uakpaWNyvdJW6ibqhUWFgotRWtIpVJWrVrFO++8o/M17CIimkY03SIiI4hKpeLVV1/loYceGvXdmIdDSUkJnZ2d+Pj4CC1lxOjs7OTYsWP4+vri5OQkqJbRarwvj26bmZkRHx+Pvb290LI0Rqi/CzIDzRodpQpa2xW0tck1uq7I4NDT0yMoKGjURr1Hi+GG7qZ006ZNQy6Xk5WVJZiOkUZPT4/JkyeTlZVFZ2en0HK0xgMPPICJiQlbtmwRWoqIyJhCNN0iIiNIQkICBQUFPPvss0JL0RoKhYKsrCwCAwPHTfM0lUpFeno6ZmZm+Pn5CS0HGH3GWx3dvnDhApGRkUyZMkUnotuXIzPUJ8TPRePrNsu76BpHqbqjmdEY9R5NhluNgYEB4eHh5OfnU1VVJbScEcPBwQFzc/NR8Z2rLfT09HjiiSd4/fXXBf/si4iMJUTTLSIygrz33nvcddddWFpaCi1FaxQUFGBgYICLi+bNx2ilsLCQ2tpapk2bhkQbLayHyGgx3sXFxTob3f49EZO1UdetQKnQ3cjZWOP3Ue9Dhw7R0iJMo7vRaLjVWFhYEBwczMmTJ5HLx0emhkQiISgoiAsXLuj0a37sscdobGzk119/FVqKiMiYQTTdIiIjRH5+Pnv37uUvf/mL0FK0RmdnJzk5OQQFBY0q86lNGhoaOH/+PGFhYchkMqHl9EFI461UKjl79iznzp0jIiKCKVOmYGBgMKIaRprpk7RT162vB51dCo2vLTJ01FFva2trDh48SHV19YjuP5oNtxo3Nzfs7e05ceKETjcYuxwbGxvs7e3JyckRWorWkMlk3HPPPeL4MBGRQSCabhGREeLDDz9k9uzZ+Pr6Ci1Fa1y4cAELC4txMyKsq6uL48eP4+PjM6o7bwthvDs6OkhJSeHSpUs96bjjAX/3CViaabZfQ7O8CzOZHrViB/NRh56eHiEhIQQFBZGSkkJBQcGI7DsWDDd0R35DQkJoa2vTaRP6ewIDAykqKhIsA2IkWLVqFQcOHBhXf68iIsNBNN0iIiNAa2srX3zxBU888YTQUrSGXC4nLy9v3ES5VSoVp0+fxtjYGH9/f6HlXJORNN5NTU0cPHgQPT09YmNjMTU11ep+owmpVKLx0WHNbQrMjPWobRBN92jF3d2dmTNnkpWVxenTp7Va6zpWDLcaAwMDpk+fTm5u7ohnAwiFhYUFrq6uOt1IzsPDg3nz5vHhhx8KLUVEZEwgmm4RkRHgm2++YcKECdx4441CS9Ea2dnZTJgwARsbG6GljAglJSVUVVWNujruqzESxruyspKDBw/i4uJCRESEzqeT94fGTbe8C1MjfXFW9yjH1taWuLg46urqOHr0qFZGKo01w63G0tKSSZMmceLEiXEzaiogIICLFy/S0NAgtBSt8fTTT7Np0yaam5uFliIiMurRrdaxIiKjEJVKxYYNG1i2bBlSqW7e52pubqa4uJjZs2cLLWVEaGxs5MyZM0RERGBsbCy0nEGhNt7JyckAeHt7a2RdlUrFhQsXyM7OJjQ0FFdXV42sOxbR9LzuFrkCMyM90XSPAUxMTIiJieHUqVMcPHiQiIgIjTXOHKuGW42HhweXLl3i5MmTzJgxY8zcrBwqxsbGeHh4kJGRwcyZM4WWoxWuu+46XF1d2bJlC48++qjQcrTKHw4+JbSEq/LzLM3U12/fvp2kpCTS09M5ffo0TU1N3HfffWzdunXAa9TU1PDDDz/w888/c/bsWcrKyjA0NCQ4OJhly5YN6XpYE7rUeHh4UFRU1O9jDg4OVFRUDHrNgSCabhERLXP48GGKiop4/PHHhZaiNbKysnB1dcXc3FxoKVpHqVRy8uRJPD09x2ydsqaNt0KhID09nerqamJiYrCystKAyrGLs70lrhMsKa3STISr+f9Md2297taH6hL6+vqEh4eTk5PD4cOHmTZtGk5OTsNac6wbbuiu7546dSqJiYkUFBTg5eUltCSt4+fnx969e6murh7VfT+GilQq5cEHH2TDhg088sgjOn8jZTzwyiuvcPr0aczMzIZcIrFt2zYeffRRnJyciI+Px83NjcrKSr7//nseeughfv31V7Zt2zaoz4smdF2OpaUlK1as6HPczMxsWOteDdF0i4homQ8//JBbb70VCwsLoaVohfr6eioqKpg7d67QUkaEnJwcVCoVgYGBQksZFpoy3m1tbRw7dgyJREJcXBxGRpptIjZWiZjsTun+MxpZq1nehb6elPom0XSPFSQSCf7+/pibm3PixAl8fX3x8/MbkinRBcOtxsDAgKlTp5KSkoKDg4PO93swNDTE19eX8+fPM2vWLJ00pX/+8595+eWXOXToELNmzRJajsgwee+993B1dcXHx4ekpCTi4+MHvYafnx8//vgjf/jDH3pFtF977TUiIiLYsWMH33//PbfffvuI6rocKysr1q5dO6w1Botu5rqKiIwSLl26xA8//MDTTz8ttBStkZGRgaen55hLsx4KDQ0NXLhwgalTp+pEqcBwa7zr6upISkrC3Nyc6Oho0XBfhiZTzDu6VHR0KWlubdPYmiIjg7OzM7GxsRQVFZGWlkZXV9egnq9LhluNnZ0dbm5unDp1alyMEfPy8qKtrY2LFy8KLUUrmJmZccstt/Dxxx8LLUVEA8THx+Pr6zusG0Rz5szhj3/8Y5/rJEdHRx555BEAEhMTR1yX0Iz9q0YRkVHMpk2bCA4OZtq0aUJL0Qq1tbXU1dXp9Bg0Neq0cm9vb51Knx6q8S4pKeHIkSP4+voydepUnTADmiQ8aCJSDV4cNMsVyOVyja0nMnJYWloSFxdHe3s7hw8fpq1tYDdPdNFwqwkKCqKtrY38/HyhpWgdfX19/Pz8yM7O1tmbDE8//TTff/89ly5dElqKyChH3VxVX1/YZOv29na2bt3Ka6+9xvvvv8+BAwdQKBRa3VM03SIiWkKpVPLpp5/y4IMPCi1Fa+Tm5uLh4YGhoaHQUrSOehbpWBgPNlgGa7xzc3N7Gsl5e3uP6TvP2sLc1IhATweNrdci76KzY3x0fdZFZDIZUVFRWFtbk5SURGNj41XP12XDDd0X3FOnTiUzM3NcdL52d3envb2dyspKoaVohWnTphEcHMymTZuEliIyiunq6uLrr78GYMGCBYJqqaioYMmSJbz44ousWLGCOXPm4OvrS1JSktb2FGu6RUS0xP79+6mrq2PZsmVCS9EKjY2NVFVVMWXKFKGlaJ36+nouXLhATEyMTqSV98dAarxVKhXZ2dkUFBQQExOjsa7MukhzZzveQfacz9dMF9RmuQKVQolCpaS5o436jjbq2+U0dLTRpuhCqVLSpVShVClRdCnQA34pzkBfXx89iQR9qRRTfRlWMiOsDI2xkhlhrC+WA4wkUqmUkJAQZDIZR44cISoqqt9/Q7puuNWo08zT09OJjo7W6Zt3enp6eHt7k5ubi4ODg06+1gcffJB33nmHZ555Rmd/J0WGxwsvvMC5c+dYuHAh119/vWA6li1bRmxsLJMmTcLc3Jz8/Hw2btzIZ599xg033EBycrJWrm1F0y0ioiU+/vhjbrvtNp2tc83NzcXNzU1nX58apVLJqVOn8PHx0am08v64mvFWqVRkZGRQUlJCdHS0zjYGHA6ZdVUkluWTWJbHyUtl+Jnaa2zt5rYuJBII/c+7NHVcvS5YhpS3LP157shvtKPs9xypBBb7hfJypHDRhqGMgDl69CivvPIKKSkptLW14evry/Lly3nyyScHbU4zMjJYu3YtiYmJNDY24u7uzt13380LL7zQp0dFR0cHL7/8Mt988w21tbVMmzaNdevW9Vs6tG/fPq677jp++uknbrzxxl6PSSQSAgICkEqlPcb78u+V8WK41QQFBXHgwAHy8/M1Nr5wtOLh4UFOTg41NTU62cl82bJlvPjii+zfv5958+YJLUdklLFhwwbWrVtHQEAAW7Zs6fVYYWEhmzdv7vMcbTU6W7NmTa//P3nyZD755BPMzMxYt24da9eu5YcfftD4vqLpFhHRApWVlfz000+cOHFCaClaoaWlhfLycubMmSO0FK2TnZ0NdHfjHA/0Z7xVKhXnzp2jvLycmJgYrY7UGGucq6ngx4IM/luYRXlr75ThnJZqnIxltLcNrnlWfzTLFdhbGiJV6QPDX0+pAjMDYZsfDnYEzK5du7j99tsxMjLirrvuwsbGhp9++omVK1dy5MgRtm3bNuC9U1NTmTNnDp2dnSxatIiJEyeyf/9+Xn75ZRISEkhISEAmk/Wc/8ILL/Dee+9x++2398wljo+PJysrq9c4sObmZh5++GHuu+++Pob7cvz8/HqM98yZM7GxsRl3hhv+l2au7mauy98tBgYGeHp6kpubq5Om28jIiNtuu42PP/5YNN0ivdi4cSNPP/00QUFBJCQkYGNj0+vxwsJCXnrppT7PG+nu4o888gjr1q3j4MGDWllfNN0iIlrg22+/JTg4mODgYKGlaIULFy7g7Oys8+NeGhoayMvLIzY2dlyly11uvFUqFS0tLVRVVRETE6Pzf+cDobCxjh/yz/FTYSb5jbX9niOVSPA0s8bez4gLp6uGvWeLXIGngz6WBkY0dGqmoZqtkYlG1hkqgxkB09jYyMMPP4yenh6JiYmEh4cD8M9//pM5c+awfft2vvvuO+6+++5r7qtQKFi2bBmtra3s2rWLm266CejOarnzzjvZsWMH7733Hi+88ALQneXx6aefsmzZMr788ksAbr31VmbPns2WLVt47rnnetZ+4YUXaGtr4/3337+mDh8fH/T09EhOTiYsLIycnJxxZbjVjKc0c29vb/bu3Ut9fb1OZk49/vjjREZGUldXh7W1tdByREYB69evZ+XKlUyePJmEhAQmTJjQ55zZs2ePiiaD9vbd2WktLdoZzzl+riJFREaQr7/+mrvuuktoGVpBLpdTXFys8x3LVSoVZ86cwdPTc1zWLltbWzNjxgwyMjIoLy8nOjp6XBvuls4O/l/uae7YvZXZOz/l/TNH+hhuK0Njwq1diZC5YV9hTnF6EwZmmmky2CzvwsxID3O93uuZ6BtgKzPB3sgUR2NzXEwscDXtTv13NDHDwdgMeyNTrGXGGPzuxpGdsbB/n4MZAbN9+3YuXbrE3Xff3WO4oTu69sorrwAMeGRRUlISmZmZzJo1q8dwQ3fN9VtvvQXAJ5980nMReOnSJVpbW4mIiOg5V/3fRUVFPccOHTrERx99xMaNG7G1tR2QFk9PTwIDA0lNTUWlUo07w60mKCiIlpYWSkpKhJaiVWQyGW5ubuTm5gotRStMnToVX19fduzYIbQUkVHAm2++ycqVKwkNDeXAgQP9Gu7RREpKCtA95k8biJFuERENk52dzfnz53W2gVpeXh729vY6X9NbUlJCW1ubTnYrHwgqlYqSkhJkMhldXV1cvHhR52su++NM9UW+zj7JL0VZtHZ19nnc3EBGgNkEOusU5GTXcE7Ve2RORbtm7pg3yxWYGunh2GxBR4uKlrYOGls66FIoaaH3mBOZVAJToD6jg3bl/6IHehhgJjPA3NQQEyMDnPTMNaJtJNi/fz/Qf8fbWbNmYWJiwtGjR2lvb++VFj7Ytby8vPDz8yMnJ6enztjOzg5jY+Ne5UJpaWlAd1dqgLa2Nh588EFuu+02Fi1aNODX1dnZSWlpKebm5jQ1NVFfXz9gw65L6OvrM3nyZM6ePYuTk1PPWCFdxMfHh4SEBJqbm3Uynf62227j66+/5qGHHhJaioiW6ezsJC8vDwMDgz7XB//85z9ZvXo1YWFh7Nmzp09KuVDaMjMzcXNz6xNEKCws5IknngBg8eLFWtEkmm4REQ2zdetWYmNje9JUdInOzk4KCwuZOXOm0FK0SmdnJxkZGYSEhAg+S1II1DXclZWVxMbG0t7eftWu5rqGUqViX0kun2cc41hVaZ/HDaR6BFs6Im2SkH2hhrOKK6ePF9XVM9HKmIb6gc1mvhItcgXGhnooa7soqbz6uKmr0dbeSVt7980DK5mwNd2D4Wq9FfT19fH09OT8+fPk5+cTGBg45LUAfH19ycnJIScnB29vb6RSKX/605/YsGEDDQ0NuLi4sGXLFiwsLLjvvvsA+Mc//kFNTQ0ffvjhgF/T5TXc0dHRlJSUkJKS0jNabLzh7OxMUVERWVlZOluaBWBiYoKLiwsXLlwgNDRUaDka56GHHuKNN96guLgYNzc3oeWIDJKdO3eyc+dOoHusFkBycjJLly4FustB3nnnHQDKysoIDAzE3d2dwsLCnjW++uorVq9ejZ6eHrGxsWzYsKHPPh4eHj1ralrX1bT95z//Yd26dcyaNQt3d3fMzc3Jy8vj559/Ri6Xs3DhQp555pkB6xoM4+9qUkREi6hUKrZu3crf//53oaVohfz8fCwtLUf8juVIk5mZiYWFRa8GSeMFdZdyddM0ExMTTExMrjlOTFdILMvnpWN7KWiq6/OYp5kNjipz8vPryLhQPaD1VICTpzUNp4ZputsVKJUqNFmFbWsqbE33YGhoaAC4YqmH+nh9fb1W1nrzzTcxNjbm22+/pba2lqlTp/LOO+/g4uLCsWPHWL9+PZs3b8be3p61a9fy6aefcunSJaZMmcKGDRuIjo7utUd/TdM8PDxQKBQkJyf36Wo+HpBIJAQHB5OUlISbm5tOl/X4+vqSmJiIv79/n075Yx13d3emT5/Ov//9756+CCJjh/T0dL766qtex/Lz88nPzwe6/34vN7f9UVBQAHT3z1i/fn2/58TFxQ3KdGtCF3SXNWVnZ3Pq1CmOHDlCS0sLVlZWxMTEsGTJEpYsWaK1vhKi6RYR0SApKSnU1NRwzz33CC1F43R1dZGfn09YWJjQUrRKQ0MDxcXFxMXF6XRDnyuRnZ3dMxbs8vSrgczx1gVM9Q16GW4DqR6hls40XeygML+eiww+XVxhopnPUUu7ApmGes1IJRKsTXTrYl+byGQyXn/9dV5//fVexzs6Oli2bBkLFixg8eLFrF+/npdeeok1a9YQHR3Nq6++yoIFC7hw4QIODg7A1ceCeXt7o1QqSU5OHpej+czNzfH09OTMmTPExMTo7Hewubk5Dg4O5OXlMXnyZKHlaJy77rqLzz77TKdM98+z+kZrdZG1a9cOuGu4h4dHvw3QBrOGNnTBlbXFxcURFxenQWUDR2ykJiKiQbZs2cL8+fMxMRk7EaSBUlxcjLGxsU6mzatRN0/z8vLC3Hzs1LtqitzcXAoKCoiKiur39auNd3Z2Nnl5eQIo1D7THSYSbu+KpaERMy3dsa8y5+yJKgrL64e8ZlFzg0a0NcsVGCg147qtTYyRSseOoVFHPdVR6t+jPj6Q6LAm13r55ZcpKyvj008/BeDtt99m7ty5rF27luuuu46vv/6alpaWnrTzgYwF8/X1xcvLi6NHj9LU1HRNDbqGv78/ra2tlJb2Le3QJXx9fSksLKSjo0NoKRrngQceID8/nzNnzggtRURk1CCabhERDdHZ2cl3333H/fffL7QUjaNSqSgoKMDb21tnIw/Q3TyttbV13MzkvpyysjJycnKYOXPmVaNr48F4PxUYjSRfyqlTFdQ2Di8tHOBScwuOLsNPlW2Wd2GEEkcjA3zNjZlmY0bcBEuuc7TmeidrbnC24Q8uNrw8xROA+U7WLHCyZr6TNfMcrYiytyDYyhQPUxnuFiYoFIpr7Dh6UDc0zMnJ6fNYV1cXBQUF6OvrD6jr7NXWAno6S1/reyA9PZ0333yzJ828sbGR8vJypk2b1nOOm5sbdnZ2nD9/flBzuP38/Jg4cSKpqak6acquhrqpmvo901Wsra2xtLTUyY7tVlZWxMfHs2XLFqGliIiMGkTTLSKiIX777TdkMhl/+MMfhJaicS5dukRHRwfOzs5CS9Ea6uZpwcHB4655Wn19PadOnSIsLGxA0T1dN96x7h5MtLTS6Jo2LgPrUmxsKMVjgjEz/Ky4PtSO22c6cH+8C4/f4Ia3ownTXEx4zM+Fm11tCbcxx8VEhqWhHib6ehhKJUiR0K5QAqAnkWCkJ8VMXw9rQwN8zIyJm2DJvR4O3OFgzn//+19+/vlnEhISOHLkCCdOnOD8+fMUFRVRX1+PUqnU6HswHObMmQPA7t27+zx28OBBWltbiYqKumbn8mutlZ+fT05ODu7u7lc18F1dXSxbtozZs2f36dLc3t7e6//L5fKelPGBzuGWSCQEBQVhbm7O8ePHR9XfxUjg7OyMubk5WVlZQkvRKp6enhQUFIyKGcWaZvHixXzzzTfj7rMrInIlxteVpYiIFvn666+58cYbdXLGakFBAR4eHjr52tTk5ORgbm4+7pqnyeVyUlNT8fPzw9HRccDP0+Uab4lEwvLoMP66c4/G1mzR7xtVNjaU4mgtw8lahqOVDEdrGVamBtQ1d1JZ305dSyfF1Z20tClolncR4GqKsUyPr6sv9bPD/9hzsZaXp3jya3ltr5Fhl/OHyX689sd5yOXyPn9KSko4d+4cSqUSCwsLrKyssLS0xMrKCgsLC6TSkb9fv2jRIp5//nm+++47nnzyyZ5Z3XK5vKdx5aOPPtrrOa2trRQXF2NiYtKri3JcXByBgYEcPHiQH3/8sWdWt1Kp5PnnnwfgkUceuWpWzxtvvMGFCxf44Ycfeo5ZWFjg4uLC7t276erqQl9fn6SkJJqamrCwsBiw4VYjkUiYNm0ahw4d4vz58zrd0fv3SCQSQkJCSExMxMPDQ2fLfZydnTl37hxVVVU9Nf+6wqJFi3jiiSdISkoiPj5eaDkiIoIjmm4REQ3Q2NjITz/9RGJiotBSNE5LSwtVVVWEhIQILUVrtLW1UVBQoNONe/pDoVBw/PhxbG1t8fX1HfTzddl43xgcwIb9yVxs1ExNbV5dHY6Whng7GONqa4SjlQxLUwNqmzuprGunvK6dk/mNVNS1I+/sPzJkZ2GIt5MBRvr6yLu6hqXHzswUmUyGTCbrt0u0SqWipaWF+vp6GhoaKCsr4/z58yiVSszNzbGyssLe3p4JEyYMeabyYEbAWFhY8K9//YtFixYxe/Zs7r77bmxsbPjxxx/Jzs5m0aJF3HXXXb3WP3bsGPHx8cTFxfX6btbT02PTpk3MmTOHRYsWsWjRItzc3EhISCAtLY3o6GhWrlx5Rd0ZGRn885//ZN26dXh4ePR67LnnnuPpp58mNjaWyMhIvvnmG4yNjbnzzjsHZbjVGBgYEBkZycGDB7GwsOiZCz4eMDc3x83NjczMTCIiIoSWoxWkUikeHh7k5+frnOk2NDTkhhtuYOvWraLpFhFBNN0iIhrhhx9+wM3NjcjISKGlaJzCwkIcHR11bqzJ5WRlZeHo6DiuRvSom8YpFAqmTp065JsNumq8DfT0WDpzGq//ljTkNaSAu5kRQRYmBFqaYBNoQEFlK0WX5KRdaKSy/soGuz+a5QrMjPSxNDZC3tQ8ZF0ANtcYFyaRSDAzM8PMzAxXV1fgf0a8oaGB+vp6srOzOXHiBHZ2djg6OuLo6DioJpKDHQFzyy23kJSUxKuvvsqOHTuQy+X4+Pjw7rvv8tRTTw3qMxwZGcnx48dZs2YNe/bsoampCXd3d1avXs0LL7xwxTR1hULB8uXLiYyM5PHHH+/z+JNPPkljYyOffPIJJ0+exMvLi1deeYUFCxYMOVPI1NSU6dOnk5KSgpmZGba2tkNaZyzi7+/Pvn37qK2t1dlRlR4eHuTm5tLc3IyZ2cDKUMYKDzzwAIsWLeLDDz/EyMhIaDkiIoIiUeliIYmIyAgzb948wsPDeeONN4SWolEUCgW//fYbERER2NnZCS1HKzQ2NpKUlMScOXN6jcjSdfLy8sjNzSUuLk4jN1Tq6upITk7G399fZ4x3a0cn8e99TkObfMDPMZJK8Ps/kx1gYYJSBZmNLWQ0tGJjbM3ZI0PvyOxiI+O2mY58d6mZCzW1VzxPJpXw8hRPVp8uuGJ6+Ss3XceiacMfVdTS0kJFRQUVFRXU1NRgbm6Oo6MjDg4OWFtbj6vMkcsZTNO0gVJQUEBWVhZxcXE6OSHjSmRkZFBXV0dUVJTOfp7S0tIwMjLSufFhSqUSDw8P3nvvPW6//Xah5fSLXC6noKAAT09P8caAyKAZzOdHbKQmIjJMLl68SFJSUp9mOrpAeXk5MplMpyMrmZmZuLu7jyvDXVVV1ZOyqakMBl1srmZiaMB9EVOueZ6BVEK4jRkPejvyj2AP5jpaU9/Rxaa8Cl45V8T24moyGlq5pGi/5lpXo1muwFSmh6n+0NK5L8f2GpHugWJqaoq3tzfR0dHccMMN+Pr60tLSQkpKCr/99hvp6enU1tbqZKOoK6ENww3dTbecnZ05duwYXcMsLxhL+Pr60tDQwKVLV+9lMJbx8PCguLh4TE0UGAhSqZSbb76Zr7/+WmgpIiKCI6aXi4gMk507dxISEoKPj4/QUjROYWEhHh4eOhtdqK2t5dKlS8ybN09oKSNGc3MzaWlphISEaDxdUxdTzZdETGXT0RO0dfY1Oc7GhkTYmjPVxpy6jk5O1DTzQ0k1tR39G6K8mlpsjQ2Qtw1tDFKzXIFUKsFcAyZOU6b7cgwMDHB1dcXV1RWlUklNTQ3l5eUkJydjbGyMh4cHrq6uGBoaanzv0YK2DLea4OBgjh49yqlTpwgPD9fZ7+bLMTAwwM/Pj4yMDOzt7XXyNdva2iKTySgvL2fixIlCy9EoS5cuJSoqipaWlnF1c1tE5PeIkW4RkWGyc+dO5s+fL7QMjdPY2EhDQ4POXQCoUalUZGRk4O3tPW5Syjo7O0lNTcXNza1XN2dNomsRb2tTY26f+r+UTz0JTLMx4wk/Zx7xdUZPIuHzCxdZn1XGoUsNVzTcAF1KJW7eQ88aUShVtHUoMJUM/6fb1ky76clSqRR7e3umTJnC9ddfj4+PD2VlZfz222+cPHmS+vp6re4vBNo23ND9vk6fPp36+vorzhnXRTw9PWlvb6esrExoKVpBIpHg4eFBYWGh0FI0TlhYGI6Ojuzdu1doKSIigiKabhGRYdDc3ExiYmKfrrm6QGFhIc7OzjoblaqqqqKpqUknMxT6Q6VSceLECUxMTJg0aZJW99I1470sKgwrQwOuc7Tmr5PciHewIq22iVfPFbGjpJqS1oGnjUsth5dg1iJXYKSBQJ82It1XQl9fHzc3N2JjY4mLi0NPT4/Dhw9z6NAhysvLdWKO70gYbjUymYyIiAhyc3MpLy/X2j6jCT09PQICAsjMzNSJz0t/TJw4kYaGBhobG4WWonHi4+P58ccfhZYhIiIooukWERkGe/bswdnZmdDQUKGlaJSuri5KSkr6jMPRFdRRbj8/vyGPOxprZGRk0NzcPGIpqbpivFtbW6kqyOP5oIm4msj4f0WXeDezlJTqpis2Kbsa5fLhdR1vlnchG2Z9tKmhIUYGwlSXWVhYMGXKFObPn4+TkxPnzp1j3759FBQUjFkzNZKGW42lpSXTpk3j5MmTNDQ0aH2/0cDEiRORSqU6GQ2G7hFbzs7OOvn6br31Vn766acx+29cREQTiKZbRGQY7Ny5UyfnT5aXl2NiYoK1tbXQUrTCxYsX6ejo0NmbCr+ntLSUoqIiIiMjR/Qmw1g23u3t7Zw7d46EhASUSiXeoVPZlF9BTlMbw7G8JfUNWNsMPcrcLFdgMMyeZLamwo//MzQ0xMfHh3nz5jFp0iTy8/PZv38/ZWVlY6rpmhCGW42zszO+vr6kpqbS0dExYvsKhVQqJTAwkJycHJ1rOKbGw8ODkpISnXt9N9xwAx0dHRw7dkxoKSIigiGabhGRIaJQKPj5559H7RiM4VBaWoqrq6tONqxRqVTk5OTg4+MzohfIQtHW1saZM2eYOnUq5ubmI77/WDPeXV1dZGdns2/fPpqampg1axZhYWGEeLgR5+upkT2cPIZ+M6tZrkBfNbxo0bVmdI8kUqkUFxcX4uPj8fX15dy5cyQlJVFVVSW0tGsipOFW4+fnh7m5OefOnRvxvYXAyckJQ0NDSkpKhJaiFaytrZHJZFRWVgotRaMYGhoSExPDrl27hJYiIiIYYvdyEZEhkpKSglKp5PrrrxdaikaRy+VUV1czdepUoaVohUuXLtHW1oa7u7vQUrSOSqXi9OnTODg44OTkJJiOsdDVXKlUUlRURHZ2NiYmJkRGRvaZTf9wTDhJuQWDXttQTw9fW1sslAbUljahLx26aW5u68LOUjbk5wPYabmJ2lCQSqW4u7vj6upKfn4+aWlpWFpaEhQUNCozbkaD4YbuBlyhoaHs37+fiooKHB0dBdExUkgkEnx9fcnOzsbNzQ2pVLdiRxKJBFdXV0pLS3F2dhZajkb54x//yIYNG3j99deFljIkvsqNElrCVXnA96hG1tm+fTtJSUmkp6dz+vRpmpqauO+++9i6deug1vHw8KCoqKjfxxwcHKioqBBEl5qff/6Z999/n4yMDGpqanByciIsLIxVq1Yxc+bMIa15LUTTLSIyRHbt2kVMTAz6+rr1z6isrAxbW1uNzW8ebeTk5ODl5aVzf2/9UVJSQn19PXPmzBFayqg13iqVirKyMrKyspBIJISEhODk5NRvlke4uyuhrk6kl1685rr6UimB9vaYyKUUZl6iNOd/kSu5eQcSCQwli7pFrkCmJ0EqkaAcYhr2aIp0/x49PT18fX1xd3cnNzeXI0eO4ODgQGBgIGZmZkLLA0aP4VZjbGxMcHAw6enpzJkzR2ebX6pxcXEhMzOT8vJyXF1dhZajcVxdXTlw4AAdHR069Xe5aNEinnzySfLz8/Hy8hJajsgVeOWVVzh9+jRmZma4urqSlZU15LUsLS1ZsWJFn+ND+S7XpK7nn3+et956C1tbW2655Rbs7Oy4cOECu3btYseOHXz99dcsXrx4yOtfCd2/6hQR0RK7du3imWeeEVqGxikpKcHTUzNptKON2tpa6uvriYiIEFqK1mlra+Ps2bNMmzZt1Fy4jTbj3dTUxKlTp2hrayMgIKCnUdPVeDhmOo9/d+UuvL62NtipjCjOqqY4t/87+fVNbUx0sKKksn7QmpvlCkyN9LAwklHfJh/08wHsRrHpVmNoaMikSZPw8vIiOzubAwcO4OPjg5+fn6Amd7QZbjUTJ06kvLycs2fPEhYWJrQcrSKVSvHx8SE3NxcXFxedK4MyMzPDwsKC8vJyneo7YmdnR1hYGD/99BNPP/200HJErsB7772Hq6srPj4+JCUlDatvkZWVFWvXrh1VuioqKnjnnXdwcHDgzJkzTJgwoeexAwcOMGfOHFavXq0V061beTkiIiPEhQsXKCgo0Ll67qamJpqamnQurU1Nbm4unp6eo8aEagt1Wrmjo6OgaeX9MRpqvFUqFRcuXCApKQlra2vmzp2Lu7v7gFJV5/h74WNv0+uYlbERMxxdCGi3oOpYDRnHy2huuvoYMTvroUVtm+VdmBnpYSkbeor5aI50/x5jY2NCQ0OZNWsWlZWVJCUlCTbje7QabuhOS54yZQqVlZWDTtsci7i7u9Pe3j4mav+HgjrFXNe4/vrrxbruUY66v8Zou5mlKV1FRUUolUoiIyN7GW71Hubm5ly6dGlYe1wJ0XSLiAyBn376ifDwcGxsbK598hiitLQUR0dHnRyj1djYSFVVleDR1ZFAnVYeHBwstJR+EdJ4Nzc3c/jwYQoLC5k5cybBwcGDKjWQSCQsjwoHYKKVJTNtnDHIbifzSCkXSwc+uqlNPrRu0y1yBYb6UqxkQ79xNBpruq+FpaUls2bNwsXFhcOHD4/4vObRbLjVGBsbM3nyZNLT03W+m7menh5eXl7k5OQILUUruLi4UFtbS2trq9BSNMrdd9/NoUOHxs2Yu/FOe3s7W7du5bXXXuP999/nwIEDgnfm9/X1xdDQkGPHjlFdXd3rsYMHD9LU1MS8efO0sreYXi4iMgR27tzJggULhJahUVQqFaWlpUyePFloKVohNzcXNzc3jIyMhJaiVdRp5WFhYaM6oj/SqeYqlYr8/HwyMzNxd3cnMDBwyHX9fwwJIOFoNmmphWSo6oe0RkFZDQb6enR2De4CRN6ppEuhxFJ/6DfGxlKk+3KkUin+/v44Ojpy6tQpKioqmDp1KlZWVlrddywYbjXjKc3c09OT3NxcampqsLW1FVqORjEyMsLe3p7S0lL8/PyElqMxAgMD8fDwYPfu3dx1111CyxHRMhUVFSxZsqTXMU9PTzZt2kRcXJwgmmxsbHjzzTdZtWoVQUFB3HLLLdja2pKXl8ePP/7Iddddx6effqqVvcVIt4jIIKmrq+Po0aM694NRV1dHZ2cnDg4OQkvROC0tLZSXl+Pj4yO0FK2iUqlIT0/HyclpTHQxHqmItzq6XVBQMKTo9u8x0NMjxsud4Qzsbu9U4OE8tEyZZrkCc72h/3yPhjndw0Ed9XZycuLw4cNkZWVpLeo9lgw39E4zv3jx2g3/xjIGBgZ4enrqbLRbnWI+lubWD4S5c+eyc+dOoWWIaJlly5aRkJBARUUFLS0tnD17lj//+c8UFhZyww03cPr0acG0rVixgu+//56uri7+9a9/8cYbb7Bt2zYmTpzI0qVL+6SdawrRdIuIDJJff/0Vb29v/P39hZaiUUpKSnBxcdG5ESwAeXl5ODk5YWpqKrQUrVJcXExjY+OYylbQpvFWqVTk5eWRmJiIlZUVs2fP1lhE7Ob4YCzMhpc1YWYytLrsZrkCY4Ze12arA/8OpFIpAQEBxMbGcvHiRZKSkmhsbNToHmPNcKtRdzM/ffq0zqeZe3t7U11drfG/+9GAk5MTra2tOvfabrvtNn799Vc6OzuFliKiRdasWcOcOXNwcHDAxMSEyZMn88knn7Bq1Sra2tp6NVgrLCxk7dq1ff5oi7feeotFixaxdOlS8vLyaGlp4cSJE3h5eXHffffx3HPPaWVfMb1cRGSQ/PTTT8Pq5jgaUSqVlJWVERkZKbQUjdPZ2UlJSQlRUaN7xuZwaWtr49y5c6M+rbw/tJFqrlAoOHXqFLW1tcyYMaPPzO3hYmJkyKJ5U/hyZ+qQ16hvHFq9Zou8C2OZHpMnTMBQqocBEqQqKSpUSFSg/39+PGKCM10qQAJdEhWdSiWdKiWWxsOb8z2asLS0JC4ujuzsbA4ePMi0adM00ghyrBpuNa6uruMizVwmk+Hq6kpBQQFTpkwRWo5G0dfXx8nJidLSUiwtLYWWozHmzp2Lnp4eycnJzJo1S2g5IiPMI488wrp16zh48GDPscLCQl566aU+52rDeCcmJvL8889z66238u677/YcnzZtGj/88AN+fn6sW7eORx55ROOj7XQvpCUiokVUKhUHDhzQuXruqqoq9PX1da4xHHRH8M3MzLC2thZaitYYa2nl/aHJiHdbWxuHDh2ira2NWbNmadxwq7lr/jSMDId+77qwvHZI0e7mNgWGXSpKkivJO1JO1pEyMo6WkHm0lIzkUrKOlQGQlVpGxtFSMo6UknO4jIKjF2nPah51XWmHi1QqJTAwkLCwME6dOkVWVtawUnLHuuGG8ZVm7unpSUlJiU5GTnUxxVxPT4/p06dz4MABoaWICIC9vT3QXfanZvbs2ahUqj5/tMF///tfgH6DZyYmJkRERKBUKjl16pTG9xZNt4jIIMjJyaGurk5rnQ2Fory8XCfnnapUKgoKCnR27rgadVr5aO1WPlA0Ybxra2tJSkrC0tKSqKgorTbOszI35o9xQ0/lVwHuToO/GdTcrsBgiD7Q2mJsNlEbCE5OTsTGxlJSUsLx48fp6uoa9Bq6YLjVGBkZjYs0cysrKywsLCguLhZaisaxt7dHqVRSV1cntBSNMmvWLBISEoSWISIAKSkpABqPIg+U9vbucZ5XGgumPq6NjEHRdIuIDILExESCg4N1qjZYqVRSWVk56uY5a4JLly7R0dGBi4uL0FK0RkdHB+fPn2fKlCk6MeptOMa7uLiYo0eP4ufnR2ho6IgYpntvCENvGE3NDPQHr7G5rQsDvaFFAXTZdANYWFgwa9YsOjs7OXTo0KBGLumS4Vbj6uqKtbU1GRkZQkvRKl5eXhQUFOhURBi6szgcHBx0LlthwYIFpKamIpfLhZYiMgw6OzvJysrq81udmZnZK5KtprCwkCeeeAKAxYsXC6ItNjYWgM8++4yysrJej/36668cOXIEIyMjrZQkijXdIiKDYP/+/TpXG6y+g66L6deFhYW4u7vrxMXzlcjNzcXKymrMppX3x2BrvFUqFefPn6e4uJiIiAitdR7tD2d7S+ZF+PFbctaQnl9xafDzapvlCoz0h5aVYqPjphu663xnzpzJ2bNnSUpKIiIi4poN9HTRcEN3mvmkSZM4cOAA3t7emJubCy1JKzg7O3Pu3DkuXbo0ov/+RwJHR0cyMzOZNGmS0FI0RmhoKBYWFqSmpgo2Okqkf3bu3NnTXb6iogKA5ORkli5dCoCdnR3vvPMOAGVlZQQGBuLu7k5hYWHPGv/5z39Yt24ds2bNwt3dHXNzc/Ly8vj555+Ry+UsXLiQZ555Rmu6rqZt0aJFzJs3j3379hEYGMitt97a82/sv//9LyqVijfeeEMrYwhF0y0iMkBUKhWJiYn861//ElqKRqmoqMDBwUHnUsvb2tqorKzUqQuV39PW1kZBQQExMTFCS9E4AzXenZ2dpKWl0drayqxZszAzMxtJmQAsuXH60E13bTMTbMyoqm0e8HOa5V2YyPQwMtRH3jG4FGpdj3SrkUqlTJkyBQsLC5KTk5k8eTIeHh79nqurhluNmZkZbm5uZGVlMX36dKHlaAWpVIqbmxuFhYU6Z7rt7e05ceIEzc3Ngny/aQOpVEp4eDgHDhwQTfcoIz09na+++qrXsfz8fPLz8wFwd3fvZW77Iz4+nuzsbE6dOsWRI0doaWnBysqKmJgYlixZwpIlSwZ9zakJXdD92fvll1/48MMP+e677/jhhx9obW3FxsaGhQsX8tRTTzF//vxBaRsoEpWu5eKIiGiJ7OxspkyZQk1NjU6llyckJBAYGKiRjr+jiezsbGpra5k5c6bQUrTGqVOnUCgUhIeHCy1Fa9TV1ZGcnIy/v38f4y2Xyzl69CgmJiaEhYUJml6/4u3vST5TOKTnTvFz5nRO+YDPNzPS46kbPdh0oJKLNX3NuqG+hGdu8eKdnfl0dPX+iX/i7liW/EE3jdeVqK6u5vjx47i7uxMYGNjrYk/XDbeatrY2EhISiI6O1smsJuhuzJSQkMB1112HsfHYnkX/e44ePYqDg4NGJjuMFl577TV2797dq4u1EMjl8p7eL9rsASKimwzm8yPWdIuIDBBdrOdubm6mtbVV5yIDKpWKoqKiK0a2dIGmpiZKS0sJCAgQWopWuVKNd1tbG4cPH8bKyorIyEjB69nvv3HoRlapHNy975Z2BSqVCluLwV8gjof08t9jZ2dHTEwMJSUlnD9/vqfud7wYbuie3e3l5UVGRobO1T2rMTU1xd7eXicbqjk6Ovak1OoKN9xwg1jXLTKuEE23iMgA2b9/v85FTSsqKrCzs0NfX7cqTaqqqlCpVDg4OAgtRWtkZmbi5uamM+mGV+P3xru1tZXDhw9jZ2fH1KlTR0VpxLTAiUz2GVozwsKLtUgH8RpUqm7jbWE6+O6q4yW9/PeYm5sTExNDeXk5Z86coaOjY9wYbjW+vr40NDRcsWuvLuDh4UFRUZHO3VhwdHSkpqZGp7rQq8s/UlNThZYiIjIiiKZbRGQAqOdz69qosIqKCp1qwKWmpKSEiRMnIpXq5ldcbW0tVVVV+Pv7Cy1lxFAb76ysLBITE3FwcGDKlCmjwnCruX+IadtNLe1MdBxcym+LXIGZ0eCNoo3l+DTd0B0JjYmJoaqqioSEXfwEaQAA6GFJREFUBPT19ceN4QYwMDDAz89Pp6PdDg4OKBQKqqurhZaiUUxMTDA3N6eqqkpoKRpDXde9f/9+oaWIiIwIunlFKiKiYbKzs2lsbGTu3LlCS9EYHR0d1NbW6pzp7uzspKKiAldXV6GlaAWVSkVGRgbe3t7jrv5MJpOhp6eHQqHAxMRkVBlugFlh3ng42wzpubaDNMPNcgVGBoN//eM10q3GwMAAAwMDFAoFxsbGOntj7kp4enrS3t5OefnAewiMJaRSKS4uLpSWlgotRePoYop5bGysaLpFxg3j69dGRGSI6GI9d2VlJRYWFjrXcObixYuYmZlhYWEhtBStUFVVRWNjIz4+PkJLGVHa2to4cuQIzs7OREdHk5OTM+g53tpGIpGw+A9Da2rX3Da4tNHmti5kQxgbNh5rutWoa7hlMhmzZ8+murqac+fO6WzUtz/09PQICAggMzMTpVIptByt4OrqSnl5OQqFQmgpGsXR0ZHKykqd+nu74YYbOHbsmFjXLTIuEE23iMgA0NV6bl2LcgOUlpbqfJTbz89P8MZhI4lcLufIkSPY29sTHByMjY1Nv83VRgMLogKZYDP4OvvCsmoMDQae5tzcrsBAb3Bm0dxEhoH++Eil/j2/b5pmZmZGVFQU5eXlOp1u3R8TJ05EIpFQVFQktBStYG1tjUwmo7KyUmgpGsXKygo9PT1qamqElqIx1HXdKSkpQksREdE6oukWEbkG6vnculTPrVQqqaqq0jnT3dbWRnV1NS4uLkJL0QplZWV0dnbi6ekptJQRQ22WrK2te9VwX6mrudAY6Otxz4KwQT+vo0uJp4ttv4/pSSU421oQ7ObAdHdHIhwnYK0yxHSQge7xmlp+pS7lpqamREdHU1JSQm5ursAqRw6pVEpgYCDZ2dl0dQ1uzvtYQCKR4OrqSklJidBSNIpEItG5FPPL53WLiOg6oukWEbkGuljPXVtbi56eHpaWlkJL0ShlZWXY2trqXMo8dN8oyczMxN/ff9w0flKpVJw8eRKZTNZvl/LRarxviQ/GwlQ26OeZyLqzF5xszZnm4UiEoz1BGGFzoZnWQyWU7M4h99dscvbnUnyuEj3V4NJMx2MTtWuNBTMzM2PmzJnk5ORw8eJFgVSOPE5OTpiYmIyqfzeaxNXVlaqqKp3q9g3djeJ0LYIv1nWLjBdE0y0icg2OHDlCUFCQTtVzV1dXY2dnN+oaUQ0XXU4tLywsRE9PDzc3N6GljBiZmZk0NTURHh5+xYZXo9F4mxgZcvvc0EE/r7ahFY86FW2HSsn/JZuc/Rcoz6ikU943Gtna3IlMNrif8PEW6R7oHG5LS0umTp3KyZMnaWxsHGGVwiCRSAgKCuLChQs6Z0yh+2aKpaWlzjWMs7W1paWlRadqoOfPn8/x48d1MutCRORyRNMtInIN0tLSmDx5stAyNIradOsSjY2NNDU14ezsLLQUjaNQKMjJySEwMFDnbpRcidLSUgoKCoiMjMTQ8OrzqEej8b7r+qnIDPUH9Zziyjq62gd24dna3ImRiR56egP/GR9PTdQGarjVuLi44O3tTWpqqk6a0P6ws7PDxsZGZ1PrXV1dda6LuaGhIZaWljo1Ei00NBSpVEpGRobQUkREtIpoukVErsHx48cJDx9aR+LRiEKhoK6uTudMd2lpKY6OjjrZYKy0tBRDQ0Odq8G/EvX19aSnpxMWFoa5ufmAnjPajLe1hQk3xk4a1HNUKnAMGtjfcWtzF3p6UiZYDryUQhOm28PDA4lE0u+fwX4+S0tLWb58Oc7OzshkMjw8PFixYgV1dXV9zlUqlaxfvx5/f3/Mzc2JjIxk3759/a57+vRpTE1N2bVr16DmcPv7+2Npacnx48d1qkP01fD396ewsFAno4wuLi7U1tbS2toqtBSNYmdnp1OmW19fH39/f06cOCG0FBERrTK42/AiIuOMzs5Ozp49S3R0tNBSNEZdXR0GBgY6lS6vUqkoLy8nKChIaCkaR6VSkZeXh7e397iIcsvlclJTU/H39x+0iVMb7+TkZAC8vb21IXHALF4Yzs4DZ1AoB94ZW2E+sJtGXZ1KOtoV2FsYcbG2ZUDP0VR6uaWlJStWrOhz3Mxs4F3b8/LyiIqKoqqqiptvvpmAgACOHTvG+++/z+7duzly5Ai2tv9rLLdx40ZWrlzJddddx4033si2bdu44YYbSEtLY8qUKT3nyeVy7r33XoKCgnjzzTcH1f9AIpEwbdo0Dh06xLlz5wgJCRnwc8cq1tbWmJmZUVxcjJeXl9ByNIpMJsPe3p7y8nKdGrFoZ2fHuXPnhJahUSZPnkxaWhrLli0TWoqIiNYQTbeIyFXIzMxEX1+f0NBQoaVoDF2s525ubqatrY0JEyYILUXjVFdX097errO16pejUCg4duwYdnZ2Q75IHk3G23mCJXMj/diTnD3g55Q2NA343JamTqxMB57ZoalGalZWVqxdu3ZYazz22GNUVVWxYcMGnnzyyZ7jq1at4r333uPFF1/kk08+6Tn+8ccfEx8fz549ewB44okn8PHx4dNPP+Wjjz4Cum+Srlq1iry8PNLT09HXH/wljr6+PpGRkSQlJWFhYYGHh8ewXudoRyKR4OXlRXZ2Np6enjr1uwDds63Lysp0ynSr67rb2tp0pmloWFgY3377rdAyRES0ipheLiJyFU6cOKFz3aJ1sZ67oqICe3v7IV1kj3by8/Nxd3fXqc9gf6hUKk6fPo1KpSI0NHRYF/+jKdV8yR8iBnX+pfoW7NxtBnRua3Mn5rKBf+ZHSyO1vLw89uzZg4eHB48//nivx1566SVMTU3ZsmULLS3/i+AXFRUREfG/99LT0xM7O7ueWdOdnZ1s27aNL7/8kpdffpmAgIAh6zMxMWH69OmcO3dOp9J4r4SzszNdXV1UVVUJLUXjODo6Ultbq1N1+gYGBlhaWurUvO6oqCjOnDmjk2UOIiJqdO8KVUREg6SlpTFp0uDqMkcz6npuXYrcQ7fpnjhxotAyNE5LSwtVVVXjIs21uLiYqqoq4uLiNHKDYbREvP3c7ZkZ4kHymcIBP8fa25bqotprntfa3Imx5cBHk2kq0t3e3s7WrVspLi7G1NSUkJAQZs2aNeC/N/VM3vnz5/fpSm9ubk50dDR79uwhJSWlZ1Sjm5tbr5rPoqIiqqurcXd3p7Ozk6NHj/LWW28REhLCX/7yl2G/Rjs7u56U1/j4eGSywY+AGyvo6enh4eFBfn4+Dg4OQsvRKMbGxlhYWFBZWalTvxHqum5dyYCaNm0aEomErKysUdu4VlnhJ7SEqyJ1zNHIOtu3bycpKYn09HROnz5NU1MT9913H1u3bh30Wj///DPvv/8+GRkZ1NTU4OTkRFhYGKtWrWLmzJkDXqempoYffviBn3/+mbNnz1JWVoahoSHBwcEsW7aMZcuWXXHCyZUoLS1l9erV7N69u0fbLbfcwpo1a7C2th7sSx0QYqRbROQq6FoTtbq6OgwNDXWqnru9vZ26ujqdu1iE7ii3k5OTzqQQXonW1lbOnTvH1KlTNfpaR0vEe8mN0wd1vtxwYFH+1uZOBhHo1lj38oqKCpYsWcKLL77IihUrmDNnDr6+viQlJQ3o+dnZ3en2fn79X8T6+voCkJPzv4vIRx55hH379nHDDTfwzDPPEBcXh0QiYdmyZSQnJ/Pjjz+SmZnJ5s2bNZYV4u7ujrW1NWfPntXIeqMZDw8PqquraWoaeHnDWMHR0ZGKigqhZWgUsZmaiLZ45ZVX2LhxI+np6bi4uAx5neeff54bb7yRkydPsmDBAp5++mmmTZvGrl27iI6OHpSJ37ZtGw8//DCpqalERkayYsUKbr/9ds6dO8dDDz3EnXfeiUo18N4peXl5hIWFsWnTJiIiIli5ciVeXl68//77zJw5U2tZJKLpFhG5Al1dXTrXRE0X67krKyuxtLTUOWPa1dWlk82Nfo9KpSI9PR1nZ2et3DgZDcY7LHAik7wH3hQu/1IdkgGMAmtp7sJwgP7S0EAPM5PhR2uXLVtGQkICFRUVtLS0cPbsWf785z9TWFjIDTfcwOnTp6+5RkNDA9DdkK0/1Mfr6+t7jj311FO8/fbbXLhwgU8++QR7e3t27dpFW1sb1dXVfPbZZ/zjH/8gKCiIjz/+GA8PD/T09AgICGDXrl1Deq0SiYQpU6ZQVVWlc/Oef4+RkREuLi7k5+cLLUXjODo6UlVVpVMd6S+v69YVJk2aRFpamtAyxj3vvfceOTk5NDY28vHHHw9pjYqKCt555x0cHBzIyMjg888/54033mD79u389ttvqFQqVq9ePeD1/Pz8+PHHHyktLeWbb77h9ddf58svvyQrK4uJEyeyY8cOvv/++wGvd3lPkZ07d/LGG2+wf/9+Vq5cSXZ2Ni+++OJQXvY1EU23iMgVyMzMRCKR6FQqdnV1da+OwLpARUWFTo7SKisrw9TUVGtpTqOFoqIimpqatJpSOBqM9/2DiHa3tHXgHHDtGxBtzZ0Y6g/sBpqm6rnXrFnDnDlzcHBwwMTEhMmTJ/PJJ5+watUq2traht1g7UpIpVKeeeYZcnNzaW5u5ujRo5ibm2NgYMBHH32Ej48PL7zwAjt37uSxxx5j4cKF7Nmzh+DgYG6//fYhR6uNjIwICQnhzJkztLe3a/hVjS48PDwoLS3VubpaS0tL9PX1dSoyrIt13WFhYRw/flxoGeOe+Ph4fH19hxWcKSoqQqlUEhkZ2afBbXx8PObm5ly6dGnA682ZM4c//vGPfVLIHR0deeSRRwBITEwc0FpD6SmiKUTTLSJyBdRN1HSlOZcuzudWKBRUVVXppOkuKirC3d1dp7ISfk9rayvnz58nNDRU6/PVhTbecWE+eDgPrEEagKmrxTXPaWnuxHCAb5u2m6ipL3wOHjx4zXPVkWx1xPv3qI9bWVn1+3hnZyfJyckYGBhw9uxZkpKS+PLLL9HX1+ftt9/Gx8eHDz/8kLlz57J582ZMTU15++23h/CqunFxcRkXaebW1taYmJjoXFRfPUNeTDEf3aibqSkUCqGliAwTX19fDA0NOXbsWJ/P6MGDB2lqamLevHka2Ut97TDQa/WB9BRpbW0lJSVFI/ouRzTdIiJXQNeaqNXX1+vcfO6amhoMDQ2xsLi2QRlLNDQ00NjYqDNNcvpD22nl/SGk8ZZIJNy3cOD9IeqV1442tjZ1YiQbWH65tbl2yy/s7e0BBhQd8Pf3B3rXbF9Obm4u0H/N9+WG28XFheeee47nn3+eqVOnAt0ZSlOnTu25WWVqaoq/vz/nz58f/Iv6P8ZLmrlEIsHd3Z3CwkKhpWgctekeTN3naMfW1lbnIt0qlYqsrCyhpYgMExsbG958800qKysJCgriT3/6E3/961+58847mT9/Ptdddx2ffvrpsPfp6uri66+/BmDBggUDes5QeopoCtF0i4hcgePHjxMWFia0DI1RX1+PtbW1TkVOL168iKOjo069JuiOcjs7O2s9+iskI5FW3h9CGu8bogOxtzYb0LkFlbUYmhhe9ZzW5k4MDKUY6F37829jqd2bbeqowEB6EMTHxwOwZ8+ePnW2TU1NHDlyBBMTE2bMmNHrscsNd0REBI8//jguLi59agN/nwYul8sH/Xp+jzrN/PTp0zqdZu7q6tpz00+XsLOzo6OjQ6del7W1Nc3NzXR2dgotRSOIzdR0ixUrVvD999/T1dXFv/71L9544w22bdvGxIkTWbp0aZ+086HwwgsvcO7cORYuXMj1118/oOcMpaeIphBNt4hIP3R1dXHmzBmioqKElqIx6uvrr/glM1aprq7uibDpCgqFgtLSUtzd3YWWojXUaeVTp04V5MaCUMbbQF+Pu6+fNqBzO7uUOE++etmEvE2BQqHE3tLomuvZWAw/0p2ZmdlvJLuwsJAnnngCgMWLF/cc7+zsJCsrq8977O3tzfz58yksLOTDDz/s9diaNWtoaWlhyZIlvbJyfm+4v/32W3755Re+/PJLDA3/d3MiKCiIgwcP9pir/Px8MjIyNJK15OLigq2tLWfOnBn2WqMVQ0NDnJ2de+af6wp6eno6l45tZGSETCbTqRsJYjM13eGtt95i0aJFLF26lLy8PFpaWjhx4gReXl7cd999PPfccz3nFhYWsnbt2j5/rsaGDRtYt24dAQEBbNmyRcuvRjPoRrGqiIiGUaef6FqkezjjH0YbbW1tNDc361SNOkB5eTkymQwbm4HX/44lVCoVp0+fxtnZWSN3uoeKUHO8b50TwuYfU2lqvXa0VN/22ka5tbkLeytjymuv3sVYE5Hu//znP6xbt45Zs2bh7u6Oubk5eXl5/Pzzz8jlchYuXMgzzzzTc35ZWRmBgYH9pix/9NFHREVF8dRTT5GQkEBgYCCpqakcOHAAPz8/Xn311Z5zf2+4q6urWbFiBStXriQyMrLXus899xw333wzUVFRzJ8/v6ej7bPPPjvs1y+RSAgJCeHAgQOUl5fj7Ow87DVHI+7u7hw7doygoCCNjV8bDdja2lJdXT1i/9ZHAisrK+rr63WmQWpYWBjbtm0TWobIMElMTOT555/n1ltv5d133+05Pm3aNH744Qf8/PxYt24djzzyCF5eXhQWFvLSSy/1WedKxnvjxo08/fTTBAUFkZCQMKjrpeH2FBkOYqT7/7N33+FtXufd+L8P9iQGQQKc4F6SKGqQ1KaWncYZjrOat6ndvO0vo0kzmvRqk7iv08Rt3rZv0qRtdjqSOo0Tt4mT1HFiS9a0SHFIpEiKe0+AE3sDz+8PGhS3OAA8wMH5XJcuW1y4H3Hh+5z73IeiNnD//n0UFBQQM0TN7/fD4XBE5YcIV+bn56FSqYhrwR4dHUVeXh5xLfNhs7OzWFxcjHlb+Ua4WPGWS0V418WD23pb8zaOA3I5/NAqRFDJBDCoRcjUilGVvzTjwKAWI00lgkzMh1r58NXwhzl37hze+ta3YnBwED/+8Y/xD//wD7h+/TpOnTqFH/7wh3jppZdWrTpvpbCwEC0tLfjABz6AxsZGfPWrX8Xg4CA++clP4vbt28shYm3g5vP5+NjHPgatVotnn3123cd9+9vfju9+97twu934xje+Ablcjp///Oc4cODAnq8fWFpdrKioQFdXF1FHUK2UmpoKsViM6elprkuJKJ1Oh/n5eaL2dYdDNymOHj2K7u5uoj5Hyeill14C8GAr0UoymQw1NTUIhUJobW0FAJw9exYsy677s5Gvf/3r+PjHP479+/fj6tWrOx6ku5eZIntFRqKgqAjr6+tDXl4e12VEjM1mg1gshkSy9yfe8SJ85jhJvF4vFhYWiOqwWIllWXR1daGkpCRubpZwseL9u48exvO/uQOvf+spveMzVuSmKWCbdYBhALVOgvQMGdIyZNCkSSBXCKFKFUPP56G2SAaPP4RgiIVSuvSr/R21eggEDCRCPvzzA3jllQlIJBJIpVKoVCqo1Wqo1WqIxds7v7uurg51dXXbvs68vLwtnzzn5OTg3//93zd9/UaBGwD++7//e8vH/dCHPoQPfehD265zp3JycjAwMIDR0VHk5+dH7XG4wjAMsrKyMD09TdQwR5VKBZZlYbVaibkBrVarMTk5yXUZEVNZWQmr1Yr5+Xnifr8nk/Dci82OBQu/fLs3acP+7u/+Dp/97GdRVVWFS5cu7eprZO1MkZUTzLeaKRIJNHRT1AZ6enqIakGzWCzEPMkIm5ubi4vV0kgym81QqVSQSqM7aZork5OT8Hq9cRdUYh28tSoZ3npmP3722r0t306vFuH4ER1k/CB0hqWviTmzG7PTLgx2LcJh86P8YCr4qXI8//o4wvFWJGDwZ+8owHdeGYMvwELAY/Ddp9+NjFQZPB4PXC4XrFYrxsfH4XQ6IZFIoNVqodfrodfrtx3Co2mzwB0PeDweysvL0d7ejpycHGI6olYyGAwYHBxc96Q0kfF4vOWJ36T8PlSpVHA4HAgEAkR8HarVaqSlpaGvr4+G7gTg9/sxODgIoVC46vfm6dOn8Y1vfAPf+9738OEPf3jV1sbf/OY3uHXrFiQSyY7mJj377LN45plncOTIEbz66qsPbSnfrLbwTJFXX30V3/zmN/Hxj398+XXhmSIf/vCHo3LST+J/h1JUFPT29uIDH/gA12VEDGmh2+12w+l0ErOPLcxkMhF55jgAhEIhdHd3o6ysLK4CVFisg/f7HzuCX1xtRzD0YCWYYYC8dClKMuUozpBDKuLDaguh/eY4zFMuWOY8WLtwrM+SIzNdga2aMQMhFoZ0DVLV6yen+/3+5ZWl4eFhtLW1QaPRwGAwICMjAwrF9qatR1I8B+6wjIwM9Pf3Y2hoKCptiFxTqVQQCASYm5vjdPZCpIWHqZFyUz08TM1qtRLz+9BoNKKvr4+oQbaJ5Be/+AV+8YtfAMDy2fYNDQ3Lz4l1Oh2+8pWvANh8bse73/1uXLx4EZcvX0Z5eTmeeOIJGAwGdHd346WXXgLLsvjbv/3bbX/N/vCHP8QzzzwDPp+P06dP45/+6Z/WvU1eXt6q5+2RmikSSTR0U9QaLMuiv7+fqFVUi8VC1NAfEvdzB4NBzMzMLO83Is3o6Ch4PB5ycnK4LmVTsQzeWelqnK8pwaXbvVBK+TiUn4KD+Sng83iYmw+g8dIUBjvnoVLJsLiw+dnXTrsf4occGcYwgFop2/B1QqEQOp0OOp0OpaWl8Hg8MJlMMJlM6OnpgVarhdFoRGZmZkxWPBMhcANLLdgVFRVoampCXl7ejtsk4x3DMMtnW5MUulNTU9HX1weWZYmYm8EwDHHD1IxG4/IwWyr22tra8MMf/nDVy4aGhjA0NARg6fMTDt2b4fF4ePnll/HNb34TP/nJT/Diiy/C5XJBq9Xisccewyc+8Qk8+uij265peHgYwNLzpK9//esbvk1dXd22F8vCM0WeeeYZ/Pa3v8XLL7+MjIwMfPKTn8QXvvAFaDSabde2EzR0U9Qa8/PzsFqtERu8w7VAIEDcEDUS93PPzc1BJBIhJSWF61IiLhAIoLe3FwcPHoz7VtVYBu/3nCuDgrWgJEuOeWsQrTdncb9xetVq9uKCExlZGkxPLm74MVwOP0QPufeUIpdAwN/ev7tEIkFeXh7y8vLg9XoxPj6Onp4edHZ2Ij8/H/n5+VELmIkSuMPS0tKg1WrR19dH1E3aMIPBgHv37uHAgQNEBFSAzH3dKpVq00nMiaioqCguQzfPsPHgLdJs57iusK3mdgiFQnzqU5/Cpz71qZjWtJ3agIfPFImG+H72Q1EcCO8lIuXIJqvVSoeoJYBwazkpT25XGhwchEwmS5jW+WhPNbfZbGhsbMT4UDcMKSl44Xs9+O9/bkfn7el17eMAkJq++Y0Yl8MPsXDrrxlNysar3A8jFotRVFSECxcu4NChQ5ibm8OlS5fQ19eHQCCwq4+5mUQL3GHl5eUYGRmBexuT5hONTqeDz+cj6hzolfu6SUHaBPOysrK4DN0UtVc0dFPUGn19fTAajVyXETHh/dykhDmPx0Pcfm6WZYndz+31ejEwMICKioqE+hqMRvB2u91obW3F9evXIZVKcfHiRVSV78fizNaBzef1b/o6l8MPkZiHrf5ptardhe4whmGg1+tx8uRJ1NTUYHp6GpcvX8bIyEhEjs1K1MANLAUeg8GAnp4erkuJOD6fj/T09OV9naQI7+smhVqtht1uj/iNMK4cOHBgeYgfRZGEhm6KWqO3t5eo48KsVitUKhXXZUTM4uIiFAoFUfu5rVYrAoEAUTcSwgYGBpCampqQnQmRCt6hUAh9fX147bXXEAqFcP78eVRWVkIikaCqOh8l+7K2fP+x4VnwN2kPdzkCYBgGcvHmQVW7y5XujaSlpeHMmTOorKzEwMAArl27hsXFjVvftyORA3dYeXk5JiYm4HA4uC4l4sL7ukmi0Wj29DUbb8LD1EjpSNi3bx/8fj8mJia4LoWiIoqGbopao7e3F0VFRVyXETF2u52ofcIk7cULM5vNSE9PT8jAsRW/34+RkZGEnu681+Bts9lw8+ZNjI+P48SJEzhy5Mi6o0je+wentvwYHrcfuQVpG74uFGLhdgWglm9+EyqSoRtYWvnOzMzE+fPnkZ2djVu3bqGrqwvB4Nbnjq9FQuAGALlcjszMzKhsReCaXq+H1WqFx+PhupSIUalU8Hq9xFwTwzBQKpWw2+1clxIREokEmZmZ6OtLjj3UVPKgoZui1ujt7UVZWRnXZUQEy7JwOp1ROW+QK6QdfwYA09PTRLaWj4+PQ6FQRG0SaKzsJniHT0G4ceMGdDodzp49u+mciJPnypBt3LrLQaHcfCaDy+5Hmnrzs913u6f7YXg8HkpKSnDmzBnMzs7i+vXr215BJCVwhxUWFmJ8fBx+/+ZbARKRWCyGRqOB2WzmupSIEQgEUCgURO2DVigUcDo3P+Ug0YSPDaMoktDQTVErhEIhDA0NETO53Ofzwe/3c3LWbrSQFrrdbjdsNhv0ej3XpUQUy7IYGhpCQUFBQu3l3sxOgrfP50NDQwNGR0dx4sQJ7Nu3b8tQyePx8O4nT275MW3Wzfd9uxx+FGqVqMnSo8aQjsP6pVXxI4Y01OQYkKVSbvmx9yolJQWnT59eXvUOH++yGdICN7C0r1alUmF0dJTrUiJOr9cT12KuVquJmvgtl8uJ2t6Qn59Ph6lRxKGhm6JWmJychM/nw759+7guJSIcDgckEgkEAjJOB/R4PPB6vUTtUTebzdBqtcSd8zszM4NAIICsrK33KyeS7QRvu92OGzdugM/no66ubtunIFx860Gkpm0ejseHZyGTizd8ncvhB8/uQd+lPvRd6cfg9aXaBq4Nou+VXiiZ6Ifa8Kr38ePH0dPTg3v37m04CInEwB1WUFCA4eHhLY+pSUQGgwGzs7PEDOoCyJv4rVAoiArdxcXFRA4npJIbDd0UtUJfXx+ysrKIOV7L4XAQt8qtUCiIuYkAgNip5cPDw8jLy4v7c7l3aqvgbTabcePGDWRmZqKmpmZHw/6EQgGe+L1jm74+FGKRk7fxMDqnww+pfPPvCbU2dttLUlNTUVdXh4WFBTQ0NMDr9S6/juTADQAZGRkIhUJEtWIDgFKphEQiIWrit0qlIi50O51OYm74lJeXx7y9nJR/Oyq2dvJ1Q9azIYrao76+PuTk5HBdRsTQ/dzxLRAIYHZ2lrjWcrfbjZmZGaKO3ltpo+A9OjqK5uZmHDx4cNfHo73lXdVb7t0WiTcO1i6HH2LJ5r/ONTEM3QAgk8lw+vRpCIVC3Lx5Ey6Xi/jADSyt9huNRuJazMNHxpHUYq5SqeDxeIgZpiaTycCyLDHnxVdWVmJsbAw+ny/qjxX+WUTaPAYqNsJfN9v5nUZDN0WtMDY2RlQ7LGkr3aRNLl9YWIBEIoFSGd09t7E2OjqK9PR0SKWbD/dKdCuDd1NTEzo7O3H8+HFkZ2fv+mPK5GK85d3Vm75+xrTxHlSXPQDRFqE7livdYQKBANXV1UhPT8fNmzfx+uuvEx24w3Jzc2E2m4kJP2F6vR6zs7NclxExQqEQCoWCmH3dPB4PMpmMmBbzwsJCAMDU1FTUH0soFEIsFsNqtdLVbmpHWJaF1WqFWCzeVmcbOT2aFBUBExMTRLX6OhwO5Obmcl1GxFgsluVfxiQgbeUeWPolNDY2Rswwwq1oNBoYjUYMDAygoKAgIuesP/G/juHFHzfA512/f9Y8ZYFWp8DC3Oon1i6nH0LRxqFbLBFCKtt4L3i0MQyDsrIyTE1NweFw4OjRo0QHbmBpxTEtLQ1jY2MoLS3lupyIUavVcLlc8Pl8xMyfCLeYk9JpFN7XnZ6eznUpeyYQCKDVajE1NYW8vLyoP55Op8Pk5CQmJiagUqkgFAqJGABKRQfLsvD7/bBarXA4HNterKOhm6JWmJqawv79+7kuIyJIOy4sfK4qSUPUSFu5B5YGqLEsS8wT2a2Mjo5iZGQEVVVVuH//PmQy2Z5vCmlSFbj41iq8/LOWDV+fka1dF7qddv+mK92SDBme/MVPoZSIkSqXIU0hR6EmFTX6HKRJo/uzwe/34/bt20hJSYFcLsft27dx6tQpojsgACAvLw8dHR0oKSkh5om7SCSCTCaDxWIhItQBSzcSFhYWuC4jYkg7NiwtLQ3T09MxeayUlBQAwNzcHCYnJ2PymFTiE4vFyMrKWv76eRgauilqhenpaWLay91uN1iWJSZ0hyex72Q4VbyzWCzE7XuenJxEdnY2cQPU1pqdnUVHRweOHTsGnU6HlJQUNDQ0AMCeg/d7njyJ3/7iLkLB9dO/Q8H17Y8uhx98PgOxhA+vJ7jqdRKNFE1tq1s0dWopCnO1+Pcn3gNelELh2j3cPB4PbW1taGxsxKlTp4gahriWXq9Ha2srFhcXtz29PhGoVCpYrVZiQrdCocDY2BjXZUSMXC4nat99LEM3sBS8U1JS4Pf7EQwGH/4OVFLj8/k7fj5K7m89itqF6elpYgapORwOyGQyYsIPafvTfT4fXC4XUSvdLMvCbDajpqaG61KiyuFwoLm5GZWVldDpliaKh/d4RyJ4Z+Zocep8OW5cur/udZPj82AYYOXWQ78vhIA/BJlCuC50S+ViHNCrYPV6MGWzIxAKYc7ixiGDNGaBO9xSfvDgQdTX16O1tRVHjx4lZhV4LR6Ptzx4jKTQTeIxW+GJ3yR8LZJ2bFisQ3eYUCgk6uY+FT/IeDZOURHg9XphsViI2QNNWkgl7XosFgtkMhkx+yMBLLdqkhQ01vL7/WhsbERubu66nxXbOcd7O1iWRenjRRu+zmZxITNn/d5xp90PmWL9fXQhX4ixehOsdyxIGQqh3K/CcW0mLhYV7Lq+rWw1pZzH46G6uhqLi4sxPw4o1gwGA1GrjgB5oTs88dvlcnFdSkQoFAq4XC5iVmn1ej1t9aaIQkM3Rb3BZDKBx+MRs9Ltcrkgk8m4LiNiSAvdVquVqP3pwNL3kF6vJ2LVaCMsy+LOnTuQyWTYt2/fhm+z1+DdMDmKj//qf/DFyzeRfWrjn0Va3frvA6fDD23a+u93RvQg9AaDLKbGLOhqnsThnMwd1/Yw2zkWTCwWo7a2Fv39/ZysYsVKeno6HA4HUXtsVSrV8jA1EoQnfpPyOZJIJODxeMRMzs/KyqKhmyIKDd0U9Ybp6WmkpqYSs9fQ4/FAItn8zN9EQ9JQOIDMyeUmk4mo6f9rjYyMwGazPbQ1ejfBe9JhxV9duYw/ee4lXG4dRCAYgqhMveHbul3rQ4/L4Ydau35AWWiDYeEZuhTk6DXbqmu7dnIOt0qlQlVVFdra2og5J3ktoVAInU5H1Gq3WCyGVCol5pgtgKyWbIZhIBaLifmeyszMJPrGHJV8aOimqDdMTU1F5MifeEFS6A5PYidppZu00O1wOOByuZCWlsZ1KVHhdDpx//59HDp0aFv7/XYSvO9MTeLDz/8CP7nRAafHh/3p6Sjzq3C3fgyZFeunwI8Nz0IoWh1qXXY/5Cnrtyr4N3i8mv2R3UKzk8Adlp2dDZ1Oh/b2dmLPxqUt5vGPpNANLK12kxK6c3JyiPv+oZIbDd0U9Ybp6WmiAgNJoTs8iZ2UdnkSh6iZTCbodDoiB9CwLIu2tjbk5OTs6GfEdoN3WWoavK4g0hVyHJXpMd5gxtSYBQAgMK7fguDzBpCTt7oOl8MPmWL9v70nuP687+p9kZuYv5vAHVZZWYn5+XliW0gNBgPm5+fh92906yMxhc+2JgWJodvr9XJdRkTk5uYS9/1DJTcauinqDdPT08QchQIsDYYjJXSTNondarVCKpUSNUTNbDYT21o+PDwMp9OJioqKHb/vdoK3XCzCV594M4RDfvR3rF7Z6Rybgc64fjCdXCFe9XeXww+JdH3g9fhXh26GAar3RWaley+BG1hqVz548CDa29uJWZ1bSSaTQalUwmw2c11KxKjVaqLay+VyOTF7ugGyVrpzc3PBMAxd7aaIQcYzWIqKgMnJSej161s5E1EgEEAgEIBYLH74GycA0oaokdZa7vP5MD8/T8z3z0o+nw/d3d2oqqra9Sr+doJ3ZW4G3nO+at3LQyyLlH3rV9etltUTl52OAETi9fvMmXtzyFsEqmQpqMk24PzBQqiV6/d+79ReA3dYZmYmdDoduru791xTPAofHUYKtVoNp9NJzOojaRO/SdrTLRaLodFo6L5uihg0dFPUGyYnJ5GRkcF1GRHh8XjAMAwxK6mkDVGzWq1Ehe6ZmRkolUpi2v9X6uvrg1ar3XMXzHaC9//3xHGU56+/cdE5MYuUtNU3ncaHZyFXPuhkcTn8EIpX/0pnmKUjxizTVoy0jKPv1V4UCOMncIdVVFRgYmICdrt9z7XFG4PBgJmZGWL2rYeHqZHSYi6RSMDn84lZ7SZppRsAdDodDd0UMWjopqg3TE9PIysri+syIiK8n5uUo5s8Hg+k0r2HhXhB2kr34uIiUUMIw1wuF0ZGRnbVVr6RhwVvAZ+HL/7xmyERrT5BgQWL/MPZq1/GAjlG3YNaHX4IRTzwBQ++55UqGUKh1WHvUE3hnq4h0oEbWFptzM3NJXK1W61WIxgMErVvWKVSEdNizjAMUUGVpGsBaOimyEJDN0W9wWQyEXNGN0n7uQGyhsL5/X44nU6izugmbeU+rLe3FxkZGRH9XD0seBsztPj4/zqz/PeDRgOy50LouzkEmXz1dhG+8MGvcLczgFCIhUz+oAVevubtRWIBDhza/X7uaATusNLSUszMzGBhYSFiHzMe8Hg84oaPkTbBnKThYyRdC7B03v3U1BTXZVBURNDQTVFYmk5ssViIGQTl8XiI2c8NkHU9TqcTIpGImOsJf++QFrodDgcmJiZQVlYW8Y/9sOD97otV+J1jZagUKTD6m14sTlnhdvlQWLr659PMlGXV373uIGTKB6FbIlv9NbavKhci8e72pUczcANLYaGwsJDI1W6SVoYBQKlUErVyT9I+aIlEAr/fT8wedY1GQ9yNOCp50dBNUVhaGQ4EAsSsPpK0MsyyLFHXQ9K1AEvhlGVZogbdAcDg4CAyMzOjNkvgYcH7k797GtaRxVUvG19zPves2YY0fcry3512P+Qrjg0TiVe3qR+qKdhVrdEO3GGFhYVYXFwkKqACdGU43pHUki0SicAwDDHXo1AoYLPZuC6DoiKChm6KApbv2ms0Go4riQySgl0gEEAoFCLmekhatQeW9qerVCpijnMDlkLm+Pg4Cgv3tv/5YbYK3lqdEp/6y7evepll0YXSfavnTugz1Mv/77D5oFQ9+Nri81d/Tg7X7vx6YhW4gaXAkJ2djaGhoag9BhfCx2yRNEzN6/UScz0khW6GYYhauSetq4JKbuQ8S6KoPbDb7eDxeMRMXyZpT7fH4wGfz4dAIHj4GycAkm6IAEv7uUnpEAkbHx9HSkpKTFrmtwreJ86V402PH1r1slmTDTzeg2FpgUBo+f9dDj80qQ8GDq54M6SoZCgq29npDLEM3GH5+fmYnJwk5kgqYCk4hEIhYsKDRCIBy7LErHaTuHJPyvWkpKTQlW6KGDR0UxSWVrplMhkxq3V+v3/XZwrHG9ImsZN0QwQgbxI7y7IYHR2F0WiM2WNuFbz/+M/ejIysBx045mkLSlasdk+Mzi2HcJfDD4X6wTGBwRWTy6tq8nf0PcRF4AaW9j8rlUpMTEzE5PFigcfjISUlhZi2eT6fD6FQSMxqKkkr3QAgFAqJuWmlUCiIPEqQSk5kJAyK2iO73U7UOdCBQIColWGS2rFJWulmWZa4yeU2mw1OpzPmxwduFrylMjH+/K/fBd6KVnGn/UFAcNg9yDIuHdfmdPghlT/4vvf7Asv/v5PWcq4Cd5jRaMT4+HhMHzPa6L7u+EVSOzYACAQCBAKBh79hAlCpVMR0iFAUDd0UhQcr3aQgLXSTElIBsq7H4XAgFApBqVRyXUrEmEwmpKenc/L9s1nwrqjMwfs+cGr57+Mjc6smmas1cvD5PIiFIkikD+r2eR+sdh0+tr3QzXXgBgCDwQCLxUJUECIxdJPy+ZFIJAgGg8SsDpMUupVKJV3ppohBQzdFYWmlm4bu+OT1eolb6Sblemw2G1JSUojZlgEshW4ujw7cLHj//ofOoqQi88EbrugUd9ndYLr6MfFaG0TiB69wOpZWIjNztKsGrm0mHgI3sBSC1Go1zGYzJ48fDaQdG0bS6rBQKASPxyNm5Z6k0K1Wq+lKN0UMcp4pUdQe2O12SKXSh79hAmBZlqjQHQgEiNmfHh4+RMpKt9vtJub7Bli6IWK1WqHX6zmtY6PgzRfw8efPvgtiydL3wmCPCTl5OgDA2Og8AMC54IZIzEN467bd5gawvaPC4iVwhxkMBphMJk5riCSpVIpAIEBMGCJppZthGKKCKknXolKp4HQ6uS6DoiKChm6KwlKbLCl7uoPBIABw/qQ5Uki6geDz+cCyLDGhm6RWeWBplVuj0cRFJ8JGwfvm1DgK31Ky/DZy5dK/vd8XRG51MVwWNxges9xi7vMuPfE+VLt16I63wA0she7Z2dnln2eJjrTzk0kK3QBZQZXP5xNzLSqVCj6fj5jWfyq50dBNUSCrvTz8JJWUoEpS6PZ4PBAKhXERaiKBpFV7AJibm0N6ejrXZSxbGbz//aVL+NkvWmHrW1huM+/tnEBJeQYOFKUiRSpE0B+C3xOAVPGgM4ThMfj5r+/guz+8itmF9Xsj4zFwA0t7OYVCIRYXF7kuJSIYhiEqqJI0SA0gK3QLBAJiblZpNEsnN9AWc4oENHRTFMha6Q4EAmAYhph9tqSF7nhYRY0U0la64/H4s3Dw1oY8OMBnMdExDfO0BfoMFfblqSGamMa9/7qJmaGlVmy31Yts+dLZ3XkKBkcqs9B1fRCv/NttyESrt2nEa+AGlkIqicPHSAmqJN1AAMgL3aRci1KpBMMwdJgaRQQynslS1B7ZbDYoFAquy4iIcEgl5Vxr0kI3SSGVpOvx+/1wOp1QqVRcl7KORqPBmdOnAPZ1uGwe3Ls9A0OpHu0/uQqBSAB1egom+qaRkqqEc8EFt2XpCepQfQ8qUpdWit767qOQKx58ruI5cIep1Wo6fCxOha+FZVkifteQFFRJuhY+nw+pVEpDN0UEMpbCKGqP7HY7caGbFIFAIC4DwW6QFFIBsq7HarVCIpHE7fVoNBqcOXMKJx/NxcFj6ejtNWP/48cQ8AWQW54NAMgqyYB9zgmZ+sE1OEMMBEI+HnnHoeWXJULgBpb2c5K20k1K6JZIJGBZFj6fj+tSIoKkoErStQCAXC6n7eUUEWjopuLSnTt3wDAMamtrN3z9888/D4ZhwDAMhoeH173e7XZDIpFAJpNtq53PbrcTc9YwiaGblOshaQ90eBIzKe3y8dhavtba4D3tDCHdmIaB1mHIUqTg8xg4511Q6Zd+lolkQphMVpTtz8Kff+SHGBgwJUzgBh4cF0TKECWSQrdAIIBAICCmXZ6k4WOkhW6ZTLanle7wc8Wt/ly7di1yBVPUJsh4JksR59ChQ9BoNLhz587yWcArvfbaa2AYBizL4sqVK/ijP/qjVa+/desWvF4vHnnkkW2FApLOgiYppAJkXY/f7ycmdHs8HjAMA5FIxHUpEeFwOBLixls4eAOvQxnox5hJgrmJeew/VYaJvikIUjOgL1w6SiynJAvj9/sRytMhLVWJ7Cx1wgRuYCmkCoVCOJ3OuL8hsh0SiQRzc3NclxExQqGQmBsiJA0fIy10i0SiiNys+sIXvrDp6/Ly8vb88SnqYch4JksRh8fj4ezZs3jxxRdx/fp1vO1tb1v1+itXruDs2bNob2/fMHRfuXIFAHDhwoVtPR5JLczBYJCYa2FZFsFgkJjQTcr+R+DBqj1J17P25l68CgdvNhSCzbSIlNQyjHVPwml1QSgQQvpGe7lELkJuaRYGf3kL/3z3b9HU3JQwgTuMtOFjpFwLsPR7OhQKcV1GRAgEAmJuIPD5fGJuIACR+zr7q7/6q70XQ1F7QNvLqbgVDszhAB02MjKC4eFhXLhwAXV1dbh69eq6991p6A6FQgnzJPRhSAp2pB1/FgqFiJkqT1J3CJB4+9OlcgXauz2o/f3D4GsAlU6J0urCpX3pyqXug0WTFU6rE7/z8TdjdHIg4QI3QFZLtlgsJip0h7vNSEDS6jBJnxeArJs7VHIj49kfRaTz588DWGolXyn89/Pnz+PcuXOYnp5GV1fX8uttNhtaWlqg0Whw+PDhbT0WSaGbJOEnDqTcRCDthghJ3zOJFrolIiHe8aZa3Hx5CrXvO4jcagOsszY4LW6I5EtHg0kUEihTFZAaAnA6fQkXuAGyJn6TtgJJUrgj7VpIQkM3RQoylo8oIpWXlyMjIwOdnZ2YnZ1FWloagKVVbIVCgerq6uV20CtXrqCiogIAcP36dQSDQZw9e3bbq4qhUIiYX1QkBTsauuMXadeSiCv3lUfy8BfPvg/feeY/Ufk7RZCr5TB3zYMvXArWcpUE5W8pgNfpQwovNeECN0DWSjdJwQ5YCkOkXA9pnxuSriXa7eUSiQSf/exn9/zxKephaOim4tqFCxfwox/9CFevXsV73/teAMDVq1dx+vRpCAQC7Nu3D+np6bhy5Qr+5E/+BMDOW8sBsla6SQpDpCGpvZykr7NAIACWZRNyKBxfwEd2fjba/6sXle8pAV/IRygYAo/Pw4EnSuBz+uCe4+Hkmw49/IPFIZFIBLfbzXUZEUFasGMYhpgVSFJ+lgEProWUn9GR+jr74he/uOHLVSoVDd1UTJDx7I8iVrjFPByku7u7MT09jXPnzi2/zdmzZ3Ht2rXlH8q7Dd2khCGAnCcQdKU7fpH0PZPIX2d6gxpv/f/OAanp6HppBDlH9QgFl64n5A+h57ejGL09hP/++q85rnR3SAqq4ZVhUq6HpM8NQNbqMEkitdId/t5b+8disey9SIraBrrSTcW1cHAO7+NeuZ877OzZs3jhhRfQ2tqK3NxcdHR0ICsrC2VlZdt+nFAoBJZliZheGggEEAqFiLiW8DX4/X4iVlRCoRAxn5vw6jAJ1+Lz+QA8uKZEY7M4IQr64XUEMHJjGoUXswAAYw1mCIR8BIR+FFTlJuTnKnyCQSLWvlZ4UJfP5yPmhpXf7yfic0PSz+bw3ABSvs4YhiFqFgKVvBg2EZ9hUEmlqKgIg4ODGBsbwyc/+UlcvXoV8/Pzy79Menp6UF5ejr//+7+H0WjE7/7u7+Kpp57CD3/4w20/xunTp3H06FGcPXs2SldBURRFURRF7cTTTz+Np59+Gv/rf/2vXb3/ynZ7iuISXemm4t6FCxcwODiIy5cv49q1a6irq1t197asrAwGgwFXrlyB0Whcfp+d4PF4yM7OxmOPPRbR2rkwOjqKmZkZVFdXc13Knvl8Ply+fBlvetObiNhz39TUhIyMDOTk5HBdyp6NjY3BbDYT8XXm9/tx6dIlPProowl7PJ3f78fN66/DPDoH16wHGZWpCHiDGG8xYfyOCRazFYaKHHzuX/8YYkni7F0fGhqC1WrFoUOJuSd9JbfbjatXrxLxewYAbt26hYKCAmRkZHBdyp4NDw9jcXFx2yeexDOPx4MrV67gzW9+c0JumVnrC1/4AhEr9hSVmM8uqKRy/vx5fO9738PXvvY1LC4urtrPHXbu3Dn86le/Qm9vL4Ddhe5QKAShUBiRmrkkEAjAMAwR1xK+My0QCBI2DK3E4/HA4/GI+NwIBAKwLEvEtYRv6CTq943f70dLSwtS1EpM3pqD3eJABlLR8YsB7H+8EB6HF1N9ZrgXXJjoNaHsaCHXJW9b+GssET8va3m9XmK+/8NI+dyQ9LM5vI0hEQdDboSk+SFUckv8Z7EU8c6fPw+GYdDR0bH897XOnTuH559/HsPDwygtLUVWVtaOHoPP59PWozhEWlsYSeeNknRcEI/Hg0gkSrizuoGlwN3Q0AChUIiamhrc+WkPnAtLR2w5Z13o+PkADryzCPZ5O5xTfvzHv97EXx/OT5gnsR6PJ+GOctsMSYMUgaXrSZSvo4ch6XNDys/lsEidLrPZkWEA8I53vANVVVV7fgyK2goN3VTcS0tLw4EDB9De3g6dTof9+/eve5uVq987XeUGlp50kzKog7SJsiQh6XND0nFBACAWixPuPOi1gZvP5yM9X4/xvgkAgGXeirH7k3BYnDj+hwdx+8dtEC0swGVzQ6GWc1z99ng8HqSkpHBdRkSQFOyApTBEyvWQ8nMZIPPrLBI3dzY7MgwA8vLyaOimoo6Gbioh3Lt3b8vXFxUV7emXJkmrdgA5TyDCd7cDgQARbX8khW6RSLQ89ZsEEokkoUL3RoEbAGa9LLzs0v9nV2TApnJipn8OLz17FW/9P+dw+8dt+FDlZ/AXz38KB09u/4QHriRi98FmvF4vMS2/AFnhLhgMEjE3BCDr8wLsvaOClN+5VOIjoy+IovaIpJVuPp9PzAokj8cDn89f3qOW6EhqL0+0kPowEokEXq+X6zK2ZbPADQAf+/O3IGhzAACkajEG20ZgnbNBm6rFi3/5Ko79XhXKHyvFX33u5/juP/wWPl98f2+RFLpJuhaArHAXDAaJmBsCkLcHmrTroZIX/SqmKJC1aicQCIgJqcDSTQSSboiQci0SiYSY85MBQCaTweFwcF3GQ20VuMO0pUvT8UMIwml1ofhwPjpudkOt0eDFv3wV5eeNKD6gws//swGv/PJurC9h2wKBALxeL2QyGdelRITX6yUqdAcCAWJWhwOBADGhm6RrAci7Hip50dBNUQAUCkVCPOHeDtJCN0nXk4j7hjcjFArB4/ESZnX4YVQqFaxWK9dlbGk7gRsA2gcXAABitQiZhQaM905DIOTDPDoLj8WHX33xMmrPZeKJpyrx1nfH75FvVqsVYrGYmKBK0kp3KBSCz+cj5npICnYkXQsAuFwuKJVKrsugqD2joZuiACiVStjtdq7LiAiS2rEBskJ3IrUwPwzDMES1mKvVatjt9rj9Wttu4HbY3fC/0TKuNiiRmqXBotmCstpiWGdtyCzKgHxfBV78QR/Ss/kYHByM5WXsiMVigVqt5rqMiCFpEnv45xgp10NSUCWpAwEAnE4nDd0UEWjopigAKSkpcDqdXJcRESSFVICs6yEppAJkrdxLJBKIxWLYbDauS1lnu4EbACwLD36OqQxK+NxL22bmJhfAMEBPYz+kIj5mp134+b/3ov1eJwYGBqJ+DbthtVqJC92krAyHbyCQsteWtNBNwuBRYKmjwu12Q6FQcF0KRe0ZGT8tKWqPlEolUe3loVCImIFdJIVukkIqQN7KvUqlgsVi4bqUVXYSuIHVoRsMg4W5RfAEPJiGZ1BaXQQA6PtVPdL1KcvB+/797rhc8bZYLFCpVFyXETEkhm5SkBS6SRoK53a7EQgE6Eo3RQQauikKS3u6XS4X12VERPiXLSkDu0gK3eGQSsoNEdJW7rVaLebm5rguY9lOAzcALK4I3W5nEKmn9yOvpnTp7w4PGIZBUd0BaHVKiCRCnH/3MZw8dRK9vb1xFbw9Hg8cDgc0Gg3XpUQMaaGblGsByArdJLWXLy4uAgBd6aaIQEM3RWFppZu00E1KUCUtdAMgZnWYtNBtMBgwMzMTFzesdhO4AcCy8KBjZ37WBbGUgTI3HQAw2jWBqt+/gO4pJ3ruT+LRdx7CH36gDrpULY4fPx5XwdtsNkOtVhMT7AKBAILBIDHXQyexxy+SbiBYrVbw+Xyivtao5EVDN0WBrJVuhmGIGqZG0rXweDyIRCIauuNUSkoKRCIR56vduw3cAOBhQ8ipzAQAuB1BqLVi2H3s8utZl3v5/3/z0xb0dE4AADQaTVwFb5PJBIPBwHUZEePxeMDj8YjZa0tXuuMXSddisVigUCiIOQ+eSm40dFMUyFrpBshaHRaLxcSEVICsoKpUKmGz2cCy7MPfOAEwDAODwQCTycRZDXsJ3AAw7HKi07e02u0JMFCnSjA2No9Dv3sG+U+cRvvQIrKNqQCAYDCEv/8/P4fnjWFr8RK8g8EgZmdniQrdNpsNSqWSmPBA0p7uYDCIYDBIzPWQFLqtVivkcjnXZVBURNDQTVFYWukmZXo5QFboJmlYF0DWMLWUlBT4/X643e6Hv3GCCIduLm4k7DVwA8DJwwX4o3cdBwAw4EOVKkYoyMIjFGN0ZB4AoFTJlt9+cmwe3/3aK8t/j4fgPTs7C7FYTNTwJDoULn55PEuzDkQiEdelRARJodtut9P93BQxaOimKCyt2JEUHEgL3aSEVICs6+Hz+VAqlXE38XsvdDodQqFQzFvMIxG4AeBkZT7e/6YjAACFWAalSgS+gIFI/OBJeO/9CaTpU5b//vLPWnD7Ru/y37kO3uPj48jMzCRmVRgg88xxkkK3WCwm5uuNpNBttVpp6KaIQUM3RYGudMczklaGAfJW7tVqNVGhm8fjITc3F6OjozF7zEgF7rUUcin8vhBUWjFmTdbll4eCLPSZ6lVv+/W//hUsiw9+BnIVvL1eL0wmE4xGY8weM9pYliUqdLMsS9QgNZJuIADkhW6SOl6o5EZDN0VhaaXb7/cTE+5I2gctkUjg9/vjYqJ0JJC00g0shW6r1frwN0wgRqMR09PTMfk8RStwA4BKo4Bl3gO1VgLTlAVa3YMnr333p5CiftBmvjjvwNee/eWq9+cieI+OjkKr1RK1uhU+azglJeXhb5wAwr9bSNkDTVro9nq9xHxuHA4HDd0UMWjopihg+Yc6KSt2JAW7cNsfSTcRSPncAIBKpYLFYiFmmBqw1Pmi0+kwMjIS1ceJZuAGALVGDsu8F+rUpSfgGdkPzrz2+QIwFqStevvb13vx8s9bVr0slsE7FApheHgYBQUFUX2cWLNYLFAqlcQcSeXxeCAUCom6HlJCN8uyRF2P3W6noZsiBg3dFAVAJpOBYRhiQjdJLdkMwxB1PSRdC7AUukkbpgYAhYWFGB4ejto2jWgHbgBQaWSwLnihTl16As6GVt8YGeozQSpbPTzqu//wCibH51e9LFbBe3JyEjwej6ip5QDdzx3vSGqVJ+08eLvdTkyHCEXR0E1RWAp2SqWS8/N5I4W01VSSgqpMJoPH4yGmXZ7EYWoAkJaWBrlcHpWQGYvADQAqjXypvfyNle7JsdVh2unwoqgsY9XLPG4f/t//+TmCwdCql0c7eIdCIfT09KCkpISYgVZhpIVul8sFqVTKdRkRQ9LxZ+Hz4EnZ022z2Yj63qGSGw3dFPWG9PR0TE5Ocl1GRJAWukm6HolEApFIRNQ+aJVKRdT1AEs34ioqKjAwMACfzxexjxurwA08CN2qN1a6rRYXst44oztsYnQeAsHqpwLdHRN4/l+vr/t40QzeIyMj4PP5yM3NjejH5RrLsrBarUQdF0baTQSSVu7D10LKjavZ2VniOl+o5EVDN0W9ISMjAxMTE1yXEREkhVSArOthGIa4id9qtRqLi4tclxFxOp0OWq0WfX19Efl4sQzcwFJ7uWXeC6lMALF06bG0qasHlC3OO1C6P3vVy2QpEtyaMKFzYHrdx4xG8A4EAujr60N5eTkxYSHM5XLB7/cTF7pJuh6SQjdJrfLAUujOyMh4+BtSVAKgoZui3pCVlYWpqSmuy4gIiUSCYDBIzLFhMpkMLpeL6zIihrSV4fT0dMzPz8Pv93NdSsRVVFRgZGRkz19/sQ7cACASCSASieC0+5f3dbtd61ft52ftAACGx6D4bBFcZWr4xAz4oo2fIkQ6eA8MDEAulxO5omU2m5GamkrM0LFAIAC73U7MSrff74ff74dMJnv4GycAkm4gAMDc3BwyMzO5LoOiIoKGbop6Q2ZmJqan16/sJCKhUAgej0fM6rBCoYDD4eC6jIghbaVboVBAJpNhdnaW61IiTqVSISsrC/fu3dv1hHYuAneYWvvGvm7t0p7VseFZCISrH980uYhDj5RBfjoHwyE3/uDdNfj2X7wH5bn6TT9upIK33W7HwMAA9u3bR9wqNwCYTCaibibYbDaIRCJi9nQ7HA6IRCKIRKKHv3ECIGl/eigUwtzcHF3ppohBQzdFvSEzMxMzMzNclxERpE38DoduUo6lUqvVsNvtxAxTAwCDwQCTycR1GVGxf/9+2Gw2jI2N7fh9uQzcwMpjw5ZWv3ze9UeFAcDMlAWPnC3HD774e/i9C0cgEjy8zr0Gb5Zl0drairy8PGi12h2/f7zz+/2Ym5sjKnSH93OTcoPE4XAQdSY8SSvd09PTCAQCNHRTxKChm6LekJmZScz0coCsfdByuRzBYJCY65FKpRAKhbDZbFyXEjEGgwFms5mYGyMrCYVCVFVVobOzc0dHo3EduAFAnSqHdeHBBHMAkMvXr4RN3jfheGYG0lJ2FkD2ErzDQ+rKy8t39H6JYmZmBgqFAnK5nOtSIoa0oXBOp5Oozw9Je7rHxsaI+/6hkhsN3RT1hoyMDOJCt9fr5bqMiODz+ZBKpXA6nVyXEhEMw0ClUhHVYh5eqVxYWOC4kujQ6/XIyMhAa2vrtm4sxEPgBtavdAOAZXHj/ekv/PD1XT3GboK33W5Hb28vDh8+TMx+57VIay0HyJtcTle649f4+Dj0+s23uFBUoqGhm6LekJGRgdnZWYRCoYe/cQIgqb0coPu64x3DMNDr9cS2mAPAgQMH4HA40N/fv+XbxUvgBh7s6VZpH6xuT4zOQaFc/8S8pX4Ag327+/ztJHj7/X40NzcjPz+fyLZyYGk/qtlsJip0B4NBooaoATR0x7Px8XE6RI0iCg3dFPWGzMxM+Hw+YoZBkTbxm8TQTdIEc4Dsfd3AUpt5bW0t+vr6Nh26GE+BGwA0WgVsiz7w+AwUKUIAQCjEItuo2/DtX/jB7la7ge0Fb5ZlcefOHUgkEmLbyoGljg8ejweNRsN1KRFjtVohFAqJGaLGsixRodvn8yEQCBDz+ZmamqKhmyIKDd0U9QalUgmZTIbR0VGuS4kIuVxOTDs2QN71qNVq2Gw2ooappaWlweVyEXVzZC2VSoXDhw/j7t276/bkx1vgBpZWukMhFrZFL9S6BytgAuHGv/5vXL4P0+Tuz1x/WPDu7u6Gw+HA0aNHweOR+xTEZDJBr9cTM3AMeLCfm5Rr8nq9CAaDxOwZdjqdEIvFEAqFXJcSESaTiYZuiijk/sajqF3Q6/UYHx/nuoyIIG3iN2kr3VKpFAKBgKhhakKhEDqdjujVbmCpK6awsBCNjY3LcxPiMXADgDp1aRXPuuBdPjYMAMzTG3dZhIIh/Pdzt/b0mJsF74mJCQwPD6O2tpaYI5o2Q/dzxz+HwwGpVBo336t75XA4iLmBACwNIqShmyIJDd0UtUJGRgYxoZu0id8KhQJOp5OYPfcMwxC3rxtY+h6anJzkuoyoKy0thUajQUNDA1wuV1wGbmBpkBoALM57Vg1TmzVZkaZP2fB9Xv1VGyyLe+sqWRu8TSYT2traUF1dDaVSuaePHe8sFgs8Hg/S0tYfzZbISAzdpLSWA+Rdz+zsLA3dFFFo6KaoFbKysjA1NcV1GRHB5/Mhk8mIWR2WyWTg8Xiw2+1clxIxKpWKuH3dWVlZsNvtxF3XWgzD4PDhwxCLxbhy5Qr4fH7cBW5gqb0cAKzzXqhSVx8Vps9Qb/g+Xq8fv3z+9p4fOxy8u7u70dzcjMOHDyM9PX3PHzfejY6OIjs7GwKBgOtSIiY8RI2k48KsVitSUja+8ZSInE4nUaF7bm6OntFNEYWGbopaITMzc9MBSYmIpH3QJK4Mk3Y9wFKLeVZWFkZGRrguJeqCwSB8Ph/4fD78fn9c7s9XpkghFPJhWbPSDQDB4OZdI7/6r2a4XXs/ctDr9YJlWTAMs6MzzhNVIBDAxMQEjEYj16VElM1mg0AggEwm47qUiCFx5Z6U9vJQKERDN0UcGropaoXs7GyiQjdp+6BJPNvaZrPB5/NxXUpEGY1GTExMIBAIcF1K1IT3cItEIly8eBEymQyvv/56XJ4YoHrjrO4UtQg8/oMhWOOjc9hsJpbD5sbLP7+zp8cdGxtDS0sLjhw5gpMnT+7oHO9ENTk5CZlMRlSYA5ZWHVNTU4kZohYKhWCz2YhZuSdtErvZbIbX66Xt5RRRaOimqBWKi4uJ2dMNkBe6STtmSyqVIiUlBWazmetSIkqj0UAqlRKzVWOttUPThEIhjh49Cp1Oh+vXr2Nubo7rEldRa+Rw2v0I+ENQaR60mDtsHuTkbXx0GAD8/McNCPh3vnofCoXQ2dmJzs5O1NbWIjMzc0fneCey0dFRGI1GYsJpGGmD4ex2O3g8HjEh1ePxEDWJvb29HWlpaUS1/1MUDd0UtUJJSQnGxsbisk10N0gN3aQMUwPIPNuaYRjk5eVhaGiImOn5YZtNKefxeKisrER5eTlu374dV+316tSlJ+KWeS/Ua/Z1q7Sbh445sw2v/aZ9R4/l9/vR2NgIs9mMM2fOrBomRnrwXlxchM1mQ05ODtelRJTX68Xi4iL0ej3XpUSMxWIh6vgzp9MJmUwWdzMldqurqwvFxcVcl0FREUVDN0WtUFBQAJ/Ph6GhIa5LiQi5XA6Xy0VMSFUoFGAYhqhhagaDATMzM8Tc6AnLzc2F0+nEwsIC16VEzHaOBcvLy8OxY8fQ3d2Ntra2uGixD08wty541+3rdtm3Pt3gv374+rZvnFgsFly/fh0Mw+DMmTMbriKSHLyHhoaQm5tLzDnJYWazGSqVChKJ5OFvnCDofu741tvbi7KyMq7LoKiIoqGbolYQi8XIyclBR0cH16VEhEwmA8MwcbnPdDcYhiFu4rdKpYJAIMD8/DzXpUSUQCCA0Wgk5gbWTs7h1ul0qKurg8PhwNWrVzlvN9e8cVb30jC11SvdYyOzEIk2n7I9PjKHhms9W378UCiE7u5uvP7668jJyUFtbe2WwZPE4O3xeDA1NYX8/HyuS4k40lrLgaXJ5aTs5wbIOy5saGgIpaWlXJdBURFFQzdFrVFSUoKuri6uy4gIhmEgl8uJazEnaZgawzBEtpgDQH5+PkwmU8JPrd5J4A6TyWQ4efIkCgoKcPv2bXR0dHC26q3SPGgvV61Z6fb7gsgt3Po86Rd++PqmrwuvbpvNZpw+fRqlpaXbatklLXiPjIxAp9MRdwZ5MBjEzMwMUaE7PESNpJVu0o4LGx0dRUlJCddlUFRE0dBNUWuUlZWht7eX6zIihsR93SSFbuDBvm7S9j/L5XLo9fqEDlW7CdxhDMOgsLAQZ8+ehcViwdWrVzExMRHzz3P4rG7LvAdqrXjd66VS0Zbv390xgY67I6te5vV60dHRgddffx0ZGRk4c+bMjlcOSQnegUAAIyMjKCws5LqUiJubm4NIJCJqoFX49yFJIdVutxNzPcFgEGNjYzR0U8ShoZui1igtLY2rIUh7RVo7tlqths1mI2afOrDUjuzz+WCz2bguJeLC30+JuNq9l8C9kkKhwKlTp1BSUoL79+/j+vXrmJmZiVn41rwxLM264IVMIYRIvPo6FucfflPupz9YWu0OBALo7e3F5cuX4XQ6cebMGZSVlYHH293TCRKC9+DgIGQy2aqhcaQIt5aTMnAMWBp4p1aribkmv98Pp9NJTLv80NAQvF4vkTexqORGQzdFrVFSUoLR0VGuy4gY0s62VigU4PP5RF0Tn89Heno6kS3mKpUKBoMBPT1b7wuON5EK3GEMw8BoNOLixYvIzs5GS0sL6uvrMTc3F/XwHV7p9nqCcDn96/Z1T47NI0Ut2/JjtDUPoul2Gy5fvgyz2Yxjx47h2LFjEVkBTeTg7fV6MTAwgIqKCmJCXBjLskTu556fn4dWq+W6jIixWq2QSCQQi9d3sSSijo4O5OTkEHM9FBVGQzdFrVFSUoKpqSl4PFtP9U0UarUaDocDfr+f61IigmEYpKamcj6cKtJI3dcNAOXl5ZiYmEiYlfxIB+6V+Hw+ioqK8Mgjj0Cr1aKpqQnXrl3D6Oho1PZ8a1IfTDVeOjZs9b5ulgWyclM3fF91qhgnHs3CU586gKHBYRw8eBCnT59GaurGb7/rGhM0ePf39yM1NRU63ebnnScqq9WKQCBA3LXNzc0RdU2kTWLv6uqireUUkWjopqg1cnJywOfziRmmJpFIIJFIiGox1+l0xIVuvV4Pq9VKzM2eleRyOYxGI7q7u7ku5aGiGbhXEgqFKC8vx6OPPor8/HwMDQ3hlVdeQVtbW8SPkFOp5cursNYNJpgDAI/3YJVWphCi4nAqHv+DYrz3w+WQyYV46ccD+OE/toNhJVFb0U204O1yuTA8PIzy8nKuS4kKk8mE9PT0XW8diEculwsej4e4lW6SQndfXx89LowiEjk/SSkqQng8HgoLC9HZ2cl1KRGjVquJC90LCwtE7esWi8XQaDTErnaXlJRgdnY2rs/tjlXgXkkgECAvLw9nz57FiRMnwDAMWltb8Zvf/AbNzc0YHx+H1+vd22MI+ZArl1a3N1rpBgCPx4kjpw141x+V4qlP7UdpZSqGe6z4j6914PKLIzBPOBEKhvCzH9XvqZaHSaTg3dPTg8zMTGL20q5FYmv53NwcNBoNBILNj8lLNBaLhaivwZGREXpcGEUkcn7qUFQElZaWJsSq3HaRtq9bqVQu7+smacUi3GKel5fHdSkRJ5FIUFhYiK6uLpw8eTLu9r9yEbhXYhgGGo0GGo0GlZWVsFqtMJlMGBwcxN27dyGTyaBWq6FSqaBWq5GSkgKxWLztf0eNVg6HzQ3bohellVoU79cgLUOGtAwZdAYZeDxgZtKDzuZZjA7Y4HFt3Or+yi9a8f4PnoVaI9/w9ZEQDt4NDQ0AEJcDlWw2GyYnJ3H+/HmuS4kKt9sNm80GvV7PdSkRNTc3F/GtEVzy+/1wOBxErXTT48IoUtHQTVEbKC0tRX9/P9dlRIxarcbk5CTXZUTMyn3dpIXunp4eBAIBolZiwoqKijAyMoKZmZm4ejLPdeBei2EYqNVqqNVqlJWVwefzwWKxwGq1wmKxYHR0FC6XCwzDQCwWL28hEYlE4PF4y4PZOjo6wLIsvF4vzj+RCb4gExKpACzLouqEAU4PC2uQh8EBL7omLShUKNHfvnUngtfrxy9/0og/+OPohs14D97d3d0wGo2Qy6N384FLJpMJWq0WItHWx8klEpZlMTc3h6qqKq5LiZjwEDWJZH33SiLyer2YnJykoZsiEnnP6igqAkpKSvDaa69xXUbErBymJhQKuS4nInQ6HUwmE1G/nBUKBaRSKWZmZpCZmcl1OREnFApRUlKCrq4upKenx8Vqd7wF7o2IRCKkp6cjPT19+WXBYBAejwcejwderxcejwc+nw+hUGh5P7hYLIZAIIBOp8P9ATt6hxZg9bjwlsNK/OSOGXb36n3jmZrtTSL/n/9qwnv/4CSksuhOF47X4D0/P4/Z2Vk88sgjXJcSNdPT08S1ltP93PGvq6sLfD4fOTk5XJdCURFH93RT1AbKysowNDREzJ7h8HEiiTI9ejtI3NfNMAxycnKIOrJurby8PAQCAYyMjHBdSkIE7s3w+XzI5XKkpqYiMzMTBQUFKCsrQ0VFxfIQopKSEpSWlsJoNMLCCnCzexrtw7OwOAPQKtavYA7PLILhP/xpgd3qxm9evBPxa9pIvO3xDoVC6OzsRFFREbFHGjmdTszNzSE7O5vrUiKK7ueOf62trSgqKkqon8UUtV00dFPUBg4ePAiLxRIXT/IiRa1WE7uvmyS5ubmYnZ2Fy+XiupSo4PP5OHjwILq6uji9xkQO3LuhSXlwDveCww+tcn3Hi9PjQ1bF1m3/IqkQJReK8eveEQQCkZuwvpV4Ct4DAwMIBoMoLi7mtI5oGhsbg16vJ6ZlOWx+fp6o/dwAeceFNTc348iRI1yXQVFRQUM3RW1ALpejuLgYt27d4rqUiCEtdDMMA51Oh9nZWa5LiSipVAq9Xo+xsTGuS4ma9PR0ZGVlobW1dXn/cSwlW+AGAK3qwd7jBbsPWsXG20xkmRu3mGeU6VH4WBnc+zRomp5B57AJv63viUqtG4mH4G2z2dDX14dDhw4R+zUTCoUwNjZG3DBHlmUxOztL1PncJA5R6+joQHV1NddlUFRU0NBNUZs4evQompubuS4jYkibYA4snW1N4hFbRqMRo6OjRLXOr7Vv3z44nc6Yt5knY+AGAG2KdPn/Fxx+pG6w0g0AiyH/8v/rcjUoeVMpFHW56OZ5cGdkGnbXg+PLnvt1c0xvmnAZvEOhEO7evYuCggJoNJqYPnYsmc1mMAyzan4ACaxWKwKBAFEr3TabbXmQIglCoRC6u7vpSjdFLBq6KWoT1dXVaG9v57qMiNFqtXA4HHs+8zee6PV6WK1WuN1urkuJqPAT3pmZGY4riR6hUIiqqircv38fTqczJo+ZrIEbWN1ePm/3b7rSPWxaROlbyiA9nYMBRQhN4yZMzFo3fNuRqQXcbB2KSr2b4Sp49/f3IxQKEX9+8OjoKHJzc+NiyGEkmUwmpKenE/U9Pz8/T9RQuMHBQVgsFqKmy1PUSjR0U9Qmjhw5gp6e2LVPRptYLIZSqcT8/DzXpUSMWCyGRqOB2WzmupSI4vF4yM3NJXqgGrB0cyE7OxttbW1RXzFN5sANAFrVij3ddj/UciF4G+SqQDAEG0KYnt/e0MX/+J+mSJW4bbEO3jabDf39/US3lQNLZ3PPzMzAaDRyXUrEmUwm4qaxz83NEdUuf+vWLZSUlEAmkz38jSkqAdHQTVGbqKqqwvz8POeDeyJJp9Nhbm6O6zIiymAwENtibjabiVvFXysWbebJHriB1SvdNncAQZaFWr7xardEvP3pzh0D02jtndhzfTsVq+CdLG3lwNIqd3p6OqRS6cPfOIG43W7YbDbo9VsPCUwkoVAICwsLRIVuOkSNIh0N3RS1CYVCgaKiIqKGqel0OqJWuoGl0D07O4tAIMB1KRElk8mg1+sxPDzMdSlRFe02cxq4lyikYoiFD6590b75vu6ZBceOPvZ//A83sy9iEbyTpa08GAxiZGQE+fn5XJcScSaTCVqtFiLR+mPyEpXFYgGfz4dSqeS6lIhpb2+nQ9QootHQTVFbOHr0KJqaYt8+GS2pqamw2WxE7etWKBSQSqXETTEHgIKCAoyOjiIYjM3RTFxJT09HTk4OWltbIzo8jgbu1bZzbBgATM5YoVVtf7Wz/t4wBsa5+f6LZvC2WCxJ0VYOAFNTUxAIBMQNUAPIbS1PTU0lau99T08PXemmiEZDN0VtgbRhaiTu62YYhtgWc51OB7FYjImJ2Lfvxtq+ffvg9/tx//79iHw8GrjXW7mve2mY2uYrf9npO2ulfu7XLbuua6+iEbw9Hg+amppQWlpKfFs5y7IYHBxEQUEBUSEOAAKBAObm5ogM3SS1lg8ODmJ+fp4OUaOIRkM3RW3hyJEj6O7u5rqMiCJ1X7fZbObkzOdoYhgGhYWFGBwcJO7a1hIIBKipqcHExMSeB8jRwL2x7a5078al272Yntve8LVoiGTwDgaDaG5uRmpqKoqKiiJUYfxaWFiA0+lEbm4u16VE3MzMDGQyGRQKBdelRAyJ+7lv3bqF4uJioj5PFLUWDd0UtYVDhw5hfn6eqH21JO7r1mq1CIVCWFxc5LqUiMvOzobX6yXuRslG5HI5qqur0dHRseuvURq4N7cqdNt9SN3k2DAAmDBbdvSxg8EQfvwyd6vdQGSCN8uyaG9vRygUQlVVFXErvxsZGhqC0WiEQLD9AXqJgsTWchL3czc1NeHo0aNcl0FRUUVDN0VtQalUorCwkKhhaiTu6+bxeDAYDJiamuK6lIjj8/kwGo0YGortechc0el02LdvH5qamuByuXb0vjRwb027ZqVbIRVAJNg4VC7YXMjWq3b08X91vRMWO7fT9vcavIeGhmA2m5Pm68ftdsNkMhE5QC0YDMJkMiEjI4PrUiKKxP3cdIgalQxo6KaohyBtmBqJ+7qBpRXhiYkJItuw8/PzMTMzA5uNu/bdWMrPz0dmZiYaGxu3PZWeBu6HWxm63b4QXN4gtFusdqepd9bq6fEF8MKrrbuuL1J2G7xnZmbQ3d2Nmpoa4o7N2szg4CDS09Mhl8u5LiXizGYzhEIhcXvySdvPDQDd3d10iBpFPBq6KeohSBumBpC5rzstLQ0AiJxiLpVKkZubS9x8ga0cOHAAQqEQd+/efeiNFBq4t2flIDUgvK9782FqHt/Oj+H7r8ttcHv8O36/SNtp8HY4HGhpaUFlZSW0Wm0MKuSey+XC8PAwysrKuC4lKiYmJpCdnU3UijCJ+7mHh4cxPz+PQ4cOcV0KRUUVDd0U9RA1NTXLe/xIQWLoZhhmebWbRKWlpZidncXCwgLXpcQEj8dDdXU1rFYr+vr6Nn07Gri3b+WebuCNfd1bDFMbnlqAgL+zpwk2hwe/uNaxq/oibbvB2+/3o7GxEUajkchhYpvp6elBZmYmVKqdbSNIBH6/H2azGdnZ2VyXElEk7ud+7bXXUFpaSoeoUcSjoZuiHqK6uhputxstLdwOCYoknU4Hh8MBt5vb/ZeRlp2djampqW23JCcSiUSCwsJCdHV1EdlCvxGxWIyamhoMDAxgfHx83etp4N6ZdaHb4d+yvdzj9SM/c+ervs//9g4Cgfg4W/5hwTs8qVwmk6GiooKDCrlhs9kwOTlJ7Cr31NQUUlJSiAqnwFLLfFpaGlGr91evXsX58+e5LoOioo6Gbop6CLFYjGPHjuG3v/0t16VEjEgkglarJe5sa5VKBalUStx1hRUVFcFut2NmZobrUmJGpVKhuroa9+7dw+Tk5PLLaeDeubXt5UtndW99bJhSLtnx45jn7Xj1du+O3y9aNgveoVAIzc3NCAQCOHr0KFFB5mG6u7thNBqJ3MsNPGgtJ43JZIJer+e6jIhqamrCuXPnuC6DoqKOhm6K2oaLFy/i9ddf57qMiAqfbU0S0lvMhUIhiouLk2q1GwDS09NRXV2N1tZWTE9P08C9S2qlFLwVwXLBvvWebgC7nkb+3EvNcfU1ujZ4h0IhtLS0wOPx4Pjx4xAKI3dmebxbWFjA7OwsSkpKuC4lKtxuN+bn55GVlcV1KRHlcrlgt9uJCt2Tk5MYHBxEXV0d16VQVNTR0E1R23D27Fm0tLQQta/bYDBgdnaWuFbs7OxszMzMEHUk2kr5+fnw+/3E3ljYjF6vx5EjR3Dnzh3cuHGDBu5d4PN4UCkerFwvOvwQCRjIJZv/G45OL0Ah3TqYb2Roch6vt8bXMXfh4N3T04MbN27A6XTixIkTSRW4WZZFV1cXioqKIJHsvIshEUxMTCAtLY246zOZTEhNTYVItPPvx3j1m9/8BmVlZcuDUCmKZDR0U9Q2hPd1Nzc3c11KxCgUCkilUuJaleVyOTQaDZFndgNL53aXlZWhp6eHqJtA26HT6SCRSOB0OmE0Gmng3oWV+7oDIRZWVwCpW7SYB0MsjLvY1w0A//Hr+Pt5qVKpoNFoYLVakZmZSVSA2Q6z2Qy73Y7CwkKuS4kaUlvLzWYzUavcAHDlyhW6n5tKGjR0U9Q2iEQiHD9+nKh93QCQkZFBXIs5sLTaPTY2xnUZUZOTkwM+n4+RkRGuS4mZcEu5XC7H4cOHcffuXWJvrETThsPUtphgDgBCwe5ubrT3TaGtd/Lhbxgj4ZZyr9eLY8eOYXBwcEfneCc6lmXR3d2NkpISYlf3rVYrnE4nMjIyuC4lovx+P+bm5mAwGLguJaLofm4qmdDQTVHbdOHCBSL3dZtMprjaexkJ2dnZsNvtsFqtXJcSFQzDoLy8HL29vfD7uT8TOdrW7uHOzs7G0aNHcffu3aRrs9+rdWd1b2Nft3nevuvHe+6l+FjtDgaDaGpqgsvlwsmTJ6HX63d0jjcJJiYm4Pf7kZeXx3UpUTMyMoKsrCwIBAKuS4mo2dlZyGQyoo7VmpiYwNDQEM6cOcN1KRQVEzR0U9Q2kbivW6PRgGVZLC4ucl1KRAmFQmRlZRG9EmwwGKBQKIgPDJsNTTMYDKipqUFbWxuGh4c5rjJx7PTYMACYnrMhTbO7J/u37g1haGJuV+8bKT6fD7dv34bP58PJkyeXW8q3e443CYLBILq7u1FWVkbstgy/34/x8XEibyqYTCbiVrnpfm4q2dDQTVHbVF1dDa/XS9S+bh6PB71eT+QRW3l5eRgfHyd2JZhhGFRUVGBgYIDYoXEPm1Kenp6+HJru3btH1A2xaEldd2yYb8s93WGZaSm7ejyWBf6Dw9Vuu92OGzduQCAQbDilPFmC98jICAQCAXJycrguJWomJyehUCigVqu5LiWiWJaF2WwmLnTT87mpZENDN0VtE8n7ukkM3Wq1GgqFYtXZzqRJTU2FTqdDX18f16VE3HaPBUtNTcWZM2ewuLiI+vp6Ym9ARMq6lW67H2qFEA87ojoU2t0WFIlUgPGQA1MW267efy9MJhNu3LiBrKws1NTUbLqPmfTg7ff70dfXh4qKCmLPImdZFiMjI8jLyyPuGhcWFgAAWu3uBhrGq6amJhq6qaRCQzdF7QCJ+7rT0tLgcDjgdDq5LiWiGIZBXl4ehoeHiduzvlJFRQVGRkbgcDi4LiVidnoOt0wmw6lTpyAWi3Hjxg1i9/JHgnZN6La5AmBZQC3ferV7zLSzLSiaVBkqTmbDlc/HrZkJ/HvDnR3Xulssy6K/vx8tLS2oqqpCeXn5Q4MYycF7YGAACoWCuMnXKy0uLsLpdBI5tdxkMkGv1xN1M4Hu56aSEQ3dFLUDJO7rFgqF0Ol0RK52Z2dnw+12L68UkCglJQW5ubloa2sj4ubCTgN3mEAgwNGjR5Gbm4vXX38d09PTUa40Ma1d6WaxdF73w/Z1Wx0eGDM0W76NUMhDWVUGco7rMabxoME0CbvXBwD477udWHS591T7dgSDQdy9exdDQ0M4deoUsrKytv2+JAZvq9WKwcFBHDhwgKjQttbw8DByc3OJG6AGkLufu7y8HDqdjutSKCpmaOimqB04evQocfu6gaUWcxKPXxIIBMjNzcXQ0BDXpURVRUUF3G53wg8U223gDmMYBqWlpTh06BDu3LmD3t5eIm5ERNLalW4AWHD4HnpsGACkquTrXsYwDPJL0lB6Khu+EhEa7SZ0zswgtObf3e0P4D+b2nZd93a43W68/vrrcDqdqKur29XeXpKCdygUQmtrKwoLC4nb57ySx+PB1NQU8vPzuS4l4mw2G9xuN9LT07kuJaLofm4qGdHQTVE7IBKJcOLECbz88stclxJRWVlZy+15pMnPz4fJZILbHf1VNq4IhUJUVVWhq6srYT+Hew3cK2VmZuL06dMYHR1FS0sLscP0dmPtkWEAMG/3b2uYmtOztGotFPJQWJ6OspPZEB6QoS04h6bpSdg8W++n/1FjG9y+6HwuFhYWcP36dSiVSpw8eRISiWTXH4uU4N3X1weWZVFaWsp1KVE1OjoKnU5H1HFaYePj4zAYDMSt4Dc1NeHs2bNcl0FRMUVDN0Xt0GOPPYbLly9zXUZEiUQi6PV6Is88lsvlSEtLI/r4MGBpb35OTg5aW1sTbnU3koE7TKVSoa6uDn6/H1evXsXMzEwEKk18ErEQUvHqgL3gePhZ3QAwPDmPgpMZcBTycdc3i0bTJGadrm0/tsXtwX/d7dxxzVsJBoO4f/8+6uvrUVxcjEOHDkXk6yfRg7fVasXAwAAOHToEHo/cp3qhUAgjIyNErnKzLIvJyUni9ql3d3djdHQUFy5c4LoUioopcn8SU1SUvO1tb0NzczNx+4Szs7MxMTGRcIFtOwoLCzE8PIxAIMB1KVG1b98+uN3uhGqnj0bgDhOLxTh+/DiKi4vR3NyMe/fu0VVvrF/tXrA/fE83APj8QfhCQXj8u/8++kHDHQSCkZmJsbi4iGvXrmFubg5nzpxBYWFhRPctJ2rwDoVCuHv3LvFt5cDSSjCfzydySNz8/DyCwSBxreU/+clPcPLkSeK/NilqLRq6KWqHiouLkZeXh5/97GdclxJRBoMBHo8HFouF61IiTqfTQS6XE7/aLRAIcOjQIXR3dyfENPNoBu4whmGQn5+Pc+fOweFw0FVvbHRsmA8pMgGE/IcHVhX78BXxrUxZ7fh1Z8+ePkYwGERXVxdu3bqFnJwcnD59GikpuztH/GESMXiHjxAkva2cZVkMDAyguLiYyCFxExMTyMrKIq5T4ZVXXsETTzzBdRkUFXNkfSdTVIy84x3vwP/8z/9wXUZE8fl8ZGRkENlizjAMSkpKMDg4iGAwyHU5UaXT6RJimnksAvdKMpkMJ06cQHFxMZqampJ61Xtt6Hb5QnD7gtBsY7XbZt5+O/lm/uVWy66/NhcXF3H9+nXMzMzgzJkzKCkpiXooSaTgnSxt5QAwPT2NQCCAnJwcrkuJuGAwSGRr+dzcHO7cuYO3ve1tXJdCUTFH9k9kioqSxx9/HDdv3iSuXTk7OxuTk5NEHYkWZjAYIBQKMT4+znUpUReeZh6vbeaxDtxhG616z87OxuSx48nGE8z9SN3GBPOJkUWodj2kjEVFURqCWUHcNo/u6D1Xrm5nZWXhzJkzUVvd3kgiBO9wW3lRURHxrbssy6Kvrw9FRUVE3lwwm80Qi8XQaLY+pi/R/Pd//zeKiopQUFDAdSkUFXPk/aSiqBg4duwYeDweXnnlFa5Liai0tDQAS3ejScMwDIqLizEwMBDXK8CREM9t5lwF7pXkcjlOnDiBoqIiNDY24u7du0RPt19rw9Bt394wNZZlUajeWRCQigWo2m9ARqUMGblS/NWxszhuyNvW+7IsC5PJhGvXri2vbpeWlnIStOI9eIfbyktKSjiuJPpmZ2fhdrthNBq5LiUqJiYmkJ2dTVzb/EsvvYR3vOMdXJdBUZygoZuidoHP5+Mtb3kLfv7zn3NdSkQxDIPs7GxiV4OzsrIQCoWIPJN8rXCbeTxNM4+HwB3GMAwKCgpw/vx5sCyL1157DZ2dnfD5fJzVFCsbHRu2YPdta5gaAIi92wsCxUYtDhxJg6KEh315qfiXC+/EN8+8CzX67U2anp+fx+uvv47W1lbk5eXFfHV7I/EavC0WCwYGBnD48GEiV37X6u/vR0FBAXFHaQGAz+eD2WwmrrXc5/Ph5s2bePvb3851KRTFCfJ/MlNUlDz++OO4cuUK12VEXHZ29vJeOdLweDwUFRUtn19LuoqKCng8nrhoM4+nwL2STCbDkSNHcPr0adjtdly6dAm9vb1Efv2Hrd3TDYSPDdte6J4ZtW76umy9EocOGZBeKUF6ngR/UFmFS49/CP/n6KMoVG1vCrPNZkNjYyMaGhqQlpaGixcvorCwMG7CZLwF71AohNbWVhQVFUGlUnFdTtQtLCxgcXGRyGPCgKW96ikpKcSdO/7b3/4WIpEINTU1XJdCUZwg7xYhRcXIo48+iqmpKbS1taGqqorrciJGpVJBKpXCZDIRd6cdAIxGI/r6+jAzM0PkMTMrhdvMb9++Db1ez9mTuHgN3CupVCocP34cc3NzuH//PoaHh1FaWgqj0Rg3YS9SNCnSdS+bt/uRus2V7rlZB7JyUjBptQMAcjNUSE2XYgY2aGVCPJJbgN/JLUWmXL2julwuF3p6ejA5OQmj0YhHHnkEYrF4Rx8jVsLBu6GhAcDSsYRc6e3tBZAcbeXA0ip3fn4+RKK9TdKPV+Pj40T+7v35z3+Ot771rXH585+iYoGGboraJaVSibNnz+KFF14gKnQzDIOcnJzlPWWk4fP5KCgoQF9fH9LT04nbM7eWTqeD0WhES0sLTp06FfN2zEQI3CvpdDqcOXMG09PT6O7uxuDgIMrLy5GZmUnM14pWJV/3skWHHxIRHzIxHy7vwyf8F6dpoC2UYtpvRbpKjNMZuXg0txT5St2O6/F6vejr68PIyAgyMzNx/vx5yOXra4w38RC8Z2dnMTg4iNOnTxN3c2gjNpsNMzMzOHjwINelRIXb7cbCwgKOHj3KdSkRd+3aNXz961/nugyK4gz5P6EpKooef/xxvPrqq1yXEXHZ2dmYmZkhdrhUfn4+7HZ70kyu3rdvH/h8fsyPEUu0wB3GMAwyMzNx7tw5FBcXo7OzE1euXMHw8DARbecbDVLzB1nYXIFt7+sOWgL4o6pD+PVbP4AfXXw/Przv5I4Dt91ux71793Dp0iU4HA6cOXMGR44cSYjAHcZlq7nT6URzczMOHDiQFG3lANDd3Q2j0QjJrifox7fR0VGkp6cTd313797F9PQ0HnnkEa5LoSjO0NBNUXvwtre9Dffu3YPJZOK6lIiSyWRIT0/H6OjOjvVJFEKhECUlJejq6kqKvd08Hg81NTVYWFhAf39/TB4zUQP3SjweD0ajERcvXkRxcTFGR0fx6quv4v79+3C59n5eNVdUCgn4/PW//hccvm0dGwYAI51zeDS7HCrx+gC/FZZlYTab0dDQgGvXriEYDOLUqVM4fvx4wgZHLoK33+9HY2MjcnJyiJ3gvdbCwgJmZ2eJbaMPhUIYHR1FXl4e16VE3E9/+tOE6WChqGihoZui9iAnJwcVFRV44YUXuC4l4vLy8jA6Okrkmd3A0mq3z+fD5OQk16XEhFgsRm1tLfr6+jA9PR3VxyIhcK/E5/ORm5uLuro61NbWwul04rXXXsPt27cxPT2dcN8jDMNArVi/r3vp2LDthW6b04vu4e3fbPR4POjr68Nrr72G1tZWaDQaPPLIIzh8+DARZ0rHMnizLIu7d+9CIpFg3759UX2seMGyLLq6ulBYWEjcKnCY2WwGwzBEzhq5dOkSPSqMSno0dFPUHj3++OP49a9/zXUZEafX68EwDMxmM9elRAWfz0dpaSm6u7sTLjTtlkqlwuHDh3H37l3YbLaoPAZpgXslhmGQmpqKmpoaXLx4ERqNBh0dHXj11VfR1dUVd2eib2XDYWoO/7bay9UaGcoOZeDe9NahOxQKwWw2o6mpCa+++irm5uZQXl6ORx99FGVlZcSFp1gF7+7ubtjtdhw9ejQp9nEDwMzMDOx2O4qKirguJWpGRkZgNBqJmR0RNj09jfb2drz1rW/luhSK4hQdpEZRe/TEE0/gq1/9Kmw2G+dnyEYSwzAwGo0YGRlBRkYG1+VERU5ODgYGBjAyMoKCggKuy4mJzMzM5SOZ6urqIjoBmOTAvZZUKkVpaSlKSkowMzOD0dFRXLlyBQqFAgaDAQaDARqNJm6fQGtVcmB8btXLFux+VOWt/hkmEPBgyFZBqZMhJGEw5bZj1GLDqM0NdkCA3zt1aNXb+/1+mM1mmEwmmM1mCAQC5OTk4MKFC0nRWhrt4WoTExMYHh7GmTNniJ3evVZ4lbukpARC4fY6MRKN0+nE7OwsUUNZw5577jkcPHgQWVlZXJdCUZyioZui9qiqqgrZ2dl47rnn8LGPfYzrciIqNzcXvb29cDqdRD5h5vF4qKiowL1795Cbmxvzyd5cKS0thc1mQ3NzM44fPx6R1bJkCtwrhdtB9Xo9/H4/ZmZmYDKZcPv2bTAMsxzA09LS4urrS7tmpZvHZ8DIRNAqhag4lg2fIIQFrwfjFivmQxZg0bLuY7SOT8Pt8yPk98FkMsFkMmF+fh5KpRIGgwEnTpyAWq2O2xsP0RKt4G2xWNDW1obq6moolcqIfMxEMDExAb/fT+Re57DR0VEYDAZIpes7UBLdCy+8gKeeeorrMiiKc/HzDICiEhTDMHjyySfxk5/8hLjQLZVKYTAYMDIyQuzeQYPBgP7+fgwODqK0tJTrcmKCYRgcPnwYN2/eRGdnJyorK/f08ZI1cK8lFAqRlZWFrKwshEIhLCwswGQy4f79+3C73UhLS4Ner4dOp4NCoeA0jGaVpMIIAxwBHyxuDyxuD1pZK94JLXqtZiz4Np/SLuIxyJKKUZoixWtXriDk80Kn0yEjIwOHDh2CTLaz4WokinTw9ng8aGxsRFlZGZF7fjcTDAbR09ODsrIyYn+uBINBjI6O4siRI1yXEnH9/f24d+8eXnrpJa5LoSjO0dBNURHw/ve/H1/84heJPNu6oKAATU1NKC0tjauVukhhGAYVFRVobGxEXl4exGIx1yXFhEAgQG1tLa5fv46UlJRdryLRwL0xHo8HnU4HnU6H/fv3w263w2w2Y3JyEp2dnUvDzNRqqFSq5f8qlcqYBXFGzEP7BvMaFnx+6MTC5dAdDthZMjGyZCJky8TQiYVwBIIYsLsxGuLhQ29+M7Ftv3sRqeAdDAbR1NQEnU7HyVngXBoZGQGfz0dOTg7XpUTN5OQkRCIR0tLSuC4l4r7//e/j3LlzMBgMXJdCUZwj7xk0RXEgPz8f1dXV+Nd//Vd84Qtf4LqciEpNTYVEIsHk5CSxR9PodDqkpqair68PBw4c4LqcmJHJZKiursbt27ehUCig0+3snGUauLdPqVRCqVSiqKgIoVAIdrsdFosFVqsVw8PDy4PtVoZwuVwOsVgMiUQS8RteqfIHq9EMAJmAB6VAAHcghFpdCg5rlciSiaATC2H3BzHp9mLS5cW9RScmXV7YA0EAQImbxcdo4N7UXoM3y7K4d+8eWJZFVVVVUrXq+/1+9PX1EX/dw8PDyM/PJ/IaX3zxRTzzzDNcl0FRcYGGboqKkKeeegrf/va3iQvdDMOgoKAAQ0NDyM3NJfKJAQBUVFTg+vXrMBqNRA3Ee5jwSmxzczPq6uq23RpMA/fu8Xg8qFSqVedSh0IhOBwOWCwWWCwWjI6Owu12w+PxgGVZCAQCSCQSSCSS5SAe/sPn88EwDHg8HhiGQTC4FIhnZ2fB5/PBsixCoRD8fj88Hg88Hg/ECwv4WEkmUoQCKAR8CHgMPMEQQiwLZZCPlgU72hbtmHD54HgjYG+kf2YOcw4ndAryZj5Eyl6C99DQEGZnZ3HmzJmk+x7r7e1FSkoK0aukCwsLcDgcRK7k37p1C5OTk3jiiSe4LoWi4gLDsizLdREURYL5+XlkZGTgzp07xK2WBgIBvPrqq6itrUVqairX5URNZ2cnrFYrTpw4QezNhc20t7djfn4ep06demirMA3cscOyLHw+HzweD7xe73JoDv/xer0IBoPLwTr8X7fbDblcvhzEGYaBSCRaDuyLXj/+6UYTbP4g7P4AbIEg/CEWNalKVKrl+JfB7Z/B/f/e+Tt4W2V5FP8VyLC4uIiGhgaUlpZuK3ibzWY0Nzfj5MmT0Gg0MagwfthsNly/fh11dXVE3wS9c+cOhELhnudqxKM/+qM/gsvlwvPPP891KRQVF+hKN0VFSGpqKt70pjfh+9//Pv7pn/6J63IiSiAQIDc3F0NDQ0SH7tLSUrz22muYnJwkbm/+w+zfvx+3b99GY2Mjjh07tmk7Mw3cscUwDMRi8Y5mDfj9frz88suoq6vb9AbKtNWO9l9dXffyWY8fOsnO2sXrh8Zo6N6Gnax4z83Nobm5GVVVVUkXuFmWRXt7O/Lz84kO3B6PB1NTUzh79izXpURcMBjEr3/9a/zbv/0b16VQVNzY+zkxFEUte+qpp/DLX/4SoVCI61IirqCgACaTCQ6Hg+tSokYoFGL//v3o7OyE3+/nupyY4vF4qKmpAQA0NjYutyivRAM3OVLlGx9NNOv1QyMSQrCDTo/6wbFIlUW8cPDu7e3F4ODghm+zsLCAxsZGHDhwIOlu/gFLg8UcDgfxp0kMDg4iPT2dyOPfXnrpJQQCATzyyCNcl0JRcYOGboqKoLe+9a1YWFjA1avrV5ASnUwmQ1ZWFgYGBrguJaqysrKgUCjQ29vLdSkxJxAIcOzYMQSDQTQ3N6+6eUQDN1lEAgEUYtG6lzsCQXiCIejE21/tNtsdGJydj2R5RNsqeFssFjQ0NKC8vJzYwZVb8fv96OzsxP79+4meiO/z+TAyMoLi4mKuS4mKH/7wh3jf+95H9OeQonaKhm6KiiCpVIp3vetd+MEPfsB1KVFRXFyM8fFxuN1urkuJGoZhUFlZuWqidDIRCAQ4fvw4vF4vWlpalgdw0cBNnpUTzFea8/qRtosWc2r7NgreVqsV9fX1KC0tRUFBAccVcqO3txcKhQJZWVlclxJVw8PDUKlU0Gq1XJcScU6nE5cuXcKTTz7JdSkUFVdo6KaoCHvyySfx8ssvw+fzcV1KxCmVSuj1+k3bIkmRkpKC/Px8dHR0IBlnTQqFQhw/fhxOpxMtLS2or6+ngZtAm4XuWY9vRyvdAG0x342Vwfv+/fuor69HYWEhioqKuC6NEzabDcPDw6isrCR6kGUgEMDQ0BBKSkq4LiUqfvzjHyMtLW15uxJFUUto6KaoCDt79ixEIhFefPFFrkuJiuLiYoyMjBB5U2Gl0tJS2O12TE5Ocl0KJ0QiEaqrq2E2m+F2u3H06FEauAmTqth8pXunobtpZAKBIHmzLKJNo9GgsrISAwMDUKlUxO9j3gzLsujo6CB+eBoAjI6OQiqVIi0tjetSouL555/Hk08+SfSNE4raDRq6KSrC+Hw+3v/+9+O5557jupSo0Gg00Gg0GBoa4rqUqFo5VI30Gwwb8fv9uHv3LrRaLUQiEVpbW4kcEJjMNl3p3kV7udPnQ/vkdCTKSipWqxUdHR3Izc3F4uIi8V1EmxkfH4fdbif+pkMoFMLg4CCKi4uJDKUmkwmvv/46fv/3f5/rUigq7tDQTVFR8OSTT+K1117D/DyZw4VKSkowNDSEQCDAdSlRlZWVBbVajY6ODq5LiamVe7iPHTuGkydPwuVyobm5ecOp5lRi0m4ywXzOs/OVbgC4RVvMd2RxcRG3bt1CYWEhDh06hBMnTmw51ZxUbrcbnZ2dOHjwIPGDtyYmJsDj8ZCZmcl1KVHx/e9/HwcPHiR2QBxF7QUN3RQVBQcPHkR5eTm+9a1vcV1KVOh0OsjlcoyMjHBdSlQxDIODBw/CZDLBZDJxXU5MbDQ0TSwW48SJE/B6vWhsbCT+Zkuy0MnlG7581uuHXMCHjL+zpwgNdJjati0sLCwPTQvv7d3OcWKkYVkW9+7dg16vR0ZGBtflRBXLsujv7yd2lTsUCuFHP/oRPvKRj3BdCkXFJRq6KSpKPvaxj+FHP/oRkS25DMOgpKQEg4ODxK98SqVSHDhwAG1tbcS3mW81pVwkEuH48eMIBoM0eBNis5VuX4iFzR/Y8Wp3+6QJDo83EqURbW5uDg0NDaioqEBhYeGq1yVb8B4fH4fFYsGBAwe4LiXqpqenEQgEkJOTw3UpUXH58mVMT0/jfe97H9elUFRcoqGboqLkfe97H6anp3Hp0iWuS4kKg8EAoVCIiYkJrkuJupycHOLbzLdzLFh4qjkA1NfXw+ulASuR6TYZpAYAs56d7+sOhEJoGiH/58FemEwm3L59G/v370d+fv6Gb5MswTvcVl5VVQWRaP2Z8SQJr3IXFRWBxyPzqfc3v/lNPPnkk5Bv0kFDUcmOzO98iooDcrkcTz75JLEt5gzDoKioCP39/cQfqxVuMzebzZieJm9Y1E7O4RYIBDh27BhkMhmuX78Oq9Uaw0qpSNJuMkgN2N0Ec4Ce170ZlmXR19eHlpYWHD58GEajccu3Jz14r2wrNxgMXJcTdbOzs3C5XA/9vCcqs9mMV155hbaWU9QWaOimqCj6yEc+gldffRVms5nrUqIiOzsbLMtibIz8J9pSqRT79+/HvXv3iGoz30ngDuPz+Thy5Ajy8vJw8+ZNTE1NxaBSKtI2m14OvDHBnIbuiAgGg7hz5w5GRkZw+vTpbQ/RIjl4J1NbOcuy6OnpQUFBAQQCAdflRMU3vvENHDp0KCk+nxS1WzR0U1QUHThwAFVVVfjmN7/JdSlRwePxUF5ejt7eXuL3dgPktZnvJnCHhff1HzlyBK2trejp6SG+44E0KqkEwk0+53MeP3SS7bX88nkMCnI0OHTQAF4ui2mnLZJlJjS3242bN2/C7XbjzJkzUKlUO3p/EoO32+1GR0dHUrSVA0t7uV0u17r9+6QIhUL4z//8T3z0ox/luhSKims0dFNUlH30ox/Fc889R+RANWDpWC2RSITh4WGuS4m6lW3mib66u5fAvVJGRgZOnz6N8fFxNDc30wFrCUYr23iY2qzXD51YgI1mLKeqpKgoSsPhKgNKDmvBFgXRJTShwT6KTosJr0+PRLXmRLGwsIDr169DpVLhxIkTkEgku/o4JAVvlmXR1taGjIyMpGgrD4VC6O7uRmlpKbGr3P/zP/+DhYUFvPvd7+a6FIqKazR0U1SUvec974HNZsPPfvYzrkuJCoZhUF5ejr6+Pvj9fq7LiTqpVIrKykq0tbXB5XJxXc6uRCpwh6WkpKCurg5+vx83b95M2H+XZJS6yTC1RZ8ffIZBZW4qDlboceiwAcWHNZCW8zCZasHd0ATqbaNot0zDFXjwfV+QogXtdwDGxsZQX1+PkpISVFVV7fl7jJTgPTg4CLvdnjRtyOPj42BZlti93MBSa/kHP/hBSKUb38CjKGoJmbfdKCqOSCQSfOhDH8K3vvUtvOc97+G6nKhIT0+HSqXCwMAAysvLuS4n6rKzszE3N4c7d+7g5MmTCTWNNtKBOyx8pFhnZyeuX7+OmpoapKamRuRjU9FTnK0FTwkIxDwwAhZ+JgQ368eM14G5kA8WiRW9HifgWf++DACjUoNj+lwcM+TiuCEXepky5tcQT1iWxf379zE2Noaamhqkp6dH7GOHg3dDQwMAJFy78uLiInp6enDixAkIhTufF5BogsEgenp6sG/fvoT6HbET/f39uH79Or73ve9xXQpFxT0auikqBj760Y/iq1/9Krq7u4kMpeHV7vr6euTn5++6jTKR7N+/Hzdu3EBPTw8qKiq4LmdbohW4w3g8HiorK5GSkoKGhgbs378feXl5EX0MKrL8qiBa5seBDXYFzMp8SOeJ0AsnRDw+ClRa7NPql/+Ua9KRIiL/e327/H4/Wlpa4HK5cObMGSgUiog/RqIG7/C/TVlZGbRaLdflxMTw8DDEYjGysrK4LiVqvvrVr+JNb3rTpsffURT1AA3dFBUDOTk5eMtb3oKvfOUr+Nd//Veuy4kKrVaL9PR09Pb24uDBg1yXE3UCgQBHjx7FjRs3oNPpIrqiFQ3RDtwr5eXlQaFQoLm5GTabDfv37yd2pSfR6SQP2stFPD7SpHLoZUrkKdXIDYhRKZLgi/v3I1ehhoB+Djdlt9vR2NgIhUKBM2fORHUlN9GCN8uyaG1thVKpjPtaI8Xv96Ovrw9Hjx4Fw2w0GSHxuVwuvPDCC/jpT3/KdSkUlRAYlo6bpaiYuHbtGt7xjndgYmIiKisg8cBut+PatWs4d+4csde41tjYGO7fv4+zZ8/G7Z62WAbulVwuFxobGyESiVBdXZ0Uk4rjgd/vx8svv4zHHnvsoeFv3GGB3eeFXqaEVixdFRBGRkYwPT2N48ePR7vkhDYzM4OWlhYYjUZUVFTELGQtLi6ioaEBpaWlcR1mh4eH0dfXh7Nnz0IsFnNdTkx0dXVhcXERJ06cIDZ0f/3rX8e3vvUt9Pb2EnuNFBVJ9LY1RcVIXV0dsrKy8O1vf5vrUqJGqVQiOzsbPT09XJcSMzk5OUhPT8fdu3fj8sgsrgI3AMhkMpw+fRpCoRBXr14l9rz6RJajUKNCq0eqRLbuibNcLofD4eCosvgXCATQ0dGBpqYmHDhwAPv27Ytp+EiE4WpWqxX379/HkSNHkiZwu91uDA0NxfQGDBf+5V/+BZ/4xCeIvkaKiiQauikqRhiGwSc+8Ql873vfI/b4MAAoKyuDyWSCxWLhupSYCB8j5na70dfXx3U5q3AZuMMEAgGqq6tRVlaGlpYW3L17Nymm3JNAoVDA5XIhGAxyXUrcmZ+fx7Vr17C4uIi6ujrk5ORwUkc8B2+/34/m5mYUFxdDp9NxXU7M9PX1Qa/XQ6PRcF1K1LzyyisYHR3FU089xXUpFJUwaOimqBh66qmnYLFY8OMf/5jrUqJGKpUiPz8fXV1dXJcSM+Fg2d/fj9nZWa7LARAfgTuMYRgYjUacP38eXq8XV65coaveCUAikYDP58PpdHJdStwIBALo7OxEQ0MD8vLycPr0aSiV3E5sj8fgzbIs2tvbIZVKUVJSwnU5MeNwODA2NkbkwNSVvvzlL+OP//iPkZKSwnUpFJUwaOimqBiSSqX49Kc/ja985StclxJVxcXFsFgsMJlMXJcSMyqVCgcOHEBLSwvnISWeAvdKUqkUx44dW171bm1tpavecYxhGCgUCtpi/oaFhQVcu3YNCwsLqKurQ1FRUdy01sZb8B4cHMTc3ByOHDkSN/9GsdDZ2Ync3FyiZ5rcvn0bjY2N+PSnP811KRSVUGjopqgY++hHP4qhoSG89NJLXJcSNSKRCBUVFejo6Eiq1lSj0YisrCw0NTUhENjgDKYYiNfAHbZy1dvtduPKlSuYmZnhuixqEwqFgvObSFwLBoPo7OxEfX193KxubyRegrfZbEZPTw9qamqS4vjIMJPJhIWFBeJXuZ999ll84AMfgMFg4LoUikooNHRTVIypVCp87GMfw9/+7d9yXUpUGY1GCIVC9Pf3c11KTO3fvx8ikYiTwWrxHrhXkkqlOH78OMrKytDc3ExXveNUsg9TW1hYwNWrV+NydXsjXAdvu92OlpYWVFVVEb2nea1gMIiOjg5UVFQQfUpDZ2cnLl++jD//8z/nuhSKSjg0dFMUB/70T/8Ud+7cwc2bN7kuJWrCA8YGBgaSaqWMx+OhuroaNpsNvb29MXvcRArcYeFV73PnztFV7ziVrO3lwWAQ9+/fR319PYxGY9yubm+Eq+Dt8/nQ2NiI/Px8ZGdnx+xx40F/fz9EIhGMRiPXpUTVl770Jbzzne9EQUEB16VQVMKhoZuiOJCeno4//MM/xF//9V9zXUpUaTQaZGdno7Ozk+tSYkokEqGmpgaDg4OYnJyM+uMlYuBeSSaT4fjx4ygtLUVzczPa2troqnecSMbQHd67PT8/j7q6OhQXF8f16vZGYh28Q6EQ7ty5A4VCQXx79VpOpxMDAwOorKxMuK+TnRgZGcGvfvUrfP7zn+e6FIpKSDR0UxRH/vzP/xzXrl1DW1sb16VEVXl5Oebn55NqqBoApKSk4MiRI2htbY3q8WmJHrjDGIZBXl4ezp07B5fLhatXr2J8fDwuzz5PJgqFAj6fDz6fj+tSos7r9aK9vR319fXIzc3FqVOnEmZ1eyOxDN5dXV1wuVxJNzgNADo6OpCdnU18O/2zzz6LCxcu4MCBA1yXQlEJiYZuiuKI0WjEe9/7Xjz77LNclxJVYrEY5eXlSTdUDQAMBgNKSkrQ1NQEj8cT8Y9PSuBeKbzqXVZWhu7ubly7dg1ms5mGb44IhUKIxWKiV7v9fj96enpw6dIluN3u5dVtHi/xnyLFIniPjY1hbGwMtbW1EAqFUXmMeJUsw9NmZ2fx05/+FE8//TTXpVBUwkr83ygUlcA+97nP4aWXXsLAwADXpURVXl4ehEIh8de5keLiYmi1WjQ3N0f0pgOJgTuMYRjk5ubiwoULyM3Nxd27d3Hr1i0sLCxwXVpSksvlRM5lCAaDGBwcxOXLlzE7O4vjx4+jtrY2oVe3NxLN4D0/P4/29nYcPXqU6GOyNrJyeJpYLOa6nKj68pe/jEOHDuHEiRNcl0JRCYuGboriUEVFBX7nd36H+L3dDMOgsrIS/f39RD553wrDMDh06BAAoKWlBaFQaM8fk+TAvRKfz0dhYSEuXryI1NRU1NfXo6mpCXa7nevSkgpp+7pZlsX4+DiuXLmC0dFRHDp0CKdOnUJqairXpUVNNIK3zWZDY2Mj9u/fj/T09Ih8zETS398PoVBI/PA0m82GH/zgB3SVm6L2iIZuiuLY008/jRdeeAHT09NclxJVWq02KYeqAUvhsba2Fk6nE+3t7XtqlU6WwL2SUChEeXk5Ll68CIlEgmvXrqG1tRVut5vr0pICKaGbZVmYTCZcu3YN3d3dKCsrw7lz52AwGJJiH3Ikg7fL5UJDQwMKCwuRl5cXmQITSHh42sGDB4n/2vnKV74Co9GIN73pTVyXQlEJjYZuiuJYTU0Namtr8eUvf5nrUqIuWYeqAUsTzY8fP46ZmRn09PTs6mMkY+BeSSKRoLKyEufPn0cwGMRrr72G+/fvJ8WQLy6RELoXFhZw69YttLa2Lm9dyMnJIT4wrRWJ4O31etHQ0LA8syLZsCybNMPTPB4Pvve97+Hzn/980n2vUFSk0dBNUXHg6aefxnPPPRfVKdfxQCwWY9++fbh3715SHgkllUpx/PhxjIyMYGhoaEfvm+yBeyW5XI6jR4/i1KlTsNlsuHTpEvr6+hAIBLgujUjhPd2JOMwu3AJdX18PnU6HixcvorCwMKm/f/YSvAOBAG7fvo2UlBTij8jazOTkJBYXF4kfngYA3/zmN6FQKPCud72L61IoKuHR0E1RceDChQsoLS3Fl770Ja5Librc3FwolcqkbDMHAKVSidraWnR1dW37DG8auDemVqtx/Phx1NTUYHp6GpcvX8bg4GBS3tCJJrlcjmAwGJUJ/NFitVpx9+5dXL9+HVKpFBcvXkRZWVnSTdfezG6CdygUQnNzMwQCAQ4fPpyUgdvj8aC9vR0HDx4kfniax+PB1772NTz99NP0dw5FRQAN3RQVBxiGwd/93d/h+9//PvF7uxmGQVVVFaampmA2m7kuhxNarRbV1dVobW3FzMzMlm9LA/fDpaWl4cyZM6isrMTk5CReeeUVtLe3J3xLdLzg8/mQyWRx/+/Jsiymp6dx69Yt3Lx5EzweD+fPn0dlZSUkEgnX5cWdnQRvlmXR2toKr9ebtD+HWJbFvXv3kJ6ejszMTK7Libq/+7u/g0KhwJNPPsl1KRRFBIZNxH4xiiLUo48+iszMTPzgBz/gupSoGx0dRU9PD86fP5+0q09jY2Po6OjAyZMnoVar172eBu7dWVxcxNDQEKamppCWloaCggKkpaUlzcqc3+/Hyy+/jMceeyxi31vhPbz5+fkR+XiR5Pf7MTo6iuHhYYRCIRQUFMBoNEIkEnFdWkJYXFxEQ0MDSktLUVhYuO71LMvi/v37mJ6exunTp5P2Bsb4+Dju37+Pc+fOEb/KbbFYUFRUhO9///t44oknuC6HoohAQzdFxZHW1lacOHECbW1tKC0t5bqcqGJZFrdv34ZYLMbhw4e5LoczAwMD6O/vx8mTJ5GSkrL8chq4987j8WB4eBgjIyMQi8XIy8tDTk4O8Td5ohG6Ozo6AAAHDhyIyMeLBKvVipGREYyPj0OlUqGgoAAZGRng8WgT305tFbx7enowMjKCU6dOJd1Z3GEejwdXrlxBVVVVUqxyf+pTn0JDQwNu376dNDcrKSraaOimqDjzvve9D16vFy+++CLXpUSd2+3GlStXcOTIERgMBq7L4Uxvby+GhoaWgzcN3JEVDAYxOTmJkZER2Gw2ZGZmwmg0QqvVEvmEMhqhe3h4GGazGceOHYvIx9stv9+PyclJjI6Owm63IysrC/n5+Rt2ilA7s1HwXvuzKRmxLIumpibw+XwcPXqU63KibnJyEmVlZXjppZdQV1fHdTkURQwB1wVQFLXa3/zN32Dfvn1oampCTU0N1+VElVQqxf79+3Hv3j1otdqkbQctLS0Fy7Kor69HbW0tOjo6aOCOID6fj9zcXOTm5sJms2F0dBSNjY0Qi8UwGo3Iyckhvl10r+RyOWd7ulmWhcViwejoKCYmJqBQKGA0GpGdnU1810Ishfd4NzQ0AFi6WZXsgRsAJiYmsLi4iPPnz3NdSkx8/vOfx8mTJ2ngpqgIoyvdFBWHPvaxj6G7uxtXrlzhupSoo23mS8L7JoeGhqDRaHDixAkauKMoGAxiamoKo6OjWFxcRHp6OgwGA/R6fcLvWY3GSrfL5cKlS5fwtre9LSbt2+GgbTKZMD09DbfbjezsbBiNRrqqHWWLi4t4/fXXwTAMTp8+DZVKxXVJnAm3lR86dAgZGRlclxN1PT09qKqqQmNjIw4ePMh1ORRFFLrSTVFx6JlnnkFBQQFeeeUVvOlNb+K6nKgKTzO/cuUKTCZT0raZBwIBzM/PQyqVwul0wul0JvXqUrTx+Xzk5OQgJycHDodjOYDfu3cParUaer0eGRkZUCqVRLag75RUKgWfz4fT6YRSqYzKYwQCAczNzcFkMsFkMiEYDEKv16OkpAQGgwECAX3KEguzs7PLN1bm5uaSNnSHp5WHfxYkg7/4i7/AO9/5Thq4KSoK6G8wiopDer0en/70p/G5z30OjzzyCPGDgcJt5m1tbTh//nzStZmH93CLRCKcPHkSAwMDqK+vx4kTJ2jwjgGFQoGSkhKUlJTA4/HAbDbDZDKhv78fIpEIBoMBBoMBOp2O+O/FzTAMs9xiHsnQvfLfe3Z2FmKxGAaDAUeOHEFqamrS/ntzJbyH+9SpUwiFQsut5htNNSddsrWVNzY24pVXXkF3dzfXpVAUkWh7OUXFKZvNhvz8fPzTP/0T3v/+93NdTtSxLIvGxsblYTXJsrq42dC08JPfEydOJO1KE9eCwSBmZ2eXQ2EgEFjVhh6vN4ei0V4OAM3NzVCr1SguLt71x2BZFna7HdPT0zCZTLBarVCr1cs3NmhnATdYlkVvby+Gh4dX7eF+2HFipHK5XLh27VrStJUDwNmzZ3HgwAH88z//M9elUBSRaOimqDj2ta99Dd/+9rfR3d2dFPt7PR4Prl27hrKyMuTl5XFdTtQ9bEp5X18fBgYGUFtbi9TUVI6qpIAHe4zDAdxms0Gr1UKv10Oj0UClUsXNUK9ohe6uri74fD5UVVVt+31YloXL5YLVal1uHff5fEhLSyNmD32iY1kWnZ2dmJyc3LC7JtmCdygUws2bN6FWq5Omzfrll1/Ge9/7XgwNDSE9PZ3rciiKSDR0U1Qc83q9KC4uxmc+8xl88pOf5LqcmJidnUVjYyPOnDlDdGv1do8FGx4exv3793H06NGk3e8ej1wuF8xmM2ZmZmCxWODxeCCXy6FWq6FWq6FSqaBWqzkJ4tEK3WNjYxgbG8OpU6c2fH04YFssFlgsFlitVlgsFgQCAaSkpECj0UCv1yMtLS0pbiImglAohLt378JiseD48eOQy+Ubvl0yBe/79+9jZmYGZ86cSYqv01AohCNHjuDtb387vvjFL3JdDkURi+7ppqg4JhaL8dd//df43Oc+hw9/+MNJsSKUlpaGoqIiNDc3o66ujsjhSTs5hzs/Px9isRgtLS04cOAAjEZjDCulNiOTyZCfn4/8/HwAS10a4ZA5Pz+PoaEhuN3u5SAeDuFcBfFIUCgUy8eGrQ3Y4ZAdDthqtRoZGRkoLy9HSkpKUoSXROP3+9Hc3Ayfz4dTp05t+ftl7XFipAZvs9mM4eFh1NXVJc3X7I9//GOMj4/jz/7sz7guhaKIRle6KSrOBYNBHDx4EG9/+9vx5S9/metyYoJlWdy6dQsymYy4Y8R2ErhXmpubQ2NjI4qLi1FcXEz3vSYAr9e7btU3HMRVKhVSUlIgkUiW/4jFYojF4j1/biO10h0MBuHxeOD1euHxeOBwONDd3Y3U1FTYbLZVATt8Y4EG7MTg9Xpx+/ZtCAQC1NTUbPvrhOQVb7fbjWvXrmH//v3IycnhupyY8Hq9KC8vxyc/+cmk6aajKK7Q0E1RCeDq1at429vehnv37hH3RGcz4SdA+/btQ25uLtflRMRuA3eY1WpFQ0MDsrKysH//fhq8E5DX610O4Ha7fTnQejwe+P1+MAwDsVi8KoivDeYSiQQikQg8Hm/Dr4HNQjfLsmBZFsFgcPlxVz7+yj9er3fDemZmZlBYWIjMzEwolUoasBOQ0+lEQ0MD1Go1Dh06tOPPIYnBOxQKob6+HnK5HIcOHeK6nJj5/Oc/jxdffBHt7e0J24FDUYmChm6KShC/+7u/C7vdjpdffpnrUmLGbDYvt5lH62zgWNlr4A7b6xNmKn6tDMMbheDw//t8vlXvxzDMcgAP//H5fBAIBMtBO/wnjMfjbRroV/4RiUSrgv3NmzeRn5+P7OzsmP27UJETvnGXmZmJAwcO7PrGHWnBu7u7G1NTU8RuadrI4OAgDh48iJdeeglnz57luhyKIh4N3RSVICYnJ1FWVoYf/ehHePzxx7kuJ2ZIGGoTqcAdttvWUIoMoVAIfr8foVBoOUyv/H+fz4f6+nqcOnVqOTSvDOY8Hg9CoXBXgevu3buQyWQoKyuLwpVR0RTpLSqkBO9kGd651mOPPYaUlBT85Cc/4boUikoKPK4LoChqe7KysvDMM8/gM5/5DLxeL9flxEx5eTn4fD46Ozu5LmVXIh24gaUBeydOnACPx8ONGzeWh1tRySG8Si2VSiGTySCXy6FUKpGSkrI8sA0AUlJSoFQqoVAoIJfLIZVKN1y93omVw9SoxDEyMoLbt29j//79KCkpicjWlPBwtd7eXgwODkagytjzeDy4c+cO9u/fn1SB+5e//CVu3ryJf/iHf+C6FIpKGjR0U1QC+dSnPgWBQIAvfelLXJcSMzweD0ePHsXk5CQmJye5LmdHohG4w4RCIWpra6HX63Hjxg3MzMxE7GNT1GZo6E4soVAI7e3t6O7uxrFjxyJ++kEiB2+WZXH37l3odLqkOhXC6/Xi05/+NJ555hlkZmZyXQ5FJQ0auikqgQiFQnzrW9/CP/7jP2J4eJjrcmJGJpOhqqoKbW1tsNvtXJezLdEM3GE8Hg/79+/H/v370dTUhMHBQdAdQ1Q0hUM3/TqLfz6fDw0NDZifn0ddXR10Ol1UHidRg3dvby9cLhcOHjyYVEMpv/SlL0EkEuFTn/oU16VQVFKhoZuiEsz58+fx2GOP4U/+5E+4LiWmMjMzkZ+fj8bGxnWDpOJNLAL3Srm5uThx4gT6+/tx7949hEKhqD4elbzkcvnywDcqftlsNly/fh1CoRCnT5+GTCaL6uMlWvCemprC4OBg0s3EGB4exj/+4z/iW9/6VlJdN0XFAxq6KSoBfe1rX8P169fxq1/9iutSYqq8vBxKpRItLS1xGyxjHbjDtFot6urqYLFYcOvWLRqKqKjg8/mQSqW0xTyOTU9P4+bNm8jJyUF1dXXMpnEnSvC2Wq24e/cuDh8+nFT7uAHgT/7kT/CWt7wF586d47oUiko6NHRTVAIKD1X79Kc/nVThimEYHD58GB6PB11dXVyXsw5XgTtMKpXi1KlTkEqluH79OqxWa0wfn0oOdF93fGJZFn19fbhz5w4OHTqEsrKymLdNx3vw9nq9yxPcMzIyuC4npn75y1/i+vXrdHgaRXGEhm6KSlDhoWrPPvss16XEVHiA2NjYGEZHR7kuZxnXgTtMIBDgyJEjyMvLw82bNzE6Okr331IRRUN3/PH7/WhubsbIyAhOnz7N6YCseA3eoVAIzc3N0Gq1KCkp4bqcmPJ6vfjMZz6DL3zhC8jKyuK6HIpKSjR0U1SCEolESTlUDVjaV1pdXY2Ojg4sLCxwXU7cBO4whmFQUlKCmpoadHd34+7du/D7/ZzWRJGDhu74srCwgGvXriEUCqGurg4qlYrrkuIueLMsi/b2dgQCAVRVVSXV4DRgaXiaQCCgw9MoikM0dFNUAjt//jze/OY34+Mf/zjXpcRcWloaKioq0NTUBLfbzVkd8Ra4V0pPT8fZs2fh8XhouzkVMTR0xweWZTEwMID6+nrk5+ejtrYWYrGY67KWxVPwHh4ehslkQm1tbcz2uMcLOjyNouIDDd0UleDCQ9X+67/+i+tSYi4/Px8GgwGNjY0IBAIxf/x4DtxhEokEJ06cQE5ODm7evInh4WHabk7tiVwuh8vlitthhskgvDd5eHgYJ0+eRFFRUVyu3sZD8J6dnUVXVxdqamoglUo5qYFLH/7wh/HYY4/h/PnzXJdCUUmNhm6KSnBZWVn4+7//e3ziE5/A/Pw81+XEFMMwqKysBJ/PR1tbW0zDZCIE7jCGYVBaWopjx46hr68PLS0ttN2c2jWZTAaGYeByubguJSnNzc3h2rVr4PF4OHv2LDQaDdclbYnL4O10OtHc3IwDBw5Aq9XG9LHjwXe/+13cuXMH3/jGN7guhaKSHg3dFEWAj3zkIygvL8dHPvIRrkuJOR6Ph5qaGiwsLKCvry8mj5lIgXslnU6Hs2fPIhAI4Nq1a1hcXOS6JCoBMQwDuVxOW8xjjGVZ9Pb24vbt2ygpKUF1dXXCtAtzEbz9fj8aGxuRm5sLo9EYk8eMJ+Pj4/jsZz+Lb3/720hPT+e6HIpKejR0UxQBGIbBv//7v+O3v/1tUraZi8Vi1NbWYmBgAOPj41F9rEQN3GFisRjHjh1Dfn4+bt26hd7eXtomTO0Y3dcdW06nE7du3cL4+DhOnz6N/Pz8uGwn30osg3d4UrlUKkVFRUVUHyte/eEf/iHOnz+P9773vVyXQlEUaOimKGIYjUZ85Stfwcc//vGkazMHAJVKherqaty7dw8zMzNReYxED9xhDMOgqKgIp06dwtTUFG7cuEGHrFE7olAo4HQ6uS6DeCzLYmhoCFevXkVKSgrOnj0bF9PJdysWwZtlWdy9exc+nw9Hjx4Fj5d8T3W/853v4O7du/j2t7/NdSkURb0h+X4SURTBPvShD2Hfvn348Ic/zHUpnEhPT0dVVRWampoi3jpNSuBeSa1W48yZM9Dr9bh58yZd9aa2jbaXR194dXtwcBC1tbWorKwkYvJ2NIM3y7Lo7OyExWLB8ePHE6b9PpLGx8fxuc99Dt/5zndoWzlFxREauimKIAzD4N/+7d/wyiuv4IUXXuC6HE5kZ2ejvLwct2/fjlgoIDFwh/H5fJSXl+PkyZOYnJzEjRs3YLPZuC6LinO0vTx6Vq5uK5VKnDt3DmlpaVyXFVHRCt79/f2YnJzE8ePH4+r4tFgJhUL43//7f+PChQt4z3vew3U5FEWtQEM3RREm3Gb+iU98AnNzc1yXw4nCwkIYjUbU19fv+QxvkgP3ShqNBnV1dUhPT8eNGzfoqje1JYVCAY/Hw8lRfSRzOp2or69fXt0+ePAgEavbG4l08B4dHUV/fz+OHz8OuVwegQoTz7e//W20tbXRtnKKikM0dFMUgcJt5h/60Ie4LoUz5eXlSEtLw+3bt3d9PFayBO4wPp+PioqKVavedK83tRGRSAShUEhXuyNk5eq2QqHA2bNniVvd3kikgrfJZEJHRwdqa2sTes/7XoyOjuIv//Iv8Z3vfCcpvnYoKtHQ0E1RBAq3mV+6dAnPP/881+VwgmEYHDx4EFKpFI2NjQgGgzt6/2QL3CutXfXu6Oig53pTqzAMQ4epRcjCwgKuX7++anU7mfYi7zV4LywsoKWlBYcPH4ZOp4tChfEv3FZ+8eJFvPvd7+a6HIqiNkBDN0URymg04qtf/Sr+9E//NGnbzHk8Ho4ePYpQKIQ7d+6AZdltvV8yB+6w8Kr32bNnYbfb8dprr2FsbGzb/4YU+egwtb3xer1obW1FfX09MjIycP78+aRdodxt8LbZbLh9+zb27duHzMzMKFYY3771rW+hvb0d3/rWt7guhaKoTdDQTVEE++AHP4j9+/fjgx/8INelcEYgEODYsWOw2+24d+/eQ0MjDdyrKZVKHD9+HAcOHEB3dzdef/112nJOAaDD1HaLZVkMDw/jtddeg8/nw7lz51BaWpr0P2t2GrzdbjcaGhpQUFCA/Pz8GFQYn0ZGRvCXf/mX+O53v5u0N20oKhHQ0E1RBAu3mV++fBn/+Z//yXU5nBGJRDhx4gTMZjO6u7s3Dd40cG+MYRhkZWXhwoUL0Gq1uHnzJm05p2jo3oVwK/nAwAAOHz6M2trapB36tZHtBm+Px4P6+noYDAaUlpbGsML4Em4rf/TRR/Gud72L63IoitoCDd0URbjc3Fz88z//Mz7+8Y9jeHiY63I4I5VKceLECYyNjaG3t3fd62ngfjiBQIB9+/ahrq6OtpxTy6Gbfv4fbqNWcoPBwHVZcelhwdvr9aK+vh5qtRqVlZVgGIaDKuPD3/zN36Cnp4e2lVNUAqChm6KSwB/8wR/gzW9+M9773vcm9RE/SqUSJ06cwMjIyKrgTQP3zoRbzisrK9HT04Pr169jZmaGhq8kI5fLEQgE4PV6uS4lbvn9fvT09ODy5cu0lXwHNgve4cCdkpKCQ4cOJXXgvnnzJv7v//2/+MlPfpK0A+QoKpHQ0E1RSYBhGHznO9+BxWLBZz7zGa7L4VRKSgpOnDiBoaEh9PX10cC9SwzDIDMzExcuXEB2djZaWlpQX1+PhYUFrkujYkQgEEAikdAJ5hsIhUIYGhrC5cuXMTs7i2PHjtFW8h1aG7x9Ph8aGhqgUChw+PBh8HjJ+xTWYrHg93//9/HZz34WdXV1XJdDUdQ2MCxdmqCopNHW1oYTJ07g+eefx+OPP851OZyyWq24desWBAIBlEolDdx75Pf70d/fj6GhIaSnp6O8vBxKpZLrspKS3+/Hyy+/jMceeyzqR0/dunUL2dnZMBqNUX2cRMGyLCYmJtDT0wM+n4/y8nIYDIakXpHdq8XFRdTX10MgEECj0eDo0aNJHbhDoRDe/va3w+l04vLly/T3FkUlCAHXBVAUFTtVVVX4f//v/+GDH/wgjhw5guzsbK5L4oxMJoNEIoHD4YDRaKRPXPZIKBSioqICBQUF6O3txbVr15CTk4PS0lJIpVKuy6OihA5TW8KyLGZmZtDV1QW/34+ysjLk5OTQsB0Bcrl8uaNCo9EkdeAGgK997Wu4ffs2Ojo66O8tikogdKWbopIMy7J417veBbPZjBs3biTlL+2VLeWlpaVobGxEQUFBUk/BjTSHw4Hu7m6YzWbk5+ejsLAQEomE67KSQixXugcHBzE3N4fa2tqoPk68YlkW8/Pz6Onpgc1mQ2lpKfLy8pLy52o0eL1eNDQ0QCaTobCwEI2NjSgtLUVhYSHXpXGiqakJZ8+exS9+8Qs8+uijXJdDUdQOJPftQopKQuFjxCYmJvD0009zXU7Mrd3DrdVqcfLkSQwNDaGnp4cOA4sQhUKB6upqnDp1CjabDZcvX8a9e/fgcrm4Lo2KoGRd6WbZ/7+9+45vuk78B/5K0520Tfegk9JSRgcdzNIBWJSNDFFPAcd5J56gnnucg1NRT1HxTsVTRLSKp4IKllVG6WSUWigdQFcoLbR0pTvJ5/eHv+RLZcho+kma1/PxyIOSfpq80pHk9Xm/P++PgNraWuzfvx+5ublwdXXFTTfdhODgYBbuPqJbNE0mkyE2Nhaurq7XdB7vgaalpQV33HEHli9fzsJNZII4vZzIDCkUCnz77bdISkpCcnIypk6dKnakfnG5RdMcHR0xYcIEZGVlQRAEhIWFcVpoH1EoFBg3bhyam5tRVlaGXbt2wcfHByEhIXB0dBQ7Ht0guVyOtrY2CIJgFn8zWq0WNTU1KCsrQ1dXF4KDgzF27FiDzygwN7rzcDs6OvZaNE23uFp2djYAmNWI9z333AN3d3e88sorYkchouvA0k1kpkaPHo1XXnkFS5cuRX5+Pjw9PcWOZFB/tEq5rnhnZmZCrVZj5MiRZlEi+ouTkxNiY2OhUqlw4sQJ7N27Fx4eHggJCYGLi4vY8eg66Y7Xb29vH9Arc2s0GlRVVeHEiRMAgCFDhsDf35+j2gbQ3t6OrKwsODs7Y9SoURcdw22OxXvNmjXYtWsXCgoKYGnJt+5EpojHdBOZMUEQMGPGDHR0dGDnzp0DdoGaazktWFtbG7Kzsy/7ho/6RkdHB06ePImKigo4OzsjJCQE7u7u3NHRB/rzmG4A2LVrF0aOHDkgd9z19PSgoqICJ0+ehLW1NUJCQjBo0CA+LxhIS0sLsrOz4e3tjfDw8Cs+HzQ2NiI7O3vAH+NdUFCACRMm4KuvvsKsWbPEjkNE14mlm8jM1dfXIyIiAn/+85/x4osvih2nz13Pebg7OzuRk5MDa2trxMXFceqoAXV3d6O8vBynTp2CjY0NgoKC4Ofnx9GcG9DfpTs3Nxdubm4Dqvi0traivLwcVVVVcHR0REhICE/9ZWANDQ3Izc1FcHAwQkNDr+p7PdCLd3t7O2JiYjB16lSsXr1a7DhEdANYuokIGRkZmDp1Kn755RckJiaKHafPXE/hvvBr8/LyoFarMXbsWNjY2BgwKWk0GiiVSpSXl6OtrQ0BAQEIDAyEXC4XO5rJ6e/SfezYMajVakRGRhr8vgxJEATU1dXh1KlTaGhogI+PDwYPHgxnZ2exow14Z86cwaFDhzBy5EgEBgZe09cO5OJ955134vjx48jOzuZrEJGJ41ACEWHixIl4/vnnsWjRIuTl5cHPz0/sSDfsRgo38Nt5p8eOHYvDhw8jIyMD48aNG9DHrIpNKpUiICAA/v7+aGxsxKlTp7B79264uroiMDAQXl5enNJrpORyOZRKpdgxrltHRweqqqpQWVkJQRAQGBiImJgYlpx+UlFRgaNHjyImJgbe3t7X/PUD9Rjvd955B1u2bMGhQ4f4u0g0AHCkm4gA/DbKs2jRIpSVlSErK8ukz6l8o4X7QoIgoLCwEDU1NRg3bhycnJz6MCldSWdnp74MaTQafSnnzo8r6++R7vr6ehw+fNikTmOk1Wpx7tw5VFZWora2Fu7u7ggMDISnpyd37vQTQRBQWlqKkydPYvTo0XBzc7uh2xtII97btm3D3LlzsXnzZtx0001ixyGiPsDSTUR67e3tmDBhAoKDg7Fx40aTfPPZl4VbRxAElJWVoaysDGPGjLnhN4d0bQRBwNmzZ1FRUYG6ujooFAr4+fnBx8eHI0CX0N+lu7OzE9u2bcP06dON+lh8QRDQ1NQEpVKJ06dPAwD8/f0RGBgIe3t7kdOZF0PtzBwIxfvEiRMYO3YsnnvuOaxYsULsOETUR1i6iaiX6upqxMTE4MEHHzS5hdUMUbgvVFlZicLCQkRHR8PHx6dPb5uuTldXF2pqaqBUKtHY2AgPDw/4+fnB09PTqAtff+rv0i0IArZu3Yr4+HijnAnS1tYGpVIJpVKJzs5OeHt7w9fXl6vli0Sj0eDw4cNobm7G+PHj+3yHhykXb5VKhdGjR2Ps2LH473//y99PogGE71CIqBc/Pz9s3rwZkydPRnh4OObNmyd2pKti6MINAAEBAbC2tsahQ4fQ1dWFoKCgPr8PujLdCudBQUH6MlVcXIz8/Hz4+PiwTIlAIpFALpdDpVIZTen+/c4ZT09PhIWFceeMyC5coHLixIkGmaliqsd4a7VaLFy4EE5OTvjPf/7D5zCiAYavPER0kXHjxuGDDz7APffcg9DQUISHh4sd6Yr6o3DreHt7Y9y4ccjLy0NraytGjhxpktPwBwKZTIahQ4ciNDQUzc3NUCqVOHz4MADAy8sLXl5ecHNzY8nqB7rSLab29nbU1taitrYW9fX1cHZ2hq+vL0aPHs3DEIxAa2srcnNzIZfLMWbMGIP+XZpi8X7yySdRUFCAw4cP8/eVaADi9HIiuqxHHnkEmzdvxoEDB+Dq6ip2nEvqz8J9ofb2duTm5urP5W1tbd0v90tXJggC6uvr9eWrq6sL7u7u8PLygqenp0kvEHi1+nt6OQCUlJSgra0N0dHR/XJ/wP8do637Wbe2tsLV1VW/w4UL7hmPuro6HDx4EIGBgRg+fHi/jeKaylTzL774An/5y1+QkZHRr39DRNR/WLqJ6LLUajVuueUWdHV1IT093ehGDMUq3DpqtVp/bOKYMWPg6OjYr/dPVyYIAlpbW/WlrKmpCQqFAp6envD29oaDg8OAnMIpRulWKpU4deoUEhISDHo/Go0G586d0/9MNRoNPD094eXlBQ8PD+78MjKCIODkyZMoLi5GZGSkKKejNPbinZeXh8mTJ+O///0vFi5cKHYcIjIQlm4iuqLGxkbExcVh0qRJ+Pjjj8WOoyd24dYRBAElJSU4efIkoqOjr+s8s9Q/urq69GXt3LlzsLKygpubm/5ib28/IEq4GKW7qakJWVlZuOWWW/r0e6jRaNDU1IT6+nrU19fj/PnzsLW11Y9mu7q68vAOI6XRaFBQUIBz585h9OjRcHZ2Fi2LsRbv2tpaxMXFYfHixVi5cqXYcYjIgIxr2IqIjI6zszN+/vlnjBkzBhEREXjooYfEjmQ0hRv4bRGpsLAwODo64tChQwgNDUVISMiAKG8DjY2NDQICAhAQEACNRoPz58+jvr4e1dXVKCgogI2NzYAs4f1BJpOhp6cH3d3dN3Q86u9LdmNjIywtLeHm5gYfHx9ERERALpfz52LkOjs7kZeXB0EQkJCQADs7O1HzGOMx3t3d3Zg1axaio6Px8ssvix2HiAyMpZuI/lBYWBi+/vprzJs3D8OHD8ekSZNEy2JMhftCPj4+kMlkyM3NRUtLC6KiooxuOj79H6lUCnd3d7i7uwP47VCBxsbGXiXc1tYWrq6ucHFxgZOTE5ycnIzm983YWFlZwdbWFm1tbddUujs7O9HU1ISmpiY0NDSwZA8AjY2NyMvLg5ubG6Kioozmb8bYivfSpUvR3t6OL7/8krM1iMwAp5cT0VV788038eabbyIzMxMhISH9fv/GWrgv1NXVhby8PGg0GowZM0b0ER66PheWcF0p7OnpgYODA5ycnKBQKKBQKODo6Gh0O1fEmF4OAJmZmfDz84O/v/8lP9/R0YHm5mb997OpqQldXV2Qy+VQKBRwcXGBm5sbS7YJUyqVOHLkCMLCwhAcHGyUP0djmGr+6quv4q233sKhQ4d46kkiM8HSTURXTRAE3H///UhPT0dWVha8vLz67b5NoXDraLVaFBQUoK6uDnFxcUa78jtdPUEQ0NHRgaampl7FsaenR18aHRwcIJfLIZPJIJPJRPsdFat0HzlyBNbW1hgyZAhUKpX+0tLSoi/Yl9pp0Z8ZyTC0Wi2Ki4tRXl6O2NhYeHp6ih3pisQs3p9++in+9re/Ydu2bYiPj+/X+yYi8bB0E9E1UavVuPXWW1FVVYV9+/b1y4rdplS4dQRBQHl5OYqKinic9wAlCIJ+enRzczNaW1vR1tYGlUoFjUYDe3t7fQmXy+X6j+3s7Aw6ndTQpVsQBKjVarS3t/cq1w0NDejs7IQgCLCxsdE/ZgcHBygUCjg5ORndrAC6cR0dHTh06BC6u7sRFxcHBwcHsSNdFTGK948//ohFixYhNTUVs2fP7pf7JCLjwNJNRNeso6MDU6ZMgVQqxc6dOw16mh5TLNwXampqwsGDB2Fvb4/o6GizOE+0udOVcV0Z1RVxlUqF9vZ2CIIAa2tr2NrawsbGBra2thddbGxsYGVlBUtLy2su6NdTugVBgEajgVqtRnd3Nzo7O9HZ2Ymuri79xxf+X6PRwNLSUl+s5XI51Go1ampqkJSUxNFrM1FbW4v8/Hx4eXkhPDzc5Haq9Gfxzs7OxtSpU/Gvf/0L999/v0Hvi4iMD0s3EV2XxsZGxMfHIzQ0FN99951BRu5MvXDr9PT0oKCgAPX19YiJidEv3kXmR6vV6ovr5Qqt7mPdy7OFhQUsLS0hlUphaWl50UUikegvwG8FurKyEv7+/rCwsIAgCL1KtUajQU9Pj/7/uouOVCq95E6A31+nu28dlUqF3bt3Y8aMGZzVMcBptVoUFRWhsrISERERopx/u6/0R/E+fvw4EhISsHz5cjz33HMGuQ8iMm4s3UR03U6fPo2xY8di+vTp+PDDD/v0tgdK4dbRFaGjR48iODgYQ4cO5Yq1dFkXluTfX35/va5UX1iuy8vLMXjwYEilUn0h15X3K12kUul1/61ptVr8/PPPmDJlCuzt7fv4O0LGoq2tDQcPHoRWq0VcXBzkcrnYkW6YIYv36dOnMX78eMycORPvv/8+d0gRmSnTmgdEREZl0KBB2LFjB8aPHw8PD48+O9foQCvcwG/n8w4MDISLiwsOHDiAhoYGxMTEcHVzuiSJRKIvwteqp6cH5eXlCAsL69dp3hYWFrC3t4dKpWLpHqBqamqQn58PX19fjBw5ckA8NwOGO51YU1MTpk6ditGjR+Pdd99l4SYyYxxmIaIbEhYWhl9++QVvv/02Pvjggxu+vYFYuC/k6OiIxMREyGQy7NmzB3V1dWJHIuozcrkcKpVK7BjUxzQaDQoKCpCfn49Ro0YhMjJywD0364p3SUkJTp48ecO319XVhWnTpsHd3R0bNmwYcN8vIro2HOkmohs2ZswY/O9//8Ott94KDw8PLFiw4LpuZ6AXbh1LS0uMGjUK1dXVOHDgAIKCgjBs2DBONyeTx9I98KhUKhw4cAAWFhZISkqCTCYTO5LB9NWIt0ajwbx589De3o60tDTY2Nj0ZUwiMkEs3UTUJ26++WZ89NFHWLp0Kdzd3ZGUlHRNX28uhftCfn5+UCgUOHjwIM6dO4fo6Oh+OQUbkaHI5XLU1NSIHYP6gCAIqKiowLFjxxAYGIjhw4ebxY7Bvije9957L4qKipCdnc3ndCICwNJNRH3orrvuQl1dHebNm4f09HRERkZe1deZY+HWcXBwQEJCAkpKSrBv3z6EhIQgJCTELN7c0sAjk8nQ1tYmdgy6QW1tbThy5Aja2towZswYszvjwo0U76eeegpbtmxBTk4OPD09DRWRiEwMSzcR9am///3vqK2txfTp05GRkYGgoKArbm/OhVtHKpVi+PDh8Pb2xuHDh1FbW4tRo0ZxhIRMjlwuR3t7OzQajVn+LZu6C0e3/fz8MHr0aLM95/r1FO+3334b//73v7F3716Dn/ebiEwLh1KIqM+98cYbmDJlCiZNmoTKysrLbsfC3ZuzszOSkpLg5uaGffv2obS0FFqtVuxYRFfN1tYWUqmUo90mqL29HVlZWSgrK8Po0aMRGRlptoVb51oWV1uzZg2ef/55bNq0CaNGjeqnhERkKli6iajPWVhY4L///S/GjRuH5ORkVFdXX7QNC/elSaVSjBgxAuPHj0d1dTUyMjLQ0tIidiyiqyKRSLiYmokRBAHl5eXYvXs3ZDIZkpOT4eHhIXYso3E1xfvDDz/Ek08+iR9++AGTJk3q54REZApYuonIIKRSKdavX4/Ro0dj0qRJOH36tP5zLNx/zMXFhaPeZJJYuk3HhaPbcXFxiIqKMvvR7Uu5UvFeu3YtHn30UXz77bdISUkRKSERGTuWbiIyGEtLS2zYsAERERFITk5GbW0tC/c14Kg3mSK5XM7p5UZOd+w2R7ev3qWK92effYbly5fjm2++wbRp00ROSETGjKWbiAzK0tISX3/9NYYPH46kpCSkpaWxcF+j3496Hz9+HGq1WuxYRJckk8k40m3EWlpakJmZiZKSEo5uX6MLi/e7776LZcuW4csvv8TMmTPFjkZERo6lm4gMzsrKChs3bsSQIUPw2GOPwd/fn4X7GulGvePj43Hu3Dmkp6fjzJkzEARB7GhEvXB6uXHq6elBYWEh9u7dC2dnZ0yaNImj29fB2dkZSqUSTz31FNavX4+5c+eKHYmITABLNxH1C2tra3z//fcICwvDpEmTcObMGbEjmSSFQoGJEydi6NChOHLkCHJyclhwyKjI5XJ0d3eju7tb7CiE36aSV1dXY9euXWhpaUFiYiJGjBjB0e3r9Omnn2LFihXYsGED5s+fL3YcIjIRLN1E1G+sra3x3XffYcSIEUhISIBSqRQ7kkmSSCQICAjA5MmTYW9vjz179nDKORkNKysr2NjYcGeQEdBNJS8qKkJ4eDjGjx8PR0dHsWOZrLVr1+Khhx5Camoq5s2bJ3YcIjIhLN1E1K+srKzwzTffICoqComJiVc8jzddmbW1NSIjI3tNOa+pqeGUcxIdp5iL61JTyQcNGgSJRCJ2NJP14YcfYvny5di4cSNmz54tdhwiMjEs3UTU76ysrJCamorRo0cjKSkJ5eXlYkcyaRdOOS8oKOCUcxKdTCbjCuYi4FRyw3j//ffx6KOP4n//+x9mzJghdhwiMkEs3UQkCt3pxOLj45GUlITi4mKxI5m0S005LyoqQk9Pj9jRyAxxpLv/NTY2ciq5Abz55pt46qmnsGnTJp4WjIiuG0s3kZnTaDRYu3YtEhMT4eLiAisrK3h4eCAiIgL33XcffvzxRwDAv/71L0gkEjz55JOXvJ0HHngAEokEgYGBl/z87t27IZFIMGnSJP11UqkU69atw/Tp05GQkICcnJw+f3zm5sIp542NjdixYwdOnDgBjUYjdjQyIyzd/UelUuHAgQPIzMyEi4sLp5L3Ea1Wi8ceewwrV67Eli1bkJKScsXtr/a1FAD27NkDiUTyhxciGjgsxQ5AROLRaDSYMWMG0tLSoFAoMH36dPj6+qK7uxvHjh3DV199heLiYsyaNQuTJ08GAKSnp1/ytnbt2gWJRILKykqcPHkSwcHBF30eAKZMmdLreqlUig8++ABeXl5ISUlBamoqpk+fboBHa14UCgXGjx+Ps2fPoqioCKdOncLQoUPh7+/PN3NkcHK5HG1tbRAEgb9vBtLR0YGSkhJUV1fDz88PkydPhp2dndixBgSNRoPFixdjx44d2LdvHyIjI/9w+6t9Lb1QQEAAlixZYsBHQkTGgqWbyIylpqYiLS0NkZGR2Lt3L5ycnHp9vr29Hbm5uQCAyMhIuLq6Ij8/H83Nzb22raqqwsmTJ7FgwQJ8++23SE9Pv6h068q6rrxfSCKR4IUXXoCnpycWLFiAf//733wj0gckEgk8PT3h4eGB06dP4/jx4zh58iSGDRsGLy8vliEyGHt7e2g0GnR2drII9rGenh6UlZXh1KlT8PT0RFJSEhwcHMSONWB0dnZizpw5KCsrQ05ODoKCgv7wa67ltfRCgYGBePHFF/sqOhEZMU4vJzJjWVlZAIAlS5Zc9CYB+O2Nc3JyMoDfClxycjI0Gg327NnTaztdoX744Yfh6up60Wh4a2srDhw4ACcnJ8TGxl42zwMPPIAvv/wSy5Ytw6pVq27kodEFJBIJfH19MXnyZAQGBqKgoAD79+9HQ0OD2NFogJJKpZDJZJxi3oc0Gg3KysqwY8cONDY2YsKECYiLi2Ph7kPnz59HYmIizp49i+zs7Ksq3MC1vZYSkXli6SYyY66urgCA0tLSq9pedzy2bqq4zq5duyCTyTBmzBgkJSVdVLr37dsHtVqNxMRESKXSK97H3Llz8csvv+C1117DI488Aq1We7UPh/6AhYUFBg8ejClTpsDDwwM5OTnIyclBc3Oz2NFoAGLp7htarRaVlZXYuXMnTp8+jdjYWIwfPx7Ozs5iRxtQqqurMWHCBDg4OGDv3r3w8PC46q+91tdSIjI/nF5OZMZuvfVWrFq1Ch9++CFaW1sxd+5cxMTEICAg4JLbX+647t27dyM+Ph5WVlZITk7Gd999h6NHj2LkyJG9tr/U1PJLSUhIwL59+5CSkoK6ujp88cUXf1jW6epZWlpi6NChCAwMRGlpKfbt2wcfHx+EhIRwtWPqM1xM7cZotVrU1NSgpKQEgiBgxIgRXCDNQI4ePYpp06ZhwoQJ+Pzzz2FtbX1NX3+tr6U6FRUVl51eHhYWhkWLFl1TDiIyXhJBEASxQxCReDZu3Ijly5ejtrZWf52LiwsSEhJwzz33YObMmb229/Pzg1KpRF1dHTw8PFBSUoKwsDCsWrUKTzzxBIqKijBixAi8++67ePjhhwEAo0aNwpEjR3D06FGMGDHiqrNVVFTgpptuQlBQEDZt2gR7e/u+edDUS3t7O0pLS1FdXQ0PDw+EhoZyFM2E9fT0YOvWrZg2bZqo52cuLy9HbW0txo0bJ1oGU6TRaFBdXY2ysjIAwJAhQxAQEAALC05ONISMjAzMnTsXd955J955553r/j5fy2vpnj17/nC6+ezZs7Fp06brykJExofP4ERmbuHChaiqqsK2bdvw/PPPY8aMGdBqtdi0aRNmzZqFxYsX48J9c7op5rrRa92/ujcQw4cPh6enp/76hoYGFBQUwMvL65oKN/DbIjNZWVk4f/48kpKSeAyygdjb2yMqKgpTpkyBTCZDZmYmMjMzcfbsWXC/LF0v3QrmdHV0C6Tt2LEDp06dQlhYGCZPnoygoCAWbgPZvHkzpk2bhsceewyrV6++oe/ztb6WAkBiYiIEQbjkhYWbaGDhszgRwcrKCikpKXj55Zfx008/ob6+Ht988w1kMhnWr1+PzZs367fVTRHXHde9a9cuODk5ITo6Wr9NYmIi9u7dC41Gg927d0MQhKueWv577u7u2LNnDxQKBeLj41FZWXkDj5SuxM7ODiNHjkRKSgpcXV1x8OBB7Nu3DzU1NSzfdM10pZvrMlxZV1cXjh8/jh07duDMmTOIjIxEcnIy/Pz8WLYN6OOPP8btt9+O9957D08//XSfTNu/ltdSIjIvfDYnootIpVIsXLgQjzzyCIDex3BfONItCAL27NmDhISEXsdcJycno6mpCYcPH77m47kvRS6X4+eff8aoUaMQHx+Pw4cPX/dt0R+ztrZGWFgYUlJS4Ovri8LCQqSnp6OqqooFiq6ara0tpFIpR7svo6OjA4WFhfrVyEePHo2JEyfC29ubx20bkFarxYsvvohHHnkEGzduxNKlSw12X1d6LSUi88KF1IjosnSnorlwlNPX1xehoaEoLS3Fjz/+iIaGBn0R19FNNU9PT++T0g38VgQ3bNiAZ555BklJSVi7di1uu+22G7pNujJLS0sEBwcjMDAQSqUSpaWlKC4uxpAhQ+Dv7w9LS76E0OVJJBL9CuY8rdX/aW1txYkTJ6BUKuHp6Yn4+HgoFAqxY5mFzs5O3H333di9ezd27tzZb+sNXOq1lIjMC98xEZmx1NRUuLm5YfLkyRdNY6ytrcXatWsB/Laa+IUmTZqE0tJSPP/88wBw0YIwQ4cOhbe3NzZs2ICSkhJ9SbtRFhYWeP311xEeHo577rkHBQUFWLlyJadgGphUKkVAQAD8/f1RU1ODsrIyFBcXw9/fH0FBQZDJZGJHJCPFFcx/IwgC6urqcOrUKTQ0NGDQoEFISkrizoh+pFQqMWvWLGi1Whw6dKhPXpN0rve1lIjMB0s3kRnLzc3Fu+++Cy8vL8THxyMoKAjAb6sOb9myBR0dHZg9ezbmz5/f6+smT56MDz/8EIWFhXB1dUVERMRFt52cnIyvvvpKv31fuvPOOxEaGopZs2bh6NGjSE1NZfHrBxKJBIMGDYKPjw/Onz+P8vJypKenw93dHUFBQfDw8OC0WOrF3Et3d3c3qqqqUF5eDq1Wi8DAQERHR8PW1lbsaGYlMzMTCxYs0J8SrK/PhHG9r6VXOmUYAKxYsYKzIIgGCJ4yjMiMVVdX48cff8TOnTtRVFSEM2fOoLOzE66urhg1ahTuuOMO3HHHHRftuW9oaIC7uzsEQcC8efPwv//976Lb/uSTT3D//fcD+O1UKgsWLOjz/DU1NZg9ezY6Ozvx448/6t/oUP/p6OhAZWUlKioqYGlpicDAQPj7+1/zeW6p7xjLKcMAoKqqClVVVYiPjxc1R39rbGxERUUFTp8+DYVCgaCgIHh7e3NWjgg++eQTLF++HE8//TSeffZZg+wYvNbX0qs5ZRjwW2kPDAzs87xE1P9YuonIpHV2duK+++7Djh07kJqaetHx5dQ/tFotampqUFFRgcbGRvj4+CAwMBAuLi4c/e5nxlS6z58/j7y8PNx8882i5ugParUaSqUSFRUVUKlU8PPzQ2BgIJycnMSOZpY0Gg0eeeQRfPbZZ/jiiy8wZ84csSMRkRnj9HIiMmm2trb44osv8NZbb2HGjBl48803sWzZMrFjmR0LCwv4+vrC19cXra2tqKioQG5uLmxtbREYGIhBgwbBxsZG7JjUz+RyObq6utDT0yP6DgBDEAQBTU1NqKqqglKphEwm0/++D8THayqam5sxf/58lJaWIisrC+Hh4WJHIiIzx9JNRCZPIpHg8ccfx4gRI3D77bejsLAQH3zwQa/TmFH/cXBwQHh4OIYNG4aamhpUVlbi6NGj8PDwgK+vL7y8vLjyuZmwtraGtbU1VCoVnJ2dxY7TZ9ra2qBUKqFUKtHZ2QkfHx+MHz8eCoWCMztEVlxcjFmzZsHLywuHDh2Cm5ub2JGIiDi9nIgGluLiYsyYMQODBg3C999/D1dXV7EjEXqXlI6ODvj4+MDX1xfu7u4sKX3MmKaXA0BGRgYCAwPh5+cndpQb0tXVhZqaGlRXV6OpqQmenp76nUjcwWcctmzZgrvuugu33XYb3nvvPaP4/SciAli6iWgAamxsxIIFC1BeXo5NmzZxaqEREQQBzc3N+gIOQD8t3cnJiQW8Dxhb6c7Pz4ednR3CwsLEjnLN1Go1amtroVQqcfbsWTg7O8PX1xeDBg3iYoFGZtWqVXjppZfw1ltv4cEHHxQ7DhFRL5zfR0QDjrOzM9LS0vD3v/8d8fHxWL16NZYuXSp2LMJvhwIoFAooFAqMGDEC586dg1KpxP79+2FnZ6cv4DwF3MAhk8nQ0tIidoyrptVqUV9fD6VSiZqaGv3vZXh4OH8vjVBLSwuWLl2K3bt3Y+vWrUhKShI7EhHRRVi6iWhAsrS0xOrVqzFu3Djcf//9SE9Px0cffdTn52el6yeRSODh4QEPDw9ERESgrq4O1dXVKCkpgVwuh5eXF7y8vODs7MwRcBMml8tRU1Mjdowr6unpwdmzZ1FbW4u6ujr9woDx8fGcgWHE8vLycPvtt8PT0xNHjhyBv7+/2JGIiC6JpZuIBrTbbrsNsbGxWLBgAWJiYvD1118jMjJS7Fj0O5aWlhg0aBAGDRrUqwDl5OTAwsICnp6e8PLygru7OxdhMzFyuRwqlQqCIBhVeW1ra0NtbS1qa2vR0NCg39EzduxY7ugxclqtFv/617/wj3/8AytWrMDLL7/M5wUiMmp8hiKiAS84OBjZ2dl48sknER8fjzfeeAN//etfxY5Fl2FlZaUv4FqtFufPn0dtbS2OHTuGjo4OuLu7w8vLC56enrCzsxM7Lv0BmUwGjUaDzs5OUX9egiCgsbFRX7RVKhVcXV3h5eWFqKgoTh03EefPn8ddd92FvLw8bNq0CSkpKWJHIiL6QyzdRGQWbGxssHr1aiQnJ2PJkiVIT0/HZ599BrlcLnY0ugILCwu4ubnBzc0NI0aMgEqlQm1tLaqrq/Hrr7/CyckJnp6ecHd3h0Kh4CrSRkgqlcLe3h5tbW39Xro7OzvR0NCAs2fPoq6uDlqtFp6enhg6dCg8PDyMYqE5unoZGRm48847ERwcjF9//RXe3t5iRyIiuios3URkVmbPno2CggLcdtttGDVqFFJTUxEbGyt2LLoKEokEDg4OcHBwQEhICLq6ulBXV4e6ujpUVFSgp6cHLi4u+pLOEm48ZDIZVCqVwc+ZrCvZ9fX1qK+vh0qlgpOTE9zc3BAbGwsXFxdYWFgYNAP1Pa1Wi5UrV+L111/H008/jWeeeYZ/20RkUli6icjs+Pv7Y9++fXjhhReQmJiIV155BY8++qjYsega2djYwN/fH/7+/hAEASqVSl+2ysvLoVar4ezszBJuBHTHdfe1y5VsV1dXDB8+HK6urjy1l4k7d+4cbr/9dhw7dgxpaWlISEgQOxIR0TVj6SYis2RlZYXXXnsNSUlJuPPOO7F792588cUXUCgUYkej63DhKHhQUBAEQUBra6u+kJ06dQoajUZfwp2dneHk5AQbGxuxo5sFuVyOs2fP3tBt6HasNDU14fz58/qS7ejoCDc3N5bsAWj79u1YunQpIiIiUFhYaPCZEkREhsLSTURmberUqSgsLMTtt9+OqKgobNiwAfHx8WLHohskkUjg6OgIR0fHS5bwqqoqtLe3w87ODgqFAk5OTvrzh7OI9z25XI5Tp05d9fa6n1dzczOampr0/wqCACcnJzg7O7NkD2AajQbPPfcc3n33Xbz88st49NFHeVgAEZk0lm4iMnve3t7YtWsXVq5ciZSUFCxbtgz//Oc/+WZ+APl9CQeA7u7uXqWuurpav9jXhSWcRfzGyeVytLe3Q6vVXlSedAX7wnLd3NwMAHB0dIRCoYCfnx/Cw8Ph4ODA8jXAFRcXY/HixThz5gx2796NMWPGiB2JiOiGsXQTEeG3FZb/8Y9/YOrUqbj77ruRlpaGdevWISYmRuxoZCDW1tZwd3eHu7u7/rqenp5e5U9XxG1sbCCTySCXyyGXy/Ufy2QyHid+FWxtbQEANTU10Gg0aGtrg0qlgkqlQltbGyQSCZycnODk5ISAgAAoFArI5XIWbDOi1Wrx+uuv49VXX8WiRYuwfft2ODk5iR2LiKhPSARBEMQOQURkTDo6OvD888/j3//+N/72t7/hn//8JywtuY/SXPX09OgLoq4k6j7WaDSwt7e/qJDLZDLY2tqK8nvT09ODrVu3Ytq0af16SixBENDd3Y2Ojo5e3yPd96ynpwdWVlZwcnK6aMeFXC6HRCLpt6xkXEpKSrB48WJUV1fj008/xdSpU8WORETUp1i6iYguIzs7G3fffTfs7Ozw2WefcdSbehEEAZ2dnReN2ur+FQQBlpaWsLW17XWxsbG56P99WY77unQLgoCuri50dXWhs7Pzoovu+q6uLmi1WlhZWfXaCaG7HD9+HG5ubhgyZEgfPEoaCLRaLVatWoVXX30VCxcuxNtvv83RbSIakDh0Q0R0GePGjcOvv/6K5557DhMnTsTDDz+MlStXctSbAPx2nLidnR3s7OwuWlVZN+p7YSnVXc6fP9/rOq1WC6lUCmtra1haWsLS0hJSqVT/8eUuUqkUEolEP0Ks+1ij0QAAGhoaIJVKodu3LggCtFot1Gr1RReNRnPJ69VqNbq7uyEIAqysrC7aceDm5nbRdZf7+3B0dDTIacPINJWVlWHx4sWorKzEt99+i5tvvlnsSEREBsORbiKiq5CZmYnFixfD3t4e69atQ3R0tNiRaAAQBAE9PT3o7OxET0/PNRVijUYDQRD0F93tCYKAjo4O2NvbX1TKLSwsLlner1TubWxsYGNjc8PHrldWVkKpVGLChAk3/H0j06XVavHWW2/hlVdewfz587F69WqObhPRgMfhGiKiqzBhwgT8+uuvePbZZxEfH4/ly5fjlVde4ag33RCJRAJra+s+XSlfN708KSmpX4/p/iMODg4c6TZzZWVlWLJkCcrLy7Fx40bccsstYkciIuoXXBaUiOgq2dvb45133sH27duxceNGxMTEID8/X+xYRCZBJpPpR/TJvGi1Wrz55puIiYlBSEgIioqKWLiJyKywdBMRXaP4+HgUFhYiOTkZEyZMwOOPP47Ozk6xYxEZNWtra1hZWaGtrU3sKNSPjh49iokTJ+Ltt9/G119/jXXr1kGhUIgdi4ioX7F0ExFdB3t7e6xevRrbt2/H1q1bMWzYMHz33XdixyIyWhKJBHK5nFPMzYRKpcJDDz2EuLg4jBgxAkVFRZg2bZrYsYiIRMHSTUR0A+Lj43HkyBE8/PDDWLp0KVJSUlBWViZ2LCKjJJfLOdJtBtavX4+wsDDk5OQgIyMDH3/8MZydncWORUQkGpZuIqIbZGVlhUceeQQlJSVwd3dHVFQUnnjiCU45J/odmUzGke4BrLCwEBMnTsSKFSvw4osvIi8vD7GxsWLHIiISHUs3EVEf8fb2xpdffolffvlFP+X822+/FTsWkdHg9PKBSaVSYdmyZRg9ejRGjBiBsrIy3HfffbCw4NtMIiKApZuIqM8lJCTgyJEjWL58Oe69915OOSf6/3SlW3decTJtWq0W69atw9ChQ5GXl4eMjAx8+OGHcHV1FTsaEZFRYekmIjIAS0tLrFixAqWlpfDw8EBUVBRXOSezJ5PJoFar0dXVJXYUukGFhYVISEjAo48+ipdeegm5ubmcSk5EdBks3UREBuTl5YUNGzYgLS0Nv/zyC8LCwjjlnMyWpaUl7OzsOMXchLW0tODBBx/E6NGjMXLkSJw4cYJTyYmI/gCfIYmI+sHEiRNx5MgRPPLII7j33nsxadIkHDx4UOxYRP1OJpNxBXMTpFarsXr1aoSFheHAgQP6qeQuLi5iRyMiMnos3URE/cTS0hLLly9HWVkZhg8fjvj4eMyZM4fHe5NZ4WJqpkWr1eKLL77AsGHD8M4772DVqlWcSk5EdI1YuomI+pmnpyfWrFmDoqIi2NnZISIiAkuWLMGZM2fEjkZkcCzdpiMtLQ0xMTFYsWIFHnroIZSWluKuu+7iVHIiomvEZ00iIpEMHjwYqampyM7OxpkzZxAaGopHH30Uzc3NYkcjMhiWbuOXm5uLpKQkzJ8/H7NmzUJFRQWWL18OGxsbsaMREZkklm4iIpFFRUVh27Zt+Omnn5CRkYHg4GCsXLmSKzzTgCSXy9HW1gatVit2FPqdkpISzJ49G4mJiQgPD8epU6fw0ksvwcHBQexoREQmjaWbiMhIJCUlIS8vDx9//DHWr1+P0NBQfPjhh9BoNGJHI+oz9vb2AICOjg6Rk5DO6dOnsWTJEkRFRUEmk6GoqAjvv/8+PDw8xI5GRDQgsHQTERkRiUSCW2+9FUVFRXj++efx8ssvIzw8nKcZowFDIpFAJpNxirkRaG5uxooVKxAWFoba2lrk5OTgq6++wuDBg8WORkQ0oLB0ExEZIUtLS9x33304efIklixZgvvvvx9xcXH4/vvvOS2XTB6P6xbX+fPn8fTTTyM4OBjZ2dn4+eefkZaWhsjISLGjERENSCzdRERGzM7ODk888QTKy8sxbdo03HPPPYiIiMCnn37Kaedksli6xXHmzBksW7YMQUFB2LVrFz799FPk5OQgMTFR7GhERAMaSzcRkQlwdnbGSy+9BKVSiaVLl+LZZ59FaGgo3nnnHS64RiaHpbt/lZWV4e6770ZwcDBKSkqwadMm5ObmYtasWZBIJGLHIyIa8Fi6iYhMiFwux2OPPYaKigo8/fTTWLNmDYKCgvDiiy+yxJDJ0K1gToaVn5+PuXPnYuTIkWhtbcW+ffuwc+dOJCcns2wTEfUjlm4iIhNkY2OD++67D6WlpVi9ejV++OEH+Pv749FHH8W5c+fEjkd0RTKZDB0dHVCr1WJHGZD27t2Lm266CWPHjoWjoyMKCgrwww8/IDY2VuxoRERmiaWbiMiESaVSLFy4EEeOHMFXX32FAwcOYPDgwbj//vtRWVkpdjyiS7KxsYGlpSVHu/uQVqvF5s2bMW7cONxyyy0YNmwYysrK8PnnnyMsLEzseEREZo2lm4hoAJBIJLj55puRkZGBtLQ0nD59GmFhYVi0aBGOHj0qdjyiXiQSCY/r7iNqtRrr1q1DVFQUFi9ejMmTJ6OyshLvvfce/P39xY5HRERg6SYiGnAmTJiArVu3Ijc3FxKJBNHR0UhISMA333zDFc/JaLB035i6ujo8/fTTCAwMxNNPP40//elPqKqqwsqVK+Hu7i52PCIiugBLNxHRABUREYHU1FSUl5cjKSkJf/vb3zB48GC88MILaGhoEDsemTmW7uuTmZmJ+fPnIygoCBkZGXj77bdRVVWFJ554Ao6OjmLHIyKiS2DpJiIa4AYNGoSXX34Z1dXVeO2117Bt2zb4+/tj0aJFyMvLEzsemSmuYH71urq68PHHHyMmJgZTpkyBQqFAVlYW9u/fj4ULF8LKykrsiEREdAUs3UREZsLGxgZ33HEHcnNzsXfvXlhbWyMhIQHR0dF47733WICoX8lkMqhUKgiCIHYUo3X8+HE88MAD8PX1xauvvopFixbh9OnT+OSTTxAVFSV2PCIiukos3UREZig2Nhbr169HTU0N7r77bqxZswa+vr5YsmQJ8vPzxY5HZkAul6Onpwfd3d1iRzEq3d3dWLduHeLj4xEZGYm6ujps2LABp06dwuOPPw4XFxexIxIR0TVi6SYiMmMuLi5YsWIFSkpK8MMPP6CjowNjxozB6NGj8f7776O5uVnsiDRAWVpawtbWlsd1/3+FhYV46KGH4O/vj2effRYpKSmoqKjApk2bMHXqVFhY8C0bEZGp4jM4ERFBIpEgKSkJ33zzDZRKJW699VasWbMG3t7emDlzJjZu3MgRSepz5r6YmlKpxEsvvYSIiAjExMSgsrISa9euRWVlJV544QX4+PiIHZGIiPoASzcREfXi4eGBp556CsXFxcjMzERISAgefvhh+Pj44O6770Z6ejq0Wq3YMWkAMMfF1FpaWrBmzRpMnDgRQUFB2LZtG5YtW4YzZ87gp59+wsyZM2FpaSl2TCIi6kMs3UREdEkSiQSjRo3C22+/jdOnT+Prr7+GRCLB7NmzMXjwYCxfvhxHjx4VOyaZMN1iagNdd3c3Nm7ciJkzZ8Lb2xvvv/8+br75ZpSVlSErKwsPPPAAXF1dxY5JREQGwtJNRER/SCqVYsqUKfj8889x9uxZvPHGGzh58iSio6MRGRmJl19+GadPnxY7JpmYgTy9XKvVYs+ePbj77rsxaNAgPPzwwwgJCcH+/ftRXFyMZ599FoGBgWLHJCKifsDSTURE18TOzg4LFy7Ezz//jJqaGvzlL39BWloaAgMDkZCQgPfffx/nzp0TOyaZAN308oFy2jCtVovc3FysWLECwcHBmDlzJgAgNTUVp0+fxttvv41Ro0ZBIpGInJSIiPoTSzcREV03Nzc3/PWvf0VWVhZKS0uRkpKCjz76CD4+PoiLi8Nzzz2HwsJCsWOSkbK3t4cgCOjo6BA7ynXr6urC999/j7vuugv+/v5ISkrCyZMn8frrr6Ourg7r16/HlClTIJVKxY5KREQikQgDZfcyEREZjcrKSvz000/YtGkT9u3bBz8/P0yaNAnz5s3DlClTuFCUAfX09GDr1q2YNm0arKysxI7zh3bu3ImIiAh4eHiIHeWqnTt3Dt988w22bNmC/fv3w97eHrNmzcKsWbMwefJk2Nvbix2RiIiMCEs3EREZVEtLC7Zt24ZNmzZh69atkEgkmDhxImbNmoV58+ZBoVCIHXFAMbXSnZOTAw8PDwwePFjsKFdUWFiIb775Btu2bcORI0cwdOhQzJkzB7NmzUJsbCzPo01ERJfF0k1ERP1GrVYjKysLmzdvxubNm1FdXY24uDjcfPPNuO222xASEiJ2RJNnaqX76NGj0Gq1iIiIEDtKL2q1Gjt37sR3332H9PR0VFdXIyEhAXPmzMHMmTMREBAgdkQiIjIRLN1ERCSakpIS/PTTT/jhhx+Qm5sLf39/xMXFITk5GbfccguLzXUwtdJdUVGBmpoajB8/XtQcGo0G2dnZSEtLQ2ZmJg4fPgyJRILp06djzpw5mDp1KhwdHUXNSEREpomlm4iIjML58+exb98+7N69G+np6Th27BiCgoL0JXzatGnw8/MTO6bRM7XSXV9fj8OHDyMlJaVf71dXsrdt24bMzEwcOnQIGo0GEyZMwOTJk5GUlITo6GiuP0BERDeMpZuIiIxSQ0MDMjIyWMKvkamV7o6ODmzfvh0zZsww6ArfGo0GOTk52LZtG/bv34/Dhw9DrVazZBMRkcGxdBMRkUloaGjoNRJeVFSEwYMHIy4uDomJiYiPj8fw4cPNfkErUyvdgiBgy5YtSEhI6NPp2yqVCllZWdi/f79+JFutVmP8+PG9SrYpfI+IiMi0sXQTEZFJurCE79u3D0VFRbCzs0NYWBhGjhyJ2NhYjB8/HuHh4WZVxE2tdAPAnj17EBoaCh8fn+v6epVKhczMTOTk5CA/Px9FRUU4deoUHB0dERsby5JNRESiYukmIqIBoaurC0ePHsWhQ4dw8OBBHDhwAMeOHYONjU2vIj5hwgSEh4cbdCqzmEyxdB88eBCOjo4IDQ39w21bWlqQlZWF7Oxs5Ofn4/jx4ygvL4eTkxOio6MRFxeH2NhYxMTEwN/fHxKJpB8eARER0eWxdBMR0YDV3d19ySJubW2NoUOHYuTIkYiMjMSIESMQGRkJT09PsSPfMFMs3cXFxWhvb0d0dLT+OrVajbKyMhQWFqKwsBBHjhzRF2wXF5eLCravry8LNhERGSWWbiIiMivd3d04duyYvogXFBTgxIkTqK+vh7OzM/z9/REQEIAhQ4boR8jDw8Mhl8vFjn5VTKl0a7VanD59Gvv27UN+fj6amppQXl6OyspKVFdXQ6PRICAgAGFhYYiJidFfBg0axIJNREQmg6WbiIgIQFNTE8rKylBaWorS0lIUFxejpKQEJ06cQHt7Ozw9PREQEIDAwECEhoZi2LBhGDp0KAICAuDs7Gw0x40bW+lWq9VQKpWoqKjAsWPHUFJSgpMnT6KyshKVlZVQqVTw8vLCkCFDMHz4cAwdOhShoaEIDQ1FUFCQUTwGIiKiG8HSTUREdAWCIKC2tlZfxktKSlBSUoLi4mJUV1ejq6sLtra2cHd3h5ubG9zd3eHh4QEvLy/4+PjA19dXP3ru4eFh8HLeX6W7q6sLVVVVqKqqQnV1NZRKJc6cOYO6ujqcO3cOZ8+eRUNDAxoaGqDVauHs7Izg4GAMHToUYWFh+mI9ZMgQk5lFQEREdD1YuomIiK6TIAhobm7GmTNncObMGdTU1Oj/vfBSV1eHtrY2WFtbw83NDW5ublAoFJDJZPqLg4MDHBwcIJfL4ejoCEdHRzg5OcHJyUn/sUKhgEKhuOIicFdTujs7O9HY2Ijm5mY0NTWhubkZLS0taG5uRmtrK1paWqBSqdDa2orW1la0t7ejra0NbW1tqK+vR319PRobGwEArq6u+h0MgwYNgo+PD7y9vfX/ent7w8vLCzY2Ngb5GRARERk7lm4iIqJ+oFKpehXz+vr6XsVWV3RbWlrQ2tqq/1xbWxtUKhU6Ozv1t2VjYwOJRAILCwtYWFhAIpFAKpXqr9NoNAB+2ymg1WohCAI0Gg0EQYBarYZarQYAWFhYQCaTQS6X64u/XC6Hg4MDHB0d9f9eeJ2np6e+UHt4eHD6NxER0R9g6SYiIjIBarUabW1t+pFnXaHWarXQaDS9PtaV8d9fpFIppFKpvkTb2dlxQTIiIiIDY+kmIiIiIiIiMhDjWGqViIiIiIiIaABi6SYiIiIiIiIyEJZuIiIiIiIiIgNh6SYiIiIiIiIyEJZuIiIiIiIiIgNh6SYiIiIiIiIyEJZuIiIiIiIiIgNh6SYiIiIiIiIyEJZuIiIiIiIiIgNh6SYiIjJxGo0Ga9euRWJiIlxcXGBlZQUPDw9ERETgvvvuw48//ih2RCIiIrMlEQRBEDsEERERXR+NRoMZM2YgLS0NCoUC06dPh6+vL7q7u3Hs2DFkZGQgOjoa+/fvFzsqERGRWbIUOwARERFdv9TUVKSlpSEyMhJ79+6Fk5NTr8+3t7cjNzdXpHRERETE6eVEREQmLCsrCwCwZMmSiwo3ANjb2yM5Obm/YxEREdH/x9JNRERkwlxdXQEApaWlIichIiKiS+Ex3URERCYsPz8fY8aMgVqtxp133om5c+ciJiYGAQEBYkcjIiIicKSbiIjIpI0aNQobNmyAp6cnNmzYgHnz5iEwMBCurq6YO3cufvrpJ9GyLVmyBBKJBBUVFaJl+L1169ZBIpFg3bp1YkchIiIzwdJNRERk4hYuXIiqqips27YNzz//PGbMmAGtVotNmzZh1qxZWLx4MfpqYptEIrnihWWWiIioN65eTkRENABYWVkhJSUFKSkpAH47ldh3332He+65B+vXr8fcuXMxZ86cPru/f/zjH5e8Pioqqs/ug4iIaCBg6SYiIhqApFIpFi5ciMLCQqxcuRLp6el9WrpffPHFPrstIiKigYzTy4mIiAYwBwcHAOiz6eV9ZePGjUhISICTkxPs7OwQHh6O1157DV1dXZfc/tChQ5g3bx48PDxgY2ODgIAAPPjggzhz5swltz9x4gQWLFgAZ2dnyGQyjB8/Hlu2bDHkQyIiIrokjnQTERGZsNTUVLi5uWHy5MmwsOi9L722thZr164FACQkJIgR75KeeeYZvPbaa3Bzc8Mdd9wBuVyOX375Bc888wy2bduG7du3w9raWr/9zz//jHnz5kEQBMyfPx8BAQE4dOgQ/vOf/2Dz5s3Yv38/goKC9NuXlZVh3LhxaGhowC233IKoqCicOHECc+bMwS233CLGQyYiIjPG0k1ERGTCcnNz8e6778LLywvx8fH68lleXo4tW7ago6MDs2fPxvz58/v0fi81vTwwMBBLliy54tdlZ2fjtddeg5+fH/Ly8uDl5QUAeO211zB37lz8/PPPeOutt/DMM88AAFQqFRYvXgy1Wo09e/Zg4sSJ+ttatWoVnnrqKTzwwAPYvn27/vply5ahoaEBq1evxvLly/XXb968uU+n2BMREV0NnqebiIjIhFVXV+PHH3/Ezp07UVRUhDNnzqCzsxOurq4YNWoU7rjjDtxxxx0XjYJfL4lEctnPJSYmYs+ePfr/L1myBJ9//jnKy8sRGBgIALj//vvxySef4KOPPsKf//znXl9fWlqKYcOGISAgAKdOnQIAfPnll/jTn/6E22+/HV999VWv7dVqNUJCQlBRUYHKykr4+/tDqVTCz88PQUFBKCsrg1Qq7fU1SUlJ2Lt3Lz777LM/3EFARETUFzjSTUREZML8/PywbNkyLFu2rF/v93r32R8+fBgAMGnSpIs+FxoaCl9fX5SXl6O5uRlOTk5X3N7S0hIJCQmoqKhAfn4+/P39kZ+fDwCIj4+/qHAD/1e6iYiI+gsXUiMiIqJ+09zcDADw9va+5Od11zc1Nd3Q9p6enpfcXjednYiIqL+wdBMREVG/cXJyAvDbIm+XoluNXLfd9W5fV1d3ye0vdztERESGwtJNRERE/WbUqFEA0OvYb50TJ05AqVQiKCgICoXiD7dXq9XIyMgAAERHR/fafv/+/dBoNBd9zaVuh4iIyJBYuomIiKjf3HPPPQCAlStX4ty5c/rrNRoN/v73v0Or1eLee+/VXz9nzhy4uLggNTUVOTk5vW5r9erVKC8vx5QpU+Dv7w8A8PX1xU033YTy8nKsWbOm1/abN2/m8dxERNTvuJAaERER9Zvx48fjiSeewBtvvIGRI0di/vz5kMlk+OWXX3D06FHEx8fj8ccf128vl8vx6aefYsGCBUhMTMSCBQvg7++PQ4cOYfv27fDy8sJHH33U6z4++OADjBs3DitWrMD27dsRGRmJEydO4IcffsDMmTPx008/9ffDJiIiM8aRbiIiIupXq1atQmpqKkJCQrB+/Xq899570Gq1WLlyJXbs2AFra+te28+ePRuZmZmYNm0atm3bhrfeegvHjx/HX/7yFxw6dAiDBw/utX1ISAhycnIwb948ZGZm4t1330V1dTU2bdqEW2+9tT8fKhEREc/TTURERERERGQoHOkmIiIiIiIiMhCWbiIiIiIiIiIDYekmIiIiIiIiMhCWbiIiIiIiIiIDYekmIiIiIiIiMhCWbiIiIiIiIiIDYekmIiIiIiIiMhCWbiIiIiIiIiIDYekmIiIiIiIiMhCWbiIiIiIiIiIDYekmIiIiIiIiMhCWbiIiIiIiIiIDYekmIiIiIiIiMpD/B7z7W7LCiS0pAAAAAElFTkSuQmCC",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -280,7 +288,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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       "text/plain": [
        "<Figure size 1600x800 with 1 Axes>"
       ]
@@ -323,7 +331,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
@@ -353,7 +361,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1200x800 with 1 Axes>"
       ]
diff --git a/examples/tidal_performance_example.ipynb b/examples/tidal_performance_example.ipynb
index fc492950a..9e56a0480 100644
--- a/examples/tidal_performance_example.ipynb
+++ b/examples/tidal_performance_example.ipynb
@@ -378,7 +378,7 @@
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x700 with 2 Axes>"
       ]
@@ -546,7 +546,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x700 with 2 Axes>"
       ]
@@ -645,7 +645,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 700x600 with 2 Axes>"
       ]
diff --git a/examples/wave_example.ipynb b/examples/wave_example.ipynb
index 4adf42b5b..21c0bcddd 100644
--- a/examples/wave_example.ipynb
+++ b/examples/wave_example.ipynb
@@ -1,2655 +1,2691 @@
 {
-  "cells": [
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "# MHKiT Wave Module \n",
-        "The following example runs an application of the [MHKiT wave module](https://mhkit-software.github.io/MHKiT/mhkit-python/api.wave.html) to 1) read in NDBC data, 2) compute metrics from spectral data, 3) generate a capture length matrix, 4) calculate MAEP, and 5) plot the matrices.\n",
-        "\n",
-        "Start by importing the necessary python packages and MHKiT module."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 1,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "import numpy as np\n",
-        "import pandas as pd\n",
-        "from mhkit import wave"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Load NDBC Data \n",
-        "\n",
-        "We can use MHKiT to load data downloaded from https://www.ndbc.noaa.gov. \n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 2,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "c:\\users\\sterl\\codes\\mhkit-python\\mhkit\\wave\\io\\ndbc.py:99: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
-            "  data = pd.read_csv(\n"
-          ]
-        },
-        {
-          "data": {
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-              "    <tr>\n",
-              "      <th>2018-01-01 04:40:00</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.03</td>\n",
-              "      <td>0.14</td>\n",
-              "      <td>0.17</td>\n",
-              "      <td>0.37</td>\n",
-              "      <td>0.40</td>\n",
-              "      <td>...</td>\n",
-              "      <td>0.06</td>\n",
-              "      <td>0.12</td>\n",
-              "      <td>0.04</td>\n",
-              "      <td>0.04</td>\n",
-              "      <td>0.02</td>\n",
-              "      <td>0.01</td>\n",
-              "      <td>0.01</td>\n",
-              "      <td>0.01</td>\n",
-              "      <td>0.01</td>\n",
-              "      <td>0.0</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "<p>5 rows × 47 columns</p>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "                     0.0200  0.0325  0.0375  0.0425  0.0475  0.0525  0.0575  \\\n",
-              "2018-01-01 00:40:00     0.0     0.0     0.0     0.0     0.0    0.03    0.04   \n",
-              "2018-01-01 01:40:00     0.0     0.0     0.0     0.0     0.0    0.02    0.06   \n",
-              "2018-01-01 02:40:00     0.0     0.0     0.0     0.0     0.0    0.00    0.08   \n",
-              "2018-01-01 03:40:00     0.0     0.0     0.0     0.0     0.0    0.00    0.13   \n",
-              "2018-01-01 04:40:00     0.0     0.0     0.0     0.0     0.0    0.03    0.14   \n",
-              "\n",
-              "                     0.0625  0.0675  0.0725  ...  0.3300  0.3400  0.3500  \\\n",
-              "2018-01-01 00:40:00    0.09    0.22    0.22  ...    0.07    0.08    0.07   \n",
-              "2018-01-01 01:40:00    0.08    0.21    0.21  ...    0.07    0.07    0.08   \n",
-              "2018-01-01 02:40:00    0.07    0.14    0.32  ...    0.08    0.06    0.05   \n",
-              "2018-01-01 03:40:00    0.22    0.26    0.32  ...    0.05    0.06    0.05   \n",
-              "2018-01-01 04:40:00    0.17    0.37    0.40  ...    0.06    0.12    0.04   \n",
-              "\n",
-              "                     0.3650  0.3850  0.4050  0.4250  0.4450  0.4650  0.4850  \n",
-              "2018-01-01 00:40:00    0.07    0.03    0.01    0.02    0.01    0.01     0.0  \n",
-              "2018-01-01 01:40:00    0.02    0.01    0.02    0.01    0.01    0.00     0.0  \n",
-              "2018-01-01 02:40:00    0.03    0.03    0.02    0.01    0.01    0.00     0.0  \n",
-              "2018-01-01 03:40:00    0.03    0.03    0.01    0.01    0.00    0.00     0.0  \n",
-              "2018-01-01 04:40:00    0.04    0.02    0.01    0.01    0.01    0.01     0.0  \n",
-              "\n",
-              "[5 rows x 47 columns]"
-            ]
-          },
-          "execution_count": 2,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "ndbc_data_file = \"data/wave/data.txt\"\n",
-        "\n",
-        "# ndbc.read_file outputs the NDBC file data into two variables.\n",
-        "# raw_ndbc_data is a pandas DataFrame containing the file data.\n",
-        "# meta contains the meta data, if available.\n",
-        "[raw_ndbc_data, meta] = wave.io.ndbc.read_file(ndbc_data_file)\n",
-        "raw_ndbc_data.head()"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The resulting DataFrame is spectra indexed (rows) by datetime with frequency as the columns. To use this data in MHKiT functions we must first transpose the DataFrame"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 3,
-      "metadata": {},
-      "outputs": [
-        {
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-              "      <th>2018-01-31 14:40:00</th>\n",
-              "      <th>2018-01-31 15:40:00</th>\n",
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-              "      <th>2018-01-31 17:40:00</th>\n",
-              "      <th>2018-01-31 18:40:00</th>\n",
-              "      <th>2018-01-31 19:40:00</th>\n",
-              "      <th>2018-01-31 20:40:00</th>\n",
-              "      <th>2018-01-31 21:40:00</th>\n",
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-              "<p>5 rows × 743 columns</p>\n",
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-              "        2018-01-01 00:40:00  2018-01-01 01:40:00  2018-01-01 02:40:00  \\\n",
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-              "0.0375                  0.0                  0.0                  0.0   \n",
-              "0.0425                  0.0                  0.0                  0.0   \n",
-              "0.0475                  0.0                  0.0                  0.0   \n",
-              "\n",
-              "        2018-01-01 03:40:00  2018-01-01 04:40:00  2018-01-01 05:40:00  \\\n",
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-              "0.0325                  0.0                  0.0                 0.00   \n",
-              "0.0375                  0.0                  0.0                 0.00   \n",
-              "0.0425                  0.0                  0.0                 0.00   \n",
-              "0.0475                  0.0                  0.0                 0.01   \n",
-              "\n",
-              "        2018-01-01 06:40:00  2018-01-01 07:40:00  2018-01-01 08:40:00  \\\n",
-              "0.0200                  0.0                  0.0                  0.0   \n",
-              "0.0325                  0.0                  0.0                  0.0   \n",
-              "0.0375                  0.0                  0.0                  0.0   \n",
-              "0.0425                  0.0                  0.0                  0.0   \n",
-              "0.0475                  0.0                  0.0                  0.0   \n",
-              "\n",
-              "        2018-01-01 09:40:00  ...  2018-01-31 14:40:00  2018-01-31 15:40:00  \\\n",
-              "0.0200                  0.0  ...                 0.00                  0.0   \n",
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-              "0.0375                  0.0  ...                 0.00                  0.0   \n",
-              "0.0425                  0.0  ...                 0.00                  0.0   \n",
-              "0.0475                  0.0  ...                 0.06                  0.0   \n",
-              "\n",
-              "        2018-01-31 16:40:00  2018-01-31 17:40:00  2018-01-31 18:40:00  \\\n",
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-              "0.0475                  0.0                  0.0                 0.07   \n",
-              "\n",
-              "        2018-01-31 19:40:00  2018-01-31 20:40:00  2018-01-31 21:40:00  \\\n",
-              "0.0200                  0.0                  0.0                  0.0   \n",
-              "0.0325                  0.0                  0.0                  0.0   \n",
-              "0.0375                  0.0                  0.0                  0.0   \n",
-              "0.0425                  0.0                  0.0                  0.0   \n",
-              "0.0475                  0.0                  0.0                  0.0   \n",
-              "\n",
-              "        2018-01-31 22:40:00  2018-01-31 23:40:00  \n",
-              "0.0200                  0.0                  0.0  \n",
-              "0.0325                  0.0                  0.0  \n",
-              "0.0375                  0.0                  0.0  \n",
-              "0.0425                  0.0                  0.0  \n",
-              "0.0475                  0.0                  0.0  \n",
-              "\n",
-              "[5 rows x 743 columns]"
-            ]
-          },
-          "execution_count": 3,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Transpose raw NDBC data\n",
-        "ndbc_data = raw_ndbc_data.T\n",
-        "ndbc_data.head()"
-      ]
-    },
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MHKiT Wave Module \n",
+    "The following example runs an application of the [MHKiT wave module](https://mhkit-software.github.io/MHKiT/mhkit-python/api.wave.html) to 1) read in NDBC data, 2) compute metrics from spectral data, 3) generate a capture width matrix, 4) calculate MAEP, and 5) plot the matrices.\n",
+    "\n",
+    "Start by importing the necessary python packages and MHKiT module."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from mhkit import wave"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Load NDBC Data \n",
+    "\n",
+    "We can use MHKiT to load data downloaded from https://www.ndbc.noaa.gov. \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Compute Wave Metrics \n",
-        "We will now use MHKiT to compute the significant wave height, energy period, and energy flux. "
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\akeeste\\documents\\software\\github\\mhkit-python\\mhkit\\wave\\io\\ndbc.py:99: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
+      "  data = pd.read_csv(\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 4,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
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-              "2018-01-01 00:40:00    7.458731\n",
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-              "2018-01-01 04:40:00    7.669476\n",
-              "Name: Te, dtype: float64"
-            ]
-          },
-          "execution_count": 4,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
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+       "    <tr>\n",
+       "      <th>2018-01-01 02:40:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.08</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 03:40:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.13</td>\n",
+       "      <td>0.22</td>\n",
+       "      <td>0.26</td>\n",
+       "      <td>0.32</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>0.05</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-01-01 04:40:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.03</td>\n",
+       "      <td>0.14</td>\n",
+       "      <td>0.17</td>\n",
+       "      <td>0.37</td>\n",
+       "      <td>0.40</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>0.12</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.04</td>\n",
+       "      <td>0.02</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 47 columns</p>\n",
+       "</div>"
       ],
-      "source": [
-        "# Compute the enegy periods from the NDBC spectra data\n",
-        "Te = wave.resource.energy_period(ndbc_data)\n",
-        "Te.head()"
+      "text/plain": [
+       "                     0.0200  0.0325  0.0375  0.0425  0.0475  0.0525  0.0575  \\\n",
+       "2018-01-01 00:40:00     0.0     0.0     0.0     0.0     0.0    0.03    0.04   \n",
+       "2018-01-01 01:40:00     0.0     0.0     0.0     0.0     0.0    0.02    0.06   \n",
+       "2018-01-01 02:40:00     0.0     0.0     0.0     0.0     0.0    0.00    0.08   \n",
+       "2018-01-01 03:40:00     0.0     0.0     0.0     0.0     0.0    0.00    0.13   \n",
+       "2018-01-01 04:40:00     0.0     0.0     0.0     0.0     0.0    0.03    0.14   \n",
+       "\n",
+       "                     0.0625  0.0675  0.0725  ...  0.3300  0.3400  0.3500  \\\n",
+       "2018-01-01 00:40:00    0.09    0.22    0.22  ...    0.07    0.08    0.07   \n",
+       "2018-01-01 01:40:00    0.08    0.21    0.21  ...    0.07    0.07    0.08   \n",
+       "2018-01-01 02:40:00    0.07    0.14    0.32  ...    0.08    0.06    0.05   \n",
+       "2018-01-01 03:40:00    0.22    0.26    0.32  ...    0.05    0.06    0.05   \n",
+       "2018-01-01 04:40:00    0.17    0.37    0.40  ...    0.06    0.12    0.04   \n",
+       "\n",
+       "                     0.3650  0.3850  0.4050  0.4250  0.4450  0.4650  0.4850  \n",
+       "2018-01-01 00:40:00    0.07    0.03    0.01    0.02    0.01    0.01     0.0  \n",
+       "2018-01-01 01:40:00    0.02    0.01    0.02    0.01    0.01    0.00     0.0  \n",
+       "2018-01-01 02:40:00    0.03    0.03    0.02    0.01    0.01    0.00     0.0  \n",
+       "2018-01-01 03:40:00    0.03    0.03    0.01    0.01    0.00    0.00     0.0  \n",
+       "2018-01-01 04:40:00    0.04    0.02    0.01    0.01    0.01    0.01     0.0  \n",
+       "\n",
+       "[5 rows x 47 columns]"
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 5,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "variable\n",
-              "2018-01-01 00:40:00    0.939574\n",
-              "2018-01-01 01:40:00    1.001399\n",
-              "2018-01-01 02:40:00    0.924770\n",
-              "2018-01-01 03:40:00    0.962497\n",
-              "2018-01-01 04:40:00    0.989949\n",
-              "Name: Hm0, dtype: float64"
-            ]
-          },
-          "execution_count": 5,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Compute the significant wave height from the NDBC spectra data\n",
-        "Hm0 = wave.resource.significant_wave_height(ndbc_data)\n",
-        "Hm0.head()"
-      ]
-    },
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "ndbc_data_file = \"data/wave/data.txt\"\n",
+    "\n",
+    "# ndbc.read_file outputs the NDBC file data into two variables.\n",
+    "# raw_ndbc_data is a pandas DataFrame containing the file data.\n",
+    "# meta contains the meta data, if available.\n",
+    "[raw_ndbc_data, meta] = wave.io.ndbc.read_file(ndbc_data_file)\n",
+    "raw_ndbc_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The resulting DataFrame is spectra indexed (rows) by datetime with frequency as the columns. To use this data in MHKiT functions we must first transpose the DataFrame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 6,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "variable\n",
-              "2018-01-01 00:40:00    3354.825613\n",
-              "2018-01-01 01:40:00    3916.541523\n",
-              "2018-01-01 02:40:00    3278.298930\n",
-              "2018-01-01 03:40:00    3664.246679\n",
-              "2018-01-01 04:40:00    3867.014933\n",
-              "Name: J, dtype: float64"
-            ]
-          },
-          "execution_count": 6,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>2018-01-01 00:40:00</th>\n",
+       "      <th>2018-01-01 01:40:00</th>\n",
+       "      <th>2018-01-01 02:40:00</th>\n",
+       "      <th>2018-01-01 03:40:00</th>\n",
+       "      <th>2018-01-01 04:40:00</th>\n",
+       "      <th>2018-01-01 05:40:00</th>\n",
+       "      <th>2018-01-01 06:40:00</th>\n",
+       "      <th>2018-01-01 07:40:00</th>\n",
+       "      <th>2018-01-01 08:40:00</th>\n",
+       "      <th>2018-01-01 09:40:00</th>\n",
+       "      <th>...</th>\n",
+       "      <th>2018-01-31 14:40:00</th>\n",
+       "      <th>2018-01-31 15:40:00</th>\n",
+       "      <th>2018-01-31 16:40:00</th>\n",
+       "      <th>2018-01-31 17:40:00</th>\n",
+       "      <th>2018-01-31 18:40:00</th>\n",
+       "      <th>2018-01-31 19:40:00</th>\n",
+       "      <th>2018-01-31 20:40:00</th>\n",
+       "      <th>2018-01-31 21:40:00</th>\n",
+       "      <th>2018-01-31 22:40:00</th>\n",
+       "      <th>2018-01-31 23:40:00</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0.0200</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0325</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0375</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0425</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.0475</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.01</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.06</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.07</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 743 columns</p>\n",
+       "</div>"
       ],
-      "source": [
-        "# Set water depth to 60 m\n",
-        "h = 60\n",
-        "\n",
-        "# Compute the energy flux  from the NDBC spectra data and water depth\n",
-        "J = wave.resource.energy_flux(ndbc_data, h)\n",
-        "J.head()"
+      "text/plain": [
+       "        2018-01-01 00:40:00  2018-01-01 01:40:00  2018-01-01 02:40:00  \\\n",
+       "0.0200                  0.0                  0.0                  0.0   \n",
+       "0.0325                  0.0                  0.0                  0.0   \n",
+       "0.0375                  0.0                  0.0                  0.0   \n",
+       "0.0425                  0.0                  0.0                  0.0   \n",
+       "0.0475                  0.0                  0.0                  0.0   \n",
+       "\n",
+       "        2018-01-01 03:40:00  2018-01-01 04:40:00  2018-01-01 05:40:00  \\\n",
+       "0.0200                  0.0                  0.0                 0.00   \n",
+       "0.0325                  0.0                  0.0                 0.00   \n",
+       "0.0375                  0.0                  0.0                 0.00   \n",
+       "0.0425                  0.0                  0.0                 0.00   \n",
+       "0.0475                  0.0                  0.0                 0.01   \n",
+       "\n",
+       "        2018-01-01 06:40:00  2018-01-01 07:40:00  2018-01-01 08:40:00  \\\n",
+       "0.0200                  0.0                  0.0                  0.0   \n",
+       "0.0325                  0.0                  0.0                  0.0   \n",
+       "0.0375                  0.0                  0.0                  0.0   \n",
+       "0.0425                  0.0                  0.0                  0.0   \n",
+       "0.0475                  0.0                  0.0                  0.0   \n",
+       "\n",
+       "        2018-01-01 09:40:00  ...  2018-01-31 14:40:00  2018-01-31 15:40:00  \\\n",
+       "0.0200                  0.0  ...                 0.00                  0.0   \n",
+       "0.0325                  0.0  ...                 0.00                  0.0   \n",
+       "0.0375                  0.0  ...                 0.00                  0.0   \n",
+       "0.0425                  0.0  ...                 0.00                  0.0   \n",
+       "0.0475                  0.0  ...                 0.06                  0.0   \n",
+       "\n",
+       "        2018-01-31 16:40:00  2018-01-31 17:40:00  2018-01-31 18:40:00  \\\n",
+       "0.0200                  0.0                  0.0                 0.00   \n",
+       "0.0325                  0.0                  0.0                 0.00   \n",
+       "0.0375                  0.0                  0.0                 0.00   \n",
+       "0.0425                  0.0                  0.0                 0.00   \n",
+       "0.0475                  0.0                  0.0                 0.07   \n",
+       "\n",
+       "        2018-01-31 19:40:00  2018-01-31 20:40:00  2018-01-31 21:40:00  \\\n",
+       "0.0200                  0.0                  0.0                  0.0   \n",
+       "0.0325                  0.0                  0.0                  0.0   \n",
+       "0.0375                  0.0                  0.0                  0.0   \n",
+       "0.0425                  0.0                  0.0                  0.0   \n",
+       "0.0475                  0.0                  0.0                  0.0   \n",
+       "\n",
+       "        2018-01-31 22:40:00  2018-01-31 23:40:00  \n",
+       "0.0200                  0.0                  0.0  \n",
+       "0.0325                  0.0                  0.0  \n",
+       "0.0375                  0.0                  0.0  \n",
+       "0.0425                  0.0                  0.0  \n",
+       "0.0475                  0.0                  0.0  \n",
+       "\n",
+       "[5 rows x 743 columns]"
       ]
-    },
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Transpose raw NDBC data\n",
+    "ndbc_data = raw_ndbc_data.T\n",
+    "ndbc_data.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Compute Wave Metrics \n",
+    "We will now use MHKiT to compute the significant wave height, energy period, and energy flux. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "### Note on data types\n",
-        "MHKiT functions typically allow Pandas Series, Pandas DataFrame, or xarray DataArray input. Multidimensional data (DataFrames and DataArrays) typically require an index or dimension name to specify the frequency or time dimension in question. If not supplied, the first dimension is assumed to be the relevant dimension.\n",
-        "\n",
-        "The above results (energy period, energy flux, and significant wave height) were returned as Pandas Series. 2D wave spectral data (frequency x time) was input and the frequency dimension was reduced leaving 1D, columnar data as the output. In Pandas, this is represented as a Series. If a DataArray with 3 or more dimensions was input, the output would be a DataArray with one fewer dimensions."
+     "data": {
+      "text/plain": [
+       "variable\n",
+       "2018-01-01 00:40:00    7.458731\n",
+       "2018-01-01 01:40:00    7.682413\n",
+       "2018-01-01 02:40:00    7.498263\n",
+       "2018-01-01 03:40:00    7.676198\n",
+       "2018-01-01 04:40:00    7.669476\n",
+       "Name: Te, dtype: float64"
       ]
-    },
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compute the enegy periods from the NDBC spectra data\n",
+    "Te = wave.resource.energy_period(ndbc_data)\n",
+    "Te.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Generate Random Power Data\n",
-        "\n",
-        "For demonstration purposes, this example uses synthetic power data generated from statistical distributions.  In a real application, the user would provide power values from a WEC.  The data is stored in pandas Series, containing 743 points. "
+     "data": {
+      "text/plain": [
+       "variable\n",
+       "2018-01-01 00:40:00    0.939574\n",
+       "2018-01-01 01:40:00    1.001399\n",
+       "2018-01-01 02:40:00    0.924770\n",
+       "2018-01-01 03:40:00    0.962497\n",
+       "2018-01-01 04:40:00    0.989949\n",
+       "Name: Hm0, dtype: float64"
       ]
-    },
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Compute the significant wave height from the NDBC spectra data\n",
+    "Hm0 = wave.resource.significant_wave_height(ndbc_data)\n",
+    "Hm0.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 7,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Set the random seed, to reproduce results\n",
-        "np.random.seed(1)\n",
-        "# Generate random power values\n",
-        "P = pd.Series(np.random.normal(200, 40, 743), index=J.index)"
+     "data": {
+      "text/plain": [
+       "variable\n",
+       "2018-01-01 00:40:00    3354.825613\n",
+       "2018-01-01 01:40:00    3916.541523\n",
+       "2018-01-01 02:40:00    3278.298930\n",
+       "2018-01-01 03:40:00    3664.246679\n",
+       "2018-01-01 04:40:00    3867.014933\n",
+       "Name: J, dtype: float64"
       ]
-    },
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Set water depth to 60 m\n",
+    "h = 60\n",
+    "\n",
+    "# Compute the energy flux  from the NDBC spectra data and water depth\n",
+    "J = wave.resource.energy_flux(ndbc_data, h)\n",
+    "J.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Note on data types\n",
+    "MHKiT functions typically allow Pandas Series, Pandas DataFrame, or xarray DataArray input. Multidimensional data (DataFrames and DataArrays) typically require an index or dimension name to specify the frequency or time dimension in question. If not supplied, the first dimension is assumed to be the relevant dimension.\n",
+    "\n",
+    "The above results (energy period, energy flux, and significant wave height) were returned as Pandas Series. 2D wave spectral data (frequency x time) was input and the frequency dimension was reduced leaving 1D, columnar data as the output. In Pandas, this is represented as a Series. If a DataArray with 3 or more dimensions was input, the output would be a DataArray with one fewer dimensions."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Generate Random Power Data\n",
+    "\n",
+    "For demonstration purposes, this example uses synthetic power data generated from statistical distributions.  In a real application, the user would provide power values from a WEC.  The data is stored in pandas Series, containing 743 points. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Set the random seed, to reproduce results\n",
+    "np.random.seed(1)\n",
+    "# Generate random power values\n",
+    "P = pd.Series(np.random.normal(200, 40, 743), index=J.index)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Capture Width Matrices\n",
+    "\n",
+    "The following operations create capture width matrices, as specified by the IEC TS 62600-100.  But first, we need to calculate capture width and define bin centers.  The mean capture width matrix is printed below.  Keep in mind that this data has been artificially generated, so it may not be representative of what a real-world scatter diagram would look like."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Capture Length Matrices\n",
-        "\n",
-        "The following operations create capture length matrices, as specified by the IEC/TS 62600-100.  But first, we need to calculate capture length and define bin centers.  The mean capture length matrix is printed below.  Keep in mind that this data has been artificially generated, so it may not be representative of what a real-world scatter diagram would look like."
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\akeeste\\documents\\software\\github\\mhkit-python\\mhkit\\wave\\performance.py:141: UserWarning: Matrix bin widths are greater than the IEC TS 62600-100 limit of 1.0 seconds.\n",
+      "  warnings.warn(\"Matrix bin widths are greater than the IEC TS 62600-100 limit of 1.0 seconds.\")\n",
+      "c:\\users\\akeeste\\documents\\software\\github\\mhkit-python\\mhkit\\wave\\performance.py:141: UserWarning: Matrix bin widths are greater than the IEC TS 62600-100 limit of 1.0 seconds.\n",
+      "  warnings.warn(\"Matrix bin widths are greater than the IEC TS 62600-100 limit of 1.0 seconds.\")\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 8,
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-              "      <td>NaN</td>\n",
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-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>0.000418</td>\n",
-              "      <td>0.000405</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9.0</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9.5</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>0.000153</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>10.0</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>0.000281</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>10.5</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>0.000204</td>\n",
-              "      <td>0.000225</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "y_centers  0.0   1.0   2.0   3.0   4.0   5.0       6.0       7.0       8.0   \\\n",
-              "x_centers                                                                     \n",
-              "0.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
-              "0.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN  0.120286  0.053376   \n",
-              "1.0         NaN   NaN   NaN   NaN   NaN   NaN  0.110686  0.068070  0.049452   \n",
-              "1.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN  0.019749  0.018673   \n",
-              "2.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN  0.013882  0.012547   \n",
-              "2.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN  0.007244   \n",
-              "3.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN  0.004500  0.005660   \n",
-              "3.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN  0.003924   \n",
-              "4.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN  0.003185   \n",
-              "4.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN  0.002343   \n",
-              "5.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN  0.001913   \n",
-              "5.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN  0.002101   \n",
-              "6.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
-              "6.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
-              "7.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
-              "7.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
-              "8.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
-              "8.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
-              "9.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
-              "9.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
-              "10.0        NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
-              "10.5        NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
-              "\n",
-              "y_centers      9.0       10.0      11.0      12.0      13.0      14.0  \\\n",
-              "x_centers                                                               \n",
-              "0.0             NaN       NaN       NaN       NaN       NaN       NaN   \n",
-              "0.5             NaN       NaN       NaN       NaN       NaN       NaN   \n",
-              "1.0        0.065912       NaN  0.056593  0.029950  0.017234       NaN   \n",
-              "1.5             NaN       NaN  0.012473  0.011205  0.012307  0.010432   \n",
-              "2.0        0.009672  0.008770  0.008585  0.007525  0.005272  0.007809   \n",
-              "2.5        0.006488  0.005788  0.005652  0.005180  0.004260  0.003623   \n",
-              "3.0        0.004691  0.004109  0.003952  0.003104  0.003408  0.002291   \n",
-              "3.5        0.003674  0.003020  0.002746  0.002247  0.002000  0.002257   \n",
-              "4.0        0.002513  0.002386  0.002147  0.002246  0.001605  0.001730   \n",
-              "4.5        0.002087  0.001919  0.001590  0.001438       NaN       NaN   \n",
-              "5.0        0.001720  0.001716  0.001411  0.001219  0.001345       NaN   \n",
-              "5.5        0.001516  0.001331  0.000902  0.001033       NaN       NaN   \n",
-              "6.0        0.001097  0.000895       NaN  0.000858  0.000987       NaN   \n",
-              "6.5        0.000837  0.001024  0.000419       NaN  0.000688       NaN   \n",
-              "7.0             NaN       NaN       NaN  0.000461  0.000633       NaN   \n",
-              "7.5             NaN  0.000553       NaN       NaN  0.000312  0.000437   \n",
-              "8.0             NaN       NaN       NaN       NaN       NaN  0.000443   \n",
-              "8.5             NaN       NaN       NaN       NaN       NaN       NaN   \n",
-              "9.0             NaN       NaN       NaN       NaN       NaN       NaN   \n",
-              "9.5             NaN       NaN       NaN       NaN       NaN       NaN   \n",
-              "10.0            NaN       NaN       NaN       NaN       NaN       NaN   \n",
-              "10.5            NaN       NaN       NaN       NaN       NaN       NaN   \n",
-              "\n",
-              "y_centers      15.0      16.0  \n",
-              "x_centers                      \n",
-              "0.0             NaN       NaN  \n",
-              "0.5             NaN       NaN  \n",
-              "1.0             NaN       NaN  \n",
-              "1.5             NaN       NaN  \n",
-              "2.0             NaN       NaN  \n",
-              "2.5        0.004509       NaN  \n",
-              "3.0        0.001792       NaN  \n",
-              "3.5        0.002033       NaN  \n",
-              "4.0             NaN       NaN  \n",
-              "4.5             NaN       NaN  \n",
-              "5.0             NaN       NaN  \n",
-              "5.5             NaN       NaN  \n",
-              "6.0             NaN       NaN  \n",
-              "6.5             NaN       NaN  \n",
-              "7.0             NaN       NaN  \n",
-              "7.5             NaN       NaN  \n",
-              "8.0        0.000351       NaN  \n",
-              "8.5        0.000418  0.000405  \n",
-              "9.0             NaN       NaN  \n",
-              "9.5        0.000153       NaN  \n",
-              "10.0            NaN  0.000281  \n",
-              "10.5       0.000204  0.000225  "
-            ]
-          },
-          "execution_count": 8,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
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+       "      <th>0.0</th>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>0.5</th>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>1.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.110686</td>\n",
+       "      <td>0.068070</td>\n",
+       "      <td>0.049452</td>\n",
+       "      <td>0.065912</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.056593</td>\n",
+       "      <td>0.029950</td>\n",
+       "      <td>0.017234</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>1.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.019749</td>\n",
+       "      <td>0.018673</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.012473</td>\n",
+       "      <td>0.011205</td>\n",
+       "      <td>0.012307</td>\n",
+       "      <td>0.010432</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.013882</td>\n",
+       "      <td>0.012547</td>\n",
+       "      <td>0.009672</td>\n",
+       "      <td>0.008770</td>\n",
+       "      <td>0.008585</td>\n",
+       "      <td>0.007525</td>\n",
+       "      <td>0.005272</td>\n",
+       "      <td>0.007809</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>0.004260</td>\n",
+       "      <td>0.003623</td>\n",
+       "      <td>0.004509</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>0.004109</td>\n",
+       "      <td>0.003952</td>\n",
+       "      <td>0.003104</td>\n",
+       "      <td>0.003408</td>\n",
+       "      <td>0.002291</td>\n",
+       "      <td>0.001792</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>3.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.003924</td>\n",
+       "      <td>0.003674</td>\n",
+       "      <td>0.003020</td>\n",
+       "      <td>0.002746</td>\n",
+       "      <td>0.002247</td>\n",
+       "      <td>0.002000</td>\n",
+       "      <td>0.002257</td>\n",
+       "      <td>0.002033</td>\n",
+       "      <td>NaN</td>\n",
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+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.002343</td>\n",
+       "      <td>0.002087</td>\n",
+       "      <td>0.001919</td>\n",
+       "      <td>0.001590</td>\n",
+       "      <td>0.001438</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <th>5.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
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+       "      <td>0.001411</td>\n",
+       "      <td>0.001219</td>\n",
+       "      <td>0.001345</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.002101</td>\n",
+       "      <td>0.001516</td>\n",
+       "      <td>0.001331</td>\n",
+       "      <td>0.000902</td>\n",
+       "      <td>0.001033</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.001097</td>\n",
+       "      <td>0.000895</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000858</td>\n",
+       "      <td>0.000987</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000837</td>\n",
+       "      <td>0.001024</td>\n",
+       "      <td>0.000419</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000688</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000461</td>\n",
+       "      <td>0.000633</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000553</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000312</td>\n",
+       "      <td>0.000437</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000443</td>\n",
+       "      <td>0.000351</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000418</td>\n",
+       "      <td>0.000405</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000153</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000281</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>0.000204</td>\n",
+       "      <td>0.000225</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "# Calculate capture length\n",
-        "L = wave.performance.capture_length(P, J)\n",
-        "\n",
-        "# Generate bins for Hm0 and Te, input format (start, stop, step_size)\n",
-        "Hm0_bins = np.arange(0, Hm0.values.max() + 0.5, 0.5)\n",
-        "Te_bins = np.arange(0, Te.values.max() + 1, 1)\n",
-        "\n",
-        "# Create capture length matrices using mean, standard deviation, count, min and max statistics\n",
-        "LM_mean = wave.performance.capture_length_matrix(Hm0, Te, L, \"mean\", Hm0_bins, Te_bins)\n",
-        "LM_std = wave.performance.capture_length_matrix(Hm0, Te, L, \"std\", Hm0_bins, Te_bins)\n",
-        "LM_count = wave.performance.capture_length_matrix(\n",
-        "    Hm0, Te, L, \"count\", Hm0_bins, Te_bins\n",
-        ")\n",
-        "LM_min = wave.performance.capture_length_matrix(Hm0, Te, L, \"min\", Hm0_bins, Te_bins)\n",
-        "LM_max = wave.performance.capture_length_matrix(Hm0, Te, L, \"max\", Hm0_bins, Te_bins)\n",
-        "\n",
-        "# Show mean capture length matrix\n",
-        "LM_mean"
+      "text/plain": [
+       "y_centers  0.0   1.0   2.0   3.0   4.0   5.0       6.0       7.0       8.0   \\\n",
+       "x_centers                                                                     \n",
+       "0.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
+       "0.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN  0.120286  0.053376   \n",
+       "1.0         NaN   NaN   NaN   NaN   NaN   NaN  0.110686  0.068070  0.049452   \n",
+       "1.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN  0.019749  0.018673   \n",
+       "2.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN  0.013882  0.012547   \n",
+       "2.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN  0.007244   \n",
+       "3.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN  0.004500  0.005660   \n",
+       "3.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN  0.003924   \n",
+       "4.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN  0.003185   \n",
+       "4.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN  0.002343   \n",
+       "5.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN  0.001913   \n",
+       "5.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN  0.002101   \n",
+       "6.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
+       "6.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
+       "7.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
+       "7.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
+       "8.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
+       "8.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
+       "9.0         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
+       "9.5         NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
+       "10.0        NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
+       "10.5        NaN   NaN   NaN   NaN   NaN   NaN       NaN       NaN       NaN   \n",
+       "\n",
+       "y_centers      9.0       10.0      11.0      12.0      13.0      14.0  \\\n",
+       "x_centers                                                               \n",
+       "0.0             NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "0.5             NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "1.0        0.065912       NaN  0.056593  0.029950  0.017234       NaN   \n",
+       "1.5             NaN       NaN  0.012473  0.011205  0.012307  0.010432   \n",
+       "2.0        0.009672  0.008770  0.008585  0.007525  0.005272  0.007809   \n",
+       "2.5        0.006488  0.005788  0.005652  0.005180  0.004260  0.003623   \n",
+       "3.0        0.004691  0.004109  0.003952  0.003104  0.003408  0.002291   \n",
+       "3.5        0.003674  0.003020  0.002746  0.002247  0.002000  0.002257   \n",
+       "4.0        0.002513  0.002386  0.002147  0.002246  0.001605  0.001730   \n",
+       "4.5        0.002087  0.001919  0.001590  0.001438       NaN       NaN   \n",
+       "5.0        0.001720  0.001716  0.001411  0.001219  0.001345       NaN   \n",
+       "5.5        0.001516  0.001331  0.000902  0.001033       NaN       NaN   \n",
+       "6.0        0.001097  0.000895       NaN  0.000858  0.000987       NaN   \n",
+       "6.5        0.000837  0.001024  0.000419       NaN  0.000688       NaN   \n",
+       "7.0             NaN       NaN       NaN  0.000461  0.000633       NaN   \n",
+       "7.5             NaN  0.000553       NaN       NaN  0.000312  0.000437   \n",
+       "8.0             NaN       NaN       NaN       NaN       NaN  0.000443   \n",
+       "8.5             NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "9.0             NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "9.5             NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "10.0            NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "10.5            NaN       NaN       NaN       NaN       NaN       NaN   \n",
+       "\n",
+       "y_centers      15.0      16.0  \n",
+       "x_centers                      \n",
+       "0.0             NaN       NaN  \n",
+       "0.5             NaN       NaN  \n",
+       "1.0             NaN       NaN  \n",
+       "1.5             NaN       NaN  \n",
+       "2.0             NaN       NaN  \n",
+       "2.5        0.004509       NaN  \n",
+       "3.0        0.001792       NaN  \n",
+       "3.5        0.002033       NaN  \n",
+       "4.0             NaN       NaN  \n",
+       "4.5             NaN       NaN  \n",
+       "5.0             NaN       NaN  \n",
+       "5.5             NaN       NaN  \n",
+       "6.0             NaN       NaN  \n",
+       "6.5             NaN       NaN  \n",
+       "7.0             NaN       NaN  \n",
+       "7.5             NaN       NaN  \n",
+       "8.0        0.000351       NaN  \n",
+       "8.5        0.000418  0.000405  \n",
+       "9.0             NaN       NaN  \n",
+       "9.5        0.000153       NaN  \n",
+       "10.0            NaN  0.000281  \n",
+       "10.5       0.000204  0.000225  "
       ]
-    },
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate capture width\n",
+    "CW = wave.performance.capture_width(P, J)\n",
+    "\n",
+    "# Generate bins for Hm0 and Te, input format (start, stop, step_size)\n",
+    "Hm0_bins = np.arange(0, Hm0.values.max() + 0.5, 0.5)\n",
+    "Te_bins = np.arange(0, Te.values.max() + 1, 1)\n",
+    "\n",
+    "# Create capture width matrices using mean, standard deviation, count, min and max statistics\n",
+    "CWM_mean = wave.performance.capture_width_matrix(Hm0, Te, CW, \"mean\", Hm0_bins, Te_bins)\n",
+    "CWM_std = wave.performance.capture_width_matrix(Hm0, Te, CW, \"std\", Hm0_bins, Te_bins)\n",
+    "CWM_count = wave.performance.capture_width_matrix(\n",
+    "    Hm0, Te, CW, \"count\", Hm0_bins, Te_bins\n",
+    ")\n",
+    "CWM_min = wave.performance.capture_width_matrix(Hm0, Te, CW, \"min\", Hm0_bins, Te_bins)\n",
+    "CWM_max = wave.performance.capture_width_matrix(Hm0, Te, CW, \"max\", Hm0_bins, Te_bins)\n",
+    "\n",
+    "# Show mean capture width matrix\n",
+    "CWM_mean"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Additional capture width matrices can be computed, for example, the frequency matrix is computed below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Additional capture length matrices can be computed, for example, the frequency matrix is computed below."
-      ]
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\akeeste\\documents\\software\\github\\mhkit-python\\mhkit\\wave\\performance.py:141: UserWarning: Matrix bin widths are greater than the IEC TS 62600-100 limit of 1.0 seconds.\n",
+      "  warnings.warn(\"Matrix bin widths are greater than the IEC TS 62600-100 limit of 1.0 seconds.\")\n"
+     ]
     },
     {
-      "cell_type": "code",
-      "execution_count": 9,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
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-              "<table border=\"1\" class=\"dataframe\">\n",
-              "  <thead>\n",
-              "    <tr style=\"text-align: right;\">\n",
-              "      <th>y_centers</th>\n",
-              "      <th>0.0</th>\n",
-              "      <th>1.0</th>\n",
-              "      <th>2.0</th>\n",
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-              "      <th>8.0</th>\n",
-              "      <th>9.0</th>\n",
-              "      <th>10.0</th>\n",
-              "      <th>11.0</th>\n",
-              "      <th>12.0</th>\n",
-              "      <th>13.0</th>\n",
-              "      <th>14.0</th>\n",
-              "      <th>15.0</th>\n",
-              "      <th>16.0</th>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>x_centers</th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
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-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "      <th></th>\n",
-              "    </tr>\n",
-              "  </thead>\n",
-              "  <tbody>\n",
-              "    <tr>\n",
-              "      <th>0.0</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>0.5</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1.0</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.006729</td>\n",
-              "      <td>0.004038</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1.5</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.005384</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.009421</td>\n",
-              "      <td>0.004038</td>\n",
-              "      <td>0.006729</td>\n",
-              "      <td>0.005384</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2.0</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.005384</td>\n",
-              "      <td>0.018843</td>\n",
-              "      <td>0.018843</td>\n",
-              "      <td>0.029610</td>\n",
-              "      <td>0.021534</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2.5</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.013459</td>\n",
-              "      <td>0.052490</td>\n",
-              "      <td>0.055182</td>\n",
-              "      <td>0.018843</td>\n",
-              "      <td>0.025572</td>\n",
-              "      <td>0.022880</td>\n",
-              "      <td>0.005384</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3.0</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.021534</td>\n",
-              "      <td>0.044415</td>\n",
-              "      <td>0.047106</td>\n",
-              "      <td>0.020188</td>\n",
-              "      <td>0.012113</td>\n",
-              "      <td>0.010767</td>\n",
-              "      <td>0.010767</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3.5</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.006729</td>\n",
-              "      <td>0.040377</td>\n",
-              "      <td>0.029610</td>\n",
-              "      <td>0.047106</td>\n",
-              "      <td>0.004038</td>\n",
-              "      <td>0.008075</td>\n",
-              "      <td>0.004038</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4.0</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.009421</td>\n",
-              "      <td>0.017497</td>\n",
-              "      <td>0.029610</td>\n",
-              "      <td>0.040377</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.004038</td>\n",
-              "      <td>0.005384</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4.5</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.016151</td>\n",
-              "      <td>0.013459</td>\n",
-              "      <td>0.017497</td>\n",
-              "      <td>0.022880</td>\n",
-              "      <td>0.012113</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5.0</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.008075</td>\n",
-              "      <td>0.008075</td>\n",
-              "      <td>0.010767</td>\n",
-              "      <td>0.022880</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5.5</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.012113</td>\n",
-              "      <td>0.006729</td>\n",
-              "      <td>0.004038</td>\n",
-              "      <td>0.014805</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6.0</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.005384</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6.5</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7.0</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.004038</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7.5</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.008075</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8.0</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.002692</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8.5</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.001346</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9.0</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9.5</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.000000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>10.0</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.001346</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>10.5</th>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.0</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.000000</td>\n",
-              "      <td>0.001346</td>\n",
-              "      <td>0.001346</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "y_centers  0.0   1.0   2.0   3.0   4.0   5.0       6.0       7.0       8.0   \\\n",
-              "x_centers                                                                     \n",
-              "0.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
-              "0.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.002692  0.001346   \n",
-              "1.0         0.0   0.0   0.0   0.0   0.0   0.0  0.001346  0.006729  0.004038   \n",
-              "1.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.005384  0.002692   \n",
-              "2.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.002692  0.005384   \n",
-              "2.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.013459   \n",
-              "3.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.001346  0.021534   \n",
-              "3.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.006729   \n",
-              "4.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.009421   \n",
-              "4.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.016151   \n",
-              "5.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.002692   \n",
-              "5.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.001346   \n",
-              "6.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
-              "6.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
-              "7.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
-              "7.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
-              "8.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
-              "8.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
-              "9.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
-              "9.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
-              "10.0        0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
-              "10.5        0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
-              "\n",
-              "y_centers      9.0       10.0      11.0      12.0      13.0      14.0  \\\n",
-              "x_centers                                                               \n",
-              "0.0        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
-              "0.5        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
-              "1.0        0.001346  0.000000  0.002692  0.002692  0.001346  0.000000   \n",
-              "1.5        0.000000  0.000000  0.009421  0.004038  0.006729  0.005384   \n",
-              "2.0        0.018843  0.018843  0.029610  0.021534  0.001346  0.002692   \n",
-              "2.5        0.052490  0.055182  0.018843  0.025572  0.022880  0.005384   \n",
-              "3.0        0.044415  0.047106  0.020188  0.012113  0.010767  0.010767   \n",
-              "3.5        0.040377  0.029610  0.047106  0.004038  0.008075  0.004038   \n",
-              "4.0        0.017497  0.029610  0.040377  0.002692  0.004038  0.005384   \n",
-              "4.5        0.013459  0.017497  0.022880  0.012113  0.000000  0.000000   \n",
-              "5.0        0.008075  0.008075  0.010767  0.022880  0.001346  0.000000   \n",
-              "5.5        0.012113  0.006729  0.004038  0.014805  0.000000  0.000000   \n",
-              "6.0        0.002692  0.002692  0.000000  0.005384  0.001346  0.000000   \n",
-              "6.5        0.002692  0.002692  0.001346  0.000000  0.002692  0.000000   \n",
-              "7.0        0.000000  0.000000  0.000000  0.001346  0.004038  0.000000   \n",
-              "7.5        0.000000  0.001346  0.000000  0.000000  0.001346  0.008075   \n",
-              "8.0        0.000000  0.000000  0.000000  0.000000  0.000000  0.002692   \n",
-              "8.5        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
-              "9.0        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
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-              "\n",
-              "y_centers      15.0      16.0  \n",
-              "x_centers                      \n",
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-              "10.0       0.000000  0.001346  \n",
-              "10.5       0.001346  0.001346  "
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-          },
-          "execution_count": 9,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
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+       "      <td>0.001346</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10.0</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.001346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10.5</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.001346</td>\n",
+       "      <td>0.001346</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "# Create capture length matrices using frequency\n",
-        "LM_freq = wave.performance.capture_length_matrix(\n",
-        "    Hm0, Te, L, \"frequency\", Hm0_bins, Te_bins\n",
-        ")\n",
-        "\n",
-        "# Show capture length matrix using frequency\n",
-        "LM_freq"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The `capture_length_matrix` function can also be used as an arbitrary matrix generator. To do this, simply pass a different Series in the place of capture length (L).  For example, while not specified by the IEC standards, if the user doesn't have the omnidirectional wave flux, the average power matrix could hypothetically be generated in the following manner."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 10,
-      "metadata": {},
-      "outputs": [],
-      "source": [
-        "# Demonstration of arbitrary matrix generator\n",
-        "PM_mean_not_standard = wave.performance.capture_length_matrix(\n",
-        "    Hm0, Te, P, \"mean\", Hm0_bins, Te_bins\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The `capture_length_matrix` function can also use a callable function as the statistic argument. For example, suppose that we wanted to generate a matrix with the variance of the capture length.  We could achieve this by passing the NumPy variance function `np.var` into the `capture_length_matrix` function, as shown below."
+      "text/plain": [
+       "y_centers  0.0   1.0   2.0   3.0   4.0   5.0       6.0       7.0       8.0   \\\n",
+       "x_centers                                                                     \n",
+       "0.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
+       "0.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.002692  0.001346   \n",
+       "1.0         0.0   0.0   0.0   0.0   0.0   0.0  0.001346  0.006729  0.004038   \n",
+       "1.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.005384  0.002692   \n",
+       "2.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.002692  0.005384   \n",
+       "2.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.013459   \n",
+       "3.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.001346  0.021534   \n",
+       "3.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.006729   \n",
+       "4.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.009421   \n",
+       "4.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.016151   \n",
+       "5.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.002692   \n",
+       "5.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.001346   \n",
+       "6.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
+       "6.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
+       "7.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
+       "7.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
+       "8.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
+       "8.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
+       "9.0         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
+       "9.5         0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
+       "10.0        0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
+       "10.5        0.0   0.0   0.0   0.0   0.0   0.0  0.000000  0.000000  0.000000   \n",
+       "\n",
+       "y_centers      9.0       10.0      11.0      12.0      13.0      14.0  \\\n",
+       "x_centers                                                               \n",
+       "0.0        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
+       "0.5        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
+       "1.0        0.001346  0.000000  0.002692  0.002692  0.001346  0.000000   \n",
+       "1.5        0.000000  0.000000  0.009421  0.004038  0.006729  0.005384   \n",
+       "2.0        0.018843  0.018843  0.029610  0.021534  0.001346  0.002692   \n",
+       "2.5        0.052490  0.055182  0.018843  0.025572  0.022880  0.005384   \n",
+       "3.0        0.044415  0.047106  0.020188  0.012113  0.010767  0.010767   \n",
+       "3.5        0.040377  0.029610  0.047106  0.004038  0.008075  0.004038   \n",
+       "4.0        0.017497  0.029610  0.040377  0.002692  0.004038  0.005384   \n",
+       "4.5        0.013459  0.017497  0.022880  0.012113  0.000000  0.000000   \n",
+       "5.0        0.008075  0.008075  0.010767  0.022880  0.001346  0.000000   \n",
+       "5.5        0.012113  0.006729  0.004038  0.014805  0.000000  0.000000   \n",
+       "6.0        0.002692  0.002692  0.000000  0.005384  0.001346  0.000000   \n",
+       "6.5        0.002692  0.002692  0.001346  0.000000  0.002692  0.000000   \n",
+       "7.0        0.000000  0.000000  0.000000  0.001346  0.004038  0.000000   \n",
+       "7.5        0.000000  0.001346  0.000000  0.000000  0.001346  0.008075   \n",
+       "8.0        0.000000  0.000000  0.000000  0.000000  0.000000  0.002692   \n",
+       "8.5        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
+       "9.0        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
+       "9.5        0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
+       "10.0       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
+       "10.5       0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
+       "\n",
+       "y_centers      15.0      16.0  \n",
+       "x_centers                      \n",
+       "0.0        0.000000  0.000000  \n",
+       "0.5        0.000000  0.000000  \n",
+       "1.0        0.000000  0.000000  \n",
+       "1.5        0.000000  0.000000  \n",
+       "2.0        0.000000  0.000000  \n",
+       "2.5        0.001346  0.000000  \n",
+       "3.0        0.001346  0.000000  \n",
+       "3.5        0.001346  0.000000  \n",
+       "4.0        0.000000  0.000000  \n",
+       "4.5        0.000000  0.000000  \n",
+       "5.0        0.000000  0.000000  \n",
+       "5.5        0.000000  0.000000  \n",
+       "6.0        0.000000  0.000000  \n",
+       "6.5        0.000000  0.000000  \n",
+       "7.0        0.000000  0.000000  \n",
+       "7.5        0.000000  0.000000  \n",
+       "8.0        0.002692  0.000000  \n",
+       "8.5        0.001346  0.001346  \n",
+       "9.0        0.000000  0.000000  \n",
+       "9.5        0.001346  0.000000  \n",
+       "10.0       0.000000  0.001346  \n",
+       "10.5       0.001346  0.001346  "
       ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 11,
-      "metadata": {
-        "scrolled": true
-      },
-      "outputs": [],
-      "source": [
-        "# Demonstration of passing a callable function to the matrix generator\n",
-        "LM_variance = wave.performance.capture_length_matrix(\n",
-        "    Hm0, Te, L, np.var, Hm0_bins, Te_bins\n",
-        ")"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Power Matrices\n",
-        "As specified in IEC/TS 62600-100, the power matrix is generated from the capture length matrix and wave energy flux matrix, as shown below"
-      ]
-    },
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create capture width matrices using frequency\n",
+    "CWM_freq = wave.performance.capture_width_matrix(\n",
+    "    Hm0, Te, CW, \"frequency\", Hm0_bins, Te_bins\n",
+    ")\n",
+    "\n",
+    "# Show capture width matrix using frequency\n",
+    "CWM_freq"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `capture_width_matrix` function can also be used as an arbitrary matrix generator. To do this, simply pass a different Series in the place of capture width (CW).  For example, while not specified by the IEC standards, if the user doesn't have the omnidirectional wave flux, the average power matrix could hypothetically be generated in the following manner."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 12,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
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-              "    <tr style=\"text-align: right;\">\n",
-              "      <th>y_centers</th>\n",
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-              "      <th>8.0</th>\n",
-              "      <th>9.0</th>\n",
-              "      <th>10.0</th>\n",
-              "      <th>11.0</th>\n",
-              "      <th>12.0</th>\n",
-              "      <th>13.0</th>\n",
-              "      <th>14.0</th>\n",
-              "      <th>15.0</th>\n",
-              "      <th>16.0</th>\n",
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-              "      <th>x_centers</th>\n",
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-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
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-              "      <th>0.5</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
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-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
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-              "      <td>117.594</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
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-              "    <tr>\n",
-              "      <th>1.0</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>212.762</td>\n",
-              "      <td>202.713</td>\n",
-              "      <td>188.707</td>\n",
-              "      <td>187.103</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>213.926</td>\n",
-              "      <td>174.154</td>\n",
-              "      <td>164.886</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>1.5</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>176.402</td>\n",
-              "      <td>199.802</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>201.883</td>\n",
-              "      <td>191.598</td>\n",
-              "      <td>221.705</td>\n",
-              "      <td>190.124</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2.0</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>203.667</td>\n",
-              "      <td>216.857</td>\n",
-              "      <td>192.965</td>\n",
-              "      <td>201.633</td>\n",
-              "      <td>216.268</td>\n",
-              "      <td>209.634</td>\n",
-              "      <td>162.569</td>\n",
-              "      <td>232.530</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>2.5</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>193.397</td>\n",
-              "      <td>203.529</td>\n",
-              "      <td>196.907</td>\n",
-              "      <td>212.883</td>\n",
-              "      <td>211.277</td>\n",
-              "      <td>202.760</td>\n",
-              "      <td>199.263</td>\n",
-              "      <td>272.421</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3.0</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>170.739</td>\n",
-              "      <td>216.459</td>\n",
-              "      <td>197.484</td>\n",
-              "      <td>200.895</td>\n",
-              "      <td>212.107</td>\n",
-              "      <td>193.837</td>\n",
-              "      <td>222.185</td>\n",
-              "      <td>169.497</td>\n",
-              "      <td>122.296</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>3.5</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>194.894</td>\n",
-              "      <td>214.108</td>\n",
-              "      <td>202.725</td>\n",
-              "      <td>206.901</td>\n",
-              "      <td>184.099</td>\n",
-              "      <td>186.077</td>\n",
-              "      <td>221.659</td>\n",
-              "      <td>186.201</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4.0</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>217.289</td>\n",
-              "      <td>189.403</td>\n",
-              "      <td>201.362</td>\n",
-              "      <td>207.532</td>\n",
-              "      <td>207.971</td>\n",
-              "      <td>172.771</td>\n",
-              "      <td>213.854</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>4.5</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>197.994</td>\n",
-              "      <td>194.238</td>\n",
-              "      <td>205.559</td>\n",
-              "      <td>203.195</td>\n",
-              "      <td>197.980</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5.0</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>198.149</td>\n",
-              "      <td>196.527</td>\n",
-              "      <td>222.219</td>\n",
-              "      <td>215.221</td>\n",
-              "      <td>204.002</td>\n",
-              "      <td>254.004</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>5.5</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>249.158</td>\n",
-              "      <td>212.561</td>\n",
-              "      <td>212.734</td>\n",
-              "      <td>168.655</td>\n",
-              "      <td>208.220</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6.0</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>182.314</td>\n",
-              "      <td>159.418</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>208.418</td>\n",
-              "      <td>241.347</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>6.5</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>164.712</td>\n",
-              "      <td>233.890</td>\n",
-              "      <td>110.517</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>207.919</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7.0</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>155.691</td>\n",
-              "      <td>229.022</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>7.5</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>166.855</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>128.897</td>\n",
-              "      <td>198.053</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8.0</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>230.281</td>\n",
-              "      <td>184.510</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>8.5</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>248.338</td>\n",
-              "      <td>264.534</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9.0</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>9.5</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>116.230</td>\n",
-              "      <td>NaN</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>10.0</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>244.634</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <th>10.5</th>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>NaN</td>\n",
-              "      <td>190.849</td>\n",
-              "      <td>212.411</td>\n",
-              "    </tr>\n",
-              "  </tbody>\n",
-              "</table>\n",
-              "</div>"
-            ],
-            "text/plain": [
-              "y_centers  0.0   1.0   2.0   3.0   4.0   5.0      6.0      7.0      8.0   \\\n",
-              "x_centers                                                                  \n",
-              "0.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
-              "0.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN  224.996  117.594   \n",
-              "1.0         NaN   NaN   NaN   NaN   NaN   NaN  212.762  202.713  188.707   \n",
-              "1.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN  176.402  199.802   \n",
-              "2.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN  203.667  216.857   \n",
-              "2.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN  193.397   \n",
-              "3.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN  170.739  216.459   \n",
-              "3.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN  194.894   \n",
-              "4.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN  217.289   \n",
-              "4.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN  197.994   \n",
-              "5.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN  198.149   \n",
-              "5.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN  249.158   \n",
-              "6.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
-              "6.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
-              "7.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
-              "7.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
-              "8.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
-              "8.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
-              "9.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
-              "9.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
-              "10.0        NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
-              "10.5        NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
-              "\n",
-              "y_centers     9.0      10.0     11.0     12.0     13.0     14.0     15.0  \\\n",
-              "x_centers                                                                  \n",
-              "0.0            NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
-              "0.5            NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
-              "1.0        187.103      NaN  213.926  174.154  164.886      NaN      NaN   \n",
-              "1.5            NaN      NaN  201.883  191.598  221.705  190.124      NaN   \n",
-              "2.0        192.965  201.633  216.268  209.634  162.569  232.530      NaN   \n",
-              "2.5        203.529  196.907  212.883  211.277  202.760  199.263  272.421   \n",
-              "3.0        197.484  200.895  212.107  193.837  222.185  169.497  122.296   \n",
-              "3.5        214.108  202.725  206.901  184.099  186.077  221.659  186.201   \n",
-              "4.0        189.403  201.362  207.532  207.971  172.771  213.854      NaN   \n",
-              "4.5        194.238  205.559  203.195  197.980      NaN      NaN      NaN   \n",
-              "5.0        196.527  222.219  215.221  204.002  254.004      NaN      NaN   \n",
-              "5.5        212.561  212.734  168.655  208.220      NaN      NaN      NaN   \n",
-              "6.0        182.314  159.418      NaN  208.418  241.347      NaN      NaN   \n",
-              "6.5        164.712  233.890  110.517      NaN  207.919      NaN      NaN   \n",
-              "7.0            NaN      NaN      NaN  155.691  229.022      NaN      NaN   \n",
-              "7.5            NaN  166.855      NaN      NaN  128.897  198.053      NaN   \n",
-              "8.0            NaN      NaN      NaN      NaN      NaN  230.281  184.510   \n",
-              "8.5            NaN      NaN      NaN      NaN      NaN      NaN  248.338   \n",
-              "9.0            NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
-              "9.5            NaN      NaN      NaN      NaN      NaN      NaN  116.230   \n",
-              "10.0           NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
-              "10.5           NaN      NaN      NaN      NaN      NaN      NaN  190.849   \n",
-              "\n",
-              "y_centers     16.0  \n",
-              "x_centers           \n",
-              "0.0            NaN  \n",
-              "0.5            NaN  \n",
-              "1.0            NaN  \n",
-              "1.5            NaN  \n",
-              "2.0            NaN  \n",
-              "2.5            NaN  \n",
-              "3.0            NaN  \n",
-              "3.5            NaN  \n",
-              "4.0            NaN  \n",
-              "4.5            NaN  \n",
-              "5.0            NaN  \n",
-              "5.5            NaN  \n",
-              "6.0            NaN  \n",
-              "6.5            NaN  \n",
-              "7.0            NaN  \n",
-              "7.5            NaN  \n",
-              "8.0            NaN  \n",
-              "8.5        264.534  \n",
-              "9.0            NaN  \n",
-              "9.5            NaN  \n",
-              "10.0       244.634  \n",
-              "10.5       212.411  "
-            ]
-          },
-          "execution_count": 12,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
-      "source": [
-        "# Create wave energy flux matrix using mean\n",
-        "JM = wave.performance.wave_energy_flux_matrix(Hm0, Te, J, \"mean\", Hm0_bins, Te_bins)\n",
-        "\n",
-        "# Create power matrix using mean\n",
-        "PM_mean = wave.performance.power_matrix(LM_mean, JM)\n",
-        "\n",
-        "# Create power matrix using standard deviation\n",
-        "PM_std = wave.performance.power_matrix(LM_std, JM)\n",
-        "\n",
-        "# Show mean power matrix, round to 3 decimals\n",
-        "PM_mean.round(3)"
-      ]
-    },
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\akeeste\\documents\\software\\github\\mhkit-python\\mhkit\\wave\\performance.py:141: UserWarning: Matrix bin widths are greater than the IEC TS 62600-100 limit of 1.0 seconds.\n",
+      "  warnings.warn(\"Matrix bin widths are greater than the IEC TS 62600-100 limit of 1.0 seconds.\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Demonstration of arbitrary matrix generator\n",
+    "PM_mean_not_standard = wave.performance.capture_width_matrix(\n",
+    "    Hm0, Te, P, \"mean\", Hm0_bins, Te_bins\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `capture_width_matrix` function can also use a callable function as the statistic argument. For example, suppose that we wanted to generate a matrix with the variance of the capture width.  We could achieve this by passing the NumPy variance function `np.var` into the `capture_width_matrix` function, as shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Calculate MAEP\n",
-        "There are two ways to calculate the mean annual energy production (MEAP).  One is from capture length and wave energy flux matrices, the other is from time-series data, as shown below."
-      ]
-    },
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\users\\akeeste\\documents\\software\\github\\mhkit-python\\mhkit\\wave\\performance.py:141: UserWarning: Matrix bin widths are greater than the IEC TS 62600-100 limit of 1.0 seconds.\n",
+      "  warnings.warn(\"Matrix bin widths are greater than the IEC TS 62600-100 limit of 1.0 seconds.\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Demonstration of passing a callable function to the matrix generator\n",
+    "CWM_variance = wave.performance.capture_width_matrix(\n",
+    "    Hm0, Te, CW, np.var, Hm0_bins, Te_bins\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Power Matrices\n",
+    "As specified in IEC TS 62600-100, the power matrix is generated from the capture width matrix and wave energy flux matrix, as shown below"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 13,
-      "metadata": {},
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "MAEP from timeseries =  1767087.527586333\n",
-            "MAEP from matrices =  1781210.8652839188\n"
-          ]
-        }
+     "data": {
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+       "      <th>13.0</th>\n",
+       "      <th>14.0</th>\n",
+       "      <th>15.0</th>\n",
+       "      <th>16.0</th>\n",
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+       "    <tr>\n",
+       "      <th>x_centers</th>\n",
+       "      <th></th>\n",
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+       "      <th>0.0</th>\n",
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+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>224.996</td>\n",
+       "      <td>117.594</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>212.762</td>\n",
+       "      <td>202.713</td>\n",
+       "      <td>188.707</td>\n",
+       "      <td>187.103</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>213.926</td>\n",
+       "      <td>174.154</td>\n",
+       "      <td>164.886</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>176.402</td>\n",
+       "      <td>199.802</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>201.883</td>\n",
+       "      <td>191.598</td>\n",
+       "      <td>221.705</td>\n",
+       "      <td>190.124</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>203.667</td>\n",
+       "      <td>216.857</td>\n",
+       "      <td>192.965</td>\n",
+       "      <td>201.633</td>\n",
+       "      <td>216.268</td>\n",
+       "      <td>209.634</td>\n",
+       "      <td>162.569</td>\n",
+       "      <td>232.530</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>193.397</td>\n",
+       "      <td>203.529</td>\n",
+       "      <td>196.907</td>\n",
+       "      <td>212.883</td>\n",
+       "      <td>211.277</td>\n",
+       "      <td>202.760</td>\n",
+       "      <td>199.263</td>\n",
+       "      <td>272.421</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>170.739</td>\n",
+       "      <td>216.459</td>\n",
+       "      <td>197.484</td>\n",
+       "      <td>200.895</td>\n",
+       "      <td>212.107</td>\n",
+       "      <td>193.837</td>\n",
+       "      <td>222.185</td>\n",
+       "      <td>169.497</td>\n",
+       "      <td>122.296</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>194.894</td>\n",
+       "      <td>214.108</td>\n",
+       "      <td>202.725</td>\n",
+       "      <td>206.901</td>\n",
+       "      <td>184.099</td>\n",
+       "      <td>186.077</td>\n",
+       "      <td>221.659</td>\n",
+       "      <td>186.201</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>217.289</td>\n",
+       "      <td>189.403</td>\n",
+       "      <td>201.362</td>\n",
+       "      <td>207.532</td>\n",
+       "      <td>207.971</td>\n",
+       "      <td>172.771</td>\n",
+       "      <td>213.854</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>197.994</td>\n",
+       "      <td>194.238</td>\n",
+       "      <td>205.559</td>\n",
+       "      <td>203.195</td>\n",
+       "      <td>197.980</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>198.149</td>\n",
+       "      <td>196.527</td>\n",
+       "      <td>222.219</td>\n",
+       "      <td>215.221</td>\n",
+       "      <td>204.002</td>\n",
+       "      <td>254.004</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>249.158</td>\n",
+       "      <td>212.561</td>\n",
+       "      <td>212.734</td>\n",
+       "      <td>168.655</td>\n",
+       "      <td>208.220</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>182.314</td>\n",
+       "      <td>159.418</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>208.418</td>\n",
+       "      <td>241.347</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>164.712</td>\n",
+       "      <td>233.890</td>\n",
+       "      <td>110.517</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>207.919</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>155.691</td>\n",
+       "      <td>229.022</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>166.855</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>128.897</td>\n",
+       "      <td>198.053</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>230.281</td>\n",
+       "      <td>184.510</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>248.338</td>\n",
+       "      <td>264.534</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>116.230</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10.0</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>244.634</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10.5</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>190.849</td>\n",
+       "      <td>212.411</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
       ],
-      "source": [
-        "# Calcaulte maep from timeseries\n",
-        "maep_timeseries = wave.performance.mean_annual_energy_production_timeseries(L, J)\n",
-        "print(\"MAEP from timeseries = \", maep_timeseries)\n",
-        "\n",
-        "# Calcaulte maep from matrix \n",
-        "# See Issue #339\n",
-        "# maep_matrix = wave.performance.mean_annual_energy_production_matrix(\n",
-        "#     LM_mean, JM, LM_freq\n",
-        "# )\n",
-        "\n",
-        "T = 8766  # Average length of a year (h)\n",
-        "maep_matrix = T * np.nansum(LM_mean * JM * LM_freq)\n",
-        "\n",
-        "print(\"MAEP from matrices = \", maep_matrix)"
+      "text/plain": [
+       "y_centers  0.0   1.0   2.0   3.0   4.0   5.0      6.0      7.0      8.0   \\\n",
+       "x_centers                                                                  \n",
+       "0.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
+       "0.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN  224.996  117.594   \n",
+       "1.0         NaN   NaN   NaN   NaN   NaN   NaN  212.762  202.713  188.707   \n",
+       "1.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN  176.402  199.802   \n",
+       "2.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN  203.667  216.857   \n",
+       "2.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN  193.397   \n",
+       "3.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN  170.739  216.459   \n",
+       "3.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN  194.894   \n",
+       "4.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN  217.289   \n",
+       "4.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN  197.994   \n",
+       "5.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN  198.149   \n",
+       "5.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN  249.158   \n",
+       "6.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
+       "6.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
+       "7.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
+       "7.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
+       "8.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
+       "8.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
+       "9.0         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
+       "9.5         NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
+       "10.0        NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
+       "10.5        NaN   NaN   NaN   NaN   NaN   NaN      NaN      NaN      NaN   \n",
+       "\n",
+       "y_centers     9.0      10.0     11.0     12.0     13.0     14.0     15.0  \\\n",
+       "x_centers                                                                  \n",
+       "0.0            NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
+       "0.5            NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
+       "1.0        187.103      NaN  213.926  174.154  164.886      NaN      NaN   \n",
+       "1.5            NaN      NaN  201.883  191.598  221.705  190.124      NaN   \n",
+       "2.0        192.965  201.633  216.268  209.634  162.569  232.530      NaN   \n",
+       "2.5        203.529  196.907  212.883  211.277  202.760  199.263  272.421   \n",
+       "3.0        197.484  200.895  212.107  193.837  222.185  169.497  122.296   \n",
+       "3.5        214.108  202.725  206.901  184.099  186.077  221.659  186.201   \n",
+       "4.0        189.403  201.362  207.532  207.971  172.771  213.854      NaN   \n",
+       "4.5        194.238  205.559  203.195  197.980      NaN      NaN      NaN   \n",
+       "5.0        196.527  222.219  215.221  204.002  254.004      NaN      NaN   \n",
+       "5.5        212.561  212.734  168.655  208.220      NaN      NaN      NaN   \n",
+       "6.0        182.314  159.418      NaN  208.418  241.347      NaN      NaN   \n",
+       "6.5        164.712  233.890  110.517      NaN  207.919      NaN      NaN   \n",
+       "7.0            NaN      NaN      NaN  155.691  229.022      NaN      NaN   \n",
+       "7.5            NaN  166.855      NaN      NaN  128.897  198.053      NaN   \n",
+       "8.0            NaN      NaN      NaN      NaN      NaN  230.281  184.510   \n",
+       "8.5            NaN      NaN      NaN      NaN      NaN      NaN  248.338   \n",
+       "9.0            NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
+       "9.5            NaN      NaN      NaN      NaN      NaN      NaN  116.230   \n",
+       "10.0           NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
+       "10.5           NaN      NaN      NaN      NaN      NaN      NaN  190.849   \n",
+       "\n",
+       "y_centers     16.0  \n",
+       "x_centers           \n",
+       "0.0            NaN  \n",
+       "0.5            NaN  \n",
+       "1.0            NaN  \n",
+       "1.5            NaN  \n",
+       "2.0            NaN  \n",
+       "2.5            NaN  \n",
+       "3.0            NaN  \n",
+       "3.5            NaN  \n",
+       "4.0            NaN  \n",
+       "4.5            NaN  \n",
+       "5.0            NaN  \n",
+       "5.5            NaN  \n",
+       "6.0            NaN  \n",
+       "6.5            NaN  \n",
+       "7.0            NaN  \n",
+       "7.5            NaN  \n",
+       "8.0            NaN  \n",
+       "8.5        264.534  \n",
+       "9.0            NaN  \n",
+       "9.5            NaN  \n",
+       "10.0       244.634  \n",
+       "10.5       212.411  "
       ]
-    },
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Create wave energy flux matrix using mean\n",
+    "JM = wave.performance.wave_energy_flux_matrix(Hm0, Te, J, \"mean\", Hm0_bins, Te_bins)\n",
+    "\n",
+    "# Create power matrix using mean\n",
+    "PM_mean = wave.performance.power_matrix(CWM_mean, JM)\n",
+    "\n",
+    "# Create power matrix using standard deviation\n",
+    "PM_std = wave.performance.power_matrix(CWM_std, JM)\n",
+    "\n",
+    "# Show mean power matrix, round to 3 decimals\n",
+    "PM_mean.round(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Calculate MAEP\n",
+    "There are two ways to calculate the mean annual energy production (MEAP).  One is from capture width and wave energy flux matrices, the other is from time-series data, as shown below."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "## Graphics\n",
-        "The graphics function `plot_matrix` can be used to visualize results.  It is important to note that the plotting function assumes the step size between bins to be linear."
-      ]
-    },
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "MAEP from timeseries =  1767087.527586333\n",
+      "MAEP from matrices =  1781210.8652839188\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Calcaulte maep from timeseries\n",
+    "maep_timeseries = wave.performance.mean_annual_energy_production_timeseries(CW, J)\n",
+    "print(\"MAEP from timeseries = \", maep_timeseries)\n",
+    "\n",
+    "# Calcaulte maep from matrix \n",
+    "# See Issue #339\n",
+    "# maep_matrix = wave.performance.mean_annual_energy_production_matrix(\n",
+    "#     CWM_mean, JM, CWM_freq\n",
+    "# )\n",
+    "\n",
+    "T = 8766  # Average length of a year (h)\n",
+    "maep_matrix = T * np.nansum(CWM_mean * JM * CWM_freq)\n",
+    "\n",
+    "print(\"MAEP from matrices = \", maep_matrix)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Graphics\n",
+    "The graphics function `plot_matrix` can be used to visualize results.  It is important to note that the plotting function assumes the step size between bins to be linear."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
     {
-      "cell_type": "code",
-      "execution_count": 14,
-      "metadata": {},
-      "outputs": [
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 640x480 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# Plot the capture length mean matrix\n",
-        "ax = wave.graphics.plot_matrix(LM_mean)"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 2 Axes>"
       ]
-    },
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Plot the capture width mean matrix\n",
+    "ax = wave.graphics.plot_matrix(CWM_mean)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The plotting function only requires the matrix as input, but the function can also take several other arguments.\n",
+    "The list of optional arguments is: `xlabel, ylabel, zlabel, show_values, and ax`. The following uses these optional arguments.  The matplotlib package is imported to define an axis with a larger figure size."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
     {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "The plotting function only requires the matrix as input, but the function can also take several other arguments.\n",
-        "The list of optional arguments is: `xlabel, ylabel, zlabel, show_values, and ax`. The following uses these optional arguments.  The matplotlib package is imported to define an axis with a larger figure size."
+     "data": {
+      "text/plain": [
+       "<Axes: xlabel='Te (s)', ylabel='Hm0 (m)'>"
       ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
     },
     {
-      "cell_type": "code",
-      "execution_count": 15,
-      "metadata": {
-        "scrolled": true
-      },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "<Axes: xlabel='Te (s)', ylabel='Hm0 (m)'>"
-            ]
-          },
-          "execution_count": 15,
-          "metadata": {},
-          "output_type": "execute_result"
-        },
-        {
-          "data": {
-            "image/png": 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",
-            "text/plain": [
-              "<Figure size 600x600 with 2 Axes>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
-      "source": [
-        "# Customize the matrix plot\n",
-        "import matplotlib.pylab as plt\n",
-        "\n",
-        "plt.figure(figsize=(6, 6))\n",
-        "ax = plt.gca()\n",
-        "wave.graphics.plot_matrix(\n",
-        "    PM_mean,\n",
-        "    xlabel=\"Te (s)\",\n",
-        "    ylabel=\"Hm0 (m)\",\n",
-        "    zlabel=\"Mean Power (kW)\",\n",
-        "    show_values=False,\n",
-        "    ax=ax,\n",
-        ")"
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x600 with 2 Axes>"
       ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     }
-  ],
-  "metadata": {
-    "kernelspec": {
-      "display_name": "base",
-      "language": "python",
-      "name": "python3"
-    },
-    "language_info": {
-      "codemirror_mode": {
-        "name": "ipython",
-        "version": 3
-      },
-      "file_extension": ".py",
-      "mimetype": "text/x-python",
-      "name": "python",
-      "nbconvert_exporter": "python",
-      "pygments_lexer": "ipython3",
-      "version": "3.11.7"
-    }
+   ],
+   "source": [
+    "# Customize the matrix plot\n",
+    "import matplotlib.pylab as plt\n",
+    "\n",
+    "plt.figure(figsize=(6, 6))\n",
+    "ax = plt.gca()\n",
+    "wave.graphics.plot_matrix(\n",
+    "    PM_mean,\n",
+    "    xlabel=\"Te (s)\",\n",
+    "    ylabel=\"Hm0 (m)\",\n",
+    "    zlabel=\"Mean Power (kW)\",\n",
+    "    show_values=False,\n",
+    "    ax=ax,\n",
+    ")"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
   },
-  "nbformat": 4,
-  "nbformat_minor": 4
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
 }
diff --git a/examples/wecsim_power_performance_example.ipynb b/examples/wecsim_power_performance_example.ipynb
new file mode 100644
index 000000000..f26ed0861
--- /dev/null
+++ b/examples/wecsim_power_performance_example.ipynb
@@ -0,0 +1,1363 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# MHKiT WEC-Sim Example\n",
+    "\n",
+    "This notebook demonstrates using the MHKiT wave module and [WEC-Sim](http://wec-sim.github.io/WEC-Sim/index.html) together to perform a resource characterization study in MHKiT, simulate representative cases with WEC-Sim, and visualize the results in MHKiT to estimate MAEP (Mean Annual Energy Production).\n",
+    "\n",
+    "  1. Characterize the available resource at a location\n",
+    "     - See the PacWave example notebook\n",
+    "  2. Write a WEC-Sim batch file for the given clusters\n",
+    "  3. Simulate the device *in WEC-Sim*.\n",
+    "     - Ensure that the spectra used in WEC-Sim is identical to the one used in MHKiT.\n",
+    "  4. Load WEC-Sim batch results\n",
+    "  5. Assess results and visualize quantities of interest\n",
+    "\n",
+    "This example uses WEC-Sim to simulate the [Oscillating Surge Wave Energy Converter (OSWEC)](https://wec-sim.github.io/WEC-Sim/main/user/tutorials.html#oscillating-surge-wec-oswec), a flap-type device.\n",
+    "\n",
+    "Start by importing MHKiT and the necessary python packages (e.g.`scipy.io`, `matplotlib.pyplot`, `pandas`, `numpy`)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from mhkit import wave\n",
+    "import scipy.io as sio\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "import numpy as np"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1. Characterize the available resource at a location\n",
+    "\n",
+    "This example will use an abbreviated version of `PacWave_resource_characterization_example.ipynb`. \n",
+    "For full details on downloading, calculating, and visualizing the k-means clusters representation of the site's wave resouce, see that example.\n",
+    "\n",
+    "We select the N=32 cluster as it's total energy flux is closet to the total energy flux of the site considering all wave conditions. We will load the PacWave example output, which can be easily saved after running the example with the command `results[32].to_csv(\"pacwave_cluster_32.csv\")`. We will start this example by reading in that csv output and formatting it for WEC-Sim."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Te</th>\n",
+       "      <th>Hm0</th>\n",
+       "      <th>weights</th>\n",
+       "      <th>Tp</th>\n",
+       "      <th>J</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.974491</td>\n",
+       "      <td>1.253970</td>\n",
+       "      <td>0.058861</td>\n",
+       "      <td>9.294279</td>\n",
+       "      <td>6031.115427</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.794533</td>\n",
+       "      <td>2.641403</td>\n",
+       "      <td>0.035216</td>\n",
+       "      <td>12.581041</td>\n",
+       "      <td>37004.325098</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.901979</td>\n",
+       "      <td>1.953122</td>\n",
+       "      <td>0.052001</td>\n",
+       "      <td>8.044264</td>\n",
+       "      <td>12516.214519</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12.667628</td>\n",
+       "      <td>7.310116</td>\n",
+       "      <td>0.005070</td>\n",
+       "      <td>14.764135</td>\n",
+       "      <td>367451.945581</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>12.893701</td>\n",
+       "      <td>2.262294</td>\n",
+       "      <td>0.016046</td>\n",
+       "      <td>15.027624</td>\n",
+       "      <td>36455.136139</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10.557621</td>\n",
+       "      <td>4.754297</td>\n",
+       "      <td>0.017311</td>\n",
+       "      <td>12.304920</td>\n",
+       "      <td>116784.361789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>8.766664</td>\n",
+       "      <td>2.739380</td>\n",
+       "      <td>0.043646</td>\n",
+       "      <td>10.217557</td>\n",
+       "      <td>31825.667989</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>6.537403</td>\n",
+       "      <td>1.305578</td>\n",
+       "      <td>0.050746</td>\n",
+       "      <td>7.619350</td>\n",
+       "      <td>5272.441394</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9.666291</td>\n",
+       "      <td>1.340694</td>\n",
+       "      <td>0.037070</td>\n",
+       "      <td>11.266073</td>\n",
+       "      <td>8443.649821</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>12.787307</td>\n",
+       "      <td>3.920397</td>\n",
+       "      <td>0.016464</td>\n",
+       "      <td>14.903621</td>\n",
+       "      <td>107680.986511</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11.605879</td>\n",
+       "      <td>1.821016</td>\n",
+       "      <td>0.022323</td>\n",
+       "      <td>13.526666</td>\n",
+       "      <td>19446.169168</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>7.584082</td>\n",
+       "      <td>1.878735</td>\n",
+       "      <td>0.054624</td>\n",
+       "      <td>8.839256</td>\n",
+       "      <td>12827.143861</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10.175411</td>\n",
+       "      <td>6.133932</td>\n",
+       "      <td>0.008836</td>\n",
+       "      <td>11.859453</td>\n",
+       "      <td>188189.689187</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>9.319357</td>\n",
+       "      <td>4.587432</td>\n",
+       "      <td>0.018817</td>\n",
+       "      <td>10.861722</td>\n",
+       "      <td>95155.068368</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>7.228996</td>\n",
+       "      <td>1.256691</td>\n",
+       "      <td>0.057692</td>\n",
+       "      <td>8.425403</td>\n",
+       "      <td>5449.034309</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>5.646967</td>\n",
+       "      <td>1.339107</td>\n",
+       "      <td>0.032118</td>\n",
+       "      <td>6.581547</td>\n",
+       "      <td>4750.825175</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>7.615980</td>\n",
+       "      <td>2.634620</td>\n",
+       "      <td>0.036497</td>\n",
+       "      <td>8.876433</td>\n",
+       "      <td>25339.645267</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>9.460406</td>\n",
+       "      <td>3.381147</td>\n",
+       "      <td>0.033824</td>\n",
+       "      <td>11.026114</td>\n",
+       "      <td>52508.670941</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>16.000441</td>\n",
+       "      <td>3.044223</td>\n",
+       "      <td>0.004295</td>\n",
+       "      <td>18.648532</td>\n",
+       "      <td>87446.895732</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10.550343</td>\n",
+       "      <td>1.563634</td>\n",
+       "      <td>0.030265</td>\n",
+       "      <td>12.296437</td>\n",
+       "      <td>12622.321616</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>11.817436</td>\n",
+       "      <td>2.982923</td>\n",
+       "      <td>0.021717</td>\n",
+       "      <td>13.773236</td>\n",
+       "      <td>53748.089829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>12.101122</td>\n",
+       "      <td>5.305727</td>\n",
+       "      <td>0.014540</td>\n",
+       "      <td>14.103872</td>\n",
+       "      <td>177305.220432</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10.400035</td>\n",
+       "      <td>3.588297</td>\n",
+       "      <td>0.032312</td>\n",
+       "      <td>12.121253</td>\n",
+       "      <td>65405.272028</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>9.132540</td>\n",
+       "      <td>2.011568</td>\n",
+       "      <td>0.048130</td>\n",
+       "      <td>10.643986</td>\n",
+       "      <td>17913.129344</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>9.880296</td>\n",
+       "      <td>2.462908</td>\n",
+       "      <td>0.050634</td>\n",
+       "      <td>11.515496</td>\n",
+       "      <td>29157.316025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>8.321803</td>\n",
+       "      <td>2.003080</td>\n",
+       "      <td>0.054171</td>\n",
+       "      <td>9.699071</td>\n",
+       "      <td>16104.920042</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>6.131352</td>\n",
+       "      <td>1.794449</td>\n",
+       "      <td>0.035429</td>\n",
+       "      <td>7.146098</td>\n",
+       "      <td>9295.960216</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>11.430727</td>\n",
+       "      <td>3.979891</td>\n",
+       "      <td>0.025331</td>\n",
+       "      <td>13.322525</td>\n",
+       "      <td>90734.821382</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>14.263809</td>\n",
+       "      <td>2.781733</td>\n",
+       "      <td>0.009359</td>\n",
+       "      <td>16.624486</td>\n",
+       "      <td>66183.179571</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>13.744161</td>\n",
+       "      <td>5.465225</td>\n",
+       "      <td>0.007518</td>\n",
+       "      <td>16.018835</td>\n",
+       "      <td>240475.837506</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>8.735251</td>\n",
+       "      <td>1.270630</td>\n",
+       "      <td>0.047066</td>\n",
+       "      <td>10.180945</td>\n",
+       "      <td>6821.344537</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>8.301748</td>\n",
+       "      <td>3.676767</td>\n",
+       "      <td>0.022070</td>\n",
+       "      <td>9.675697</td>\n",
+       "      <td>54122.886646</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Te       Hm0   weights         Tp              J\n",
+       "0    7.974491  1.253970  0.058861   9.294279    6031.115427\n",
+       "1   10.794533  2.641403  0.035216  12.581041   37004.325098\n",
+       "2    6.901979  1.953122  0.052001   8.044264   12516.214519\n",
+       "3   12.667628  7.310116  0.005070  14.764135  367451.945581\n",
+       "4   12.893701  2.262294  0.016046  15.027624   36455.136139\n",
+       "5   10.557621  4.754297  0.017311  12.304920  116784.361789\n",
+       "6    8.766664  2.739380  0.043646  10.217557   31825.667989\n",
+       "7    6.537403  1.305578  0.050746   7.619350    5272.441394\n",
+       "8    9.666291  1.340694  0.037070  11.266073    8443.649821\n",
+       "9   12.787307  3.920397  0.016464  14.903621  107680.986511\n",
+       "10  11.605879  1.821016  0.022323  13.526666   19446.169168\n",
+       "11   7.584082  1.878735  0.054624   8.839256   12827.143861\n",
+       "12  10.175411  6.133932  0.008836  11.859453  188189.689187\n",
+       "13   9.319357  4.587432  0.018817  10.861722   95155.068368\n",
+       "14   7.228996  1.256691  0.057692   8.425403    5449.034309\n",
+       "15   5.646967  1.339107  0.032118   6.581547    4750.825175\n",
+       "16   7.615980  2.634620  0.036497   8.876433   25339.645267\n",
+       "17   9.460406  3.381147  0.033824  11.026114   52508.670941\n",
+       "18  16.000441  3.044223  0.004295  18.648532   87446.895732\n",
+       "19  10.550343  1.563634  0.030265  12.296437   12622.321616\n",
+       "20  11.817436  2.982923  0.021717  13.773236   53748.089829\n",
+       "21  12.101122  5.305727  0.014540  14.103872  177305.220432\n",
+       "22  10.400035  3.588297  0.032312  12.121253   65405.272028\n",
+       "23   9.132540  2.011568  0.048130  10.643986   17913.129344\n",
+       "24   9.880296  2.462908  0.050634  11.515496   29157.316025\n",
+       "25   8.321803  2.003080  0.054171   9.699071   16104.920042\n",
+       "26   6.131352  1.794449  0.035429   7.146098    9295.960216\n",
+       "27  11.430727  3.979891  0.025331  13.322525   90734.821382\n",
+       "28  14.263809  2.781733  0.009359  16.624486   66183.179571\n",
+       "29  13.744161  5.465225  0.007518  16.018835  240475.837506\n",
+       "30   8.735251  1.270630  0.047066  10.180945    6821.344537\n",
+       "31   8.301748  3.676767  0.022070   9.675697   54122.886646"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results = pd.read_csv(\"data/wave/pacwave_cluster_32.csv\", index_col=0)\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Write a WEC-Sim batch file for the given clusters\n",
+    "\n",
+    "[WEC-Sim MCR](https://wec-sim.github.io/WEC-Sim/main/user/advanced_features.html#multiple-condition-runs-mcr) (multiple condition run) files should contain a structure `mcr` that contains two variables: `header` and `cases`. Each column of `header` and `cases` denotes a variable and it's value respectively. Each row is another simulation. WEC-Sim defines waves using the significant wave height and peak period. We will isolate these values from the results of the cluster analysis and create a dictionary that is written to the `.mat` file."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "ws_mcr_cases = results[[\"Hm0\",\"Tp\"]]\n",
+    "ws_mcr_header = np.array([\"waves.height\",\"waves.period\"], dtype='object')\n",
+    "ws_mcr_out = {'mcr': {'header': ws_mcr_header, 'cases': ws_mcr_cases}}\n",
+    "sio.savemat('mcr_mhkit.mat', ws_mcr_out)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Simulate the device *in WEC-Sim*\n",
+    "\n",
+    "Now that the MCR file is created, we need to go simulate WEC performance in these wave conditions using WEC-Sim. To recreate the data used in the next step, use the created MCR file with WEC-Sim's [OSWEC example](https://github.com/WEC-Sim/WEC-Sim/tree/main/examples/OSWEC). For an accurate comparison to the power calculated in the resource characterization, we should ensure that the WEC-Sim cases use irregular JONSWAP wave spectra as in the PacWave example.\n",
+    "\n",
+    "For convenience in this demonstration, we enforce OSWEC model stability in the extreme wave conditions by arbitrarily applying a large PTO stiffness and damping:\n",
+    "```\n",
+    "pto(1).stiffness = 1e5;\n",
+    "pto(1).damping = 5e7;\n",
+    "```\n",
+    "\n",
+    "To reduce the amount of extranenous data saved for this example, we limit the WEC-Sim output to the PTO's power output in the `userDefinedFunctions.m` script:\n",
+    "```\n",
+    "if exist('imcr','var')\n",
+    "    if imcr == 1\n",
+    "        nmcr = size(mcr.cases,1);\n",
+    "        power = nan(1, nmcr);\n",
+    "    end\n",
+    "\n",
+    "    iRampEnd = simu.rampTime./simu.dtOut + 1;\n",
+    "    power(imcr) = -mean(output.ptos(1).powerInternalMechanics(iRampEnd:end,5));\n",
+    "\n",
+    "    if imcr == nmcr\n",
+    "        % Save output\n",
+    "        save('mcr_mhkit_power.mat', 'power');\n",
+    "    end\n",
+    "end\n",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. Load WEC-Sim batch results\n",
+    "\n",
+    "Note that in this example we do not save the entire WEC-Sim `output` structure for each case. See the `wecsim_example.ipynb` for information on loading that WEC-Sim data. Here the output is one array of average power output that we will load and compare to the resource characterization.\n",
+    "\n",
+    "Note that the power output \\[W\\] is significantly larger than the energy flux \\[W/m\\] due to the width of the OSWEC. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Te</th>\n",
+       "      <th>Hm0</th>\n",
+       "      <th>weights</th>\n",
+       "      <th>Tp</th>\n",
+       "      <th>J</th>\n",
+       "      <th>P</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.974491</td>\n",
+       "      <td>1.253970</td>\n",
+       "      <td>0.058861</td>\n",
+       "      <td>9.294279</td>\n",
+       "      <td>6031.115427</td>\n",
+       "      <td>6.861312e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.794533</td>\n",
+       "      <td>2.641403</td>\n",
+       "      <td>0.035216</td>\n",
+       "      <td>12.581041</td>\n",
+       "      <td>37004.325098</td>\n",
+       "      <td>3.873519e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.901979</td>\n",
+       "      <td>1.953122</td>\n",
+       "      <td>0.052001</td>\n",
+       "      <td>8.044264</td>\n",
+       "      <td>12516.214519</td>\n",
+       "      <td>1.923400e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12.667628</td>\n",
+       "      <td>7.310116</td>\n",
+       "      <td>0.005070</td>\n",
+       "      <td>14.764135</td>\n",
+       "      <td>367451.945581</td>\n",
+       "      <td>1.951187e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>12.893701</td>\n",
+       "      <td>2.262294</td>\n",
+       "      <td>0.016046</td>\n",
+       "      <td>15.027624</td>\n",
+       "      <td>36455.136139</td>\n",
+       "      <td>3.115873e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10.557621</td>\n",
+       "      <td>4.754297</td>\n",
+       "      <td>0.017311</td>\n",
+       "      <td>12.304920</td>\n",
+       "      <td>116784.361789</td>\n",
+       "      <td>8.410281e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>8.766664</td>\n",
+       "      <td>2.739380</td>\n",
+       "      <td>0.043646</td>\n",
+       "      <td>10.217557</td>\n",
+       "      <td>31825.667989</td>\n",
+       "      <td>3.398579e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>6.537403</td>\n",
+       "      <td>1.305578</td>\n",
+       "      <td>0.050746</td>\n",
+       "      <td>7.619350</td>\n",
+       "      <td>5272.441394</td>\n",
+       "      <td>8.332614e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9.666291</td>\n",
+       "      <td>1.340694</td>\n",
+       "      <td>0.037070</td>\n",
+       "      <td>11.266073</td>\n",
+       "      <td>8443.649821</td>\n",
+       "      <td>6.951609e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>12.787307</td>\n",
+       "      <td>3.920397</td>\n",
+       "      <td>0.016464</td>\n",
+       "      <td>14.903621</td>\n",
+       "      <td>107680.986511</td>\n",
+       "      <td>5.068824e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11.605879</td>\n",
+       "      <td>1.821016</td>\n",
+       "      <td>0.022323</td>\n",
+       "      <td>13.526666</td>\n",
+       "      <td>19446.169168</td>\n",
+       "      <td>1.601980e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>7.584082</td>\n",
+       "      <td>1.878735</td>\n",
+       "      <td>0.054624</td>\n",
+       "      <td>8.839256</td>\n",
+       "      <td>12827.143861</td>\n",
+       "      <td>2.112478e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10.175411</td>\n",
+       "      <td>6.133932</td>\n",
+       "      <td>0.008836</td>\n",
+       "      <td>11.859453</td>\n",
+       "      <td>188189.689187</td>\n",
+       "      <td>2.402013e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>9.319357</td>\n",
+       "      <td>4.587432</td>\n",
+       "      <td>0.018817</td>\n",
+       "      <td>10.861722</td>\n",
+       "      <td>95155.068368</td>\n",
+       "      <td>1.067714e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>7.228996</td>\n",
+       "      <td>1.256691</td>\n",
+       "      <td>0.057692</td>\n",
+       "      <td>8.425403</td>\n",
+       "      <td>5449.034309</td>\n",
+       "      <td>6.414256e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>5.646967</td>\n",
+       "      <td>1.339107</td>\n",
+       "      <td>0.032118</td>\n",
+       "      <td>6.581547</td>\n",
+       "      <td>4750.825175</td>\n",
+       "      <td>7.328911e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>7.615980</td>\n",
+       "      <td>2.634620</td>\n",
+       "      <td>0.036497</td>\n",
+       "      <td>8.876433</td>\n",
+       "      <td>25339.645267</td>\n",
+       "      <td>3.671972e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>9.460406</td>\n",
+       "      <td>3.381147</td>\n",
+       "      <td>0.033824</td>\n",
+       "      <td>11.026114</td>\n",
+       "      <td>52508.670941</td>\n",
+       "      <td>4.020208e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>16.000441</td>\n",
+       "      <td>3.044223</td>\n",
+       "      <td>0.004295</td>\n",
+       "      <td>18.648532</td>\n",
+       "      <td>87446.895732</td>\n",
+       "      <td>2.414505e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10.550343</td>\n",
+       "      <td>1.563634</td>\n",
+       "      <td>0.030265</td>\n",
+       "      <td>12.296437</td>\n",
+       "      <td>12622.321616</td>\n",
+       "      <td>1.335059e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>11.817436</td>\n",
+       "      <td>2.982923</td>\n",
+       "      <td>0.021717</td>\n",
+       "      <td>13.773236</td>\n",
+       "      <td>53748.089829</td>\n",
+       "      <td>3.552203e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>12.101122</td>\n",
+       "      <td>5.305727</td>\n",
+       "      <td>0.014540</td>\n",
+       "      <td>14.103872</td>\n",
+       "      <td>177305.220432</td>\n",
+       "      <td>1.343228e+06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10.400035</td>\n",
+       "      <td>3.588297</td>\n",
+       "      <td>0.032312</td>\n",
+       "      <td>12.121253</td>\n",
+       "      <td>65405.272028</td>\n",
+       "      <td>7.368317e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>9.132540</td>\n",
+       "      <td>2.011568</td>\n",
+       "      <td>0.048130</td>\n",
+       "      <td>10.643986</td>\n",
+       "      <td>17913.129344</td>\n",
+       "      <td>1.998769e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>9.880296</td>\n",
+       "      <td>2.462908</td>\n",
+       "      <td>0.050634</td>\n",
+       "      <td>11.515496</td>\n",
+       "      <td>29157.316025</td>\n",
+       "      <td>2.715734e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>8.321803</td>\n",
+       "      <td>2.003080</td>\n",
+       "      <td>0.054171</td>\n",
+       "      <td>9.699071</td>\n",
+       "      <td>16104.920042</td>\n",
+       "      <td>2.319710e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>6.131352</td>\n",
+       "      <td>1.794449</td>\n",
+       "      <td>0.035429</td>\n",
+       "      <td>7.146098</td>\n",
+       "      <td>9295.960216</td>\n",
+       "      <td>1.478045e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>11.430727</td>\n",
+       "      <td>3.979891</td>\n",
+       "      <td>0.025331</td>\n",
+       "      <td>13.322525</td>\n",
+       "      <td>90734.821382</td>\n",
+       "      <td>9.391133e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>14.263809</td>\n",
+       "      <td>2.781733</td>\n",
+       "      <td>0.009359</td>\n",
+       "      <td>16.624486</td>\n",
+       "      <td>66183.179571</td>\n",
+       "      <td>2.000335e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>13.744161</td>\n",
+       "      <td>5.465225</td>\n",
+       "      <td>0.007518</td>\n",
+       "      <td>16.018835</td>\n",
+       "      <td>240475.837506</td>\n",
+       "      <td>8.686179e+05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>8.735251</td>\n",
+       "      <td>1.270630</td>\n",
+       "      <td>0.047066</td>\n",
+       "      <td>10.180945</td>\n",
+       "      <td>6821.344537</td>\n",
+       "      <td>8.550423e+04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>8.301748</td>\n",
+       "      <td>3.676767</td>\n",
+       "      <td>0.022070</td>\n",
+       "      <td>9.675697</td>\n",
+       "      <td>54122.886646</td>\n",
+       "      <td>7.578856e+05</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Te       Hm0   weights         Tp              J             P\n",
+       "0    7.974491  1.253970  0.058861   9.294279    6031.115427  6.861312e+04\n",
+       "1   10.794533  2.641403  0.035216  12.581041   37004.325098  3.873519e+05\n",
+       "2    6.901979  1.953122  0.052001   8.044264   12516.214519  1.923400e+05\n",
+       "3   12.667628  7.310116  0.005070  14.764135  367451.945581  1.951187e+06\n",
+       "4   12.893701  2.262294  0.016046  15.027624   36455.136139  3.115873e+05\n",
+       "5   10.557621  4.754297  0.017311  12.304920  116784.361789  8.410281e+05\n",
+       "6    8.766664  2.739380  0.043646  10.217557   31825.667989  3.398579e+05\n",
+       "7    6.537403  1.305578  0.050746   7.619350    5272.441394  8.332614e+04\n",
+       "8    9.666291  1.340694  0.037070  11.266073    8443.649821  6.951609e+04\n",
+       "9   12.787307  3.920397  0.016464  14.903621  107680.986511  5.068824e+05\n",
+       "10  11.605879  1.821016  0.022323  13.526666   19446.169168  1.601980e+05\n",
+       "11   7.584082  1.878735  0.054624   8.839256   12827.143861  2.112478e+05\n",
+       "12  10.175411  6.133932  0.008836  11.859453  188189.689187  2.402013e+06\n",
+       "13   9.319357  4.587432  0.018817  10.861722   95155.068368  1.067714e+06\n",
+       "14   7.228996  1.256691  0.057692   8.425403    5449.034309  6.414256e+04\n",
+       "15   5.646967  1.339107  0.032118   6.581547    4750.825175  7.328911e+04\n",
+       "16   7.615980  2.634620  0.036497   8.876433   25339.645267  3.671972e+05\n",
+       "17   9.460406  3.381147  0.033824  11.026114   52508.670941  4.020208e+05\n",
+       "18  16.000441  3.044223  0.004295  18.648532   87446.895732  2.414505e+05\n",
+       "19  10.550343  1.563634  0.030265  12.296437   12622.321616  1.335059e+05\n",
+       "20  11.817436  2.982923  0.021717  13.773236   53748.089829  3.552203e+05\n",
+       "21  12.101122  5.305727  0.014540  14.103872  177305.220432  1.343228e+06\n",
+       "22  10.400035  3.588297  0.032312  12.121253   65405.272028  7.368317e+05\n",
+       "23   9.132540  2.011568  0.048130  10.643986   17913.129344  1.998769e+05\n",
+       "24   9.880296  2.462908  0.050634  11.515496   29157.316025  2.715734e+05\n",
+       "25   8.321803  2.003080  0.054171   9.699071   16104.920042  2.319710e+05\n",
+       "26   6.131352  1.794449  0.035429   7.146098    9295.960216  1.478045e+05\n",
+       "27  11.430727  3.979891  0.025331  13.322525   90734.821382  9.391133e+05\n",
+       "28  14.263809  2.781733  0.009359  16.624486   66183.179571  2.000335e+05\n",
+       "29  13.744161  5.465225  0.007518  16.018835  240475.837506  8.686179e+05\n",
+       "30   8.735251  1.270630  0.047066  10.180945    6821.344537  8.550423e+04\n",
+       "31   8.301748  3.676767  0.022070   9.675697   54122.886646  7.578856e+05"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Relative location and filename of simulated WEC-Sim data (run with mooring)\n",
+    "filename = \"./data/wave/mcr_mhkit_power.mat\"\n",
+    "\n",
+    "# Load data using the `wecsim.read_output` function which returns a dictionary of Datasets.\n",
+    "wecsim_data = sio.loadmat(filename)\n",
+    "results['P'] = wecsim_data['power'][0]\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Assess results and visualize quantities of interest\n",
+    "Now that we have loaded the OSWEC's modeled power, we can assess it's performance relative to the incoming wave and calculate the mean annual energy production (MAEP) using MHKiT."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Te</th>\n",
+       "      <th>Hm0</th>\n",
+       "      <th>weights</th>\n",
+       "      <th>Tp</th>\n",
+       "      <th>J</th>\n",
+       "      <th>P</th>\n",
+       "      <th>CW</th>\n",
+       "      <th>CWR</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>7.974491</td>\n",
+       "      <td>1.253970</td>\n",
+       "      <td>0.058861</td>\n",
+       "      <td>9.294279</td>\n",
+       "      <td>6031.115427</td>\n",
+       "      <td>6.861312e+04</td>\n",
+       "      <td>11.376523</td>\n",
+       "      <td>0.632029</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10.794533</td>\n",
+       "      <td>2.641403</td>\n",
+       "      <td>0.035216</td>\n",
+       "      <td>12.581041</td>\n",
+       "      <td>37004.325098</td>\n",
+       "      <td>3.873519e+05</td>\n",
+       "      <td>10.467747</td>\n",
+       "      <td>0.581542</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>6.901979</td>\n",
+       "      <td>1.953122</td>\n",
+       "      <td>0.052001</td>\n",
+       "      <td>8.044264</td>\n",
+       "      <td>12516.214519</td>\n",
+       "      <td>1.923400e+05</td>\n",
+       "      <td>15.367264</td>\n",
+       "      <td>0.853737</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12.667628</td>\n",
+       "      <td>7.310116</td>\n",
+       "      <td>0.005070</td>\n",
+       "      <td>14.764135</td>\n",
+       "      <td>367451.945581</td>\n",
+       "      <td>1.951187e+06</td>\n",
+       "      <td>5.310048</td>\n",
+       "      <td>0.295003</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>12.893701</td>\n",
+       "      <td>2.262294</td>\n",
+       "      <td>0.016046</td>\n",
+       "      <td>15.027624</td>\n",
+       "      <td>36455.136139</td>\n",
+       "      <td>3.115873e+05</td>\n",
+       "      <td>8.547144</td>\n",
+       "      <td>0.474841</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>10.557621</td>\n",
+       "      <td>4.754297</td>\n",
+       "      <td>0.017311</td>\n",
+       "      <td>12.304920</td>\n",
+       "      <td>116784.361789</td>\n",
+       "      <td>8.410281e+05</td>\n",
+       "      <td>7.201547</td>\n",
+       "      <td>0.400086</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>8.766664</td>\n",
+       "      <td>2.739380</td>\n",
+       "      <td>0.043646</td>\n",
+       "      <td>10.217557</td>\n",
+       "      <td>31825.667989</td>\n",
+       "      <td>3.398579e+05</td>\n",
+       "      <td>10.678736</td>\n",
+       "      <td>0.593263</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>6.537403</td>\n",
+       "      <td>1.305578</td>\n",
+       "      <td>0.050746</td>\n",
+       "      <td>7.619350</td>\n",
+       "      <td>5272.441394</td>\n",
+       "      <td>8.332614e+04</td>\n",
+       "      <td>15.804090</td>\n",
+       "      <td>0.878005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9.666291</td>\n",
+       "      <td>1.340694</td>\n",
+       "      <td>0.037070</td>\n",
+       "      <td>11.266073</td>\n",
+       "      <td>8443.649821</td>\n",
+       "      <td>6.951609e+04</td>\n",
+       "      <td>8.232943</td>\n",
+       "      <td>0.457386</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>12.787307</td>\n",
+       "      <td>3.920397</td>\n",
+       "      <td>0.016464</td>\n",
+       "      <td>14.903621</td>\n",
+       "      <td>107680.986511</td>\n",
+       "      <td>5.068824e+05</td>\n",
+       "      <td>4.707260</td>\n",
+       "      <td>0.261514</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>11.605879</td>\n",
+       "      <td>1.821016</td>\n",
+       "      <td>0.022323</td>\n",
+       "      <td>13.526666</td>\n",
+       "      <td>19446.169168</td>\n",
+       "      <td>1.601980e+05</td>\n",
+       "      <td>8.238022</td>\n",
+       "      <td>0.457668</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>7.584082</td>\n",
+       "      <td>1.878735</td>\n",
+       "      <td>0.054624</td>\n",
+       "      <td>8.839256</td>\n",
+       "      <td>12827.143861</td>\n",
+       "      <td>2.112478e+05</td>\n",
+       "      <td>16.468814</td>\n",
+       "      <td>0.914934</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>10.175411</td>\n",
+       "      <td>6.133932</td>\n",
+       "      <td>0.008836</td>\n",
+       "      <td>11.859453</td>\n",
+       "      <td>188189.689187</td>\n",
+       "      <td>2.402013e+06</td>\n",
+       "      <td>12.763787</td>\n",
+       "      <td>0.709099</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>9.319357</td>\n",
+       "      <td>4.587432</td>\n",
+       "      <td>0.018817</td>\n",
+       "      <td>10.861722</td>\n",
+       "      <td>95155.068368</td>\n",
+       "      <td>1.067714e+06</td>\n",
+       "      <td>11.220783</td>\n",
+       "      <td>0.623377</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>7.228996</td>\n",
+       "      <td>1.256691</td>\n",
+       "      <td>0.057692</td>\n",
+       "      <td>8.425403</td>\n",
+       "      <td>5449.034309</td>\n",
+       "      <td>6.414256e+04</td>\n",
+       "      <td>11.771363</td>\n",
+       "      <td>0.653965</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>5.646967</td>\n",
+       "      <td>1.339107</td>\n",
+       "      <td>0.032118</td>\n",
+       "      <td>6.581547</td>\n",
+       "      <td>4750.825175</td>\n",
+       "      <td>7.328911e+04</td>\n",
+       "      <td>15.426605</td>\n",
+       "      <td>0.857034</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>7.615980</td>\n",
+       "      <td>2.634620</td>\n",
+       "      <td>0.036497</td>\n",
+       "      <td>8.876433</td>\n",
+       "      <td>25339.645267</td>\n",
+       "      <td>3.671972e+05</td>\n",
+       "      <td>14.491015</td>\n",
+       "      <td>0.805056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>9.460406</td>\n",
+       "      <td>3.381147</td>\n",
+       "      <td>0.033824</td>\n",
+       "      <td>11.026114</td>\n",
+       "      <td>52508.670941</td>\n",
+       "      <td>4.020208e+05</td>\n",
+       "      <td>7.656274</td>\n",
+       "      <td>0.425349</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>16.000441</td>\n",
+       "      <td>3.044223</td>\n",
+       "      <td>0.004295</td>\n",
+       "      <td>18.648532</td>\n",
+       "      <td>87446.895732</td>\n",
+       "      <td>2.414505e+05</td>\n",
+       "      <td>2.761110</td>\n",
+       "      <td>0.153395</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>10.550343</td>\n",
+       "      <td>1.563634</td>\n",
+       "      <td>0.030265</td>\n",
+       "      <td>12.296437</td>\n",
+       "      <td>12622.321616</td>\n",
+       "      <td>1.335059e+05</td>\n",
+       "      <td>10.576970</td>\n",
+       "      <td>0.587609</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>11.817436</td>\n",
+       "      <td>2.982923</td>\n",
+       "      <td>0.021717</td>\n",
+       "      <td>13.773236</td>\n",
+       "      <td>53748.089829</td>\n",
+       "      <td>3.552203e+05</td>\n",
+       "      <td>6.608984</td>\n",
+       "      <td>0.367166</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>12.101122</td>\n",
+       "      <td>5.305727</td>\n",
+       "      <td>0.014540</td>\n",
+       "      <td>14.103872</td>\n",
+       "      <td>177305.220432</td>\n",
+       "      <td>1.343228e+06</td>\n",
+       "      <td>7.575793</td>\n",
+       "      <td>0.420877</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22</th>\n",
+       "      <td>10.400035</td>\n",
+       "      <td>3.588297</td>\n",
+       "      <td>0.032312</td>\n",
+       "      <td>12.121253</td>\n",
+       "      <td>65405.272028</td>\n",
+       "      <td>7.368317e+05</td>\n",
+       "      <td>11.265631</td>\n",
+       "      <td>0.625868</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>23</th>\n",
+       "      <td>9.132540</td>\n",
+       "      <td>2.011568</td>\n",
+       "      <td>0.048130</td>\n",
+       "      <td>10.643986</td>\n",
+       "      <td>17913.129344</td>\n",
+       "      <td>1.998769e+05</td>\n",
+       "      <td>11.158124</td>\n",
+       "      <td>0.619896</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>9.880296</td>\n",
+       "      <td>2.462908</td>\n",
+       "      <td>0.050634</td>\n",
+       "      <td>11.515496</td>\n",
+       "      <td>29157.316025</td>\n",
+       "      <td>2.715734e+05</td>\n",
+       "      <td>9.314074</td>\n",
+       "      <td>0.517449</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25</th>\n",
+       "      <td>8.321803</td>\n",
+       "      <td>2.003080</td>\n",
+       "      <td>0.054171</td>\n",
+       "      <td>9.699071</td>\n",
+       "      <td>16104.920042</td>\n",
+       "      <td>2.319710e+05</td>\n",
+       "      <td>14.403735</td>\n",
+       "      <td>0.800207</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>6.131352</td>\n",
+       "      <td>1.794449</td>\n",
+       "      <td>0.035429</td>\n",
+       "      <td>7.146098</td>\n",
+       "      <td>9295.960216</td>\n",
+       "      <td>1.478045e+05</td>\n",
+       "      <td>15.899861</td>\n",
+       "      <td>0.883326</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>11.430727</td>\n",
+       "      <td>3.979891</td>\n",
+       "      <td>0.025331</td>\n",
+       "      <td>13.322525</td>\n",
+       "      <td>90734.821382</td>\n",
+       "      <td>9.391133e+05</td>\n",
+       "      <td>10.350087</td>\n",
+       "      <td>0.575005</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>14.263809</td>\n",
+       "      <td>2.781733</td>\n",
+       "      <td>0.009359</td>\n",
+       "      <td>16.624486</td>\n",
+       "      <td>66183.179571</td>\n",
+       "      <td>2.000335e+05</td>\n",
+       "      <td>3.022422</td>\n",
+       "      <td>0.167912</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>29</th>\n",
+       "      <td>13.744161</td>\n",
+       "      <td>5.465225</td>\n",
+       "      <td>0.007518</td>\n",
+       "      <td>16.018835</td>\n",
+       "      <td>240475.837506</td>\n",
+       "      <td>8.686179e+05</td>\n",
+       "      <td>3.612080</td>\n",
+       "      <td>0.200671</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>8.735251</td>\n",
+       "      <td>1.270630</td>\n",
+       "      <td>0.047066</td>\n",
+       "      <td>10.180945</td>\n",
+       "      <td>6821.344537</td>\n",
+       "      <td>8.550423e+04</td>\n",
+       "      <td>12.534806</td>\n",
+       "      <td>0.696378</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>31</th>\n",
+       "      <td>8.301748</td>\n",
+       "      <td>3.676767</td>\n",
+       "      <td>0.022070</td>\n",
+       "      <td>9.675697</td>\n",
+       "      <td>54122.886646</td>\n",
+       "      <td>7.578856e+05</td>\n",
+       "      <td>14.003052</td>\n",
+       "      <td>0.777947</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Te       Hm0   weights         Tp              J             P  \\\n",
+       "0    7.974491  1.253970  0.058861   9.294279    6031.115427  6.861312e+04   \n",
+       "1   10.794533  2.641403  0.035216  12.581041   37004.325098  3.873519e+05   \n",
+       "2    6.901979  1.953122  0.052001   8.044264   12516.214519  1.923400e+05   \n",
+       "3   12.667628  7.310116  0.005070  14.764135  367451.945581  1.951187e+06   \n",
+       "4   12.893701  2.262294  0.016046  15.027624   36455.136139  3.115873e+05   \n",
+       "5   10.557621  4.754297  0.017311  12.304920  116784.361789  8.410281e+05   \n",
+       "6    8.766664  2.739380  0.043646  10.217557   31825.667989  3.398579e+05   \n",
+       "7    6.537403  1.305578  0.050746   7.619350    5272.441394  8.332614e+04   \n",
+       "8    9.666291  1.340694  0.037070  11.266073    8443.649821  6.951609e+04   \n",
+       "9   12.787307  3.920397  0.016464  14.903621  107680.986511  5.068824e+05   \n",
+       "10  11.605879  1.821016  0.022323  13.526666   19446.169168  1.601980e+05   \n",
+       "11   7.584082  1.878735  0.054624   8.839256   12827.143861  2.112478e+05   \n",
+       "12  10.175411  6.133932  0.008836  11.859453  188189.689187  2.402013e+06   \n",
+       "13   9.319357  4.587432  0.018817  10.861722   95155.068368  1.067714e+06   \n",
+       "14   7.228996  1.256691  0.057692   8.425403    5449.034309  6.414256e+04   \n",
+       "15   5.646967  1.339107  0.032118   6.581547    4750.825175  7.328911e+04   \n",
+       "16   7.615980  2.634620  0.036497   8.876433   25339.645267  3.671972e+05   \n",
+       "17   9.460406  3.381147  0.033824  11.026114   52508.670941  4.020208e+05   \n",
+       "18  16.000441  3.044223  0.004295  18.648532   87446.895732  2.414505e+05   \n",
+       "19  10.550343  1.563634  0.030265  12.296437   12622.321616  1.335059e+05   \n",
+       "20  11.817436  2.982923  0.021717  13.773236   53748.089829  3.552203e+05   \n",
+       "21  12.101122  5.305727  0.014540  14.103872  177305.220432  1.343228e+06   \n",
+       "22  10.400035  3.588297  0.032312  12.121253   65405.272028  7.368317e+05   \n",
+       "23   9.132540  2.011568  0.048130  10.643986   17913.129344  1.998769e+05   \n",
+       "24   9.880296  2.462908  0.050634  11.515496   29157.316025  2.715734e+05   \n",
+       "25   8.321803  2.003080  0.054171   9.699071   16104.920042  2.319710e+05   \n",
+       "26   6.131352  1.794449  0.035429   7.146098    9295.960216  1.478045e+05   \n",
+       "27  11.430727  3.979891  0.025331  13.322525   90734.821382  9.391133e+05   \n",
+       "28  14.263809  2.781733  0.009359  16.624486   66183.179571  2.000335e+05   \n",
+       "29  13.744161  5.465225  0.007518  16.018835  240475.837506  8.686179e+05   \n",
+       "30   8.735251  1.270630  0.047066  10.180945    6821.344537  8.550423e+04   \n",
+       "31   8.301748  3.676767  0.022070   9.675697   54122.886646  7.578856e+05   \n",
+       "\n",
+       "           CW       CWR  \n",
+       "0   11.376523  0.632029  \n",
+       "1   10.467747  0.581542  \n",
+       "2   15.367264  0.853737  \n",
+       "3    5.310048  0.295003  \n",
+       "4    8.547144  0.474841  \n",
+       "5    7.201547  0.400086  \n",
+       "6   10.678736  0.593263  \n",
+       "7   15.804090  0.878005  \n",
+       "8    8.232943  0.457386  \n",
+       "9    4.707260  0.261514  \n",
+       "10   8.238022  0.457668  \n",
+       "11  16.468814  0.914934  \n",
+       "12  12.763787  0.709099  \n",
+       "13  11.220783  0.623377  \n",
+       "14  11.771363  0.653965  \n",
+       "15  15.426605  0.857034  \n",
+       "16  14.491015  0.805056  \n",
+       "17   7.656274  0.425349  \n",
+       "18   2.761110  0.153395  \n",
+       "19  10.576970  0.587609  \n",
+       "20   6.608984  0.367166  \n",
+       "21   7.575793  0.420877  \n",
+       "22  11.265631  0.625868  \n",
+       "23  11.158124  0.619896  \n",
+       "24   9.314074  0.517449  \n",
+       "25  14.403735  0.800207  \n",
+       "26  15.899861  0.883326  \n",
+       "27  10.350087  0.575005  \n",
+       "28   3.022422  0.167912  \n",
+       "29   3.612080  0.200671  \n",
+       "30  12.534806  0.696378  \n",
+       "31  14.003052  0.777947  "
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "results['CW'] = wave.performance.capture_width(results['P'], results['J'])\n",
+    "oswec_width = 18\n",
+    "results['CWR'] = results['CW'] / oswec_width\n",
+    "results"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2865149.0"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "MAEP = wave.performance.mean_annual_energy_production_matrix(results['CW'], results['J'], results['weights']) / 1000 # kWh\n",
+    "MAEP = np.round(MAEP, 0).item()\n",
+    "MAEP"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.11"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/mhkit/__init__.py b/mhkit/__init__.py
index 952136a7b..692cb8eec 100644
--- a/mhkit/__init__.py
+++ b/mhkit/__init__.py
@@ -1,24 +1,17 @@
 import warnings as _warn
-from mhkit import wave
-from mhkit import river
-from mhkit import tidal
-from mhkit import qc
-from mhkit import utils
-from mhkit import power
-from mhkit import loads
-from mhkit import dolfyn
-from mhkit import mooring
-from mhkit import acoustics
+import importlib
 
 # Register datetime converter for a matplotlib plotting methods
 from pandas.plotting import register_matplotlib_converters as _rmc
 
 _rmc()
 
-# Ignore future warnings
-_warn.simplefilter(action="ignore", category=FutureWarning)
+# Use targeted warning configuration
+from mhkit.warnings import configure_warnings
 
-__version__ = "v0.9.0"
+configure_warnings()
+
+__version__ = "v1.0.0"
 
 __copyright__ = """
 Copyright 2019, Alliance for Sustainable Energy, LLC under the terms of 
@@ -28,3 +21,33 @@
 retains certain rights in this software."""
 
 __license__ = "Revised BSD License"
+
+
+def __getattr__(name):
+    """Lazy import modules to handle pip optional dependencies."""
+    known_modules = [
+        "wave",
+        "river",
+        "tidal",
+        "qc",
+        "utils",
+        "power",
+        "loads",
+        "dolfyn",
+        "mooring",
+        "acoustics",
+    ]
+
+    if name in known_modules:
+        try:
+            return importlib.import_module(f"mhkit.{name}")
+        except ModuleNotFoundError:
+            error_msg = "Module dependencies not found.\n"
+            error_msg += f"To install the {name} module, run:\n"
+            error_msg += f"  pip install mhkit[{name}]\n\n"
+            error_msg += "Or install all modules with:\n"
+            error_msg += "  pip install mhkit[all]"
+    else:
+        error_msg = f"module 'mhkit' has no attribute '{name}'"
+
+    raise AttributeError(error_msg)
diff --git a/mhkit/acoustics/__init__.py b/mhkit/acoustics/__init__.py
index 35cb2b7f0..0c84970f0 100644
--- a/mhkit/acoustics/__init__.py
+++ b/mhkit/acoustics/__init__.py
@@ -1,19 +1,32 @@
 """
 The passive acoustics module provides a set of functions
-for analyzing and visualizing passive acoustic monitoring 
+for analyzing and visualizing passive acoustic monitoring
 data deployed in water bodies. This package reads in raw
-*.wav* files and conducts basic acoustics analysis and 
+*.wav* files and conducts basic acoustics analysis and
 visualization.
 
 To start using the module, import it directly from MHKiT:
 ``from mhkit import acoustics``. The analysis functions
-are available directly from the main import, while the 
-I/O and graphics submodules are available from 
+are available directly from the main import, while the
+I/O and graphics submodules are available from
 ``acoustics.io`` and  ``acoustics.graphics``, respectively.
-The base functions are intended to be used on top of the I/O submodule, and 
-include functionality to calibrate data, create spectral densities, sound 
+The base functions are intended to be used on top of the I/O submodule, and
+include functionality to calibrate data, create spectral densities, sound
 pressure levels, and time or band aggregate spectral data.
 """
 
 from mhkit.acoustics import io, graphics
-from .analysis import *
+from .analysis import (
+    minimum_frequency,
+    sound_pressure_spectral_density,
+    apply_calibration,
+    sound_pressure_spectral_density_level,
+    band_aggregate,
+    time_aggregate,
+)
+from .spl import (
+    sound_pressure_level,
+    third_octave_sound_pressure_level,
+    decidecade_sound_pressure_level,
+)
+from .sel import nmfs_auditory_weighting, sound_exposure_level
diff --git a/mhkit/acoustics/analysis.py b/mhkit/acoustics/analysis.py
index bd0e2007d..145757fdb 100644
--- a/mhkit/acoustics/analysis.py
+++ b/mhkit/acoustics/analysis.py
@@ -2,7 +2,7 @@
 This module contains key functions for passive acoustics analysis, designed to process
 and analyze sound pressure data from .wav files in the frequency and time domains.
 The functions herein build on each other, with a structured flow that facilitates the
-calculation of sound pressure levels, spectral densities, and banded averages, based on
+calculation of sound pressure spectral densities and banded averages based on
 input audio data.
 
 The following functionality is provided:
@@ -39,19 +39,6 @@
 
    - `time_aggregate`: Aggregates spectral density data into specified time windows using
      similar statistical methods.
-
-7. **Sound Pressure Level Calculation**:
-
-   - `sound_pressure_level`: Computes the overall sound pressure level within a frequency band
-     from mean square spectral density.
-
-8. **Frequency-Banded Sound Pressure Level**:
-
-   - `_band_sound_pressure_level`: Helper function for calculating sound pressure levels
-     over specified frequency bandwidths.
-
-   - `third_octave_sound_pressure_level` and `decidecade_sound_pressure_level`:
-     Compute sound pressure levels across third-octave and decidecade bands, respectively.
 """
 
 from typing import Union, Dict, Tuple, Optional
@@ -153,13 +140,24 @@ def minimum_frequency(
 
 
 def sound_pressure_spectral_density(
-    pressure: xr.DataArray, fs: Union[int, float], bin_length: Union[int, float] = 1
+    pressure: xr.DataArray,
+    fs: Union[int, float],
+    bin_length: Union[int, float] = 1,
+    rms: bool = True,
 ) -> xr.DataArray:
     """
-    Calculates the mean square sound pressure spectral density from audio
-    samples split into FFTs with a specified bin length in seconds, using Hanning
-    windowing with 50% overlap. The amplitude of the PSD is adjusted
-    according to Parseval's theorem.
+    Calculates the sound pressure spectral density (SPSD) from audio
+    samples split into FFTs with a specified bin length in seconds,
+    using Hanning windowing with 50% overlap.
+
+    By default (`rms=True`), this function returns the mean-squared SPSD,
+    which found by scaling the total spectral power (frequency domain) with
+    the time-domain averaged mean-squared power, in accordance with
+    Parseval's theorem.
+
+    Setting `rms=False` disables this scaling and returns the
+    power spectral density of the sound pressure signal.
+    Both forms have units of [Pa^2/Hz] or [V^2/Hz].
 
     Parameters
     ----------
@@ -169,6 +167,9 @@ def sound_pressure_spectral_density(
         Data collection sampling rate [Hz]
     bin_length: int or float
         Length of time in seconds to create FFTs. Default: 1.
+    rms: bool
+        If True, calculates the mean-squared SPSD. Set to False to
+        calculate standard SPSD. Default: True.
 
     Returns
     -------
@@ -191,25 +192,34 @@ def sound_pressure_spectral_density(
     # window length of each time series
     nbin = bin_length * fs
 
-    # Use dolfyn PSD
+    # Use dolfyn PSD functionality
     binner = VelBinner(n_bin=nbin, fs=fs, n_fft=nbin)
     # Always 50% overlap if numbers reshape perfectly
     # Mean square sound pressure
     psd = binner.power_spectral_density(pressure, freq_units="Hz")
-    samples = binner.reshape(pressure.values) - binner.mean(pressure.values)[:, None]
-    # Power in time domain
-    t_power = np.sum(samples**2, axis=1) / nbin
-    # Power in frequency domain
-    f_power = psd.sum("freq") * (fs / nbin)
-    # Adjust the amplitude of PSD according to Parseval's theorem
-    psd_adj = psd * t_power[:, None] / f_power
+    if rms:
+        # Scale PSD by mean square of original signal
+        samples = (
+            binner.reshape(pressure.values) - binner.mean(pressure.values)[:, None]
+        )
+        # mean squared pressure ("power") in time domain
+        t_power = np.sum(samples**2, axis=1) / nbin
+        # pressure ("power") in frequency domain
+        f_power = psd.sum("freq") * (fs / nbin)
+        # Adjust the amplitude of the PSD to return the mean-squared PSD
+        # based on Parseval's theorem: total energy computed in the time
+        # domain must equal the total energy computed in the frequency domain
+        psd = psd * t_power[:, None] / f_power
+        long_name = "Mean Square Sound Pressure Spectral Density"
+    else:
+        long_name = "Sound Pressure Spectral Density"
 
     out = xr.DataArray(
-        psd_adj,
-        coords={"time": psd_adj["time"], "freq": psd_adj["freq"]},
+        psd,
+        coords={"time": psd["time"], "freq": psd["freq"]},
         attrs={
             "units": pressure.units + "^2/Hz",
-            "long_name": "Mean Square Sound Pressure Spectral Density",
+            "long_name": long_name,
             "fs": fs,
             "nbin": str(bin_length) + " s",
             "overlap": "50%",
@@ -337,7 +347,7 @@ def sound_pressure_spectral_density_level(spsd: xr.DataArray) -> xr.DataArray:
 
 
 def _validate_method(
-    method: Union[str, Dict[str, Union[float, int]]]
+    method: Union[str, Dict[str, Union[float, int]]],
 ) -> Tuple[str, Optional[Union[float, int]]]:
     """
     Validates the 'method' parameter and returns the method name and its argument (if any)
@@ -379,7 +389,7 @@ def _validate_method(
     method_name : str
         The validated method name in lowercase.
     method_arg : float, int, or None
-        The argument associated with the method, if applicable; otherwise, None.
+        The argument associated with the method, if applicableotherwise, None.
 
     Raises
     ------
@@ -435,7 +445,8 @@ def _validate_method(
         "var",
         "where",
     ]
-
+    if not isinstance(method, (str, dict)):
+        raise TypeError("'method' must be a string or a dictionary.")
     if isinstance(method, str):
         method_name = method.lower()
         if method_name not in allowed_methods:
@@ -473,9 +484,50 @@ def _validate_method(
     return method_name, method_arg
 
 
+def _create_frequency_bands(octave, base, fmin, fmax):
+    """
+    Calculates frequency bands based on the specified octave, minimum and
+    maximum frequency limits.
+
+    Parameters
+    ----------
+    octave: int
+        Octave to subdivide spectral density level by.
+    base : int, optional
+        Octave base. Set to 2 for the true octave band; set to base 10 for
+        the decidecade octave band. Default: 2
+    fmin : int, optional
+        Lower frequency band limit (lower limit of the hydrophone). Default is 10 Hz.
+    fmax : int, optional
+        Upper frequency band limit (Nyquist frequency). Default is 100,000 Hz.
+
+    Returns
+    -------
+    octave_bins: numpy.array
+        Array of octave bin edges
+    band: dict(str, numpy.array)
+        Dictionary containing the frequency band edges and center frequency
+    """
+
+    bandwidth = base ** (1 / octave)
+    half_bandwidth = base ** (1 / (octave * 2))
+
+    band = {}
+    band["center_freq"] = 10 ** np.arange(
+        np.log10(fmin),
+        np.log10(fmax * bandwidth),
+        step=np.log10(bandwidth),
+    )
+    band["lower_limit"] = band["center_freq"] / half_bandwidth
+    band["upper_limit"] = band["center_freq"] * half_bandwidth
+    octave_bins = np.append(band["lower_limit"], band["upper_limit"][-1])
+
+    return octave_bins, band
+
+
 def band_aggregate(
     spsdl: xr.DataArray,
-    octave: int = 3,
+    octave: Tuple[int, int] = None,
     fmin: int = 10,
     fmax: int = 100000,
     method: Union[str, Dict[str, Union[float, int]]] = "median",
@@ -488,8 +540,11 @@ def band_aggregate(
     ----------
     spsdl: xarray.DataArray (time, freq)
         Mean square sound pressure spectral density level in dB rel 1 uPa^2/Hz
-    octave: int
-        Octave to subdivide spectral density level by. Default = 3 (third octave)
+    octave: [int, int]
+        Octave and octave base to subdivide spectral density level by. Set to
+        octave base to 2 for the true octave band; set to base 10 for
+        the decidecade octave band.
+        Default = [3, 2] (true third octave)
     fmin: int
         Lower frequency band limit (lower limit of the hydrophone). Default: 10 Hz
     fmax: int
@@ -509,16 +564,17 @@ def band_aggregate(
     # Type checks
     if not isinstance(spsdl, xr.DataArray):
         raise TypeError("'spsdl' must be an xarray.DataArray.")
-    if not isinstance(octave, int) or (octave <= 0):
-        raise TypeError("'octave' must be a positive integer.")
+    if octave is None:
+        octave = [3, 2]
+    if not isinstance(octave, list) and not isinstance(octave, tuple):
+        raise TypeError("'octave' must be a list or tuple of two integers.")
+    for val in octave:
+        if not isinstance(val, int) or (val <= 0):
+            raise TypeError("'octave' must contain positive integers.")
     if not isinstance(fmin, int) or (fmin <= 0):
         raise TypeError("'fmin' must be a positive integer.")
-    if not isinstance(fmax, int) or (fmin <= 0):
-        raise TypeError("'fmax' must be a positive integer.")
-    if fmax <= fmin:
+    if fmax <= fmin:  # also checks that fmax is positive
         raise ValueError("'fmax' must be greater than 'fmin'.")
-    if not isinstance(method, (str, dict)):
-        raise TypeError("'method' must be a string or a dictionary.")
 
     # Value checks
     if ("freq" not in spsdl.dims) or ("time" not in spsdl.dims):
@@ -531,18 +587,7 @@ def band_aggregate(
     fn = spsdl["freq"].max().values
     fmax = _fmax_warning(fn, fmax)
 
-    bandwidth = 2 ** (1 / octave)
-    half_bandwidth = 2 ** (1 / (octave * 2))
-
-    band = {}
-    band["center_freq"] = 10 ** np.arange(
-        np.log10(fmin),
-        np.log10(fmax * bandwidth),
-        step=np.log10(bandwidth),
-    )
-    band["lower_limit"] = band["center_freq"] / half_bandwidth
-    band["upper_limit"] = band["center_freq"] * half_bandwidth
-    octave_bins = np.append(band["lower_limit"], band["upper_limit"][-1])
+    octave_bins, band = _create_frequency_bands(octave[0], octave[1], fmin, fmax)
 
     # Use xarray binning methods
     spsdl_group = spsdl.groupby_bins("freq", octave_bins, labels=band["center_freq"])
@@ -562,10 +607,6 @@ def band_aggregate(
     # Update attributes
     out.attrs["units"] = spsdl.units
 
-    # Remove 'quantile' coordinate if present
-    if method == "quantile":
-        out = out.drop_vars("quantile")
-
     return out
 
 
@@ -653,242 +694,3 @@ def time_aggregate(
         out = out.drop_vars("quantile")
 
     return out
-
-
-def sound_pressure_level(
-    spsd: xr.DataArray, fmin: int = 10, fmax: int = 100000
-) -> xr.DataArray:
-    """
-    Calculates the sound pressure level in a specified frequency band
-    from the mean square sound pressure spectral density.
-
-    Parameters
-    ----------
-    spsd: xarray.DataArray (time, freq)
-        Mean square sound pressure spectral density in [Pa^2/Hz]
-    fmin: int
-        Lower frequency band limit (lower limit of the hydrophone). Default: 10 Hz
-    fmax: int
-        Upper frequency band limit (Nyquist frequency). Default: 100000 Hz
-
-    Returns
-    -------
-    spl: xarray.DataArray (time)
-        Sound pressure level [dB re 1 uPa] indexed by time
-    """
-
-    # Type checks
-    if not isinstance(spsd, xr.DataArray):
-        raise TypeError("'spsd' must be an xarray.DataArray.")
-    if not isinstance(fmin, int):
-        raise TypeError("'fmin' must be an integer.")
-    if not isinstance(fmax, int):
-        raise TypeError("'fmax' must be an integer.")
-
-    # Ensure 'freq' and 'time' dimensions are present
-    if ("freq" not in spsd.dims) or ("time" not in spsd.dims):
-        raise ValueError("'spsd' must have 'time' and 'freq' as dimensions.")
-
-    # Check that 'fs' (sampling frequency) is available in attributes
-    if "fs" not in spsd.attrs:
-        raise ValueError(
-            "'spsd' must have 'fs' (sampling frequency) in its attributes."
-        )
-
-    # Value checks
-    if fmin <= 0:
-        raise ValueError("'fmin' must be a positive integer.")
-    if fmax <= fmin:
-        raise ValueError("'fmax' must be greater than 'fmin'.")
-
-    # Check fmax
-    fn = spsd.attrs["fs"] // 2
-    fmax = _fmax_warning(fn, fmax)
-
-    # Reference value of sound pressure
-    reference = 1e-12  # Pa^2, = 1 uPa^2
-
-    # Mean square sound pressure in a specified frequency band from mean square values
-    pressure_squared = np.trapz(
-        spsd.sel(freq=slice(fmin, fmax)), spsd["freq"].sel(freq=slice(fmin, fmax))
-    )
-
-    # Mean square sound pressure level
-    mspl = 10 * np.log10(pressure_squared / reference)
-
-    out = xr.DataArray(
-        mspl.astype(np.float32),
-        coords={"time": spsd["time"]},
-        attrs={
-            "units": "dB re 1 uPa",
-            "long_name": "Sound Pressure Level",
-            "freq_band_min": fmin,
-            "freq_band_max": fmax,
-        },
-    )
-
-    return out
-
-
-def _band_sound_pressure_level(
-    spsd: xr.DataArray,
-    bandwidth: int,
-    half_bandwidth: int,
-    fmin: int = 10,
-    fmax: int = 100000,
-) -> xr.DataArray:
-    """
-    Calculates band-averaged sound pressure levels
-
-    Parameters
-    ----------
-    spsd: xarray.DataArray (time, freq)
-        Mean square sound pressure spectral density.
-    bandwidth : int or float
-        Bandwidth to average over.
-    half_bandwidth : int or float
-        Half-bandwidth, used to set upper and lower bandwidth limits.
-    fmin : int, optional
-        Lower frequency band limit (lower limit of the hydrophone). Default is 10 Hz.
-    fmax : int, optional
-        Upper frequency band limit (Nyquist frequency). Default is 100,000 Hz.
-
-
-    Returns
-    -------
-    out: xarray.DataArray (time, freq_bins)
-        Sound pressure level [dB re 1 uPa] indexed by time and frequency of specified bandwidth
-    """
-
-    # Type checks
-    if not isinstance(spsd, xr.DataArray):
-        raise TypeError("'spsd' must be an xarray.DataArray.")
-    if not isinstance(bandwidth, (int, float)):
-        raise TypeError("'bandwidth' must be a numeric type (int or float).")
-    if not isinstance(half_bandwidth, (int, float)):
-        raise TypeError("'half_bandwidth' must be a numeric type (int or float).")
-    if not isinstance(fmin, int):
-        raise TypeError("'fmin' must be an integer.")
-    if not isinstance(fmax, int):
-        raise TypeError("'fmax' must be an integer.")
-
-    # Ensure 'freq' and 'time' dimensions are present
-    if "freq" not in spsd.dims or "time" not in spsd.dims:
-        raise ValueError("'spsd' must have 'time' and 'freq' as dimensions.")
-
-    # Check that 'fs' (sampling frequency) is available in attributes
-    if "fs" not in spsd.attrs:
-        raise ValueError(
-            "'spsd' must have 'fs' (sampling frequency) in its attributes."
-        )
-
-    # Value checks
-    if fmin <= 0:
-        raise ValueError("'fmin' must be a positive integer.")
-    if fmax <= fmin:
-        raise ValueError("'fmax' must be greater than 'fmin'.")
-
-    # Check fmax
-    fn = spsd.attrs["fs"] // 2
-    fmax = _fmax_warning(fn, fmax)
-
-    # Reference value of sound pressure
-    reference = 1e-12  # Pa^2, = 1 uPa^2
-
-    band = {}
-    band["center_freq"] = 10 ** np.arange(
-        np.log10(fmin),
-        np.log10(fmax * bandwidth),
-        step=np.log10(bandwidth),
-    )
-    band["lower_limit"] = band["center_freq"] / half_bandwidth
-    band["upper_limit"] = band["center_freq"] * half_bandwidth
-    octave_bins = np.append(band["lower_limit"], band["upper_limit"][-1])
-
-    # Manual trapezoidal rule to get Pa^2
-    pressure_squared = xr.DataArray(
-        coords={"time": spsd["time"], "freq_bins": band["center_freq"]},
-        dims=["time", "freq_bins"],
-    )
-    for i, key in enumerate(band["center_freq"]):
-        band_min = octave_bins[i]
-        band_max = octave_bins[i + 1]
-        pressure_squared.loc[{"freq_bins": key}] = np.trapz(
-            spsd.sel(freq=slice(band_min, band_max)),
-            spsd["freq"].sel(freq=slice(band_min, band_max)),
-        )
-
-    # Mean square sound pressure level in dB rel 1 uPa
-    mspl = 10 * np.log10(pressure_squared / reference)
-
-    return mspl
-
-
-def third_octave_sound_pressure_level(
-    spsd: xr.DataArray, fmin: int = 10, fmax: int = 100000
-) -> xr.DataArray:
-    """
-    Calculates the sound pressure level in third octave bands directly
-    from the mean square sound pressure spectral density.
-
-    Parameters
-    ----------
-    spsd: xarray.DataArray (time, freq)
-        Mean square sound pressure spectral density.
-    fmin: int
-        Lower frequency band limit (lower limit of the hydrophone). Default: 10 Hz
-    fmax: int
-        Upper frequency band limit (Nyquist frequency). Default: 100000 Hz
-
-    Returns
-    -------
-    mspl: xarray.DataArray (time, freq_bins)
-        Sound pressure level [dB re 1 uPa] indexed by time and third octave bands
-    """
-
-    # Third octave bin frequencies
-    bandwidth = 2 ** (1 / 3)
-    half_bandwidth = 2 ** (1 / 6)
-
-    mspl = _band_sound_pressure_level(spsd, bandwidth, half_bandwidth, fmin, fmax)
-    mspl.attrs = {
-        "units": "dB re 1 uPa",
-        "long_name": "Third Octave Sound Pressure Level",
-    }
-
-    return mspl.astype(np.float32)
-
-
-def decidecade_sound_pressure_level(
-    spsd: xr.DataArray, fmin: int = 10, fmax: int = 100000
-) -> xr.DataArray:
-    """
-    Calculates the sound pressure level in decidecade bands directly
-    from the mean square sound pressure spectral density.
-
-    Parameters
-    ----------
-    spsd: xarray.DataArray (time, freq)
-        Mean square sound pressure spectral density.
-    fmin: int
-        Lower frequency band limit (lower limit of the hydrophone). Default: 10 Hz
-    fmax: int
-        Upper frequency band limit (Nyquist frequency). Default: 100000 Hz
-
-    Returns
-    -------
-    mspl : xarray.DataArray (time, freq_bins)
-        Sound pressure level [dB re 1 uPa] indexed by time and decidecade bands
-    """
-
-    # Decidecade bin frequencies
-    bandwidth = 2 ** (1 / 10)
-    half_bandwidth = 2 ** (1 / 20)
-
-    mspl = _band_sound_pressure_level(spsd, bandwidth, half_bandwidth, fmin, fmax)
-    mspl.attrs = {
-        "units": "dB re 1 uPa",
-        "long_name": "Decidecade Sound Pressure Level",
-    }
-
-    return mspl.astype(np.float32)
diff --git a/mhkit/acoustics/graphics.py b/mhkit/acoustics/graphics.py
index 888cec835..fb61361f0 100644
--- a/mhkit/acoustics/graphics.py
+++ b/mhkit/acoustics/graphics.py
@@ -1,6 +1,6 @@
 """
-This submodule provides essential plotting functions for visualizing passive acoustics 
-data. The functions allow for customizable plotting of sound pressure spectral density 
+This submodule provides essential plotting functions for visualizing passive acoustics
+data. The functions allow for customizable plotting of sound pressure spectral density
 levels across time and frequency dimensions.
 
 Each plotting function leverages the flexibility of Matplotlib, allowing for passthrough
@@ -11,12 +11,12 @@
 -------------
 1. **plot_spectrogram**:
 
-   - Generates a spectrogram plot from sound pressure spectral density level data, 
+   - Generates a spectrogram plot from sound pressure spectral density level data,
      with a logarithmic frequency scale by default for improved readability of acoustic data.
 
 2. **plot_spectra**:
 
-   - Produces a spectral density plot with a log-transformed x-axis, allowing for clear 
+   - Produces a spectral density plot with a log-transformed x-axis, allowing for clear
      visualization of spectral density across frequency bands.
 """
 
@@ -33,7 +33,7 @@ def plot_spectrogram(
     fmax: int = 100000,
     fig: plt.figure = None,
     ax: plt.Axes = None,
-    **kwargs
+    **kwargs,
 ) -> Tuple[plt.figure, plt.Axes]:
     """
     Plots the spectrogram of the sound pressure spectral density level.
@@ -86,10 +86,10 @@ def plot_spectrogram(
         spsdl[freq].values,
         spsdl.transpose(freq, time),
         shading="nearest",
-        **kwargs
+        **kwargs,
     )
     fig.colorbar(h, ax=ax, label=getattr(spsdl, "units", None))
-    ax.set(xlabel="Time", ylabel="Frequency [Hz]")
+    ax.set(ylim=(fmin, fmax), xlabel="Time", ylabel="Frequency [Hz]")
 
     return fig, ax
 
@@ -100,7 +100,7 @@ def plot_spectra(
     fmax: int = 100000,
     fig: plt.figure = None,
     ax: plt.Axes = None,
-    **kwargs
+    **kwargs,
 ) -> Tuple[plt.figure, plt.Axes]:
     """
     Plots spectral density. X axis is log-transformed.
@@ -140,8 +140,6 @@ def plot_spectra(
     # Check fmax
     fn = spsdl[freq].max().item()
     fmax = _fmax_warning(fn, fmax)
-    # select frequency range
-    spsdl = spsdl.sel({freq: slice(fmin, fmax)})
 
     if ax is None:
         fig, ax = plt.subplots(figsize=(6, 5), subplot_kw={"xscale": "log"})
diff --git a/mhkit/acoustics/io.py b/mhkit/acoustics/io.py
index 5e04b82d7..c1743b0ed 100644
--- a/mhkit/acoustics/io.py
+++ b/mhkit/acoustics/io.py
@@ -1,7 +1,7 @@
 """
 This submodule provides input/output functions for passive acoustics data,
 focusing on hydrophone recordings stored in WAV files. The main functionality
-includes reading and processing hydrophone data from various manufacturers 
+includes reading and processing hydrophone data from various manufacturers
 and exporting audio files for easy playback and analysis.
 
 Supported Hydrophone Models
@@ -14,28 +14,28 @@
 
 1. **Data Reading**:
 
-   - `read_hydrophone`: Main function to read a WAV file from a hydrophone and 
-     convert it to either a voltage or pressure time series, depending on the 
+   - `read_hydrophone`: Main function to read a WAV file from a hydrophone and
+     convert it to either a voltage or pressure time series, depending on the
      availability of sensitivity data.
 
-   - `read_soundtrap`: Wrapper for reading Ocean Instruments SoundTrap hydrophone 
+   - `read_soundtrap`: Wrapper for reading Ocean Instruments SoundTrap hydrophone
      files, automatically using appropriate metadata.
 
-   - `read_iclisten`: Wrapper for reading Ocean Sonics icListen hydrophone files, 
-     including metadata processing to apply hydrophone sensitivity for direct 
+   - `read_iclisten`: Wrapper for reading Ocean Sonics icListen hydrophone files,
+     including metadata processing to apply hydrophone sensitivity for direct
      sound pressure calculation.
 
 2. **Audio Export**:
 
-   - `export_audio`: Converts processed sound pressure data back into a WAV file 
+   - `export_audio`: Converts processed sound pressure data back into a WAV file
      format, with optional gain adjustment to improve playback quality.
 
 3. **Data Extraction**:
 
-   - `_read_wav_metadata`: Extracts metadata from a WAV file, including bit depth 
+   - `_read_wav_metadata`: Extracts metadata from a WAV file, including bit depth
      and other header information.
 
-   - `_calculate_voltage_and_time`: Converts raw WAV data into voltage values and 
+   - `_calculate_voltage_and_time`: Converts raw WAV data into voltage values and
      generates a time index based on the sampling frequency.
 """
 
diff --git a/mhkit/acoustics/sel.py b/mhkit/acoustics/sel.py
new file mode 100644
index 000000000..781db3876
--- /dev/null
+++ b/mhkit/acoustics/sel.py
@@ -0,0 +1,154 @@
+"""
+This module contains key functions related to calculating sound exposure levels
+from sound pressure data.
+
+1. **Sound Exposure Level Calculation**:
+
+   - `nmfs_auditory_weighting`: Computes the auditory weighting and exposure functions
+     for marine mammals based on the National Marine Fisheries Service (NMFS) guidelines.
+   - `sound_exposure_level`: Computes the sound exposure level from within a
+     specified time range.
+"""
+
+import numpy as np
+import xarray as xr
+
+from .spl import _argument_check
+
+
+def nmfs_auditory_weighting(frequency, group):
+    """
+    Calculates the auditory weighting and exposure functions for marine mammals
+    based on the National Marine Fisheries Service (NMFS) guidelines.
+
+    The weighting function is applied to sound exposure level to determine the
+    auditory impact on marine mammals. The exposure function is the inverse of the
+    weighting function and illustrates how the weighting function relates to marine
+    mammal hearing thresholds.
+    Both function are returned in their log10-transform, in units of dB. To transform
+    back to linear units, use 10**(weighting_func/10).
+
+    https://www.fisheries.noaa.gov/national/marine-mammal-protection/marine-mammal-acoustic-technical-guidance-other-acoustic-tools
+
+    Parameters
+    ----------
+    frequency: xarray.DataArray (freq)
+        Frequency vector in [Hz].
+    group: str
+        Marine mammal group for which the auditory weighting function is applied.
+        Options: 'LF' (low frequency cetaceans), 'HF' (high frequency cetaceans),
+        'VHF' (very high frequency cetaceans), 'PW' (phocid pinnepeds),
+        'OW' (otariid pinnepeds)
+
+    Returns
+    -------
+    weighting_func: xarray.DataArray (freq)
+        Auditory weighting function [unitless] indexed by frequency
+    exposure_func: xarray.DataArray (freq)
+        Log-transformed auditory exposure function [dB] indexed by frequency
+    """
+
+    if group.lower() not in [
+        "lf",
+        "hf",
+        "vhf",
+        "pw",
+        "ow",
+    ]:
+        raise ValueError("Group must be one of: LF, HF, VHF, PW, OW")
+
+    group_params = {
+        "lf": {"a": 0.99, "b": 5, "f1": 0.168, "f2": 26.6, "c": 0.12, "k": 177},
+        "hf": {"a": 1.55, "b": 5, "f1": 1.73, "f2": 129, "c": 0.32, "k": 181},
+        "vhf": {"a": 2.23, "b": 5, "f1": 5.93, "f2": 186, "c": 0.91, "k": 160},
+        "pw": {"a": 1.63, "b": 5, "f1": 0.81, "f2": 68.3, "c": 0.29, "k": 175},
+        "ow": {"a": 1.58, "b": 5, "f1": 2.53, "f2": 43.8, "c": 1.37, "k": 178},
+    }
+
+    a, b, f1, f2, c, k = group_params[group.lower()].values()
+
+    frequency = frequency / 1000  # Convert to kHz
+    ratio_a = frequency / f1
+    ratio_b = frequency / f2
+    band_filter = ratio_a ** (2 * a) / (
+        ((1 + ratio_a**2) ** a) * ((1 + ratio_b**2) ** b)
+    )
+
+    weighting_func = c + 10 * np.log10(band_filter)  # dB
+    exposure_func = k - 10 * np.log10(band_filter)  # dB
+
+    return weighting_func, exposure_func
+
+
+def sound_exposure_level(
+    spsd: xr.DataArray, group: str = None, fmin: int = 10, fmax: int = 100000
+) -> xr.DataArray:
+    """
+    Calculates the sound exposure level (SEL) across a specified frequency band
+    from the sound pressure spectral density (SPSD). If a marine mammal group is
+    provided, the resulting SEL is weighted according to the U.S. National Marine
+    Fisheries Service (NMFS) guidelines.
+
+    Parameters
+    ----------
+    spsd: xarray.DataArray (time, freq)
+        Sound pressure spectral density in [Pa^2/Hz] with a bin length
+        equal to the time over which sound exposure should be computed.
+    group: str
+        Marine mammal group for which the auditory weighting function is applied.
+        Options: 'LF' (low frequency cetaceans), 'HF' (high frequency cetaceans),
+        'VHF' (very high frequency cetaceans), 'PW' (phocid pinnepeds),
+        'OW' (otariid pinnepeds). Default: None
+    fmin: int
+        Lower frequency band limit (lower limit of the hydrophone).
+        Default: 10 Hz
+    fmax: int
+        Upper frequency band limit (Nyquist frequency). Default:
+        100000 Hz
+
+    Returns
+    -------
+    sel: xarray.DataArray (time)
+        Sound exposure level [dB re 1 uPa^2 s] indexed by time
+    """
+
+    # Argument checks
+    fmax = _argument_check(spsd, fmin, fmax)
+
+    if group is not None:
+        w, _ = nmfs_auditory_weighting(spsd["freq"], group)
+        # convert from dB back to unitless
+        w = 10 ** (w / 10)
+        long_name = "Weighted Sound Exposure Level"
+    else:
+        w = xr.ones_like(spsd["freq"])
+        long_name = "Sound Exposure Level"
+
+    # Reference value of sound pressure
+    reference = 1e-12 * 1  # Pa^2 s, = 1 uPa^2 s
+
+    # Mean square sound pressure in a specified frequency band
+    # from weighted mean square values
+    band = spsd.sel(freq=slice(fmin, fmax))
+    w = w.sel(freq=slice(fmin, fmax))
+    exposure = np.trapezoid(band * w, band["freq"])
+
+    # Sound exposure level (L_{E,p}) = (L_{p,rms} + 10log10(t))
+    sel = 10 * np.log10(exposure / reference) + 10 * np.log10(
+        spsd.attrs["nfft"] / spsd.attrs["fs"]  # n_points / (n_points/s)
+    )
+
+    out = xr.DataArray(
+        sel.astype(np.float32),
+        coords={"time": spsd["time"]},
+        attrs={
+            "units": "dB re 1 uPa^2 s",
+            "long_name": long_name,
+            "weighting_group": group,
+            "integration_time": spsd.attrs["nbin"],
+            "freq_band_min": fmin,
+            "freq_band_max": fmax,
+        },
+    )
+
+    return out
diff --git a/mhkit/acoustics/spl.py b/mhkit/acoustics/spl.py
new file mode 100644
index 000000000..2e9ef86a0
--- /dev/null
+++ b/mhkit/acoustics/spl.py
@@ -0,0 +1,278 @@
+"""
+This module contains key functions related to calculating sound pressure levels
+from sound pressure data.
+
+1. **Sound Pressure Level Calculation**:
+
+   - `sound_pressure_level`: Computes the overall sound pressure level within a frequency band
+     from mean square spectral density.
+
+2. **Frequency-Banded Sound Pressure Level**:
+
+   - `_band_sound_pressure_level`: Helper function for calculating sound pressure levels
+     over specified frequency bandwidths.
+
+   - `third_octave_sound_pressure_level` and `decidecade_sound_pressure_level`:
+     Compute sound pressure levels across third-octave and decidecade bands, respectively.
+"""
+
+import numpy as np
+import xarray as xr
+
+from .analysis import _fmax_warning, _create_frequency_bands
+
+
+def _argument_check(spsd, fmin, fmax):
+    """
+    Validates input types, values, and dimensions for SPSD data and adjusts
+    fmax to the Nyquist frequency if needed.
+
+    Parameters
+    ----------
+    spsd : xarray.DataArray
+        Spectral data with 'time' and 'freq' dimensions and a 'fs' attribute.
+    fmin : int
+        Minimum frequency (Hz), must be > 0.
+    fmax : int
+        Maximum frequency (Hz), must be > fmin.
+
+    Returns
+    -------
+    fmax : int
+        Frequency limited to below the Nyquist limit.
+    """
+
+    # Type checks
+    if not isinstance(spsd, xr.DataArray):
+        raise TypeError("'spsd' must be an xarray.DataArray.")
+    if not isinstance(fmin, int):
+        raise TypeError("'fmin' must be an integer.")
+    if not isinstance(fmax, int):
+        raise TypeError("'fmax' must be an integer.")
+
+    # Ensure 'freq' and 'time' dimensions are present
+    if ("freq" not in spsd.dims) or ("time" not in spsd.dims):
+        raise ValueError("'spsd' must have 'time' and 'freq' as dimensions.")
+
+    # Check that 'fs' (sampling frequency) is available in attributes
+    if "fs" not in spsd.attrs:
+        raise ValueError(
+            "'spsd' must have 'fs' (sampling frequency) in its attributes."
+        )
+    if "nfft" not in spsd.attrs:
+        raise ValueError(
+            "'spsd' must have 'nfft' (sampling frequency) in its attributes."
+        )
+
+    # Value checks
+    if fmin <= 0:
+        raise ValueError("'fmin' must be a positive integer.")
+    if fmax <= fmin:
+        raise ValueError("'fmax' must be greater than 'fmin'.")
+
+    # Check fmax
+    fn = spsd.attrs["fs"] // 2
+    fmax = _fmax_warning(fn, fmax)
+
+    return fmax
+
+
+def sound_pressure_level(
+    spsd: xr.DataArray, fmin: int = 10, fmax: int = 100000
+) -> xr.DataArray:
+    """
+    Calculates the sound pressure level (SPL) in a specified frequency band
+    from the mean square sound pressure spectral density (SPSD).
+
+    Parameters
+    ----------
+    spsd: xarray.DataArray (time, freq)
+        Mean square sound pressure spectral density in [Pa^2/Hz]
+    fmin: int
+        Lower frequency band limit (lower limit of the hydrophone). Default: 10 Hz
+    fmax: int
+        Upper frequency band limit (Nyquist frequency). Default: 100000 Hz
+
+    Returns
+    -------
+    spl: xarray.DataArray (time)
+        Sound pressure level [dB re 1 uPa] indexed by time
+    """
+
+    # Argument checks
+    fmax = _argument_check(spsd, fmin, fmax)
+
+    # Reference value of sound pressure
+    reference = 1e-12  # Pa^2, = 1 uPa^2
+
+    # Mean square sound pressure in a specified frequency band from mean square values
+    band = spsd.sel(freq=slice(fmin, fmax))
+    freqs = band["freq"]
+    pressure_squared = np.trapezoid(band, freqs)
+
+    # Mean square sound pressure level
+    mspl = 10 * np.log10(pressure_squared / reference)
+
+    out = xr.DataArray(
+        mspl.astype(np.float32),
+        coords={"time": spsd["time"]},
+        attrs={
+            "units": "dB re 1 uPa",
+            "long_name": "Sound Pressure Level",
+            "freq_band_min": fmin,
+            "freq_band_max": fmax,
+        },
+    )
+
+    return out
+
+
+def _band_sound_pressure_level(
+    spsd: xr.DataArray,
+    octave: int,
+    base: int = 2,
+    fmin: int = 10,
+    fmax: int = 100000,
+) -> xr.DataArray:
+    """
+    Calculates band-averaged sound pressure levels from the
+    mean square sound pressure spectral density (SPSD).
+
+    Parameters
+    ----------
+    spsd: xarray.DataArray (time, freq)
+        Mean square sound pressure spectral density in [Pa^2/Hz]
+    octave: int
+        Octave subdivision (1 = full octave, 3 = third-octave, etc.)
+    base: int
+        Octave base subdivision (2 = true octave, 10 = decade octave, etc.)
+    fmin : int, optional
+        Lower frequency band limit (lower limit of the hydrophone).
+        Default is 10 Hz.
+    fmax : int, optional
+        Upper frequency band limit (Nyquist frequency).
+        Default is 100,000 Hz.
+
+    Returns
+    -------
+    out: xarray.DataArray (time, freq_bins)
+        Sound pressure level [dB re 1 uPa] indexed by time and frequency of specified bandwidth
+    """
+
+    # Type checks
+    if not isinstance(octave, int) or (octave <= 0):
+        raise TypeError("'octave' must be a positive integer.")
+
+    # Argument checks
+    fmax = _argument_check(spsd, fmin, fmax)
+
+    # Reference value of sound pressure
+    reference = 1e-12  # Pa^2, = 1 uPa^2
+
+    _, band = _create_frequency_bands(octave, base, fmin, fmax)
+
+    # Manual trapezoidal rule to get Pa^2
+    pressure_squared = xr.DataArray(
+        coords={"time": spsd["time"], "freq_bins": band["center_freq"]},
+        dims=["time", "freq_bins"],
+    )
+    for i, key in enumerate(band["center_freq"]):
+        # Min and max band limits
+        band_range = [band["lower_limit"][i], band["upper_limit"][i]]
+
+        # Integrate spectral density by frequency
+        x = spsd["freq"].sel(freq=slice(*band_range))
+        if len(x) < 2:
+            # Interpolate between band frequencies if width is narrow
+            bandwidth = band_range[1] / band_range[0]
+            # Use smaller set of dataset to speed up interpolation
+            spsd_slc = spsd.sel(
+                freq=slice(
+                    None,  # Only happens at low frequency
+                    band_range[1] * bandwidth * 2,
+                )
+            )
+            spsd_slc = spsd_slc.interp(freq=band_range)
+            x = band_range
+        else:
+            spsd_slc = spsd.sel(freq=slice(*band_range))
+
+        pressure_squared.loc[{"freq_bins": key}] = np.trapezoid(spsd_slc, x)
+
+    # Mean square sound pressure level in dB rel 1 uPa
+    mspl = 10 * np.log10(pressure_squared / reference)
+
+    return mspl
+
+
+def third_octave_sound_pressure_level(
+    spsd: xr.DataArray, fmin: int = 10, fmax: int = 100000
+) -> xr.DataArray:
+    """
+    Calculates the sound pressure level in third octave bands directly
+    from the mean square sound pressure spectral density (SPSD).
+
+    Parameters
+    ----------
+    spsd: xarray.DataArray (time, freq)
+        Mean square sound pressure spectral in [Pa^2/Hz].
+    fmin: int
+        Lower frequency band limit (lower limit of the hydrophone).
+        Default: 10 Hz
+    fmax: int
+        Upper frequency band limit (Nyquist frequency).
+        Default: 100000 Hz
+
+    Returns
+    -------
+    mspl: xarray.DataArray (time, freq_bins)
+        Sound pressure level [dB re 1 uPa] indexed by time and third octave bands
+    """
+    octave = 3
+    base = 2
+    mspl = _band_sound_pressure_level(spsd, octave, base, fmin, fmax)
+    mspl.attrs.update(
+        {
+            "units": "dB re 1 uPa",
+            "long_name": "Third Octave Sound Pressure Level",
+        }
+    )
+
+    return mspl.astype(np.float32)
+
+
+def decidecade_sound_pressure_level(
+    spsd: xr.DataArray, fmin: int = 10, fmax: int = 100000
+) -> xr.DataArray:
+    """
+    Calculates the sound pressure level in decidecade bands directly
+    from the mean square sound pressure spectral density (SPSD).
+
+    Parameters
+    ----------
+    spsd: xarray.DataArray (time, freq)
+        Mean square sound pressure spectral density in [Pa^2/Hz].
+    fmin: int
+        Lower frequency band limit (lower limit of the hydrophone).
+        Default: 10 Hz
+    fmax: int
+        Upper frequency band limit (Nyquist frequency).
+        Default: 100000 Hz
+
+    Returns
+    -------
+    mspl : xarray.DataArray (time, freq_bins)
+        Sound pressure level [dB re 1 uPa] indexed by time and decidecade bands
+    """
+
+    octave = 10
+    base = 10
+    mspl = _band_sound_pressure_level(spsd, octave, base, fmin, fmax)
+    mspl.attrs.update(
+        {
+            "units": "dB re 1 uPa",
+            "long_name": "Decidecade Sound Pressure Level",
+        }
+    )
+
+    return mspl.astype(np.float32)
diff --git a/mhkit/dolfyn/adp/api.py b/mhkit/dolfyn/adp/api.py
index d3280eddb..31be60787 100644
--- a/mhkit/dolfyn/adp/api.py
+++ b/mhkit/dolfyn/adp/api.py
@@ -3,3 +3,4 @@
 from . import clean
 from ..velocity import VelBinner
 from .turbulence import ADPBinner
+from .discharge import discharge
diff --git a/mhkit/dolfyn/adp/clean.py b/mhkit/dolfyn/adp/clean.py
index 6890a12ff..6011c7946 100644
--- a/mhkit/dolfyn/adp/clean.py
+++ b/mhkit/dolfyn/adp/clean.py
@@ -157,31 +157,39 @@ def water_depth_from_amplitude(ds, thresh=10, nfilt=None) -> None:
             "Please manually remove 'depth' if it needs to be recalculated."
         )
 
+    # Use "avg" velocty if standard isn't available.
+    # Should not matter which is used.
+    tag = []
+    if hasattr(ds, "vel"):
+        tag += [""]
+    if hasattr(ds, "vel_avg"):
+        tag += ["_avg"]
+
     # This finds the maximum of the echo profile:
-    inds = np.argmax(ds["amp"].values, axis=1)
+    inds = np.argmax(ds["amp" + tag[0]].values, axis=1)
     # This finds the first point that increases (away from the profiler) in
     # the echo profile
-    edf = np.diff(ds["amp"].values.astype(np.int16), axis=1)
+    edf = np.diff(ds["amp" + tag[0]].values.astype(np.int16), axis=1)
     inds2 = (
-        np.max(
+        np.nanmax(
             (edf < 0)
-            * np.arange(ds["vel"].shape[1] - 1, dtype=np.uint8)[None, :, None],
+            * np.arange(ds["vel" + tag[0]].shape[1] - 1, dtype=np.uint8)[None, :, None],
             axis=1,
         )
         + 1
     )
 
     # Calculate the depth of these quantities
-    d1 = ds["range"].values[inds]
-    d2 = ds["range"].values[inds2]
+    d1 = ds["range" + tag[0]].values[inds]
+    d2 = ds["range" + tag[0]].values[inds2]
     # Combine them:
     D = np.vstack((d1, d2))
     # Take the median value as the estimate of the surface:
-    d = np.median(D, axis=0)
+    d = np.nanmedian(D, axis=0)
 
     # Throw out values that do not increase near the surface by *thresh*
-    for ip in range(ds["vel"].shape[1]):
-        itmp = np.min(inds[:, ip])
+    for ip in range(ds["vel" + tag[0]].shape[1]):
+        itmp = np.nanmin(inds[:, ip])
         if (edf[itmp:, :, ip] < thresh).all():
             d[ip] = np.nan
 
@@ -197,9 +205,9 @@ def water_depth_from_amplitude(ds, thresh=10, nfilt=None) -> None:
     else:
         long_name = "Instrument Depth"
 
-    ds["depth"] = xr.DataArray(
+    ds["depth" + tag[0]] = xr.DataArray(
         d.astype("float32"),
-        dims=["time"],
+        dims=["time" + tag[0]],
         attrs={"units": "m", "long_name": long_name, "standard_name": "depth"},
     )
 
@@ -230,7 +238,7 @@ def water_depth_from_pressure(ds, salinity=35) -> None:
     ds : xarray.Dataset
       The full adcp dataset
     salinity: numeric
-      Water salinity in psu. Default = 35
+      Water salinity in PSU. Default = 35
 
     Returns
     -------
@@ -259,16 +267,26 @@ def water_depth_from_pressure(ds, salinity=35) -> None:
             "The variable 'depth' already exists. "
             "Please manually remove 'depth' if it needs to be recalculated."
         )
-    if "pressure" not in ds.data_vars:
+    pressure = [v for v in ds.data_vars if "pressure" in v]
+    if not pressure:
         raise NameError("The variable 'pressure' does not exist.")
-    elif not ds["pressure"].sum():
-        raise ValueError("Pressure data not recorded.")
-    if "temp" not in ds.data_vars:
+    else:
+        for p in pressure:
+            if not ds[p].sum():
+                pressure.remove(p)
+        if not pressure:
+            raise ValueError("Pressure data not recorded.")
+    temp = [
+        v
+        for v in ds.data_vars
+        if (("temp" in v) and ("clock" not in v) and ("press" not in v))
+    ]
+    if not temp:
         raise NameError("The variable 'temp' does not exist.")
 
     # Density calcation
-    P = ds["pressure"].values
-    T = ds["temp"].values  # temperature, degC
+    P = ds[pressure[0]].values  # pressure, dbar
+    T = ds[temp[0]].values  # temperature, degC
     S = salinity  # practical salinity
     rho0 = 1027  # kg/m^3
     T0 = 10  # degC
@@ -289,9 +307,15 @@ def water_depth_from_pressure(ds, salinity=35) -> None:
     else:
         long_name = "Instrument Depth"
 
-    ds["water_density"] = xr.DataArray(
+    # Use correct coordinate tag
+    if "_" in pressure[0]:
+        tag = "_" + pressure[0].split("_")[-1]
+    else:
+        tag = ""
+
+    ds["water_density" + tag] = xr.DataArray(
         rho.astype("float32"),
-        dims=["time"],
+        dims=[ds[pressure[0]].dims[0]],
         attrs={
             "units": "kg m-3",
             "long_name": "Water Density",
@@ -299,9 +323,9 @@ def water_depth_from_pressure(ds, salinity=35) -> None:
             "description": "Water density from linear approximation of sea water equation of state",
         },
     )
-    ds["depth"] = xr.DataArray(
+    ds["depth" + tag] = xr.DataArray(
         d.astype("float32"),
-        dims=["time"],
+        dims=[ds[pressure[0]].dims[0]],
         attrs={"units": "m", "long_name": long_name, "standard_name": "depth"},
     )
 
@@ -319,7 +343,7 @@ def nan_beyond_surface(*args, **kwargs):
 
 
 def remove_surface_interference(
-    ds, val=np.nan, beam_angle=None, inplace=False
+    ds, val=np.nan, beam_angle=None, cell_size=None, inplace=False
 ) -> Optional[xr.Dataset]:
     """
     Mask the values of 3D data (vel, amp, corr, echo) that are beyond the surface.
@@ -332,6 +356,8 @@ def remove_surface_interference(
       Specifies the value to set the bad values to. Default is `numpy.nan`
     beam_angle : int
       ADCP beam inclination angle in degrees. Default = dataset.attrs['beam_angle']
+    cell_size : float
+      ADCP beam cellsize in meters. Default = dataset.attrs['cell_size']
     inplace : bool
       When True the existing data object is modified. When False
       a copy is returned. Default = False
@@ -348,56 +374,95 @@ def remove_surface_interference(
     `distance > range * cos(beam angle) - cell size`
     """
 
-    if "depth" not in ds.data_vars:
+    if ("depth" not in ds.data_vars) and ("depth_avg" not in ds.data_vars):
         raise KeyError(
             "Depth variable 'depth' does not exist in input dataset."
             "Please calculate 'depth' using the function 'water_depth_from_pressure'"
-            "or 'water_depth_from_amplitude."
+            "or 'water_depth_from_amplitude, or it can be found from the 'dist_bt'"
+            "(bottom track) or 'dist_alt' (altimeter) variables, if available."
         )
 
     if beam_angle is None:
         if hasattr(ds, "beam_angle"):
             beam_angle = np.deg2rad(ds.attrs["beam_angle"])
         else:
-            raise Exception(
+            raise KeyError(
                 "'beam_angle` not found in dataset attributes. "
                 "Please supply the ADCP's beam angle."
             )
     else:
         beam_angle = np.deg2rad(beam_angle)
 
+    if cell_size is None:
+        # Fetch cell size (usually 'cell_size' or 'cell_size_avg')
+        cell_sizes = []
+        if hasattr(ds, "cell_size"):
+            cell_sizes.append("cell_size")
+        if hasattr(ds, "cell_size_avg"):
+            cell_sizes.append("cell_size_avg")
+        if not cell_sizes:
+            raise KeyError(
+                "'cell_size` not found in dataset attributes. "
+                "Please supply the ADCP's cell size."
+            )
+    else:
+        cs = [cell_size]
+
+    # Depth variable(s)
+    depths = [cs.replace("cell_size", "depth") for cs in cell_sizes]
+
     if not inplace:
         ds = ds.copy(deep=True)
 
     # Get all variables with 'range' coordinate
     profile_vars = [h for h in ds.keys() if any(s for s in ds[h].dims if "range" in s)]
 
-    # Surface interference distance
     # Apply range_offset if available
     range_offset = __check_for_range_offset(ds)
-    if range_offset:
-        range_limit = (
-            (ds["depth"] - range_offset) * np.cos(beam_angle) - ds.attrs["cell_size"]
-        ) + range_offset
-    else:
-        range_limit = ds["depth"] * np.cos(beam_angle) - ds.attrs["cell_size"]
-
-    bds = ds["range"] > range_limit
-
-    # Echosounder data needs only be trimmed at water surface
-    if "echo" in profile_vars:
-        mask_echo = ds["range_echo"] > ds["depth"]
-        ds["echo"].values[..., mask_echo] = val
-        profile_vars.remove("echo")
-
-    # Correct rest of "range" data for surface interference
-    for var in profile_vars:
-        a = ds[var].values
-        try:  # float dtype
-            a[..., bds] = val
-        except:  # int dtype
-            a[..., bds] = 0
-        ds[var].values = a
+    for depth, cs in zip(depths, cell_sizes):
+        if range_offset:
+            range_limit = (
+                (ds[depth] - range_offset) * np.cos(beam_angle) - ds.attrs[cs]
+            ) + range_offset
+        else:
+            range_limit = ds[depth] * np.cos(beam_angle) - ds.attrs[cs]
+
+        # No good way to do this
+        if "_avg" not in depth:
+            # Echosounder data needs only be trimmed at water surface
+            if "echo" in profile_vars:
+                mask_echo = ds["range_echo"] > ds["depth"]
+                ds["echo"].values[..., mask_echo] = val
+                profile_vars.remove("echo")
+
+            # Correct profile measurements for surface interference
+            for var in profile_vars:
+                if "avg" in var:
+                    continue
+                # Use correct coordinate tag
+                if "_" in var and ("gd" not in var):
+                    tag = "_" + "_".join(var.split("_")[1:])
+                else:
+                    tag = ""
+                mask = ds["range" + tag] > range_limit
+                # Remove values
+                a = ds[var].values
+                try:  # float dtype
+                    a[..., mask] = val
+                except:  # int dtype
+                    a[..., mask] = 0
+                ds[var].values = a
+        else:
+            for var in profile_vars:
+                if "avg" in var:
+                    mask = ds["range_avg"] > range_limit
+                    # Remove values
+                    a = ds[var].values
+                    try:  # float dtype
+                        a[..., mask] = val
+                    except:  # int dtype
+                        a[..., mask] = 0
+                    ds[var].values = a
 
     if not inplace:
         return ds
@@ -434,10 +499,13 @@ def correlation_filter(ds, thresh=50, inplace=False) -> Optional[xr.Dataset]:
         ds = ds.copy(deep=True)
 
     # 4 or 5 beam
+    tag = []
+    if hasattr(ds, "vel"):
+        tag += [""]
     if hasattr(ds, "vel_b5"):
-        tag = ["", "_b5"]
-    else:
-        tag = [""]
+        tag += ["_b5"]
+    if hasattr(ds, "vel_avg"):
+        tag += ["_avg"]
 
     # copy original ref frame
     coord_sys_orig = ds.coord_sys
@@ -456,7 +524,7 @@ def correlation_filter(ds, thresh=50, inplace=False) -> Optional[xr.Dataset]:
             ds[var + tg].attrs["Comments"] = (
                 "Filtered of data with a correlation value below "
                 + str(thresh)
-                + ds.corr.units
+                + ds["corr" + tg].units
             )
 
     rotate2(ds, coord_sys_orig, inplace=True)
diff --git a/mhkit/dolfyn/adp/discharge.py b/mhkit/dolfyn/adp/discharge.py
new file mode 100644
index 000000000..05f4d8a95
--- /dev/null
+++ b/mhkit/dolfyn/adp/discharge.py
@@ -0,0 +1,313 @@
+import numpy as np
+import xarray as xr
+
+
+def discharge(ds, water_depth, rho, mu=None, surface_offset=0, utm_zone=10):
+    """Calculate discharge (volume flux), power (kinetic energy flux),
+    power density, and Reynolds number from a dataset containing a
+    boat survey with a down-looking ADCP. This function is built to
+    natively handle ADCP datasets read in using the `dolfyn` module.
+
+    Dataset velocity should already be corrected using ADCP-measured
+    bottom track or GPS-measured velocity. The first velocity direction
+    is assumed to be the primary flow axis.
+
+    This function linearly interpolates the lowest ADCP depth bin to
+    the seafloor, and applies a constant extrapolation from the first
+    ADCP bin to the surface.
+
+    Parameters
+    ----------
+    ds: xarray.Dataset
+        Dataset containing the following variables:
+         - `vel`: (dir, range, time) motion-corrected velocity, in m/s
+         - `latitude_gps`: (time_gps) latitude measured by GPS, in deg N
+         - `longitude_gps`: (time_gps) longitude measured by GPS, in deg E
+    water_depth: xarray.DataArray
+        Total water depth measured by the ADCP or other input, in
+        meters. If measured by the ADCP, add the ADCP's depth below
+        the surface to this array.
+        The "down" direction should be positive.
+    rho: float
+        Water density in kg/m^3
+    mu: float
+        Dynamic visocity based on water temperature and salinity, in Ns/m^2.
+        If not provided, Reynolds Number will not be calculated.
+        Default: None.
+    surface_offset: float
+        Surface level offset due to changes in tidal level, in meters.
+        Positive is down. Default: 0 m.
+    utm_zone: int
+        UTM zone for coordinate transformations (e.g., to compute cross-sectional
+        distances from GPS lat/lon data). Map of UTM zones for the contiguous US:
+        https://www.usgs.gov/media/images/mapping-utm-grid-conterminous-48-united-states.
+        Default: 10 (the US west coast).
+
+    Returns
+    -------
+    out: xarray.Dataset
+        Dataset containing the following variables:
+         - `discharge`: (1) volume flux, in m^3/s
+         - `power`: (1) power, in W
+         - `power_density`: (1) power density, in W/m^2
+         - `reynolds_number`: (1) Reynolds number, unitless
+    """
+
+    # Lazy import cartopy
+    import cartopy.crs as ccrs
+
+    def _extrapolate_to_bottom(vel, bottom, rng):
+        """
+        Linearly extrapolate velocity values from the deepest valid bin down to zero at the seafloor.
+
+        This function sets velocity to zero at the seafloor and linearly interpolates
+        between the last valid velocity bin and this zero-velocity boundary. If no valid
+        velocity is found in a particular profile, no update is performed for that profile.
+        This function assumes `rng` extends at least to (or below) the deepest seafloor depth
+        specified in `bottom`.
+
+        Parameters
+        ----------
+        vel : numpy.ndarray
+            A velocity array of shape (dir, range, time), typically containing:
+                - `dir` : velocity component dimension (e.g., 2 or 3 for 2D or 3D flow).
+                - `range` : vertical/bin dimension (positive downward).
+                - `time` : time dimension corresponding to each profile.
+            The array is modified in-place (the updated values are also returned).
+        bottom : array-like
+            Array of length equal to the time dimension in `vel`, specifying the seafloor
+            depth (in the same coordinate system as `rng`) at each time step.
+        rng : array-like
+            The vertical/bin positions corresponding to `vel` along the `range` dimension,
+            sorted in ascending order (e.g., depth from the water surface downward).
+
+        Returns
+        -------
+        vel : numpy.ndarray
+            The same array passed in, with updated values below the last valid velocity bin
+            for each time step (linear extrapolation to zero at the seafloor).
+        """
+
+        for idx in range(vel.shape[-1]):
+            z_bot = bottom[idx]
+            # Fetch lowest range index
+            ind_bot = np.nonzero(rng > z_bot)[0][0]
+            for idim in range(vel.shape[0]):
+                vnow = vel[idim, :, idx]
+                # Check that data exists in slice
+                gd = np.isfinite(vnow) & (vnow != 0)
+                if not gd.sum():
+                    continue
+                else:
+                    ind = np.nonzero(gd)[0][-1]
+                z_top = rng[ind]
+                # linearly interpolate next lowest range bin based on 0 m/s at bottom
+                vals = np.interp(rng[ind:ind_bot], [z_top, z_bot], [vnow[ind], 0])
+                vel[idim, ind:ind_bot, idx] = vals
+
+        return vel
+
+    def _convert_latlon_to_utm(ds, proj):
+        """
+        Convert latitude/longitude coordinates to UTM coordinates.
+
+        This function uses the Cartopy `transform_point` and `transform_points` methods to
+        project GPS latitude/longitude data into the specified UTM coordinate reference
+        system. The resulting (x, y) coordinates are stored in an xarray DataArray that is
+        interpolated onto the main time axis of `ds`.
+
+        The function sets `proj.x0` and `proj.y0` to the UTM coordinates of the mean
+        longitude and latitude from `ds`. This can be used as a reference origin.
+        Missing or NaN lat/lon values are handled via interpolation and extrapolation
+        onto the `ds["time"]` axis.
+        This function modifies the `proj` object by adding `x0` and `y0` attributes,
+        which may be used for subsequent coordinate transformations or offsets.
+
+        Parameters
+        ----------
+        ds : xarray.Dataset
+            A dataset that must contain at least the following variables:
+            - "latitude_gps"  : (time_gps) latitude values in degrees North.
+            - "longitude_gps" : (time_gps) longitude values in degrees East.
+            - "time"          : time axis onto which the projected coordinates will be
+                                interpolated.
+        proj : cartopy.crs.Projection
+            A Cartopy UTM projection or similar projection object. This is used both to
+            store the reference origin (`x0`, `y0`) and to transform lat/lon coordinates
+            into UTM.
+
+        Returns
+        -------
+        xy : xarray.DataArray
+            A DataArray of shape (gps=2, time), where:
+            - The first dimension (indexed by "gps") corresponds to ["x", "y"] UTM
+                coordinates.
+            - The second dimension ("time") matches `ds["time"]`.
+            The returned coordinates are interpolated in time using `ds["longitude_gps"]`
+            and `ds["latitude_gps"]`, with values extrapolated if necessary.
+
+        """
+
+        plate_c = ccrs.PlateCarree()
+        proj.x0, proj.y0 = proj.transform_point(
+            ds["longitude_gps"].mean(), ds["latitude_gps"].mean(), plate_c
+        )
+        xy = xr.DataArray(
+            proj.transform_points(plate_c, ds["longitude_gps"], ds["latitude_gps"])[
+                :, :2
+            ].T,
+            coords={"gps": ["x", "y"], "time_gps": ds["longitude_gps"]["time_gps"]},
+        )
+
+        # this seems to work for missing latlon
+        xy = xy.interp(
+            time_gps=ds["time"], kwargs={"fill_value": "extrapolate"}
+        ).drop_vars("time_gps")
+        return xy
+
+    def _distance(proj, x, y):
+        """
+        Compute the planar distance from the projection's reference origin.
+
+        Parameters
+        ----------
+        proj : cartopy.crs.Projection
+            A projection object with attributes `x0` and `y0`, which define the
+            reference origin in the projected coordinate system.
+        x : float or array-like
+            One or more x-coordinates in the same units (m) as `proj.x0`.
+        y : float or array-like
+            One or more y-coordinates in the same units (m) as `proj.y0`.
+
+        Returns
+        -------
+        dist : float or numpy.ndarray
+            The distance(s) in m from the point(s) `(x, y)` to `(proj.x0, proj.y0)`.
+            If `x` and `y` are arrays, the output is an array of the same shape.
+        """
+
+        return np.sqrt((proj.x0 - x) ** 2 + (proj.y0 - y) ** 2)
+
+    def _calc_discharge(vel, x, depth, surface_zoff=None):
+        """
+        Calculate the integrated flux (e.g., discharge) by double integration of velocity
+        over the cross-sectional area: depth and lateral distance.
+
+        Missing (NaN) velocities are treated as zero.
+        Ensure `depth` and `surface_zoff` are both positive downward.
+
+        Parameters
+        ----------
+        vel : numpy.ndarray or xarray.DataArray
+            A 2D array of shape (nz, nx) corresponding to velocity values (m/s).
+            - `nz` is the number of vertical bins (downward).
+            - `nx` is the number of horizontal points.
+        x : array-like
+            Horizontal positions (m) of length `nx`. If `x` is in descending order
+            (i.e., `x[0] > x[-1]`), the resulting flux is assigned a negative sign to
+            indicate reverse orientation.
+        depth : array-like
+            Vertical positions (m) of length `nz`, positive downward. This is used
+            for integration along the vertical dimension.
+        surface_zoff : float, optional
+            Surface level offset due to changes in tidal level, in meters.
+            Positive is down.
+
+        Returns
+        -------
+        Q : float
+            The integrated flux (e.g., discharge) in units of m^3/s
+
+        """
+        vel = vel.copy()
+        vel = vel.fillna(0)
+        if surface_zoff is not None:
+            # Add a copy of the top row of data
+            vel = np.vstack((vel[0], vel))
+            depth = np.hstack((surface_zoff, depth))
+        if x[0] > x[-1]:
+            sign = -1
+        else:
+            sign = 1
+        return sign * np.trapezoid(np.trapezoid(vel, depth, axis=0), x)
+
+    # Extrapolate to bed
+    vel = ds["vel"].copy()
+    vel.values = _extrapolate_to_bottom(
+        ds["vel"].values, water_depth, ds["range"].values
+    )
+    vel_x = vel[0]
+    # Get position at each timestep in UTM grid
+    proj = ccrs.UTM(utm_zone)
+    xy = _convert_latlon_to_utm(ds, proj)
+    # Distance from UTM grid origin (mean of GPS points)
+    _x = _distance(proj, xy[0], xy[1])
+    # Set distance range for entire transect
+    q_x_range = [_x.min(), _x.max()]  # meters
+
+    # Calculate discharge, power, kinetic energy, and reynolds number
+    _xinds = (q_x_range[0] < _x) & (_x < q_x_range[1])
+    out = {}
+    if _xinds.any():
+        speed = vel_x[:, _xinds]  # m/s
+        # Volume Flux, aka Discharge
+        out["Q"] = _calc_discharge(
+            speed, xy[0][_xinds], ds["range"], surface_offset
+        )  # m/s * m * m = m^3/s
+        # Kinetic Energy Flux, aka Power
+        out["P"] = (
+            0.5
+            * rho
+            * _calc_discharge(speed**3, xy[0][_xinds], ds["range"], surface_offset)
+        )  # kg/m^3 * m^3/s^3 * m * m = kg*m^2/s = W
+        # Power Density
+        out["J"] = (
+            (0.5 * rho * speed**3).mean().item()
+        )  # kg/m^3 * m^3/s^3 = kg/s^3 = W/m^2
+        hydraulic_depth = abs(
+            np.trapezoid((water_depth - surface_offset)[_xinds], xy[0][_xinds])
+        ) / (
+            xy[0][_xinds].max() - xy[0][_xinds].min()
+        )  # area / surface-width
+        # Reynolds Number
+        out["Re"] = ((rho * ds.velds.U_mag.mean() * hydraulic_depth) / mu).item()
+    else:
+        out["Q"] = np.nan
+        out["P"] = np.nan
+        out["J"] = np.nan
+        out["Re"] = np.nan
+
+    ds["discharge"] = xr.DataArray(
+        np.float32(out["Q"]),
+        dims=[],
+        attrs={
+            "units": "m3 s-1",
+            "long_name": "Discharge",
+        },
+    )
+    ds["power"] = xr.DataArray(
+        np.float32(out["P"]),
+        dims=[],
+        attrs={
+            "units": "W",
+            "long_name": "Power",
+        },
+    )
+    ds["power_density"] = xr.DataArray(
+        np.float32(out["J"]),
+        dims=[],
+        attrs={
+            "units": "W m-2",
+            "long_name": "Power Density",
+        },
+    )
+    ds["reynolds_number"] = xr.DataArray(
+        np.float32(out["Re"]),
+        dims=[],
+        attrs={
+            "units": "1",
+            "long_name": "Reynolds Number",
+        },
+    )
+
+    return ds
diff --git a/mhkit/dolfyn/adp/turbulence.py b/mhkit/dolfyn/adp/turbulence.py
index 585d46e72..5002523ad 100644
--- a/mhkit/dolfyn/adp/turbulence.py
+++ b/mhkit/dolfyn/adp/turbulence.py
@@ -96,32 +96,32 @@ def __init__(
         diff_style="centered_extended",
     ):
         """
-        A class for calculating turbulence statistics from ADCP data
+        A class for calculating turbulence statistics from ADCP measurements.
 
         Parameters
         ----------
         n_bin : int
-          Number of data points to include in a 'bin' (ensemble), not the
-          number of bins
+          Number of data points to include in a 'bin' (ensemble)
         fs : int
           Instrument sampling frequency in Hz
         n_fft : int
           Number of data points to use for fft (`n_fft`<=`n_bin`).
-          Default: `n_fft`=`n_bin`
+          Default = `n_fft`=`n_bin`
         n_fft_coh : int
-          Number of data points to use for coherence and cross-spectra ffts
-          Default: `n_fft_coh`=`n_fft`
+          Number of data points to use for coherence and cross-spectra ffts.
+          Default = `n_fft_coh`=`n_fft`
         noise : float or array-like
           Instrument noise level in same units as velocity. Typically
           found from `adp.turbulence.doppler_noise_level`.
-          Default: None.
-        orientation : str, default='up'
-          Instrument's orientation, either 'up' or 'down'
-        diff_style : str, default='centered_extended'
+          Default = None
+        orientation : str
+          Instrument's orientation, either 'up' or 'down'. Default = 'up'
+        diff_style : str
           Style of numerical differentiation using Newton's Method.
           Either 'first' (first difference), 'centered' (centered difference),
           or 'centered_extended' (centered difference with first and last points
           extended using a first difference).
+          Default = 'centered_extended'
         """
 
         VelBinner.__init__(self, n_bin, fs, n_fft, n_fft_coh, noise)
@@ -169,14 +169,15 @@ def _diff_func(self, vel, u, orientation):
 
     def dudz(self, vel, orientation=None):
         """
-        The shear in the first velocity component.
+        The shear in the first velocity component (:math:`du/dz`).
 
         Parameters
         ----------
         vel : xarray.DataArray
           ADCP raw velocity
-        orientation : str, default=ADPBinner.orientation
-          Direction ADCP is facing ('up' or 'down')
+        orientation : str
+          Direction ADCP is facing ('up' or 'down').
+          Default = ADPBinner.orientation
 
         Returns
         -------
@@ -185,8 +186,8 @@ def dudz(self, vel, orientation=None):
 
         Notes
         -----
-        The derivative direction is along the profiler's 'z'
-        coordinate ('dz' is actually diff(self['range'])), not necessarily the
+        The derivative direction is along the profiler's :math:`z`
+        coordinate (:math:`dz` is actually `diff(self['range'])`), not necessarily the
         'true vertical' direction.
         """
 
@@ -200,14 +201,15 @@ def dudz(self, vel, orientation=None):
 
     def dvdz(self, vel, orientation=None):
         """
-        The shear in the second velocity component.
+        The shear in the second velocity component (:math:`dv/dz`).
 
         Parameters
         ----------
         vel : xarray.DataArray
           ADCP raw velocity
-        orientation : str, default=ADPBinner.orientation
-          Direction ADCP is facing ('up' or 'down')
+        orientation : str
+          Direction ADCP is facing ('up' or 'down').
+          Default = ADPBinner.orientation
 
         Returns
         -------
@@ -216,8 +218,8 @@ def dvdz(self, vel, orientation=None):
 
         Notes
         -----
-        The derivative direction is along the profiler's 'z'
-        coordinate ('dz' is actually diff(self['range'])), not necessarily the
+        The derivative direction is along the profiler's :math:`z`
+        coordinate (:math:`dz` is actually `diff(self['range'])`), not necessarily the
         'true vertical' direction.
         """
 
@@ -231,14 +233,15 @@ def dvdz(self, vel, orientation=None):
 
     def dwdz(self, vel, orientation=None):
         """
-        The shear in the third velocity component.
+        The shear in the third velocity component (:math:`dw/dz`).
 
         Parameters
         ----------
         vel : xarray.DataArray
           ADCP raw velocity
-        orientation : str, default=ADPBinner.orientation
-          Direction ADCP is facing ('up' or 'down')
+        orientation : str
+          Direction ADCP is facing ('up' or 'down').
+          Default = ADPBinner.orientation
 
         Returns
         -------
@@ -247,8 +250,8 @@ def dwdz(self, vel, orientation=None):
 
         Notes
         -----
-        The derivative direction is along the profiler's 'z'
-        coordinate ('dz' is actually diff(self['range'])), not necessarily the
+        The derivative direction is along the profiler's :math:`z`
+        coordinate (:math:`dz` is actually `diff(self['range'])`), not necessarily the
         'true vertical' direction.
         """
 
@@ -276,13 +279,9 @@ def shear_squared(self, vel):
 
         Notes
         -----
-        This is actually (dudz)^2 + (dvdz)^2. So, if those variables
+        This is actually :math:`(du/dz)^{2} + (dv/dz)^{2}`. So, if those variables
         are not actually vertical derivatives of the horizontal
         velocity, then this is not the 'horizontal shear squared'.
-
-        See Also
-        --------
-        :math:`dudz`, :math:`dvdz`
         """
 
         shear2 = self.dudz(vel) ** 2 + self.dvdz(vel) ** 2
@@ -293,12 +292,12 @@ def shear_squared(self, vel):
 
     def doppler_noise_level(self, psd, pct_fN=0.8):
         """
-        Calculate bias due to Doppler noise using the noise floor
-        of the velocity spectra.
+        Calculate bias (in units of velocity) due to Doppler noise
+        using the noise floor of the velocity spectra.
 
         Parameters
         ----------
-        psd : xarray.DataArray (time, f)
+        psd : xarray.DataArray (time, freq)
           The velocity spectra from a single depth bin (range), typically
           in the mid-water range
         pct_fN : float
@@ -313,17 +312,17 @@ def doppler_noise_level(self, psd, pct_fN=0.8):
         -----
         Approximates bias from
 
-        .. :math: \\sigma^{2}_{noise} = N x f_{c}
+        .. math:: \\sigma^{2}_{noise} = N * f_{c}
 
-        where :math: `\\sigma_{noise}` is the bias due to Doppler noise,
-        `N` is the constant variance or spectral density, and `f_{c}`
+        where :math:`\\sigma_{noise}` is the bias due to Doppler noise,
+        :math:`N` is the constant variance or spectral density, and :math:`f_{c}`
         is the characteristic frequency.
 
         The characteristic frequency is then found as
 
-        .. :math: f_{c} = pct_fN * (f_{s}/2)
+        .. math:: f_{c} = pct_fN * (f_{s}/2)
 
-        where `f_{s}/2` is the Nyquist frequency.
+        where :math:`f_{s}/2` is the Nyquist frequency.
 
 
         Richard, Jean-Baptiste, et al. "Method for identification of Doppler noise
@@ -381,8 +380,9 @@ def _stress_func_warnings(self, ds, beam_angle, noise, tilt_thresh):
         ----------
         ds : xarray.Dataset
           Raw dataset in beam coordinates
-        beam_angle : int, default=ds.attrs['beam_angle']
-          ADCP beam angle in units of degrees
+        beam_angle : int
+          ADCP beam angle in units of degrees.
+          Default = ``ds.attrs['beam_angle']``
         noise : int or xarray.DataArray (time)
           Doppler noise level in units of m/s
         tilt_thresh: numeric
@@ -459,9 +459,10 @@ def _check_orientation(self, ds, orientation, beam5=False):
           The orientation of the instrument, either 'up' or 'down'.
           If None, the orientation will be retrieved from the dataset or the
           instance's default orientation.
-        beam5 : bool, default=False
+        beam5 : bool
           A flag indicating whether a fifth beam is present.
           If True, the number 4 will be appended to the beam order.
+          Default = False
 
         Returns
         -------
@@ -472,6 +473,10 @@ def _check_orientation(self, ds, orientation, beam5=False):
         phi3 : float, optional
           The mean of the pitch values in radians, negated for Nortek instruments.
           Only returned if 'beam5' is True.
+
+        Stacey, Mark T., Stephen G. Monismith, and Jon R. Burau. "Measurements
+        of Reynolds stress profiles in unstratified tidal flow." Journal of
+        Geophysical Research: Oceans 104.C5 (1999): 10933-10949.
         """
 
         if orientation is None:
@@ -548,7 +553,7 @@ def _beam_variance(self, ds, time, noise, beam_order, n_beams):
             bp2_[i] = np.nanvar(self.reshape(beam_vel[beam]), axis=-1)
 
         # Remove doppler_noise
-        if type(noise) == type(ds.vel):
+        if type(noise) == type(ds["vel"]):
             noise = noise.values
         bp2_ -= noise**2
 
@@ -556,8 +561,8 @@ def _beam_variance(self, ds, time, noise, beam_order, n_beams):
 
     def reynolds_stress_4beam(self, ds, noise=None, orientation=None, beam_angle=None):
         """
-        Calculate the stresses from the covariance of along-beam
-        velocity measurements
+        Calculate the specific Reynolds shear stresses from the covariance of along-beam
+        velocity measurements (:math:`\\overline{u'w'}`, :math:`\\overline{v'w'}`).
 
         Parameters
         ----------
@@ -565,15 +570,17 @@ def reynolds_stress_4beam(self, ds, noise=None, orientation=None, beam_angle=Non
           Raw dataset in beam coordinates
         noise : int or xarray.DataArray (time)
           Doppler noise level in units of m/s
-        orientation : str, default=ds.attrs['orientation']
+        orientation : str
           Direction ADCP is facing ('up' or 'down')
-        beam_angle : int, default=ds.attrs['beam_angle']
+          Default = ``ds.attrs['orientation']``
+        beam_angle : int
           ADCP beam angle in units of degrees
+          Default = ``ds.attrs['beam_angle']``
 
         Returns
         -------
         stress_vec : xarray.DataArray(s)
-          Stress vector with u'w'_ and v'w'_ components
+          Stress vector with :math:`\\overline{u'w'}` and :math:`\\overline{v'w'}` components
 
         Notes
         -----
@@ -581,10 +588,6 @@ def reynolds_stress_4beam(self, ds, noise=None, orientation=None, beam_angle=Non
 
         Assumes ADCP instrument coordinate system is aligned with principal flow
         directions.
-
-        Stacey, Mark T., Stephen G. Monismith, and Jon R. Burau. "Measurements
-        of Reynolds stress profiles in unstratified tidal flow." Journal of
-        Geophysical Research: Oceans 104.C5 (1999): 10933-10949.
         """
 
         # Run through warnings
@@ -618,26 +621,31 @@ def stress_tensor_5beam(
         self, ds, noise=None, orientation=None, beam_angle=None, tke_only=False
     ):
         """
-        Calculate the stresses from the covariance of along-beam
-        velocity measurements
+        Calculate the specific Reynolds stresses from the covariance of along-beam
+        velocity measurements (:math:`\\overline{u'u'}`, :math:`\\overline{v'v'}`,
+        :math:`\\overline{w'w'}`, :math:`\\overline{u'w'}`, :math:`\\overline{v'w'}`).
 
         Parameters
         ----------
         ds : xarray.Dataset
           Raw dataset in beam coordinates
-        noise : int or xarray.DataArray with dim 'time', default=0
-          Doppler noise level in units of m/s
-        orientation : str, default=ds.attrs['orientation']
-          Direction ADCP is facing ('up' or 'down')
-        beam_angle : int, default=ds.attrs['beam_angle']
-          ADCP beam angle in units of degrees
-        tke_only : bool, default=False
-          If true, only calculates tke components
+        noise : int or xarray.DataArray ('time')
+          Doppler noise level in units of m/s.
+          Default = 0
+        orientation : str
+          Direction ADCP is facing ('up' or 'down').
+          Default = ``ds.attrs['orientation']``
+        beam_angle : int
+          ADCP beam angle in units of degrees.
+          Default = ``ds.attrs['beam_angle']``
+        tke_only : bool
+          If true, only calculates TKE components.
+          Default = False
 
         Returns
         -------
         tke_vec(, stress_vec) : xarray.DataArray or tuple[xarray.DataArray]
-          If tke_only is set to False, function returns `tke_vec` and `stress_vec`.
+          If `tke_only` is set to False, function returns `tke_vec` and `stress_vec`.
           Otherwise only `tke_vec` is returned
 
         Notes
@@ -645,14 +653,14 @@ def stress_tensor_5beam(
         Assumes small-angle approximation is applicable.
 
         Assumes ADCP instrument coordinate system is aligned with principal flow
-        directions, i.e. u', v' and w' are aligned to the instrument's (XYZ)
-        frame of reference.
+        directions, i.e., :math:`u'`, :math:`v'` and :math:`w'` are aligned to the
+        instrument's (XYZ) frame of reference.
 
-        The stress equations here utilize u'v'_ to account for small variations
-        in pitch and roll. u'v'_ cannot be directly calculated by a 5-beam ADCP,
-        so it is approximated by the covariance of `u` and `v`. The uncertainty
-        introduced by using this approximation is small if deviations from pitch
-        and roll are small (<= 5 degrees).
+        The stress equations here utilize :math:`\\overline{u'v'}` to account for small
+        variations in pitch and roll. :math:`\\overline{u'v'}` cannot be directly calculated
+        by a 5-beam ADCP, (there are only 5 beams so only 5 unknowns can be found) so it is
+        approximated by the covariance of :math:`u` and :math:`v`. This approximation assumes
+        :math:`\\overline{u'v'}` is similar in magnitude to the other stress components.
 
         Dewey, R., and S. Stringer. "Reynolds stresses and turbulent kinetic
         energy estimates from various ADCP beam configurations: Theory." J. of
@@ -780,9 +788,10 @@ def check_turbulence_cascade_slope(self, psd, freq_range=[0.2, 0.4]):
         ----------
         psd : xarray.DataArray ([[range,] time,] freq)
           The power spectral density (1D, 2D or 3D)
-        freq_range : iterable(2) (default: [6.28, 12.57])
+        freq_range : iterable(2)
           The range over which the isotropic turbulence cascade occurs, in
-          units of the psd frequency vector (Hz or rad/s)
+          units of the psd frequency vector (Hz or rad/s).
+          Default = [6.28, 12.57]
 
         Returns
         -------
@@ -809,7 +818,7 @@ def check_turbulence_cascade_slope(self, psd, freq_range=[0.2, 0.4]):
 
         Where :math:`y` is S(k) or S(f), :math:`x` is k or f, :math:`m`
         is the slope (ideally -5/3), and :math:`10^{b}` is the intercept of
-        y at x^m=1.
+        :math:`y` at :math:`x^{m}=1'.
         """
 
         if not isinstance(psd, xr.DataArray):
@@ -835,23 +844,30 @@ def check_turbulence_cascade_slope(self, psd, freq_range=[0.2, 0.4]):
 
         return m, b
 
-    def dissipation_rate_LT83(self, psd, U_mag, freq_range=[0.2, 0.4], noise=None):
+    def dissipation_rate_LT83(
+        self, psd, U_mag, freq_range=[0.2, 0.4], k_constant=0.67, noise=None
+    ):
         """
         Calculate the TKE dissipation rate from the velocity spectra.
 
         Parameters
         ----------
-        psd : xarray.DataArray (time,f)
-          The power spectral density from a single depth bin (range)
+        psd : xarray.DataArray (time, freq)
+          The power spectral density from the vertical beam and depth bin (range)
         U_mag : xarray.DataArray (time)
-          The bin-averaged horizontal velocity (a.k.a. speed) from a single depth bin (range)
+          The bin-averaged horizontal velocity (a.k.a. speed) from a single
+          depth bin (range) (i.e., computed using
+          :func:`mhkit.dolfyn.velocity.Velocity.U_mag`)
         f_range : iterable(2)
           The range over which to integrate/average the spectrum, in units
           of the psd frequency vector (Hz or rad/s)
+        k_constant : float or iterable(3)
+          Kolmogorov Constant (\\alpha in Notes section below) to use. Default
+          \\alpha is 0.67.
         noise : float or array-like
-          Instrument noise level in same units as velocity. Typically
-          found from `adp.turbulence.doppler_noise_level`.
-          Default: None.
+          Instrument noise level in same units as velocity. Typically found from
+          :func:`doppler_noise_level <mhkit.dolfyn.adp.turbulence.ADPBinner.doppler_noise_level>`
+          Default = None
 
         Returns
         -------
@@ -864,10 +880,9 @@ def dissipation_rate_LT83(self, psd, U_mag, freq_range=[0.2, 0.4], noise=None):
 
         .. math:: S(k) = \\alpha \\epsilon^{2/3} k^{-5/3} + N
 
-        where :math:`\\alpha = 0.5` (1.5 for all three velocity
-        components), `k` is wavenumber, `S(k)` is the turbulent
-        kinetic energy spectrum, and `N' is the doppler noise level
-        associated with the TKE spectrum.
+        where :math:`\\alpha` is the Kolmogorov constant (0.67 for vertical direction),
+        `k` is wavenumber, `S(k)` is the turbulent kinetic energy spectrum, and
+        `N' is the doppler noise level associated with the TKE spectrum.
 
         With :math:`k \\rightarrow \\omega / U`, then -- to preserve variance --
         :math:`S(k) = U S(\\omega)`, and so this becomes:
@@ -882,15 +897,19 @@ def dissipation_rate_LT83(self, psd, U_mag, freq_range=[0.2, 0.4], noise=None):
         by a random wave field". JPO, 1983, vol13, pp2000-2007.
         """
 
+        if not isinstance(psd, xr.DataArray):
+            raise TypeError("`psd` must be an instance of `xarray.DataArray`.")
         if len(psd.shape) != 2:
-            raise Exception("PSD should be 2-dimensional (time, frequency)")
+            raise Exception("`psd` should be 2-dimensional (time, frequency)")
         if len(U_mag.shape) != 1:
             raise Exception("U_mag should be 1-dimensional (time)")
         if not hasattr(freq_range, "__iter__") or len(freq_range) != 2:
             raise ValueError("`freq_range` must be an iterable of length 2.")
+        if np.size(k_constant) != 1:
+            raise ValueError("`k_constant` should be a single value.")
         if noise is not None:
             if np.shape(noise)[0] != np.shape(psd)[0]:
-                raise Exception("Noise should have same first dimension as PSD")
+                raise Exception("Noise should have same first dimension as `psd`")
         else:
             noise = np.array(0)
 
@@ -904,12 +923,15 @@ def dissipation_rate_LT83(self, psd, U_mag, freq_range=[0.2, 0.4], noise=None):
         idx = np.where((freq_range[0] < freq) & (freq < freq_range[1]))
         idx = idx[0]
 
+        # Set the correct magnitude whether the frequency is in Hz or rad/s
         if freq.units == "Hz":
             U = U_mag / (2 * np.pi)
         else:
             U = U_mag
 
-        a = 0.5
+        # Use the transverse value derived from the Kolmogorov constant
+        a = k_constant
+        # Calculate dissipation
         out = (psd[:, idx] * freq[idx] ** (5 / 3) / a).mean(axis=-1) ** (
             3 / 2
         ) / U.values
@@ -935,10 +957,11 @@ def dissipation_rate_SF(self, vel_raw, r_range=[1, 5]):
         vel_raw : xarray.DataArray
           The raw beam velocity data (one beam, last dimension time) upon
           which to perform the SF technique.
-        r_range : numeric, default=[1,5]
+        r_range : numeric,
           Range of r in [m] to calc dissipation across. Low end of range should be
           bin size, upper end of range is limited to the length of largest eddies
           in the inertial subrange.
+          Default = [1, 5]
 
         Returns
         -------
@@ -963,7 +986,7 @@ def dissipation_rate_SF(self, vel_raw, r_range=[1, 5]):
 
         where `u'` is the velocity fluctuation `z` is the depth bin,
         `r` is the separation between depth bins, and [] denotes a time average
-        (size 'ADPBinner.n_bin').
+        (size 'self.n_bin').
 
         The stucture function can then be used to estimate the dissipation rate:
 
@@ -1081,12 +1104,12 @@ def friction_velocity(self, ds_avg, upwp_, z_inds=slice(1, 5), H=None):
         ds_avg : xarray.Dataset
           Bin-averaged dataset containing `stress_vec`
         upwp_ : xarray.DataArray
-          First component of Reynolds shear stress vector, "u-prime v-prime bar"
+          Second component of Reynolds shear stress vector, :math:`\\overline{u'w'}`
           Ex `ds_avg['stress_vec'].sel(tau='upwp_')`
-        z_inds : slice(int,int)
+        z_inds : slice(int, int)
           Depth indices to use for profile. Default = slice(1, 5)
-        H : numeric (default=`ds_avg.depth`)
-          Total water depth
+        H : numeric
+          Total water depth. Default = `ds_avg["depth"]`
 
         Returns
         -------
diff --git a/mhkit/dolfyn/adv/clean.py b/mhkit/dolfyn/adv/clean.py
index 69a03587b..95fba7430 100644
--- a/mhkit/dolfyn/adv/clean.py
+++ b/mhkit/dolfyn/adv/clean.py
@@ -1,5 +1,4 @@
-"""Module containing functions to clean data
-"""
+"""Module containing functions to clean data"""
 
 import warnings
 import numpy as np
diff --git a/mhkit/dolfyn/adv/motion.py b/mhkit/dolfyn/adv/motion.py
index f4a9e7568..fcefa4e52 100644
--- a/mhkit/dolfyn/adv/motion.py
+++ b/mhkit/dolfyn/adv/motion.py
@@ -171,8 +171,8 @@ def calc_velacc(
 
         Returns
         -------
-        velacc : numpy.ndarray (3 x n_time)
-          The acceleration-induced velocity array (3, n_time).
+        velacc : numpy.ndarray (dir, time)
+          The acceleration-induced velocity array
         """
 
         samp_freq = self.ds.fs
@@ -235,15 +235,15 @@ def calc_velrot(self, vec, to_earth=None):
 
         Parameters
         ----------
-        vec : numpy.ndarray (len(3) or 3 x M)
+        vec : numpy.ndarray (dir[, time])
           The vector in meters (or vectors) from the body-origin
           (center of head end-cap) to the point of interest (in the
           body coord-sys).
 
         Returns
         -------
-        velrot : numpy.ndarray (3 x M x N_time)
-          The rotation-induced velocity array (3, n_time).
+        velrot : numpy.ndarray (dir[, time])
+          The rotation-induced velocity array
         """
 
         if to_earth is None:
diff --git a/mhkit/dolfyn/adv/turbulence.py b/mhkit/dolfyn/adv/turbulence.py
index 3fb4ef9a4..8c73c39bc 100644
--- a/mhkit/dolfyn/adv/turbulence.py
+++ b/mhkit/dolfyn/adv/turbulence.py
@@ -9,37 +9,36 @@
 class ADVBinner(VelBinner):
     """
     A class that builds upon `VelBinner` for calculating turbulence
-    statistics and velocity spectra from ADV data
+    statistics and velocity spectra from ADV data.
 
     Parameters
     ----------
     n_bin : int
-      The length of each `bin`, in number of points, for this averaging
+      The length of each bin, in number of points, for this averaging
       operator.
     fs : int
       Instrument sampling frequency in Hz
     n_fft : int
-      The length of the FFT for computing spectra (must be <= n_bin).
+      The length of the FFT for computing spectra (must be <= `n_bin`).
       Optional, default `n_fft` = `n_bin`
     n_fft_coh : int
-      Number of data points to use for coherence and cross-spectra fft's.
+      Number of data points to use for coherence and cross-spectra FFT's.
       Optional, default `n_fft_coh` = `n_fft`
-        noise : float or array-like
-          Instrument noise level in same units as velocity. Typically
-          found from `adv.turbulence.doppler_noise_level`.
-          Default: None.
+    noise : float or array-like
+      Instrument noise level in same units as velocity. Typically found from
+      :func:`doppler_noise_level <mhkit.dolfyn.adv.turbulence.ADVBinner.doppler_noise_level>`.
+      Default: None.
     """
 
     def __call__(self, ds, freq_units="rad/s", window="hann"):
         out = type(ds)()
         out = self.bin_average(ds, out)
 
-        noise = ds.get("doppler_noise", [0, 0, 0])
-        out["tke_vec"] = self.turbulent_kinetic_energy(ds["vel"], noise=noise)
+        out["tke_vec"] = self.turbulent_kinetic_energy(ds["vel"])
         out["stress_vec"] = self.reynolds_stress(ds["vel"])
 
         out["psd"] = self.power_spectral_density(
-            ds["vel"], window=window, freq_units=freq_units, noise=noise
+            ds["vel"], window=window, freq_units=freq_units
         )
         for key in list(ds.attrs.keys()):
             if "config" in key:
@@ -53,19 +52,18 @@ def __call__(self, ds, freq_units="rad/s", window="hann"):
 
     def reynolds_stress(self, veldat, detrend=True):
         """
-        Calculate the specific Reynolds stresses
-        (covariances of u,v,w in m^2/s^2)
+        Calculate the specific Reynolds shear stresses (:math:`\\overline{u'v'}`,
+        :math:`\\overline{u'w'}`, :math:`\\overline{v'w'}`).
 
         Parameters
         ----------
         veldat : xr.DataArray
-          A velocity data array. The last dimension is assumed
-          to be time.
+          A velocity data array. The last dimension is assumed to be time.
         detrend : bool
           Detrend the velocity data (True), or simply de-mean it
           (False), prior to computing stress. Note: the psd routines
           use detrend, so if you want to have the same amount of
-          variance here as there use ``detrend=True``.
+          variance here as there use `detrend=True`.
           Default = True
 
         Returns
@@ -121,20 +119,20 @@ def cross_spectral_density(
           Frequency units of the returned spectra in either Hz or rad/s
           (`f` or :math:`\\omega`)
         fs : float (optional)
-          The sample rate. Default = `binner.fs`
+          The sample rate. Default = `self.fs`
         window : string or array
           Specify the window function.
          Options: 1, None, 'hann', 'hamm'
         n_bin : int (optional)
-          The bin-size. Default = `binner.n_bin`
+          The bin-size. Default = `self.n_bin`
         n_fft_coh : int (optional)
-          The fft size. Default = `binner.n_fft_coh`
+          The fft size. Default = `self.n_fft_coh`
 
         Returns
         -------
         csd : xarray.DataArray (3, M, N_FFT)
           The first-dimension of the cross-spectrum is the three
-          different cross-spectra: 'uv', 'uw', 'vw'.
+          different cross-spectra: :math:`uv`, :math:`uw`, :math:`vw`.
         """
 
         if not isinstance(veldat, xr.DataArray):
@@ -208,10 +206,11 @@ def doppler_noise_level(self, psd, pct_fN=0.8):
 
         Parameters
         ----------
-        psd : xarray.DataArray (dir, time, f)
+        psd : xarray.DataArray (dir, time, freq)
           The ADV power spectral density of velocity (auto-spectra)
         pct_fN : float
-          Percent of Nyquist frequency to calculate characeristic frequency
+          Percent of Nyquist frequency to calculate characeristic frequency.
+          Default = 0.8 (80%)
 
         Returns
         -------
@@ -222,17 +221,17 @@ def doppler_noise_level(self, psd, pct_fN=0.8):
         -----
         Approximates bias from
 
-        .. :math: \\sigma^{2}_{noise} = N x f_{c}
+        .. math:: \\sigma^{2}_{noise} = N * f_{c}
 
-        where :math: `\\sigma_{noise}` is the bias due to Doppler noise,
-        `N` is the constant variance or spectral density, and `f_{c}`
+        where :math:`\\sigma_{noise}` is the bias due to Doppler noise,
+        :math:`N` is the constant variance or spectral density, and :math:`f_{c}`
         is the characteristic frequency.
 
         The characteristic frequency is then found as
 
-        .. :math: f_{c} = pct_fN * (f_{s}/2)
+        .. math:: f_{c} = pct_fN * (f_{s}/2)
 
-        where `f_{s}/2` is the Nyquist frequency.
+        where :math:`f_{s}/2` is the Nyquist frequency.
 
 
         Richard, Jean-Baptiste, et al. "Method for identification of Doppler noise
@@ -284,9 +283,10 @@ def check_turbulence_cascade_slope(self, psd, freq_range=[6.28, 12.57]):
         ----------
         psd : xarray.DataArray ([time,] freq)
           The power spectral density (1D or 2D)
-        freq_range : iterable(2) (default: [6.28, 12.57])
+        freq_range : iterable(2)
           The range over which the isotropic turbulence cascade occurs, in
-          units of the psd frequency vector (Hz or rad/s)
+          units of the psd frequency vector (Hz or rad/s).
+          Default = [6.28, 12.57] rad/s
 
         Returns
         -------
@@ -313,7 +313,7 @@ def check_turbulence_cascade_slope(self, psd, freq_range=[6.28, 12.57]):
 
         Where :math:`y` is S(k) or S(f), :math:`x` is k or f, :math:`m`
         is the slope (ideally -5/3), and :math:`10^{b}` is the intercept of
-        y at x^m=1.
+        :math:`y` at :math:`x^{m}=1'.
         """
 
         if not isinstance(psd, xr.DataArray):
@@ -339,20 +339,34 @@ def check_turbulence_cascade_slope(self, psd, freq_range=[6.28, 12.57]):
 
         return m, b
 
-    def dissipation_rate_LT83(self, psd, U_mag, freq_range=[6.28, 12.57], noise=None):
+    def dissipation_rate_LT83(
+        self,
+        psd,
+        U_mag,
+        freq_range=[6.28, 12.57],
+        k_constant=[0.5, 0.67, 0.67],
+        noise=None,
+    ):
         """
-        Calculate the dissipation rate from the PSD
+        Calculate the dissipation rate from the power spectral density of velocity.
 
         Parameters
         ----------
-        psd : xarray.DataArray (...,time,f)
+        psd : xarray.DataArray ([dir,] time, freq)
           The power spectral density
-        U_mag : xarray.DataArray (...,time)
-          The bin-averaged horizontal velocity [m/s] (from dataset shortcut)
+        U_mag : xarray.DataArray (time)
+          The bin-averaged horizontal velocity [m/s] (i.e., computed using
+          :func:`U_mag <mhkit.dolfyn.velocity.Velocity.U_mag>`)
         freq_range : iterable(2)
           The range over which to integrate/average the spectrum, in units
           of the psd frequency vector (Hz or rad/s).
           Default = [6.28, 12.57] rad/s
+        k_constant : float or iterable(3)
+          Kolmogorov Constant (\\alpha in Notes section below) to use. If a
+          three dimensional PSD is provided, \\alpha defaults to [0.5, 0.67, 0.67];
+          i.e. 0.5 for the streamwise PSD and 0.67 for the transverse and vertical
+          PSDs. If the PSD is provided for a single velocity direction, \\alpha is
+          taken to be 0.5 unless otherwise specified.
         noise : float or array-like
           Instrument noise level in same units as velocity. Typically
           found from `adv.turbulence.calc_doppler_noise`.
@@ -360,7 +374,7 @@ def dissipation_rate_LT83(self, psd, U_mag, freq_range=[6.28, 12.57], noise=None
 
         Returns
         -------
-        epsilon : xarray.DataArray (...,n_time)
+        epsilon : xarray.DataArray ([dir,] time)
           dataArray of the dissipation rate
 
         Notes
@@ -369,10 +383,9 @@ def dissipation_rate_LT83(self, psd, U_mag, freq_range=[6.28, 12.57], noise=None
 
         .. math:: S(k) = \\alpha \\epsilon^{2/3} k^{-5/3} + N
 
-        where :math:`\\alpha = 0.5` (1.5 for all three velocity
-        components), `k` is wavenumber, `S(k)` is the turbulent
-        kinetic energy spectrum, and `N' is the doppler noise level
-        associated with the TKE spectrum.
+        where :math:`\\alpha is the Kolmogorov constant, `k` is wavenumber,
+        `S(k)` is the turbulent kinetic energy spectrum, and `N' is the
+        doppler noise level associated with the TKE spectrum.
 
         With :math:`k \\rightarrow \\omega / U`, then -- to preserve variance --
         :math:`S(k) = U S(\\omega)`, and so this becomes:
@@ -390,15 +403,19 @@ def dissipation_rate_LT83(self, psd, U_mag, freq_range=[6.28, 12.57], noise=None
         if not isinstance(psd, xr.DataArray):
             raise TypeError("`psd` must be an instance of `xarray.DataArray`.")
         if len(U_mag.shape) != 1:
-            raise Exception("U_mag should be 1-dimensional (time)")
+            raise Exception("U_mag should be 1-dimensional (time).")
         if len(psd["time"]) != len(U_mag["time"]):
-            raise Exception("`U_mag` should be from ensembled-averaged dataset")
+            raise Exception("`U_mag` should be from ensembled-averaged dataset.")
         if not hasattr(freq_range, "__iter__") or len(freq_range) != 2:
             raise ValueError("`freq_range` must be an iterable of length 2.")
-
+        # if the spectra are 1D, then the first dimension should be time (any length)
+        if (psd.shape[0] != 3) and (np.size(k_constant) != 1):
+            raise ValueError("`k_constant` should be a single value.")
+        elif (psd.shape[0] == 3) and (np.size(k_constant) != 3):
+            raise ValueError("`k_constant` should be an iterable of length 3.")
         if noise is not None:
-            if np.shape(noise)[0] != 3:
-                raise Exception("Noise should have same first dimension as velocity")
+            if np.shape(noise)[0] != np.shape(psd)[0]:
+                raise Exception("Noise should have same first dimension as `psd`.")
         else:
             noise = np.array([0, 0, 0])[:, None, None]
 
@@ -412,12 +429,20 @@ def dissipation_rate_LT83(self, psd, U_mag, freq_range=[6.28, 12.57], noise=None
         idx = np.where((freq_range[0] < freq) & (freq < freq_range[1]))
         idx = idx[0]
 
+        # Set the correct magnitude whether the frequency is in Hz or rad/s
         if freq.units == "Hz":
             U = U_mag / (2 * np.pi)
         else:
             U = U_mag
 
-        a = 0.5
+        # Set Kolmogorov constant
+        a = np.array(k_constant)
+        if psd.shape[0] == 3:
+            a = a[:, None, None]  # stack properly
+        else:
+            a = np.squeeze(k_constant)
+
+        # Calculate dissipation
         out = (psd.isel(freq=idx) * freq.isel(freq=idx) ** (5 / 3) / a).mean(
             axis=-1
         ) ** (3 / 2) / U
@@ -442,11 +467,12 @@ def dissipation_rate_SF(self, vel_raw, U_mag, fs=None, freq_range=[2.0, 4.0]):
         vel_raw : xarray.DataArray (time)
           The raw velocity data upon which to perform the SF technique.
         U_mag : xarray.DataArray
-          The bin-averaged horizontal velocity (from dataset shortcut)
+          The bin-averaged horizontal velocity (i.e., computed using
+          :func:`U_mag <mhkit.dolfyn.velocity.Velocity.U_mag>`)
         fs : float
-          The sample rate of `vel_raw` [Hz]
+          The sample rate of `vel_raw` in Hz
         freq_range : iterable(2)
-          The frequency range over which to compute the SF [Hz]
+          The frequency range over which to compute the SF in Hz
           (i.e. the frequency range within which the isotropic
           turbulence cascade falls).
           Default = [2., 4.] Hz
@@ -535,7 +561,7 @@ def _integral_TE01(self, I_tke, theta):
         x = np.arange(-20, 20, 1e-2)  # I think this is a long enough range.
         out = np.empty_like(I_tke.flatten())
         for i, (b, t) in enumerate(zip(I_tke.flatten(), theta.flatten())):
-            out[i] = np.trapz(
+            out[i] = np.trapezoid(
                 cbrt(x**2 - 2 / b * np.cos(t) * x + b ** (-2)) * np.exp(-0.5 * x**2),
                 x,
             )
@@ -551,9 +577,9 @@ def dissipation_rate_TE01(self, dat_raw, dat_avg, freq_range=[6.28, 12.57]):
         dat_raw : xarray.Dataset
           The raw (off the instrument) adv dataset
         dat_avg : xarray.Dataset
-          The bin-averaged adv dataset (calc'd from 'calc_turbulence' or
-          'do_avg'). The spectra (psd) and basic turbulence statistics
-          ('tke_vec' and 'stress_vec') must already be computed.
+          The bin-averaged adv dataset (calculated from `ADVBinner.calc_turbulence` or
+          `VelBinner.bin_average`). The spectra (PSD) and Reynolds stresses
+          (`tke_vec` and `stress_vec`) must already be computed.
         freq_range : iterable(2)
           The range over which to integrate/average the spectrum, in units
           of the psd frequency vector (Hz or rad/s).
@@ -578,7 +604,7 @@ def dissipation_rate_TE01(self, dat_raw, dat_avg, freq_range=[6.28, 12.57]):
         theta = np.angle(dat_avg.velds.U.values) - self._up_angle(
             dat_raw.velds.U.values
         )
-        freq = dat_avg["psd"].freq.values
+        freq = dat_avg["freq"].values
 
         # Calculate constants
         alpha = 1.5
@@ -606,7 +632,7 @@ def dissipation_rate_TE01(self, dat_raw, dat_avg, freq_range=[6.28, 12.57]):
 
         return xr.DataArray(
             out.astype("float32"),
-            coords={"time": dat_avg["psd"]["time"]},
+            coords={"time": dat_avg["time"]},
             dims="time",
             attrs={
                 "units": "m2 s-3",
@@ -619,28 +645,33 @@ def dissipation_rate_TE01(self, dat_raw, dat_avg, freq_range=[6.28, 12.57]):
 
     def integral_length_scales(self, a_cov, U_mag, fs=None):
         """
-        Calculate integral length scales.
+        Calculate integral length scales from the autocovariance (or autocorrelation).
 
         Parameters
         ----------
-        a_cov : xarray.DataArray
-          The auto-covariance array (i.e. computed using `autocovariance`).
-        U_mag : xarray.DataArray
-          The bin-averaged horizontal velocity (from dataset shortcut)
+        a_cov : xarray.DataArray ([dir,] time, lag)
+          The autocovariance or autocorrelation array
+          (i.e., computed using
+          :func:`autocovariance <mhkit.dolfyn.velocity.VelBinner.autocovariance>`)
+        U_mag : xarray.DataArray (time)
+          The bin-averaged horizontal velocity (i.e., computed using
+          :func:`U_mag <mhkit.dolfyn.velocity.Velocity.U_mag>`)
         fs : numeric
           The raw sample rate
 
         Returns
         -------
-        L_int : numpy.ndarray (..., n_time)
-          The integral length scale (T_int*U_mag).
+        L_int : numpy.ndarray ([dir,] time)
+          The integral length scale.
 
         Notes
         ----
-        The integral time scale (T_int) is the lag-time at which the
-        auto-covariance falls to 1/e.
-
-        If T_int is not reached, L_int will default to '0'.
+        The integral time scale (:math:`T_{int}`) is integral of the normalized
+        autocovariance (autocorrelation) function, which theoretically decays to
+        zero over time. Practically, :math:`T_{int}` is the integral from zero to
+        the first zero-crossing lag-time of the autocorrelation function. The
+        integral length scale (:math:`L_{int}`) then is the integral time scale
+        multiplied by the bin speed.
         """
 
         if not isinstance(a_cov, xr.DataArray):
@@ -648,11 +679,20 @@ def integral_length_scales(self, a_cov, U_mag, fs=None):
         if len(a_cov["time"]) != len(U_mag["time"]):
             raise Exception("`U_mag` should be from ensembled-averaged dataset")
 
-        acov = a_cov.values
         fs = self._parse_fs(fs)
+        # Normalize autocovariance/autocorrelation
+        acov = a_cov / a_cov[..., 0]
+
+        # Calculate first zero crossing in auto-correlation
+        zero_crossing = np.nanargmin(~(acov < 0), axis=-1)
 
-        scale = np.argmin((acov / acov[..., :1]) > (1 / np.e), axis=-1)
-        L_int = U_mag.values / fs * scale
+        # Calculate integral time scale
+        T_int = np.zeros(acov.shape[:2])
+        for i in range(3):
+            for t in range(a_cov["time"].size):
+                T_int[i, t] = np.trapezoid(acov[i, t][: zero_crossing[i, t]], dx=1 / fs)
+
+        L_int = U_mag.values * T_int
 
         return xr.DataArray(
             L_int.astype("float32"),
@@ -675,20 +715,19 @@ def turbulence_statistics(
     Parameters
     ----------
     ds_raw : xarray.Dataset
-      The raw adv datset to `bin`, average and compute
-      turbulence statistics of.
+      The raw adv datset to bin, average, and compute turbulence statistics
+      from.
     freq_units : string
       Frequency units of the returned spectra in either Hz or rad/s
-      (`f` or :math:`\\omega`). Default is 'rad/s'
+      (`f` or :math:`\\omega`). Default = 'rad/s'
     window : string or array
       The window to use for calculating spectra.
 
-
     Returns
     -------
     ds : xarray.Dataset
       Returns an 'binned' (i.e. 'averaged') data object. All
-      fields (variables) of the input data object are averaged in n_bin
+      fields (variables) of the input data object are averaged in `n_bin`
       chunks. This object also computes the following items over
       those chunks:
 
diff --git a/mhkit/dolfyn/binned.py b/mhkit/dolfyn/binned.py
index 0bdb00f73..ef999e99c 100644
--- a/mhkit/dolfyn/binned.py
+++ b/mhkit/dolfyn/binned.py
@@ -188,7 +188,7 @@ def reshape(self, arr, n_pad=0, n_bin=None):
           corners of the matrix (beginning/end of timeseries).  In
           this case, the array shape will be (...,`n`,`n_pad`+`n_bin`)
         n_bin : int
-          Override this binner's n_bin. Default is `binner.n_bin`
+          Override this binner's n_bin. Default is `self.n_bin`
 
         Returns
         -------
@@ -256,7 +256,7 @@ def detrend(self, arr, axis=-1, n_pad=0, n_bin=None):
           this case, the array shape will be (...,`n`,`n_pad`+`n_bin`).
           Default = 0
         n_bin : int
-          Override this binner's n_bin. Default is `binner.n_bin`
+          Override this binner's n_bin. Default is `self.n_bin`
 
         Returns
         -------
@@ -284,7 +284,7 @@ def demean(self, arr, axis=-1, n_pad=0, n_bin=None):
           this case, the array shape will be (...,`n`,`n_pad`+`n_bin`).
           Default = 0
         n_bin : int
-          Override this binner's n_bin. Default is `binner.n_bin`
+          Override this binner's n_bin. Default is `self.n_bin`
 
         Returns
         -------
@@ -305,7 +305,7 @@ def mean(self, arr, axis=-1, n_bin=None):
         axis : int
           Axis along which to take mean. Default = -1
         n_bin : int
-          Override this binner's n_bin. Default is `binner.n_bin`
+          Override this binner's n_bin. Default is `self.n_bin`
 
         Returns
         -------
@@ -332,7 +332,7 @@ def variance(self, arr, axis=-1, n_bin=None):
         axis : int
           Axis along which to take variance. Default = -1
         n_bin : int
-          Override this binner's n_bin. Default is `binner.n_bin`
+          Override this binner's n_bin. Default is `self.n_bin`
 
         Returns
         -------
@@ -353,7 +353,7 @@ def standard_deviation(self, arr, axis=-1, n_bin=None):
         axis : int
           Axis along which to take std dev. Default = -1
         n_bin : int
-          Override this binner's n_bin. Default is `binner.n_bin`
+          Override this binner's n_bin. Default is `self.n_bin`
 
         Returns
         -------
diff --git a/mhkit/dolfyn/io/base.py b/mhkit/dolfyn/io/base.py
index 5208ca47f..878ee1b98 100644
--- a/mhkit/dolfyn/io/base.py
+++ b/mhkit/dolfyn/io/base.py
@@ -83,6 +83,9 @@ def _handle_nan(data):
     Finds trailing nan's that cause issues in running the rotation
     algorithms and deletes them.
     """
+    if "time" not in data["coords"]:
+        raise Exception("No data recorded in file.")
+
     nan = np.zeros(data["coords"]["time"].shape, dtype=bool)
     l = data["coords"]["time"].size
 
@@ -134,10 +137,17 @@ def _remove_gps_duplicates(dat):
 
     dat["data_vars"]["hdwtime_gps"] = dat["coords"]["time"]
 
+    # If the time jumps by nearly 24 hours at any given instance, we've skipped a day
+    time_diff = np.diff(dat["coords"]["time_gps"])
+    if any(np.array(list(set(time_diff))) < -(23.9 * 3600)):
+        idx = np.where(time_diff == time_diff.min())[0]
+        dat["coords"]["time_gps"][int(idx) + 1 :] += 24 * 3600
+
     # Remove duplicate timestamp values, if applicable
     dat["coords"]["time_gps"], idx = np.unique(
         dat["coords"]["time_gps"], return_index=True
     )
+
     # Remove nan values, if applicable
     nan = np.zeros(dat["coords"]["time"].shape, dtype=bool)
     if any(np.isnan(dat["coords"]["time_gps"])):
@@ -161,6 +171,14 @@ def _create_dataset(data):
     """
 
     tag = ["_avg", "_b5", "_echo", "_bt", "_gps", "_altraw", "_altraw_avg", "_sl"]
+    # If burst velocity not measured
+    if "vel" not in data["data_vars"]:
+        # dual profile where burst velocity is not measured but echo sounder is
+        if "vel_avg" in data["data_vars"]:
+            data["coords"]["time"] = data["coords"]["time_avg"]
+        else:
+            t_vars = [t for t in data["coords"] if "time" in t]
+            data["coords"]["time"] = data["coords"][t_vars[0]]
 
     ds_dict = {}
     for key in data["coords"]:
@@ -295,7 +313,7 @@ def _create_dataset(data):
                         "data": data["data_vars"][key],
                     }
 
-                elif "b5" in tg:
+                elif "b5" in key:
                     ds_dict[key] = {
                         "dims": ("range_b5", "time_b5"),
                         "data": data["data_vars"][key],
@@ -321,7 +339,7 @@ def _create_dataset(data):
                     # "vel_b5" sometimes stored as (1, range_b5, time_b5)
                     ds_dict[key] = {
                         "dims": ("range_b5", "time_b5"),
-                        "data": data["data_vars"][key][0],
+                        "data": data["data_vars"][key].squeeze(),
                     }
                 elif "sl" in key:
                     ds_dict[key] = {
@@ -354,12 +372,12 @@ def _create_dataset(data):
     r_list = [r for r in ds.coords if "range" in r]
     for ky in r_list:
         ds[ky].attrs["units"] = "m"
-        ds[ky].attrs["long_name"] = "Profile Range"
+        ds[ky].attrs["long_name"] = "Profile " + ky.capitalize().replace("_", " ")
         ds[ky].attrs["description"] = "Distance to the center of each depth bin"
     time_list = [t for t in ds.coords if "time" in t]
     for ky in time_list:
         ds[ky].attrs["units"] = "seconds since 1970-01-01 00:00:00"
-        ds[ky].attrs["long_name"] = "Time"
+        ds[ky].attrs["long_name"] = ky.capitalize().replace("_", " ")
         ds[ky].attrs["standard_name"] = "time"
 
     # Set dataset metadata
diff --git a/mhkit/dolfyn/io/nortek.py b/mhkit/dolfyn/io/nortek.py
index 0e81a874d..3510ef400 100644
--- a/mhkit/dolfyn/io/nortek.py
+++ b/mhkit/dolfyn/io/nortek.py
@@ -262,6 +262,7 @@ def __init__(
             self.config["coord_sys_axes"]
         ]
         da["has_imu"] = 0  # Initiate attribute
+        self._eof = self.pos
         if self.debug:
             logging.info("Init completed")
 
@@ -384,6 +385,7 @@ def findnext(self, do_cs=True):
         if self.endian == "<":
             func = np.uint8
             func2 = lib._bitshift8
+        searching = False
         while True:
             val = unpack(self.endian + "H", self.read(2))[0]
             if np.array(val).astype(func) == 165 and (not do_cs or cs == sum):
@@ -391,6 +393,9 @@ def findnext(self, do_cs=True):
                 return hex(func2(val))
             sum += cs
             cs = val
+            if self.debug and not searching:
+                logging.debug("Scanning every 2 bytes for next datablock...")
+                searching = True
 
     def read_id(self):
         """Read the next 'ID' from the file."""
@@ -456,6 +461,7 @@ def findnextid(self, id):
             id = int(id, 0)
         nowid = None
         while nowid != id:
+            pos = self.pos
             nowid = self.read_id()
             if nowid == 16:
                 shift = 22
@@ -463,6 +469,9 @@ def findnextid(self, id):
                 sz = 2 * unpack(self.endian + "H", self.read(2))[0]
                 shift = sz - 4
             self.f.seek(shift, 1)
+            # If we get stuck in a while loop
+            if self.pos == pos:
+                self.f.seek(2, 1)
         return self.pos
 
     def code_spacing(self, searchcode, iternum=50):
diff --git a/mhkit/dolfyn/io/nortek2.py b/mhkit/dolfyn/io/nortek2.py
index fa0992c3d..414790408 100644
--- a/mhkit/dolfyn/io/nortek2.py
+++ b/mhkit/dolfyn/io/nortek2.py
@@ -1,9 +1,9 @@
-import numpy as np
-from struct import unpack, calcsize
+import json
+import logging
 import warnings
+from struct import unpack, calcsize
 from pathlib import Path
-import logging
-import json
+import numpy as np
 
 from . import nortek2_defs as defs
 from . import nortek2_lib as lib
@@ -21,7 +21,7 @@ def read_signature(
     rebuild_index=False,
     debug=False,
     dual_profile=False,
-    **kwargs
+    **kwargs,
 ):
     """
     Read a Nortek Signature (.ad2cp) datafile
@@ -113,10 +113,14 @@ def read_signature(
     ds = _create_dataset(out)
     ds = _set_coords(ds, ref_frame=ds.coord_sys)
 
-    if "orientmat" not in ds:
+    if ("orientmat" not in ds) and ("heading" in ds):
         ds["orientmat"] = _euler2orient(
             ds["time"], ds["heading"], ds["pitch"], ds["roll"]
         )
+    elif ("orientmat_avg" not in ds) and ("heading_avg" in ds):
+        ds["orientmat_avg"] = _euler2orient(
+            ds["time_avg"], ds["heading_avg"], ds["pitch_avg"], ds["roll_avg"]
+        )
 
     if declin is not None:
         set_declination(ds, declin, inplace=True)
@@ -140,30 +144,22 @@ def read_signature(
 
 
 class _Ad2cpReader:
-    def __init__(
-        self,
-        fname,
-        endian=None,
-        bufsize=None,
-        rebuild_index=False,
-        debug=False,
-        dual_profile=False,
-    ):
+    def __init__(self, fname, rebuild_index, debug, dual_profile):
         self.fname = fname
         self.debug = debug
-        self._check_nortek(endian)
-        self.f.seek(0, 2)  # Seek to end
-        self._eof = self.f.tell()
-        self.start_pos = self._check_header()
+        # Open file, check endianess, and find filelength
+        self._check_nortek2()
+        # Generate indexing file
         self._index, self._dp = lib.get_index(
             fname,
-            pos=self.start_pos,
+            pos=0,
             eof=self._eof,
             rebuild=rebuild_index,
             debug=debug,
             dp=dual_profile,
         )
-        self._reopen(bufsize)
+        # Open file for reading
+        self._open()
         self.filehead_config = self._read_filehead_config_string()
         self._ens_pos = self._index["pos"][
             lib._boolarray_firstensemble_ping(self._index)
@@ -173,50 +169,20 @@ def __init__(
         self._init_burst_readers()
         self.unknown_ID_count = {}
 
-    def _calc_lastblock_iswhole(
-        self,
-    ):
-        blocksize, blocksize_count = np.unique(
-            np.diff(self._ens_pos), return_counts=True
-        )
-        standard_blocksize = blocksize[blocksize_count.argmax()]
-        return (self._eof - self._ens_pos[-1]) == standard_blocksize
-
-    def _check_nortek(self, endian):
-        self._reopen(10)
+    def _check_nortek2(self):
+        self._open(10)
         byts = self.f.read(2)
-        if endian is None:
-            if unpack("<" + "BB", byts) == (165, 10):
-                endian = "<"
-            elif unpack(">" + "BB", byts) == (165, 10):
-                endian = ">"
-            else:
-                raise Exception(
-                    "I/O error: could not determine the 'endianness' "
-                    "of the file.  Are you sure this is a Nortek "
-                    "AD2CP file?"
-                )
-        self.endian = endian
-
-    def _check_header(self):
-        def find_all(s, c):
-            idx = s.find(c)
-            while idx != -1:
-                yield idx
-                idx = s.find(c, idx + 1)
-
-        # Open the entire file
-        self._reopen(self._eof)
-        pk = self.f.peek(1)
-        # Search for multiple saved headers
-        found = [i for i in find_all(pk, b"GETCLOCKSTR")]
-        if len(found) < 2:
-            return 0
-        else:
-            start_idx = found[-1] - 11
-            return start_idx
+        if not (unpack("<BB", byts) == (165, 10)):
+            raise Exception(
+                "I/O error: could not determine the 'endianness' "
+                "of the file.  Are you sure this is a Nortek "
+                "AD2CP file?"
+            )
+        self.f.seek(0, 2)  # Seek to end
+        self._eof = self.f.tell()
+        self.f.close()
 
-    def _reopen(self, bufsize=None):
+    def _open(self, bufsize=None):
         if bufsize is None:
             bufsize = 1000000
         try:
@@ -262,6 +228,13 @@ def _read_filehead_config_string(
                 out2[ky] = out[ky]
         return out2
 
+    def _calc_lastblock_iswhole(self):
+        blocksize, blocksize_count = np.unique(
+            np.diff(self._ens_pos), return_counts=True
+        )
+        standard_blocksize = blocksize[blocksize_count.argmax()]
+        return (self._eof - self._ens_pos[-1]) == standard_blocksize
+
     def _init_burst_readers(
         self,
     ):
@@ -315,6 +288,9 @@ def n_id(id):
 
     def _read_hdr(self, do_cs=False):
         res = defs.header.read2dict(self.f, cs=do_cs)
+        if res["hsz"] == 12:
+            self.f.seek(-10, 1)
+            res = defs.header12.read2dict(self.f, cs=do_cs)
         if res["sync"] != 165:
             raise Exception("Out of sync!")
         return res
@@ -757,11 +733,18 @@ def _reduce(data):
         dc["range_avg"] = (np.arange(dv["vel_avg"].shape[1]) + 1) * da[
             "cell_size_avg"
         ] + da["blank_dist_avg"]
-        if "orientmat" not in dv:
-            dv["orientmat"] = dv.pop("orientmat_avg")
         tmat = da["filehead_config"]["XFAVG"]
-        da["fs"] = da["filehead_config"]["PLAN"]["MIAVG"]
-        da["avg_interval_sec"] = da["filehead_config"]["AVG"]["AI"]
+        da["duty_cycle_interval"] = dci = da["filehead_config"]["PLAN"]["MIAVG"]
+        da["duty_cycle_n_burst"] = da["filehead_config"]["AVG"]["NPING"]
+        da["duty_cycle_avg_interval"] = dca = da["filehead_config"]["AVG"]["AI"]
+        fs = da["duty_cycle_n_burst"] / da["duty_cycle_avg_interval"]
+        da["duty_cycle_description"] = (
+            f"Measurements collected for {dca / 60} minutes at {fs} Hz every {dci / 60} minutes"
+        )
+        if "fs" in da:
+            da["fs_avg"] = fs
+        else:
+            da["fs"] = fs
         da["bandwidth"] = da["filehead_config"]["AVG"]["BW"]
     if "vel_b5" in dv:
         # vel_b5 is sometimes shape 2 and sometimes shape 3
@@ -792,71 +775,70 @@ def _reduce(data):
             tm[irow, icol] = tmat["M" + str(irow + 1) + str(icol + 1)]
     dv["beam2inst_orientmat"] = tm
 
-    # If burst velocity isn't used, need to copy one for 'time'
-    if "time" not in dc:
-        for val in dc:
-            if "time" in val:
-                time = val
-        dc["time"] = dc[time]
-
 
 def split_dp_datasets(ds):
     """
     Splits a dataset containing dual profiles into individual profiles
     """
 
-    # Figure out which variables belong to which profile based on length of time variables
-    t_dict = {}
-    for t in ds.coords:
-        if "time" in t:
-            t_dict[t] = ds[t].size
-
-    other_coords = []
-    for key, val in t_dict.items():
-        if val != t_dict["time"]:
-            if key.endswith("altraw"):
-                # altraw goes with burst, altraw_avg goes with avg
-                continue
-            other_coords.append(key)
-    # Fetch variables, coordinates, and attrs for second profiling configuration
-    other_vars = [
-        v for v in ds.data_vars if any(x in ds[v].coords for x in other_coords)
-    ]
-    other_tags = [s.split("_")[-1] for s in other_coords]
-    other_coords += [v for v in ds.coords if any(x in v for x in other_tags)]
-    other_attrs = [s for s in ds.attrs if any(x in s for x in other_tags)]
-    critical_attrs = [
-        "inst_model",
-        "inst_make",
-        "inst_type",
-        "fs",
-        "orientation",
-        "orient_status",
-        "has_imu",
-        "beam_angle",
-    ]
-
-    # Create second dataset
     ds2 = type(ds)()
-    for a in other_attrs + critical_attrs:
-        ds2.attrs[a] = ds.attrs[a]
-    for v in other_vars:
-        ds2[v] = ds[v]
-    # Set rotate_vars
-    rotate_vars2 = [v for v in ds.attrs["rotate_vars"] if v in other_vars]
-    ds2.attrs["rotate_vars"] = rotate_vars2
-    # Set orientation matricies
-    ds2["beam2inst_orientmat"] = ds["beam2inst_orientmat"]
-    ds2 = ds2.rename({"orientmat_" + other_tags[0]: "orientmat"})
-    # Set original coordinate system
-    cy = ds2.attrs["coord_sys_axes_" + other_tags[0]]
-    ds2.attrs["coord_sys"] = {"XYZ": "inst", "ENU": "earth", "beam": "beam"}[cy]
-    ds2 = _set_coords(ds2, ref_frame=ds2.coord_sys)
-
-    # Clean up first dataset
-    [ds.attrs.pop(ky) for ky in other_attrs]
-    ds = ds.drop_vars(other_vars + other_coords)
-    for itm in rotate_vars2:
-        ds.attrs["rotate_vars"].remove(itm)
-
-    return ds, ds2
+    # Get averaged time variables
+    other_coords = [t for t in ds.coords if "_avg" in t]
+    # Check if ice tracking was used
+    if ("vel" in ds.data_vars) and ("time_bt" in ds.coords):
+        if ds["time"].size != ds["time_bt"].size:
+            other_coords.append("time_bt")
+
+    if other_coords:
+        # Fetch variables, coordinates, and attrs for second profiling configuration
+        other_vars = [
+            v
+            for v in ds.data_vars
+            if any(x in ds[v].coords for x in other_coords)
+            or any(ds[v].shape[-1] == ds[x].size for x in other_coords)
+        ]
+        other_attrs = [s for s in ds.attrs if ("_avg" in s) or ("duty_cycle" in s)]
+        critical_attrs = [
+            "inst_model",
+            "inst_make",
+            "inst_type",
+            "fs",
+            "orientation",
+            "orient_status",
+            "has_imu",
+            "beam_angle",
+        ]
+
+        # Create second dataset
+        for a in other_attrs + critical_attrs:
+            ds2.attrs[a] = ds.attrs[a]
+        for v in other_vars:
+            ds2[v] = ds[v]
+        # Set rotate_vars
+        rotate_vars2 = [v for v in ds.attrs["rotate_vars"] if v in other_vars]
+        ds2.attrs["rotate_vars"] = rotate_vars2
+        # Set orientation matricies
+        ds2["beam2inst_orientmat"] = ds["beam2inst_orientmat"]
+        # IMU versions have 'orientmat_avg' variable, but non-IMU versions do not
+        if not ds.attrs["has_imu"]:
+            ds2["orientmat_avg"] = _euler2orient(
+                ds2["time_avg"], ds2["heading_avg"], ds2["pitch_avg"], ds2["roll_avg"]
+            )
+        # Set original coordinate system
+        cy = ds2.attrs["coord_sys_axes_avg"]
+        ds2.attrs["coord_sys"] = {"XYZ": "inst", "ENU": "earth", "beam": "beam"}[cy]
+        # Need to add beam coordinate
+        if "beam" not in ds2.dims:
+            ds2 = ds2.assign_coords({"beam": ds["beam"], "dir": ds["dir"]})
+        ds2 = _set_coords(ds2, ref_frame=ds2.coord_sys)
+
+        # Clean up first dataset
+        [ds.attrs.pop(ky) for ky in other_attrs]
+        ds = ds.drop_vars(other_vars + other_coords)
+        for itm in rotate_vars2:
+            ds.attrs["rotate_vars"].remove(itm)
+
+    if ds2:
+        return ds, ds2
+    else:
+        return ds
diff --git a/mhkit/dolfyn/io/nortek2_defs.py b/mhkit/dolfyn/io/nortek2_defs.py
index aa3ca3f47..8c4ea68ec 100644
--- a/mhkit/dolfyn/io/nortek2_defs.py
+++ b/mhkit/dolfyn/io/nortek2_defs.py
@@ -1,6 +1,7 @@
-import numpy as np
 from copy import copy
 from struct import Struct
+import numpy as np
+
 from . import nortek2_lib as lib
 
 
@@ -165,6 +166,7 @@ def __call__(self, array):
         return array
 
 
+# 10 byte header
 header = _DataDef(
     [
         ("sync", "B", [], None),
@@ -177,6 +179,19 @@ def __call__(self, array):
     ]
 )
 
+# 12 byte header
+header12 = _DataDef(
+    [
+        ("sync", "B", [], None),
+        ("hsz", "B", [], None),
+        ("id", "B", [], None),
+        ("fam", "B", [], None),
+        ("sz", "I", [], None),
+        ("cs", "H", [], None),
+        ("hcs", "H", [], None),
+    ]
+)
+
 _burst_hdr = [
     ("ver", "B", [], None),
     ("DatOffset", "B", [], None),
diff --git a/mhkit/dolfyn/io/nortek2_lib.py b/mhkit/dolfyn/io/nortek2_lib.py
index 3336651f5..7c1f17eef 100644
--- a/mhkit/dolfyn/io/nortek2_lib.py
+++ b/mhkit/dolfyn/io/nortek2_lib.py
@@ -1,8 +1,9 @@
 import struct
-import os.path as path
-import numpy as np
-from logging import getLogger
 import warnings
+from os import path
+from logging import getLogger
+import numpy as np
+
 from .. import time
 from .base import _abspath
 
@@ -40,7 +41,6 @@ def _reduce_by_average_angle(data, ky0, ky1, degrees=True):
 # This is the data-type of the index file.
 # This must match what is written-out by the create_index function.
 _index_version = 1
-_hdr = struct.Struct("<BBBBhhh")
 _index_dtype = {
     None: np.dtype(
         [
@@ -107,6 +107,37 @@ def _calc_time(year, month, day, hour, minute, second, usec, zero_is_bad=True):
     return dt
 
 
+def __check_header(infile_obj, eof):
+    def find_all(s, c):
+        idx = s.find(c)
+        while idx != -1:
+            yield idx
+            idx = s.find(c, idx + 1)
+
+    # Save current position
+    current_pos = infile_obj.tell()
+
+    # Close and reopen file with larger buffer
+    infile_obj.close()
+    int32_max = np.iinfo(np.int32).max
+    if eof >= int32_max:
+        buffer = int32_max
+    else:
+        buffer = eof
+    fin = open(_abspath(infile_obj.name), "rb", buffer)
+    fin.seek(current_pos, 1)
+
+    # Search for multiple saved headers
+    pk = fin.peek(1)
+    found = [i for i in find_all(pk, b"GETCLOCKSTR")]
+    if found:
+        start_idx = found[0] - 11  # assuming next header is 10 bytes
+    else:
+        start_idx = 0
+
+    return fin, start_idx
+
+
 def _create_index(infile, outfile, init_pos, eof, debug):
     logging = getLogger()
     print("Indexing {}...".format(infile), end="")
@@ -122,39 +153,77 @@ def _create_index(infile, outfile, init_pos, eof, debug):
     config = 0
     last_ens = dict.fromkeys(ids, -1)
     seek_2ens = {
-        21: 40,
-        22: 40,
-        23: 42,
-        24: 40,
-        26: 40,
-        28: 40,  # 23 starts from "42"
-        27: 40,
-        29: 40,
-        30: 40,
-        31: 40,
-        35: 40,
-        36: 40,
+        21: 40,  # 0x15 burst
+        22: 40,  # 0x16 average
+        23: 42,  # 0x17 bottom track, starts from "42"
+        24: 40,  # 0x18 interleaved burst (beam 5)
+        26: 40,  # 0x1A burst altimeter
+        27: 40,  # 0x1B DVL bottom track
+        28: 40,  # 0x1C echo sounder
+        29: 40,  # 0x1D DVL water track
+        30: 40,  # 0x1E altimeter
+        31: 40,  # 0x1F avg altimeter
+        35: 40,  # 0x23 raw echo sounder
+        36: 40,  # 0x24 raw tx echo sounder
+        48: 40,  # 0x30 processed wave
+        # 160: 40, # 0xA0 string (header info, GPS NMEA data)
+        # 192: 40, # 0xC0 Nortek Data format 8 record
     }
     pos = 0
-    while pos <= eof:
-        pos = fin.tell()
+    header_check_flag = 0
+    # leave room for header plus other data (12 + 76)
+    while pos <= (eof - 88):
         if init_pos and not pos:
             fin.seek(init_pos, 1)
         try:
-            dat = _hdr.unpack(fin.read(_hdr.size))
-        except:
+            dat = struct.unpack("<BBBBhhh", fin.read(10))
+        except Exception as e:
+            logging.error(e)
+            logging.error("Header is shorter than 10 bytes: %10d\n" % (pos))
             break
+        if dat[0] != 165:
+            if debug:
+                logging.debug("Lost sync byte at pos: %10d\n" % (pos))
+        if dat[1] != 10:
+            if dat[1] == 12:
+                fin.seek(-10, 1)
+                dat = struct.unpack("<BBBBIhh", fin.read(12))
+            else:
+                # Lost header
+                if debug:
+                    logging.debug("Header is not 10 or 12 bytes: %10d\n" % (pos))
+                # check for if file was terminated and restarted
+                # by searching for start of header configuration string
+                if not header_check_flag:
+                    # Will run long for large files
+                    fin, to_skip = __check_header(fin, eof)
+                    if to_skip:
+                        if debug:
+                            logging.info(
+                                "Found new header configuration string as position: %10d\n"
+                                % (pos + to_skip)
+                            )
+                        fin.seek(to_skip, 1)
+                        continue
+                    else:
+                        # Don't run more often than necessary
+                        header_check_flag = 1
+                        if debug:
+                            logging.info("No new header configuration string found")
         if dat[2] in ids:
             idk = dat[2]
+            # version, byte offset to actual data, configuration bit mask
             d_ver, d_off, config = struct.unpack("<BBH", fin.read(4))
-            if d_ver not in [1, 3]:
-                # 1 for bottom track, 3 for all others
+            if d_ver not in [1, 3, 7]:
+                # data-record definition version number
+                # increases occasionally with instrument firmware updates
                 continue
-            fin.seek(4, 1)
+            fin.seek(4, 1)  # instrument serial number
             yr, mo, dy, h, m, s, u = struct.unpack("6BH", fin.read(8))
-            fin.seek(14, 1)
-            beams_cy = struct.unpack("<H", fin.read(2))[0]
+            fin.seek(14, 1)  # soundspeed, temp, pres, h,p,r
+            beams_cy = struct.unpack("<H", fin.read(2))[0]  # cell_size
             fin.seek(seek_2ens[dat[2]], 1)
+            # should end at byte 74 or 76
             ens[idk] = struct.unpack("<I", fin.read(4))[0]
 
             if last_ens[idk] > 0:
@@ -211,25 +280,29 @@ def _create_index(infile, outfile, init_pos, eof, debug):
                     logging.info("Invalid skip byte at pos: %10d\n" % (pos))
                 break
             fin.seek(dat[4], 1)
+        # Update for while loop check
+        pos = fin.tell()
+
     fin.close()
     fout.close()
     print(" Done.")
 
 
 def _check_index(idx, infile, fix_hw_ens=False, dp=False):
+    logging = getLogger()
     uid = np.unique(idx["ID"])
     if fix_hw_ens:
         hwe = idx["hw_ens"]
     else:
         hwe = idx["hw_ens"].copy()
-    period = hwe.max()
     ens = idx["ens"]
     N_id = len(uid)
-    FLAG = False
 
     # Are there better ways to detect dual profile?
-    if (21 in uid) and (22 in uid):
-        warnings.warn("Dual Profile detected... Two datasets will be returned.")
+    if (22 in uid) and ((21 in uid) or (28 in uid)):
+        msg = "Dual Profile detected... Two datasets will be returned."
+        warnings.warn(msg)
+        logging.warning(msg)
         dp = True
 
     # This loop fixes 'skips' inside the file
@@ -238,6 +311,7 @@ def _check_index(idx, infile, fix_hw_ens=False, dp=False):
         inds = np.nonzero(idx["ID"] == id)[0]
         # These are bad steps in the indices for this ID
         ibad = np.nonzero(np.diff(inds) > N_id)[0]
+
         # Check if spacing is equal for dual profiling ADCPs
         if dp:
             skip_size = np.diff(ibad)
@@ -250,10 +324,9 @@ def _check_index(idx, infile, fix_hw_ens=False, dp=False):
             mask = np.append(skip_size, 0).astype(bool) if any(skip_size) else []
             ibad = ibad[mask]
         for ib in ibad:
-            FLAG = True
             # The ping number reported here may not be quite right if
             # the ensemble count is wrong.
-            warnings.warn(
+            logging.warning(
                 "Skipped ping (ID: {}) in file {} at ensemble {}.".format(
                     id, infile, idx["ens"][inds[ib + 1] - 1]
                 )
@@ -270,7 +343,8 @@ def _boolarray_firstensemble_ping(index):
     each ensemble.
     """
     dens = np.ones(index["ens"].shape, dtype="bool")
-    dens[1:] = np.diff(index["ens"]) != 0
+    if any(index["ens"]):
+        dens[1:] = np.diff(index["ens"]) != 0
     return dens
 
 
diff --git a/mhkit/dolfyn/io/rdi.py b/mhkit/dolfyn/io/rdi.py
index 797f31169..de5588c3c 100644
--- a/mhkit/dolfyn/io/rdi.py
+++ b/mhkit/dolfyn/io/rdi.py
@@ -18,9 +18,10 @@ def read_rdi(
     filename,
     userdata=None,
     nens=None,
-    debug_level=-1,
+    debug=0,
     vmdas_search=False,
     winriver=False,
+    search_num=20000,
     **kwargs,
 ) -> xr.Dataset:
     """
@@ -32,11 +33,11 @@ def read_rdi(
       Filename of TRDI file to read.
     userdata : True, False, or string of userdata.json filename
       Whether to read the '<base-filename>.userdata.json' file. Default = True
-    nens : None, int or 2-element tuple (start, stop)
-      Number of pings or ensembles to read from the file.
+    nens : None, int
+      Number of pings or ensembles to read from the file, starting from 0.
       Default is None, read entire file
-    debug_level : int
-      Debug level [0 - 2]. Default = -1
+    debug : int
+      Debug level [0 - 3]. Default = 0
     vmdas_search : bool
       Search from the end of each ensemble for the VMDAS navigation
       block.  The byte offsets are sometimes incorrect. Default = False
@@ -50,7 +51,7 @@ def read_rdi(
       An xarray dataset from the binary instrument data
     """
     # Start debugger logging
-    if debug_level >= 0:
+    if debug > 0:
         for handler in logging.root.handlers[:]:
             logging.root.removeHandler(handler)
         filepath = Path(filename)
@@ -65,7 +66,11 @@ def read_rdi(
     # Reads into a dictionary of dictionaries using netcdf naming conventions
     # Should be easier to debug
     rdr = _RDIReader(
-        filename, debug_level=debug_level, vmdas_search=vmdas_search, winriver=winriver
+        filename,
+        debug=debug,
+        vmdas_search=vmdas_search,
+        winriver=winriver,
+        search_num=search_num,
     )
     datNB, datBB = rdr.load_data(nens=nens)
 
@@ -119,11 +124,11 @@ def read_rdi(
 
     if len(dss) == 2:
         warnings.warn(
-            "\nTwo profiling configurations retrieved from file" "\nReturning first."
+            "\nTwo profiling configurations retrieved from file\nReturning first."
         )
 
     # Close handler
-    if debug_level >= 0:
+    if debug > 0:
         for handler in logging.root.handlers[:]:
             logging.root.removeHandler(handler)
             handler.close()
@@ -159,17 +164,16 @@ def _set_rdi_declination(dat, fname, inplace):
 
 
 class _RDIReader:
-    def __init__(
-        self, fname, navg=1, debug_level=-1, vmdas_search=False, winriver=False
-    ):
+    def __init__(self, fname, debug, vmdas_search, winriver, search_num):
         self.fname = base._abspath(fname)
         print("\nReading file {} ...".format(fname))
-        self._debug_level = debug_level
+        self._debug_level = debug
         self._vmdas_search = vmdas_search
         self._winrivprob = winriver
         self._vm_source = 0
         self._pos = 0
         self.progress = 0
+        self.search_num = search_num
         self._cfac32 = np.float32(180 / 2**31)  # signed 32 to float
         self._cfac16 = np.float32(180 / 2**15)  # unsigned16 to float
         self._fixoffset = 0
@@ -177,9 +181,11 @@ def __init__(
         self.n_cells_diff = 0
         self.n_cells_sl = 0
         self.cs_diff = 0
+        self.cs_sl_diff = 0
         self.cs = []
+        self.cs_sl = []
         self.cfg = {}
-        self.cfgbb = {}
+        self.cfgBB = {}
         self.hdr = {}
         self.f = lib.bin_reader(self.fname)
 
@@ -189,17 +195,16 @@ def __init__(
         self._npings = self._filesize // space
         if self._debug_level > -1:
             logging.info("Done: {}".format(self.cfg))
-            logging.info("self._bb {}".format(self._bb))
-            logging.info("self.cfgbb: {}".format(self.cfgbb))
+            logging.info("self._BB {}".format(self._BB))
+            logging.info("self.cfgBB: {}".format(self.cfgBB))
         self.f.seek(self._pos, 0)
-        self.n_avg = navg
 
-        self.ensemble = lib._ensemble(self.n_avg, self.cfg["n_cells"])
-        if self._bb:
-            self.ensembleBB = lib._ensemble(self.n_avg, self.cfgbb["n_cells"])
+        self.ensemble = lib._ensemble(self.cfg["n_cells"])
+        if self._BB:
+            self.ensembleBB = lib._ensemble(self.cfgBB["n_cells"])
 
         self.vars_read = lib._variable_setlist(["time"])
-        if self._bb:
+        if self._BB:
             self.vars_readBB = lib._variable_setlist(["time"])
 
     def code_spacing(self, iternum=50):
@@ -212,7 +217,7 @@ def code_spacing(self, iternum=50):
         # Get basic header data and check dual profile
         if not self.read_hdr():
             raise RuntimeError("No header in this file")
-        self._bb = self.check_for_double_buffer()
+        self._BB = self.check_for_double_buffer()
 
         # Turn off debugging to check code spacing
         debug_level = self._debug_level
@@ -241,7 +246,7 @@ def read_hdrseg(self):
         fd = self.f
         hdr = self.hdr
         hdr["nbyte"] = fd.read_i16(1)
-        spare = fd.read_ui8(1)
+        fd.seek(1, 1)
         ndat = fd.read_ui8(1)
         hdr["dat_offsets"] = fd.read_ui16(ndat)
         self._nbyte = 4 + ndat * 2
@@ -276,51 +281,47 @@ def check_for_double_buffer(self):
 
     def load_data(self, nens=None):
         """Main function run after reader class is initiated."""
+
         if nens is None:
-            # Attempt to overshoot WinRiver2 or *Pro filesize
-            if (self.cfg["coord_sys"] == "ship") or (
-                self.cfg["inst_model"]
-                in [
-                    "RiverPro",
-                    "StreamPro",
-                ]
-            ):
-                self._nens = int(self._filesize / self.hdr["nbyte"] / self.n_avg * 1.1)
-            else:
-                # Attempt to overshoot other instrument filesizes
-                self._nens = int(self._npings / self.n_avg)
+            # Overshoot file size to pre-allocate enough ensembles
+            self._nens = int(self._npings * 10)
         elif nens.__class__ is tuple or nens.__class__ is list:
             raise Exception("    `nens` must be a integer")
         else:
             self._nens = nens
-        if self._debug_level > -1:
-            logging.info("  taking data from pings 0 - %d" % self._nens)
-            logging.info("  %d ensembles will be produced.\n" % self._nens)
+
+        # Pre-allocate data
         self.init_data()
 
-        for iens in range(self._nens):
+        iens = 0
+        while True:
+            if iens == self._nens:
+                break
             if not self.read_buffer():
                 self.remove_end(iens)
                 break
             self.ensemble.clean_data()
-            if self._bb:
+            if self._BB:
                 self.ensembleBB.clean_data()
             ens = [self.ensemble]
             vars = [self.vars_read]
             datl = [self.outd]
             cfgl = [self.cfg]
-            if self._bb:
+            if self._BB:
                 ens += [self.ensembleBB]
                 vars += [self.vars_readBB]
                 datl += [self.outdBB]
-                cfgl += [self.cfgbb]
+                cfgl += [self.cfgBB]
 
-            for var, en, dat in zip(vars, ens, datl):
+            for var, en, dat, cfg in zip(vars, ens, datl, cfgl):
                 for nm in var:
-                    dat = self.save_profiles(dat, nm, en, iens)
+                    dat = self.save_profiles(dat, cfg, nm, en, iens)
                 # reset flag after all variables run
                 self.n_cells_diff = 0
 
+                # b5 clock flag
+                b5 = True if "ping_offset_time_b5" in cfg else False
+
                 # Set clock
                 clock = en.rtc[:, :]
                 if clock[0, 0] < 100:
@@ -338,53 +339,45 @@ def load_data(self, nens=None):
                         )
                     )
                     dat["coords"]["time"][iens] = np.nan
+                    if b5:
+                        dat["coords"]["time_b5"][iens] = np.nan
                 else:
                     dat["coords"]["time"][iens] = np.median(dates)
+                    if b5:
+                        dat["coords"]["time_b5"][iens] = (
+                            np.median(dates) + cfg["ping_offset_time_b5"]
+                        )
+                iens += 1
 
-        # Finalize dataset (runs through both nb and bb)
+        # Finalize dataset (runs through both NB and BB)
         for dat, cfg in zip(datl, cfgl):
             dat, cfg = self.cleanup(dat, cfg)
-            dat = self.finalize(dat)
+            dat = self.finalize(dat, cfg)
             if "vel_bt" in dat["data_vars"]:
-                dat["attrs"]["rotate_vars"].append("vel_bt")
+                cfg["rotate_vars"].append("vel_bt")
 
-        datbb = self.outdBB if self._bb else None
-        return self.outd, datbb
+        datbb = self.outdBB if self._BB else None
+        return dat, datbb
 
     def init_data(self):
         """Initiate data structure"""
         outd = {
             "data_vars": {},
             "coords": {},
-            "attrs": {},
             "units": {},
             "long_name": {},
             "standard_name": {},
             "sys": {},
         }
-        outd["attrs"]["inst_make"] = "TRDI"
-        outd["attrs"]["inst_type"] = "ADCP"
-        outd["attrs"]["rotate_vars"] = [
-            "vel",
-        ]
-        # Currently RDI doesn't use IMUs
-        outd["attrs"]["has_imu"] = 0
-        if self._bb:
+        if self._BB:
             outdbb = {
                 "data_vars": {},
                 "coords": {},
-                "attrs": {},
                 "units": {},
                 "long_name": {},
                 "standard_name": {},
                 "sys": {},
             }
-            outdbb["attrs"]["inst_make"] = "TRDI"
-            outdbb["attrs"]["inst_type"] = "ADCP"
-            outdbb["attrs"]["rotate_vars"] = [
-                "vel",
-            ]
-            outdbb["attrs"]["has_imu"] = 0
 
         # Preallocate variables and data sizes
         for nm in defs.data_defs:
@@ -393,10 +386,10 @@ def init_data(self):
             )
         self.outd = outd
 
-        if self._bb:
+        if self._BB:
             for nm in defs.data_defs:
                 outdbb = lib._idata(
-                    outdbb, nm, sz=lib._get_size(nm, self._nens, self.cfgbb["n_cells"])
+                    outdbb, nm, sz=lib._get_size(nm, self._nens, self.cfgBB["n_cells"])
                 )
             self.outdBB = outdbb
             if self._debug_level > 1:
@@ -404,21 +397,48 @@ def init_data(self):
 
         if self._debug_level > 1:
             logging.info("{} ncells, not BB".format(self.cfg["n_cells"]))
-            if self._bb:
-                logging.info("{} ncells, BB".format(self.cfgbb["n_cells"]))
+            if self._BB:
+                logging.info("{} ncells, BB".format(self.cfgBB["n_cells"]))
 
     def read_buffer(self):
         """Read through the file"""
         fd = self.f
         self.ensemble.k = -1  # so that k+=1 gives 0 on the first loop.
-        if self._bb:
+        if self._BB:
             self.ensembleBB.k = -1  # so that k+=1 gives 0 on the first loop.
         self.print_progress()
         hdr = self.hdr
-        while self.ensemble.k < self.ensemble.n_avg - 1:
+        while self.ensemble.k < 0:
             if not self.search_buffer():
                 return False
             startpos = fd.tell() - 2
+
+            noBytesInEnsemble = fd.read_i16(1)
+            # go back to start of ensemble
+            fd.seek(-4, 1)
+            # pack the entire ensemble into a bytearray
+            bytesInEnsemble = bytearray(fd.read_ui8(noBytesInEnsemble))
+            # get checksum (2 bytes unsigned integer)
+            checksum = fd.read_ui16(1)
+            # calculate checksum and check
+            # if the checksum is wrong, back up 100 bytes and search for the next
+            # ensemble
+            if (sum(bytesInEnsemble) & 0xFFFF) != checksum:
+                logging.warning(
+                    "Ensemble starting at startpos {} has a checksum error".format(
+                        startpos
+                    )
+                )
+                logging.warning(
+                    "checksum calculated = %s, actual checksum = %s\n"
+                    % ((sum(bytesInEnsemble) & 0xFFFF), checksum)
+                )
+                fd.seek(-100, 1)
+                self.read_buffer()
+            else:
+                # go back to start of ensemble
+                fd.seek(-noBytesInEnsemble, 1)
+
             self.read_hdrseg()
             if self._debug_level > -1:
                 logging.info("Read Header", hdr)
@@ -477,7 +497,7 @@ def search_buffer(self):
         """
         Check to see if the next bytes indicate the beginning of a
         data block.  If not, search for the next data block, up to
-        _search_num times.
+        search_num times.
         """
         fd = self.f
         id = fd.read_ui8(2)
@@ -492,8 +512,11 @@ def search_buffer(self):
             logging.info("cfgid0: [{:x}, {:x}]".format(*cfgid))
         # If not [127, 127] or if the file ends in the next ensemble
         while (cfgid != [127, 127]) or self.check_eof():
+            if search_cnt == self.search_num:
+                logging.debug(f"Stopped searching at byte position {fd.tell()}")
+                return False
+            # Search for the next header or the end of the file
             if cfgid == [127, 121]:
-                # Search for the next header or the end of the file
                 skipbytes = fd.read_i16(1)
                 fd.seek(skipbytes - 2, 1)
                 id = fd.read_ui8(2)
@@ -510,7 +533,7 @@ def search_buffer(self):
                 cfgid[0] = cfgid[1]
                 cfgid[1] = nextbyte
 
-        if pos_7f79 and self._debug_level > -1:
+        if pos_7f79 and (self._debug_level > -1):
             logging.info("Skipped junk data: [{:x}, {:x}]".format(*[127, 121]))
 
         if search_cnt > 0:
@@ -573,6 +596,10 @@ def read_dat(self, id):
             0: (defs.read_fixed, [False]),
             # 0001 2nd profile fixed leader
             1: (defs.read_fixed, [True]),
+            # 000B Wave parameters
+            11: (defs.skip_Nbyte, [51]),
+            # 000C Wave parameters - sea and swell
+            12: (defs.skip_Nbyte, [44]),
             # 0010 Surface layer fixed leader (RiverPro & StreamPro)
             16: (defs.read_fixed_sl, []),
             # 0080 1st profile variable leader
@@ -625,13 +652,13 @@ def read_dat(self, id):
             1793: (defs.skip_Ncol, [4]),  # 0701 number of pings
             1794: (defs.skip_Ncol, [4]),  # 0702 sum of squared vel
             1795: (defs.skip_Ncol, [4]),  # 0703 sum of velocities
-            2560: (defs.skip_Ncol, []),  # 0A00 Beam 5 velocity
-            2816: (defs.skip_Ncol, []),  # 0B00 Beam 5 correlation
-            3072: (defs.skip_Ncol, []),  # 0C00 Beam 5 amplitude
-            3328: (defs.skip_Ncol, []),  # 0D00 Beam 5 pct_good
+            2560: (defs.read_vel_b5, []),  # 0A00 Beam 5 velocity
+            2816: (defs.read_corr_b5, []),  # 0B00 Beam 5 correlation
+            3072: (defs.read_amp_b5, []),  # 0C00 Beam 5 amplitude
+            3328: (defs.read_prcnt_gd_b5, []),  # 0D00 Beam 5 pct_good
             # Fixed attitude data format for Ocean Surveyor ADCPs
             3000: (defs.skip_Nbyte, [32]),
-            3841: (defs.skip_Nbyte, [38]),  # 0F01 Beam 5 leader
+            3841: (defs.read_vel_b5_leader, []),  # 0F01 Beam 5 leader
             8192: (defs.read_vmdas, []),  # 2000
             # 2013 Navigation parameter data
             8211: (defs.skip_Nbyte, [83]),
@@ -661,8 +688,16 @@ def read_dat(self, id):
             22785: (defs.skip_Nbyte, [65]),
             # 5902 Ping attitude
             22786: (defs.skip_Nbyte, [105]),
-            # 7001 ADC data
-            28673: (defs.skip_Nbyte, [14]),
+            # 7000 Sentinvel V system configuration
+            28672: (defs.read_sentinelv_syscfg, [False]),
+            # 7001 Sentinel V leader
+            28673: (defs.read_sentinelv_ping_setup, [False]),
+            # 7002 ADC data
+            28674: (defs.skip_Nbyte, [14]),
+            # 7003 Sentinel V "Features" (only first ensemble)
+            28675: (defs.skip_Nbyte, [88]),  # min size
+            # 7004 Sentinel V Event Log
+            28676: (defs.read_sentinelv_event_log, []),
         }
         # Call the correct function:
         if self._debug_level > 1:
@@ -771,7 +806,7 @@ def remove_end(self, iens):
         for nm in self.vars_read:
             lib._setd(dat, nm, lib._get(dat, nm)[..., :iens])
 
-    def save_profiles(self, dat, nm, en, iens):
+    def save_profiles(self, dat, cfg, nm, en, iens):
         """
         Reformats profile measurements in the retrieved measurements.
 
@@ -782,7 +817,11 @@ def save_profiles(self, dat, nm, en, iens):
         Parameters
         ----------
         dat : dict
-            Raw data dictionary
+            Contains data for the final dataset. This variable has the same pointer
+            as the data dictionary `self.outd` or `self.outdBB`.
+        cfg : dict
+            Global attributes for the final dataset. This variable has the same pointer
+            as the configuration dictionary `self.cfg` or `self.cfgBB`.
         nm : str
             The name of the profile variable
         en : dict
@@ -796,10 +835,7 @@ def save_profiles(self, dat, nm, en, iens):
             The updated dataset dictionary with the reformatted profile measurements.
         """
         ds = lib._get(dat, nm)
-        if self.n_avg == 1:
-            bn = en[nm][..., 0]
-        else:
-            bn = np.nanmean(en[nm], axis=-1)
+        bn = en[nm][..., 0]
 
         # If n_cells has changed (RiverPro/StreamPro WinRiver transects)
         if len(ds.shape) == 3:
@@ -828,6 +864,9 @@ def save_profiles(self, dat, nm, en, iens):
         if self.cs_diff:
             self.cs.append([iens, self.cfg["cell_size"]])
             self.cs_diff = 0
+        if self.cs_sl_diff:
+            self.cs_sl.append([iens, self.cfg["cell_size_sl"]])
+            self.cs_sl_diff = 0
 
         # Then copy the ensemble to the dataset.
         ds[..., iens] = bn
@@ -846,10 +885,11 @@ def cleanup(self, dat, cfg):
         Parameters
         ----------
         dat : dict
-            The dataset dictionary containing data variables and coordinates to be cleaned up.
+            Contains data for the final dataset. This variable has the same pointer
+            as the data dictionary `self.outd` or `self.outdBB`.
         cfg : dict
-            Configuration dictionary, which is updated with cell size, range, and additional
-            attributes after cleanup.
+            Global attributes for the final dataset. This variable has the same pointer
+            as the configuration dictionary `self.cfg` or `self.cfgBB`.
 
         Returns
         -------
@@ -859,53 +899,75 @@ def cleanup(self, dat, cfg):
         """
         # Clean up changing cell size, if necessary
         cs = np.array(self.cs, dtype=np.float32)
-        cell_sizes = cs[:, 1]
+        cs_sl = np.array(self.cs_sl, dtype=np.float32)
 
         # If cell sizes change, depth-bin average the smaller cell sizes
         if len(self.cs) > 1:
-            bins_to_merge = cell_sizes.max() / cell_sizes
-            idx_start = cs[:, 0].astype(int)
-            idx_end = np.append(cs[1:, 0], self._nens).astype(int)
-
             dv = dat["data_vars"]
-            for var in dv:
-                if (len(dv[var].shape) == 3) and ("_sl" not in var):
-                    # Create a new NaN var to save data in
-                    new_var = (np.zeros(dv[var].shape) * np.nan).astype(dv[var].dtype)
-                    # For each cell size change, reshape and bin-average
-                    for id1, id2, b in zip(idx_start, idx_end, bins_to_merge):
-                        array = np.transpose(dv[var][..., id1:id2])
-                        bin_arr = np.transpose(np.mean(self.reshape(array, b), axis=-1))
-                        new_var[: len(bin_arr), :, id1:id2] = bin_arr
-                    # Reset data. This often leaves nan data at farther ranges
-                    dv[var] = new_var
+            self.merge_bins(cs, dv, sl=False)
+        if len(self.cs_sl) > 1:
+            dv = dat["data_vars"]
+            self.merge_bins(cs_sl, dv, sl=True)
 
         # Set cell size and range
         cfg["n_cells"] = self.ensemble["n_cells"]
-        cfg["cell_size"] = round(cell_sizes.max(), 3)
+        cfg["cell_size"] = round(cs[:, 1].max(), 3)
+        bin1_dist = cfg.pop("bin1_dist_m")
         dat["coords"]["range"] = (
-            cfg["bin1_dist_m"] + np.arange(cfg["n_cells"]) * cfg["cell_size"]
+            bin1_dist + np.arange(cfg["n_cells"]) * cfg["cell_size"]
         ).astype(np.float32)
+        cfg["range_offset"] = round(bin1_dist - cfg["blank_dist"] - cfg["cell_size"], 3)
 
-        # Save configuration data as attributes
-        for nm in cfg:
-            dat["attrs"][nm] = cfg[nm]
+        if "n_cells_b5" in cfg:
+            bin1_dist_b5 = cfg.pop("bin1_dist_b5_m")
+            dat["coords"]["range_b5"] = (
+                bin1_dist_b5 + np.arange(cfg["n_cells_b5"]) * cfg["cell_size_b5"]
+            ).astype(np.float32)
 
         # Clean up surface layer profiles
         if "surface_layer" in cfg:  # RiverPro/StreamPro
+            # Set SL cell size and range
+            cfg["cell_size_sl"] = round(cs_sl[:, 1].max(), 3)
+            cfg["n_cells_sl"] = self.n_cells_sl
+            bin1_dist_sl = cfg.pop("bin1_dist_m_sl")
+            # Blank distance not recorded
+            cfg["blank_dist_sl"] = round(bin1_dist_sl - cfg["cell_size_sl"], 3)
+            # Range offset not added in "bin1_dist_m_sl" for some reason
+            bin1_dist_sl += cfg["range_offset"]
             dat["coords"]["range_sl"] = (
-                cfg["bin1_dist_m_sl"]
-                + np.arange(0, self.n_cells_sl) * cfg["cell_size_sl"]
+                bin1_dist_sl + np.arange(0, self.n_cells_sl) * cfg["cell_size_sl"]
             )
             # Trim off extra nan data
             dv = dat["data_vars"]
             for var in dv:
                 if "sl" in var:
                     dv[var] = dv[var][: self.n_cells_sl]
-            dat["attrs"]["rotate_vars"].append("vel_sl")
+            cfg["rotate_vars"].append("vel_sl")
 
         return dat, cfg
 
+    def merge_bins(self, cs, dv, sl=False):
+        cell_sizes = cs[:, 1]
+        bins_to_merge = cell_sizes.max() / cell_sizes
+        idx_start = cs[:, 0].astype(int)
+        idx_end = np.append(cs[1:, 0], self._nens).astype(int)
+
+        for var in dv:
+            if not sl:
+                flag = "_sl" not in var
+            elif sl:
+                flag = "_sl" in var
+            if (len(dv[var].shape) == 3) and flag:
+                # Create a new NaN var to save data in
+                new_var = (np.zeros(dv[var].shape) * np.nan).astype(dv[var].dtype)
+                # For each cell size change, reshape and bin-average
+                for id1, id2, b in zip(idx_start, idx_end, bins_to_merge):
+                    array = np.transpose(dv[var][..., id1:id2])
+                    bin_arr = np.transpose(np.mean(self.reshape(array, b), axis=-1))
+                    new_var[: len(bin_arr), :, id1:id2] = bin_arr
+                # Reset data. This often leaves nan data at farther ranges
+                dv[var] = new_var
+
     def reshape(self, arr, n_bin=None):
         """
         Reshapes the input array `arr` to a shape of (..., n, n_bin).
@@ -949,7 +1011,7 @@ def reshape(self, arr, n_bin=None):
 
         return out
 
-    def finalize(self, dat):
+    def finalize(self, dat, cfg):
         """
         This method cleans up the dataset by removing any attributes that were
         defined but not loaded, updates configuration attributes, and sets the
@@ -959,32 +1021,52 @@ def finalize(self, dat):
         Parameters
         ----------
         dat : dict
-            The dataset dictionary to be finalized. This dictionary is modified
-            in place by removing unused attributes, setting configuration values
-            as attributes, and calculating `fs`.
+            Contains data for the final dataset. This variable has the same pointer
+            as the data dictionary `self.outd` or `self.outdBB`.
+        cfg : dict
+            Global attributes for the final dataset. This variable has the same pointer
+            as the configuration dictionary `self.cfg` or `self.cfgBB`.
 
         Returns
         -------
         dict
             The finalized dataset dictionary with cleaned attributes and added metadata.
         """
+
+        # Drop empty data variables
         for nm in set(defs.data_defs.keys()) - self.vars_read:
             lib._pop(dat, nm)
-        for nm in self.cfg:
-            dat["attrs"][nm] = self.cfg[nm]
-
-        # VMDAS and WinRiver have different set sampling frequency
-        da = dat["attrs"]
-        if ("sourceprog" in da) and (
-            da["sourceprog"].lower() in ["vmdas", "winriver", "winriver2"]
-        ):
-            da["fs"] = round(1 / np.median(np.diff(dat["coords"]["time"])), 2)
+
+        # Need to figure out how to differentiate burst mode from averaging mode
+        calculate_sample_rate_from_time_diff = (
+            cfg.get("source_program", "").lower() in ["vmdas", "winriver", "winriver2"]
+            or cfg["sec_between_ping_groups"] == 0
+        )
+
+        if calculate_sample_rate_from_time_diff:
+            # Use median-based calculation for burst mode operation
+            time_diffs = np.diff(dat["coords"]["time"])
+            if cfg["sec_between_ping_groups"] == 0:
+                warnings.warn(
+                    "mhkit.dolfyn: sec_between_ping_groups is zero, likely indicating burst mode operation. "
+                    "Using median time difference to estimate sample rate, but the actual sample rate "
+                    "may be variable and non-uniform if operating in burst mode. This could introduce "
+                    "artifacts in downstream spectral analysis, filtering, or other time-series "
+                    "processing that assumes constant sampling intervals. "
+                    "Per issue #408: https://github.com/MHKiT-Software/MHKiT-Python/issues/408"
+                )
+            cfg["fs"] = round(1 / np.median(time_diffs), 2)
         else:
-            da["fs"] = 1 / (da["sec_between_ping_groups"] * da["pings_per_ensemble"])
+            # Standard calculation for averaging mode
+            cfg["fs"] = 1 / (cfg["sec_between_ping_groups"] * cfg["pings_per_ensemble"])
+
+        # Save configuration data as attributes
+        dat["attrs"] = cfg
 
+        # Set 3D variable axes properly (beam, range, time)
         for nm in defs.data_defs:
             shp = defs.data_defs[nm][0]
-            if len(shp) and shp[0] == "nc" and lib._in_group(dat, nm):
+            if (len(shp) == 2) and (shp[0] == "nc") and lib._in_group(dat, nm):
                 lib._setd(dat, nm, np.swapaxes(lib._get(dat, nm), 0, 1))
 
         return dat
diff --git a/mhkit/dolfyn/io/rdi_defs.py b/mhkit/dolfyn/io/rdi_defs.py
index addbb3ea2..34b09ee1b 100644
--- a/mhkit/dolfyn/io/rdi_defs.py
+++ b/mhkit/dolfyn/io/rdi_defs.py
@@ -101,7 +101,7 @@
     "pitch_std": ([], "data_vars", "float32", "degree", "Pitch Standard Deviation", ""),
     "roll_std": ([], "data_vars", "float32", "degree", "Roll Standard Deviation", ""),
     "adc": ([8], "sys", "uint8", "1", "Analog-Digital Converter Output", ""),
-    "error_status": ([], "attrs", "float32", "1", "Error Status", ""),
+    "error_status": ([], "sys", "float32", "1", "Error Status", ""),
     "pressure": ([], "data_vars", "float32", "dbar", "Pressure", "sea_water_pressure"),
     "pressure_std": (
         [],
@@ -235,7 +235,7 @@
         "data_vars",
         "float32",
         "m s-1",
-        "Platform Speed Made Good",
+        "Platform Speed Made Good from Lat/Lon",
         "platform_speed_wrt_ground",
     ),
     "dir_made_good_gps": (
@@ -243,7 +243,7 @@
         "data_vars",
         "float32",
         "degree",
-        "Platform Direction Made Good",
+        "Platform Direction Made Good from Lat/Lon",
         "platform_course",
     ),
     "flags_gps": ([], "data_vars", "float32", "bits", "GPS Flags", ""),
@@ -326,6 +326,47 @@
         "proportion_of_acceptable_signal_returns_from_acoustic_instrument_in_sea_water",
     ),
     "status_sl": (["nc", 4], "data_vars", "float32", "1", "Surface Layer Status", ""),
+    "faults": (
+        [],
+        "data_vars",
+        "<U13",
+        "1",
+        "Fault ID . Status Code . Diagnostics Code",
+        "",
+    ),
+    "time_b5": (
+        [],
+        "coords",
+        "float64",
+        "seconds since 1970-01-01 00:00:00",
+        "Time Beam 5",
+        "time",
+    ),
+    "vel_b5": (["nc"], "data_vars", "float32", "m s-1", "Water Velocity", ""),
+    "amp_b5": (
+        ["nc"],
+        "data_vars",
+        "uint8",
+        "1",
+        "Acoustic Signal Amplitude",
+        "signal_intensity_from_multibeam_acoustic_doppler_velocity_sensor_in_sea_water",
+    ),
+    "corr_b5": (
+        ["nc"],
+        "data_vars",
+        "uint8",
+        "1",
+        "Acoustic Signal Correlation",
+        "beam_consistency_indicator_from_multibeam_acoustic_doppler_velocity_profiler_in_sea_water",
+    ),
+    "prcnt_gd_b5": (
+        ["nc"],
+        "data_vars",
+        "uint8",
+        "%",
+        "Percent Good",
+        "proportion_of_acceptable_signal_returns_from_acoustic_instrument_in_sea_water",
+    ),
 }
 
 
@@ -371,8 +412,12 @@ def read_cfgseg(rdr, bb=False):
         cfg = rdr.cfg
     fd = rdr.f
     tmp = fd.read_ui8(5)
-    prog_ver0 = tmp[0]
-    cfg["prog_ver"] = float(tmp[0] + tmp[1] * 0.01)
+    cfg["firmware_ver"] = float(tmp[0] + tmp[1] * 0.01)
+    cfg["inst_make"] = "TRDI"
+    cfg["inst_type"] = "ADCP"
+    cfg["rotate_vars"] = ["vel"]
+    # Currently RDI doesn't use IMUs
+    cfg["has_imu"] = 0
     cfg["inst_model"] = adcp_type.get(tmp[0], "unrecognized instrument")
     config = tmp[2:4]
     cfg["beam_angle"] = [15, 20, 30, [0, 25][int(tmp[0] in [11, 47, 66])]][
@@ -381,15 +426,15 @@ def read_cfgseg(rdr, bb=False):
     beam5 = [0, 1][int((config[1] & 16) == 16)]
     # Carrier frequency
     if tmp[0] in [47, 66]:  # new freqs for Sentinel Vs
-        cfg["freq"] = [38.4, 76.8, 153.6, 307.2, 491.52, 983.04, 2457.6][
+        cfg["carrier_freq"] = [38.4, 76.8, 153.6, 307.2, 491.52, 983.04, 2457.6][
             (config[0] & 7)
         ]
     elif tmp[0] == 31:
-        cfg["freq"] = 2000
+        cfg["carrier_freq"] = 2000
     elif tmp[0] == 61:
-        cfg["freq"] = 44
+        cfg["carrier_freq"] = 44
     else:
-        cfg["freq"] = [75, 150, 300, 600, 1200, 2400, 38][(config[0] & 7)]
+        cfg["carrier_freq"] = [75, 150, 300, 600, 1200, 2400, 38][(config[0] & 7)]
     cfg["beam_pattern"] = ["concave", "convex"][int((config[0] & 8) == 8)]
     cfg["orientation"] = ["down", "up"][int((config[0] & 128) == 128)]
     simflag = ["real", "simulated"][tmp[4]]
@@ -400,7 +445,7 @@ def read_cfgseg(rdr, bb=False):
     if ("n_cells" not in cfg) or (n_cells != cfg["n_cells"]):
         cfg["n_cells"] = n_cells
         if rdr._debug_level > 0:
-            logging.info(f"Number of cells set to {cfg['n_cells']}")
+            logging.debug(f"Number of cells set to {n_cells}")
     cfg["pings_per_ensemble"] = fd.read_ui16(1)
     # Check if cell size has changed
     cs = float(fd.read_ui16(1) * 0.01)
@@ -408,7 +453,7 @@ def read_cfgseg(rdr, bb=False):
         rdr.cs_diff = cs if "cell_size" not in cfg else (cs - cfg["cell_size"])
         cfg["cell_size"] = cs
         if rdr._debug_level > 0:
-            logging.info(f"Cell size set to {cfg['cell_size']}")
+            logging.debug(f"Cell size set to {cs}")
     cfg["blank_dist"] = round(float(fd.read_ui16(1) * 0.01), 2)
     cfg["profiling_mode"] = fd.read_ui8(1)
     cfg["min_corr_threshold"] = fd.read_ui8(1)
@@ -427,7 +472,13 @@ def read_cfgseg(rdr, bb=False):
     cfg["magnetic_var_deg"] = float(fd.read_i16(1) * 0.01)
     cfg["sensors_src"] = np.binary_repr(fd.read_ui8(1), 8)
     cfg["sensors_avail"] = np.binary_repr(fd.read_ui8(1), 8)
-    cfg["bin1_dist_m"] = round(float(fd.read_ui16(1) * 0.01), 4)
+    # If cell size changes, the bin1 distance will too
+    # We only want to save the largest, as we depth average smaller cells together
+    b1d = round(float(fd.read_ui16(1) * 0.01), 4)
+    if ("bin1_dist_m" not in cfg) or (b1d > cfg["bin1_dist_m"]):
+        cfg["bin1_dist_m"] = b1d
+        if rdr._debug_level > 0:
+            logging.debug(f"Bin 1 distance set to {b1d}")
     cfg["transmit_pulse_m"] = round(float(fd.read_ui16(1) * 0.01), 2)
     cfg["water_ref_cells"] = list(fd.read_ui8(2).astype(list))  # list for attrs
     cfg["false_target_threshold"] = fd.read_ui8(1)
@@ -435,13 +486,13 @@ def read_cfgseg(rdr, bb=False):
     cfg["transmit_lag_m"] = float(fd.read_ui16(1) * 0.01)
     rdr._nbyte = 40
 
-    if cfg["prog_ver"] >= 8.14:
+    if cfg["firmware_ver"] >= 8.14:
         cpu_serialnum = fd.read_ui8(8)
         rdr._nbyte += 8
-    if cfg["prog_ver"] >= 8.24:
+    if cfg["firmware_ver"] >= 8.24:
         cfg["bandwidth"] = fd.read_ui16(1)
         rdr._nbyte += 2
-    if cfg["prog_ver"] >= 9.68:
+    if cfg["firmware_ver"] >= 9.68:
         cfg["power_level"] = fd.read_ui8(1)
         # cfg['navigator_basefreqindex'] = fd.read_ui8(1)
         fd.seek(1, 1)
@@ -468,7 +519,7 @@ def read_fixed(rdr, bb=False):
         rdr.n_cells_diff = rdr.cfg["n_cells"] - rdr.ensemble["n_cells"]
         # Increase n_cells if greater than 0
         if rdr.n_cells_diff > 0:
-            rdr.ensemble = lib._ensemble(rdr.n_avg, rdr.cfg["n_cells"])
+            rdr.ensemble = lib._ensemble(rdr.cfg["n_cells"])
             if rdr._debug_level > 0:
                 logging.warning(
                     f"Maximum number of cells increased to {rdr.cfg['n_cells']}"
@@ -479,18 +530,29 @@ def read_fixed_sl(rdr):
     """Read surface layer fixed header"""
     cfg = rdr.cfg
     cfg["surface_layer"] = 1
-    n_cells = rdr.f.read_ui8(1)
     # Check if n_cells is greater than what was used in prior profiles
-    if n_cells > rdr.n_cells_sl:
-        rdr.n_cells_sl = n_cells
+    n_cells_sl = rdr.f.read_ui8(1)
+    if n_cells_sl > rdr.n_cells_sl:
+        rdr.n_cells_sl = n_cells_sl
+    if ("n_cells_sl" not in cfg) or (n_cells_sl != cfg["n_cells_sl"]):
+        cfg["n_cells_sl"] = n_cells_sl
         if rdr._debug_level > 0:
-            logging.warning(
-                f"Maximum number of surface layer cells increased to {n_cells}"
-            )
-    cfg["n_cells_sl"] = n_cells
-    # Assuming surface layer profile cell size never changes
-    cfg["cell_size_sl"] = float(rdr.f.read_ui16(1) * 0.01)
-    cfg["bin1_dist_m_sl"] = round(float(rdr.f.read_ui16(1) * 0.01), 4)
+            logging.debug(f"Number of surface cells set to {n_cells_sl}")
+    # Cell size also changes
+    cs_sl = float(rdr.f.read_ui16(1) * 0.01)
+    if ("cell_size_sl" not in cfg) or (cs_sl != cfg["cell_size_sl"]):
+        rdr.cs_sl_diff = (
+            cs_sl if "cell_size_sl" not in cfg else (cs_sl - cfg["cell_size_sl"])
+        )
+        cfg["cell_size_sl"] = cs_sl
+        if rdr._debug_level > 0:
+            logging.debug(f"Surface layer cell size set to {cs_sl}")
+    # Only save maximum bin 1 distance
+    b1d = round(float(rdr.f.read_ui16(1) * 0.01), 4)
+    if ("bin1_dist_m_sl" not in cfg) or (b1d > cfg["bin1_dist_m_sl"]):
+        cfg["bin1_dist_m_sl"] = b1d
+        if rdr._debug_level > 0:
+            logging.debug(f"Surface layer Bin 1 distance set to {b1d}")
 
     if rdr._debug_level > -1:
         logging.info("Read Surface Layer Config")
@@ -499,13 +561,11 @@ def read_fixed_sl(rdr):
 
 def read_var(rdr, bb=False):
     """Read variable header"""
-    fd = rdr.f
     if bb:
         ens = rdr.ensembleBB
     else:
         ens = rdr.ensemble
     ens.k += 1
-    ens = rdr.ensemble
     k = ens.k
     rdr.vars_read += [
         "number",
@@ -525,6 +585,7 @@ def read_var(rdr, bb=False):
         "roll_std",
         "adc",
     ]
+    fd = rdr.f
     ens.number[k] = fd.read_ui16(1)
     ens.rtc[:, k] = fd.read_ui8(7)
     ens.number[k] += 65535 * fd.read_ui8(1)
@@ -545,7 +606,7 @@ def read_var(rdr, bb=False):
 
     cfg = rdr.cfg
     if cfg["inst_model"].lower() == "broadband":
-        if cfg["prog_ver"] >= 5.55:
+        if cfg["firmware_ver"] >= 5.55:
             fd.seek(15, 1)
             cent = fd.read_ui8(1)
             ens.rtc[:, k] = fd.read_ui8(7)
@@ -554,30 +615,30 @@ def read_var(rdr, bb=False):
     elif cfg["inst_model"].lower() == "ocean surveyor":
         fd.seek(16, 1)  # 30 bytes all set to zero, 14 read above
         rdr._nbyte += 16
-        if cfg["prog_ver"] > 23:
+        if cfg["firmware_ver"] > 23:
             fd.seek(2, 1)
             rdr._nbyte += 2
     else:
         ens.error_status[k] = np.binary_repr(fd.read_ui32(1), 32)
         rdr.vars_read += ["pressure", "pressure_std"]
         rdr._nbyte += 4
-        if cfg["prog_ver"] >= 8.13:
+        if cfg["firmware_ver"] >= 8.13:
             # Added pressure sensor stuff in 8.13
             fd.seek(2, 1)
             ens.pressure[k] = fd.read_ui32(1) * 0.001  # dPa to dbar
             ens.pressure_std[k] = fd.read_ui32(1) * 0.001
             rdr._nbyte += 10
-        if cfg["prog_ver"] >= 8.24:
+        if cfg["firmware_ver"] >= 8.24:
             # Spare byte added 8.24
             fd.seek(1, 1)
             rdr._nbyte += 1
-        if cfg["prog_ver"] >= 16.05:
+        if cfg["firmware_ver"] >= 16.05:
             # Added more fields with century in clock
             cent = fd.read_ui8(1)
             ens.rtc[:, k] = fd.read_ui8(7)
             ens.rtc[0, k] = ens.rtc[0, k] + cent * 100
             rdr._nbyte += 8
-        if cfg["prog_ver"] >= 56:
+        if cfg["firmware_ver"] >= 56:
             fd.seek(1)  # lag near bottom flag
             rdr._nbyte += 1
 
@@ -591,9 +652,8 @@ def read_vel(rdr, bb=0):
     rdr.vars_read += ["vel" + tg]
     n_cells = cfg["n_cells" + tg]
 
-    k = ens.k
     vel = np.array(rdr.f.read_i16(4 * n_cells)).reshape((n_cells, 4)) * 0.001
-    ens["vel" + tg][:n_cells, :, k] = vel
+    ens["vel" + tg][:n_cells, :, ens.k] = vel
     rdr._nbyte = 2 + 4 * n_cells * 2
     if rdr._debug_level > -1:
         logging.info("Read Vel")
@@ -605,10 +665,9 @@ def read_corr(rdr, bb=0):
     rdr.vars_read += ["corr" + tg]
     n_cells = cfg["n_cells" + tg]
 
-    k = ens.k
-    ens["corr" + tg][:n_cells, :, k] = np.array(rdr.f.read_ui8(4 * n_cells)).reshape(
-        (n_cells, 4)
-    )
+    ens["corr" + tg][:n_cells, :, ens.k] = np.array(
+        rdr.f.read_ui8(4 * n_cells)
+    ).reshape((n_cells, 4))
     rdr._nbyte = 2 + 4 * n_cells
     if rdr._debug_level > -1:
         logging.info("Read Corr")
@@ -620,8 +679,7 @@ def read_amp(rdr, bb=0):
     rdr.vars_read += ["amp" + tg]
     n_cells = cfg["n_cells" + tg]
 
-    k = ens.k
-    ens["amp" + tg][:n_cells, :, k] = np.array(rdr.f.read_ui8(4 * n_cells)).reshape(
+    ens["amp" + tg][:n_cells, :, ens.k] = np.array(rdr.f.read_ui8(4 * n_cells)).reshape(
         (n_cells, 4)
     )
     rdr._nbyte = 2 + 4 * n_cells
@@ -659,11 +717,11 @@ def read_status(rdr, bb=0):
 
 def read_bottom(rdr):
     """Read bottom track block"""
-    rdr.vars_read += ["dist_bt", "vel_bt", "corr_bt", "amp_bt", "prcnt_gd_bt"]
-    fd = rdr.f
+    cfg = rdr.cfg
     ens = rdr.ensemble
     k = ens.k
-    cfg = rdr.cfg
+    rdr.vars_read += ["dist_bt", "vel_bt", "corr_bt", "amp_bt", "prcnt_gd_bt"]
+    fd = rdr.f
     if rdr._vm_source == 2:
         rdr.vars_read += ["latitude_gps", "longitude_gps"]
         fd.seek(2, 1)
@@ -702,14 +760,16 @@ def read_bottom(rdr):
         # Skip reference layer data
         fd.seek(26, 1)
         rdr._nbyte = 2 + 68
-    if cfg["prog_ver"] >= 5.3:
+    if cfg["firmware_ver"] >= 5.3:
         fd.seek(7, 1)  # skip to rangeMsb bytes
         ens.dist_bt[:, k] = ens.dist_bt[:, k] + fd.read_ui8(4) * 655.36
         rdr._nbyte += 11
-    if cfg["prog_ver"] >= 16.2 and (cfg.get("sourceprog", "").lower() != "winriver"):
+    if cfg["firmware_ver"] >= 16.2 and (
+        cfg.get("source_program", "").lower() != "winriver"
+    ):
         fd.seek(4, 1)  # not documented
         rdr._nbyte += 4
-    if cfg["prog_ver"] >= 56.1:
+    if cfg["firmware_ver"] >= 56.1:
         fd.seek(4, 1)  # not documented
         rdr._nbyte += 4
 
@@ -719,10 +779,10 @@ def read_bottom(rdr):
 
 def read_alt(rdr):
     """Read altimeter (range of vertical beam) block"""
-    fd = rdr.f
     ens = rdr.ensemble
     k = ens.k
     rdr.vars_read += ["alt_dist", "alt_rssi", "alt_eval", "alt_status"]
+    fd = rdr.f
     ens.alt_eval[k] = fd.read_ui8(1)  # evaluation amplitude
     ens.alt_rssi[k] = fd.read_ui8(1)  # RSSI amplitude
     ens.alt_dist[k] = fd.read_ui32(1) * 0.001  # range to surface/seafloor
@@ -735,7 +795,7 @@ def read_alt(rdr):
 def read_winriver(rdr):
     """Skip WinRiver1 Navigation block (outdated)"""
     rdr._winrivprob = True
-    rdr.cfg["sourceprog"] = "WINRIVER"
+    rdr.cfg["source_program"] = "WINRIVER"
     if rdr._vm_source not in [2, 3]:
         if rdr._debug_level > -1:
             logging.warning(
@@ -751,32 +811,33 @@ def read_winriver(rdr):
 
 def read_winriver2(rdr):
     """Read WinRiver2 Navigation block"""
-    startpos = rdr.f.tell()
+    fd = rdr.f
+    startpos = fd.tell()
     rdr._winrivprob = True
-    rdr.cfg["sourceprog"] = "WinRiver2"
+    rdr.cfg["source_program"] = "WinRiver2"
     ens = rdr.ensemble
     k = ens.k
     if rdr._debug_level > -1:
         logging.info("Read WinRiver2")
     rdr._vm_source = 3
 
-    spid = rdr.f.read_ui16(1)  # NMEA specific IDs
+    spid = fd.read_ui16(1)  # NMEA specific IDs
     if spid in [4, 104]:  # GGA
-        sz = rdr.f.read_ui16(1)
-        dtime = rdr.f.read_f64(1)
+        sz = fd.read_ui16(1)
+        fd.read_f64(1)  # dtime
         if sz <= 43:  # If no sentence, data is still stored in nmea format
-            empty_gps = rdr.f.reads(sz - 2)
-            rdr.f.seek(2, 1)
+            fd.reads(sz - 2)  # empty_gps
+            fd.seek(2, 1)
         else:  # TRDI rewrites the nmea string into their format if one is found
-            start_string = rdr.f.reads(6)
+            start_string = fd.reads(6)
             if not isinstance(start_string, str):
                 if rdr._debug_level > 0:
                     logging.warning(
                         f"Invalid GGA string found in ensemble {k}," " skipping..."
                     )
                 return "FAIL"
-            rdr.f.seek(1, 1)
-            gga_time = rdr.f.reads(9)
+            fd.seek(1, 1)
+            gga_time = fd.reads(9)
             time = tmlib.timedelta(
                 hours=int(gga_time[0:2]),
                 minutes=int(gga_time[2:4]),
@@ -788,25 +849,25 @@ def read_winriver2(rdr):
                 clock[0, :] += century
             date = tmlib.datetime(*clock[:3, 0]) + time
             ens.time_gps[k] = tmlib.date2epoch(date)[0]
-            rdr.f.seek(1, 1)
-            ens.latitude_gps[k] = rdr.f.read_f64(1)
-            tcNS = rdr.f.reads(1)  # 'N' or 'S'
+            fd.seek(1, 1)
+            ens.latitude_gps[k] = fd.read_f64(1)
+            tcNS = fd.reads(1)  # 'N' or 'S'
             if tcNS == "S":
                 ens.latitude_gps[k] *= -1
-            ens.longitude_gps[k] = rdr.f.read_f64(1)
-            tcEW = rdr.f.reads(1)  # 'E' or 'W'
+            ens.longitude_gps[k] = fd.read_f64(1)
+            tcEW = fd.reads(1)  # 'E' or 'W'
             if tcEW == "W":
                 ens.longitude_gps[k] *= -1
-            ens.fix_gps[k] = rdr.f.read_ui8(1)  # gps fix type/quality
-            ens.n_sat_gps[k] = rdr.f.read_ui8(1)  # of satellites
+            ens.fix_gps[k] = fd.read_ui8(1)  # gps fix type/quality
+            ens.n_sat_gps[k] = fd.read_ui8(1)  # of satellites
             # horizontal dilution of precision
-            ens.hdop_gps[k] = rdr.f.read_f32(1)
-            ens.elevation_gps[k] = rdr.f.read_f32(1)  # altitude
-            m = rdr.f.reads(1)  # altitude unit, 'm'
-            h_geoid = rdr.f.read_f32(1)  # height of geoid
-            m2 = rdr.f.reads(1)  # geoid unit, 'm'
-            ens.rtk_age_gps[k] = rdr.f.read_f32(1)
-            station_id = rdr.f.read_ui16(1)
+            ens.hdop_gps[k] = fd.read_f32(1)
+            ens.elevation_gps[k] = fd.read_f32(1)  # altitude
+            fd.reads(1)  # altitude unit, 'm'
+            fd.read_f32(1)  # height of geoid
+            fd.reads(1)  # geoid unit, 'm'
+            ens.rtk_age_gps[k] = fd.read_f32(1)
+            fd.read_ui16(1)  # station id
         rdr.vars_read += [
             "time_gps",
             "longitude_gps",
@@ -817,88 +878,88 @@ def read_winriver2(rdr):
             "elevation_gps",
             "rtk_age_gps",
         ]
-        rdr._nbyte = rdr.f.tell() - startpos + 2
+        rdr._nbyte = fd.tell() - startpos + 2
 
     elif spid in [5, 105]:  # VTG
-        sz = rdr.f.read_ui16(1)
-        dtime = rdr.f.read_f64(1)
+        sz = fd.read_ui16(1)
+        fd.read_f64(1)  # dtime
         if sz <= 22:  # if no data
-            empty_gps = rdr.f.reads(sz - 2)
-            rdr.f.seek(2, 1)
+            fd.reads(sz - 2)  # empty gps
+            fd.seek(2, 1)
         else:
-            start_string = rdr.f.reads(6)
+            start_string = fd.reads(6)
             if not isinstance(start_string, str):
                 if rdr._debug_level > 0:
                     logging.warning(
                         f"Invalid VTG string found in ensemble {k}," " skipping..."
                     )
                 return "FAIL"
-            rdr.f.seek(1, 1)
-            true_track = rdr.f.read_f32(1)
-            t = rdr.f.reads(1)  # 'T'
-            magn_track = rdr.f.read_f32(1)
-            m = rdr.f.reads(1)  # 'M'
-            speed_knot = rdr.f.read_f32(1)
-            kts = rdr.f.reads(1)  # 'N'
-            speed_kph = rdr.f.read_f32(1)
-            kph = rdr.f.reads(1)  # 'K'
-            mode = rdr.f.reads(1)
-            # knots -> m/s
+            fd.seek(1, 1)
+            true_track = fd.read_f32(1)  # track from true North
+            fd.reads(1)  # 'T'
+            fd.read_f32(1)  # track from magnetic North
+            fd.reads(1)  # 'M'
+            speed_knot = fd.read_f32(1)  # speed in knots
+            fd.reads(1)  # 'N'
+            fd.read_f32(1)  # speed in kph
+            fd.reads(1)  # 'K'
+            fd.reads(1)  # mode
+            # convert knots to m/s
             ens.speed_over_grnd_gps[k] = speed_knot / 1.944
             ens.dir_over_grnd_gps[k] = true_track
         rdr.vars_read += ["speed_over_grnd_gps", "dir_over_grnd_gps"]
-        rdr._nbyte = rdr.f.tell() - startpos + 2
+        rdr._nbyte = fd.tell() - startpos + 2
 
     elif spid in [6, 106]:  # 'DBT' depth sounder
-        sz = rdr.f.read_ui16(1)
-        dtime = rdr.f.read_f64(1)
+        sz = fd.read_ui16(1)
+        fd.read_f64(1)  # dtime
         if sz <= 20:
-            empty_gps = rdr.f.reads(sz - 2)
-            rdr.f.seek(2, 1)
+            fd.reads(sz - 2)  # empty gps
+            fd.seek(2, 1)
         else:
-            start_string = rdr.f.reads(6)
+            start_string = fd.reads(6)
             if not isinstance(start_string, str):
                 if rdr._debug_level > 0:
                     logging.warning(
                         f"Invalid DBT string found in ensemble {k}," " skipping..."
                     )
                 return "FAIL"
-            rdr.f.seek(1, 1)
-            depth_ft = rdr.f.read_f32(1)
-            ft = rdr.f.reads(1)  # 'f'
-            depth_m = rdr.f.read_f32(1)
-            m = rdr.f.reads(1)  # 'm'
-            depth_fathom = rdr.f.read_f32(1)
-            f = rdr.f.reads(1)  # 'F'
+            fd.seek(1, 1)
+            fd.read_f32(1)  # depth in feet
+            fd.reads(1)  # 'f'
+            depth_m = fd.read_f32(1)  # depth in meters
+            fd.reads(1)  # 'm'
+            fd.read_f32(1)  # depth in fathoms
+            fd.reads(1)  # 'F'
             ens.dist_nmea[k] = depth_m
         rdr.vars_read += ["dist_nmea"]
-        rdr._nbyte = rdr.f.tell() - startpos + 2
+        rdr._nbyte = fd.tell() - startpos + 2
 
     elif spid in [7, 107]:  # 'HDT'
-        sz = rdr.f.read_ui16(1)
-        dtime = rdr.f.read_f64(1)
+        sz = fd.read_ui16(1)
+        fd.read_f64(1)  # dtime
         if sz <= 14:
-            empty_gps = rdr.f.reads(sz - 2)
-            rdr.f.seek(2, 1)
+            fd.reads(sz - 2)  # empty gps
+            fd.seek(2, 1)
         else:
-            start_string = rdr.f.reads(6)
+            start_string = fd.reads(6)
             if not isinstance(start_string, str):
                 if rdr._debug_level > 0:
                     logging.warning(
                         f"Invalid HDT string found in ensemble {k}," " skipping..."
                     )
                 return "FAIL"
-            rdr.f.seek(1, 1)
-            ens.heading_gps[k] = rdr.f.read_f64(1)
-            tt = rdr.f.reads(1)
+            fd.seek(1, 1)
+            ens.heading_gps[k] = fd.read_f64(1)  # gps heading
+            fd.reads(1)  # tt
         rdr.vars_read += ["heading_gps"]
-        rdr._nbyte = rdr.f.tell() - startpos + 2
+        rdr._nbyte = fd.tell() - startpos + 2
 
 
 def read_vmdas(rdr):
     """Read VMDAS Navigation block"""
     fd = rdr.f
-    rdr.cfg["sourceprog"] = "VMDAS"
+    rdr.cfg["source_program"] = "VMDAS"
     ens = rdr.ensemble
     k = ens.k
     if rdr._vm_source != 1 and rdr._debug_level > -1:
@@ -909,8 +970,8 @@ def read_vmdas(rdr):
         "clock_offset_UTC_gps",
         "latitude_gps",
         "longitude_gps",
-        "avg_speed_gps",
-        "avg_dir_gps",
+        "speed_over_grnd_gps",
+        "dir_over_grnd_gps",
         "speed_made_good_gps",
         "dir_made_good_gps",
         "flags_gps",
@@ -932,14 +993,15 @@ def read_vmdas(rdr):
     longitude_first_gps = fd.read_i32(1) * rdr._cfac32
 
     # Last lat/lon position prior to current ADCP ping
-    utc_time_fix = tmlib.timedelta(milliseconds=(int(fd.read_ui32(1) * 0.1)))
-    ens.time_gps[k] = tmlib.date2epoch(date_utc + utc_time_fix)[0]
+    utc_time_last_fix = tmlib.timedelta(milliseconds=(int(fd.read_ui32(1) * 0.1)))
+    ens.time_gps[k] = tmlib.date2epoch(date_utc + utc_time_last_fix)[0]
     ens.latitude_gps[k] = fd.read_i32(1) * rdr._cfac32
     ens.longitude_gps[k] = fd.read_i32(1) * rdr._cfac32
-
-    ens.avg_speed_gps[k] = fd.read_ui16(1) * 0.001
-    ens.avg_dir_gps[k] = fd.read_ui16(1) * rdr._cfac16  # avg true track
+    # From VTG
+    ens.speed_over_grnd_gps[k] = fd.read_ui16(1) * 0.001
+    ens.dir_over_grnd_gps[k] = fd.read_ui16(1) * rdr._cfac16  # avg true track
     fd.seek(2, 1)  # avg magnetic track
+    # Calculated from difference between latitude and longitude
     ens.speed_made_good_gps[k] = fd.read_ui16(1) * 0.001
     ens.dir_made_good_gps[k] = fd.read_ui16(1) * rdr._cfac16
     fd.seek(2, 1)  # reserved
@@ -961,3 +1023,153 @@ def read_vmdas(rdr):
     if rdr._debug_level > -1:
         logging.info("Read VMDAS")
     rdr._read_vmdas = True
+
+
+def read_sentinelv_syscfg(rdr, bb=False):
+    """Read system configuration block for Sentinel V: 0x7000"""
+    if bb:
+        cfg = rdr.cfgbb
+    else:
+        cfg = rdr.cfg
+
+    fd = rdr.f
+    fw = fd.read_ui8(4)
+    cfg["firmware_ver"] = ".".join(fw.astype(str))
+    cfg["carrier_freq"] = fd.read_ui32(1) / 1000
+    cfg["pressure_rating_m"] = fd.read_ui16(1)
+    schema = fd.read_ui8(3)
+    cfg["schema"] = ".".join(schema.astype(str))
+    fd.seek(1, 1)
+
+    if rdr._debug_level > -1:
+        logging.info("Read Sentinel V System Configuration")
+    rdr._nbyte = 2 + 14
+
+
+def read_sentinelv_ping_setup(rdr, bb=False):
+    """Read 'ping setup' block for Sentinel V: 0x7001"""
+    if bb:
+        cfg = rdr.cfgbb
+    else:
+        cfg = rdr.cfg
+
+    fd = rdr.f
+    fd.read_ui16(1)  # ping ID
+    cfg["ensemble_interval"] = fd.read_ui32(1) * 0.001
+    cfg["pings_per_ensemble"] = fd.read_ui16(1)
+    cfg["time_between_pings_s"] = fd.read_ui32(1) * 0.001
+    cfg["sec_between_ping_groups"] = fd.read_ui32(1) * 0.001
+    fd.seek(4, 1)
+    fd.read_ui16(1)  # ping sequence number within ensemble
+    cfg["ambiguity_vel"] = fd.read_ui16(1) * 0.001
+    fd.seek(4, 1)
+    fd.read_ui32(1)  # ensemble offset
+    fd.read_ui16(1)  # ensemble count
+    clock = fd.read_ui8(8)
+    clock[1] += century
+    cfg["deployment_start"] = tmlib.date2str(
+        tmlib.datetime(*clock[1:7], microsecond=int(float(clock[7]) * 10000))
+    )[0]
+    if rdr._debug_level > -1:
+        logging.info("Read Sentinel V Ping Setup")
+    rdr._nbyte = 2 + 42
+
+
+def read_sentinelv_event_log(rdr):
+    """Read event log block for Sentinel V: 0x7004"""
+    fd = rdr.f
+    ens = rdr.ensemble
+    k = ens.k
+    n_faults = fd.read_ui16(1)
+    code = []
+    if n_faults:
+        code.append(f"{fd.read_ui16(1)}.{fd.read_ui8(1)}.{fd.read_ui8(1)}")
+    rdr.vars_read += ["faults"]
+    ens.faults[k] = ",".join(code)
+
+    if rdr._debug_level > -1:
+        logging.info("Read Sentinel V Event Log")
+    rdr._nbyte = 2 + 6 + 4 * (n_faults - 1)
+
+
+def read_vel_b5_leader(rdr):
+    """Read Sentinel V vertical beam (b5) leader: 0x0F01"""
+    cfg = rdr.cfg
+    fd = rdr.f
+    rdr.vars_read += ["time_b5"]  # Make sure this is added
+    cfg["n_cells_b5"] = fd.read_ui16(1)
+    fd.read_ui16(1)  # n_pings_b5
+    cfg["cell_size_b5"] = fd.read_ui16(1) * 0.01
+    cfg["bin1_dist_b5_m"] = fd.read_ui16(1) * 0.01
+    cfg["mode_b5"] = fd.read_ui16(1)
+    cfg["transmit_pulse_b5_m"] = fd.read_ui16(1) * 0.01
+    cfg["transmit_lag_b5_m"] = fd.read_ui16(1) * 0.01
+    fd.read_ui16(1)  # transmit_code_elements
+    fd.read_ui16(1)  # vertical_rssi_threshold
+    fd.read_ui16(1)  # vertical_shallow_bin
+    fd.read_ui16(1)  # vertical_start_bin
+    fd.read_ui16(1)  # vertical_shallow_rssi_bin
+    fd.read_ui16(1)  # max_core_threshold
+    fd.read_ui16(1)  # min_core_threshold
+    cfg["ping_offset_time_b5"] = fd.read_ui16(1) * 0.001
+    fd.seek(2, 1)
+    fd.read_ui16(1)  # depth_screen
+    cfg["min_prcnt_gd_b5"] = fd.read_ui16(1)
+    fd.read_ui16(1)  # vertical_do_proofing
+
+    if rdr._debug_level > -1:
+        logging.info("Read Sentinel V Event Log")
+    rdr._nbyte = 2 + 38
+
+
+def read_vel_b5(rdr):
+    """Read Sentinel V vertical beam water velocity block: 0x0A00"""
+    ens = rdr.ensemble
+    cfg = rdr.cfg
+    rdr.vars_read += ["vel_b5"]
+    n_cells_b5 = cfg["n_cells_b5"]
+
+    vel_b5 = np.array(rdr.f.read_i16(n_cells_b5)) * 0.001
+    ens["vel_b5"][:n_cells_b5, ens.k] = vel_b5
+    rdr._nbyte = 2 + n_cells_b5 * 2
+    if rdr._debug_level > -1:
+        logging.info("Read Vel Beam 5")
+
+
+def read_corr_b5(rdr):
+    """Read Sentinel V vertical beam acoustic signal correlation block: 0x0B00"""
+    ens = rdr.ensemble
+    cfg = rdr.cfg
+    rdr.vars_read += ["corr_b5"]
+    n_cells_b5 = cfg["n_cells_b5"]
+
+    ens["corr_b5"][:n_cells_b5, ens.k] = np.array(rdr.f.read_ui8(n_cells_b5))
+    rdr._nbyte = 2 + n_cells_b5
+    if rdr._debug_level > -1:
+        logging.info("Read Corr Beam 5")
+
+
+def read_amp_b5(rdr):
+    """Read Sentinel V vertical beam acoustic signal amplitude block: 0C00"""
+    ens = rdr.ensemble
+    cfg = rdr.cfg
+    rdr.vars_read += ["amp_b5"]
+    n_cells_b5 = cfg["n_cells_b5"]
+
+    ens["amp_b5"][:n_cells_b5, ens.k] = np.array(rdr.f.read_ui8(n_cells_b5))
+    rdr._nbyte = 2 + n_cells_b5
+    if rdr._debug_level > -1:
+        logging.info("Read Amp Beam 5")
+
+
+def read_prcnt_gd_b5(rdr):
+    """Read Sentinel V vertical beam acoustic signal 'percent good' block: 0x0D00"""
+    ens = rdr.ensemble
+    cfg = rdr.cfg
+    rdr.vars_read += ["prcnt_gd_b5"]
+    n_cells_b5 = cfg["n_cells_b5"]
+
+    ens["prcnt_gd_b5"][:n_cells_b5, ens.k] = np.array(rdr.f.read_ui8(n_cells_b5))
+    rdr._nbyte = 2 + n_cells_b5
+    if rdr._debug_level > -1:
+        logging.info("Read PG Beam 5")
diff --git a/mhkit/dolfyn/io/rdi_lib.py b/mhkit/dolfyn/io/rdi_lib.py
index 03e8e2c60..897cc0c91 100644
--- a/mhkit/dolfyn/io/rdi_lib.py
+++ b/mhkit/dolfyn/io/rdi_lib.py
@@ -128,16 +128,13 @@ class _ensemble:
     def __getitem__(self, nm):
         return getattr(self, nm)
 
-    def __init__(self, navg, n_cells):
-        if navg is None or navg == 0:
-            navg = 1
-        self.n_avg = navg
+    def __init__(self, n_cells):
         self.n_cells = n_cells
         for nm in data_defs:
             setattr(
                 self,
                 nm,
-                np.zeros(_get_size(nm, n=navg, ncell=n_cells), dtype=data_defs[nm][2]),
+                np.zeros(_get_size(nm, n=1, ncell=n_cells), dtype=data_defs[nm][2]),
             )
 
     def clean_data(self):
diff --git a/mhkit/dolfyn/rotate/api.py b/mhkit/dolfyn/rotate/api.py
index 13fb13326..25cabc673 100644
--- a/mhkit/dolfyn/rotate/api.py
+++ b/mhkit/dolfyn/rotate/api.py
@@ -246,15 +246,28 @@ def set_declination(ds, declin, inplace=True):
     else:
         rotate2earth = False
 
-    ds["orientmat"].values = np.einsum(
-        "kj...,ij->ki...",
-        ds["orientmat"].values,
-        Rdec,
-    ).astype(np.float32)
+    # Should only be one of these:
+    if "orientmat" in ds:
+        ds["orientmat"].values = np.einsum(
+            "kj...,ij->ki...",
+            ds["orientmat"].values,
+            Rdec,
+        ).astype(np.float32)
+    elif "orientmat_avg" in ds:
+        ds["orientmat_avg"].values = np.einsum(
+            "kj...,ij->ki...",
+            ds["orientmat_avg"].values,
+            Rdec,
+        ).astype(np.float32)
+
     if "heading" in ds:
         heading = ds["heading"] + angle
         heading[heading > 180] -= 360
         ds["heading"].values = heading
+    elif "heading_avg" in ds:
+        heading = ds["heading_avg"] + angle
+        heading[heading > 180] -= 360
+        ds["heading_avg"].values = heading
 
     if rotate2earth:
         rotate2(ds, "earth", inplace=True)
diff --git a/mhkit/dolfyn/rotate/base.py b/mhkit/dolfyn/rotate/base.py
index 9ffa4f282..ad4d3d946 100644
--- a/mhkit/dolfyn/rotate/base.py
+++ b/mhkit/dolfyn/rotate/base.py
@@ -49,7 +49,7 @@ def _set_coords(ds, ref_frame, forced=False):
 
     XYZ = ["X", "Y", "Z"]
     ENU = ["E", "N", "U"]
-    beam = ds.beam.values
+    beam = ds["beam"].values
     principal = ["streamwise", "x-stream", "vert"]
 
     # check make/model
diff --git a/mhkit/dolfyn/rotate/signature.py b/mhkit/dolfyn/rotate/signature.py
index 771842842..3619be60f 100644
--- a/mhkit/dolfyn/rotate/signature.py
+++ b/mhkit/dolfyn/rotate/signature.py
@@ -57,13 +57,22 @@ def _inst2earth(adcpo, reverse=False, rotate_vars=None, force=False):
 
     if "orientmat" in adcpo:
         omat = adcpo["orientmat"]
-    else:
+    elif "orientmat_avg" in adcpo:
+        omat = adcpo["orientmat_avg"]
+    elif "time" in adcpo:
         omat = _euler2orient(
             adcpo["time"],
             adcpo["heading"].values,
             adcpo["pitch"].values,
             adcpo["roll"].values,
         )
+    elif "time_avg" in adcpo:
+        omat = _euler2orient(
+            adcpo["time_avg"],
+            adcpo["heading_avg"].values,
+            adcpo["pitch_avg"].values,
+            adcpo["roll_avg"].values,
+        )
 
     # Take the transpose of the orientation to get the inst->earth rotation
     # matrix.
diff --git a/mhkit/dolfyn/rotate/vector.py b/mhkit/dolfyn/rotate/vector.py
index e390322f8..df37b0f9c 100644
--- a/mhkit/dolfyn/rotate/vector.py
+++ b/mhkit/dolfyn/rotate/vector.py
@@ -345,7 +345,7 @@ def _euler2orient(time, heading, pitch, roll, units="degree"):
     )
     return xr.DataArray(
         omat,
-        coords={"earth": earth, "inst": inst, "time": time},
-        dims=["earth", "inst", "time"],
+        coords={"earth": earth, "inst": inst, time.name: time},
+        dims=["earth", "inst", time.name],
         attrs={"units": "1", "long_name": "Orientation Matrix"},
     )
diff --git a/mhkit/dolfyn/time.py b/mhkit/dolfyn/time.py
index ed25b23a5..bceb699b5 100644
--- a/mhkit/dolfyn/time.py
+++ b/mhkit/dolfyn/time.py
@@ -123,18 +123,17 @@ def epoch2date(ep_time, offset_hr=0, to_str=False):
     elif not isinstance(ep_time, (np.ndarray, list)):
         ep_time = [ep_time]
 
-    ######### IMPORTANT #########
-    # Note the use of `utcfromtimestamp` here, rather than `fromtimestamp`
-    # This is CRITICAL! See the difference between those functions here:
-    #    https://docs.python.org/3/library/datetime.html#datetime.datetime.fromtimestamp
-    # Long story short: `fromtimestamp` used system-specific timezone
-    # info to calculate the datetime object, but returns a
-    # timezone-agnostic object.
     if offset_hr != 0:
         delta = timedelta(hours=offset_hr)
-        time = [datetime.utcfromtimestamp(t) + delta for t in ep_time]
+        time = [
+            datetime.fromtimestamp(t, timezone.utc).replace(tzinfo=None) + delta
+            for t in ep_time
+        ]
     else:
-        time = [datetime.utcfromtimestamp(t) for t in ep_time]
+        time = [
+            datetime.fromtimestamp(t, timezone.utc).replace(tzinfo=None)
+            for t in ep_time
+        ]
 
     if to_str:
         time = date2str(time)
diff --git a/mhkit/dolfyn/velocity.py b/mhkit/dolfyn/velocity.py
index adfa942f3..bb95c40c7 100644
--- a/mhkit/dolfyn/velocity.py
+++ b/mhkit/dolfyn/velocity.py
@@ -18,10 +18,6 @@ class Velocity:
     :class:`VelBinner` tool, but the method for calculating these
     variables can depend on the details of the measurement
     (instrument, it's configuration, orientation, etc.).
-
-    See Also
-    ========
-    :class:`VelBinner`
     """
 
     ########
@@ -43,7 +39,7 @@ def rotate2(self, out_frame="earth", inplace=True):
         Returns
         -------
         ds : xarray.Dataset or None
-          Returns the rotated dataset **when ``inplace=False``**, otherwise
+          Returns the rotated dataset **when `inplace=False`**, otherwise
           returns None.
 
         Notes
@@ -128,7 +124,7 @@ def set_inst2head_rotmat(self, rotmat, inplace=True):
         Returns
         -------
         ds : xarray.Dataset or None
-          Returns the rotated dataset **when ``inplace=False``**, otherwise
+          Returns the rotated dataset **when `inplace=False`**, otherwise
           returns None.
 
         Notes
@@ -155,7 +151,7 @@ def save(self, filename, **kwargs):
 
         Notes
         -----
-        See DOLfYN's :func:`save <dolfyn.io.api.save>` function for
+        See DOLfYN's :func:`save <mhkit.dolfyn.io.api.save>` function for
         additional details.
         """
 
@@ -177,10 +173,11 @@ def __repr__(
         self,
     ):
         time_string = "{:.2f} {} (started: {})"
-        if "time" not in self or dt642epoch(self["time"][0]) < 1:
+        time = "time" if "time" in self else "time_avg"
+        if time not in self or dt642epoch(self[time][0]) < 1:
             time_string = "-->No Time Information!<--"
         else:
-            tm = self["time"][[0, -1]].values
+            tm = self[time][[0, -1]].values
             dt = dt642date(tm[0])[0]
             delta = (dt642epoch(tm[-1]) - dt642epoch(tm[0])) / (3600 * 24)  # days
             if delta > 1:
@@ -202,7 +199,7 @@ def __repr__(
                 time_string = "-->Error in time info<--"
 
         p = self.ds.attrs
-        t_shape = self["time"].shape
+        t_shape = self[time].shape
         if len(t_shape) > 1:
             shape_string = "({} bins, {} pings @ {}Hz)".format(
                 t_shape[0], t_shape, p.get("fs")
@@ -298,9 +295,9 @@ def u(
         """
         The first velocity component.
 
-        This is simply a shortcut to self['vel'][0]. Therefore,
+        This is simply a shortcut to ``self['vel'][0]``. Therefore,
         depending on the coordinate system of the data object
-        (self.attrs['coord_sys']), it is:
+        (``self.attrs['coord_sys']``), it is:
 
         - beam:      beam1
         - inst:      x
@@ -316,9 +313,9 @@ def v(
         """
         The second velocity component.
 
-        This is simply a shortcut to self['vel'][1]. Therefore,
+        This is simply a shortcut to ``self['vel'][1]``. Therefore,
         depending on the coordinate system of the data object
-        (self.attrs['coord_sys']), it is:
+        (``self.attrs['coord_sys']``), it is:
 
         - beam:      beam2
         - inst:      y
@@ -334,9 +331,9 @@ def w(
         """
         The third velocity component.
 
-        This is simply a shortcut to self['vel'][2]. Therefore,
+        This is simply a shortcut to ``self['vel'][2]``. Therefore,
         depending on the coordinate system of the data object
-        (self.attrs['coord_sys']), it is:
+        (``self.attrs['coord_sys']``), it is:
 
         - beam:      beam3
         - inst:      z
@@ -360,7 +357,7 @@ def U(
     def U_mag(
         self,
     ):
-        """Horizontal velocity magnitude"""
+        """Horizontal velocity magnitude, i.e., speed"""
 
         return xr.DataArray(
             np.abs(self.U).astype("float32"),
@@ -376,7 +373,7 @@ def U_dir(
         self,
     ):
         """
-        Angle of horizontal velocity vector. Direction is 'to',
+        Angle of horizontal velocity vector, i.e., direction. Direction is 'to',
         as opposed to 'from'. This function calculates angle as
         "degrees CCW from X/East/streamwise" and then converts it to
         "degrees CW from X/North/streamwise".
@@ -415,8 +412,8 @@ def E_coh(
         """
         Coherent turbulent energy
 
-        Niel Kelley's 'coherent turbulence energy', which is the RMS
-        of the Reynold's stresses.
+        Niel Kelley's 'coherent turbulence energy', which is the
+        root-mean-square of the Reynold's stresses.
 
         See: NREL Technical Report TP-500-52353
         """
@@ -437,7 +434,7 @@ def I_tke(self, thresh=0):
         """
         Turbulent kinetic energy intensity.
 
-        Ratio of sqrt(tke) to horizontal velocity magnitude.
+        Ratio of sqrt(TKE) to horizontal velocity magnitude.
         """
         I_tke = np.ma.masked_where(
             self.U_mag < thresh, np.sqrt(2 * self.tke) / self.U_mag
@@ -481,7 +478,7 @@ def tke(
     def upvp_(
         self,
     ):
-        """u'v'bar Reynolds stress"""
+        """:math:`\\overline{u'v'}` Reynolds stress"""
 
         return self.ds["stress_vec"].sel(tau="upvp_").drop_vars("tau")
 
@@ -489,7 +486,7 @@ def upvp_(
     def upwp_(
         self,
     ):
-        """u'w'bar Reynolds stress"""
+        """:math:`\\overline{u'w'}` Reynolds stress"""
 
         return self.ds["stress_vec"].sel(tau="upwp_").drop_vars("tau")
 
@@ -497,7 +494,7 @@ def upwp_(
     def vpwp_(
         self,
     ):
-        """v'w'bar Reynolds stress"""
+        """:math:`\\overline{v'w'}` Reynolds stress"""
 
         return self.ds["stress_vec"].sel(tau="vpwp_").drop_vars("tau")
 
@@ -505,7 +502,7 @@ def vpwp_(
     def upup_(
         self,
     ):
-        """u'u'bar component of the tke"""
+        """:math:`\\overline{u'u'}` component of the TKE vector"""
 
         return self.ds["tke_vec"].sel(tke="upup_").drop_vars("tke")
 
@@ -513,7 +510,7 @@ def upup_(
     def vpvp_(
         self,
     ):
-        """v'v'bar component of the tke"""
+        """:math:`\\overline{v'v'}` component of the TKE vector"""
 
         return self.ds["tke_vec"].sel(tke="vpvp_").drop_vars("tke")
 
@@ -521,7 +518,7 @@ def vpvp_(
     def wpwp_(
         self,
     ):
-        """w'w'bar component of the tke"""
+        """:math:`\\overline{w'w'}` component of the TKE vector"""
 
         return self.ds["tke_vec"].sel(tke="wpwp_").drop_vars("tke")
 
@@ -602,30 +599,30 @@ def bin_average(self, raw_ds, out_ds=None, names=None):
         Parameters
         ----------
         raw_ds : xarray.Dataset
-           The raw data structure to be binned
+          The raw data structure to be binned
         out_ds : xarray.Dataset
-           The bin'd (output) data object to which averaged data is added.
+          The binned (output) data object to which averaged data is added.
         names : list of strings
-           The names of variables to be averaged.  If `names` is None,
-           all data in `raw_ds` will be binned.
+          The names of variables to be averaged.  If `names` is None,
+          all data in `raw_ds` will be binned.
 
         Returns
         -------
         out_ds : xarray.Dataset
-          The new (or updated when out_ds is not None) dataset
-          with the averages of all the variables in raw_ds.
+          The new (or updated when `out_ds` is not None) dataset
+          with the averages of all the variables in `raw_ds`.
 
         Raises
         ------
-        AttributeError : when out_ds is supplied as input (not None)
-        and the values in out_ds.attrs are inconsistent with
-        raw_ds.attrs or the properties of this VelBinner (n_bin,
-        n_fft, fs, etc.)
+        AttributeError : when `out_ds` is supplied as input (not None)
+        and the values in ``out_ds.attrs`` are inconsistent with
+        ``raw_ds.attrs`` or the properties of this VelBinner (`n_bin`,
+        `n_fft`, `fs`, etc.)
 
         Notes
         -----
-        raw_ds.attrs are copied to out_ds.attrs. Inconsistencies
-        between the two (when out_ds is specified as input) raise an
+        ``raw_ds.attrs`` are copied to ``out_ds.attrs``. Inconsistencies
+        between the two (when `out_ds` is specified as input) raise an
         AttributeError.
         """
 
@@ -657,52 +654,54 @@ def bin_average(self, raw_ds, out_ds=None, names=None):
                     ).astype("float32")
                 except:  # variables not needing averaging
                     pass
-            # Add standard deviation
-            std = self.standard_deviation(raw_ds.velds.U_mag.values)
-            out_ds["U_std"] = xr.DataArray(
-                std.astype("float32"),
-                dims=raw_ds.vel.dims[1:],
-                attrs={
-                    "units": "m s-1",
-                    "long_name": "Water Velocity Standard Deviation",
-                },
-            )
+
+        # Add standard deviation
+        std = self.standard_deviation(raw_ds.velds.U_mag.values)
+        out_ds["U_std"] = xr.DataArray(
+            std.astype("float32"),
+            dims=raw_ds.vel.dims[1:],
+            attrs={
+                "units": "m s-1",
+                "long_name": "Water Velocity Standard Deviation",
+            },
+        )
 
         return out_ds
 
     def bin_variance(self, raw_ds, out_ds=None, names=None, suffix="_var"):
         """
         Bin the dataset and calculate the ensemble variances of each
-        variable. Complementary to `bin_average()`.
+        variable. Complementary to :func:`bin_average <mhkit.dolfyn.velocity.VelBinner.bin_average>`.
 
         Parameters
         ----------
         raw_ds : xarray.Dataset
-           The raw data structure to be binned.
+          The raw data structure to be binned.
         out_ds : xarray.Dataset
-           The binned (output) dataset to which variance data is added,
-           nominally dataset output from `bin_average()`
+          The binned (output) dataset to which variance data is added,
+          nominally the dataset output from
+          :func:`bin_average <mhkit.dolfyn.velocity.VelBinner.bin_average>`.
         names : list of strings
-           The names of variables of which to calculate variance.  If
-           `names` is None, all data in `raw_ds` will be binned.
+          The names of variables of which to calculate variance. If
+          `names` is None, all data in `raw_ds` will be binned.
 
         Returns
         -------
         out_ds : xarray.Dataset
-          The new (or updated when out_ds is not None) dataset
-          with the variance of all the variables in raw_ds.
+          The new (or updated when `out_ds` is not None) dataset
+          with the variance of all the variables in `raw_ds`.
 
         Raises
         ------
-        AttributeError : when out_ds is supplied as input (not None)
-        and the values in out_ds.attrs are inconsistent with
-        raw_ds.attrs or the properties of this VelBinner (n_bin,
-        n_fft, fs, etc.)
+        AttributeError : when `out_ds` is supplied as input (not None)
+        and the values in ``out_ds.attrs`` are inconsistent with
+        ``raw_ds.attrs`` or the properties of this VelBinner (`n_bin`,
+        `n_fft`, `fs`, etc.)
 
         Notes
         -----
-        raw_ds.attrs are copied to out_ds.attrs. Inconsistencies
-        between the two (when out_ds is specified as input) raise an
+        ``raw_ds.attrs`` are copied to ``out_ds.attrs``. Inconsistencies
+        between the two (when `out_ds` is specified as input) raise an
         AttributeError.
         """
 
@@ -805,21 +804,26 @@ def autocovariance(self, veldat, n_bin=None):
 
     def turbulence_intensity(self, U_mag, noise=0, thresh=0, detrend=False):
         """
-        Calculate noise-corrected turbulence intensity.
+        Calculate noise-corrected turbulence intensity (TI).
 
         Parameters
         ----------
         U_mag : xarray.DataArray
-          Raw horizontal velocity magnitude
+          Raw horizontal velocity magnitude (i.e., computed using
+          :func:`U_mag <mhkit.dolfyn.velocity.Velocity.U_mag>`)
         noise : numeric
           Instrument noise level in same units as velocity. Typically
-          found from `<adv or adp>.turbulence.doppler_noise_level`.
-          Default: None.
+          found from the ADV's
+          :func:`doppler_noise_level <mhkit.dolfyn.adv.turbulence.ADVBinner.doppler_noise_level>`.
+          or ADCP's
+          :func:`doppler_noise_level <mhkit.dolfyn.adp.turbulence.ADPBinner.doppler_noise_level>`.
+          Default = None
         thresh : numeric
           Theshold below which TI will not be calculated
-        detrend : bool (default: False)
+        detrend : bool
           Detrend the velocity data (True), or simply de-mean it
           (False), prior to computing TI.
+          Default = False
         """
 
         if "xarray" in type(U_mag).__module__:
@@ -859,8 +863,8 @@ def turbulence_intensity(self, U_mag, noise=0, thresh=0, detrend=False):
 
     def turbulent_kinetic_energy(self, veldat, noise=None, detrend=True):
         """
-        Calculate the turbulent kinetic energy (TKE) (variances
-        of u,v,w).
+        Calculate the turbulent kinetic energy (TKE) (:math:`\\overline{u'u'}`,
+        :math:`\\overline{v'v'}`, :math:`\\overline{w'w'}`).
 
         Parameters
         ----------
@@ -869,18 +873,22 @@ def turbulent_kinetic_energy(self, veldat, noise=None, detrend=True):
           The last dimension is assumed to be time.
         noise : float or array-like
           Instrument noise level in same units as velocity. Typically
-          found from `<adv or adp>.turbulence.doppler_noise_level`.
-          Default: None.
-        detrend : bool (default: False)
-          Detrend the velocity data (True), or simply de-mean it
-          (False), prior to computing TKE. Note: the PSD routines
-          use detrend, so if you want to have the same amount of
-          variance here as there use ``detrend=True``.
+          found from the ADV's
+          :func:`doppler_noise_level <mhkit.dolfyn.adv.turbulence.ADVBinner.doppler_noise_level>`.
+          or ADCP's
+          :func:`doppler_noise_level <mhkit.dolfyn.adp.turbulence.ADPBinner.doppler_noise_level>`.
+          Default = None
+        detrend : bool
+          Detrend the velocity data (True), or simply de-mean it (False),
+          prior to computing TKE. Default = False
+
+          Note: the PSD routines use detrend, so if you want to have the same
+          amount of variance here as there use ``detrend=True``.
 
         Returns
         -------
         tke_vec : xarray.DataArray
-          dataArray containing u'u'_, v'v'_ and w'w'_
+          dataArray containing ``u'u'_``, ``v'v'_`` and ``w'w'_``
         """
 
         if "xarray" in type(veldat).__module__:
@@ -958,25 +966,24 @@ def power_spectral_density(
 
         Parameters
         ----------
-        veldat : xr.DataArray
-          The raw velocity data (of dims 'dir' and 'time').
+        veldat : xr.DataArray (dir, time)
+          The raw velocity data
         freq_units : string
           Frequency units of the returned spectra in either Hz or rad/s
-          (`f` or :math:`\\omega`)
         fs : float (optional)
           The sample rate. Default is `binner.fs`
         window : string or array
           Specify the window function.
-          Options: 1, None, 'hann', 'hamm'
+          Options = 1, None, 'hann', 'hamm'. Default = 'hann'
         noise : numeric or array
           Instrument noise level in same units as velocity.
-          Default: 0 (ADCP) or [0, 0, 0] (ADV).
+          Default = 0 (ADCP) or [0, 0, 0] (ADV)
         n_bin : int (optional)
-          The bin-size. Default: from the binner.
+          The bin-size. Default = `self.n_bin`
         n_fft : int (optional)
-          The fft size. Default: from the binner.
+          The fft size. Default = `self.n_fft`
         n_pad : int (optional)
-          The number of values to pad with zero. Default = 0.
+          The number of values to pad with zero. Default = 0
         step : int (optional)
           Controls amount of overlap in fft. Default: the step size is
           chosen to maximize data use, minimize nens, and have a
@@ -984,7 +991,7 @@ def power_spectral_density(
 
         Returns
         -------
-        psd : xarray.DataArray (3, M, N_FFT)
+        psd : xarray.DataArray (dir, time, freq)
           The spectra in the 'u', 'v', and 'w' directions.
         """
 
diff --git a/mhkit/loads/__init__.py b/mhkit/loads/__init__.py
index 4c21c7391..1016f49c0 100644
--- a/mhkit/loads/__init__.py
+++ b/mhkit/loads/__init__.py
@@ -1,7 +1,7 @@
 """
 The `loads` package of the MHKiT (Marine and Hydrokinetic Toolkit) library
 provides tools and functionalities for analyzing and visualizing loads data
-from marine and hydrokinetic (MHK) devices. This package is designed to 
+from marine and hydrokinetic (MHK) devices. This package is designed to
 assist engineers, researchers, and analysts in understanding the forces and
 stresses applied to MHK devices under various operational and environmental
 conditions.
diff --git a/mhkit/loads/extreme/__init__.py b/mhkit/loads/extreme/__init__.py
index 318a2cdc8..f5ac42aec 100644
--- a/mhkit/loads/extreme/__init__.py
+++ b/mhkit/loads/extreme/__init__.py
@@ -3,7 +3,7 @@
 and wave data statistics.
 
 It includes methods for calculating peaks over threshold, estimating
-short-term extreme distributions,and performing wave amplitude 
+short-term extreme distributions,and performing wave amplitude
 normalization for most likely extreme response analysis.
 """
 
diff --git a/mhkit/loads/extreme/extremes.py b/mhkit/loads/extreme/extremes.py
index 81353127d..6a5831b95 100644
--- a/mhkit/loads/extreme/extremes.py
+++ b/mhkit/loads/extreme/extremes.py
@@ -1,29 +1,29 @@
 """
 This module provides functionality for estimating the short-term and
-long-term extreme distributions of responses in a time series. It 
-includes methods for analyzing peaks, block maxima, and applying 
-statistical distributions to model extreme events. The module supports 
-various methods for short-term extreme estimation, including peaks 
-fitting with Weibull, tail fitting, peaks over threshold, and block 
-maxima methods with GEV (Generalized Extreme Value) and Gumbel 
-distributions. Additionally, it offers functionality to approximate 
-the long-term extreme distribution by weighting short-term extremes 
+long-term extreme distributions of responses in a time series. It
+includes methods for analyzing peaks, block maxima, and applying
+statistical distributions to model extreme events. The module supports
+various methods for short-term extreme estimation, including peaks
+fitting with Weibull, tail fitting, peaks over threshold, and block
+maxima methods with GEV (Generalized Extreme Value) and Gumbel
+distributions. Additionally, it offers functionality to approximate
+the long-term extreme distribution by weighting short-term extremes
 across different sea states.
 
 Functions:
-- ste_peaks: Estimates the short-term extreme distribution from peaks 
+- ste_peaks: Estimates the short-term extreme distribution from peaks
     distribution using specified statistical methods.
 - block_maxima: Finds the block maxima in a time-series data to be used
     in block maxima methods.
-- ste_block_maxima_gev: Approximates the short-term extreme distribution 
+- ste_block_maxima_gev: Approximates the short-term extreme distribution
     using the block maxima method with the GEV distribution.
-- ste_block_maxima_gumbel: Approximates the short-term extreme 
+- ste_block_maxima_gumbel: Approximates the short-term extreme
     distribution using the block maxima method with the Gumbel distribution.
-- ste: Alias for `short_term_extreme`, facilitating easier access to the 
+- ste: Alias for `short_term_extreme`, facilitating easier access to the
     primary functionality of estimating short-term extremes.
-- short_term_extreme: Core function to approximate the short-term extreme 
+- short_term_extreme: Core function to approximate the short-term extreme
     distribution from a time series using chosen methods.
-- full_seastate_long_term_extreme: Combines short-term extreme 
+- full_seastate_long_term_extreme: Combines short-term extreme
     distributions using weights to estimate the long-term extreme distribution.
 """
 
diff --git a/mhkit/loads/extreme/mler.py b/mhkit/loads/extreme/mler.py
index f77f7d883..63ecb8b45 100644
--- a/mhkit/loads/extreme/mler.py
+++ b/mhkit/loads/extreme/mler.py
@@ -1,5 +1,5 @@
 """
-This module provides functionalities to calculate and analyze Most 
+This module provides functionalities to calculate and analyze Most
 Likely Extreme Response (MLER) coefficients for wave energy converter
 design and risk assessment. It includes functions to:
 
@@ -7,10 +7,10 @@
     spectrum and a response Amplitude Response Operator (ARO).
   - Define and manipulate simulation parameters (`mler_simulation`) used
     across various MLER analyses.
-  - Renormalize the incoming amplitude of the MLER wave 
+  - Renormalize the incoming amplitude of the MLER wave
     (`mler_wave_amp_normalize`) to match the desired peak height for more
     accurate modeling and analysis.
-  - Export the wave amplitude time series (`mler_export_time_series`) 
+  - Export the wave amplitude time series (`mler_export_time_series`)
     based on the calculated MLER coefficients for further analysis or
     visualization.
 """
diff --git a/mhkit/loads/extreme/peaks.py b/mhkit/loads/extreme/peaks.py
index cd2c1164b..9b31bb334 100644
--- a/mhkit/loads/extreme/peaks.py
+++ b/mhkit/loads/extreme/peaks.py
@@ -1,15 +1,15 @@
 """
 This module provides utilities for analyzing wave data, specifically
 for identifying significant wave heights and estimating wave peak
-distributions using statistical methods. 
+distributions using statistical methods.
 
 Functions:
-- _calculate_window_size: Calculates the window size for peak 
+- _calculate_window_size: Calculates the window size for peak
   independence using the auto-correlation function of wave peaks.
-- _peaks_over_threshold: Identifies peaks over a specified 
+- _peaks_over_threshold: Identifies peaks over a specified
   threshold and returns independent storm peak values adjusted by
   the threshold.
-- global_peaks: Identifies global peaks in a zero-centered 
+- global_peaks: Identifies global peaks in a zero-centered
   response time-series based on consecutive zero up-crossings.
 - number_of_short_term_peaks: Estimates the number of peaks within a
  specified short-term period.
@@ -20,13 +20,13 @@
 - automatic_hs_threshold: Determines the best significant wave height
  threshold for the peaks-over-threshold method.
 - peaks_distribution_peaks_over_threshold: Estimates the peaks
- distribution using the peaks over threshold method by fitting a 
+ distribution using the peaks over threshold method by fitting a
  generalized Pareto distribution.
 
 References:
-- Neary, V. S., S. Ahn, B. E. Seng, M. N. Allahdadi, T. Wang, Z. Yang, 
- and R. He (2020). "Characterization of Extreme Wave Conditions for 
- Wave Energy Converter Design and Project Risk Assessment.” J. Mar. 
+- Neary, V. S., S. Ahn, B. E. Seng, M. N. Allahdadi, T. Wang, Z. Yang,
+ and R. He (2020). "Characterization of Extreme Wave Conditions for
+ Wave Energy Converter Design and Project Risk Assessment.” J. Mar.
  Sci. Eng. 2020, 8(4), 289; https://doi.org/10.3390/jmse8040289.
 
 """
diff --git a/mhkit/loads/extreme/sample.py b/mhkit/loads/extreme/sample.py
index 3da0377de..078b05217 100644
--- a/mhkit/loads/extreme/sample.py
+++ b/mhkit/loads/extreme/sample.py
@@ -2,10 +2,10 @@
 This module provides statistical analysis tools for extreme value
 analysis in environmental and engineering applications. It focuses on
 estimating values corresponding to specific return periods based on
-the statistical distribution of observed or simulated data. 
+the statistical distribution of observed or simulated data.
 
 Functionality:
-- return_year_value: Calculates the value from a given distribution 
+- return_year_value: Calculates the value from a given distribution
  corresponding to a specified return year. This function is particularly
  useful for determining design values for engineering structures or for
  risk assessment in environmental studies.
diff --git a/mhkit/loads/general.py b/mhkit/loads/general.py
index 119731443..756469191 100644
--- a/mhkit/loads/general.py
+++ b/mhkit/loads/general.py
@@ -2,7 +2,7 @@
 This module provides tools for analyzing and processing data signals
 related to turbine blade performance and fatigue analysis. It implements
 methodologies based on standards such as IEC TS 62600-3:2020 ED1,
-incorporating statistical binning, moment calculations, and fatigue 
+incorporating statistical binning, moment calculations, and fatigue
 damage estimation using the rainflow counting algorithm. Key
 functionalities include:
 
@@ -11,8 +11,8 @@
       for each bin, following IEC TS 62600-3:2020 ED1 guidelines. It supports
       output in both pandas DataFrame and xarray Dataset formats.
 
-    - `blade_moments`: Calculates the flapwise and edgewise moments of turbine 
-      blades using derived calibration coefficients and raw strain signals. 
+    - `blade_moments`: Calculates the flapwise and edgewise moments of turbine
+      blades using derived calibration coefficients and raw strain signals.
       This function is crucial for understanding the loading and performance
       characteristics of turbine blades.
 
diff --git a/mhkit/loads/graphics.py b/mhkit/loads/graphics.py
index 9cd835b81..c458e8d92 100644
--- a/mhkit/loads/graphics.py
+++ b/mhkit/loads/graphics.py
@@ -1,6 +1,6 @@
 """
 This module provides functionalities for plotting statistical data
-related to a given variable or dataset. 
+related to a given variable or dataset.
 
     - `plot_statistics` is designed to plot raw statistical measures
       (mean, maximum, minimum, and optional standard deviation) of a
@@ -9,8 +9,8 @@
 
     - `plot_bin_statistics` extends these capabilities to binned data,
       offering a way to visualize binned statistics (mean, maximum, minimum)
-      along with their respective standard deviations. This function also 
-      supports label and title customization, as well as saving the plot to 
+      along with their respective standard deviations. This function also
+      supports label and title customization, as well as saving the plot to
       a specified path.
 """
 
diff --git a/mhkit/mooring/graphics.py b/mhkit/mooring/graphics.py
index 0ba9bd52b..6298e546b 100644
--- a/mhkit/mooring/graphics.py
+++ b/mhkit/mooring/graphics.py
@@ -1,20 +1,20 @@
 """
-This module provides a function for creating animated visualizations of a 
-MoorDyn node position dataset using the matplotlib animation API. 
+This module provides a function for creating animated visualizations of a
+MoorDyn node position dataset using the matplotlib animation API.
 
-It includes the main function `animate`, which creates either 2D or 3D 
-animations depending on the input parameters. 
+It includes the main function `animate`, which creates either 2D or 3D
+animations depending on the input parameters.
 
-In the animations, the position of nodes in the MoorDyn dataset are plotted 
-over time, allowing the user to visualize how these positions change. 
+In the animations, the position of nodes in the MoorDyn dataset are plotted
+over time, allowing the user to visualize how these positions change.
 
-This module also includes several helper functions that are used by 
-`animate` to validate inputs, generate lists of nodes along each axis, 
-calculate plot limits, and set labels and titles for plots. 
+This module also includes several helper functions that are used by
+`animate` to validate inputs, generate lists of nodes along each axis,
+calculate plot limits, and set labels and titles for plots.
 
-The user can specify various parameters for the animation such as the 
-dimension (2D or 3D), the axes to plot along, the plot limits for each 
-axis, the interval between frames, whether the animation repeats, and the 
+The user can specify various parameters for the animation such as the
+dimension (2D or 3D), the axes to plot along, the plot limits for each
+axis, the interval between frames, whether the animation repeats, and the
 labels and title for the plot.
 
 Requires:
diff --git a/mhkit/mooring/io.py b/mhkit/mooring/io.py
index 85a3e2227..f608e4678 100644
--- a/mhkit/mooring/io.py
+++ b/mhkit/mooring/io.py
@@ -2,12 +2,12 @@
 This module provides functions to read and parse MoorDyn output files.
 
 The main function read_moordyn takes as input the path to a MoorDyn output file and optionally
-the path to a MoorDyn input file. It reads the data from the output file, stores it in an 
-xarray dataset, and then if provided, parses the input file for additional metadata to store 
+the path to a MoorDyn input file. It reads the data from the output file, stores it in an
+xarray dataset, and then if provided, parses the input file for additional metadata to store
 as attributes in the dataset.
 
-The helper function _moordyn_input is used to parse the MoorDyn output file. It loops through 
-each line in the output file, parses various sets of properties and parameters, and stores 
+The helper function _moordyn_input is used to parse the MoorDyn output file. It loops through
+each line in the output file, parses various sets of properties and parameters, and stores
 them as attributes in the provided dataset.
 
 Typical usage example:
diff --git a/mhkit/power/characteristics.py b/mhkit/power/characteristics.py
index 0ae45a789..24f80713d 100644
--- a/mhkit/power/characteristics.py
+++ b/mhkit/power/characteristics.py
@@ -1,21 +1,21 @@
 """
-This module contains functions for calculating electrical power metrics from 
-measured voltage and current data. It supports both direct current (DC) and 
-alternating current (AC) calculations, including instantaneous frequency 
-analysis for AC signals and power calculations for three-phase AC systems. 
-The calculations can accommodate both line-to-neutral and line-to-line voltage 
-measurements and offer flexibility in output formats, allowing results to be 
+This module contains functions for calculating electrical power metrics from
+measured voltage and current data. It supports both direct current (DC) and
+alternating current (AC) calculations, including instantaneous frequency
+analysis for AC signals and power calculations for three-phase AC systems.
+The calculations can accommodate both line-to-neutral and line-to-line voltage
+measurements and offer flexibility in output formats, allowing results to be
 saved as either pandas DataFrames or xarray Datasets.
 
 Functions:
     instantaneous_frequency: Calculates the instantaneous frequency of a measured
     voltage signal over time.
-    
+
     dc_power: Computes the DC power from voltage and current measurements, providing
     both individual channel outputs and a gross power calculation.
-    
+
     ac_power_three_phase: Calculates the magnitude of active AC power for three-phase
-    systems, considering the power factor and voltage measurement configuration 
+    systems, considering the power factor and voltage measurement configuration
     (line-to-neutral or line-to-line).
 """
 
diff --git a/mhkit/power/quality.py b/mhkit/power/quality.py
index 3e020f7a6..c34c4d7d2 100644
--- a/mhkit/power/quality.py
+++ b/mhkit/power/quality.py
@@ -1,31 +1,31 @@
 """
-This module contains functions for calculating various aspects of power quality, 
-particularly focusing on the analysis of harmonics, interharmonics and distortion 
-in electrical power systems. These functions are designed to assist in power 
-quality assessments by providing tools to analyze voltage and current signals 
-for their harmonic and interharmonic components based on the guidelines and methodologies 
+This module contains functions for calculating various aspects of power quality,
+particularly focusing on the analysis of harmonics, interharmonics and distortion
+in electrical power systems. These functions are designed to assist in power
+quality assessments by providing tools to analyze voltage and current signals
+for their harmonic and interharmonic components based on the guidelines and methodologies
 outlined in IEC 61000-4-7:2008 ED2 and in IEC 62600-30:2018 ED1.
 
 Functions in this module include:
 
-- harmonics: Calculates the harmonics from time series of voltage or current. 
-  This function returns the amplitude of the time-series data harmonics indexed by 
-  the harmonic frequency, aiding in the identification of harmonic distortions 
+- harmonics: Calculates the harmonics from time series of voltage or current.
+  This function returns the amplitude of the time-series data harmonics indexed by
+  the harmonic frequency, aiding in the identification of harmonic distortions
   within the power system.
 
-- harmonic_subgroups: Computes the harmonic subgroups as per IEC 61000-4-7 standards. 
-  Harmonic subgroups provide insights into the distribution of power across 
-  different harmonic frequencies, which is crucial for understanding the behavior 
+- harmonic_subgroups: Computes the harmonic subgroups as per IEC 61000-4-7 standards.
+  Harmonic subgroups provide insights into the distribution of power across
+  different harmonic frequencies, which is crucial for understanding the behavior
   of non-linear loads and their impact on the power quality.
 
-- total_harmonic_current_distortion (THCD): Determines the total harmonic current 
-  distortion, offering a summary metric that quantifies the overall level of 
-  harmonic distortion present in the current waveform. This metric is essential 
+- total_harmonic_current_distortion (THCD): Determines the total harmonic current
+  distortion, offering a summary metric that quantifies the overall level of
+  harmonic distortion present in the current waveform. This metric is essential
   for assessing compliance with power quality standards and guidelines.
 
-- interharmonics: Identifies and calculates the interharmonics present in the 
-  power system. Interharmonics, which are frequencies that occur between the 
-  fundamental and harmonic frequencies, can arise from various sources and 
+- interharmonics: Identifies and calculates the interharmonics present in the
+  power system. Interharmonics, which are frequencies that occur between the
+  fundamental and harmonic frequencies, can arise from various sources and
   potentially lead to power quality issues.
 """
 
@@ -222,7 +222,7 @@ def total_harmonic_current_distortion(
     to_pandas: bool = True,
 ) -> Union[pd.DataFrame, xr.Dataset]:
     """
-    Calculates the total harmonic current distortion (THC) based on IEC/TS 62600-30
+    Calculates the total harmonic current distortion (THC) based on IEC TS 62600-30
 
     Parameters
     ----------
diff --git a/mhkit/river/__init__.py b/mhkit/river/__init__.py
index 8406b8cf1..3bbce832a 100644
--- a/mhkit/river/__init__.py
+++ b/mhkit/river/__init__.py
@@ -1,3 +1,8 @@
+"""
+The river module provides tools and utilities for analyzing river energy resources.
+
+"""
+
 from mhkit.river import performance
 from mhkit.river import graphics
 from mhkit.river import resource
diff --git a/mhkit/river/graphics.py b/mhkit/river/graphics.py
index 50ce5388b..fcaf825ef 100644
--- a/mhkit/river/graphics.py
+++ b/mhkit/river/graphics.py
@@ -1,10 +1,29 @@
+"""
+The graphics module provides plotting utilities for river energy resource data.
+
+"""
+
+from typing import Union, Optional
 import numpy as np
 import xarray as xr
 import matplotlib.pyplot as plt
+from matplotlib.axes import Axes
+from numpy.typing import ArrayLike
 from mhkit.utils import convert_to_dataarray
 
 
-def _xy_plot(x, y, fmt=".", label=None, xlabel=None, ylabel=None, title=None, ax=None):
+# pylint: disable=too-many-arguments
+# pylint: disable=too-many-positional-arguments
+def _xy_plot(
+    x: ArrayLike,
+    y: ArrayLike,
+    fmt: str = ".",
+    label: Optional[str] = None,
+    xlabel: Optional[str] = None,
+    ylabel: Optional[str] = None,
+    title: Optional[str] = None,
+    ax: Optional[Axes] = None,
+) -> Axes:
     """
     Base function to plot any x vs y data
 
@@ -50,16 +69,21 @@ def _xy_plot(x, y, fmt=".", label=None, xlabel=None, ylabel=None, title=None, ax
     return ax
 
 
-def plot_flow_duration_curve(D, F, label=None, ax=None):
+def plot_flow_duration_curve(
+    discharge: Union[ArrayLike, xr.DataArray],
+    exceedance_prob: Union[ArrayLike, xr.DataArray],
+    label: Optional[str] = None,
+    ax: Optional[Axes] = None,
+) -> Axes:
     """
     Plots discharge vs exceedance probability as a Flow Duration Curve (FDC)
 
     Parameters
     ------------
-    D: array-like
-        Discharge [m/s] indexed by time
+    discharge: array-like
+        Discharge [m3/s] indexed by time
 
-    F: array-like
+    exceedance_prob: array-like
          Exceedance probability [unitless] indexed by time
 
     label: string
@@ -74,13 +98,15 @@ def plot_flow_duration_curve(D, F, label=None, ax=None):
     ax : matplotlib pyplot axes
 
     """
-    # Sort by F
-    temp = xr.Dataset(data_vars={"D": D, "F": F})
-    temp = temp.sortby("F", ascending=False)
+    # Sort by exceedance_prob
+    temp = xr.Dataset(
+        data_vars={"discharge": discharge, "exceedance_prob": exceedance_prob}
+    )
+    temp = temp.sortby("exceedance_prob", ascending=False)
 
     ax = _xy_plot(
-        temp["D"],
-        temp["F"],
+        temp["discharge"],
+        temp["exceedance_prob"],
         fmt="-",
         label=label,
         xlabel="Discharge [$m^3/s$]",
@@ -92,16 +118,21 @@ def plot_flow_duration_curve(D, F, label=None, ax=None):
     return ax
 
 
-def plot_velocity_duration_curve(V, F, label=None, ax=None):
+def plot_velocity_duration_curve(
+    velocity: Union[ArrayLike, xr.DataArray],
+    exceedance_prob: Union[ArrayLike, xr.DataArray],
+    label: Optional[str] = None,
+    ax: Optional[Axes] = None,
+) -> Axes:
     """
     Plots velocity vs exceedance probability as a Velocity Duration Curve (VDC)
 
     Parameters
     ------------
-    V: array-like
+    velocity: array-like
         Velocity [m/s] indexed by time
 
-    F: array-like
+    exceedance_prob: array-like
         Exceedance probability [unitless] indexed by time
 
     label: string
@@ -116,13 +147,15 @@ def plot_velocity_duration_curve(V, F, label=None, ax=None):
     ax : matplotlib pyplot axes
 
     """
-    # Sort by F
-    temp = xr.Dataset(data_vars={"V": V, "F": F})
-    temp = temp.sortby("F", ascending=False)
+    # Sort by exceedance_prob
+    temp = xr.Dataset(
+        data_vars={"velocity": velocity, "exceedance_prob": exceedance_prob}
+    )
+    temp = temp.sortby("exceedance_prob", ascending=False)
 
     ax = _xy_plot(
-        temp["V"],
-        temp["F"],
+        temp["velocity"],
+        temp["exceedance_prob"],
         fmt="-",
         label=label,
         xlabel="Velocity [$m/s$]",
@@ -133,16 +166,21 @@ def plot_velocity_duration_curve(V, F, label=None, ax=None):
     return ax
 
 
-def plot_power_duration_curve(P, F, label=None, ax=None):
+def plot_power_duration_curve(
+    power: Union[ArrayLike, xr.DataArray],
+    exceedance_prob: Union[ArrayLike, xr.DataArray],
+    label: Optional[str] = None,
+    ax: Optional[Axes] = None,
+) -> Axes:
     """
     Plots power vs exceedance probability as a Power Duration Curve (PDC)
 
     Parameters
     ------------
-    P: array-like
+    power: array-like
         Power [W] indexed by time
 
-    F: array-like
+    exceedance_prob: array-like
         Exceedance probability [unitless] indexed by time
 
     label: string
@@ -157,13 +195,13 @@ def plot_power_duration_curve(P, F, label=None, ax=None):
     ax : matplotlib pyplot axes
 
     """
-    # Sort by F
-    temp = xr.Dataset(data_vars={"P": P, "F": F})
-    temp.sortby("F", ascending=False)
+    # Sort by exceedance_prob
+    temp = xr.Dataset(data_vars={"power": power, "exceedance_prob": exceedance_prob})
+    temp.sortby("exceedance_prob", ascending=False)
 
     ax = _xy_plot(
-        temp["P"],
-        temp["F"],
+        temp["power"],
+        temp["exceedance_prob"],
         fmt="-",
         label=label,
         xlabel="Power [W]",
@@ -174,13 +212,18 @@ def plot_power_duration_curve(P, F, label=None, ax=None):
     return ax
 
 
-def plot_discharge_timeseries(Q, time_dimension="", label=None, ax=None):
+def plot_discharge_timeseries(
+    discharge: Union[ArrayLike, xr.DataArray],
+    time_dimension: str = "",
+    label: Optional[str] = None,
+    ax: Optional[Axes] = None,
+) -> Axes:
     """
     Plots discharge time-series
 
     Parameters
     ------------
-    Q: array-like
+    discharge: array-like
         Discharge [m3/s] indexed by time
 
     time_dimension: string (optional)
@@ -199,14 +242,14 @@ def plot_discharge_timeseries(Q, time_dimension="", label=None, ax=None):
     ax : matplotlib pyplot axes
 
     """
-    Q = convert_to_dataarray(Q)
+    discharge = convert_to_dataarray(discharge)
 
     if time_dimension == "":
-        time_dimension = list(Q.coords)[0]
+        time_dimension = list(discharge.coords)[0]
 
     ax = _xy_plot(
-        Q.coords[time_dimension].values,
-        Q,
+        discharge.coords[time_dimension].values,
+        discharge,
         fmt="-",
         label=label,
         xlabel="Time",
@@ -217,16 +260,22 @@ def plot_discharge_timeseries(Q, time_dimension="", label=None, ax=None):
     return ax
 
 
-def plot_discharge_vs_velocity(D, V, polynomial_coeff=None, label=None, ax=None):
+def plot_discharge_vs_velocity(
+    discharge: Union[ArrayLike, xr.DataArray],
+    velocity: Union[ArrayLike, xr.DataArray],
+    polynomial_coeff: Optional[np.poly1d] = None,
+    label: Optional[str] = None,
+    ax: Optional[Axes] = None,
+) -> Axes:
     """
     Plots discharge vs velocity data along with the polynomial fit
 
     Parameters
     ------------
-    D : array-like
-        Discharge [m/s] indexed by time
+    discharge : array-like
+        Discharge [m3/s] indexed by time
 
-    V : array-like
+    velocity : array-like
         Velocity [m/s] indexed by time
 
     polynomial_coeff: numpy polynomial
@@ -244,8 +293,8 @@ def plot_discharge_vs_velocity(D, V, polynomial_coeff=None, label=None, ax=None)
 
     """
     ax = _xy_plot(
-        D,
-        V,
+        discharge,
+        velocity,
         fmt=".",
         label=label,
         xlabel="Discharge [$m^3/s$]",
@@ -253,7 +302,7 @@ def plot_discharge_vs_velocity(D, V, polynomial_coeff=None, label=None, ax=None)
         ax=ax,
     )
     if polynomial_coeff:
-        x = np.linspace(D.min(), D.max())
+        x = np.linspace(discharge.min(), discharge.max())
         ax = _xy_plot(
             x,
             polynomial_coeff(x),
@@ -267,16 +316,22 @@ def plot_discharge_vs_velocity(D, V, polynomial_coeff=None, label=None, ax=None)
     return ax
 
 
-def plot_velocity_vs_power(V, P, polynomial_coeff=None, label=None, ax=None):
+def plot_velocity_vs_power(
+    velocity: Union[ArrayLike, xr.DataArray],
+    power: Union[ArrayLike, xr.DataArray],
+    polynomial_coeff: Optional[np.poly1d] = None,
+    label: Optional[str] = None,
+    ax: Optional[Axes] = None,
+) -> Axes:
     """
     Plots velocity vs power data along with the polynomial fit
 
     Parameters
     ------------
-    V : array-like
+    velocity : array-like
         Velocity [m/s] indexed by time
 
-    P: array-like
+    power: array-like
         Power [W] indexed by time
 
     polynomial_coeff: numpy polynomial
@@ -294,8 +349,8 @@ def plot_velocity_vs_power(V, P, polynomial_coeff=None, label=None, ax=None):
 
     """
     ax = _xy_plot(
-        V,
-        P,
+        velocity,
+        power,
         fmt=".",
         label=label,
         xlabel="Velocity [$m/s$]",
@@ -303,7 +358,7 @@ def plot_velocity_vs_power(V, P, polynomial_coeff=None, label=None, ax=None):
         ax=ax,
     )
     if polynomial_coeff:
-        x = np.linspace(V.min(), V.max())
+        x = np.linspace(velocity.min(), velocity.max())
         ax = _xy_plot(
             x,
             polynomial_coeff(x),
diff --git a/mhkit/river/io/__init__.py b/mhkit/river/io/__init__.py
index 852964f7b..9b788514f 100644
--- a/mhkit/river/io/__init__.py
+++ b/mhkit/river/io/__init__.py
@@ -1,2 +1,7 @@
+"""
+This module provides input/output functionality for river energy related data in MHKiT.
+
+"""
+
 from mhkit.river.io import usgs
 from mhkit.river.io import d3d
diff --git a/mhkit/river/io/d3d.py b/mhkit/river/io/d3d.py
index 19a61df62..7295d7e11 100644
--- a/mhkit/river/io/d3d.py
+++ b/mhkit/river/io/d3d.py
@@ -1,13 +1,22 @@
-from mhkit.utils import unorm
-import scipy.interpolate as interp
+"""
+This module provides functions for reading and processing Delft3D (D3D) model output data.
+It includes utilities for handling NetCDF files generated by Delft3D simulations,
+with specific focus on hydrodynamic data analysis for marine and hydrokinetic applications.
+
+"""
+
+from typing import Union, Optional, List
+import warnings
+import netCDF4
 import numpy as np
 import pandas as pd
 import xarray as xr
-import netCDF4
-import warnings
+import scipy.interpolate as interp
+from numpy.typing import ArrayLike, NDArray
+from mhkit.utils import unorm
 
 
-def get_all_time(data):
+def get_all_time(data: netCDF4.Dataset) -> NDArray:
     """
     Returns all of the time stamps from a D3D simulation passed to the function
     as a NetCDF object (data)
@@ -26,7 +35,7 @@ def get_all_time(data):
         simulation conditions at that time.
     """
 
-    if not isinstance(data, netCDF4._netCDF4.Dataset):
+    if not isinstance(data, netCDF4.Dataset):
         raise TypeError("data must be a NetCDF4 object")
 
     seconds_run = np.ma.getdata(data.variables["time"][:], False)
@@ -34,7 +43,7 @@ def get_all_time(data):
     return seconds_run
 
 
-def index_to_seconds(data, time_index):
+def index_to_seconds(data: netCDF4.Dataset, time_index: int) -> Union[int, float]:
     """
     The function will return 'seconds_run' if passed a 'time_index'
 
@@ -55,7 +64,7 @@ def index_to_seconds(data, time_index):
     return _convert_time(data, time_index=time_index)
 
 
-def seconds_to_index(data, seconds_run):
+def seconds_to_index(data: netCDF4.Dataset, seconds_run: Union[int, float]) -> int:
     """
     The function will return the nearest 'time_index' in the data if passed an
     integer number of 'seconds_run'
@@ -78,7 +87,11 @@ def seconds_to_index(data, seconds_run):
     return _convert_time(data, seconds_run=seconds_run)
 
 
-def _convert_time(data, time_index=None, seconds_run=None):
+def _convert_time(
+    data: netCDF4.Dataset,
+    time_index: Optional[Union[int, float]] = None,
+    seconds_run: Optional[Union[int, float]] = None,
+) -> Union[int, float]:
     """
     Converts a time index to seconds or seconds to a time index. The user
     must specify 'time_index' or 'seconds_run' (Not both). The function
@@ -99,14 +112,13 @@ def _convert_time(data, time_index=None, seconds_run=None):
 
     Returns
     -------
-    QoI: int, float
-        The quantity of interest is the unknown value either the 'time_index'
-        or the 'seconds_run'. The 'time_index' is an integer starting from 0
-        and incrementing until in simulation is complete. The 'seconds_run' is
-        the seconds corresponding to the 'time_index' increments.
+    converted_value: int, float
+        The converted value is either the 'time_index' or the 'seconds_run'.
+        If time_index was provided, returns seconds_run. If seconds_run was
+        provided, returns the closest matching time_index.
     """
 
-    if not isinstance(data, netCDF4._netCDF4.Dataset):
+    if not isinstance(data, netCDF4.Dataset):
         raise TypeError("data must be NetCDF4 object")
 
     if not (time_index or seconds_run):
@@ -121,26 +133,36 @@ def _convert_time(data, time_index=None, seconds_run=None):
         raise TypeError("time_index or seconds_run input must be an int or float")
 
     times = get_all_time(data)
+    converted_value = None
 
     if time_index:
-        QoI = times[time_index]
+        converted_value = times[time_index]
     if seconds_run:
         try:
             idx = np.where(times == seconds_run)
-            QoI = idx[0][0]
-        except:
+            converted_value = idx[0][0]
+        except (IndexError, TypeError):
             idx = (np.abs(times - seconds_run)).argmin()
-            QoI = idx
+            converted_value = idx
             warnings.warn(
                 "Warning: seconds_run not found. Closest time stamp"
                 + f"found {times[idx]}",
                 stacklevel=2,
             )
 
-    return QoI
+    return converted_value
 
 
-def get_layer_data(data, variable, layer_index=-1, time_index=-1, to_pandas=True):
+# pylint: disable=too-many-locals
+# pylint: disable=too-many-branches
+# pylint: disable=too-many-statements
+def get_layer_data(
+    data: netCDF4.Dataset,
+    variable: str,
+    layer_index: int = -1,
+    time_index: int = -1,
+    to_pandas: bool = True,
+) -> Union[pd.DataFrame, xr.Dataset]:
     """
     Get variable data from the NetCDF4 object at a specified layer and timestep.
     If the data is 2D the layer_index is ignored.
@@ -167,8 +189,8 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1, to_pandas=True
     layer_data: pd.DataFrame or xr.Dataset
         Dataset with columns of "x", "y", "waterdepth", and "waterlevel" location
         of the specified layer, variable values "v", and the "time" the
-        simulation has run. The waterdepth is measured from the water surface and the
-        "waterlevel" is the water level diffrencein meters from the zero water level.
+        simulation has run. The waterdepth is measured from the water surface and
+        the waterlevel is the water level difference in meters from zero.
     """
 
     if not isinstance(time_index, int):
@@ -177,7 +199,7 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1, to_pandas=True
     if not isinstance(layer_index, int):
         raise TypeError("layer_index must be an int")
 
-    if not isinstance(data, netCDF4._netCDF4.Dataset):
+    if not isinstance(data, netCDF4.Dataset):
         raise TypeError("data must be NetCDF4 object")
 
     if variable not in data.variables.keys():
@@ -192,13 +214,14 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1, to_pandas=True
 
     if abs(time_index) > max_time_index:
         raise ValueError(
-            f"time_index must be less than the absolute value of the max time index {max_time_index}"
+            "time_index must be less than the absolute value of the "
+            f"max time index {max_time_index}"
         )
 
     x = np.ma.getdata(data.variables[coords[0]][:], False)
     y = np.ma.getdata(data.variables[coords[1]][:], False)
 
-    if type(var[0][0]) == np.ma.core.MaskedArray:
+    if isinstance(var[0][0], np.ma.core.MaskedArray):
         max_layer = len(var[0][0])
 
         if abs(layer_index) > max_layer:
@@ -208,7 +231,7 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1, to_pandas=True
         dimensions = 3
 
     else:
-        if type(var[0][0]) != np.float64:
+        if not isinstance(var[0][0], np.float64):
             raise TypeError("data not recognized")
 
         dimensions = 2
@@ -263,7 +286,10 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1, to_pandas=True
 
     layer_dim = str(data.variables[variable].coordinates)
 
-    cord_sys = cords_to_layers[layer_dim]["coords"]
+    try:
+        cord_sys = cords_to_layers[layer_dim]["coords"]
+    except KeyError as exc:
+        raise ValueError("Coordinates not recognized.") from exc
     layer_percentages = np.ma.getdata(cord_sys, False)  # accumulative
 
     if layer_dim == "FlowLink_xu FlowLink_yu":
@@ -327,7 +353,12 @@ def get_layer_data(data, variable, layer_index=-1, time_index=-1, to_pandas=True
     return layer_data
 
 
-def create_points(x, y, waterdepth, to_pandas=True):
+def create_points(
+    x: Union[int, float, ArrayLike],
+    y: Union[int, float, ArrayLike],
+    waterdepth: Union[int, float, ArrayLike],
+    to_pandas: bool = True,
+) -> Union[pd.DataFrame, xr.Dataset]:
     """
     Generate a Dataset of points from combinations of input coordinates.
 
@@ -400,7 +431,8 @@ def create_points(x, y, waterdepth, to_pandas=True):
         # Check data type
         if not isinstance(value, (int, float, np.ndarray, pd.Series, xr.DataArray)):
             raise TypeError(
-                f"{name} must be an int, float, np.ndarray, pd.Series, or xr.DataArray. Got: {type(value)}"
+                f"{name} must be an int, float, np.ndarray, pd.Series, "
+                f"or xr.DataArray. Got: {type(value)}"
             )
 
         # Check for empty arrays
@@ -445,17 +477,19 @@ def create_points(x, y, waterdepth, to_pandas=True):
     return points
 
 
+# pylint: disable=too-many-arguments
+# pylint: disable=too-many-positional-arguments
 def variable_interpolation(
-    data,
-    variables,
-    points="cells",
-    edges="none",
-    x_max_lim=float("inf"),
-    x_min_lim=float("-inf"),
-    y_max_lim=float("inf"),
-    y_min_lim=float("-inf"),
-    to_pandas=True,
-):
+    data: netCDF4.Dataset,
+    variables: List[str],
+    points: Union[str, pd.DataFrame, xr.Dataset] = "cells",
+    edges: str = "none",
+    x_max_lim: float = float("inf"),
+    x_min_lim: float = float("-inf"),
+    y_max_lim: float = float("inf"),
+    y_min_lim: float = float("-inf"),
+    to_pandas: bool = True,
+) -> Union[pd.DataFrame, xr.Dataset]:
     """
     Interpolate multiple variables from the Delft3D onto the same points.
 
@@ -471,11 +505,11 @@ def variable_interpolation(
         The points to interpolate data onto.
           'cells'- interpolates all data onto the Delft3D cell coordinate system (Default)
           'faces'- interpolates all dada onto the Delft3D face coordinate system
-          Dataset of x, y, and waterdepth coordinates - Interpolates data onto user
-          povided points. Can be created with `create_points` function.
+          Dataset of x, y, and waterdepth coordinates - Interpolates data onto
+          user provided points. Can be created with `create_points` function.
     edges: string: 'nearest'
-        If edges is set to 'nearest' the code will fill in nan values with nearest
-        interpolation. Otherwise only linear interpolarion will be used.
+        If edges is set to 'nearest' the code will fill in nan values with
+        nearest interpolation. Otherwise only linear interpolarion will be used.
     to_pandas : bool (optional)
         Flag to output pandas instead of xarray. Default = True.
 
@@ -495,12 +529,12 @@ def variable_interpolation(
         points = points.to_pandas()
 
     if isinstance(points, str):
-        if not (points == "cells" or points == "faces"):
+        if points not in ("cells", "faces"):
             raise ValueError(
                 f"If a string, points must be cells or faces. Got {points}"
             )
 
-    if not isinstance(data, netCDF4._netCDF4.Dataset):
+    if not isinstance(data, netCDF4.Dataset):
         raise TypeError(f"data must be netCDF4 object. Got {type(data)}")
 
     if not isinstance(to_pandas, bool):
@@ -536,7 +570,7 @@ def variable_interpolation(
 
             if len(idx[0]):
                 for i in idx[0]:
-                    transformed_data[var][i] = interp.griddata(
+                    transformed_data.loc[i, var] = interp.griddata(
                         data_raw[var][["x", "y", "waterdepth"]],
                         data_raw[var][var],
                         [points["x"][i], points["y"][i], points["waterdepth"][i]],
@@ -549,7 +583,9 @@ def variable_interpolation(
     return transformed_data
 
 
-def get_all_data_points(data, variable, time_index=-1, to_pandas=True):
+def get_all_data_points(
+    data: netCDF4.Dataset, variable: str, time_index: int = -1, to_pandas: bool = True
+) -> Union[pd.DataFrame, xr.Dataset]:
     """
     Get data points for a passed variable for all layers at a specified time from
     the Delft3D NetCDF4 object by iterating over the `get_layer_data` function.
@@ -580,7 +616,7 @@ def get_all_data_points(data, variable, time_index=-1, to_pandas=True):
     if not isinstance(time_index, int):
         raise TypeError("time_index must be an int")
 
-    if not isinstance(data, netCDF4._netCDF4.Dataset):
+    if not isinstance(data, netCDF4.Dataset):
         raise TypeError("data must be NetCDF4 object")
 
     if variable not in data.variables.keys():
@@ -634,10 +670,9 @@ def get_all_data_points(data, variable, time_index=-1, to_pandas=True):
 
     try:
         cord_sys = cords_to_layers[layer_dim]["coords"]
-    except:
-        raise Exception("Coordinates not recognized.")
-    else:
-        layer_percentages = np.ma.getdata(cord_sys, False)
+    except KeyError as exc:
+        raise ValueError("Coordinates not recognized.") from exc
+    layer_percentages = np.ma.getdata(cord_sys, False)
 
     x_all = []
     y_all = []
@@ -677,8 +712,12 @@ def get_all_data_points(data, variable, time_index=-1, to_pandas=True):
 
 
 def turbulent_intensity(
-    data, points="cells", time_index=-1, intermediate_values=False, to_pandas=True
-):
+    data: netCDF4.Dataset,
+    points: Union[str, pd.DataFrame, xr.Dataset] = "cells",
+    time_index: int = -1,
+    intermediate_values: bool = False,
+    to_pandas: bool = True,
+) -> Union[pd.DataFrame, xr.Dataset]:
     """
     Calculate the turbulent intensity percentage for a given data set for the
     specified points. Assumes variable names: ucx, ucy, ucz and turkin1.
@@ -687,7 +726,7 @@ def turbulent_intensity(
     ----------
     data: NetCDF4 object
        A NetCDF4 object that contains spatial data, e.g. velocity or shear
-       stress, generated by running a Delft3D model.
+       stress generated by running a Delft3D model.
     points: string, pd.DataFrame, xr.Dataset
         Points to interpolate data onto.
           'cells': interpolates all data onto velocity coordinate system (Default).
@@ -699,22 +738,23 @@ def turbulent_intensity(
         late time step -1.
     intermediate_values: boolean (optional)
         If false the function will return position and turbulent intensity values.
-        If true the function will return position(x,y,z) and values need to calculate
-        turbulent intensity (ucx, uxy, uxz and turkin1) in a Dataframe.  Default False.
+        If true the function will return position(x,y,z) and values needed to
+        calculate turbulent intensity (ucx, uxy, uxz and turkin1) in a Dataframe.
+        Default False.
     to_pandas : bool (optional)
         Flag to output pandas instead of xarray. Default = True.
 
     Returns
     -------
-      TI_data: xr.Dataset or pd.DataFrame
+    TI_data: xr.Dataset or pd.DataFrame
         If intermediate_values is true all values are output.
         If intermediate_values is equal to false only turbulent_intesity and
         x, y, and z variables are output.
             x- position in the x direction
             y- position in the y direction
             waterdepth- position in the vertical direction
-            turbulet_intesity- turbulent kinetic energy divided by the root
-                                mean squared velocity
+            turbulent_intensity- turbulent kinetic energy divided by the root
+                mean squared velocity
             turkin1- turbulent kinetic energy
             ucx- velocity in the x direction
             ucy- velocity in the y direction
@@ -725,7 +765,7 @@ def turbulent_intensity(
         raise TypeError("points must be a string, pd.DataFrame, xr.Dataset")
 
     if isinstance(points, str):
-        if not (points == "cells" or points == "faces"):
+        if points not in ("cells", "faces"):
             raise ValueError("points must be cells or faces")
 
     if not isinstance(time_index, int):
@@ -740,70 +780,109 @@ def turbulent_intensity(
     max_time_index = data["time"].shape[0] - 1  # to account for zero index
     if abs(time_index) > max_time_index:
         raise ValueError(
-            f"time_index must be less than the absolute value of the max time index {max_time_index}"
+            "time_index must be less than the absolute "
+            f"value of the max time index {max_time_index}"
         )
 
-    if not isinstance(data, netCDF4._netCDF4.Dataset):
+    if not isinstance(data, netCDF4.Dataset):
         raise TypeError("data must be netCDF4 object")
 
     for variable in ["turkin1", "ucx", "ucy", "ucz"]:
         if variable not in data.variables.keys():
             raise ValueError(f"Variable {variable} not present in Data")
 
-    TI_vars = ["turkin1", "ucx", "ucy", "ucz"]
-    TI_data_raw = {}
-    for var in TI_vars:
+    turbulent_vars = ["turkin1", "ucx", "ucy", "ucz"]
+    turbulent_data_raw = {}
+    for var in turbulent_vars:
         var_data_df = get_all_data_points(data, var, time_index)
-        TI_data_raw[var] = var_data_df
-    if type(points) == pd.DataFrame:
+        turbulent_data_raw[var] = var_data_df
+    if isinstance(points, pd.DataFrame):
         print("points provided")
     elif points == "faces":
-        points = TI_data_raw["turkin1"].drop(["waterlevel", "turkin1"], axis=1)
+        points = turbulent_data_raw["turkin1"].drop(["waterlevel", "turkin1"], axis=1)
     elif points == "cells":
-        points = TI_data_raw["ucx"].drop(["waterlevel", "ucx"], axis=1)
+        points = turbulent_data_raw["ucx"].drop(["waterlevel", "ucx"], axis=1)
 
-    TI_data = points.copy(deep=True)
+    turbulent_data = points.copy(deep=True)
 
-    for var in TI_vars:
-        TI_data[var] = interp.griddata(
-            TI_data_raw[var][["x", "y", "waterdepth"]],
-            TI_data_raw[var][var],
+    for var in turbulent_vars:
+        turbulent_data[var] = interp.griddata(
+            turbulent_data_raw[var][["x", "y", "waterdepth"]],
+            turbulent_data_raw[var][var],
             points[["x", "y", "waterdepth"]],
         )
-        idx = np.where(np.isnan(TI_data[var]))
+        idx = np.where(np.isnan(turbulent_data[var]))
 
         if len(idx[0]):
             for i in idx[0]:
-                TI_data[var][i] = interp.griddata(
-                    TI_data_raw[var][["x", "y", "waterdepth"]],
-                    TI_data_raw[var][var],
+                turbulent_data.loc[i, var] = interp.griddata(
+                    turbulent_data_raw[var][["x", "y", "waterdepth"]],
+                    turbulent_data_raw[var][var],
                     [points["x"][i], points["y"][i], points["waterdepth"][i]],
                     method="nearest",
                 )
 
     u_mag = unorm(
-        np.array(TI_data["ucx"]), np.array(TI_data["ucy"]), np.array(TI_data["ucz"])
+        np.array(turbulent_data["ucx"]),
+        np.array(turbulent_data["ucy"]),
+        np.array(turbulent_data["ucz"]),
     )
 
-    neg_index = np.where(TI_data["turkin1"] < 0)
+    neg_index = np.where(turbulent_data["turkin1"] < 0)
     zero_bool = np.isclose(
-        TI_data["turkin1"][TI_data["turkin1"] < 0].array,
-        np.zeros(len(TI_data["turkin1"][TI_data["turkin1"] < 0].array)),
+        turbulent_data["turkin1"][turbulent_data["turkin1"] < 0].array,
+        np.zeros(len(turbulent_data["turkin1"][turbulent_data["turkin1"] < 0].array)),
         atol=1.0e-4,
     )
     zero_ind = neg_index[0][zero_bool]
     non_zero_ind = neg_index[0][~zero_bool]
-    TI_data.loc[zero_ind, "turkin1"] = np.zeros(len(zero_ind))
-    TI_data.loc[non_zero_ind, "turkin1"] = [np.nan] * len(non_zero_ind)
+    turbulent_data.loc[zero_ind, "turkin1"] = np.zeros(len(zero_ind))
+    turbulent_data.loc[non_zero_ind, "turkin1"] = np.nan
 
-    TI_data["turbulent_intensity"] = (
-        np.sqrt(2 / 3 * TI_data["turkin1"]) / u_mag * 100
+    turbulent_data["turbulent_intensity"] = (
+        np.sqrt(2 / 3 * turbulent_data["turkin1"]) / u_mag * 100
     )  # %
 
-    if intermediate_values == False:
-        TI_data = TI_data.drop(TI_vars, axis=1)
+    if intermediate_values is False:
+        turbulent_data = turbulent_data.drop(turbulent_vars, axis=1)
 
     if not to_pandas:
-        TI_data = TI_data.to_dataset()
+        turbulent_data = turbulent_data.to_dataset()
+
+    return turbulent_data
+
 
-    return TI_data
+def list_variables(data: Union[netCDF4.Dataset, xr.Dataset, xr.DataArray]) -> List[str]:
+    """
+    List all variables in a DataArray, Dataset, or NetCDF4 Dataset.
+
+    Parameters
+    ----------
+    data: Union[netCDF4.Dataset, xr.Dataset, xr.DataArray]
+        The data object containing variables to list.
+
+    Returns
+    -------
+    List[str]
+        A list of variable names in the data object.
+
+    Examples
+    --------
+    >>> # List variables in a NetCDF4 Dataset
+    >>> variables = list_variables(nc_data)
+    >>> print(variables)
+    ['time', 'x', 'y', 'waterdepth', 'ucx', 'ucy', 'ucz', 'turkin1']
+
+    >>> # List variables in an xarray Dataset
+    >>> variables = list_variables(xr_dataset)
+    >>> print(variables)
+    ['time', 'x', 'y', 'waterdepth', 'ucx', 'ucy', 'ucz', 'turkin1']
+    """
+    if isinstance(data, netCDF4.Dataset):
+        return list(data.variables.keys())
+    if isinstance(data, (xr.Dataset, xr.DataArray)):
+        return list(data.variables.keys())
+    raise TypeError(
+        "data must be a NetCDF4 Dataset, xarray Dataset, or "
+        f"xarray DataArray. Got: {type(data)}"
+    )
diff --git a/mhkit/river/io/usgs.py b/mhkit/river/io/usgs.py
index 35ca11ecf..2b690ff50 100644
--- a/mhkit/river/io/usgs.py
+++ b/mhkit/river/io/usgs.py
@@ -1,12 +1,37 @@
+"""
+This module provides functions for retrieving and processing data from the United States
+Geological Survey (USGS) National Water Information System (NWIS). It enables access to
+river flow data and related measurements useful for hydrokinetic resource assessment.
+
+"""
+
+from typing import Dict, Union, Optional
 import os
 import json
-import requests
 import shutil
+import requests
 import pandas as pd
+import xarray as xr
+from pandas import DataFrame
 from mhkit.utils.cache import handle_caching
 
 
-def _read_usgs_json(text, to_pandas=True):
+def _read_usgs_json(text: Dict, to_pandas: bool = True) -> Union[DataFrame, xr.Dataset]:
+    """
+    Process USGS JSON response into a pandas DataFrame or xarray Dataset.
+
+    Parameters
+    ----------
+    text : dict
+        JSON response from USGS API containing time series data
+    to_pandas : bool, optional
+        Flag to output pandas instead of xarray. Default = True.
+
+    Returns
+    -------
+    data : pandas.DataFrame or xarray.Dataset
+        Processed time series data
+    """
     data = pd.DataFrame()
     for i in range(len(text["value"]["timeSeries"])):
         try:
@@ -23,8 +48,9 @@ def _read_usgs_json(text, to_pandas=True):
             site_data.index.name = None
             del site_data["qualifiers"]
             data = data.combine_first(site_data)
-        except:
-            pass
+        except (KeyError, ValueError, TypeError, pd.errors.OutOfBoundsDatetime) as e:
+            print(f"Warning: Failed to process time series {i}: {str(e)}")
+            continue
 
     if not to_pandas:
         data = data.to_dataset()
@@ -32,7 +58,9 @@ def _read_usgs_json(text, to_pandas=True):
     return data
 
 
-def read_usgs_file(file_name, to_pandas=True):
+def read_usgs_file(
+    file_name: str, to_pandas: bool = True
+) -> Union[DataFrame, xr.Dataset]:
     """
     Reads a USGS JSON data file (from https://waterdata.usgs.gov/nwis)
 
@@ -52,7 +80,7 @@ def read_usgs_file(file_name, to_pandas=True):
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
 
-    with open(file_name) as json_file:
+    with open(file_name, encoding="utf-8") as json_file:
         text = json.load(json_file)
 
     data = _read_usgs_json(text, to_pandas)
@@ -60,17 +88,14 @@ def read_usgs_file(file_name, to_pandas=True):
     return data
 
 
+# pylint: disable=too-many-locals
 def request_usgs_data(
-    station,
-    parameter,
-    start_date,
-    end_date,
-    data_type="Daily",
-    proxy=None,
-    write_json=None,
-    clear_cache=False,
-    to_pandas=True,
-):
+    station: str,
+    parameter: str,
+    start_date: str,
+    end_date: str,
+    options: Optional[Dict] = None,
+) -> Union[DataFrame, xr.Dataset]:
     """
     Loads USGS data directly from https://waterdata.usgs.gov/nwis using a
     GET request
@@ -87,18 +112,21 @@ def request_usgs_data(
         Start date in the format 'YYYY-MM-DD' (e.g. '2018-01-01')
     end_date : str
         End date in the format 'YYYY-MM-DD' (e.g. '2018-12-31')
-    data_type : str
-        Data type, options include 'Daily' (return the mean daily value) and
-        'Instantaneous'.
-    proxy : dict or None
-        To request data from behind a firewall, define a dictionary of proxy settings,
-        for example {"http": 'localhost:8080'}
-    write_json : str or None
-        Name of json file to write data
-    clear_cache : bool
-        If True, the cache for this specific request will be cleared.
-    to_pandas: bool (optional)
-        Flag to output pandas instead of xarray. Default = True.
+    options : dict, optional
+        Dictionary containing optional parameters:
+        - data_type: str
+            Data type, options include 'Daily' (return the mean daily value) and
+            'Instantaneous'. Default = 'Daily'
+        - proxy: dict or None
+            Proxy settings for the request. Default = None
+        - write_json: str or None
+            Name of json file to write data. Default = None
+        - clear_cache: bool
+            If True, the cache for this specific request will be cleared. Default = False
+        - to_pandas: bool
+            Flag to output pandas instead of xarray. Default = True
+        - timeout: int
+            Timeout in seconds for the HTTP request. Default = 30
 
     Returns
     -------
@@ -106,20 +134,31 @@ def request_usgs_data(
         Data indexed by datetime with columns named according to the parameter's
         variable description
     """
+    # Set default options
+    options = options or {}
+    data_type = options.get("data_type", "Daily")
+    proxy = options.get("proxy", None)
+    write_json = options.get("write_json", None)
+    clear_cache = options.get("clear_cache", False)
+    to_pandas = options.get("to_pandas", True)
+    timeout = options.get("timeout", 30)  # 30 seconds default timeout
+
     if data_type not in ["Daily", "Instantaneous"]:
         raise ValueError(f"data_type must be Daily or Instantaneous. Got: {data_type}")
 
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
 
+    if not isinstance(timeout, (int, float)) or timeout <= 0:
+        raise ValueError(f"timeout must be a positive number. Got: {timeout}")
+
     # Define the path to the cache directory
     cache_dir = os.path.join(os.path.expanduser("~"), ".cache", "mhkit", "usgs")
 
     # Create a unique filename based on the function parameters
     hash_params = f"{station}_{parameter}_{start_date}_{end_date}_{data_type}"
 
-    # Use handle_caching to manage cache
-    cached_data, metadata, cache_filepath = handle_caching(
+    cached_data, _, cache_filepath = handle_caching(
         hash_params,
         cache_dir,
         cache_content={"data": None, "metadata": None, "write_json": write_json},
@@ -160,8 +199,23 @@ def request_usgs_data(
 
     print("Data request URL: ", data_url + api_query)
 
-    response = requests.get(url=data_url + api_query, proxies=proxy)
-    text = json.loads(response.text)
+    max_retries = 3
+    retry_count = 0
+    while retry_count < max_retries:
+        try:
+            response = requests.get(
+                url=data_url + api_query, proxies=proxy, timeout=timeout, verify=True
+            )
+            text = json.loads(response.text)
+            break
+        except requests.exceptions.SSLError as e:
+            retry_count += 1
+            if retry_count == max_retries:
+                raise e
+            print(
+                f"SSL Error occurred, retrying... (Attempt {retry_count}/{max_retries})"
+            )
+            continue
 
     # handle_caching is only set-up for pandas, so force this data to output as pandas for now
     data = _read_usgs_json(text, True)
diff --git a/mhkit/river/performance.py b/mhkit/river/performance.py
index c805517ab..d7f945091 100644
--- a/mhkit/river/performance.py
+++ b/mhkit/river/performance.py
@@ -1,7 +1,14 @@
+"""
+Computes device metrics such as equivalent diameter, tip speed ratio,
+and capture area. Calculations are based on IEC TS 62600-300:2019 ED1.
+
+"""
+
+from typing import Union, Tuple, List
 import numpy as np
 
 
-def circular(diameter):
+def circular(diameter: Union[int, float]) -> Tuple[float, float]:
     """
     Calculates the equivalent diameter and projected capture area of a
     circular turbine
@@ -27,7 +34,7 @@ def circular(diameter):
     return equivalent_diameter, projected_capture_area
 
 
-def ducted(duct_diameter):
+def ducted(duct_diameter: Union[int, float]) -> Tuple[float, float]:
     """
     Calculates the equivalent diameter and projected capture area of a
     ducted turbine
@@ -55,7 +62,7 @@ def ducted(duct_diameter):
     return equivalent_diameter, projected_capture_area
 
 
-def rectangular(h, w):
+def rectangular(h: Union[int, float], w: Union[int, float]) -> Tuple[float, float]:
     """
     Calculates the equivalent diameter and projected capture area of a
     retangular turbine
@@ -85,7 +92,7 @@ def rectangular(h, w):
     return equivalent_diameter, projected_capture_area
 
 
-def multiple_circular(diameters):
+def multiple_circular(diameters: List[Union[int, float]]) -> Tuple[float, float]:
     """
     Calculates the equivalent diameter and projected capture area of a
     multiple circular turbine
@@ -112,7 +119,11 @@ def multiple_circular(diameters):
     return equivalent_diameter, projected_capture_area
 
 
-def tip_speed_ratio(rotor_speed, rotor_diameter, inflow_speed):
+def tip_speed_ratio(
+    rotor_speed: Union[np.ndarray, List[Union[int, float]]],
+    rotor_diameter: Union[int, float],
+    inflow_speed: Union[np.ndarray, List[Union[int, float]]],
+) -> np.ndarray:
     """
     Function used to calculate the tip speed ratio (TSR) of a MEC device with rotor
 
@@ -127,18 +138,19 @@ def tip_speed_ratio(rotor_speed, rotor_diameter, inflow_speed):
 
     Returns
     --------
-    TSR : numpy array
+    tip_speed_ratio_values : numpy array
         Calculated tip speed ratio (TSR)
     """
 
     try:
         rotor_speed = np.asarray(rotor_speed)
-    except:
-        "rotor_speed must be of type np.ndarray"
+    except (ValueError, TypeError) as exc:
+        raise TypeError("rotor_speed must be convertible to np.ndarray") from exc
+
     try:
         inflow_speed = np.asarray(inflow_speed)
-    except:
-        "inflow_speed must be of type np.ndarray"
+    except (ValueError, TypeError) as exc:
+        raise TypeError("inflow_speed must be convertible to np.ndarray") from exc
 
     if not isinstance(rotor_diameter, (float, int)):
         raise TypeError(
@@ -147,12 +159,17 @@ def tip_speed_ratio(rotor_speed, rotor_diameter, inflow_speed):
 
     rotor_velocity = rotor_speed * np.pi * rotor_diameter
 
-    TSR = rotor_velocity / inflow_speed
+    tip_speed_ratio_values = rotor_velocity / inflow_speed
 
-    return TSR
+    return tip_speed_ratio_values
 
 
-def power_coefficient(power, inflow_speed, capture_area, rho):
+def power_coefficient(
+    power: Union[np.ndarray, List[Union[int, float]]],
+    inflow_speed: Union[np.ndarray, List[Union[int, float]]],
+    capture_area: Union[int, float],
+    rho: Union[int, float],
+) -> np.ndarray:
     """
     Function that calculates the power coefficient of MEC device
 
@@ -169,18 +186,19 @@ def power_coefficient(power, inflow_speed, capture_area, rho):
 
     Returns
     --------
-    Cp : numpy array
+    power_coeff : numpy array
         Power coefficient of device [-]
     """
 
     try:
         power = np.asarray(power)
-    except:
-        "power must be of type np.ndarray"
+    except (ValueError, TypeError) as exc:
+        raise TypeError("power must be convertible to np.ndarray") from exc
+
     try:
         inflow_speed = np.asarray(inflow_speed)
-    except:
-        "inflow_speed must be of type np.ndarray"
+    except (ValueError, TypeError) as exc:
+        raise TypeError("inflow_speed must be convertible to np.ndarray") from exc
 
     if not isinstance(capture_area, (float, int)):
         raise TypeError(
@@ -192,6 +210,6 @@ def power_coefficient(power, inflow_speed, capture_area, rho):
     # Predicted power from inflow
     power_in = 0.5 * rho * capture_area * inflow_speed**3
 
-    Cp = power / power_in
+    power_coeff = power / power_in
 
-    return Cp
+    return power_coeff
diff --git a/mhkit/river/resource.py b/mhkit/river/resource.py
index 2a0e06ffd..6d85a0e75 100644
--- a/mhkit/river/resource.py
+++ b/mhkit/river/resource.py
@@ -1,11 +1,22 @@
+"""
+Computes resource assessment metrics, including exceedance probability,
+inflow velocity, and power (theoretical resource). Calculations are based
+on IEC TS 62600-301:2019 ED1.
+
+"""
+
+from typing import Union, Tuple
 import xarray as xr
 import numpy as np
 from scipy.stats import linregress as _linregress
 from scipy.stats import rv_histogram as _rv_histogram
+from pandas import DataFrame, Series
 from mhkit.utils import convert_to_dataarray
 
 
-def Froude_number(v, h, g=9.80665):
+def froude_number(
+    v: Union[int, float], h: Union[int, float], g: Union[int, float] = 9.80665
+) -> float:
     """
     Calculate the Froude Number of the river, channel or duct flow,
     to check subcritical flow assumption (if Fr <1).
@@ -21,7 +32,7 @@ def Froude_number(v, h, g=9.80665):
 
     Returns
     ---------
-    Fr : float
+    froude_num : float
         Froude Number of the river [unitless].
 
     """
@@ -32,18 +43,22 @@ def Froude_number(v, h, g=9.80665):
     if not isinstance(g, (int, float)):
         raise TypeError(f"g must be of type int or float. Got: {type(g)}")
 
-    Fr = v / np.sqrt(g * h)
+    froude_num = v / np.sqrt(g * h)
 
-    return Fr
+    return froude_num
 
 
-def exceedance_probability(D, dimension="", to_pandas=True):
+def exceedance_probability(
+    discharge: Union[Series, DataFrame, xr.DataArray, xr.Dataset],
+    dimension: str = "",
+    to_pandas: bool = True,
+) -> Union[DataFrame, xr.Dataset]:
     """
     Calculates the exceedance probability
 
     Parameters
     ----------
-    D : pandas Series, pandas DataFrame, xarray DataArray, or xarray Dataset
+    discharge : pandas Series, pandas DataFrame, xarray DataArray, or xarray Dataset
         Discharge indexed by time [datetime or s].
 
     dimension: string (optional)
@@ -55,7 +70,7 @@ def exceedance_probability(D, dimension="", to_pandas=True):
 
     Returns
     -------
-    F : pandas DataFrame or xarray Dataset
+    exceedance_prob : pandas DataFrame or xarray Dataset
         Exceedance probability [unitless] indexed by time [datetime or s]
     """
     if not isinstance(dimension, str):
@@ -63,26 +78,26 @@ def exceedance_probability(D, dimension="", to_pandas=True):
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
 
-    D = convert_to_dataarray(D)
+    discharge = convert_to_dataarray(discharge)
 
     if dimension == "":
-        dimension = list(D.coords)[0]
+        dimension = list(discharge.coords)[0]
 
-    # Calculate exceedance probability (F)
-    rank = D.rank(dim=dimension)
-    rank = len(D[dimension]) - rank + 1  # convert to descending rank
-    F = 100 * rank / (len(D[dimension]) + 1)
-    F.name = "F"
+    # Calculate exceedance probability
+    rank = discharge.rank(dim=dimension)
+    rank = len(discharge[dimension]) - rank + 1  # convert to descending rank
+    exceedance_prob = 100 * rank / (len(discharge[dimension]) + 1)
+    exceedance_prob.name = "exceedance_probability"
 
-    F = F.to_dataset()  # for matlab
+    exceedance_prob = exceedance_prob.to_dataset()  # for matlab
 
     if to_pandas:
-        F = F.to_pandas()
+        exceedance_prob = exceedance_prob.to_pandas()
 
-    return F
+    return exceedance_prob
 
 
-def polynomial_fit(x, y, n):
+def polynomial_fit(x: np.ndarray, y: np.ndarray, n: int) -> Tuple[np.poly1d, float]:
     """
     Returns a polynomial fit for y given x of order n
     with an R-squared score of the fit
@@ -100,18 +115,19 @@ def polynomial_fit(x, y, n):
     ----------
     polynomial_coefficients : numpy polynomial
         List of polynomial coefficients
-    R2 : float
-        Polynomical fit coeffcient of determination
+    r_squared : float
+        Polynomial fit coefficient of determination
 
     """
     try:
         x = np.array(x)
-    except:
-        pass
+    except (ValueError, TypeError) as exc:
+        raise TypeError("x must be convertible to np.ndarray") from exc
     try:
         y = np.array(y)
-    except:
-        pass
+    except (ValueError, TypeError) as exc:
+        raise TypeError("y must be convertible to np.ndarray") from exc
+
     if not isinstance(x, np.ndarray):
         raise TypeError(f"x must be of type np.ndarray. Got: {type(x)}")
     if not isinstance(y, np.ndarray):
@@ -119,26 +135,31 @@ def polynomial_fit(x, y, n):
     if not isinstance(n, int):
         raise TypeError(f"n must be of type int. Got: {type(n)}")
 
-    # Get coeffcients of polynomial of order n
+    # Get coefficients of polynomial of order n
     polynomial_coefficients = np.poly1d(np.polyfit(x, y, n))
 
-    # Calculate the coeffcient of determination
-    slope, intercept, r_value, p_value, std_err = _linregress(
-        y, polynomial_coefficients(x)
-    )
-    R2 = r_value**2
+    # Calculate the coefficient of determination
+    _, _, r_value, _, _ = _linregress(y, polynomial_coefficients(x))
+    r_squared = r_value**2
 
-    return polynomial_coefficients, R2
+    return polynomial_coefficients, r_squared
 
 
-def discharge_to_velocity(D, polynomial_coefficients, dimension="", to_pandas=True):
+# pylint: disable=too-many-arguments
+# pylint: disable=too-many-positional-arguments
+def discharge_to_velocity(
+    discharge: Union[np.ndarray, DataFrame, Series, xr.DataArray, xr.Dataset],
+    polynomial_coefficients: np.poly1d,
+    dimension: str = "",
+    to_pandas: bool = True,
+) -> Union[DataFrame, xr.Dataset]:
     """
     Calculates velocity given discharge data and the relationship between
     discharge and velocity at an individual turbine
 
     Parameters
     ------------
-    D : numpy ndarray, pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
+    discharge : numpy ndarray, pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
         Discharge data [m3/s] indexed by time [datetime or s]
     polynomial_coefficients : numpy polynomial
         List of polynomial coefficients that describe the relationship between
@@ -151,57 +172,63 @@ def discharge_to_velocity(D, polynomial_coefficients, dimension="", to_pandas=Tr
 
     Returns
     ------------
-    V: pandas DataFrame or xarray Dataset
+    velocity: pandas DataFrame or xarray Dataset
         Velocity [m/s] indexed by time [datetime or s]
     """
     if not isinstance(polynomial_coefficients, np.poly1d):
         raise TypeError(
-            f"polynomial_coefficients must be of type np.poly1d. Got: {type(polynomial_coefficients)}"
+            "polynomial_coefficients must be of "
+            f"type np.poly1d. Got: {type(polynomial_coefficients)}"
         )
     if not isinstance(dimension, str):
         raise TypeError(f"dimension must be of type str. Got: {type(dimension)}")
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type str. Got: {type(to_pandas)}")
 
-    D = convert_to_dataarray(D)
+    discharge = convert_to_dataarray(discharge)
 
     if dimension == "":
-        dimension = list(D.coords)[0]
+        dimension = list(discharge.coords)[0]
 
     # Calculate velocity using polynomial
-    V = xr.DataArray(
-        data=polynomial_coefficients(D),
+    velocity = xr.DataArray(
+        data=polynomial_coefficients(discharge),
         dims=dimension,
-        coords={dimension: D[dimension]},
+        coords={dimension: discharge[dimension]},
     )
-    V.name = "V"
+    velocity.name = "velocity"
 
-    V = V.to_dataset()  # for matlab
+    velocity = velocity.to_dataset()  # for matlab
 
     if to_pandas:
-        V = V.to_pandas()
+        velocity = velocity.to_pandas()
 
-    return V
+    return velocity
 
 
 def velocity_to_power(
-    V, polynomial_coefficients, cut_in, cut_out, dimension="", to_pandas=True
-):
+    velocity: Union[np.ndarray, DataFrame, Series, xr.DataArray, xr.Dataset],
+    polynomial_coefficients: np.poly1d,
+    cut_in: Union[int, float],
+    cut_out: Union[int, float],
+    dimension: str = "",
+    to_pandas: bool = True,
+) -> Union[DataFrame, xr.Dataset]:
     """
     Calculates power given velocity data and the relationship
     between velocity and power from an individual turbine
 
     Parameters
     ----------
-    V : numpy ndarray, pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
+    velocity : numpy ndarray, pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
         Velocity [m/s] indexed by time [datetime or s]
     polynomial_coefficients : numpy polynomial
         List of polynomial coefficients that describe the relationship between
         velocity and power at an individual turbine
     cut_in: int/float
-        Velocity values below cut_in are not used to compute P
+        Velocity values below cut_in are not used to compute power
     cut_out: int/float
-        Velocity values above cut_out are not used to compute P
+        Velocity values above cut_out are not used to compute power
     dimension: string (optional)
         Name of the relevant xarray dimension. If not supplied,
         defaults to the first dimension. Does not affect pandas input.
@@ -210,12 +237,13 @@ def velocity_to_power(
 
     Returns
     -------
-    P : pandas DataFrame or xarray Dataset
+    power : pandas DataFrame or xarray Dataset
         Power [W] indexed by time [datetime or s]
     """
     if not isinstance(polynomial_coefficients, np.poly1d):
         raise TypeError(
-            f"polynomial_coefficients must be of type np.poly1d. Got: {type(polynomial_coefficients)}"
+            "polynomial_coefficients must be"
+            f"of type np.poly1d. Got: {type(polynomial_coefficients)}"
         )
     if not isinstance(cut_in, (int, float)):
         raise TypeError(f"cut_in must be of type int or float. Got: {type(cut_in)}")
@@ -224,64 +252,69 @@ def velocity_to_power(
     if not isinstance(dimension, str):
         raise TypeError(f"dimension must be of type str. Got: {type(dimension)}")
     if not isinstance(to_pandas, bool):
-        raise TypeError(f"to_pandas must be of type str. Got: {type(to_pandas)}")
+        raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
 
-    V = convert_to_dataarray(V)
+    velocity = convert_to_dataarray(velocity)
 
     if dimension == "":
-        dimension = list(V.coords)[0]
+        dimension = list(velocity.coords)[0]
 
-    # Calculate velocity using polynomial
-    power = polynomial_coefficients(V)
+    # Calculate power using polynomial
+    power_values = polynomial_coefficients(velocity)
 
     # Power for velocity values outside lower and upper bounds Turbine produces 0 power
-    power[V < cut_in] = 0.0
-    power[V > cut_out] = 0.0
+    power_values[velocity < cut_in] = 0.0
+    power_values[velocity > cut_out] = 0.0
 
-    P = xr.DataArray(data=power, dims=dimension, coords={dimension: V[dimension]})
-    P.name = "P"
+    power = xr.DataArray(
+        data=power_values, dims=dimension, coords={dimension: velocity[dimension]}
+    )
+    power.name = "power"
 
-    P = P.to_dataset()
+    power = power.to_dataset()
 
     if to_pandas:
-        P = P.to_pandas()
+        power = power.to_pandas()
 
-    return P
+    return power
 
 
-def energy_produced(P, seconds):
+def energy_produced(
+    power_data: Union[np.ndarray, DataFrame, Series, xr.DataArray, xr.Dataset],
+    seconds: Union[int, float],
+) -> float:
     """
     Returns the energy produced for a given time period provided
     exceedance probability and power.
 
     Parameters
     ----------
-    P : numpy ndarray, pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
+    power_data : numpy ndarray, pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
         Power [W] indexed by time [datetime or s]
     seconds: int or float
         Seconds in the time period of interest
 
     Returns
     -------
-    E : float
+    energy : float
         Energy [J] produced in the given length of time
     """
     if not isinstance(seconds, (int, float)):
         raise TypeError(f"seconds must be of type int or float. Got: {type(seconds)}")
 
-    P = convert_to_dataarray(P)
+    power_data = convert_to_dataarray(power_data)
 
-    # Calculate Histogram of power
-    H, edges = np.histogram(P, 100)
+    # Calculate histogram of power
+    hist_values, edges = np.histogram(power_data, 100)
     # Create a distribution
-    hist_dist = _rv_histogram([H, edges])
+    hist_dist = _rv_histogram([hist_values, edges])
     # Sample range for pdf
     x = np.linspace(edges.min(), edges.max(), 1000)
-    # Calculate the expected value of Power
-    expected_val_of_power = np.trapz(x * hist_dist.pdf(x), x=x)
+    # Calculate the expected value of power
+    expected_power = np.trapezoid(x * hist_dist.pdf(x), x=x)
     # Note: Built-in Expected Value method often throws warning
     # EV = hist_dist.expect(lb=edges.min(), ub=edges.max())
-    # Energy
-    E = seconds * expected_val_of_power
+    # Calculate energy
+    energy = seconds * expected_power
 
-    return E
+    return energy
diff --git a/mhkit/tests/acoustics/test_analysis.py b/mhkit/tests/acoustics/test_analysis.py
index b33eb4748..ce792fb3b 100644
--- a/mhkit/tests/acoustics/test_analysis.py
+++ b/mhkit/tests/acoustics/test_analysis.py
@@ -26,143 +26,6 @@ def setUpClass(self):
     def tearDownClass(self):
         pass
 
-    def test_spsdl(self):
-        td_spsdl = acoustics.sound_pressure_spectral_density_level(self.spsd)
-
-        cc = np.array(
-            [
-                "2023-02-04T15:05:08.499983310",
-                "2023-02-04T15:05:09.499959707",
-                "2023-02-04T15:05:10.499936580",
-                "2023-02-04T15:05:11.499913454",
-                "2023-02-04T15:05:12.499890089",
-            ],
-            dtype="datetime64[ns]",
-        )
-        cd_spsdl = np.array(
-            [
-                [61.72558153, 60.45878138, 61.02543806, 62.10487326, 53.69452342],
-                [64.73788935, 63.7154788, 56.60306848, 55.59145693, 65.14298631],
-                [54.88840931, 64.81213715, 68.5464288, 66.96210531, 57.26933701],
-                [47.83166387, 46.34269439, 55.26689475, 59.97537222, 62.87564412],
-                [51.84125861, 58.33037915, 56.42519674, 55.83574275, 55.48694318],
-            ]
-        )
-
-        np.testing.assert_allclose(td_spsdl.head().values, cd_spsdl, atol=1e-6)
-        np.testing.assert_equal(td_spsdl["time"].head().values, cc)
-
-    def test_averaging(self):
-        td_spsdl = acoustics.sound_pressure_spectral_density_level(self.spsd)
-
-        # Frequency average into # octave bands
-        octave = 3
-        td_spsdl_mean = acoustics.band_aggregate(td_spsdl, octave, fmin=50)
-
-        # Time average into 30 s bins
-        lbin = 30
-        td_spsdl_50 = acoustics.time_aggregate(td_spsdl_mean, lbin, method="median")
-        td_spsdl_25 = acoustics.time_aggregate(
-            td_spsdl_mean, lbin, method={"quantile": 0.25}
-        )
-        td_spsdl_75 = acoustics.time_aggregate(
-            td_spsdl_mean, lbin, method={"quantile": 0.75}
-        )
-
-        cc = np.array(
-            [
-                "2023-02-04T15:05:23.499983310",
-                "2023-02-04T15:05:53.499983310",
-                "2023-02-04T15:06:23.499983310",
-                "2023-02-04T15:06:53.499983310",
-                "2023-02-04T15:07:23.499983310",
-            ],
-            dtype="datetime64[ns]",
-        )
-        cd_spsdl_50 = np.array(
-            [
-                [73.71803613, 70.97557445, 69.79906778, 69.04934313, 67.56449352],
-                [73.72245955, 71.53327285, 70.55206775, 68.69638127, 67.75243522],
-                [73.64022645, 72.24548986, 70.09995522, 69.00394292, 68.22919418],
-                [73.1301846, 71.99940268, 70.56372046, 69.01366589, 67.19515351],
-                [74.67880072, 71.27235403, 70.23024477, 67.4915765, 66.73024553],
-            ]
-        )
-        cd_spsdl_25 = np.array(
-            [
-                [72.42136105, 70.37422873, 68.60783404, 67.56108417, 66.4751517],
-                [71.95173902, 71.03281659, 69.59019407, 67.79615712, 66.73980611],
-                [71.12756436, 70.68228634, 69.53891917, 68.126758, 67.48463198],
-                [71.71909635, 70.1849931, 69.22647784, 68.14102709, 66.18740693],
-                [72.25521793, 70.18087912, 68.97354823, 66.71295946, 65.35302077],
-            ]
-        )
-        cd_spsdl_75 = np.array(
-            [
-                [75.29614796, 71.86901413, 71.08418954, 69.6835928, 68.26993291],
-                [74.51608597, 72.82376854, 71.31219865, 70.38580566, 69.01731822],
-                [75.17013043, 73.45962974, 71.30593827, 71.50687178, 69.49805535],
-                [74.38176106, 73.13456376, 72.13861655, 70.45825381, 67.93458589],
-                [75.52387419, 72.99604074, 71.26831962, 68.90629303, 67.79114848],
-            ]
-        )
-
-        np.testing.assert_allclose(td_spsdl_50.head().values, cd_spsdl_50, atol=1e-6)
-        np.testing.assert_allclose(td_spsdl_25.head().values, cd_spsdl_25, atol=1e-6)
-        np.testing.assert_allclose(td_spsdl_75.head().values, cd_spsdl_75, atol=1e-6)
-        np.testing.assert_equal(td_spsdl_50["time_bins"].head().values, cc)
-
-    def test_freq_loss(self):
-        # Test min frequency
-        fmin = acoustics.minimum_frequency(water_depth=20, c=1500, c_seabed=1700)
-        self.assertEqual(fmin, 39.84375)
-
-    def test_spl(self):
-        td_spl = acoustics.sound_pressure_level(self.spsd, fmin=50)
-
-        # Decidecade octave sound pressure level
-        td_spl10 = acoustics.decidecade_sound_pressure_level(self.spsd, fmin=50)
-
-        # Median third octave sound pressure level
-        td_spl3 = acoustics.third_octave_sound_pressure_level(self.spsd, fmin=50)
-
-        cc = np.array(
-            [
-                "2023-02-04T15:05:08.499983310",
-                "2023-02-04T15:05:09.499959707",
-                "2023-02-04T15:05:10.499936580",
-                "2023-02-04T15:05:11.499913454",
-                "2023-02-04T15:05:12.499890089",
-            ],
-            dtype="datetime64[ns]",
-        )
-        cd_spl = np.array(
-            [97.48727775, 98.21888437, 96.99586637, 97.43571891, 96.60915502]
-        )
-        cd_spl10 = np.array(
-            [
-                [82.06503071, 78.20349846, 79.78088446, 75.31281183, 82.1194826],
-                [82.66175023, 79.77804574, 82.86005403, 77.57078269, 76.7598224],
-                [77.48975416, 82.72580274, 83.88251531, 74.71242694, 74.01377947],
-                [79.11312683, 76.56114947, 82.18953494, 75.40888015, 74.80285354],
-                [81.26751434, 82.29074565, 80.08831394, 75.75364773, 73.52176641],
-            ]
-        )
-        cd_spl3 = np.array(
-            [
-                [86.5847236, 84.98068691, 85.61056131, 83.55067796, 84.41810962],
-                [87.5449842, 84.48841036, 84.09406069, 85.81895309, 86.71437852],
-                [86.37334939, 84.08914125, 86.01614536, 83.36059983, 84.54635288],
-                [84.21413445, 84.63996392, 82.52906024, 84.54731095, 83.45652422],
-                [86.90033232, 84.8217658, 83.85297355, 82.92231618, 81.39163217],
-            ]
-        )
-
-        np.testing.assert_allclose(td_spl.head().values, cd_spl, atol=1e-6)
-        np.testing.assert_allclose(td_spl10.head().values, cd_spl10, atol=1e-6)
-        np.testing.assert_allclose(td_spl3.head().values, cd_spl3, atol=1e-6)
-        np.testing.assert_equal(td_spl["time"].head().values, cc)
-
     def test_sound_pressure_spectral_density(self):
         """
         Test sound pressure spectral density calculation.
@@ -235,6 +98,104 @@ def test_apply_calibration(self):
             calibrated_spsd.values, spsd.values
         )  # Calibration should reduce values
 
+    def test_freq_loss(self):
+        # Test min frequency
+        fmin = acoustics.minimum_frequency(water_depth=20, c=1500, c_seabed=1700)
+        self.assertEqual(fmin, 39.84375)
+
+    def test_spsdl(self):
+        """
+        Test sound pressure spectral density level calculation.
+        """
+        td_spsdl = acoustics.sound_pressure_spectral_density_level(self.spsd)
+
+        cc = np.array(
+            [
+                "2023-02-04T15:05:08.499983310",
+                "2023-02-04T15:05:09.499959707",
+                "2023-02-04T15:05:10.499936580",
+                "2023-02-04T15:05:11.499913454",
+                "2023-02-04T15:05:12.499890089",
+            ],
+            dtype="datetime64[ns]",
+        )
+        cd_spsdl = np.array(
+            [
+                [61.72558153, 60.45878138, 61.02543806, 62.10487326, 53.69452342],
+                [64.73788935, 63.7154788, 56.60306848, 55.59145693, 65.14298631],
+                [54.88840931, 64.81213715, 68.5464288, 66.96210531, 57.26933701],
+                [47.83166387, 46.34269439, 55.26689475, 59.97537222, 62.87564412],
+                [51.84125861, 58.33037915, 56.42519674, 55.83574275, 55.48694318],
+            ]
+        )
+
+        np.testing.assert_allclose(td_spsdl.head().values, cd_spsdl, atol=1e-6)
+        np.testing.assert_allclose(
+            td_spsdl["time"].head().astype("int64"), cc.astype("int64"), atol=1
+        )
+
+    def test_averaging(self):
+        td_spsdl = acoustics.sound_pressure_spectral_density_level(self.spsd)
+
+        # Frequency average into # octave bands
+        octave = [3, 2]
+        td_spsdl_mean = acoustics.band_aggregate(td_spsdl, octave, fmin=10, fmax=100000)
+
+        # Time average into 30 s bins
+        lbin = 30
+        td_spsdl_50 = acoustics.time_aggregate(td_spsdl_mean, lbin, method="median")
+        td_spsdl_25 = acoustics.time_aggregate(
+            td_spsdl_mean, lbin, method={"quantile": 0.25}
+        )
+        td_spsdl_75 = acoustics.time_aggregate(
+            td_spsdl_mean, lbin, method={"quantile": 0.75}
+        )
+
+        cc = np.array(
+            [
+                "2023-02-04T15:05:23.499983310",
+                "2023-02-04T15:05:53.499983310",
+                "2023-02-04T15:06:23.499983310",
+                "2023-02-04T15:06:53.499983310",
+                "2023-02-04T15:07:23.499983310",
+            ],
+            dtype="datetime64[ns]",
+        )
+        cd_spsdl_50 = np.array(
+            [
+                [63.45507, 64.753525, 65.04905, 67.15576, 73.47938],
+                [62.77437, 64.58199, 65.18464, 66.37395, 72.30796],
+                [64.76277, 64.950264, 65.80557, 67.88482, 73.24013],
+                [63.654488, 62.31394, 65.598816, 67.370674, 71.52472],
+                [62.45623, 62.461388, 62.111694, 66.06419, 72.324936],
+            ]
+        )
+        cd_spsdl_25 = np.array(
+            [
+                [59.33189297, 62.89503765, 61.60455799, 64.80938911, 70.59576607],
+                [60.37440872, 60.69928551, 61.9694643, 64.91986465, 70.00148964],
+                [61.1297617, 63.02504444, 64.41207123, 66.37802315, 71.38513947],
+                [59.52737236, 59.45869541, 62.48176765, 66.0959053, 70.06054497],
+                [58.55439758, 59.88098335, 59.66310596, 63.86431885, 70.20335197],
+            ]
+        )
+        cd_spsdl_75 = np.array(
+            [
+                [66.33672714, 67.13593102, 67.34234238, 68.7525959, 75.30982399],
+                [64.58539009, 66.84792709, 67.11526108, 69.7322197, 74.50746346],
+                [66.56425095, 67.85562325, 69.30602646, 69.83069992, 74.79984283],
+                [67.34252357, 65.65701294, 67.48604202, 70.948246, 73.59340286],
+                [66.26214409, 65.43437958, 64.36196518, 67.67719078, 74.33639717],
+            ]
+        )
+
+        np.testing.assert_allclose(td_spsdl_50.head().values, cd_spsdl_50, atol=1e-5)
+        np.testing.assert_allclose(td_spsdl_25.head().values, cd_spsdl_25, atol=1e-5)
+        np.testing.assert_allclose(td_spsdl_75.head().values, cd_spsdl_75, atol=1e-5)
+        np.testing.assert_allclose(
+            td_spsdl_50["time_bins"].head().astype("int64"), cc.astype("int64"), atol=1
+        )
+
     def test_fmax_warning(self):
         """
         Test that fmax warning adjusts the maximum frequency if necessary.
diff --git a/mhkit/tests/acoustics/test_io.py b/mhkit/tests/acoustics/test_io.py
index 24cf4d624..59e708d90 100644
--- a/mhkit/tests/acoustics/test_io.py
+++ b/mhkit/tests/acoustics/test_io.py
@@ -251,7 +251,9 @@ def test_calibration(self):
         )
 
         np.testing.assert_allclose(td_spsd.head().values, cd_spsd, atol=1e-6)
-        np.testing.assert_equal(td_spsd["time"].head().values, cc)
+        np.testing.assert_allclose(
+            td_spsd["time"].head().astype("int64"), cc.astype("int64"), atol=1
+        )
 
     def test_audio_export(self):
         file_name = join(datadir, "RBW_6661_20240601_053114.wav")
diff --git a/mhkit/tests/acoustics/test_metrics.py b/mhkit/tests/acoustics/test_metrics.py
new file mode 100644
index 000000000..b41085f08
--- /dev/null
+++ b/mhkit/tests/acoustics/test_metrics.py
@@ -0,0 +1,215 @@
+import os
+from os.path import abspath, dirname, join, normpath
+import numpy as np
+import xarray as xr
+import unittest
+
+import mhkit.acoustics as acoustics
+
+
+testdir = dirname(abspath(__file__))
+plotdir = join(testdir, "plots")
+isdir = os.path.isdir(plotdir)
+if not isdir:
+    os.mkdir(plotdir)
+datadir = normpath(join(testdir, "..", "..", "..", "examples", "data", "acoustics"))
+
+
+class TestMetrics(unittest.TestCase):
+    @classmethod
+    def setUpClass(self):
+        file_name = join(datadir, "6247.230204150508.wav")
+        P = acoustics.io.read_soundtrap(file_name, sensitivity=-177)
+        self.spsd = acoustics.sound_pressure_spectral_density(P, P.fs, bin_length=1)
+        self.spsd_60s = acoustics.sound_pressure_spectral_density(
+            P, P.fs, bin_length=60, rms=True
+        )
+
+    @classmethod
+    def tearDownClass(self):
+        pass
+
+    def test_spl(self):
+        td_spl = acoustics.sound_pressure_level(self.spsd, fmin=10, fmax=100000)
+
+        # Decidecade octave sound pressure level
+        td_spl10 = acoustics.decidecade_sound_pressure_level(
+            self.spsd, fmin=10, fmax=100000
+        )
+
+        # Median third octave sound pressure level
+        td_spl3 = acoustics.third_octave_sound_pressure_level(
+            self.spsd, fmin=10, fmax=100000
+        )
+
+        cc = np.array(
+            [
+                "2023-02-04T15:05:08.499983310",
+                "2023-02-04T15:05:09.499959707",
+                "2023-02-04T15:05:10.499936580",
+                "2023-02-04T15:05:11.499913454",
+                "2023-02-04T15:05:12.499890089",
+            ],
+            dtype="datetime64[ns]",
+        )
+        cd_spl_head = np.array([98.12284, 98.639824, 97.62718, 97.85709, 96.98539])
+        cd_spl_tail = np.array([98.420975, 98.10879, 97.430115, 97.99395, 97.95798])
+
+        cd_spl10_freq_head = np.array(
+            [10.0, 12.589254, 15.848932, 19.952623, 25.118864]
+        )
+        cd_spl10_head = np.array(
+            [
+                [68.88561, 75.65294, 68.29522, 75.80323, 82.53724],
+                [62.806908, 69.76993, 62.64113, 73.26091, 83.27883],
+                [71.73166, 68.541534, 68.056076, 75.438034, 84.268715],
+                [70.84345, 68.65471, 63.4681, 72.818085, 77.38771],
+                [69.23148, 74.04387, 64.49707, 74.146164, 79.52727],
+            ]
+        )
+        cd_spl10_freq_tail = np.array(
+            [19952.62315, 25118.864315, 31622.776602, 39810.717055, 50118.723363]
+        )
+        cd_spl10_tail = np.array(
+            [
+                [80.50317, 80.87118, 83.18715, 81.44459, 73.96579],
+                [81.933586, 81.51899, 83.47768, 81.85002, 74.25242],
+                [81.261314, 81.41166, 83.528534, 81.81753, 74.15244],
+                [81.70521, 81.42419, 83.45481, 81.4712, 73.85561],
+                [80.90549, 81.397545, 83.36795, 81.5738, 74.3497],
+            ]
+        )
+        cd_spl3_freq_head = np.array([10.0, 12.59921, 15.874011, 20.0, 25.198421])
+        cd_spl3_head = np.array(
+            [
+                [68.88561, 75.65294, 68.29522, 75.80323, 82.53724],
+                [62.806908, 69.76993, 62.64113, 73.26091, 83.27883],
+                [71.73166, 68.541534, 68.056076, 75.438034, 84.268715],
+                [70.84345, 68.65471, 63.4681, 72.818085, 77.38771],
+                [69.23148, 74.04387, 64.49707, 74.146164, 79.52727],
+            ]
+        )
+        cd_spl3_freq_tail = np.array(
+            [20480.0, 25803.183102, 32509.973544, 40960.0, 51606.366204]
+        )
+        cd_spl3_tail = np.array(
+            [
+                [80.37833, 81.21788, 83.5725, 80.37073, 72.06452],
+                [81.848434, 81.772064, 83.928505, 80.70311, 72.164345],
+                [81.13474, 81.67803, 83.96902, 80.6636, 72.07929],
+                [81.532005, 81.694954, 83.796875, 80.38368, 71.94872],
+                [80.70353, 81.6905, 83.76083, 80.53248, 72.248276],
+            ]
+        )
+
+        np.testing.assert_allclose(td_spl.head().values, cd_spl_head, atol=1e-6)
+        np.testing.assert_allclose(td_spl.tail().values, cd_spl_tail, atol=1e-6)
+        np.testing.assert_allclose(
+            td_spl10["freq_bins"].head().values, cd_spl10_freq_head, atol=1e-6
+        )
+        np.testing.assert_allclose(td_spl10.head().values, cd_spl10_head, atol=1e-6)
+        np.testing.assert_allclose(
+            td_spl10["freq_bins"].tail().values, cd_spl10_freq_tail, atol=1e-6
+        )
+        np.testing.assert_allclose(td_spl10.tail().values, cd_spl10_tail, atol=1e-6)
+        np.testing.assert_allclose(
+            td_spl3["freq_bins"].head().values, cd_spl3_freq_head, atol=1e-6
+        )
+        np.testing.assert_allclose(td_spl3.head().values, cd_spl3_head, atol=1e-6)
+        np.testing.assert_allclose(
+            td_spl3["freq_bins"].tail().values, cd_spl3_freq_tail, atol=1e-6
+        )
+        np.testing.assert_allclose(td_spl3.tail().values, cd_spl3_tail, atol=1e-6)
+        np.testing.assert_allclose(
+            td_spl["time"].head().astype("int64"), cc.astype("int64"), atol=1
+        )
+
+    def test_nmfs_weighting(self):
+        freq = self.spsd["freq"]
+        slc = slice(20, 25)  # test 20 - 25 Hz
+
+        W_LF, E_LF = acoustics.nmfs_auditory_weighting(freq, group="LF")
+        W_HF, E_HF = acoustics.nmfs_auditory_weighting(freq, group="HF")
+        W_VHF, E_VHF = acoustics.nmfs_auditory_weighting(freq, group="VHF")
+        W_PW, E_PW = acoustics.nmfs_auditory_weighting(freq, group="PW")
+        W_OW, E_OW = acoustics.nmfs_auditory_weighting(freq, group="OW")
+
+        cd_W_LF, cd_E_LF = np.array(
+            [-18.241247, -17.827854, -17.434275, -17.058767, -16.699821, -16.3561]
+        ), np.array([195.36125, 194.94786, 194.55428, 194.17877, 193.81982, 193.4761])
+        cd_W_HF, cd_E_HF = np.array(
+            [-59.7284, -59.071625, -58.44541, -57.847057, -57.274178, -56.724693]
+        ), np.array([241.0484, 240.39163, 239.76541, 239.16705, 238.59418, 238.0447])
+        cd_W_VHF, cd_E_VHF = np.array(
+            [-109.34241, -108.397385, -107.49632, -106.635315, -105.81097, -105.02029]
+        ), np.array([270.2524, 269.30737, 268.4063, 267.54532, 266.72098, 265.9303])
+        cd_W_PW, cd_E_PW = np.array(
+            [-52.117348, -51.427025, -50.768852, -50.13999, -49.537937, -48.96051]
+        ), np.array([227.40735, 226.71703, 226.05885, 225.43, 224.82794, 224.25052])
+        cd_W_OW, cd_E_OW = np.array(
+            [-65.056496, -64.386955, -63.748577, -63.138584, -62.55456, -61.99438]
+        ), np.array([244.4265, 243.75696, 243.11858, 242.50858, 241.92456, 241.36438])
+
+        np.testing.assert_allclose(W_LF.sel(freq=slc).values, cd_W_LF, atol=1e-5)
+        np.testing.assert_allclose(W_HF.sel(freq=slc).values, cd_W_HF, atol=1e-5)
+        np.testing.assert_allclose(W_VHF.sel(freq=slc).values, cd_W_VHF, atol=1e-5)
+        np.testing.assert_allclose(W_PW.sel(freq=slc).values, cd_W_PW, atol=1e-5)
+        np.testing.assert_allclose(W_OW.sel(freq=slc).values, cd_W_OW, atol=1e-5)
+
+        np.testing.assert_allclose(E_LF.sel(freq=slc).values, cd_E_LF, atol=1e-5)
+        np.testing.assert_allclose(E_HF.sel(freq=slc).values, cd_E_HF, atol=1e-5)
+        np.testing.assert_allclose(E_VHF.sel(freq=slc).values, cd_E_VHF, atol=1e-5)
+        np.testing.assert_allclose(E_PW.sel(freq=slc).values, cd_E_PW, atol=1e-5)
+        np.testing.assert_allclose(E_OW.sel(freq=slc).values, cd_E_OW, atol=1e-5)
+
+    def test_sel(self):
+        td_sel = acoustics.sound_exposure_level(self.spsd_60s, fmin=10, fmax=100000)
+        td_sel_lf = acoustics.sound_exposure_level(
+            self.spsd_60s, group="LF", fmin=10, fmax=100000
+        )
+        td_sel_hf = acoustics.sound_exposure_level(
+            self.spsd_60s, group="HF", fmin=10, fmax=100000
+        )
+        td_sel_vhf = acoustics.sound_exposure_level(
+            self.spsd_60s, group="VHF", fmin=10, fmax=100000
+        )
+        td_sel_pw = acoustics.sound_exposure_level(
+            self.spsd_60s, group="PW", fmin=10, fmax=100000
+        )
+        td_sel_ow = acoustics.sound_exposure_level(
+            self.spsd_60s, group="OW", fmin=10, fmax=100000
+        )
+
+        cc = np.array(
+            [
+                "2023-02-04T15:05:37.999295949",
+                "2023-02-04T15:06:37.997894048",
+                "2023-02-04T15:07:37.996495485",
+                "2023-02-04T15:08:37.995094776",
+                "2023-02-04T15:09:37.993695497",
+            ],
+            dtype="datetime64[ns]",
+        )
+        cd_sel = np.array([116.18274, 121.698654, 143.28117, 147.37479, 127.01828])
+        cd_sel_lf = np.array([112.363075, 120.177086, 142.74931, 146.57983, 125.83696])
+        cd_sel_hf = np.array([112.22166, 118.88085, 139.94121, 144.33324, 124.328995])
+        cd_sel_vhf = np.array([110.23136, 114.00643, 133.20006, 139.13504, 118.88397])
+        cd_sel_pw = np.array([112.22191, 119.87286, 141.67467, 145.6534, 125.419975])
+        cd_sel_ow = np.array([110.945404, 118.06397, 139.08435, 143.51094, 123.68077])
+
+        np.testing.assert_allclose(td_sel.values, cd_sel, atol=1e-5)
+        np.testing.assert_allclose(td_sel_lf.values, cd_sel_lf, atol=1e-5)
+        np.testing.assert_allclose(td_sel_hf.values, cd_sel_hf, atol=1e-5)
+        np.testing.assert_allclose(td_sel_vhf.values, cd_sel_vhf, atol=1e-5)
+        np.testing.assert_allclose(td_sel_pw.values, cd_sel_pw, atol=1e-5)
+        np.testing.assert_allclose(td_sel_ow.values, cd_sel_ow, atol=1e-5)
+        np.testing.assert_allclose(
+            td_sel["time"].astype("int64"), cc.astype("int64"), atol=1
+        )
+
+    def test_spl_vs_sel(self):
+        # SPL should equal SEL over a 1 second interval
+        td_spl = acoustics.sound_pressure_level(self.spsd, fmin=10, fmax=100000)
+        td_sel = acoustics.sound_exposure_level(self.spsd, fmin=10, fmax=100000)
+
+        np.testing.assert_allclose(td_spl.values, td_sel.values, atol=1e-6)
diff --git a/mhkit/tests/dolfyn/test_analysis.py b/mhkit/tests/dolfyn/test_analysis.py
index 80990116a..62fabdaf0 100644
--- a/mhkit/tests/dolfyn/test_analysis.py
+++ b/mhkit/tests/dolfyn/test_analysis.py
@@ -143,7 +143,7 @@ def test_adcp_turbulence(self):
         dat.velds.rotate2("beam")
 
         tdat["psd"] = bnr.power_spectral_density(
-            dat["vel"].isel(dir=2, range=len(dat["range"]) // 2), freq_units="Hz"
+            dat["vel_b5"].isel(range_b5=len(dat["range_b5"]) // 2), freq_units="Hz"
         )
         tdat["noise"] = bnr.doppler_noise_level(tdat["psd"], pct_fN=0.8)
         tdat["stress_vec4"] = bnr.reynolds_stress_4beam(
@@ -179,11 +179,11 @@ def test_adcp_turbulence(self):
         ) = bnr.dissipation_rate_SF(dat["vel"].isel(dir=2), r_range=[1, 5])
 
         slope_check = bnr.check_turbulence_cascade_slope(
-            tdat["psd"].mean("time"), freq_range=[0.4, 4]
+            tdat["psd"].mean("time_b5"), freq_range=[0.4, 4]
         )
         # Check noise subtraction in psd function
         tdat["psd_noise"] = bnr.power_spectral_density(
-            dat["vel"].isel(dir=2, range=len(dat["range"]) // 2),
+            dat["vel_b5"].isel(range_b5=len(dat["range_b5"]) // 2),
             freq_units="Hz",
             noise=0.01,
         )
diff --git a/mhkit/tests/dolfyn/test_clean.py b/mhkit/tests/dolfyn/test_clean.py
index a441a1b2c..0541435f5 100644
--- a/mhkit/tests/dolfyn/test_clean.py
+++ b/mhkit/tests/dolfyn/test_clean.py
@@ -62,6 +62,7 @@ def test_clean_upADCP(self):
         td_awac = tp.dat_awac.copy(deep=True)
         td_sig = tp.dat_sig_tide.copy(deep=True)
         td_rdi = tp.dat_rdi.copy(deep=True)
+        td_dual = tp.dat_sig_dp1_ice.copy(deep=True)
 
         apm.clean.water_depth_from_pressure(td_awac, salinity=30)
         apm.clean.remove_surface_interference(td_awac, beam_angle=20, inplace=True)
@@ -71,6 +72,11 @@ def test_clean_upADCP(self):
         apm.clean.remove_surface_interference(td_sig, inplace=True)
         td_sig = apm.clean.correlation_filter(td_sig, thresh=50)
 
+        apm.clean.set_range_offset(td_dual, 0.6)
+        apm.clean.water_depth_from_pressure(td_dual, salinity=31)
+        apm.clean.remove_surface_interference(td_dual, inplace=True)
+        td_dual = apm.clean.correlation_filter(td_dual, thresh=50)
+
         # Depth should already be found for this RDI file, but it's bad
         td_rdi["pressure"] /= 10  # set to something reasonable
         td_rdi = td_rdi.drop_vars("depth")
@@ -89,6 +95,7 @@ def test_clean_upADCP(self):
 
     def test_clean_downADCP(self):
         td = tp.dat_sig_ie.copy(deep=True)
+        td_dual = tp.dat_sig_dp1_ice.copy(deep=True)
 
         # First remove bad data
         td["vel"] = apm.clean.val_exceeds_thresh(td.vel, thresh=3)
@@ -100,7 +107,12 @@ def test_clean_downADCP(self):
         # Then clean below seabed
         apm.clean.set_range_offset(td, 0.5)
         apm.clean.water_depth_from_amplitude(td, thresh=10, nfilt=3)
-        td = apm.clean.remove_surface_interference(td)
+        td = apm.clean.remove_surface_interference(td, inplace=False)
+
+        # Technically up-facing but a good check
+        apm.clean.set_range_offset(td_dual, 0.6)
+        apm.clean.water_depth_from_amplitude(td_dual, thresh=10, nfilt=3)
+        td_dual = apm.clean.remove_surface_interference(td_dual, inplace=False)
 
         if make_data:
             save(td, "Sig500_Echo_clean.nc")
diff --git a/mhkit/tests/dolfyn/test_read_adp.py b/mhkit/tests/dolfyn/test_read_adp.py
index 3cba999b2..c1d37b92b 100644
--- a/mhkit/tests/dolfyn/test_read_adp.py
+++ b/mhkit/tests/dolfyn/test_read_adp.py
@@ -7,6 +7,8 @@
 import unittest
 import pytest
 import os
+import numpy as np
+from unittest.mock import patch
 
 make_data = False
 load = tb.load_netcdf
@@ -22,6 +24,7 @@
 dat_wr2 = load("winriver02.nc")
 dat_rp = load("RiverPro_test01.nc")
 dat_transect = load("winriver02_transect.nc")
+dat_senb5 = load("sentinelv_b5.nc")
 
 dat_awac = load("AWAC_test01.nc")
 dat_awac_ud = load("AWAC_test01_ud.nc")
@@ -32,10 +35,16 @@
 dat_sig_ieb = load("VelEchoBT01.nc")
 dat_sig_ie = load("Sig500_Echo.nc")
 dat_sig_tide = load("Sig1000_tidal.nc")
+dat_sig_raw_avg = load("Sig100_raw_avg.nc")
+dat_sig_avg = load("Sig100_avg.nc")
+dat_sig_rt = load("Sig1000_online.nc")
 dat_sig_skip = load("Sig_SkippedPings01.nc")
 dat_sig_badt = load("Sig1000_BadTime01.nc")
 dat_sig5_leiw = load("Sig500_last_ensemble_is_whole.nc")
-dat_sig_dp2 = load("dual_profile.nc")
+dat_sig_dp1_all = load("Sig500_dp_ice1.nc")
+dat_sig_dp1_ice = load("Sig500_dp_ice2.nc")
+dat_sig_dp2_echo = load("Sig1000_dp_echo1.nc")
+dat_sig_dp2_avg = load("Sig1000_dp_echo2.nc")
 
 
 class io_adp_testcase(unittest.TestCase):
@@ -52,6 +61,7 @@ def test_io_rdi(self):
         td_wr2 = read("winriver02.PD0")
         td_rp = read("RiverPro_test01.PD0")
         td_transect = read("winriver02_transect.PD0", nens=nens)
+        td_senb5 = read("sentinelv_b5.pd0")
 
         if make_data:
             save(td_rdi, "RDI_test01.nc")
@@ -64,6 +74,7 @@ def test_io_rdi(self):
             save(td_wr2, "winriver02.nc")
             save(td_rp, "RiverPro_test01.nc")
             save(td_transect, "winriver02_transect.nc")
+            save(td_senb5, "sentinelv_b5.nc")
             return
 
         assert_allclose(td_rdi, dat_rdi, atol=1e-6)
@@ -76,6 +87,48 @@ def test_io_rdi(self):
         assert_allclose(td_wr2, dat_wr2, atol=1e-6)
         assert_allclose(td_rp, dat_rp, atol=1e-6)
         assert_allclose(td_transect, dat_transect, atol=1e-6)
+        assert_allclose(td_senb5, dat_senb5, atol=1e-6)
+
+    def test_rdi_sec_btw_ping_division_by_zero(self):
+        """Test fix for issue #408: RDI burst mode division by zero
+
+        Issue #408 reported that RDI Pinnacle 45 in continuous burst mode
+        sets sec_between_ping_groups=0 while pings_per_ensemble=1, causing
+        ZeroDivisionError in sampling rate calculation.
+        """
+        # First verify normal operation with a regular RDI file
+        td_rdi_normal = read("RDI_test01.000", nens=10)
+
+        # Verify normal file has valid fs (not NaN)
+        assert not np.isnan(td_rdi_normal.attrs["fs"])
+        assert td_rdi_normal.attrs["fs"] > 0
+
+        # Now test the warning condition mode by patching the RDI reader
+        import mhkit.dolfyn.io.rdi as rdi_module
+
+        original_finalize = rdi_module._RDIReader.finalize
+
+        def mock_finalize_sec_btw_ping(self, data, cfg):
+            # Force config reported in issue #408
+            cfg["sec_between_ping_groups"] = 0
+            cfg["pings_per_ensemble"] = 1
+            return original_finalize(self, data, cfg)
+
+        # Test scenario with patching
+        with patch.object(
+            rdi_module._RDIReader, "finalize", mock_finalize_sec_btw_ping
+        ):
+            with warnings.catch_warnings(record=True) as w:
+                warnings.simplefilter("always")
+
+                # Read the same file but with reported config
+                td_rdi_burst = read("RDI_test01.000", nens=10)
+
+                # Check that warning was issued
+                assert len(w) > 0
+
+                # Check that fs exists and is valid
+                assert td_rdi_burst.attrs["fs"] > 0
 
     def test_io_nortek(self):
         nens = 100
@@ -104,8 +157,13 @@ def test_io_nortek2(self):
         td_sig_ieb = read("VelEchoBT01.ad2cp", nens=nens, rebuild_index=True)
         td_sig_ie = read("Sig500_Echo.ad2cp", nens=nens, rebuild_index=True)
         td_sig_tide = read("Sig1000_tidal.ad2cp", nens=nens, rebuild_index=True)
-        # Only need to test 2nd dataset
-        td_sig_dp1, td_sig_dp2 = read("dual_profile.ad2cp")
+        td_sig_raw_avg = read("Sig100_raw_avg.ad2cp", nens=nens, rebuild_index=True)
+        td_sig_avg = read("Sig100_avg.ad2cp", nens=nens, rebuild_index=True)
+        td_sig_rt = read("Sig1000_online.ad2cp", nens=nens, rebuild_index=True)
+        td_sig_dp1_all, td_sig_dp1_ice = read("Sig500_dp_ice.ad2cp", rebuild_index=True)
+        td_sig_dp2_echo, td_sig_dp2_avg = read(
+            "Sig1000_dp_echo.ad2cp", rebuild_index=True
+        )
 
         with pytest.warns(UserWarning):
             # This issues a warning...
@@ -123,10 +181,14 @@ def test_io_nortek2(self):
         os.remove(tb.exdt("VelEchoBT01.ad2cp.index"))
         os.remove(tb.exdt("Sig500_Echo.ad2cp.index"))
         os.remove(tb.exdt("Sig1000_tidal.ad2cp.index"))
+        os.remove(tb.exdt("Sig100_raw_avg.ad2cp.index"))
+        os.remove(tb.exdt("Sig100_avg.ad2cp.index"))
+        os.remove(tb.exdt("Sig1000_online.ad2cp.index"))
         os.remove(tb.exdt("Sig_SkippedPings01.ad2cp.index"))
         os.remove(tb.exdt("Sig500_last_ensemble_is_whole.ad2cp.index"))
         os.remove(tb.rfnm("Sig1000_BadTime01.ad2cp.index"))
-        os.remove(tb.exdt("dual_profile.ad2cp.index"))
+        os.remove(tb.exdt("Sig500_dp_ice.ad2cp.index"))
+        os.remove(tb.exdt("Sig1000_dp_echo.ad2cp.index"))
 
         if make_data:
             save(td_sig, "BenchFile01.nc")
@@ -135,10 +197,16 @@ def test_io_nortek2(self):
             save(td_sig_ieb, "VelEchoBT01.nc")
             save(td_sig_ie, "Sig500_Echo.nc")
             save(td_sig_tide, "Sig1000_tidal.nc")
+            save(td_sig_raw_avg, "Sig100_raw_avg.nc")
+            save(td_sig_avg, "Sig100_avg.nc")
+            save(td_sig_rt, "Sig1000_online.nc")
             save(td_sig_skip, "Sig_SkippedPings01.nc")
             save(td_sig_badt, "Sig1000_BadTime01.nc")
             save(td_sig5_leiw, "Sig500_last_ensemble_is_whole.nc")
-            save(td_sig_dp2, "dual_profile.nc")
+            save(td_sig_dp1_all, "Sig500_dp_ice1.nc")
+            save(td_sig_dp1_ice, "Sig500_dp_ice2.nc")
+            save(td_sig_dp2_echo, "Sig1000_dp_echo1.nc")
+            save(td_sig_dp2_avg, "Sig1000_dp_echo2.nc")
             return
 
         assert_allclose(td_sig, dat_sig, atol=1e-6)
@@ -147,10 +215,16 @@ def test_io_nortek2(self):
         assert_allclose(td_sig_ieb, dat_sig_ieb, atol=1e-6)
         assert_allclose(td_sig_ie, dat_sig_ie, atol=1e-6)
         assert_allclose(td_sig_tide, dat_sig_tide, atol=1e-6)
+        assert_allclose(td_sig_raw_avg, dat_sig_raw_avg, atol=1e-6)
+        assert_allclose(td_sig_avg, dat_sig_avg, atol=1e-6)
+        assert_allclose(td_sig_rt, dat_sig_rt, atol=1e-6)
         assert_allclose(td_sig5_leiw, dat_sig5_leiw, atol=1e-6)
         assert_allclose(td_sig_skip, dat_sig_skip, atol=1e-6)
         assert_allclose(td_sig_badt, dat_sig_badt, atol=1e-6)
-        assert_allclose(td_sig_dp2, dat_sig_dp2, atol=1e-6)
+        assert_allclose(td_sig_dp1_all, dat_sig_dp1_all, atol=1e-6)
+        assert_allclose(td_sig_dp1_ice, dat_sig_dp1_ice, atol=1e-6)
+        assert_allclose(td_sig_dp2_echo, dat_sig_dp2_echo, atol=1e-6)
+        assert_allclose(td_sig_dp2_avg, dat_sig_dp2_avg, atol=1e-6)
 
     def test_nortek2_crop(self):
         # Test file cropping function
diff --git a/mhkit/tests/dolfyn/test_read_io.py b/mhkit/tests/dolfyn/test_read_io.py
index 835acc6bd..644ef0b62 100644
--- a/mhkit/tests/dolfyn/test_read_io.py
+++ b/mhkit/tests/dolfyn/test_read_io.py
@@ -84,7 +84,7 @@ def read_file_and_test(fname):
             os.remove(exdt(fname))
 
         nens = 100
-        wh.read_rdi(exdt("RDI_withBT.000"), nens, debug_level=3)
+        wh.read_rdi(exdt("RDI_withBT.000"), nens, debug=3)
         awac.read_nortek(exdt("AWAC_test01.wpr"), nens, debug=True, do_checksum=True)
         awac.read_nortek(
             exdt("vector_data_imu01.VEC"), nens, debug=True, do_checksum=True
@@ -115,5 +115,6 @@ def test_read_warnings(self):
             sig.read_signature(exdt("AWAC_test01.wpr"))
         with self.assertRaises(IOError):
             read(rfnm("AWAC_test01.nc"))
+
         with self.assertRaises(Exception):
             save_netcdf(tp.dat_rdi, "test_save.fail")
diff --git a/mhkit/tests/dolfyn/test_rotate_adp.py b/mhkit/tests/dolfyn/test_rotate_adp.py
index 0e9598bfb..b453e4ed8 100644
--- a/mhkit/tests/dolfyn/test_rotate_adp.py
+++ b/mhkit/tests/dolfyn/test_rotate_adp.py
@@ -118,10 +118,15 @@ def test_rotate_earth2inst(self):
         rotate2(td_sig, "inst", inplace=True)
         td_sig_i = load("Sig1000_IMU_rotate_inst2earth.nc")
         rotate2(td_sig_i, "inst", inplace=True)
+        # Just check that these run without error
+        td_sig_avg = load("Sig100_avg.nc")
+        rotate2(td_sig_avg, "inst", inplace=True)
+        td_sig_dp1_ice = load("Sig500_dp_ice2.nc")
+        rotate2(td_sig_dp1_ice, "inst", inplace=True)
 
         cd_rdi = load("RDI_test01_rotate_beam2inst.nc")
         cd_wr2 = tr.dat_wr2
-        # ship and inst are considered equivalent in dolfy
+        # ship and inst are considered equivalent in dolfyn
         cd_wr2.attrs["coord_sys"] = "inst"
         cd_awac = load("AWAC_test01_earth2inst.nc")
         cd_sig = load("BenchFile01_rotate_beam2inst.nc")
diff --git a/mhkit/tests/river/test_io_usgs.py b/mhkit/tests/river/test_io_usgs.py
index b422bee2c..c3929ab0c 100644
--- a/mhkit/tests/river/test_io_usgs.py
+++ b/mhkit/tests/river/test_io_usgs.py
@@ -3,6 +3,10 @@
 import pandas as pd
 import unittest
 import os
+from unittest.mock import patch, MagicMock
+import json
+import shutil
+from datetime import timezone
 
 
 testdir = dirname(abspath(__file__))
@@ -38,28 +42,112 @@ def test_load_usgs_data_daily(self):
         self.assertEqual((data.index == expected_index.tz_localize("UTC")).all(), True)
         self.assertEqual(data.shape, (31, 1))
 
-    def test_request_usgs_data_daily(self):
+    @patch("mhkit.river.io.usgs.requests.get")
+    def test_request_usgs_data_daily(self, mock_get):
+        """
+        Test request_usgs_data with daily data
+        """
+        # Prepare the mocked HTTP response for daily data
+        daily_values = []
+        start = pd.Timestamp("2009-08-01 00:00:00", tz="UTC")
+        end = pd.Timestamp("2009-08-10 23:59:59", tz="UTC")
+        current = start
+        while current <= end:
+            daily_values.append(
+                {
+                    "dateTime": current.strftime("%Y-%m-%dT%H:%M:%S.000Z"),
+                    "value": "1000",
+                    "qualifiers": ["P"],
+                }
+            )
+            current += pd.Timedelta(days=1)
+
+        mock_payload = {
+            "value": {
+                "timeSeries": [
+                    {
+                        "variable": {
+                            "variableDescription": "Discharge, cubic feet per second"
+                        },
+                        "values": [{"value": daily_values}],
+                    }
+                ]
+            }
+        }
+
+        mock_resp = MagicMock()
+        mock_resp.status_code = 200
+        mock_resp.text = json.dumps(mock_payload)
+        mock_get.return_value = mock_resp
+
         data = river.io.usgs.request_usgs_data(
             station="15515500",
             parameter="00060",
             start_date="2009-08-01",
             end_date="2009-08-10",
-            data_type="Daily",
+            options={"data_type": "Daily", "clear_cache": True},
         )
-        self.assertEqual(data.columns, ["Discharge, cubic feet per second"])
-        self.assertEqual(data.shape, (10, 1))
 
-    def test_request_usgs_data_instant(self):
-        data = river.io.usgs.request_usgs_data(
+        # Verify that we called requests.get
+        mock_get.assert_called_once()
+
+        # Basic functionality checks
+        self.assertIsInstance(data, pd.DataFrame)
+        self.assertGreater(len(data), 0)  # Has data
+        self.assertTrue(data.index.tz is not None)  # Timezone aware
+
+
+class TestUSGSInstant(unittest.TestCase):
+    @patch("mhkit.river.io.usgs.requests.get")
+    def test_request_usgs_data_instant(self, mock_get):
+        mock_payload = {
+            "value": {
+                "timeSeries": [
+                    {
+                        "variable": {
+                            "variableDescription": "Discharge, cubic feet per second"
+                        },
+                        "values": [
+                            {
+                                "value": [
+                                    {
+                                        "dateTime": "2009-08-01T00:00:00.000Z",
+                                        "value": "1000",
+                                        "qualifiers": ["P"],
+                                    },
+                                    {
+                                        "dateTime": "2009-08-01T00:15:00.000Z",
+                                        "value": "1000",
+                                        "qualifiers": ["P"],
+                                    },
+                                ]
+                            }
+                        ],
+                    }
+                ]
+            }
+        }
+
+        mock_resp = MagicMock()
+        mock_resp.status_code = 200
+        mock_resp.text = json.dumps(mock_payload)
+        mock_get.return_value = mock_resp
+
+        df = river.io.usgs.request_usgs_data(
             station="15515500",
             parameter="00060",
             start_date="2009-08-01",
             end_date="2009-08-10",
-            data_type="Instantaneous",
+            options={"data_type": "Instantaneous", "clear_cache": True},
         )
-        self.assertEqual(data.columns, ["Discharge, cubic feet per second"])
-        # Every 15 minutes or 4 times per hour
-        self.assertEqual(data.shape, (10 * 24 * 4, 1))
+
+        # Verify that we called requests.get
+        mock_get.assert_called_once()
+
+        # Basic functionality checks
+        self.assertIsInstance(df, pd.DataFrame)
+        self.assertGreater(len(df), 0)  # Has data
+        self.assertTrue(df.index.tz is not None)  # Timezone aware
 
 
 if __name__ == "__main__":
diff --git a/mhkit/tests/river/test_resource.py b/mhkit/tests/river/test_resource.py
index 8b3a73023..12d867f3a 100644
--- a/mhkit/tests/river/test_resource.py
+++ b/mhkit/tests/river/test_resource.py
@@ -35,27 +35,27 @@ def tearDownClass(self):
     def test_Froude_number(self):
         v = 2
         h = 5
-        Fr = river.resource.Froude_number(v, h)
+        Fr = river.resource.froude_number(v, h)
         self.assertAlmostEqual(Fr, 0.286, places=3)
 
     def test_froude_number_v_type_error(self):
         v = "invalid_type"  # String instead of int/float
         h = 5
         with self.assertRaises(TypeError):
-            river.resource.Froude_number(v, h)
+            river.resource.froude_number(v, h)
 
     def test_froude_number_h_type_error(self):
         v = 2
         h = "invalid_type"  # String instead of int/float
         with self.assertRaises(TypeError):
-            river.resource.Froude_number(v, h)
+            river.resource.froude_number(v, h)
 
     def test_froude_number_g_type_error(self):
         v = 2
         h = 5
         g = "invalid_type"  # String instead of int/float
         with self.assertRaises(TypeError):
-            river.resource.Froude_number(v, h, g)
+            river.resource.froude_number(v, h, g)
 
     def test_exceedance_probability(self):
         # Create arbitrary discharge between 0 and 8(N=9)
@@ -121,7 +121,7 @@ def test_discharge_to_velocity(self):
         p, r2 = river.resource.polynomial_fit(np.arange(9), 10 * np.arange(9), 1)
         # Because the polynomial line fits perfect we should expect the V to equal 10*Q
         V = river.resource.discharge_to_velocity(Q, p)
-        self.assertAlmostEqual(np.sum(10 * Q - V["V"]), 0.00, places=2)
+        self.assertAlmostEqual(np.sum(10 * Q - V["velocity"]), 0.00, places=2)
 
     def test_discharge_to_velocity_xarray(self):
         # Create arbitrary discharge between 0 and 8(N=9)
@@ -132,7 +132,7 @@ def test_discharge_to_velocity_xarray(self):
         p, r2 = river.resource.polynomial_fit(np.arange(9), 10 * np.arange(9), 1)
         # Because the polynomial line fits perfect we should expect the V to equal 10*Q
         V = river.resource.discharge_to_velocity(Q, p, to_pandas=False)
-        self.assertAlmostEqual(np.sum(10 * Q - V["V"]).values, 0.00, places=2)
+        self.assertAlmostEqual(np.sum(10 * Q - V["velocity"]).values, 0.00, places=2)
 
     def test_discharge_to_velocity_D_type_error(self):
         D = "invalid_type"  # String instead of pd.Series or pd.DataFrame
@@ -154,16 +154,18 @@ def test_velocity_to_power(self):
         # Calculate a first order polynomial on an VP_Curve x=y line 10 times greater than the V values
         p2, r22 = river.resource.polynomial_fit(np.arange(9), 10 * np.arange(9), 1)
         # Set cut in/out to exclude 1 bin on either end of V range
-        cut_in = V["V"][1]
-        cut_out = V["V"].iloc[-2]
+        cut_in = V["velocity"][1]
+        cut_out = V["velocity"].iloc[-2]
         # Power should be 10x greater and exclude the ends of V
-        P = river.resource.velocity_to_power(V["V"], p2, cut_in, cut_out)
+        P = river.resource.velocity_to_power(V["velocity"], p2, cut_in, cut_out)
         # Cut in power zero
-        self.assertAlmostEqual(P["P"][0], 0.00, places=2)
+        self.assertAlmostEqual(P["power"][0], 0.00, places=2)
         # Cut out power zero
-        self.assertAlmostEqual(P["P"].iloc[-1], 0.00, places=2)
+        self.assertAlmostEqual(P["power"].iloc[-1], 0.00, places=2)
         # Middle 10x greater than velocity
-        self.assertAlmostEqual((P["P"][1:-1] - 10 * V["V"][1:-1]).sum(), 0.00, places=2)
+        self.assertAlmostEqual(
+            (P["power"][1:-1] - 10 * V["velocity"][1:-1]).sum(), 0.00, places=2
+        )
 
     def test_velocity_to_power_xarray(self):
         # Calculate a first order polynomial on an DV_Curve x=y line 10 times greater than the Q values
@@ -175,19 +177,19 @@ def test_velocity_to_power_xarray(self):
         # Calculate a first order polynomial on an VP_Curve x=y line 10 times greater than the V values
         p2, r22 = river.resource.polynomial_fit(np.arange(9), 10 * np.arange(9), 1)
         # Set cut in/out to exclude 1 bin on either end of V range
-        cut_in = V["V"].values[1]
-        cut_out = V["V"].values[-2]
+        cut_in = V["velocity"].values[1]
+        cut_out = V["velocity"].values[-2]
         # Power should be 10x greater and exclude the ends of V
         P = river.resource.velocity_to_power(
-            V["V"], p2, cut_in, cut_out, to_pandas=False
+            V["velocity"], p2, cut_in, cut_out, to_pandas=False
         )
         # Cut in power zero
-        self.assertAlmostEqual(P["P"][0], 0.00, places=2)
+        self.assertAlmostEqual(P["power"][0], 0.00, places=2)
         # Cut out power zero
-        self.assertAlmostEqual(P["P"][-1], 0.00, places=2)
+        self.assertAlmostEqual(P["power"][-1], 0.00, places=2)
         # Middle 10x greater than velocity
         self.assertAlmostEqual(
-            (P["P"][1:-1] - 10 * V["V"][1:-1]).sum().values, 0.00, places=2
+            (P["power"][1:-1] - 10 * V["velocity"][1:-1]).sum().values, 0.00, places=2
         )
 
     def test_velocity_to_power_V_type_error(self):
@@ -278,7 +280,9 @@ def test_plot_flow_duration_curve(self):
 
         f = river.resource.exceedance_probability(self.data.Q)
         plt.figure()
-        river.graphics.plot_flow_duration_curve(self.data["Q"], f["F"])
+        river.graphics.plot_flow_duration_curve(
+            self.data["Q"], f["exceedance_probability"]
+        )
         plt.savefig(filename, format="png")
         plt.close()
 
@@ -291,7 +295,9 @@ def test_plot_power_duration_curve(self):
 
         f = river.resource.exceedance_probability(self.data.Q)
         plt.figure()
-        river.graphics.plot_flow_duration_curve(self.results["P_control"], f["F"])
+        river.graphics.plot_flow_duration_curve(
+            self.results["P_control"], f["exceedance_probability"]
+        )
         plt.savefig(filename, format="png")
         plt.close()
 
@@ -304,7 +310,9 @@ def test_plot_velocity_duration_curve(self):
 
         f = river.resource.exceedance_probability(self.data.Q)
         plt.figure()
-        river.graphics.plot_velocity_duration_curve(self.results["V_control"], f["F"])
+        river.graphics.plot_velocity_duration_curve(
+            self.results["V_control"], f["exceedance_probability"]
+        )
         plt.savefig(filename, format="png")
         plt.close()
 
diff --git a/mhkit/tests/tidal/test_io.py b/mhkit/tests/tidal/test_io.py
index 280b847ce..be34cd444 100644
--- a/mhkit/tests/tidal/test_io.py
+++ b/mhkit/tests/tidal/test_io.py
@@ -74,13 +74,16 @@ def test_request_noaa_data_basic(self):
         and verify that the returned DataFrame and metadata have the
         correct shape and columns.
         """
+        options = {
+            "proxy": None,
+            "write_json": None,
+        }
         data, metadata = tidal.io.noaa.request_noaa_data(
             station="s08010",
             parameter="currents",
             start_date="20180101",
             end_date="20180102",
-            proxy=None,
-            write_json=None,
+            options=options,
         )
         self.assertTrue(np.all(data.columns == ["s", "d", "b"]))
         self.assertEqual(data.shape, (183, 3))
@@ -92,14 +95,17 @@ def test_request_noaa_data_basic_xarray(self):
         and verify that the returned DataFrame and metadata have the
         correct shape and columns.
         """
+        options = {
+            "proxy": None,
+            "write_json": None,
+            "to_pandas": False,
+        }
         data = tidal.io.noaa.request_noaa_data(
             station="s08010",
             parameter="currents",
             start_date="20180101",
             end_date="20180102",
-            proxy=None,
-            write_json=None,
-            to_pandas=False,
+            options=options,
         )
         # Check if the variable sets are equal
         data_variables = list(data.variables)
@@ -117,13 +123,16 @@ def test_request_noaa_data_write_json(self):
         and can be loaded back into a dictionary.
         """
         test_json_file = "test_noaa_data.json"
+        options = {
+            "proxy": None,
+            "write_json": test_json_file,
+        }
         _, _ = tidal.io.noaa.request_noaa_data(
             station="s08010",
             parameter="currents",
             start_date="20180101",
             end_date="20180102",
-            proxy=None,
-            write_json=test_json_file,
+            options=options,
         )
         self.assertTrue(os.path.isfile(test_json_file))
 
@@ -142,14 +151,17 @@ def test_request_noaa_data_invalid_dates(self):
         Test the request_noaa_data function with an invalid date format
         and verify that it raises a ValueError.
         """
+        options = {
+            "proxy": None,
+            "write_json": None,
+        }
         with self.assertRaises(ValueError):
             tidal.io.noaa.request_noaa_data(
                 station="s08010",
                 parameter="currents",
                 start_date="2018-01-01",  # Invalid date format
                 end_date="20180102",
-                proxy=None,
-                write_json=None,
+                options=options,
             )
 
     def test_request_noaa_data_end_before_start(self):
@@ -157,14 +169,17 @@ def test_request_noaa_data_end_before_start(self):
         Test the request_noaa_data function with the end date before
         the start date and verify that it raises a ValueError.
         """
+        options = {
+            "proxy": None,
+            "write_json": None,
+        }
         with self.assertRaises(ValueError):
             tidal.io.noaa.request_noaa_data(
                 station="s08010",
                 parameter="currents",
                 start_date="20180102",
                 end_date="20180101",  # End date before start date
-                proxy=None,
-                write_json=None,
+                options=options,
             )
 
 
diff --git a/mhkit/tests/tidal/test_resource.py b/mhkit/tests/tidal/test_resource.py
index 7b5b6ad11..776060a77 100644
--- a/mhkit/tests/tidal/test_resource.py
+++ b/mhkit/tests/tidal/test_resource.py
@@ -29,9 +29,9 @@ def tearDownClass(self):
 
     def test_exceedance_probability(self):
         df = pd.DataFrame.from_records({"vals": np.array([1, 2, 3, 4, 5, 6, 7, 8, 9])})
-        df["F"] = tidal.resource.exceedance_probability(df.vals)
-        self.assertEqual(df["F"].min(), 10)
-        self.assertEqual(df["F"].max(), 90)
+        df["exceedance_probability"] = tidal.resource.exceedance_probability(df.vals)
+        self.assertEqual(df["exceedance_probability"].min(), 10)
+        self.assertEqual(df["exceedance_probability"].max(), 90)
 
     def test_principal_flow_directions(self):
         width_direction = 10
diff --git a/mhkit/tests/utils/test_cache.py b/mhkit/tests/utils/test_cache.py
index 3cd5fff43..b7bd85e8b 100644
--- a/mhkit/tests/utils/test_cache.py
+++ b/mhkit/tests/utils/test_cache.py
@@ -1,8 +1,8 @@
 """
 Unit Testing for MHKiT Cache Utilities
 
-This module provides unit tests for the caching utilities present in the MHKiT library. 
-These utilities help in caching and retrieving data, ensuring efficient and repeatable 
+This module provides unit tests for the caching utilities present in the MHKiT library.
+These utilities help in caching and retrieving data, ensuring efficient and repeatable
 data access without redundant computations or network requests.
 
 The tests cover:
@@ -11,7 +11,7 @@
 3. Usage of appropriate file extensions based on the type of data being cached.
 4. Clearing of cache directories as specified.
 
-By running these tests, one can validate that the caching utilities of MHKiT are functioning 
+By running these tests, one can validate that the caching utilities of MHKiT are functioning
 as expected, ensuring that users can rely on cached data and metadata when using the MHKiT library.
 
 Usage:
diff --git a/mhkit/tests/wave/io/hindcast/test_hindcast.py b/mhkit/tests/wave/io/hindcast/test_hindcast.py
index 26847dc64..456ea05b7 100644
--- a/mhkit/tests/wave/io/hindcast/test_hindcast.py
+++ b/mhkit/tests/wave/io/hindcast/test_hindcast.py
@@ -1,17 +1,17 @@
 """
-This module contains unit tests for the WPTO hindcast data retrieval 
+This module contains unit tests for the WPTO hindcast data retrieval
 functions in the mhkit.wave package. The tests are designed to verify
 the correct functioning of the following functionalities:
 
 1. Retrieval of multiple years of data for a single data type,
     latitude-longitude pair, and parameter.
-2. Retrieval of multiple parameters for a single data type, year, 
+2. Retrieval of multiple parameters for a single data type, year,
     and latitude-longitude pair.
 3. Retrieval of data for multiple locations for point data and
     directional spectrum at a single data type, year, and parameter.
 
-The tests use the unittest framework and compare the output of the 
-hindcast retrieval functions with expected output data. The expected 
+The tests use the unittest framework and compare the output of the
+hindcast retrieval functions with expected output data. The expected
 data is read from CSV files located in the examples/data/wave directory.
 
 Functions tested:
@@ -19,7 +19,7 @@
 - wave.io.hindcast.hindcast.request_wpto_directional_spectrum
 
 Usage:
-Run the script directly as a standalone program, or import the 
+Run the script directly as a standalone program, or import the
 TestWPTOhindcast class in another test suite.
 """
 
diff --git a/mhkit/tests/wave/io/test_cdip.py b/mhkit/tests/wave/io/test_cdip.py
index b77958df6..17d1bc2ad 100644
--- a/mhkit/tests/wave/io/test_cdip.py
+++ b/mhkit/tests/wave/io/test_cdip.py
@@ -77,8 +77,8 @@ def test_dates_to_timestamp(self):
             self.test_nc, start_date=start_date, end_date=end_date
         )
 
-        start_dt = datetime.utcfromtimestamp(start_stamp).replace(tzinfo=pytz.UTC)
-        end_dt = datetime.utcfromtimestamp(end_stamp).replace(tzinfo=pytz.UTC)
+        start_dt = datetime.fromtimestamp(start_stamp, pytz.UTC)
+        end_dt = datetime.fromtimestamp(end_stamp, pytz.UTC)
 
         self.assertEqual(start_dt, start_date)
         self.assertEqual(end_dt, end_date)
diff --git a/mhkit/tests/wave/io/test_wecsim.py b/mhkit/tests/wave/io/test_wecsim.py
index 52df214b9..89f5a0ef2 100644
--- a/mhkit/tests/wave/io/test_wecsim.py
+++ b/mhkit/tests/wave/io/test_wecsim.py
@@ -56,7 +56,7 @@ def test_read_wecSim_cable(self):
         )
         self.assertEqual(ws_output["wave"]["elevation"].name, "elevation")
         self.assertEqual(
-            ws_output["bodies"]["body1"]["position_dof1"].name, "position_dof1"
+            ws_output["bodies"].sel(body=1, dof=1)["position"].name, "position"
         )
         self.assertEqual(len(ws_output["mooring"]), 0)
         self.assertEqual(len(ws_output["moorDyn"]), 0)
diff --git a/mhkit/tests/wave/test_performance.py b/mhkit/tests/wave/test_performance.py
index a12c8050c..0580c08ea 100644
--- a/mhkit/tests/wave/test_performance.py
+++ b/mhkit/tests/wave/test_performance.py
@@ -6,6 +6,7 @@
 import numpy as np
 import unittest
 import os
+import pytest
 
 
 testdir = dirname(abspath(__file__))
@@ -46,20 +47,27 @@ def setUpClass(self):
     def tearDownClass(self):
         pass
 
-    def test_capture_length(self):
-        L = wave.performance.capture_length(self.data["P"], self.data["J"])
-        L_stats = wave.performance.statistics(L)
+    def test_capture_width(self):
+        with pytest.warns(FutureWarning):
+            CW = wave.performance.capture_length(self.data["P"], self.data["J"])
+        CW_stats = wave.performance.statistics(CW)
 
-        self.assertAlmostEqual(L_stats["mean"], 0.6676, 3)
+        self.assertAlmostEqual(CW_stats["mean"], 0.6676, 3)
 
-    def test_capture_length_matrix(self):
-        L = wave.performance.capture_length(self.data["P"], self.data["J"])
-        LM = wave.performance.capture_length_matrix(
-            self.data["Hm0"], self.data["Te"], L, "std", self.Hm0_bins, self.Te_bins
-        )
+    def test_capture_width_matrix(self):
+        CW = wave.performance.capture_width(self.data["P"], self.data["J"])
+        with pytest.warns(FutureWarning):
+            CWM = wave.performance.capture_length_maxtrix(
+                self.data["Hm0"],
+                self.data["Te"],
+                CW,
+                "std",
+                self.Hm0_bins,
+                self.Te_bins,
+            )
 
-        self.assertEqual(LM.shape, (38, 9))
-        self.assertEqual(LM.isna().sum().sum(), 131)
+        self.assertEqual(CWM.shape, (38, 9))
+        self.assertEqual(CWM.isna().sum().sum(), 131)
 
     def test_wave_energy_flux_matrix(self):
         JM = wave.performance.wave_energy_flux_matrix(
@@ -75,9 +83,9 @@ def test_wave_energy_flux_matrix(self):
         self.assertEqual(JM.isna().sum().sum(), 131)
 
     def test_power_matrix(self):
-        L = wave.performance.capture_length(self.data["P"], self.data["J"])
-        LM = wave.performance.capture_length_matrix(
-            self.data["Hm0"], self.data["Te"], L, "mean", self.Hm0_bins, self.Te_bins
+        CW = wave.performance.capture_width(self.data["P"], self.data["J"])
+        CWM = wave.performance.capture_width_matrix(
+            self.data["Hm0"], self.data["Te"], CW, "mean", self.Hm0_bins, self.Te_bins
         )
         JM = wave.performance.wave_energy_flux_matrix(
             self.data["Hm0"],
@@ -87,15 +95,15 @@ def test_power_matrix(self):
             self.Hm0_bins,
             self.Te_bins,
         )
-        PM = wave.performance.power_matrix(LM, JM)
+        PM = wave.performance.power_matrix(CWM, JM)
 
         self.assertEqual(PM.shape, (38, 9))
         self.assertEqual(PM.isna().sum().sum(), 131)
 
     def test_mean_annual_energy_production(self):
-        L = wave.performance.capture_length(self.data["P"], self.data["J"])
+        CW = wave.performance.capture_width(self.data["P"], self.data["J"])
         maep = wave.performance.mean_annual_energy_production_timeseries(
-            L, self.data["J"]
+            CW, self.data["J"]
         )
 
         self.assertAlmostEqual(maep, 1754020.077, 2)
@@ -122,7 +130,7 @@ def test_plot_matrix(self):
         self.assertTrue(isfile(filename))
 
     def test_powerperformance_workflow(self):
-        filename = abspath(join(plotdir, "Capture Length Matrix mean.png"))
+        filename = abspath(join(plotdir, "Capture Width Matrix mean.png"))
         if isfile(filename):
             os.remove(filename)
         P = pd.Series(np.random.normal(200, 40, 743), index=self.S.columns)
diff --git a/mhkit/tidal/__init__.py b/mhkit/tidal/__init__.py
index 2644bfdfa..b998addfb 100644
--- a/mhkit/tidal/__init__.py
+++ b/mhkit/tidal/__init__.py
@@ -1,3 +1,10 @@
+"""
+MHKiT Tidal Module
+
+The tidal module contains a set of functions to calculate
+relevant quantities of interest for tidal energy converters (TECs).
+"""
+
 from mhkit.tidal import graphics
 from mhkit.tidal import io
 from mhkit.tidal import resource
diff --git a/mhkit/tidal/graphics.py b/mhkit/tidal/graphics.py
index 151fac479..d31be42d3 100644
--- a/mhkit/tidal/graphics.py
+++ b/mhkit/tidal/graphics.py
@@ -1,15 +1,34 @@
-import numpy as np
+"""
+graphics.py
+
+This module provides functions for visualizing tidal resource and performance data.
+It includes tools for creating polar plots, velocity distributions, exceedance
+probability charts, and current time-series plots.
+
+"""
+
 import bisect
+import numpy as np
 from scipy.interpolate import interpn as _interpn
 from scipy.interpolate import interp1d
 import matplotlib.pyplot as plt
+import matplotlib as mpl
 from mhkit.river.resource import exceedance_probability
 from mhkit.tidal.resource import _histogram, _flood_or_ebb
 from mhkit.river.graphics import plot_velocity_duration_curve, _xy_plot
 from mhkit.utils import convert_to_dataarray
 
+# Explicitly declare the river functions to be exported
+__all__ = [
+    "plot_velocity_duration_curve",
+]
 
-def _initialize_polar(ax=None, metadata=None, flood=None, ebb=None):
+viridis = mpl.colormaps["viridis"]
+
+
+def _initialize_polar(
+    ax: plt.Axes = None, metadata: dict = None, flood: float = None, ebb: float = None
+) -> plt.Axes:
     """
     Initializes a polar plots with cardinal directions and ebb/flow
 
@@ -23,18 +42,18 @@ def _initialize_polar(ax=None, metadata=None, flood=None, ebb=None):
     ax: axes
     """
 
-    if ax == None:
+    if ax is None:
         # Initialize polar plot
-        fig = plt.figure(figsize=(12, 8))
+        plt.figure(figsize=(12, 8))
         ax = plt.axes(polar=True)
     # Angles are measured clockwise from true north
     ax.set_theta_zero_location("N")
     ax.set_theta_direction(-1)
     xticks = ["N", "NE", "E", "SE", "S", "SW", "W", "NW"]
     # Polar plots do not have minor ticks, insert flood/ebb into major ticks
-    xtickDegrees = [0.0, 45.0, 90.0, 135.0, 180.0, 225.0, 270.0, 315.0]
+    xtick_degrees = [0.0, 45.0, 90.0, 135.0, 180.0, 225.0, 270.0, 315.0]
     # Set title and metadata box
-    if metadata != None:
+    if metadata is not None:
         # Set the Title
         plt.title(metadata["name"])
         # List of strings for metadata box
@@ -52,35 +71,37 @@ def _initialize_polar(ax=None, metadata=None, flood=None, ebb=None):
             transform=ax.transAxes,
             fontsize=14,
             verticalalignment="top",
-            bbox=dict(facecolor="none", edgecolor="k", pad=5),
+            bbox={"facecolor": "none", "edgecolor": "k", "pad": 5},
         )
     # If defined plot flood and ebb directions as major ticks
-    if flood != None:
+    if flood is not None:
         # Get flood direction in degrees
-        floodDirection = flood
+        flood_direction = flood
         # Polar plots do not have minor ticks,
         #    insert flood/ebb into major ticks
-        bisect.insort(xtickDegrees, floodDirection)
+        bisect.insort(xtick_degrees, flood_direction)
         # Get location in list
-        idxFlood = xtickDegrees.index(floodDirection)
+        idx_flood = xtick_degrees.index(flood_direction)
         # Insert label at appropriate location
-        xticks[idxFlood:idxFlood] = ["\nFlood"]
-    if ebb != None:
-        # Get flood direction in degrees
-        ebbDirection = ebb
+        xticks[idx_flood:idx_flood] = ["\nFlood"]
+    if ebb is not None:
+        # Get ebb direction in degrees
+        ebb_direction = ebb
         # Polar plots do not have minor ticks,
         #    insert flood/ebb into major ticks
-        bisect.insort(xtickDegrees, ebbDirection)
+        bisect.insort(xtick_degrees, ebb_direction)
         # Get location in list
-        idxEbb = xtickDegrees.index(ebbDirection)
+        idx_ebb = xtick_degrees.index(ebb_direction)
         # Insert label at appropriate location
-        xticks[idxEbb:idxEbb] = ["\nEbb"]
-    ax.set_xticks(np.array(xtickDegrees) * np.pi / 180.0)
+        xticks[idx_ebb:idx_ebb] = ["\nEbb"]
+    ax.set_xticks(np.array(xtick_degrees) * np.pi / 180.0)
     ax.set_xticklabels(xticks)
     return ax
 
 
-def _check_inputs(directions, velocities, flood, ebb):
+def _check_inputs(
+    directions: np.ndarray, velocities: np.ndarray, flood: float, ebb: float
+) -> None:
     """
     Runs checks on inputs for the graphics functions.
 
@@ -111,27 +132,25 @@ def _check_inputs(directions, velocities, flood, ebb):
         raise TypeError("flood must be of type int or float")
     if not isinstance(ebb, (int, float, type(None))):
         raise TypeError("ebb must be of type int or float")
-    if flood is not None:
-        if (flood < 0) and (flood > 360):
-            raise ValueError("flood must be between 0 and 360 degrees")
-    if ebb is not None:
-        if (ebb < 0) and (ebb > 360):
-            raise ValueError("ebb must be between 0 and 360 degrees")
+    if flood is not None and not 0 <= flood <= 360:
+        raise ValueError("flood must be between 0 and 360 degrees")
+    if ebb is not None and not 0 <= ebb <= 360:
+        raise ValueError("ebb must be between 0 and 360 degrees")
 
 
 def plot_rose(
-    directions,
-    velocities,
-    width_dir,
-    width_vel,
-    ax=None,
-    metadata=None,
-    flood=None,
-    ebb=None,
-):
+    directions: np.ndarray,
+    velocities: np.ndarray,
+    width_dir: float,
+    width_vel: float,
+    ax: plt.Axes = None,
+    metadata: dict = None,
+    flood: float = None,
+    ebb: float = None,
+) -> plt.Axes:
     """
     Creates a polar histogram. Direction angles from binned histogram must
-    be specified such that 0  degrees is north.
+    be specified such that 0 degrees is north.
 
     Parameters
     ----------
@@ -145,7 +164,7 @@ def plot_rose(
         Width of velocity bins for histogram in m/s
     ax: float
         Polar plot axes to add polar histogram
-    metadata: dictonary
+    metadata: dictionary
         If provided needs keys ['name', 'lat', 'lon'] for plot title
         and information box on plot
     flood: float
@@ -157,69 +176,61 @@ def plot_rose(
     ax: figure
         Water current rose plot
     """
-
+    # pylint: disable=too-many-positional-arguments, disable=too-many-arguments, disable=too-many-locals
+    # Validate inputs inline to reduce function calls
     _check_inputs(directions, velocities, flood, ebb)
+    if not isinstance(width_dir, (int, float)) or width_dir < 0:
+        raise ValueError("width_dir must be a positive number")
+    if not isinstance(width_vel, (int, float)) or width_vel < 0:
+        raise ValueError("width_vel must be a positive number")
 
-    if not isinstance(width_dir, (int, float)):
-        raise TypeError("width_dir must be of type int or float")
-    if not isinstance(width_vel, (int, float)):
-        raise TypeError("width_vel must be of type int or float")
-    if width_dir < 0:
-        raise ValueError("width_dir must be greater than 0")
-    if width_vel < 0:
-        raise ValueError("width_vel must be greater than 0")
-
-    # Calculate the 2D histogram
-    H, dir_edges, vel_edges = _histogram(directions, velocities, width_dir, width_vel)
-    # Determine number of bins
-    dir_bins = H.shape[0]
-    vel_bins = H.shape[1]
-    # Create the angles
-    thetas = np.arange(0, 2 * np.pi, 2 * np.pi / dir_bins)
-    # Initialize the polar polt
+    # Compute histogram and bin edges
+    histogram, _, vel_edges = _histogram(directions, velocities, width_dir, width_vel)
+
+    # Initialize polar plot
     ax = _initialize_polar(ax=ax, metadata=metadata, flood=flood, ebb=ebb)
-    # Set bar color based on wind speed
-    colors = plt.cm.viridis(np.linspace(0, 1.0, vel_bins))
-    # Set the current speed bin label names
-    # Calculate the 2D histogram
+
+    # Define bin properties
+    dir_bins, vel_bins = histogram.shape
+    thetas = np.linspace(0, 2 * np.pi, dir_bins, endpoint=False)
+    colors = viridis(np.linspace(0, 1, vel_bins))
     labels = [f"{i:.1f}-{j:.1f}" for i, j in zip(vel_edges[:-1], vel_edges[1:])]
-    # Initialize the vertical-offset (polar radius) for the stacked bar chart.
+
+    # Plot histogram
     r_offset = np.zeros(dir_bins)
     for vel_bin in range(vel_bins):
-        # Plot fist set of bars in all directions
         ax.bar(
             thetas,
-            H[:, vel_bin],
+            histogram[:, vel_bin],
             width=(2 * np.pi / dir_bins),
             bottom=r_offset,
             color=colors[vel_bin],
             label=labels[vel_bin],
         )
-        # Increase the radius offset in all directions
-        r_offset = r_offset + H[:, vel_bin]
-    # Add the a legend for current speed bins
+        r_offset += histogram[
+            :, vel_bin
+        ]  # Increase the radius offset in all directions
+
+    # Configure legend and ticks
     plt.legend(
         loc="best", title="Velocity bins [m/s]", bbox_to_anchor=(1.29, 1.00), ncol=1
     )
-    # Get the r-ticks (polar y-ticks)
     yticks = plt.yticks()
-    # Format y-ticks with  units for clarity
-    rticks = [f"{y:.1f}%" for y in yticks[0]]
-    # Set the y-ticks
-    plt.yticks(yticks[0], rticks)
+    plt.yticks(yticks[0], [f"{y:.1f}%" for y in yticks[0]])
+
     return ax
 
 
 def plot_joint_probability_distribution(
-    directions,
-    velocities,
-    width_dir,
-    width_vel,
-    ax=None,
-    metadata=None,
-    flood=None,
-    ebb=None,
-):
+    directions: np.ndarray,
+    velocities: np.ndarray,
+    width_dir: float,
+    width_vel: float,
+    ax: plt.Axes = None,
+    metadata: dict = None,
+    flood: float = None,
+    ebb: float = None,
+) -> plt.Axes:
     """
     Creates a polar histogram. Direction angles from binned histogram must
     be specified such that 0 is north.
@@ -236,7 +247,7 @@ def plot_joint_probability_distribution(
         Width of velocity bins for histogram in m/s
     ax: float
         Polar plot axes to add polar histogram
-    metadata: dictonary
+    metadata: dictionary
         If provided needs keys ['name', 'Lat', 'Lon'] for plot title
         and information box on plot
     flood: float
@@ -248,61 +259,53 @@ def plot_joint_probability_distribution(
     ax: figure
         Joint probability distribution
     """
-
+    # pylint: disable=too-many-positional-arguments, disable=too-many-arguments, disable=too-many-locals
     _check_inputs(directions, velocities, flood, ebb)
 
     if not isinstance(width_dir, (int, float)):
         raise TypeError("width_dir must be of type int or float")
     if not isinstance(width_vel, (int, float)):
         raise TypeError("width_vel must be of type int or float")
-    if width_dir < 0:
-        raise ValueError("width_dir must be greater than 0")
-    if width_vel < 0:
-        raise ValueError("width_vel must be greater than 0")
-
-    # Calculate the 2D histogram
-    H, dir_edges, vel_edges = _histogram(directions, velocities, width_dir, width_vel)
-    # Initialize the polar polt
+    if width_dir < 0 or width_vel < 0:
+        raise ValueError("width_dir and width_vel must be greater than 0")
+
+    histogram, dir_edges, vel_edges = _histogram(
+        directions, velocities, width_dir, width_vel
+    )
     ax = _initialize_polar(ax=ax, metadata=metadata, flood=flood, ebb=ebb)
-    # Set the current speed bin label names
-    labels = [f"{i:.1f}-{j:.1f}" for i, j in zip(vel_edges[:-1], vel_edges[1:])]
-    # Set vel & dir bins to middle of bin except at ends
-    dir_bins = 0.5 * (dir_edges[1:] + dir_edges[:-1])  # set all bins to middle
+
+    dir_bins = 0.5 * (dir_edges[1:] + dir_edges[:-1])
     vel_bins = 0.5 * (vel_edges[1:] + vel_edges[:-1])
-    # Reset end of bin range to edge of bin
-    dir_bins[0] = dir_edges[0]
-    vel_bins[0] = vel_edges[0]
-    dir_bins[-1] = dir_edges[-1]
-    vel_bins[-1] = vel_edges[-1]
-    # Interpolate the bins back to specific data points
+    dir_bins[[0, -1]] = dir_edges[[0, -1]]
+    vel_bins[[0, -1]] = vel_edges[[0, -1]]
+
     z = _interpn(
         (dir_bins, vel_bins),
-        H,
+        histogram,
         np.vstack([directions, velocities]).T,
         method="splinef2d",
         bounds_error=False,
     )
-    # Plot the most probable data last
+
     idx = z.argsort()
-    # Convert to radians and order points by probability
-    theta, r, z = directions.values[idx] * np.pi / 180, velocities.values[idx], z[idx]
-    # Create scatter plot colored by probability density
-    sx = ax.scatter(theta, r, c=z, s=5, edgecolor=None)
-    # Create colorbar
+    theta = directions.values[idx] * np.pi / 180
+    r = velocities.values[idx]
+
+    sx = ax.scatter(theta, r, c=z[idx], s=5, edgecolor=None)
     plt.colorbar(sx, ax=ax, label="Joint Probability [%]")
 
-    # Get the r-ticks (polar y-ticks)
-    yticks = ax.get_yticks()
-    # Set y-ticks labels
-    ax.set_yticks(yticks)  # to avoid matplotlib warning
-    ax.set_yticklabels([f"{y:.1f} $m/s$" for y in yticks])
+    ax.set_yticklabels([f"{y:.1f} $m/s$" for y in ax.get_yticks()])
 
     return ax
 
 
 def plot_current_timeseries(
-    directions, velocities, principal_direction, label=None, ax=None
-):
+    directions: np.ndarray,
+    velocities: np.ndarray,
+    principal_direction: float,
+    label: str = None,
+    ax: plt.Axes = None,
+) -> plt.Axes:
     """
     Returns a plot of velocity from an array of direction and speed
     data in the direction of the supplied principal_direction.
@@ -351,7 +354,14 @@ def plot_current_timeseries(
     return ax
 
 
-def tidal_phase_probability(directions, velocities, flood, ebb, bin_size=0.1, ax=None):
+def tidal_phase_probability(
+    directions: np.ndarray,
+    velocities: np.ndarray,
+    flood: float,
+    ebb: float,
+    bin_size: float = 0.1,
+    ax: plt.Axes = None,
+) -> plt.Axes:
     """
     Discretizes the tidal series speed by bin size and returns a plot
     of the probability for each bin in the flood or ebb tidal phase.
@@ -360,47 +370,45 @@ def tidal_phase_probability(directions, velocities, flood, ebb, bin_size=0.1, ax
     ----------
     directions: array-like
         Time-series of directions [degrees]
-    speed: array-like
+    velocities: array-like
         Time-series of speeds [m/s]
     flood: float or int
         Principal component of flow in the flood direction [degrees]
     ebb: float or int
         Principal component of flow in the ebb direction [degrees]
     bin_size: float
-        Speed bin size. Optional. Deaful = 0.1 m/s
+        Speed bin size. Optional. Default = 0.1 m/s
     ax : matplotlib axes object
-        Axes for plotting.  If None, then a new figure with a single
+        Axes for plotting. If None, then a new figure with a single
         axes is used.
 
     Returns
     -------
     ax: figure
     """
-
+    # pylint: disable=too-many-positional-arguments, too-many-arguments, too-many-locals
     _check_inputs(directions, velocities, flood, ebb)
     if bin_size < 0:
         raise ValueError("bin_size must be greater than 0")
 
-    if ax == None:
-        fig, ax = plt.subplots(figsize=(12, 8))
+    if ax is None:
+        ax = plt.subplots(figsize=(12, 8))[1]
 
-    isEbb = _flood_or_ebb(directions, flood, ebb)
+    is_ebb = _flood_or_ebb(directions, flood, ebb)
 
-    decimals = round(bin_size / 0.1)
-    N_bins = int(round(velocities.max(), decimals) / bin_size)
+    n_bins = int(round(velocities.max(), round(bin_size / 0.1)) / bin_size)
 
-    H, bins = np.histogram(velocities, bins=N_bins)
-    H_ebb, bins1 = np.histogram(velocities[isEbb], bins=bins)
-    H_flood, bins2 = np.histogram(velocities[~isEbb], bins=bins)
+    bins = np.histogram_bin_edges(velocities, bins=n_bins)
+    h_ebb, _ = np.histogram(velocities[is_ebb], bins=bins)
+    h_flood, _ = np.histogram(velocities[~is_ebb], bins=bins)
 
-    p_ebb = H_ebb / H
-    p_flood = H_flood / H
+    p_ebb = h_ebb / (h_ebb + h_flood)
+    p_flood = h_flood / (h_ebb + h_flood)
 
     center = (bins[:-1] + bins[1:]) / 2
     width = 0.9 * (bins[1] - bins[0])
 
-    mask1 = np.ma.where(p_ebb >= p_flood)
-    mask2 = np.ma.where(p_flood >= p_ebb)
+    mask1 = p_ebb >= p_flood
 
     ax.bar(
         center[mask1],
@@ -420,8 +428,8 @@ def tidal_phase_probability(directions, velocities, flood, ebb, bin_size=0.1, ax
         color="orange",
     )
     ax.bar(
-        center[mask2],
-        height=p_ebb[mask2],
+        center[~mask1],
+        height=p_ebb[~mask1],
         alpha=1,
         edgecolor="black",
         width=width,
@@ -437,7 +445,14 @@ def tidal_phase_probability(directions, velocities, flood, ebb, bin_size=0.1, ax
     return ax
 
 
-def tidal_phase_exceedance(directions, velocities, flood, ebb, bin_size=0.1, ax=None):
+def tidal_phase_exceedance(
+    directions: np.ndarray,
+    velocities: np.ndarray,
+    flood: float,
+    ebb: float,
+    bin_size: float = 0.1,
+    ax: plt.Axes = None,
+) -> plt.Axes:
     """
     Returns a stacked area plot of the exceedance probability for the
     flood and ebb tidal phases.
@@ -462,22 +477,21 @@ def tidal_phase_exceedance(directions, velocities, flood, ebb, bin_size=0.1, ax=
     -------
     ax: figure
     """
-
+    # pylint: disable=too-many-positional-arguments, too-many-arguments
     _check_inputs(directions, velocities, flood, ebb)
     if bin_size < 0:
         raise ValueError("bin_size must be greater than 0")
 
-    if ax == None:
-        fig, ax = plt.subplots(figsize=(12, 8))
+    if ax is None:
+        ax = plt.subplots(figsize=(12, 8))[1]
 
-    isEbb = _flood_or_ebb(directions, flood, ebb)
+    is_ebb = _flood_or_ebb(directions, flood, ebb)
 
-    s_ebb = velocities[isEbb]
-    s_flood = velocities[~isEbb]
+    s_ebb = velocities[is_ebb]
+    s_flood = velocities[~is_ebb]
 
-    F = exceedance_probability(velocities)["F"]
-    F_ebb = exceedance_probability(s_ebb)["F"]
-    F_flood = exceedance_probability(s_flood)["F"]
+    f_ebb = exceedance_probability(s_ebb)["exceedance_probability"]
+    f_flood = exceedance_probability(s_flood)["exceedance_probability"]
 
     decimals = round(bin_size / 0.1)
     s_new = np.arange(
@@ -486,20 +500,15 @@ def tidal_phase_exceedance(directions, velocities, flood, ebb, bin_size=0.1, ax=
         bin_size,
     )
 
-    f_total = interp1d(velocities, F, bounds_error=False)
-    f_ebb = interp1d(s_ebb, F_ebb, bounds_error=False)
-    f_flood = interp1d(s_flood, F_flood, bounds_error=False)
-
-    F_total = f_total(s_new)
-    F_ebb = f_ebb(s_new)
-    F_flood = f_flood(s_new)
+    f_ebb = interp1d(s_ebb, f_ebb, bounds_error=False)
+    f_flood = interp1d(s_flood, f_flood, bounds_error=False)
 
-    F_max_total = np.nanmax(F_ebb) + np.nanmax(F_flood)
+    f_max_total = np.nanmax(f_ebb(s_new)) + np.nanmax(f_flood(s_new))
 
     ax.stackplot(
         s_new,
-        F_ebb / F_max_total * 100,
-        F_flood / F_max_total * 100,
+        f_ebb(s_new) / f_max_total * 100,
+        f_flood(s_new) / f_max_total * 100,
         labels=["Ebb", "Flood"],
     )
 
diff --git a/mhkit/tidal/io/__init__.py b/mhkit/tidal/io/__init__.py
index 3f75b8116..cfc84cfb4 100644
--- a/mhkit/tidal/io/__init__.py
+++ b/mhkit/tidal/io/__init__.py
@@ -1,2 +1,6 @@
+"""
+The io submodule contains functions to load NOAA and Delft3D data.
+"""
+
 from mhkit.tidal.io import noaa
 from mhkit.tidal.io import d3d
diff --git a/mhkit/tidal/io/d3d.py b/mhkit/tidal/io/d3d.py
index 67ec083d9..31a0836c6 100644
--- a/mhkit/tidal/io/d3d.py
+++ b/mhkit/tidal/io/d3d.py
@@ -1 +1,43 @@
-from mhkit.river.io.d3d import *
+"""
+d3d.py
+
+This module provides functions for reading, processing, and analyzing Delft3D
+data. It supports time indexing, variable interpolation, and turbulent
+intensity calculations to facilitate tidal resource assessment and modeling.
+"""
+
+from mhkit.river.io.d3d import (
+    interp,
+    np,
+    pd,
+    xr,
+    netCDF4,
+    warnings,
+    get_all_time,
+    index_to_seconds,
+    seconds_to_index,
+    get_layer_data,
+    create_points,
+    variable_interpolation,
+    get_all_data_points,
+    turbulent_intensity,
+    unorm,
+)
+
+__all__ = [
+    "interp",
+    "np",
+    "pd",
+    "xr",
+    "netCDF4",
+    "warnings",
+    "get_all_time",
+    "index_to_seconds",
+    "seconds_to_index",
+    "get_layer_data",
+    "create_points",
+    "variable_interpolation",
+    "get_all_data_points",
+    "turbulent_intensity",
+    "unorm",
+]
diff --git a/mhkit/tidal/io/noaa.py b/mhkit/tidal/io/noaa.py
index 2ab8a1d2a..8622e110e 100644
--- a/mhkit/tidal/io/noaa.py
+++ b/mhkit/tidal/io/noaa.py
@@ -1,27 +1,11 @@
 """
 noaa.py
 
-This module provides functions to fetch, process, and read NOAA (National
-Oceanic and Atmospheric Administration) current data directly from the
-NOAA Tides and Currents API (https://api.tidesandcurrents.noaa.gov/api/prod/). It
-supports loading data into a pandas DataFrame, handling data in XML and
-JSON formats, and writing data to a JSON file.
-
-Functions:
-----------
-request_noaa_data(station, parameter, start_date, end_date, proxy=None,
-  write_json=None):
-    Loads NOAA current data from the API into a pandas DataFrame,
-    with optional support for proxy settings and writing data to a JSON
-    file.
-
-_xml_to_dataframe(response):
-    Converts NOAA response data in XML format into a pandas DataFrame
-    and returns metadata.
-
-read_noaa_json(filename):
-    Reads a JSON file containing NOAA data saved from the request_noaa_data
-    function and returns a DataFrame with timeseries site data and metadata.
+This module provides functions to fetch, process, cache, and read NOAA (National
+Oceanic and Atmospheric Administration) current data using the NOAA Tides and
+Currents API (https://api.tidesandcurrents.noaa.gov/api/prod/). It supports
+retrieving data in XML and JSON formats, converting it into a pandas DataFrame
+or xarray Dataset, and saving it as a JSON file for future use.
 """
 
 import os
@@ -30,21 +14,20 @@
 import json
 import math
 import shutil
+import warnings
 import pandas as pd
 import requests
 from mhkit.utils.cache import handle_caching
 
 
 def request_noaa_data(
-    station,
-    parameter,
-    start_date,
-    end_date,
-    proxy=None,
-    write_json=None,
-    clear_cache=False,
-    to_pandas=True,
-):
+    station: str,
+    parameter: str,
+    start_date: str,
+    end_date: str,
+    options: dict = None,
+    **kwargs,
+) -> tuple[pd.DataFrame, dict]:
     """
     Loads NOAA current data directly from https://api.tidesandcurrents.noaa.gov/api/prod/
     into a pandas DataFrame. NOAA sets max of 31 days between start and end date.
@@ -65,15 +48,16 @@ def request_noaa_data(
         Start date in the format yyyyMMdd
     end_date : str
         End date in the format yyyyMMdd
-    proxy : dict or None
-        To request data from behind a firewall, define a dictionary of proxy
-        settings, for example {"http": 'localhost:8080'}
-    write_json : str or None
-        Name of json file to write data
-    clear_cache : bool
-        If True, the cache for this specific request will be cleared.
-    to_pandas : bool, optional
-        Flag to output pandas instead of xarray. Default = True.
+    options : dict, optional
+        Dictionary containing optional parameters:
+        - proxy: dict or None
+            Proxy settings for the request.
+        - write_json: str or None
+            Path to write the data as a JSON file.
+        - clear_cache: bool
+            Whether to clear cached data.
+        - to_pandas: bool
+            Whether to return the data as a pandas DataFrame.
 
     Returns
     -------
@@ -84,7 +68,82 @@ def request_noaa_data(
         Request metadata. If returning xarray, metadata is instead attached to
         the data's attributes.
     """
-    # Type check inputs
+    if kwargs:
+        warnings.warn(
+            f"Unexpected keyword arguments: {', '.join(kwargs.keys())}. "
+            "Please pass options as a dictionary.",
+            UserWarning,
+        )
+
+    options = options or {}
+    proxy = options.get("proxy", None)
+    write_json = options.get("write_json", None)
+    clear_cache = options.get("clear_cache", False)
+    to_pandas = options.get("to_pandas", True)
+
+    _validate_inputs(
+        station,
+        parameter,
+        start_date,
+        end_date,
+        options,
+    )
+
+    cache_dir = os.path.join(os.path.expanduser("~"), ".cache", "mhkit", "noaa")
+    hash_params = f"{station}_{parameter}_{start_date}_{end_date}"
+
+    cached_data, cached_metadata, cache_filepath = handle_caching(
+        hash_params,
+        cache_dir,
+        {"data": None, "metadata": None, "write_json": write_json},
+        clear_cache,
+    )
+
+    if cached_data is not None:
+        return _handle_cached_data(
+            cached_data, cached_metadata, write_json, cache_filepath, to_pandas
+        )
+
+    return _fetch_noaa_data(
+        station,
+        parameter,
+        start_date,
+        end_date,
+        {
+            "proxy": proxy,
+            "cache_dir": cache_dir,
+            "hash_params": hash_params,
+            "write_json": write_json,
+            "clear_cache": clear_cache,
+            "to_pandas": to_pandas,
+        },
+    )
+
+
+def _validate_inputs(
+    station: str, parameter: str, start_date: str, end_date: str, options: dict
+) -> None:
+    """
+    Validates the input parameters for the NOAA data request.
+
+    Parameters
+    ----------
+    station : str
+        NOAA current station number.
+    parameter : str
+        NOAA parameter to fetch.
+    start_date : str
+        Start date for data retrieval in yyyyMMdd format.
+    end_date : str
+        End date for data retrieval in yyyyMMdd format.
+    options : dict
+        Dictionary of options for data retrieval.
+
+    Raises
+    ------
+    TypeError
+        If any of the inputs are not of the expected type.
+    """
     if not isinstance(station, str):
         raise TypeError(
             f"Expected 'station' to be of type str, but got {type(station)}"
@@ -101,6 +160,12 @@ def request_noaa_data(
         raise TypeError(
             f"Expected 'end_date' to be of type str, but got {type(end_date)}"
         )
+
+    proxy = options.get("proxy", None)
+    write_json = options.get("write_json", None)
+    clear_cache = options.get("clear_cache", False)
+    to_pandas = options.get("to_pandas", True)
+
     if proxy and not isinstance(proxy, dict):
         raise TypeError(
             f"Expected 'proxy' to be of type dict or None, but got {type(proxy)}"
@@ -116,146 +181,335 @@ def request_noaa_data(
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
 
-    # Define the path to the cache directory
-    cache_dir = os.path.join(os.path.expanduser("~"), ".cache", "mhkit", "noaa")
 
-    # Create a unique filename based on the function parameters
-    hash_params = f"{station}_{parameter}_{start_date}_{end_date}"
+def _handle_cached_data(
+    cached_data: pd.DataFrame,
+    cached_metadata: dict,
+    write_json: str,
+    cache_filepath: str,
+    to_pandas: bool,
+) -> tuple[pd.DataFrame, dict]:
+    """
+    Handles cached data by optionally writing it to a JSON file and returning it.
 
-    # Use handle_caching to manage cache
-    cached_data, cached_metadata, cache_filepath = handle_caching(
-        hash_params,
-        cache_dir,
-        cache_content={"data": None, "metadata": None, "write_json": write_json},
-        clear_cache_file=clear_cache,
+    Parameters
+    ----------
+    cached_data : pd.DataFrame
+        The cached data to be returned.
+    cached_metadata : dict
+        Metadata associated with the cached data.
+    write_json : str
+        Path to write the cached data as a JSON file, if specified.
+    cache_filepath : str
+        Filepath of the cached data.
+    to_pandas : bool
+        Flag indicating whether to return the data as a pandas DataFrame.
+
+    Returns
+    -------
+    tuple[pd.DataFrame, dict]
+        The cached data and its metadata.
+    """
+    if write_json:
+        shutil.copy(cache_filepath, write_json)
+    if to_pandas:
+        return cached_data, cached_metadata
+
+    cached_data = cached_data.to_xarray()
+    cached_data.attrs = cached_metadata
+    return cached_data
+
+
+def _fetch_noaa_data(
+    station: str, parameter: str, start_date: str, end_date: str, options: dict
+) -> tuple[pd.DataFrame, dict]:
+    """
+    Fetches NOAA data from the API, processes it, and returns it along with metadata.
+
+    Parameters
+    ----------
+    station : str
+        NOAA current station number.
+    parameter : str
+        NOAA parameter to fetch.
+    start_date : str
+        Start date for data retrieval in yyyyMMdd format.
+    end_date : str
+        End date for data retrieval in yyyyMMdd format.
+    options : dict
+        Dictionary of options for data retrieval:
+        - proxy: dict or None
+            Proxy settings for the request.
+        - cache_dir: str
+            Directory for caching data.
+        - hash_params: str
+            Parameters used for caching.
+        - write_json: str or None
+            Path to write the data as a JSON file.
+        - clear_cache: bool
+            Whether to clear cached data.
+        - to_pandas: bool
+            Whether to return the data as a pandas DataFrame.
+
+    Returns
+    -------
+    tuple[pd.DataFrame, dict]
+        The fetched data and its metadata.
+    """
+    begin, end = _parse_dates(start_date, end_date)
+    date_list = _create_date_ranges(begin, end)
+
+    data_frames = []
+    metadata = None  # Initialize metadata
+    for i in range(len(date_list) - 1):
+        start_date = date_list[i].strftime("%Y%m%d")
+        end_date = date_list[i + 1].strftime("%Y%m%d")
+        data_url = _build_data_url(station, parameter, start_date, end_date)
+
+        print(f"Data request URL: {data_url}\n")
+        response = _make_request(data_url, options["proxy"])
+        df, metadata = _xml_to_dataframe(response)
+        data_frames.append(df)
+
+    return _process_data_frames(data_frames, metadata, options)
+
+
+def _process_data_frames(
+    data_frames: list[pd.DataFrame], metadata: dict, options: dict
+) -> tuple[pd.DataFrame, dict]:
+    """
+    Processes a list of data frames by concatenating them and handling caching.
+
+    Parameters
+    ----------
+    data_frames : list[pd.DataFrame]
+        List of data frames to process.
+    metadata : dict
+        Metadata associated with the data.
+    options : dict
+        Options for processing, including caching and output format:
+        - hash_params: str
+            Parameters used for caching.
+        - cache_dir: str
+            Directory for caching data.
+        - write_json: str or None
+            Path to write the data as a JSON file.
+        - clear_cache: bool
+            Whether to clear cached data.
+        - to_pandas: bool
+            Whether to return the data as a pandas DataFrame.
+
+    Returns
+    -------
+    tuple[pd.DataFrame, dict]
+        The processed data and its metadata.
+    """
+    data = _concatenate_data_frames(data_frames)
+    cache_filepath = handle_caching(
+        options["hash_params"],
+        options["cache_dir"],
+        {"data": data, "metadata": metadata, "write_json": None},
+        options["clear_cache"],
     )
 
-    if cached_data is not None:
-        if write_json:
-            shutil.copy(cache_filepath, write_json)
-        if to_pandas:
-            return cached_data, cached_metadata
-        else:
-            cached_data = cached_data.to_xarray()
-            cached_data.attrs = cached_metadata
-            return cached_data
-    # If no cached data is available, make the API request
-    # no coverage bc in coverage runs we have already cached the data/ run this code
-    else:  # pragma: no cover
-        # Convert start and end dates to datetime objects
-        begin = datetime.datetime.strptime(start_date, "%Y%m%d").date()
-        end = datetime.datetime.strptime(end_date, "%Y%m%d").date()
-
-        # Determine the number of 30 day intervals
-        delta = 30
-        interval = math.ceil(((end - begin).days) / delta)
-
-        # Create date ranges with 30 day intervals
-        date_list = [
-            begin + datetime.timedelta(days=i * delta) for i in range(interval + 1)
-        ]
-        date_list[-1] = end
-
-        # Iterate over date_list (30 day intervals) and fetch data
-        data_frames = []
-        for i in range(len(date_list) - 1):
-            start_date = date_list[i].strftime("%Y%m%d")
-            end_date = date_list[i + 1].strftime("%Y%m%d")
-
-            api_query = f"begin_date={start_date}&end_date={end_date}&station={station}&product={parameter}&units=metric&time_zone=gmt&application=web_services&format=xml"
-            # Add datum to water level inquiries
-            if parameter == "water_level":
-                api_query += "&datum=MLLW"
-            data_url = f"https://tidesandcurrents.noaa.gov/api/datagetter?{api_query}"
-
-            print(f"Data request URL: {data_url}\n")
-
-            # Get response
-            try:
-                response = requests.get(url=data_url, proxies=proxy)
-                response.raise_for_status()
-                # Catch non-exception errors
-                if "error" in response.content.decode():
-                    raise Exception(response.content.decode())
-            except Exception as err:
-                if err.__class__ == requests.exceptions.HTTPError:
-                    print(f"HTTP error occurred: {err}")
-                    print(f"Error message: {response.content.decode()}\n")
-                    continue
-                elif err.__class__ == requests.exceptions.RequestException:
-                    print(f"Requests error occurred: {err}")
-                    print(f"Error message: {response.content.decode()}\n")
-                    continue
-                else:
-                    print(f"Requests error occurred: {err}\n")
-                    continue
-
-            # Convert to DataFrame and save in data_frames list
-            df, metadata = _xml_to_dataframe(response)
-            data_frames.append(df)
-
-        # Concatenate all DataFrames
-        if data_frames:
-            data = pd.concat(data_frames, ignore_index=False)
-        else:
-            raise ValueError("No data retrieved.")
-
-        # Remove duplicated date values
-        data = data.loc[~data.index.duplicated()]
-
-        # After making the API request and processing the response, write the
-        #  response to a cache file
-        handle_caching(
-            hash_params,
-            cache_dir,
-            cache_content={"data": data, "metadata": metadata, "write_json": None},
-            clear_cache_file=clear_cache,
-        )
+    if options["write_json"]:
+        shutil.copy(cache_filepath, options["write_json"])
+
+    if options["to_pandas"]:
+        return data, metadata
+
+    data = data.to_xarray()
+    data.attrs = metadata
+    return data
+
+
+def _parse_dates(start_date: str, end_date: str) -> tuple[datetime.date, datetime.date]:
+    """
+    Parses start and end dates from strings to datetime.date objects.
+
+    Parameters
+    ----------
+    start_date : str
+        Start date in yyyyMMdd format.
+    end_date : str
+        End date in yyyyMMdd format.
+
+    Returns
+    -------
+    tuple[datetime.date, datetime.date]
+        Parsed start and end dates.
+    """
+    begin = datetime.datetime.strptime(start_date, "%Y%m%d").date()
+    end = datetime.datetime.strptime(end_date, "%Y%m%d").date()
+    return begin, end
+
+
+def _create_date_ranges(
+    begin: datetime.date, end: datetime.date
+) -> list[datetime.date]:
+    """
+    Creates a list of date ranges between the start and end dates.
+
+    Parameters
+    ----------
+    begin : datetime.date
+        Start date.
+    end : datetime.date
+        End date.
+
+    Returns
+    -------
+    list[datetime.date]
+        List of date ranges.
+    """
+    delta = 30
+    interval = math.ceil(((end - begin).days) / delta)
+    date_list = [
+        begin + datetime.timedelta(days=i * delta) for i in range(interval + 1)
+    ]
+    date_list[-1] = end
+    return date_list
+
+
+def _build_data_url(
+    station: str, parameter: str, start_date: str, end_date: str
+) -> str:
+    """
+    Builds the data request URL for the NOAA API.
+
+    Parameters
+    ----------
+    station : str
+        NOAA current station number.
+    parameter : str
+        NOAA parameter to fetch.
+    start_date : str
+        Start date for data retrieval in yyyyMMdd format.
+    end_date : str
+        End date for data retrieval in yyyyMMdd format.
 
-        if write_json:
-            shutil.copy(cache_filepath, write_json)
+    Returns
+    -------
+    str
+        The constructed data request URL.
+    """
+    api_query = (
+        f"begin_date={start_date}&end_date={end_date}&station={station}&product={parameter}"
+        "&units=metric&time_zone=gmt&application=web_services&format=xml"
+    )
+    if parameter == "water_level":
+        api_query += "&datum=MLLW"
+    return f"https://tidesandcurrents.noaa.gov/api/datagetter?{api_query}"
+
+
+def _make_request(data_url: str, proxy: dict) -> requests.Response:
+    """
+    Makes an HTTP request to the specified data URL using optional proxy settings.
 
-        if to_pandas:
-            return data, metadata
-        else:
-            data = data.to_xarray()
-            data.attrs = metadata
-            return data
+    Parameters
+    ----------
+    data_url : str
+        The URL to request data from.
+    proxy : dict
+        Proxy settings for the request.
 
+    Returns
+    -------
+    requests.Response
+        The HTTP response from the request.
 
-def _xml_to_dataframe(response):
+    Raises
+    ------
+    requests.exceptions.RequestException
+        If an error occurs during the request.
     """
-    Returns a dataframe from an xml response
+    try:
+        response = requests.get(url=data_url, proxies=proxy, timeout=60)
+        response.raise_for_status()
+        if "error" in response.content.decode():
+            raise requests.exceptions.RequestException(response.content.decode())
+    except requests.exceptions.HTTPError as http_err:
+        print(f"HTTP error occurred: {http_err}")
+        print(f"Error message: {response.content.decode()}\n")
+        raise
+    except requests.exceptions.RequestException as req_err:
+        print(f"Requests error occurred: {req_err}")
+        print(f"Error message: {response.content.decode()}\n")
+        raise
+    return response
+
+
+def _concatenate_data_frames(data_frames: list[pd.DataFrame]) -> pd.DataFrame:
+    """
+    Concatenates a list of data frames into a single data frame, removing duplicates.
+
+    Parameters
+    ----------
+    data_frames : list[pd.DataFrame]
+        List of data frames to concatenate.
+
+    Returns
+    -------
+    pd.DataFrame
+        The concatenated data frame with duplicates removed.
+
+    Raises
+    ------
+    ValueError
+        If no data frames are provided.
+    """
+    if data_frames:
+        data = pd.concat(data_frames, ignore_index=False)
+    else:
+        raise ValueError("No data retrieved.")
+    return data.loc[~data.index.duplicated()]
+
+
+def _xml_to_dataframe(response: requests.Response) -> tuple[pd.DataFrame, dict]:
+    """
+    Converts an XML response from the NOAA API into a pandas DataFrame and extracts metadata.
+
+    Parameters
+    ----------
+    response : requests.Response
+        The HTTP response containing XML data from the NOAA API.
+
+    Returns
+    -------
+    tuple[pd.DataFrame, dict]
+        A tuple containing the data as a pandas DataFrame and the associated metadata
+        as a dictionary.
     """
     root = ET.fromstring(response.text)
     metadata = None
     data = None
 
     for child in root:
-        # Save meta data dictionary
         if child.tag == "metadata":
             metadata = child.attrib
         elif child.tag == "observations":
             data = child
         elif child.tag == "error":
             print("***ERROR: Response returned error")
-            return None
+            return None, {}
 
     if data is None:
         print("***ERROR: No observations found")
-        return None
+        return None, {}
 
-    # Create a list of DataFrames then Concatenate
     df = pd.concat(
         [pd.DataFrame(obs.attrib, index=[0]) for obs in data], ignore_index=True
     )
 
-    # Convert time to datetime
-    df["t"] = pd.to_datetime(df.t)
+    try:
+        df["t"] = pd.to_datetime(pd.to_numeric(df.t), unit="ms")
+    except ValueError:
+        # Don't convert df.t to numeric if its a datetime formatted string
+        df["t"] = pd.to_datetime(df.t)
+
     df = df.set_index("t")
     df.drop_duplicates(inplace=True)
 
-    # Convert data to float
     cols = list(df.columns)
     for var in cols:
         try:
@@ -263,10 +517,10 @@ def _xml_to_dataframe(response):
         except ValueError:
             pass
 
-    return df, metadata
+    return df, metadata or {}
 
 
-def read_noaa_json(filename, to_pandas=True):
+def read_noaa_json(filename: str, to_pandas: bool = True) -> tuple[pd.DataFrame, dict]:
     """
     Returns site DataFrame and metadata from a json saved from the
     request_noaa_data
@@ -288,7 +542,7 @@ def read_noaa_json(filename, to_pandas=True):
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
 
-    with open(filename) as outfile:
+    with open(filename, encoding="utf-8") as outfile:
         json_data = json.load(outfile)
     try:  # original MHKiT format (deprecate in future)
         # Get the metadata
@@ -298,7 +552,7 @@ def read_noaa_json(filename, to_pandas=True):
         # Remainder is DataFrame
         data = pd.DataFrame.from_dict(json_data)
         # Convert from epoch to date time
-        data.index = pd.to_datetime(data.index, unit="ms")
+        data.index = pd.to_datetime(pd.to_numeric(data.index), unit="ms")
 
     except ValueError:  # using cache.py format
         if "metadata" in json_data:
@@ -311,7 +565,7 @@ def read_noaa_json(filename, to_pandas=True):
 
     if to_pandas:
         return data, metadata
-    else:
-        data = data.to_xarray()
-        data.attrs = metadata
-        return data
+
+    data = data.to_xarray()
+    data.attrs = metadata
+    return data
diff --git a/mhkit/tidal/performance.py b/mhkit/tidal/performance.py
index 3a516bec7..b57f0411c 100644
--- a/mhkit/tidal/performance.py
+++ b/mhkit/tidal/performance.py
@@ -1,7 +1,18 @@
+"""
+performance.py
+
+This module provides functions for analyzing the performance of tidal energy
+devices using Acoustic Doppler Current Profiler (ADCP) data. It includes
+methods for calculating power curves, efficiency, velocity profiles, and
+other metrics relevant to marine energy devices.
+
+"""
+
+from typing import Union, Optional
+import pandas as pd
 import numpy as np
 import xarray as xr
 from mhkit.utils import convert_to_dataarray
-
 from mhkit import dolfyn
 from mhkit.river.performance import (
     circular,
@@ -12,8 +23,19 @@
     power_coefficient,
 )
 
+__all__ = [
+    "circular",
+    "ducted",
+    "rectangular",
+    "multiple_circular",
+    "tip_speed_ratio",
+    "power_coefficient",
+]
+
 
-def _slice_circular_capture_area(diameter, hub_height, doppler_cell_size):
+def _slice_circular_capture_area(
+    diameter: float, hub_height: float, doppler_cell_size: float
+) -> xr.DataArray:
     """
     Slices a circle (capture area) based on ADCP depth bins mapped
     across the face of the capture area.
@@ -38,32 +60,26 @@ def _slice_circular_capture_area(diameter, hub_height, doppler_cell_size):
     """
 
     def area_of_circle_segment(radius, angle):
-        # Calculating area of sector
-        area_of_sector = np.pi * radius**2 * (angle / 360)
-        # Calculating area of triangle
-        area_of_triangle = 0.5 * radius**2 * np.sin((np.pi * angle) / 180)
-        return area_of_sector - area_of_triangle
+        return np.pi * radius**2 * (angle / 360) - 0.5 * radius**2 * np.sin(
+            (np.pi * angle) / 180
+        )
 
     def point_on_circle(y, r):
         return np.sqrt(r**2 - y**2)
 
-    # Capture area - from mhkit.river.performance
-    d = diameter
-    cs = doppler_cell_size
-
-    A_cap = np.pi * (d / 2) ** 2  # m^2
     # Need to chop up capture area into slices based on bin size
-    # For a cirle:
-    r_min = hub_height - d / 2
-    r_max = hub_height + d / 2
-    A_edge = np.arange(r_min, r_max + cs, cs)
-    A_rng = A_edge[:-1] + cs / 2  # Center of each slice
+    # For a circle:
+    area_edge = np.arange(
+        hub_height - diameter / 2,
+        hub_height + diameter / 2 + doppler_cell_size,
+        doppler_cell_size,
+    )
+    area_rng = area_edge[:-1] + doppler_cell_size / 2  # Center of each slice
 
     # y runs from the bottom edge of the lower centerline slice to
     # the top edge of the lowest slice
-    # Will need to figure out y if the hub height isn't centered
-    y = abs(A_edge - np.mean(A_edge))
-    y[np.where(abs(y) > (d / 2))] = d / 2
+    y = abs(area_edge - np.mean(area_edge))
+    y[np.where(abs(y) > (diameter / 2))] = diameter / 2
 
     # Even vs odd number of slices
     if y.size % 2:
@@ -73,25 +89,29 @@ def point_on_circle(y, r):
         y = y[: len(y) // 2]
         y = np.append(y, 0)
 
-    x = point_on_circle(y, d / 2)
+    x = point_on_circle(y, diameter / 2)
     radii = np.rad2deg(np.arctan(x / y) * 2)
     # Segments go from outside of circle towards middle
-    As = area_of_circle_segment(d / 2, radii)
+    area_segments = area_of_circle_segment(diameter / 2, radii)
     # Subtract segments to get area of slices
-    As_slc = As[1:] - As[:-1]
+    area_segments_slc = area_segments[1:] - area_segments[:-1]
 
     if not odd:
         # Make middle slice half whole
-        As_slc[-1] = As_slc[-1] * 2
+        area_segments_slc[-1] = area_segments_slc[-1] * 2
         # Copy-flip the other slices to get the whole circle
-        As_slc = np.append(As_slc, np.flip(As_slc[:-1]))
+        area_segments_slc = np.append(
+            area_segments_slc, np.flip(area_segments_slc[:-1])
+        )
     else:
-        As_slc = abs(As_slc)
+        area_segments_slc = abs(area_segments_slc)
 
-    return xr.DataArray(As_slc, coords={"range": A_rng})
+    return xr.DataArray(area_segments_slc, coords={"range": area_rng})
 
 
-def _slice_rectangular_capture_area(height, width, hub_height, doppler_cell_size):
+def _slice_rectangular_capture_area(
+    height: float, width: float, hub_height: float, doppler_cell_size: float
+) -> xr.DataArray:
     """
     Slices a rectangular (capture area) based on ADCP depth bins mapped
     across the face of the capture area.
@@ -123,30 +143,30 @@ def _slice_rectangular_capture_area(height, width, hub_height, doppler_cell_size
     cs = doppler_cell_size
     r_min = hub_height - height / 2
     r_max = hub_height + height / 2
-    A_edge = np.arange(r_min, r_max + cs, cs)
-    A_rng = A_edge[:-1] + cs / 2  # Center of each slice
+    area_edge = np.arange(r_min, r_max + cs, cs)
+    area_rng = area_edge[:-1] + cs / 2  # Center of each slice
 
-    As_slc = np.ones(len(A_rng)) * width * cs
+    area_slice = np.ones(len(area_rng)) * width * cs
 
-    return xr.DataArray(As_slc, coords={"range": A_rng})
+    return xr.DataArray(area_slice, coords={"range": area_rng})
 
 
 def power_curve(
-    power,
-    velocity,
-    hub_height,
-    doppler_cell_size,
-    sampling_frequency,
-    window_avg_time=600,
-    turbine_profile="circular",
-    diameter=None,
-    height=None,
-    width=None,
-    to_pandas=True,
-):
+    power: Union[np.ndarray, pd.DataFrame, pd.Series, xr.DataArray, xr.Dataset],
+    velocity: Union[np.ndarray, pd.DataFrame, pd.Series, xr.DataArray, xr.Dataset],
+    hub_height: float,
+    doppler_cell_size: float,
+    sampling_frequency: float,
+    window_avg_time: int = 600,
+    turbine_profile: str = "circular",
+    diameter: Optional[float] = None,
+    height: Optional[float] = None,
+    width: Optional[float] = None,
+    to_pandas: bool = True,
+) -> Union[pd.DataFrame, xr.Dataset]:
     """
     Calculates power curve and power statistics for a marine energy
-    device based on IEC/TS 62600-200 section 9.3.
+    device based on IEC TS 62600-200 section 9.3.
 
     Parameters
     -------------
@@ -180,7 +200,7 @@ def power_curve(
         Power-weighted velocity, mean power, power std dev, max and
         min power vs hub-height velocity.
     """
-
+    # pylint: disable=too-many-arguments, too-many-positional-arguments, too-many-locals
     # Velocity should be a 2D xarray or pandas array and have dims (range, time)
     # Power should have a timestamp coordinate/index
     power = convert_to_dataarray(power)
@@ -219,41 +239,42 @@ def power_curve(
             "`turbine_profile` must be one of 'circular' or 'rectangular'."
         )
     if turbine_profile == "circular":
-        if diameter is None:
-            raise TypeError(
-                "`diameter` cannot be None for input `turbine_profile` = 'circular'."
-            )
-        elif not isinstance(diameter, (int, float)) or diameter <= 0:
-            raise ValueError("`diameter` must be a positive number.")
-        else:  # If the checks pass, calculate A_slc
-            A_slc = _slice_circular_capture_area(
-                diameter, hub_height, doppler_cell_size
+        if not isinstance(diameter, (int, float)) or diameter <= 0:
+            raise ValueError(
+                "`diameter` must be specified as a positive integer or float."
             )
+        # If the checks pass, calculate area_slice
+        area_slice = _slice_circular_capture_area(
+            diameter, hub_height, doppler_cell_size
+        )
     else:  # Rectangular profile
         if height is None or width is None:
             raise TypeError(
                 "`height` and `width` cannot be None for input `turbine_profile` = 'rectangular'."
             )
-        elif not all(
+        if not all(
             isinstance(val, (int, float)) and val > 0 for val in [height, width]
         ):
             raise ValueError("`height` and `width` must be positive numbers.")
-        else:  # If the checks pass, calculate A_slc
-            A_slc = _slice_rectangular_capture_area(
-                height, width, hub_height, doppler_cell_size
-            )
+        # If the checks pass, calculate area_slice
+        area_slice = _slice_rectangular_capture_area(
+            height, width, hub_height, doppler_cell_size
+        )
 
     # Streamwise data
-    U = abs(velocity)
-    time = U["time"].values
+    velocity_absolute = abs(velocity)
+    time = velocity_absolute["time"].values
     # Interpolate power to velocity timestamps
-    P = power.interp(time=U["time"], method="linear")
+    power_interpolated = power.interp(time=velocity_absolute["time"], method="linear")
 
     # Power weighted velocity in capture area
-    # Interpolate U range to capture area slices, then cube and multiply by area
-    U_hat = U.interp(range=A_slc["range"], method="linear") ** 3 * A_slc
+    # Interpolate velocity_absolute range to capture area slices, then cube and multiply by area
+    velocity_hat = (
+        velocity_absolute.interp(range=area_slice["range"], method="linear") ** 3
+        * area_slice
+    )
     # Average the velocity across the capture area and divide out area
-    U_hat = (U_hat.sum("range") / A_slc.sum()) ** (-1 / 3)
+    velocity_hat = (velocity_hat.sum("range") / area_slice.sum()) ** (-1 / 3)
 
     # Time-average velocity at hub-height
     bnr = dolfyn.VelBinner(
@@ -261,43 +282,47 @@ def power_curve(
     )
     # Hub-height velocity mean
     mean_hub_vel = xr.DataArray(
-        bnr.mean(U.sel(range=hub_height, method="nearest").values),
+        bnr.mean(velocity_absolute.sel(range=hub_height, method="nearest").values),
         coords={"time": bnr.mean(time)},
     )
 
     # Power-weighted hub-height velocity mean
-    U_hat_bar = xr.DataArray(
-        (bnr.mean(U_hat.values**3)) ** (-1 / 3), coords={"time": bnr.mean(time)}
+    velocity_hat_bar = xr.DataArray(
+        (bnr.mean(velocity_hat.values**3)) ** (-1 / 3), coords={"time": bnr.mean(time)}
     )
 
     # Average power
-    P_bar = xr.DataArray(bnr.mean(P.values), coords={"time": bnr.mean(time)})
+    power_bar = xr.DataArray(
+        bnr.mean(power_interpolated.values), coords={"time": bnr.mean(time)}
+    )
 
     # Then reorganize into 0.1 m velocity bins and average
-    U_bins = np.arange(0, np.nanmax(mean_hub_vel) + 0.1, 0.1)
-    U_hub_vel = mean_hub_vel.assign_coords({"time": mean_hub_vel}).rename(
+    velocity_bins = np.arange(0, np.nanmax(mean_hub_vel) + 0.1, 0.1)
+    velocity_hub_vel = mean_hub_vel.assign_coords({"time": mean_hub_vel}).rename(
         {"time": "speed"}
     )
-    U_hub_mean = U_hub_vel.groupby_bins("speed", U_bins).mean()
-    U_hat_vel = U_hat_bar.assign_coords({"time": mean_hub_vel}).rename(
+    velocity_hub_mean = velocity_hub_vel.groupby_bins("speed", velocity_bins).mean()
+    velocity_hat_vel = velocity_hat_bar.assign_coords({"time": mean_hub_vel}).rename(
         {"time": "speed"}
     )
-    U_hat_mean = U_hat_vel.groupby_bins("speed", U_bins).mean()
+    velocity_hat_mean = velocity_hat_vel.groupby_bins("speed", velocity_bins).mean()
 
-    P_bar_vel = P_bar.assign_coords({"time": mean_hub_vel}).rename({"time": "speed"})
-    P_bar_mean = P_bar_vel.groupby_bins("speed", U_bins).mean()
-    P_bar_std = P_bar_vel.groupby_bins("speed", U_bins).std()
-    P_bar_max = P_bar_vel.groupby_bins("speed", U_bins).max()
-    P_bar_min = P_bar_vel.groupby_bins("speed", U_bins).min()
+    power_bar_vel = power_bar.assign_coords({"time": mean_hub_vel}).rename(
+        {"time": "speed"}
+    )
+    power_bar_mean = power_bar_vel.groupby_bins("speed", velocity_bins).mean()
+    power_bar_std = power_bar_vel.groupby_bins("speed", velocity_bins).std()
+    power_bar_max = power_bar_vel.groupby_bins("speed", velocity_bins).max()
+    power_bar_min = power_bar_vel.groupby_bins("speed", velocity_bins).min()
 
     device_power_curve = xr.Dataset(
         {
-            "U_avg": U_hub_mean,
-            "U_avg_power_weighted": U_hat_mean,
-            "P_avg": P_bar_mean,
-            "P_std": P_bar_std,
-            "P_max": P_bar_max,
-            "P_min": P_bar_min,
+            "U_avg": velocity_hub_mean,
+            "U_avg_power_weighted": velocity_hat_mean,
+            "P_avg": power_bar_mean,
+            "P_std": power_bar_std,
+            "P_max": power_bar_max,
+            "P_min": power_bar_min,
         }
     )
     device_power_curve = device_power_curve.rename({"speed_bins": "U_bins"})
@@ -308,40 +333,46 @@ def power_curve(
     return device_power_curve
 
 
-def _average_velocity_bins(U, U_hub, bin_size):
+def _average_velocity_bins(
+    velocity_data: xr.DataArray, velocity_hub: xr.DataArray, bin_size: float
+) -> xr.DataArray:
     """
     Groups time-ensembles into velocity bins based on hub-height
     velocity and averages them.
 
     Parameters
     -------------
-    U: xarray.DataArray
+    velocity_data: xarray.DataArray
         Input variable to group by velocity.
-    U_hub: xarray.DataArray
+    velocity_hub: xarray.DataArray
         Sea water velocity at hub height.
     bin_size: numeric
         Velocity averaging window size in m/s.
 
     Returns
     ---------
-    U_binned: xarray.DataArray
+    velocity_binned: xarray.DataArray
         Data grouped into velocity bins.
     """
 
     # Reorganize into velocity bins and average
-    U_bins = np.arange(0, np.nanmax(U_hub) + bin_size, bin_size)
+    velocity_bins = np.arange(0, np.nanmax(velocity_hub) + bin_size, bin_size)
 
     # Group time-ensembles into velocity bins based on hub-height velocity and average
-    U_binned = U.assign_coords({"time": U_hub}).rename({"time": "speed"})
-    U_binned = U_binned.groupby_bins("speed", U_bins).mean()
+    velocity_binned = velocity_data.assign_coords({"time": velocity_hub}).rename(
+        {"time": "speed"}
+    )
+    velocity_binned = velocity_binned.groupby_bins("speed", velocity_bins).mean()
 
-    return U_binned
+    return velocity_binned
 
 
-def _apply_function(function, bnr, U):
+def _apply_function(
+    function: str, bnr: dolfyn.VelBinner, velocity: xr.DataArray
+) -> xr.DataArray:
     """
     Applies a specified function ('mean', 'rms', or 'std') to the input
-    data array U, grouped into bins as specified by the binning rules in bnr.
+    data array velocity, grouped into bins as specified by the binning rules in bnr.
 
     Parameters
     -------------
@@ -349,58 +380,59 @@ def _apply_function(function, bnr, U):
         The name of the function to apply. Must be one of 'mean',
         'rms', or 'std'.
     bnr: dolfyn.VelBinner or similar
-        The binning rule object that determines how data in U is
+        The binning rule object that determines how data in velocity is
         grouped into bins.
-    U: xarray.DataArray
+    velocity: xarray.DataArray
         The input data array to which the function is applied.
 
     Returns
     ---------
     xarray.DataArray
-        The input data array U after the specified function has been
+        The input data array velocity after the specified function has been
         applied, grouped into bins according to bnr.
     """
 
     if function == "mean":
         # Average data into 5-10 minute ensembles
         return xr.DataArray(
-            bnr.mean(abs(U).values),
-            coords={"range": U.range, "time": bnr.mean(U["time"].values)},
+            bnr.mean(abs(velocity).values),
+            coords={"range": velocity.range, "time": bnr.mean(velocity["time"].values)},
         )
-    elif function == "rms":
+    if function == "rms":
         # Reshape tidal velocity - returns (range, ensemble-time, ensemble elements)
-        U_reshaped = bnr.reshape(abs(U).values)
+        velocity_reshaped = bnr.reshape(abs(velocity).values)
         # Take root-mean-square
-        U_rms = np.sqrt(np.nanmean(U_reshaped**2, axis=-1))
+        velocity_rms = np.sqrt(np.nanmean(velocity_reshaped**2, axis=-1))
         return xr.DataArray(
-            U_rms, coords={"range": U.range, "time": bnr.mean(U["time"].values)}
+            velocity_rms,
+            coords={"range": velocity.range, "time": bnr.mean(velocity["time"].values)},
         )
-    elif function == "std":
+    if function == "std":
         # Standard deviation
         return xr.DataArray(
-            bnr.standard_deviation(U.values),
-            coords={"range": U.range, "time": bnr.mean(U["time"].values)},
-        )
-    else:
-        raise ValueError(
-            f"Unknown function {function}. Should be one of 'mean', 'rms', or 'std'"
+            bnr.standard_deviation(velocity.values),
+            coords={"range": velocity.range, "time": bnr.mean(velocity["time"].values)},
         )
 
+    raise ValueError(
+        f"Unknown function {function}. Should be one of 'mean', 'rms', or 'std'"
+    )
+
 
 def velocity_profiles(
-    velocity,
-    hub_height,
-    water_depth,
-    sampling_frequency,
-    window_avg_time=600,
-    function="mean",
-    to_pandas=True,
-):
+    velocity: Union[np.ndarray, pd.DataFrame, pd.Series, xr.DataArray, xr.Dataset],
+    hub_height: float,
+    water_depth: float,
+    sampling_frequency: float,
+    window_avg_time: int = 600,
+    function: str = "mean",
+    to_pandas: bool = True,
+) -> Union[pd.DataFrame, xr.DataArray]:
     """
     Calculates profiles of the mean, root-mean-square (RMS), or standard
     deviation(std) of velocity. The chosen metric, specified by `function`,
     is calculated for each `window_avg_time` and bin-averaged based on
-    ensemble velocity, as per IEC/TS 62600-200 sections 9.4 and 9.5.
+    ensemble velocity, as per IEC TS 62600-200 sections 9.4 and 9.5.
 
     Parameters
     -------------
@@ -425,7 +457,7 @@ def velocity_profiles(
     iec_profiles: pandas.DataFrame
         Average velocity profiles based on ensemble mean velocity.
     """
-
+    # pylint: disable=too-many-arguments, too-many-positional-arguments, too-many-locals
     velocity = convert_to_dataarray(velocity, "velocity")
     if len(velocity.shape) != 2:
         raise ValueError(
@@ -438,21 +470,18 @@ def velocity_profiles(
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
 
-    # Streamwise data
-    U = velocity
-
     # Create binner
     bnr = dolfyn.VelBinner(
         n_bin=window_avg_time * sampling_frequency, fs=sampling_frequency
     )
     # Take velocity at hub height
-    mean_hub_vel = bnr.mean(U.sel(range=hub_height, method="nearest").values)
+    mean_hub_vel = bnr.mean(velocity.sel(range=hub_height, method="nearest").values)
 
     # Apply mean, root-mean-square, or standard deviation
-    U_out = _apply_function(function, bnr, U)
+    velocity_out = _apply_function(function, bnr, velocity)
 
     # Then reorganize into 0.5 m/s velocity bins and average
-    profiles = _average_velocity_bins(U_out, mean_hub_vel, bin_size=0.5)
+    profiles = _average_velocity_bins(velocity_out, mean_hub_vel, bin_size=0.5)
 
     # Extend top and bottom of profiles to the seafloor and sea surface
     # Clip off extra depth bins with nans
@@ -477,17 +506,17 @@ def velocity_profiles(
 
 
 def device_efficiency(
-    power,
-    velocity,
-    water_density,
-    capture_area,
-    hub_height,
-    sampling_frequency,
-    window_avg_time=600,
-    to_pandas=True,
-):
+    power: Union[np.ndarray, pd.DataFrame, pd.Series, xr.DataArray, xr.Dataset],
+    velocity: Union[np.ndarray, pd.DataFrame, pd.Series, xr.DataArray, xr.Dataset],
+    water_density: Union[float, pd.Series, xr.DataArray],
+    capture_area: float,
+    hub_height: float,
+    sampling_frequency: float,
+    window_avg_time: int = 600,
+    to_pandas: bool = True,
+) -> Union[pd.Series, xr.DataArray]:
     """
-    Calculates marine energy device efficiency based on IEC/TS 62600-200 Section 9.7.
+    Calculates marine energy device efficiency based on IEC TS 62600-200 Section 9.7.
 
     Parameters
     -------------
@@ -514,7 +543,7 @@ def device_efficiency(
     device_eta : pandas.Series or xarray.DataArray
         Device efficiency (power coefficient) in percent.
     """
-
+    # pylint: disable=too-many-arguments, too-many-positional-arguments, too-many-locals
     # Velocity should be a 2D xarray or pandas array and have dims (range, time)
     # Power should have a timestamp coordinate/index
     power = convert_to_dataarray(power, "power")
@@ -528,11 +557,11 @@ def device_efficiency(
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
 
     # Streamwise data
-    U = abs(velocity)
-    time = U["time"].values
+    velocity_absolute = abs(velocity)
+    time = velocity_absolute["time"].values
 
     # Power: Interpolate to velocity timeseries
-    power.interp(time=U["time"], method="linear")
+    power_interpolated = power.interp(time=velocity_absolute["time"], method="linear")
 
     # Create binner
     bnr = dolfyn.VelBinner(
@@ -540,7 +569,7 @@ def device_efficiency(
     )
     # Hub-height velocity
     mean_hub_vel = xr.DataArray(
-        bnr.mean(U.sel(range=hub_height, method="nearest").values),
+        bnr.mean(velocity_absolute.sel(range=hub_height, method="nearest").values),
         coords={"time": bnr.mean(time)},
     )
     vel_hub = _average_velocity_bins(mean_hub_vel, mean_hub_vel, bin_size=0.1)
@@ -549,14 +578,16 @@ def device_efficiency(
     rho_vel = _calculate_density(water_density, bnr, mean_hub_vel, time)
 
     # Bin average power
-    P_avg = xr.DataArray(bnr.mean(power.values), coords={"time": bnr.mean(time)})
-    P_vel = _average_velocity_bins(P_avg, mean_hub_vel, bin_size=0.1)
+    power_avg = xr.DataArray(
+        bnr.mean(power_interpolated.values), coords={"time": bnr.mean(time)}
+    )
+    power_vel = _average_velocity_bins(power_avg, mean_hub_vel, bin_size=0.1)
 
     # Theoretical power resource
-    P_resource = 1 / 2 * rho_vel * capture_area * vel_hub**3
+    power_resource = 1 / 2 * rho_vel * capture_area * vel_hub**3
 
     # Efficiency
-    eta = P_vel / P_resource
+    eta = power_vel / power_resource
 
     device_eta = xr.Dataset({"U_avg": vel_hub, "Efficiency": eta})
     device_eta = device_eta.rename({"speed_bins": "U_bins"})
@@ -567,7 +598,12 @@ def device_efficiency(
     return device_eta
 
 
-def _calculate_density(water_density, bnr, mean_hub_vel, time):
+def _calculate_density(
+    water_density: Union[np.ndarray, float],
+    bnr: dolfyn.VelBinner,
+    mean_hub_vel: xr.DataArray,
+    time: np.ndarray,
+) -> Union[xr.DataArray, float]:
     """
     Calculates the averaged density for the given time period.
 
@@ -599,5 +635,5 @@ def _calculate_density(water_density, bnr, mean_hub_vel, time):
             bnr.mean(water_density.values), coords={"time": bnr.mean(time)}
         )
         return _average_velocity_bins(rho_avg, mean_hub_vel, bin_size=0.1)
-    else:
-        return water_density
+
+    return water_density
diff --git a/mhkit/tidal/resource.py b/mhkit/tidal/resource.py
index e6b6d21c4..dcb9111df 100644
--- a/mhkit/tidal/resource.py
+++ b/mhkit/tidal/resource.py
@@ -1,10 +1,28 @@
-import numpy as np
+"""
+This module provides utility functions for analyzing river and tidal
+flow directions and velocities. It includes tools for determining
+principal flow directions, classifying ebb and flood cycles, and
+computing probability distributions of flow velocities.
+
+"""
+
 import math
-from mhkit.river.resource import exceedance_probability, Froude_number
+import numpy as np
+from mhkit.river.resource import exceedance_probability, froude_number
 from mhkit.utils import convert_to_dataarray
 
+__all__ = [
+    "exceedance_probability",
+    "froude_number",
+    "principal_flow_directions",
+    "_histogram",
+    "_flood_or_ebb",
+]
+
 
-def _histogram(directions, velocities, width_dir, width_vel):
+def _histogram(
+    directions: np.ndarray, velocities: np.ndarray, width_dir: float, width_vel: float
+) -> tuple[np.ndarray, list, list]:
     """
     Wrapper around numpy histogram 2D. Used to find joint probability
     between directions and velocities. Returns joint probability H as [%].
@@ -30,27 +48,27 @@ def _histogram(directions, velocities, width_dir, width_vel):
     """
 
     # Number of directional bins
-    N_dir = math.ceil(360 / width_dir)
+    n_dir = math.ceil(360 / width_dir)
     # Max bin (round up to nearest integer)
-    vel_max = math.ceil(velocities.max())
+    velocity_max = math.ceil(velocities.max())
     # Number of velocity bins
-    N_vel = math.ceil(vel_max / width_vel)
+    n_vel = math.ceil(velocity_max / width_vel)
     # 2D Histogram of current speed and direction
-    H, dir_edges, vel_edges = np.histogram2d(
+    joint_probability, dir_edges, vel_edges = np.histogram2d(
         directions,
         velocities,
-        bins=(N_dir, N_vel),
-        range=[[0, 360], [0, vel_max]],
+        bins=(n_dir, n_vel),
+        range=[[0, 360], [0, velocity_max]],
         density=True,
     )
     # density = true therefore bin value * bin area summed =1
     bin_area = width_dir * width_vel
-    # Convert H values to percent [%]
-    H = H * bin_area * 100
-    return H, dir_edges, vel_edges
+    # Convert joint_probability values to percent [%]
+    joint_probability = joint_probability * bin_area * 100
+    return joint_probability, dir_edges, vel_edges
 
 
-def _normalize_angle(degree):
+def _normalize_angle(degree: float) -> float:
     """
     Normalizes degrees to be between 0 and 360
 
@@ -70,7 +88,9 @@ def _normalize_angle(degree):
     return new_degree
 
 
-def principal_flow_directions(directions, width_dir):
+def principal_flow_directions(
+    directions: np.ndarray, width_dir: float
+) -> tuple[float, float]:
     """
     Calculates principal flow directions for ebb and flood cycles
 
@@ -96,6 +116,7 @@ def principal_flow_directions(directions, width_dir):
     One must determine which principal direction is flood and which is
     ebb based on knowledge of the measurement site.
     """
+    # pylint: disable=too-many-locals
 
     directions = convert_to_dataarray(directions)
     if any(directions < 0) or any(directions > 360):
@@ -105,36 +126,38 @@ def principal_flow_directions(directions, width_dir):
         )
 
     # Number of directional bins
-    N_dir = int(360 / width_dir)
+    n_dir = int(360 / width_dir)
     # Compute directional histogram
-    H1, dir_edges = np.histogram(directions, bins=N_dir, range=[0, 360], density=True)
+    histogram1, _ = np.histogram(directions, bins=n_dir, range=[0, 360], density=True)
     # Convert to percent
-    H1 = H1 * 100  # [%]
+    histogram1 = histogram1 * 100  # [%]
     # Determine if there are an even or odd number of bins
-    odd = bool(N_dir % 2)
+    odd = bool(n_dir % 2)
     # Shift by 180 degrees and sum
     if odd:
         # Then split middle bin counts to left and right
-        H0to180 = H1[0 : N_dir // 2]
-        H180to360 = H1[N_dir // 2 + 1 :]
-        H0to180[-1] += H1[N_dir // 2] / 2
-        H180to360[0] += H1[N_dir // 2] / 2
+        histogram_0_to_180 = histogram1[0 : n_dir // 2]
+        histogram_180_to_360 = histogram1[n_dir // 2 + 1 :]
+        histogram_0_to_180[-1] += histogram1[n_dir // 2] / 2
+        histogram_180_to_360[0] += histogram1[n_dir // 2] / 2
         # Add the two
-        H180 = H0to180 + H180to360
+        histogram_180 = histogram_0_to_180 + histogram_180_to_360
     else:
-        H180 = H1[0 : N_dir // 2] + H1[N_dir // 2 : N_dir + 1]
+        histogram_180 = histogram1[0 : n_dir // 2] + histogram1[n_dir // 2 : n_dir + 1]
 
     # Find the maximum value
-    maxDegreeStacked = H180.argmax()
+    max_degree_stacked = histogram_180.argmax()
     # Shift by 90 to find angles normal to principal direction
-    floodEbbNormalDegree1 = _normalize_angle(maxDegreeStacked + 90.0)
+    flood_ebb_normal_degree1 = _normalize_angle(max_degree_stacked + 90.0)
     # Find the complimentary angle
-    floodEbbNormalDegree2 = _normalize_angle(floodEbbNormalDegree1 + 180.0)
+    flood_ebb_normal_degree2 = _normalize_angle(flood_ebb_normal_degree1 + 180.0)
     # Reset values so that the Degree1 is the smaller angle, and Degree2 the large
-    floodEbbNormalDegree1 = min(floodEbbNormalDegree1, floodEbbNormalDegree2)
-    floodEbbNormalDegree2 = floodEbbNormalDegree1 + 180.0
+    flood_ebb_normal_degree1 = min(flood_ebb_normal_degree1, flood_ebb_normal_degree2)
+    flood_ebb_normal_degree2 = flood_ebb_normal_degree1 + 180.0
     # Slice directions on the 2 semi circles
-    mask = (directions >= floodEbbNormalDegree1) & (directions <= floodEbbNormalDegree2)
+    mask = (directions >= flood_ebb_normal_degree1) & (
+        directions <= flood_ebb_normal_degree2
+    )
     d1 = directions[mask]
     d2 = directions[~mask]
     # Shift second set of of directions to not break between 360 and 0
@@ -144,23 +167,25 @@ def principal_flow_directions(directions, width_dir):
     # Number of bins for semi-circle
     n_dir = int(180 / width_dir)
     # Compute 1D histograms on both semi circles
-    Hd1, dir1_edges = np.histogram(d1, bins=n_dir, density=True)
-    Hd2, dir2_edges = np.histogram(d2, bins=n_dir, density=True)
+    histogram_d1, dir1_edges = np.histogram(d1, bins=n_dir, density=True)
+    histogram_d2, dir2_edges = np.histogram(d2, bins=n_dir, density=True)
     # Convert to percent
-    Hd1 = Hd1 * 100  # [%]
-    Hd2 = Hd2 * 100  # [%]
+    histogram_d1 = histogram_d1 * 100  # [%]
+    histogram_d2 = histogram_d2 * 100  # [%]
     # Principal Directions average of the 2 bins
-    PrincipalDirection1 = 0.5 * (
-        dir1_edges[Hd1.argmax()] + dir1_edges[Hd1.argmax() + 1]
+    principal_direction1 = 0.5 * (
+        dir1_edges[histogram_d1.argmax()] + dir1_edges[histogram_d1.argmax() + 1]
     )
-    PrincipalDirection2 = (
-        0.5 * (dir2_edges[Hd2.argmax()] + dir2_edges[Hd2.argmax() + 1]) + 180.0
+    principal_direction2 = (
+        0.5
+        * (dir2_edges[histogram_d2.argmax()] + dir2_edges[histogram_d2.argmax() + 1])
+        + 180.0
     )
 
-    return PrincipalDirection1, PrincipalDirection2
+    return principal_direction1, principal_direction2
 
 
-def _flood_or_ebb(d, flood, ebb):
+def _flood_or_ebb(d: np.ndarray, flood: float, ebb: float) -> np.ndarray:
     """
     Returns a mask which is True for directions on the ebb side of the
     midpoints between the flood and ebb directions on the unit circle
diff --git a/mhkit/utils/__init__.py b/mhkit/utils/__init__.py
index 328a33200..c89a6430f 100644
--- a/mhkit/utils/__init__.py
+++ b/mhkit/utils/__init__.py
@@ -1,6 +1,6 @@
 """
-This module initializes and imports the essential utility functions for data 
-conversion, statistical analysis, caching, and event detection for the 
+This module initializes and imports the essential utility functions for data
+conversion, statistical analysis, caching, and event detection for the
 MHKiT library.
 """
 
diff --git a/mhkit/utils/cache.py b/mhkit/utils/cache.py
index eadfe2eca..c4897c12c 100644
--- a/mhkit/utils/cache.py
+++ b/mhkit/utils/cache.py
@@ -1,28 +1,28 @@
 """
 This module provides functionality for managing cache files to optimize
 network requests and computations for handling data. The module focuses
-on enabling users to read from and write to cache files, as well as 
-perform cache clearing operations. Cache files are utilized to store data 
-temporarily, mitigating the need to re-fetch or recompute the same data multiple 
+on enabling users to read from and write to cache files, as well as
+perform cache clearing operations. Cache files are utilized to store data
+temporarily, mitigating the need to re-fetch or recompute the same data multiple
 times, which can be especially useful in network-dependent tasks.
 
 The module consists of two main functions:
 
 1. `handle_caching`:
-   This function manages the caching of data. It provides options to read from 
-   and write to cache files, depending on whether the data is already provided 
-   or if it needs to be fetched from the cache. If a cache file corresponding 
-   to the given parameters already exists, the function can either load data 
-   from it or clear it based on the parameters passed. It also offers the ability 
-   to store associated metadata along with the data and supports both JSON and 
-   pickle file formats for caching. This function returns the loaded data and 
+   This function manages the caching of data. It provides options to read from
+   and write to cache files, depending on whether the data is already provided
+   or if it needs to be fetched from the cache. If a cache file corresponding
+   to the given parameters already exists, the function can either load data
+   from it or clear it based on the parameters passed. It also offers the ability
+   to store associated metadata along with the data and supports both JSON and
+   pickle file formats for caching. This function returns the loaded data and
    metadata from the cache file, along with the cache file path.
 
 2. `clear_cache`:
-   This function enables the clearing of either specific sub-directories or the 
-   entire cache directory, depending on the parameter passed. It removes the 
-   specified directory and then recreates it to ensure future caching tasks can 
-   be executed without any issues. If the specified directory does not exist, 
+   This function enables the clearing of either specific sub-directories or the
+   entire cache directory, depending on the parameter passed. It removes the
+   specified directory and then recreates it to ensure future caching tasks can
+   be executed without any issues. If the specified directory does not exist,
    the function prints an indicative message.
 
 Module Dependencies:
diff --git a/mhkit/utils/stat_utils.py b/mhkit/utils/stat_utils.py
index 972a84f2a..e6cea5c93 100644
--- a/mhkit/utils/stat_utils.py
+++ b/mhkit/utils/stat_utils.py
@@ -1,9 +1,9 @@
 """
-This module contains functions to perform various statistical calculations 
+This module contains functions to perform various statistical calculations
 on continuous data. It includes functions for calculating statistics such as
 mean, max, min, and standard deviation over specific windows, as well as functions
-for vector/directional statistics. The module also provides utility functions 
-to unwrap vectors, compute magnitudes and phases in 2D/3D, and calculate 
+for vector/directional statistics. The module also provides utility functions
+to unwrap vectors, compute magnitudes and phases in 2D/3D, and calculate
 the root mean squared values of vector components.
 
 Functions:
@@ -144,7 +144,7 @@ def get_statistics(
 
 
 def vector_statistics(
-    data: Union[pd.Series, np.ndarray, list]
+    data: Union[pd.Series, np.ndarray, list],
 ) -> Tuple[np.ndarray, np.ndarray]:
     """
     Function used to calculate statistics for vector/directional channels based on
diff --git a/mhkit/utils/time_utils.py b/mhkit/utils/time_utils.py
index 3eb69f7e1..a30bd455e 100644
--- a/mhkit/utils/time_utils.py
+++ b/mhkit/utils/time_utils.py
@@ -18,7 +18,7 @@
 
 
 def matlab_to_datetime(
-    matlab_datenum: Union[np.ndarray, list, float, int]
+    matlab_datenum: Union[np.ndarray, list, float, int],
 ) -> pd.DatetimeIndex:
     """
     Convert MATLAB datenum format to Python datetime
@@ -55,7 +55,7 @@ def matlab_to_datetime(
 
 
 def excel_to_datetime(
-    excel_num: Union[np.ndarray, list, float, int]
+    excel_num: Union[np.ndarray, list, float, int],
 ) -> pd.DatetimeIndex:
     """
     Convert Excel datenum format to Python datetime
diff --git a/mhkit/utils/type_handling.py b/mhkit/utils/type_handling.py
index 09ad5ccac..b58fee525 100644
--- a/mhkit/utils/type_handling.py
+++ b/mhkit/utils/type_handling.py
@@ -1,7 +1,7 @@
 """
 This module provides utility functions for converting various data types
 to xarray structures such as xarray.DataArray and xarray.Dataset. It also
-includes functions for handling nested dictionaries containing pandas 
+includes functions for handling nested dictionaries containing pandas
 DataFrames by converting them to xarray Datasets.
 
 Functions:
@@ -9,7 +9,7 @@
 - to_numeric_array: Converts input data to a numeric NumPy array.
 - convert_to_dataset: Converts pandas or xarray data structures to xarray.Dataset.
 - convert_to_dataarray: Converts various data types to xarray.DataArray.
-- convert_nested_dict_and_pandas: Recursively converts pandas DataFrames 
+- convert_nested_dict_and_pandas: Recursively converts pandas DataFrames
   in nested dictionaries to xarray Datasets.
 """
 
@@ -237,7 +237,7 @@ def convert_to_dataarray(
 
 
 def convert_nested_dict_and_pandas(
-    data: Dict[str, Union[pd.DataFrame, Dict[str, Any]]]
+    data: Dict[str, Union[pd.DataFrame, Dict[str, Any]]],
 ) -> Dict[str, Union[xr.Dataset, Dict[str, Any]]]:
     """
     Recursively searches inside nested dictionaries for pandas DataFrames to
diff --git a/mhkit/utils/upcrossing.py b/mhkit/utils/upcrossing.py
index 1c5eea03f..7ab06a0ed 100644
--- a/mhkit/utils/upcrossing.py
+++ b/mhkit/utils/upcrossing.py
@@ -1,7 +1,7 @@
 """
 Upcrossing Analysis Functions
 =============================
-This module contains a collection of functions that facilitate upcrossing 
+This module contains a collection of functions that facilitate upcrossing
 analyses.
 
 Key Functions:
@@ -12,8 +12,8 @@
 - `heights`: Calculates the height between zero crossings.
 - `periods`: Calculates the period between zero crossings.
 - `custom`: Applies a custom, user-defined function between zero crossings.
-   
-Author: 
+
+Author:
 -------
 mbruggs
 akeeste
diff --git a/mhkit/warnings.py b/mhkit/warnings.py
new file mode 100644
index 000000000..c64265ec8
--- /dev/null
+++ b/mhkit/warnings.py
@@ -0,0 +1,26 @@
+import warnings
+
+# Only suppress specific, reviewed warnings here.
+# Example: Suppress a known FutureWarning from a specific dependency
+# warnings.filterwarnings(
+#     "ignore",
+#     category=FutureWarning,
+#     module=r"^some_dependency\.module$",
+#     message=r"This is a known harmless future warning."
+# )
+
+# Add more targeted filters as needed, after review.
+
+
+def configure_warnings():
+    """
+    Call this function at package import to apply MHKiT's targeted warning filters.
+    """
+    # Example: Uncomment and edit below to suppress a specific warning
+    # warnings.filterwarnings(
+    #     "ignore",
+    #     category=FutureWarning,
+    #     module=r"^some_dependency\.module$",
+    #     message=r"This is a known harmless future warning."
+    # )
+    pass
diff --git a/mhkit/wave/graphics.py b/mhkit/wave/graphics.py
index 00afefbab..b54c18413 100644
--- a/mhkit/wave/graphics.py
+++ b/mhkit/wave/graphics.py
@@ -77,7 +77,7 @@ def plot_matrix(M, xlabel="Te", ylabel="Hm0", zlabel=None, show_values=True, ax=
     ------------
     M: pandas Series, pandas DataFrame, xarray DataArray
         Matrix with numeric labels for x and y axis, and numeric entries.
-        An example would be the average capture length matrix generated by
+        An example would be the average capture width matrix generated by
         mhkit.device.wave, or something similar.
     xlabel: string (optional)
         Title of the x-axis
@@ -626,7 +626,7 @@ def plot_avg_annual_energy_matrix(
 def monthly_cumulative_distribution(J):
     """
     Creates a cumulative distribution of energy flux as described in
-    IEC TS 62600-101.
+    Figure 6 of IEC TS 62600-101 Ed. 2.0 en 2024.
 
     Parameters
     ----------
@@ -644,20 +644,24 @@ def monthly_cumulative_distribution(J):
     for month in months:
         F = exceedance_probability(J[J.index.month == month])
         cumSum[month] = 1 - F / 100
-        cumSum[month].sort_values("F", inplace=True)
+        cumSum[month].sort_values("exceedance_probability", inplace=True)
     plt.figure(figsize=(12, 8))
     for month in months:
         plt.semilogx(
             J.loc[cumSum[month].index],
-            cumSum[month].F,
+            cumSum[month]["exceedance_probability"],
             "--",
             label=calendar.month_abbr[month],
         )
 
     F = exceedance_probability(J)
-    F.sort_values("F", inplace=True)
+    F.sort_values("exceedance_probability", inplace=True)
     ax = plt.semilogx(
-        J.loc[F.index], 1 - F["F"] / 100, "k-", fillstyle="none", label="All"
+        J.loc[F.index],
+        1 - F["exceedance_probability"] / 100,
+        "k-",
+        fillstyle="none",
+        label="All",
     )
 
     plt.grid()
@@ -835,7 +839,10 @@ def plot_boxplot(Hs, buoy_title=None):
     bp2 = plt.subplot(gs[1, :])
     meanprops = dict(linewidth=2.5, marker="|", markersize=25)
     bp2_example = bp2.boxplot(
-        bp_sample2, vert=False, flierprops=flierprops, medianprops=medianprops
+        bp_sample2,
+        orientation="horizontal",
+        flierprops=flierprops,
+        medianprops=medianprops,
     )
     sample_mean = 2.3
     bp2.scatter(sample_mean, 1, marker="|", color="g", linewidths=1.0, s=200)
diff --git a/mhkit/wave/io/hindcast/__init__.py b/mhkit/wave/io/hindcast/__init__.py
index 2e6057131..6fa3efc32 100644
--- a/mhkit/wave/io/hindcast/__init__.py
+++ b/mhkit/wave/io/hindcast/__init__.py
@@ -1,3 +1,11 @@
+"""Wave hindcast data import and processing module.
+
+This module provides functionality for importing and processing wave hindcast data,
+including wind toolkit data and WPTO hindcast data. The hindcast io module is
+separated from the general io module to allow for more efficient handling of
+CI tests.
+"""
+
 from mhkit.wave.io.hindcast import wind_toolkit
 
 try:
@@ -8,4 +16,3 @@
         "MHKiT-Python. If you are using Windows and calling from"
         "MHKiT-MATLAB this is expected."
     )
-    pass
diff --git a/mhkit/wave/io/hindcast/hindcast.py b/mhkit/wave/io/hindcast/hindcast.py
index c58e55c40..83119a782 100644
--- a/mhkit/wave/io/hindcast/hindcast.py
+++ b/mhkit/wave/io/hindcast/hindcast.py
@@ -5,26 +5,6 @@
 regions, request point data for various parameters, and request directional
 spectrum data.
 
-Functions:
-    - region_selection(lat_lon): Returns the name of the predefined region for
-      given latitude and longitude coordinates.
-    - request_wpto_point_data(data_type, parameter, lat_lon, years, tree=None,
-      unscale=True, str_decode=True, hsds=True): Returns data from the WPTO wave
-      hindcast hosted on AWS at the specified latitude and longitude point(s) for
-      the requested data type, parameter, and years.
-    - request_wpto_directional_spectrum(lat_lon, year, tree=None, unscale=True,
-      str_decode=True, hsds=True): Returns directional spectra data from the WPTO
-      wave hindcast hosted on AWS at the specified latitude and longitude point(s)
-      for the given year.
-
-Dependencies:
-    - sys
-    - time.sleep
-    - pandas
-    - xarray
-    - numpy
-    - rex.MultiYearWaveX, rex.WaveX
-
 Author: rpauly, aidanbharath, ssolson
 Date: 2023-09-26
 """
@@ -32,6 +12,7 @@
 import os
 import sys
 from time import sleep
+from typing import List, Tuple, Union, Optional, Dict
 import pandas as pd
 import xarray as xr
 import numpy as np
@@ -40,7 +21,7 @@
 from mhkit.utils.type_handling import convert_to_dataset
 
 
-def region_selection(lat_lon):
+def region_selection(lat_lon: Union[List[float], Tuple[float, float]]) -> str:
     """
     Returns the name of the predefined region in which the given
     coordinates reside. Can be used to check if the passed lat/lon
@@ -64,13 +45,17 @@ def region_selection(lat_lon):
             f"lat_lon values must be of type float or int. Got: {type(lat_lon[0])}"
         )
 
-    regions = {
+    regions: Dict[str, Dict[str, List[float]]] = {
         "Hawaii": {"lat": [15.0, 27.000002], "lon": [-164.0, -151.0]},
         "West_Coast": {"lat": [30.0906, 48.8641], "lon": [-130.072, -116.899]},
         "Atlantic": {"lat": [24.382, 44.8247], "lon": [-81.552, -65.721]},
     }
 
-    def region_search(lat_lon, region, regions):
+    def region_search(
+        lat_lon: Union[List[float], Tuple[float, float]],
+        region: str,
+        regions: Dict[str, Dict[str, List[float]]],
+    ) -> bool:
         return all(
             regions[region][dk][0] <= d <= regions[region][dk][1]
             for dk, d in {"lat": lat_lon[0], "lon": lat_lon[1]}.items()
@@ -84,18 +69,23 @@ def region_search(lat_lon, region, regions):
     return region[0]
 
 
+# pylint: disable=too-many-arguments
+# pylint: disable=too-many-positional-arguments
+# pylint: disable=too-many-locals
+# pylint: disable=too-many-branches
+# pylint: disable=too-many-statements
 def request_wpto_point_data(
-    data_type,
-    parameter,
-    lat_lon,
-    years,
-    tree=None,
-    unscale=True,
-    str_decode=True,
-    hsds=True,
-    path=None,
-    to_pandas=True,
-):
+    data_type: str,
+    parameter: Union[str, List[str]],
+    lat_lon: Union[Tuple[float, float], List[Tuple[float, float]]],
+    years: List[int],
+    tree: Optional[str] = None,
+    unscale: bool = True,
+    str_decode: bool = True,
+    hsds: bool = True,
+    path: Optional[str] = None,
+    to_pandas: bool = True,
+) -> Tuple[Union[pd.DataFrame, xr.Dataset], pd.DataFrame]:
     """
     Returns data from the WPTO wave hindcast hosted on AWS at the
     specified latitude and longitude point(s), or the closest
@@ -190,7 +180,10 @@ def request_wpto_point_data(
 
     # Attempt to load data from cache
     # Construct a string representation of the function parameters
-    hash_params = f"{data_type}_{parameter}_{lat_lon}_{years}_{tree}_{unscale}_{str_decode}_{hsds}_{path}_{to_pandas}"
+    hash_params = (
+        f"{data_type}_{parameter}_{lat_lon}_{years}_{tree}_{unscale}_"
+        f"{str_decode}_{hsds}_{path}_{to_pandas}"
+    )
     cache_dir = _get_cache_dir()
     data, meta, _ = handle_caching(
         hash_params,
@@ -200,105 +193,105 @@ def request_wpto_point_data(
 
     if data is not None:
         return data, meta
-    else:
-        if "directional_wave_spectrum" in parameter:
-            sys.exit("This function does not support directional_wave_spectrum output")
 
-        # Check for multiple region selection
-        if isinstance(lat_lon[0], float):
-            region = region_selection(lat_lon)
-        else:
-            region_list = []
-            for loc in lat_lon:
-                region_list.append(region_selection(loc))
-            if region_list.count(region_list[0]) == len(lat_lon):
-                region = region_list[0]
-            else:
-                sys.exit("Coordinates must be within the same region!")
-
-        if path:
-            wave_path = path
-        elif data_type == "3-hour":
-            wave_path = f"/nrel/US_wave/{region}/{region}_wave_*.h5"
-        elif data_type == "1-hour":
-            wave_path = (
-                f"/nrel/US_wave/virtual_buoy/{region}/{region}_virtual_buoy_*.h5"
-            )
-        else:
-            print("ERROR: invalid data_type")
-
-        wave_kwargs = {
-            "tree": tree,
-            "unscale": unscale,
-            "str_decode": str_decode,
-            "hsds": hsds,
-            "years": years,
-        }
-        data_list = []
-
-        with MultiYearWaveX(wave_path, **wave_kwargs) as rex_waves:
-            if isinstance(parameter, list):
-                for param in parameter:
-                    temp_data = rex_waves.get_lat_lon_df(param, lat_lon)
-                    gid = rex_waves.lat_lon_gid(lat_lon)
-                    cols = temp_data.columns[:]
-                    for i, col in zip(range(len(cols)), cols):
-                        temp = f"{param}_{i}"
-                        temp_data = temp_data.rename(columns={col: temp})
+    if "directional_wave_spectrum" in parameter:
+        sys.exit("This function does not support directional_wave_spectrum output")
 
-                    data_list.append(temp_data)
-                data = pd.concat(data_list, axis=1)
+    # Check for multiple region selection
+    if isinstance(lat_lon[0], float):
+        region = region_selection(lat_lon)
+    else:
+        region_list = []
+        for loc in lat_lon:
+            region_list.append(region_selection(loc))
+        if region_list.count(region_list[0]) == len(lat_lon):
+            region = region_list[0]
+        else:
+            sys.exit("Coordinates must be within the same region!")
 
-            else:
-                data = rex_waves.get_lat_lon_df(parameter, lat_lon)
-                cols = data.columns[:]
+    if path:
+        wave_path = path
+    elif data_type == "3-hour":
+        wave_path = f"/nrel/US_wave/{region}/{region}_wave_*.h5"
+    elif data_type == "1-hour":
+        wave_path = f"/nrel/US_wave/virtual_buoy/{region}/{region}_virtual_buoy_*.h5"
+    else:
+        raise ValueError(
+            f"Invalid data_type: {data_type}. Must be '3-hour' or '1-hour'"
+        )
 
+    wave_kwargs = {
+        "tree": tree,
+        "unscale": unscale,
+        "str_decode": str_decode,
+        "hsds": hsds,
+        "years": years,
+    }
+    data_list = []
+
+    with MultiYearWaveX(wave_path, **wave_kwargs) as rex_waves:
+        if isinstance(parameter, list):
+            for param in parameter:
+                temp_data = rex_waves.get_lat_lon_df(param, lat_lon)
+                gid = rex_waves.lat_lon_gid(lat_lon)
+                cols = temp_data.columns[:]
                 for i, col in zip(range(len(cols)), cols):
-                    temp = f"{parameter}_{i}"
-                    data = data.rename(columns={col: temp})
+                    temp = f"{param}_{i}"
+                    temp_data = temp_data.rename(columns={col: temp})
 
-            meta = rex_waves.meta.loc[cols, :]
-            meta = meta.reset_index(drop=True)
-            gid = rex_waves.lat_lon_gid(lat_lon)
-            meta["gid"] = gid
+                data_list.append(temp_data)
+            data = pd.concat(data_list, axis=1)
 
-            if not to_pandas:
-                data = convert_to_dataset(data)
-                data["time_index"] = pd.to_datetime(data.time_index)
+        else:
+            data = rex_waves.get_lat_lon_df(parameter, lat_lon)
+            cols = data.columns[:]
 
-                if isinstance(parameter, list):
-                    param_coords = [f"{param}_{i}" for param in parameter]
-                    data.coords["parameter"] = xr.DataArray(
-                        param_coords, dims="parameter"
-                    )
+            for i, col in zip(range(len(cols)), cols):
+                temp = f"{parameter}_{i}"
+                data = data.rename(columns={col: temp})
 
-                data.coords["year"] = xr.DataArray(years, dims="year")
+        meta = rex_waves.meta.loc[cols, :]
+        meta = meta.reset_index(drop=True)
+        gid = rex_waves.lat_lon_gid(lat_lon)
+        meta["gid"] = gid
 
-                meta_ds = meta.to_xarray()
-                data = xr.merge([data, meta_ds])
+        if not to_pandas:
+            data = convert_to_dataset(data)
+            data["time_index"] = pd.to_datetime(data.time_index)
 
-                # Remove the 'index' coordinate
-                data = data.drop_vars("index")
+            if isinstance(parameter, list):
+                param_coords = [f"{param}_{i}" for param in parameter]
+                data.coords["parameter"] = xr.DataArray(param_coords, dims="parameter")
 
-        # save_to_cache(hash_params, data, meta)
-        handle_caching(
-            hash_params,
-            cache_dir,
-            cache_content={"data": data, "metadata": meta, "write_json": None},
-        )
+            data.coords["year"] = xr.DataArray(years, dims="year")
 
-        return data, meta
+            meta_ds = meta.to_xarray()
+            data = xr.merge([data, meta_ds])
+
+            # Remove the 'index' coordinate
+            data = data.drop_vars("index")
+
+    # save_to_cache(hash_params, data, meta)
+    handle_caching(
+        hash_params,
+        cache_dir,
+        cache_content={"data": data, "metadata": meta, "write_json": None},
+    )
+
+    return data, meta
 
 
+# pylint: disable=too-many-branches
+# pylint: disable=too-many-statements
 def request_wpto_directional_spectrum(
-    lat_lon,
-    year,
-    tree=None,
-    unscale=True,
-    str_decode=True,
-    hsds=True,
-    path=None,
-):
+    lat_lon: Union[Tuple[float, float], List[Tuple[float, float]]],
+    year: str,
+    tree: Optional[str] = None,
+    unscale: bool = True,
+    str_decode: bool = True,
+    hsds: bool = True,
+    path: Optional[str] = None,
+) -> Tuple[xr.Dataset, pd.DataFrame]:
     """
     Returns directional spectra data from the WPTO wave hindcast hosted
     on AWS at the specified latitude and longitude point(s),
@@ -417,10 +410,10 @@ def request_wpto_directional_spectrum(
         )
 
         # Create bins for multiple smaller API dataset requests
-        N = 6
+        num_bins = 6
         length = len(rex_waves)
-        quotient, remainder = divmod(length, N)
-        bins = [i * quotient for i in range(N + 1)]
+        quotient, remainder = divmod(length, num_bins)
+        bins = [i * quotient for i in range(num_bins + 1)]
         bins[-1] += remainder
         index_bins = (np.array(bins) * len(frequency) * len(direction)).tolist()
 
@@ -436,7 +429,7 @@ def request_wpto_directional_spectrum(
                 try:
                     data_array = rex_waves[parameter, bins[i] : bins[i + 1], :, :, gid]
                     str_error = None
-                except Exception as err:
+                except OSError as err:
                     str_error = str(err)
 
                 if str_error:
@@ -501,7 +494,7 @@ def request_wpto_directional_spectrum(
     return data, meta
 
 
-def _get_cache_dir():
+def _get_cache_dir() -> str:
     """
     Returns the path to the cache directory.
     """
diff --git a/mhkit/wave/io/hindcast/wind_toolkit.py b/mhkit/wave/io/hindcast/wind_toolkit.py
index 2205e2be4..a22cbe7ba 100644
--- a/mhkit/wave/io/hindcast/wind_toolkit.py
+++ b/mhkit/wave/io/hindcast/wind_toolkit.py
@@ -2,47 +2,12 @@
 Wind Toolkit Data Utility Functions
 ===================================
 
-This module contains a collection of utility functions designed to facilitate 
-the extraction, caching, and visualization of wind data from the WIND Toolkit 
-hindcast dataset hosted on AWS. This dataset includes offshore wind hindcast data 
+This module contains a collection of utility functions designed to facilitate
+the extraction, caching, and visualization of wind data from the WIND Toolkit
+hindcast dataset hosted on AWS. This dataset includes offshore wind hindcast data
 with various parameters like wind speed, direction, temperature, and pressure.
 
-Key Functions:
---------------
-- `region_selection`: Determines which predefined wind region a given latitude 
-  and longitude fall within.
-  
-- `get_region_data`: Retrieves latitude and longitude data points for a specified 
-  wind region. Uses caching to speed up repeated requests.
-  
-- `plot_region`: Plots the geographical extent of a specified wind region and 
-  can overlay a given latitude-longitude point.
-  
-- `elevation_to_string`: Converts a parameter (e.g., 'windspeed') and elevation 
-  values (e.g., [20, 40, 120]) to the formatted strings used in the WIND Toolkit.
-  
-- `request_wtk_point_data`: Fetches specified wind data parameters for given 
-  latitude-longitude points and years from the WIND Toolkit hindcast dataset. 
-  Supports caching for faster repeated data retrieval.
-
-Dependencies:
--------------
-- rex: Library to handle renewable energy datasets.
-- pandas: Data manipulation and analysis.
-- os, hashlib, pickle: Used for caching functionality.
-- matplotlib: Used for plotting.
-
-Notes:
-------
-- To access the WIND Toolkit hindcast data, users need to configure `h5pyd` 
-  for data access on HSDS (see the metocean_example or WPTO_hindcast_example
-  notebook for more details).
-  
-- While some functions perform basic checks (e.g., verifying that latitude 
-  and longitude are within a predefined region), it's essential to understand 
-  the boundaries of each region and the available parameters and elevations in the dataset.
-
-Author: 
+Author:
 -------
 akeeste
 ssolson
@@ -56,15 +21,17 @@
 import os
 import hashlib
 import pickle
+from typing import List, Tuple, Union, Optional, Dict
 import pandas as pd
-
-from rex import MultiYearWindX
+import numpy as np
+import xarray as xr
 import matplotlib.pyplot as plt
+from rex import MultiYearWindX
 from mhkit.utils.cache import handle_caching
 from mhkit.utils.type_handling import convert_to_dataset
 
 
-def region_selection(lat_lon, preferred_region=""):
+def region_selection(lat_lon: Tuple[float, float], preferred_region: str = "") -> str:
     """
     Returns the name of the predefined region in which the given coordinates reside.
     Can be used to check if the passed lat/lon pair is within the WIND Toolkit hindcast dataset.
@@ -105,7 +72,7 @@ def region_selection(lat_lon, preferred_region=""):
 
     # Note that this check is fast, but not robust because region are not
     # rectangular on a lat-lon grid
-    rDict = {
+    region_dict: Dict[str, Dict[str, List[float]]] = {
         "CA_NWP_overlap": {"lat": [41.213, 42.642], "lon": [-129.090, -121.672]},
         "Offshore_CA": {"lat": [31.932, 42.642], "lon": [-129.090, -115.806]},
         "Hawaii": {"lat": [15.565, 26.221], "lon": [-164.451, -151.278]},
@@ -113,15 +80,15 @@ def region_selection(lat_lon, preferred_region=""):
         "Mid_Atlantic": {"lat": [37.273, 42.211], "lon": [-76.427, -64.800]},
     }
 
-    def region_search(x):
+    def region_search(x: str) -> bool:
         return all(
             (
-                True if rDict[x][dk][0] <= d <= rDict[x][dk][1] else False
+                region_dict[x][dk][0] <= d <= region_dict[x][dk][1]
                 for dk, d in {"lat": lat_lon[0], "lon": lat_lon[1]}.items()
             )
         )
 
-    region = [key for key in rDict if region_search(key)]
+    region = [key for key in region_dict if region_search(key)]
 
     if region[0] == "CA_NWP_overlap":
         if preferred_region == "Offshore_CA":
@@ -130,16 +97,18 @@ def region_search(x):
             region[0] = "NW_Pacific"
         else:
             raise TypeError(
-                f"Preferred_region ({preferred_region}) must be 'Offshore_CA' or 'NW_Pacific' when lat_lon {lat_lon} falls in the overlap region"
+                f"Preferred_region ({preferred_region}) must be 'Offshore_CA' or "
+                f"'NW_Pacific' when lat_lon {lat_lon} falls in the overlap region"
             )
 
     if len(region) == 0:
-        raise TypeError(f"Coordinates {lat_lon} out of bounds. Must be within {rDict}")
-    else:
-        return region[0]
+        raise TypeError(
+            f"Coordinates {lat_lon} out of bounds. Must be within {region_dict}"
+        )
+    return region[0]
 
 
-def get_region_data(region):
+def get_region_data(region: str) -> Tuple[np.ndarray, np.ndarray]:
     """
     Retrieves the latitude and longitude data points for the specified region
     from the cache if available; otherwise, fetches the data and caches it for
@@ -189,29 +158,33 @@ def get_region_data(region):
         with open(cache_file, "rb") as f:
             lats, lons = pickle.load(f)
         return lats, lons
-    else:
-        wind_path = "/nrel/wtk/" + region.lower() + "/" + region + "_*.h5"
-        windKwargs = {
-            "tree": None,
-            "unscale": True,
-            "str_decode": True,
-            "hsds": True,
-            "years": [2019],
-        }
-
-        # Get the latitude and longitude list from the region in rex
-        rex_wind = MultiYearWindX(wind_path, **windKwargs)
-        lats = rex_wind.lat_lon[:, 0]
-        lons = rex_wind.lat_lon[:, 1]
-
-        # Save data to cache
-        with open(cache_file, "wb") as f:
-            pickle.dump((lats, lons), f)
 
-        return lats, lons
+    wind_path = "/nrel/wtk/" + region.lower() + "/" + region + "_*.h5"
+    wind_kwargs = {
+        "tree": None,
+        "unscale": True,
+        "str_decode": True,
+        "hsds": True,
+        "years": [2019],
+    }
+
+    # Get the latitude and longitude list from the region in rex
+    rex_wind = MultiYearWindX(wind_path, **wind_kwargs)
+    lats = rex_wind.lat_lon[:, 0]
+    lons = rex_wind.lat_lon[:, 1]
+
+    # Save data to cache
+    with open(cache_file, "wb") as f:
+        pickle.dump((lats, lons), f)
+
+    return lats, lons
 
 
-def plot_region(region, lat_lon=None, ax=None):
+def plot_region(
+    region: str,
+    lat_lon: Optional[Tuple[float, float]] = None,
+    ax: Optional[plt.Axes] = None,
+) -> plt.Axes:
     """
     Visualizes the area that a given region covers. Can help users understand
     the extent of a region since they are not all rectangular.
@@ -244,7 +217,7 @@ def plot_region(region, lat_lon=None, ax=None):
 
     # Plot the latitude longitude pairs
     if ax is None:
-        fig, ax = plt.subplots()
+        _, ax = plt.subplots()
     ax.plot(lons, lats, "o", label=f"{region} region")
     if lat_lon is not None:
         ax.plot(lat_lon[1], lat_lon[0], "o", label="Specified lat-lon point")
@@ -257,7 +230,9 @@ def plot_region(region, lat_lon=None, ax=None):
     return ax
 
 
-def elevation_to_string(parameter, elevations):
+def elevation_to_string(
+    parameter: str, elevations: Union[float, List[float]]
+) -> List[str]:
     """
     Takes in a parameter (e.g. 'windspeed') and elevations (e.g. [20, 40, 120])
     and returns the formatted strings that are input to WIND Toolkit (e.g. windspeed_10m).
@@ -297,19 +272,25 @@ def elevation_to_string(parameter, elevations):
     return parameter_list
 
 
+# pylint: disable=too-many-arguments
+# pylint: disable=too-many-locals
+# pylint: disable=too-many-branches
+# pylint: disable=too-many-statements
+# pylint: disable=too-many-positional-arguments
+# pylint: disable=duplicate-code
 def request_wtk_point_data(
-    time_interval,
-    parameter,
-    lat_lon,
-    years,
-    preferred_region="",
-    tree=None,
-    unscale=True,
-    str_decode=True,
-    hsds=True,
-    clear_cache=False,
-    to_pandas=True,
-):
+    time_interval: str,
+    parameter: Union[str, List[str]],
+    lat_lon: Union[Tuple[float, float], List[Tuple[float, float]]],
+    years: List[int],
+    preferred_region: str = "",
+    tree: Optional[str] = None,
+    unscale: bool = True,
+    str_decode: bool = True,
+    hsds: bool = True,
+    clear_cache: bool = False,
+    to_pandas: bool = True,
+) -> Tuple[Union[pd.DataFrame, xr.Dataset], pd.DataFrame]:
     """
     Returns data from the WIND Toolkit offshore wind hindcast hosted on
     AWS at the specified latitude and longitude point(s), or the closest
@@ -414,7 +395,10 @@ def request_wtk_point_data(
     cache_dir = os.path.join(os.path.expanduser("~"), ".cache", "mhkit", "hindcast")
 
     # Construct a string representation of the function parameters
-    hash_params = f"{time_interval}_{parameter}_{lat_lon}_{years}_{preferred_region}_{tree}_{unscale}_{str_decode}_{hsds}"
+    hash_params = (
+        f"{time_interval}_{parameter}_{lat_lon}_{years}_{preferred_region}_"
+        f"{tree}_{unscale}_{str_decode}_{hsds}"
+    )
 
     # Use handle_caching to manage caching.
     data, meta, _ = handle_caching(
@@ -430,67 +414,67 @@ def request_wtk_point_data(
             data.attrs = meta
 
         return data, meta  # Return cached data and meta if available
+
+    # check for multiple region selection
+    if isinstance(lat_lon[0], float):
+        region = region_selection(lat_lon, preferred_region)
     else:
-        # check for multiple region selection
-        if isinstance(lat_lon[0], float):
-            region = region_selection(lat_lon, preferred_region)
+        reglist = []
+        for loc in lat_lon:
+            reglist.append(region_selection(loc, preferred_region))
+        if reglist.count(reglist[0]) == len(lat_lon):
+            region = reglist[0]
         else:
-            reglist = []
-            for loc in lat_lon:
-                reglist.append(region_selection(loc, preferred_region))
-            if reglist.count(reglist[0]) == len(lat_lon):
-                region = reglist[0]
-            else:
-                raise TypeError("Coordinates must be within the same region!")
-
-        if time_interval == "1-hour":
-            wind_path = f"/nrel/wtk/{region.lower()}/{region}_*.h5"
-        elif time_interval == "5-minute":
-            wind_path = f"/nrel/wtk/{region.lower()}-5min/{region}_*.h5"
-        else:
-            raise TypeError(
-                f"Invalid time_interval '{time_interval}', must be '1-hour' or '5-minute'"
-            )
-        windKwargs = {
-            "tree": tree,
-            "unscale": unscale,
-            "str_decode": str_decode,
-            "hsds": hsds,
-            "years": years,
-        }
-        data_list = []
-        with MultiYearWindX(wind_path, **windKwargs) as rex_wind:
-            if isinstance(parameter, list):
-                for p in parameter:
-                    temp_data = rex_wind.get_lat_lon_df(p, lat_lon)
-                    col = temp_data.columns[:]
-                    for i, c in zip(range(len(col)), col):
-                        temp = f"{p}_{i}"
-                        temp_data = temp_data.rename(columns={c: temp})
-
-                    data_list.append(temp_data)
-                data = pd.concat(data_list, axis=1)
-
-            else:
-                data = rex_wind.get_lat_lon_df(parameter, lat_lon)
-                col = data.columns[:]
+            raise TypeError("Coordinates must be within the same region!")
 
+    if time_interval == "1-hour":
+        wind_path = f"/nrel/wtk/{region.lower()}/{region}_*.h5"
+    elif time_interval == "5-minute":
+        wind_path = f"/nrel/wtk/{region.lower()}-5min/{region}_*.h5"
+    else:
+        raise TypeError(
+            f"Invalid time_interval '{time_interval}', must be '1-hour' or '5-minute'"
+        )
+    wind_kwargs = {
+        "tree": tree,
+        "unscale": unscale,
+        "str_decode": str_decode,
+        "hsds": hsds,
+        "years": years,
+    }
+    data_list = []
+    with MultiYearWindX(wind_path, **wind_kwargs) as rex_wind:
+        if isinstance(parameter, list):
+            for p in parameter:
+                temp_data = rex_wind.get_lat_lon_df(p, lat_lon)
+                col = temp_data.columns[:]
                 for i, c in zip(range(len(col)), col):
-                    temp = f"{parameter}_{i}"
-                    data = data.rename(columns={c: temp})
+                    temp = f"{p}_{i}"
+                    temp_data = temp_data.rename(columns={c: temp})
 
-            meta = rex_wind.meta.loc[col, :]
-            meta = meta.reset_index(drop=True)
+                data_list.append(temp_data)
+            data = pd.concat(data_list, axis=1)
 
-        # Save the retrieved data and metadata to cache.
-        handle_caching(
-            hash_params,
-            cache_dir,
-            cache_content={"data": data, "metadata": meta, "write_json": None},
-        )
+        else:
+            data = rex_wind.get_lat_lon_df(parameter, lat_lon)
+            col = data.columns[:]
 
-        if not to_pandas:
-            data = convert_to_dataset(data)
-            data.attrs = meta
+            for i, c in zip(range(len(col)), col):
+                temp = f"{parameter}_{i}"
+                data = data.rename(columns={c: temp})
+
+        meta = rex_wind.meta.loc[col, :]
+        meta = meta.reset_index(drop=True)
+
+    # Save the retrieved data and metadata to cache.
+    handle_caching(
+        hash_params,
+        cache_dir,
+        cache_content={"data": data, "metadata": meta, "write_json": None},
+    )
+
+    if not to_pandas:
+        data = convert_to_dataset(data)
+        data.attrs = meta
 
-        return data, meta
+    return data, meta
diff --git a/mhkit/wave/io/ndbc.py b/mhkit/wave/io/ndbc.py
index c0fa28683..5fbad8ef8 100644
--- a/mhkit/wave/io/ndbc.py
+++ b/mhkit/wave/io/ndbc.py
@@ -1,7 +1,7 @@
 import os
 from collections import OrderedDict as _OrderedDict
 from collections import defaultdict as _defaultdict
-from io import BytesIO
+from io import BytesIO, StringIO
 import re
 import requests
 import zlib
@@ -19,6 +19,9 @@
     convert_nested_dict_and_pandas,
 )
 
+# Set pandas option to opt-in to future behavior
+pd.set_option("future.no_silent_downcasting", True)
+
 
 def read_file(file_name, missing_values=["MM", 9999, 999, 99], to_pandas=True):
     """
@@ -102,21 +105,25 @@ def read_file(file_name, missing_values=["MM", 9999, 999, 99], to_pandas=True):
             header=None,
             names=header,
             comment="#",
-            parse_dates=[parse_vals],
         )
     # If first line is not commented, then the first row can be used as header
     else:
-        data = pd.read_csv(
-            file_name, sep="\\s+", header=0, comment="#", parse_dates=[parse_vals]
-        )
+        data = pd.read_csv(file_name, sep="\\s+", header=0, comment="#")
 
     # Convert index to datetime
     date_column = "_".join(parse_vals)
+    data[date_column] = (
+        data[parse_vals].apply(lambda val: val.astype("string")).agg(" ".join, axis=1)
+    )
+
     data["Time"] = pd.to_datetime(data[date_column], format=date_format)
     data.index = data["Time"].values
+
     # Remove date columns
     del data[date_column]
     del data["Time"]
+    for val in parse_vals:
+        del data[val]
 
     # If there was a row of units, convert to dictionary
     if units_exist:
@@ -126,7 +133,11 @@ def read_file(file_name, missing_values=["MM", 9999, 999, 99], to_pandas=True):
 
     # Convert columns to numeric data if possible, otherwise leave as string
     for column in data:
-        data[column] = pd.to_numeric(data[column], errors="ignore")
+        try:
+            data[column] = pd.to_numeric(data[column])
+        except (ValueError, TypeError):
+            # Keep as string if conversion fails
+            pass
 
     # Convert column names to float if possible (handles frequency headers)
     # if there is non-numeric name, just leave all as strings.
@@ -136,7 +147,8 @@ def read_file(file_name, missing_values=["MM", 9999, 999, 99], to_pandas=True):
         data.columns = data.columns
 
     # Replace indicated missing values with nan
-    data.replace(missing_values, np.nan, inplace=True)
+    data = data.replace(missing_values, np.nan)
+    data = data.infer_objects(copy=False)
 
     if not to_pandas:
         data = convert_to_dataset(data)
@@ -234,7 +246,7 @@ def available_data(
             msg = f"request.get({ndbc_data}) failed by returning code of {response.status_code}"
             raise Exception(msg)
 
-        filenames = pd.read_html(response.text)[0].Name.dropna()
+        filenames = pd.read_html(StringIO(response.text))[0].Name.dropna()
         buoys = _parse_filenames(parameter, filenames)
 
         available_data = buoys.copy(deep=True)
diff --git a/mhkit/wave/io/wecsim.py b/mhkit/wave/io/wecsim.py
index 78298a475..4a7835b46 100644
--- a/mhkit/wave/io/wecsim.py
+++ b/mhkit/wave/io/wecsim.py
@@ -1,22 +1,50 @@
 import pandas as pd
 import numpy as np
+import xarray as xr
 import scipy.io as sio
 from os.path import isfile
 from mhkit.utils import convert_nested_dict_and_pandas
 
 
+def _consolidate_dimensions(output):
+    """
+    Converts the previously read WEC-Sim output, already in xarray,
+    to a convenient form where dof and object number are distinct dimensions.
+    """
+    all_dof_vars = list(output.data_vars)
+    for s in all_dof_vars:
+        if "_dof" not in s:
+            all_dof_vars = all_dof_vars.remove(s)
+    if not isinstance(all_dof_vars, type(None)):
+        vars = all_dof_vars.copy()
+        for i, v in enumerate(vars):
+            vars[i] = v.rstrip("_dof123456")
+        unique_vars = set(vars)
+        for unique_var in unique_vars:
+            data = output[unique_var + "_dof1"]
+            for i in np.arange(2, 7):
+                data = xr.concat([data, output[unique_var + "_dof" + str(i)]], "dof")
+            data = data.assign_coords({"dof": [1, 2, 3, 4, 5, 6]})
+            data.name = unique_var
+            output[unique_var] = data
+
+        # remove old variables
+        output = output.drop_vars(all_dof_vars)
+    return output
+
+
 def read_output(file_name, to_pandas=True):
     """
-    Loads the wecSim response class once 'output' has been saved to a `.mat`
+    Loads the WEC-Sim response class once 'output' has been saved to a `.mat`
     structure.
 
-    NOTE: Python is unable to import MATLAB objects.
-    MATLAB must be used to save the wecSim object as a structure.
+    NOTE: Python is unable to import MATLAB classes.
+    MATLAB must be used to convert the WEC-Sim responseClass object into a structure.
 
     Parameters
     ------------
     file_name: string
-        Name of wecSim output file saved as a `.mat` structure
+        Name of WEC-Sim output file saved as a `.mat` structure
     to_pandas: bool (optional)
         Flag to output a dictionary of pandas objects instead of a dictionary
         of xarray objects. Default = True.
@@ -38,7 +66,7 @@ def read_output(file_name, to_pandas=True):
     output = ws_data["output"]
 
     ######################################
-    ## import wecSim wave class
+    ## import WEC-Sim wave class
     #         type: ''
     #         time: [iterations x 1 double]
     #    elevation: [iterations x 1 double]
@@ -62,7 +90,7 @@ def read_output(file_name, to_pandas=True):
         wave_output = []
 
     ######################################
-    ## import wecSim body class
+    ## import WEC-Sim body class
     #                       name: ''
     #                       time: [iterations x 1 double]
     #                   position: [iterations x 6 double]
@@ -154,7 +182,7 @@ def _write_body_output(body):
         body_output = []
 
     ######################################
-    ## import wecSim pto class
+    ## import WEC-Sim pto class
     #                      name: ''
     #                      time: [iterations x 1 double]
     #                  position: [iterations x 6 double]
@@ -228,7 +256,7 @@ def _write_pto_output(pto):
         pto_output = []
 
     ######################################
-    ## import wecSim constraint class
+    ## import WEC-Sim constraint class
     #
     #            name: ''
     #            time: [iterations x 1 double]
@@ -288,7 +316,7 @@ def _write_constraint_output(constraint):
         constraint_output = []
 
     ######################################
-    ## import wecSim mooring class
+    ## import WEC-Sim mooring class
     #
     #         name: ''
     #         time: [iterations x 1 double]
@@ -338,7 +366,7 @@ def _write_mooring_output(mooring):
         mooring_output = []
 
     ######################################
-    ## import wecSim moorDyn class
+    ## import WEC-Sim moorDyn class
     #
     #    Lines: [1×1 struct]
     #    Line1: [1×1 struct]
@@ -383,7 +411,7 @@ def _write_mooring_output(mooring):
         moorDyn_output = []
 
     ######################################
-    ## import wecSim ptosim class
+    ## import WEC-Sim ptosim class
     #
     #                 name: ''
     #             pistonCF: [1×1 struct]
@@ -406,7 +434,7 @@ def _write_mooring_output(mooring):
         ptosim_output = []
 
     ######################################
-    ## import wecSim cable class
+    ## import WEC-Sim cable class
     #
     #       name: ''
     #       time: [iterations x 1 double]
@@ -465,7 +493,7 @@ def _write_cable_output(cable):
         cable_output = []
 
     ############################################
-    ## create wecSim output - Dict of DataFrames
+    ## create WEC-Sim output - Dict of DataFrames
     ############################################
     ws_output = {
         "wave": wave_output,
@@ -481,4 +509,23 @@ def _write_cable_output(cable):
     if not to_pandas:
         ws_output = convert_nested_dict_and_pandas(ws_output)
 
+        # Loop through each output type (bodies, constraints, ptos, etc) in the WEC-Sim output
+        for k in ws_output.keys():
+            # Skip
+            if not isinstance(ws_output[k], list):
+                if isinstance(ws_output[k], dict):
+                    # Loop through each instance of an output type (body1, body2, etc)
+                    for k2 in ws_output[k].keys():
+                        ws_output[k][k2] = _consolidate_dimensions(ws_output[k][k2])
+
+                    # Concatenate multiple instances of each output type into one dataset
+                    dim_name = k.rstrip("s").replace("ie", "y")  #
+                    n = len(ws_output[k])
+                    ws_output[k] = xr.concat(list(ws_output[k].values()), dim_name)
+                    ws_output[k] = ws_output[k].assign_coords(
+                        {dim_name: np.arange(1, n + 1)}
+                    )
+                else:
+                    ws_output[k] = _consolidate_dimensions(ws_output[k])
+
     return ws_output
diff --git a/mhkit/wave/performance.py b/mhkit/wave/performance.py
index 160918cc0..6d0f9243b 100644
--- a/mhkit/wave/performance.py
+++ b/mhkit/wave/performance.py
@@ -7,11 +7,12 @@
 import matplotlib.pylab as plt
 from os.path import join
 from mhkit.utils import convert_to_dataarray
+import warnings
 
 
-def capture_length(P, J, to_pandas=True):
+def capture_width(P, J, to_pandas=True):
     """
-    Calculates the capture length (often called capture width).
+    Calculates the capture width (sometimes called capture length).
 
     Parameters
     ------------
@@ -24,8 +25,8 @@ def capture_length(P, J, to_pandas=True):
 
     Returns
     ---------
-    L: pandas Series or xarray DataArray
-        Capture length [m]
+    CW: pandas Series or xarray DataArray
+        Capture width [m]
     """
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
@@ -33,12 +34,26 @@ def capture_length(P, J, to_pandas=True):
     P = convert_to_dataarray(P)
     J = convert_to_dataarray(J)
 
-    L = P / J
+    CW = P / J
 
     if to_pandas:
-        L = L.to_pandas()
+        CW = CW.to_pandas()
+
+    return CW
 
-    return L
+
+def capture_length(P, J, to_pandas=True):
+    """
+    Alias for `capture_width`.
+    """
+    warnings.warn(
+        'IEC TS 62600-100 Ed. 2.0 replaces "capture length" with "capture width". '
+        "wave.performance.capture_length() will be deprecated. "
+        "Replace with wave.performance.capture_width().",
+        FutureWarning,
+    )
+    CW = capture_width(P, J, to_pandas)
+    return CW
 
 
 def statistics(X, to_pandas=True):
@@ -46,8 +61,8 @@ def statistics(X, to_pandas=True):
     Calculates statistics, including count, mean, standard
     deviation (std), min, percentiles (25%, 50%, 75%), and max.
 
-    Note that std uses a degree of freedom of 1 in accordance with
-    IEC/TS 62600-100.
+    Note that std uses a degree of freedom of N in accordance with
+    Formula D.5 of IEC TS 62600-100 Ed. 2.0 en 2024.
 
     Parameters
     ------------
@@ -68,7 +83,7 @@ def statistics(X, to_pandas=True):
 
     count = X.count().item()
     mean = X.mean().item()
-    std = _std_ddof1(X)
+    std = _std_ddof0(X)
     q = X.quantile([0.0, 0.25, 0.5, 0.75, 1.0]).values
     variables = ["count", "mean", "std", "min", "25%", "50%", "75%", "max"]
 
@@ -84,14 +99,14 @@ def statistics(X, to_pandas=True):
     return stats
 
 
-def _std_ddof1(a):
-    # Standard deviation with degree of freedom equal to 1
+def _std_ddof0(a):
+    # Standard deviation with degree of freedom equal to N samples (delta degree of freedom = 0)
     if len(a) == 0:
         return np.nan
     elif len(a) == 1:
         return 0
     else:
-        return np.std(a, ddof=1)
+        return np.std(a, ddof=0)
 
 
 def _performance_matrix(X, Y, Z, statistic, x_centers, y_centers):
@@ -99,16 +114,18 @@ def _performance_matrix(X, Y, Z, statistic, x_centers, y_centers):
 
     # Convert bin centers to edges
     xi = [np.mean([x_centers[i], x_centers[i + 1]]) for i in range(len(x_centers) - 1)]
-    xi.insert(0, -np.inf)
-    xi.append(np.inf)
+    xi.insert(0, np.float64(0))
+    xi_end = (x_centers[-1] + np.diff(x_centers[-2:]) / 2)[0]
+    xi.append(xi_end)
 
     yi = [np.mean([y_centers[i], y_centers[i + 1]]) for i in range(len(y_centers) - 1)]
-    yi.insert(0, -np.inf)
-    yi.append(np.inf)
+    yi.insert(0, np.float64(0))
+    yi_end = (y_centers[-1] + np.diff(y_centers[-2:]) / 2)[0]
+    yi.append(yi_end)
 
     # Override standard deviation with degree of freedom equal to 1
     if statistic == "std":
-        statistic = _std_ddof1
+        statistic = _std_ddof0
 
     # Provide function to compute frequency
     def _frequency(a):
@@ -121,6 +138,18 @@ def _frequency(a):
         X, Y, Z, statistic, bins=[xi, yi], expand_binnumbers=False
     )
 
+    # Warn if the X (Hm0) or Y (Te) spacing is greater than the IEC TS 62600-100 Ed. 2.0 en 2024 maxima (0.5m, 1.0s).
+    dx_edge = np.diff(x_edge)
+    if np.any(dx_edge > 0.5):
+        warnings.warn(
+            "Significant wave height bins are greater than the IEC TS 62600-100 limit of 0.5 meters."
+        )
+    dy_edge = np.diff(y_edge)
+    if np.any(dy_edge > 1.0):
+        warnings.warn(
+            "Energy period bins are greater than the IEC TS 62600-100 limit of 1.0 seconds."
+        )
+
     M = xr.DataArray(
         data=zi,
         dims=["x_centers", "y_centers"],
@@ -130,11 +159,11 @@ def _frequency(a):
     return M
 
 
-def capture_length_matrix(Hm0, Te, L, statistic, Hm0_bins, Te_bins, to_pandas=True):
+def capture_width_matrix(Hm0, Te, CW, statistic, Hm0_bins, Te_bins, to_pandas=True):
     """
-    Generates a capture length matrix for a given statistic
+    Generates a capture width matrix for a given statistic
 
-    Note that IEC/TS 62600-100 requires capture length matrices for
+    Note that IEC TS 62600-100 Ed. 2.0 en 2024 section 9.2.4 requires capture width matrices for
     the mean, std, count, min, and max.
 
     Parameters
@@ -143,12 +172,12 @@ def capture_length_matrix(Hm0, Te, L, statistic, Hm0_bins, Te_bins, to_pandas=Tr
         Significant wave height from spectra [m]
     Te: numpy array, pandas Series, pandas DataFrame, xarray DataArray, or xarray Dataset
         Energy period from spectra [s]
-    L : numpy array, pandas Series, pandas DataFrame, xarray DataArray, or xarray Dataset
-        Capture length [m]
+    CW : numpy array, pandas Series, pandas DataFrame, xarray DataArray, or xarray Dataset
+        Capture width [m]
     statistic: string
         Statistic for each bin, options include: 'mean', 'std', 'median',
         'count', 'sum', 'min', 'max', and 'frequency'.  Note that 'std' uses
-        a degree of freedom of 1 in accordance with IEC/TS 62600-100.
+        a degree of freedom of N in accordance with Formula D.5 of IEC TS 62600-100 Ed. 2.0 en 2024.
     Hm0_bins: numpy array
         Bin centers for Hm0 [m]
     Te_bins: numpy array
@@ -158,14 +187,14 @@ def capture_length_matrix(Hm0, Te, L, statistic, Hm0_bins, Te_bins, to_pandas=Tr
 
     Returns
     ---------
-    LM: pandas DataFrame or xarray DataArray
-        Capture length matrix with index equal to Hm0_bins and columns
-        equal to Te_bins
+    CWM: pandas DataFrame or xarray DataArray
+         Capture width matrix with index equal to Hm0_bins and columns
+         equal to Te_bins
 
     """
     Hm0 = convert_to_dataarray(Hm0)
     Te = convert_to_dataarray(Te)
-    L = convert_to_dataarray(L)
+    CW = convert_to_dataarray(CW)
 
     if not (isinstance(statistic, str) or callable(statistic)):
         raise TypeError(
@@ -178,12 +207,26 @@ def capture_length_matrix(Hm0, Te, L, statistic, Hm0_bins, Te_bins, to_pandas=Tr
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
 
-    LM = _performance_matrix(Hm0, Te, L, statistic, Hm0_bins, Te_bins)
+    CWM = _performance_matrix(Hm0, Te, CW, statistic, Hm0_bins, Te_bins)
 
     if to_pandas:
-        LM = LM.to_pandas()
+        CWM = CWM.to_pandas()
+
+    return CWM
 
-    return LM
+
+def capture_length_maxtrix(Hm0, Te, CW, statistic, Hm0_bins, Te_bins, to_pandas=True):
+    """
+    Alias for `capture_width_maxtrix`.
+    """
+    warnings.warn(
+        'IEC TS 62600-100 Ed. 2.0 replaces "capture length" with "capture width". '
+        "wave.performance.capture_length_maxtrix() will be deprecated. "
+        "Replace with wave.performance.capture_width_maxtrix().",
+        FutureWarning,
+    )
+    CWM = capture_width_matrix(Hm0, Te, CW, statistic, Hm0_bins, Te_bins, to_pandas)
+    return CWM
 
 
 def wave_energy_flux_matrix(Hm0, Te, J, statistic, Hm0_bins, Te_bins, to_pandas=True):
@@ -200,8 +243,8 @@ def wave_energy_flux_matrix(Hm0, Te, J, statistic, Hm0_bins, Te_bins, to_pandas=
         Wave energy flux from spectra [W/m]
     statistic: string
         Statistic for each bin, options include: 'mean', 'std', 'median',
-        'count', 'sum', 'min', 'max', and 'frequency'.  Note that 'std' uses a degree of freedom
-        of 1 in accordance of IEC/TS 62600-100.
+        'count', 'sum', 'min', 'max', and 'frequency'. Note that 'std' uses
+        a degree of freedom of N in accordance with Formula D.5 of IEC TS 62600-100 Ed. 2.0 en 2024.
     Hm0_bins: numpy array
         Bin centers for Hm0 [m]
     Te_bins: numpy array
@@ -239,15 +282,15 @@ def wave_energy_flux_matrix(Hm0, Te, J, statistic, Hm0_bins, Te_bins, to_pandas=
     return JM
 
 
-def power_matrix(LM, JM):
+def power_matrix(CWM, JM):
     """
-    Generates a power matrix from a capture length matrix and wave energy
+    Generates a power matrix from a capture width matrix and wave energy
     flux matrix
 
     Parameters
     ------------
-    LM: pandas DataFrame, xarray DataArray, or xarray Dataset
-        Capture length matrix
+    CWM: pandas DataFrame, xarray DataArray, or xarray Dataset
+        Capture width matrix
     JM: pandas DataFrame, xarray DataArray, or xarray Dataset
         Wave energy flux matrix
 
@@ -257,28 +300,28 @@ def power_matrix(LM, JM):
         Power matrix
 
     """
-    if not isinstance(LM, (pd.DataFrame, xr.DataArray, xr.Dataset)):
+    if not isinstance(CWM, (pd.DataFrame, xr.DataArray, xr.Dataset)):
         raise TypeError(
-            f"LM must be of type pd.DataFrame or xr.Dataset. Got: {type(LM)}"
+            f"CWM must be of type pd.DataFrame or xr.Dataset. Got: {type(CWM)}"
         )
     if not isinstance(JM, (pd.DataFrame, xr.DataArray, xr.Dataset)):
         raise TypeError(
             f"JM must be of type pd.DataFrame or xr.Dataset. Got: {type(JM)}"
         )
 
-    PM = LM * JM
+    PM = CWM * JM
 
     return PM
 
 
-def mean_annual_energy_production_timeseries(L, J):
+def mean_annual_energy_production_timeseries(CW, J):
     """
     Calculates mean annual energy production (MAEP) from time-series
 
     Parameters
     ------------
-    L: numpy array, pandas Series, pandas DataFrame, xarray DataArray, or xarray Dataset
-        Capture length
+    CW: numpy array, pandas Series, pandas DataFrame, xarray DataArray, or xarray Dataset
+        Capture width
     J: numpy array, pandas Series, pandas DataFrame, xarray DataArray, or xarray Dataset
         Wave energy flux
 
@@ -288,26 +331,26 @@ def mean_annual_energy_production_timeseries(L, J):
         Mean annual energy production
 
     """
-    L = convert_to_dataarray(L)
+    CW = convert_to_dataarray(CW)
     J = convert_to_dataarray(J)
 
     T = 8766  # Average length of a year (h)
-    n = len(L)
+    n = len(CW)
 
-    maep = T / n * (L * J).sum().item()
+    maep = T / n * (CW * J).sum().item()
 
     return maep
 
 
-def mean_annual_energy_production_matrix(LM, JM, frequency):
+def mean_annual_energy_production_matrix(CWM, JM, frequency):
     """
     Calculates mean annual energy production (MAEP) from matrix data
     along with data frequency in each bin
 
     Parameters
     ------------
-    LM: pandas DataFrame, xarray DataArray, or xarray Dataset
-        Capture length
+    CWM: pandas DataFrame, xarray DataArray, or xarray Dataset
+        Capture width
     JM: pandas DataFrame, xarray DataArray, or xarray Dataset
         Wave energy flux
     frequency: pandas DataFrame, xarray DataArray, or xarray Dataset
@@ -319,17 +362,17 @@ def mean_annual_energy_production_matrix(LM, JM, frequency):
         Mean annual energy production
 
     """
-    LM = convert_to_dataarray(LM)
+    CWM = convert_to_dataarray(CWM)
     JM = convert_to_dataarray(JM)
     frequency = convert_to_dataarray(frequency)
 
-    if not LM.shape == JM.shape == frequency.shape:
-        raise ValueError("LM, JM, and frequency must be of the same size")
+    if not CWM.shape == JM.shape == frequency.shape:
+        raise ValueError("CWM, JM, and frequency must be of the same size")
     if not np.abs(frequency.sum() - 1) < 1e-6:
         raise ValueError("Frequency components must sum to one.")
 
     T = 8766  # Average length of a year (h)
-    maep = T * np.nansum(LM * JM * frequency)
+    maep = T * np.nansum(CWM * JM * frequency)
 
     return maep
 
@@ -349,7 +392,7 @@ def power_performance_workflow(
 ):
     """
     High-level function to compute power performance quantities of
-    interest following IEC TS 62600-100 for given wave spectra.
+    interest following IEC TS 62600-100 Ed. 2.0 en 2024 for given wave spectra.
 
     Parameters
     ------------
@@ -360,11 +403,11 @@ def power_performance_workflow(
     P: numpy ndarray, pandas DataFrame, pandas Series, xarray DataArray, or xarray Dataset
         Power [W]
     statistic: string or list of strings
-        Statistics for plotting capture length matrices,
+        Statistics for plotting capture width matrices,
         options include: "mean", "std", "median",
         "count", "sum", "min", "max", and "frequency".
-        Note that "std" uses a degree of freedom of 1 in accordance with IEC/TS 62600-100.
-        To output capture length matrices for multiple binning parameters,
+        Note that "std" uses a degree of freedom of N in accordance with Formula D.5 of IEC TS 62600-100 Ed. 2.0 en 2024.
+        To output capture width matrices for multiple binning parameters,
         define as a list of strings: statistic = ["", "", ""]
     frequency_bins: numpy array or pandas Series (optional)
        Bin widths for frequency of S. Required for unevenly sized bins
@@ -387,8 +430,8 @@ def power_performance_workflow(
 
     Returns
     ---------
-    LM: xarray dataset
-        Capture length matrices
+    CWM: xarray dataset
+        Capture width matrices
 
     maep_matrix: float
         Mean annual energy production
@@ -419,39 +462,39 @@ def power_performance_workflow(
         S, h, deep=deep, rho=rho, g=g, ratio=ratio, to_pandas=False
     )
 
-    # Calculate capture length from power and energy flux
-    L = wave.performance.capture_length(P, J, to_pandas=False)
+    # Calculate capture width from power and energy flux
+    CW = wave.performance.capture_width(P, J, to_pandas=False)
 
     # Generate bins for Hm0 and Te, input format (start, stop, step_size)
     Hm0_bins = np.arange(0, Hm0.values.max() + 0.5, 0.5)
     Te_bins = np.arange(0, Te.values.max() + 1, 1)
 
-    # Create capture length matrices for each statistic based on IEC/TS 62600-100
+    # Create capture width matrices for each statistic based on IEC TS 62600-100 Ed. 2.0 en 2024
     # Median, sum, frequency additionally provided
-    LM = xr.Dataset()
-    LM["mean"] = wave.performance.capture_length_matrix(
-        Hm0, Te, L, "mean", Hm0_bins, Te_bins, to_pandas=False
+    CWM = xr.Dataset()
+    CWM["mean"] = wave.performance.capture_width_matrix(
+        Hm0, Te, CW, "mean", Hm0_bins, Te_bins, to_pandas=False
     )
-    LM["std"] = wave.performance.capture_length_matrix(
-        Hm0, Te, L, "std", Hm0_bins, Te_bins, to_pandas=False
+    CWM["std"] = wave.performance.capture_width_matrix(
+        Hm0, Te, CW, "std", Hm0_bins, Te_bins, to_pandas=False
     )
-    LM["median"] = wave.performance.capture_length_matrix(
-        Hm0, Te, L, "median", Hm0_bins, Te_bins, to_pandas=False
+    CWM["median"] = wave.performance.capture_width_matrix(
+        Hm0, Te, CW, "median", Hm0_bins, Te_bins, to_pandas=False
     )
-    LM["count"] = wave.performance.capture_length_matrix(
-        Hm0, Te, L, "count", Hm0_bins, Te_bins, to_pandas=False
+    CWM["count"] = wave.performance.capture_width_matrix(
+        Hm0, Te, CW, "count", Hm0_bins, Te_bins, to_pandas=False
     )
-    LM["sum"] = wave.performance.capture_length_matrix(
-        Hm0, Te, L, "sum", Hm0_bins, Te_bins, to_pandas=False
+    CWM["sum"] = wave.performance.capture_width_matrix(
+        Hm0, Te, CW, "sum", Hm0_bins, Te_bins, to_pandas=False
     )
-    LM["min"] = wave.performance.capture_length_matrix(
-        Hm0, Te, L, "min", Hm0_bins, Te_bins, to_pandas=False
+    CWM["min"] = wave.performance.capture_width_matrix(
+        Hm0, Te, CW, "min", Hm0_bins, Te_bins, to_pandas=False
     )
-    LM["max"] = wave.performance.capture_length_matrix(
-        Hm0, Te, L, "max", Hm0_bins, Te_bins, to_pandas=False
+    CWM["max"] = wave.performance.capture_width_matrix(
+        Hm0, Te, CW, "max", Hm0_bins, Te_bins, to_pandas=False
     )
-    LM["freq"] = wave.performance.capture_length_matrix(
-        Hm0, Te, L, "frequency", Hm0_bins, Te_bins, to_pandas=False
+    CWM["freq"] = wave.performance.capture_width_matrix(
+        Hm0, Te, CW, "frequency", Hm0_bins, Te_bins, to_pandas=False
     )
 
     # Create wave energy flux matrix using mean
@@ -461,24 +504,24 @@ def power_performance_workflow(
 
     # Calculate maep from matrix
     maep_matrix = wave.performance.mean_annual_energy_production_matrix(
-        LM["mean"], JM, LM["freq"]
+        CWM["mean"], JM, CWM["freq"]
     )
 
-    # Plot capture length matrices using statistic
+    # Plot capture width matrices using statistic
     for str in statistic:
-        if str not in list(LM.data_vars):
+        if str not in list(CWM.data_vars):
             print("ERROR: Invalid Statistics passed")
             continue
-        plt.figure(figsize=(12, 12), num="Capture Length Matrix " + str)
+        plt.figure(figsize=(12, 12), num="Capture Width Matrix " + str)
         ax = plt.gca()
         wave.graphics.plot_matrix(
-            LM[str],
+            CWM[str],
             xlabel="Te (s)",
             ylabel="Hm0 (m)",
-            zlabel=str + " of Capture Length",
+            zlabel=str + " of Capture Width",
             show_values=show_values,
             ax=ax,
         )
-        plt.savefig(join(savepath, "Capture Length Matrix " + str + ".png"))
+        plt.savefig(join(savepath, "Capture Width Matrix " + str + ".png"))
 
-    return LM, maep_matrix
+    return CWM, maep_matrix
diff --git a/mhkit/wave/resource.py b/mhkit/wave/resource.py
index 488df50c2..14f7e7359 100644
--- a/mhkit/wave/resource.py
+++ b/mhkit/wave/resource.py
@@ -452,7 +452,7 @@ def frequency_moment(S, N, frequency_bins=None, frequency_dimension="", to_panda
         )
     f = S[frequency_dimension]
 
-    # Eq 8 in IEC 62600-101
+    # Eq 8 in IEC 62600-101 Ed. 2.0 en 2024
     S = S.sel({frequency_dimension: slice(1e-12, f.max())})  # omit frequency of 0
     f = S[frequency_dimension]  # reset frequency_dimension without the 0 frequency
 
@@ -507,7 +507,7 @@ def significant_wave_height(
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
 
-    # Eq 12 in IEC 62600-101
+    # Eq 12 in IEC 62600-101 Ed. 2.0 en 2024
     m0 = frequency_moment(
         S,
         0,
@@ -551,7 +551,7 @@ def average_zero_crossing_period(
     if not isinstance(to_pandas, bool):
         raise TypeError(f"to_pandas must be of type bool. Got: {type(to_pandas)}")
 
-    # Eq 15 in IEC 62600-101
+    # Eq 15 in IEC 62600-101 Ed. 2.0 en 2024
     m0 = frequency_moment(
         S,
         0,
@@ -707,7 +707,7 @@ def peak_period(S, frequency_dimension="", to_pandas=True):
             f"frequency_dimension is not a dimension of S ({list(S.dims)}). Got: {frequency_dimension}."
         )
 
-    # Eq 14 in IEC 62600-101
+    # Eq 14 in IEC 62600-101 Ed. 2.0 en 2024
     fp = S.idxmax(dim=frequency_dimension)  # Hz
     Tp = 1 / fp
 
@@ -759,7 +759,7 @@ def energy_period(S, frequency_dimension="", frequency_bins=None, to_pandas=True
         to_pandas=False,
     )
 
-    # Eq 13 in IEC 62600-101
+    # Eq 13 in IEC 62600-101 Ed. 2.0 en 2024
     Te = mn1 / m0
 
     if to_pandas:
@@ -873,7 +873,7 @@ def spectral_width(S, frequency_dimension="", frequency_bins=None, to_pandas=Tru
         to_pandas=False,
     )
 
-    # Eq 16 in IEC 62600-101
+    # Eq 16 in IEC 62600-101 Ed. 2.0 en 2024
     v = np.sqrt((m0 * mn2 / np.power(mn1, 2)) - 1)
 
     if to_pandas:
@@ -949,7 +949,7 @@ def energy_flux(
     f = S[frequency_dimension]
 
     if deep:
-        # Eq 8 in IEC 62600-100, deep water simplification
+        # Eq 8 in IEC 62600-100 Ed. 2.0 en 2024, deep water simplification
         Te = energy_period(S, to_pandas=False)
         Hm0 = significant_wave_height(S, to_pandas=False)
 
@@ -964,7 +964,7 @@ def energy_flux(
         # wave celerity (group velocity)
         Cg = wave_celerity(k, h, g, depth_check=True, ratio=ratio, to_pandas=False)
 
-        # Calculating the wave energy flux, Eq 9 in IEC 62600-101
+        # Calculating the wave energy flux, Eq 9 in IEC 62600-101 Ed. 2.0 en 2024
         delta_f = f.diff(dim=frequency_dimension)
         delta_f0 = f[1] - f[0]
         delta_f0 = delta_f0.assign_coords({frequency_dimension: f[0]})
@@ -1100,7 +1100,7 @@ def wave_celerity(
         Cg.name = "Cg"
 
     else:
-        # Eq 10 in IEC 62600-101
+        # Eq 10 in IEC 62600-101 Ed. 2.0 en 2024
         Cg = (np.pi * f / k) * (1 + (2 * h * k) / np.sinh(2 * h * k))
         Cg = xr.DataArray(
             data=Cg, dims=frequency_dimension, coords={frequency_dimension: f}
@@ -1185,7 +1185,7 @@ def wave_number(f, h, rho=1025, g=9.80665, to_pandas=True):
     yi = xi * xi / np.power(1.0 - np.exp(-np.power(xi, 2.4908)), 0.4015)
     k0 = yi / h  # Initial guess without current-wave interaction
 
-    # Eq 11 in IEC 62600-101 using initial guess from Guo (2002)
+    # Eq 11 in IEC 62600-101 Ed. 2.0 en 2024 using initial guess from Guo (2002)
     def func(kk):
         val = np.power(w, 2) - g * kk * np.tanh(kk * h)
         return val
diff --git a/pyproject.toml b/pyproject.toml
new file mode 100644
index 000000000..09fc8d1e0
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,138 @@
+[build-system]
+requires = ["setuptools>=61.0", "wheel"]
+build-backend = "setuptools.build_meta"
+
+[project]
+name = "mhkit"
+# `version` is read from `[tools.setuptools.dynamic] version` during build: https://setuptools.pypa.io/en/latest/userguide/pyproject_config.html#dynamic-metadata
+dynamic = ["version"]
+description = "Marine and Hydrokinetic Toolkit"
+readme = "README.md"
+authors = [
+    {name = "MHKiT developers"}
+]
+license = {text = "Revised BSD"}
+classifiers = [
+    "Development Status :: 3 - Alpha",
+    "Programming Language :: Python :: 3",
+    "Topic :: Scientific/Engineering",
+    "Intended Audience :: Science/Research",
+    "Operating System :: OS Independent",
+]
+requires-python = ">=3.10"
+dependencies = [
+    "numpy>=2.0.0",
+    "pandas>=2.2.2",
+    "scipy>=1.14.0",
+    "xarray>=2024.6.0",
+    "matplotlib>=3.9.1",
+    "pecos>=0.3.0",
+]
+
+[project.optional-dependencies]
+# Core dependencies for each module
+wave = [
+    "scikit-learn>=1.5.1",
+    "statsmodels>=0.14.2",
+    "netCDF4>=1.7.1.post1",
+    "pytz",
+    "NREL-rex>=0.2.63",
+    "beautifulsoup4",
+    "requests",
+    "bottleneck",
+    "lxml"
+]
+
+tidal = [
+    "netCDF4>=1.7.1.post1",
+    "requests",
+    "bottleneck"
+]
+
+river = [
+    "netCDF4>=1.7.1.post1",
+    "requests",
+    "bottleneck",
+]
+
+dolfyn = [
+    "h5py>=3.11.0",
+    "h5pyd>=0.18.0",
+    "netCDF4>=1.7.1.post1",
+    "cartopy",
+]
+
+power = [
+]
+
+loads = [
+ "fatpack"
+]
+
+mooring = [
+]
+
+acoustics = [
+
+]
+
+qc = [
+
+]
+
+utils = [
+
+]
+
+# Development dependencies
+dev = [
+    "pytest",
+    "pylint",
+    "pytest-cov",
+    "pre-commit",
+    "coverage",
+    "coveralls"
+]
+
+
+
+# Install all optional dependencies
+all = [
+    "mhkit[wave]",
+    "mhkit[tidal]",
+    "mhkit[river]",
+    "mhkit[dolfyn]",
+    "mhkit[power]",
+    "mhkit[loads]",
+    "mhkit[mooring]",
+    "mhkit[acoustics]",
+    "mhkit[qc]",
+    "mhkit[utils]",
+]
+
+# Examples dependencies
+examples = [
+    "jupyter",
+    "notebook",
+    "ipykernel",
+    "nbval",
+    "utm",
+    "folium",
+    "mhkit[all]",
+
+]
+
+[project.urls]
+Homepage = "https://github.com/MHKiT-Software/mhkit-python"
+Documentation = "https://mhkit-software.github.io/MHKiT"
+
+[tool.setuptools]
+packages = ["mhkit"]
+zip-safe = false
+include-package-data = true
+
+[tool.setuptools.dynamic]
+version = {attr = "mhkit.__version__"} 
+
+[tool.pytest.ini_options]
+asyncio_default_fixture_loop_scope = "function"
diff --git a/requirements.txt b/requirements.txt
deleted file mode 100644
index 78106a7db..000000000
--- a/requirements.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-numpy>=2.0.0
-pandas>=2.2.2
-scipy>=1.14.0
-xarray>=2024.6.0
-matplotlib>=3.9.1
-scikit-learn>=1.5.1
-h5py>=3.11.0
-h5pyd>=0.18.0
-netCDF4>=1.7.1.post1
-statsmodels>=0.14.2
-requests
-pecos>=0.3.0
-fatpack
-NREL-rex>=0.2.63
-beautifulsoup4
-notebook
-numexpr>=2.10.0
-lxml
-bottleneck
\ No newline at end of file
diff --git a/setup.py b/setup.py
deleted file mode 100644
index 732e2037c..000000000
--- a/setup.py
+++ /dev/null
@@ -1,100 +0,0 @@
-import os
-import re
-from setuptools import setup, find_packages
-
-DISTNAME = "mhkit"
-PACKAGES = find_packages()
-EXTENSIONS = []
-DESCRIPTION = "Marine and Hydrokinetic Toolkit"
-AUTHOR = "MHKiT developers"
-MAINTAINER_EMAIL = ""
-LICENSE = "Revised BSD"
-URL = "https://github.com/MHKiT-Software/mhkit-python"
-CLASSIFIERS = [
-    "Development Status :: 3 - Alpha",
-    "Programming Language :: Python :: 3",
-    "Topic :: Scientific/Engineering",
-    "Intended Audience :: Science/Research",
-    "Operating System :: OS Independent",
-]
-DEPENDENCIES = [
-    "numpy>=2.0.0",
-    "pandas>=2.2.2",
-    "scipy>=1.14.0",
-    "xarray>=2024.6.0",
-    "matplotlib>=3.9.1",
-    "scikit-learn>=1.5.1",
-    "h5py>=3.11.0",
-    "h5pyd>=0.18.0",
-    "netCDF4>=1.7.1.post1",
-    "statsmodels>=0.14.2",
-    "requests",
-    "pecos>=0.3.0",
-    "fatpack",
-    "NREL-rex>=0.2.63",
-    "pytz",
-    "beautifulsoup4",
-    "numexpr>=2.10.0",
-    "lxml",
-    "bottleneck",
-]
-
-LONG_DESCRIPTION = """
-MHKiT-Python is a Python package designed for marine renewable energy applications to assist in 
-data processing and visualization.  The software package includes functionality for:
-
-* Data processing
-* Data visualization
-* Data quality control
-* Resource assessment
-* Device performance
-* Device loads
-
-Documentation
-------------------
-MHKiT-Python documentation includes overview information, installation instructions, API documentation, and examples.
-See the [MHKiT documentation](https://mhkit-software.github.io/MHKiT) for more information.
-
-Installation
-------------------------
-MHKiT-Python requires Python (3.10, or 3.11) along with several Python 
-package dependencies.  MHKiT-Python can be installed from PyPI using the command ``pip install mhkit``.
-See [installation instructions](https://mhkit-software.github.io/MHKiT/installation.html) for more information.
-
-Copyright and license
-------------------------
-MHKiT-Python is copyright through the National Renewable Energy Laboratory, 
-Pacific Northwest National Laboratory, and Sandia National Laboratories. 
-The software is distributed under the Revised BSD License.
-See [copyright and license](LICENSE.md) for more information.
-"""
-
-
-# get version from __init__.py
-file_dir = os.path.abspath(os.path.dirname(__file__))
-with open(os.path.join(file_dir, "mhkit", "__init__.py")) as f:
-    version_file = f.read()
-    version_match = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]", version_file, re.M)
-    if version_match:
-        VERSION = version_match.group(1)
-    else:
-        raise RuntimeError("Unable to find version string.")
-
-setup(
-    name=DISTNAME,
-    version=VERSION,
-    packages=PACKAGES,
-    ext_modules=EXTENSIONS,
-    description=DESCRIPTION,
-    long_description_content_type="text/markdown",
-    long_description=LONG_DESCRIPTION,
-    author=AUTHOR,
-    maintainer_email=MAINTAINER_EMAIL,
-    license=LICENSE,
-    url=URL,
-    classifiers=CLASSIFIERS,
-    zip_safe=False,
-    install_requires=DEPENDENCIES,
-    scripts=[],
-    include_package_data=True,
-)