Short list of VMDAS updates (#405)

Discovered from a recent vessel survey that I misunderstood TDI's
documentation on a couple of VMDAS parameters. The speed and direction
parameters I thought it was pulling directly from the GPS it is
calculating from the change in latitude/longitude over time. I renamed
them and added the two variables that are actually directly from the
GPS.

Also discovered that VMDAS cannot take an RMC NMEA sentence, so the GPS
timestamps are lacking a datestamp. Which is not helpful if your ADCP's
clock is on local time and the GPS is always in UTC. So I added a code
block that checks if the GPS UTC time rolled over from 23:59:59 to
00:00:00 and manually updated the day.

---------

Co-authored-by: ssolson <[email protected]>
Co-authored-by: akeeste <[email protected]>

Author: 3 people

examples/data/dolfyn/test_data/dat_vm.mat

@@ -137,10 +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"])):

examples/data/dolfyn/test_data/vmdas01_wh.nc

@@ -235,15 +235,15 @@
         "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": (
         [],
         "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", ""),
@@ -970,8 +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",
@@ -993,14 +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

mhkit/dolfyn/io/base.py

@@ -658,16 +658,17 @@ 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