DOC: Fix broken ref

by addressing some low-hanging fruits

Author: pllim

docs/conf.py

@@ -78,6 +78,7 @@ def setup(app):
     'gwcs': ('https://gwcs.readthedocs.io/en/stable/', None),
     'stdatamodels': ('https://stdatamodels.readthedocs.io/en/latest/', None),
     'stcal': ('https://stcal.readthedocs.io/en/latest/', None),
+    'stpipe': ('https://stpipe.readthedocs.io/en/latest/', None),
     'drizzle': ('https://drizzlepac.readthedocs.io/en/latest/', None),
     'tweakwcs': ('https://tweakwcs.readthedocs.io/en/latest/', None),
 }

docs/jwst/ami_analyze/description.rst

@@ -123,7 +123,7 @@ Inputs
 
 3D calibrated image
 ^^^^^^^^^^^^^^^^^^^
-:Data model: `~jwst.datamodels.DataModel`
+:Data model: `~stdatamodels.DataModel`
 :File suffix: _calints
 
 The ``ami_analyze`` step takes a single calibrated image cube as input, which should be

docs/jwst/outlier_detection/outlier_detection_imaging.rst

@@ -6,7 +6,7 @@ Imaging Data
 This module serves as the interface for applying ``outlier_detection`` to direct
 image observations, like those taken with MIRI, NIRCam, and NIRISS.
 A :ref:`Stage 3 association <asn-level3-techspecs>`,
-which is loaded into a :py:class:`~jwst.datamodels.ModelLibrary` object,
+which is loaded into a :py:class:`~jwst.datamodels.library.ModelLibrary` object,
 serves as the basic format for all processing performed by this step.
 This routine performs the following operations:
 
@@ -107,10 +107,10 @@ Control over this memory model happens
 with the use of the ``in_memory`` parameter, which defaults to True.
 The full impact of setting this parameter to `False` includes:
 
-#. The input :py:class:`~jwst.datamodels.ModelLibrary` object is loaded with `on_disk=True`.
+#. The input :py:class:`~jwst.datamodels.library.ModelLibrary` object is loaded with `on_disk=True`.
    This ensures that input models are loaded into memory one at at time,
    and saved to a temporary file when not in use; these read-write operations are handled internally by
-   the :py:class:`~jwst.datamodels.ModelLibrary` object.
+   the :py:class:`~jwst.datamodels.library.ModelLibrary` object.
 
 #. Computing the median image works by writing the resampled data frames to appendable files
    on disk that are split into sections spatially but contain the entire ``groups``

docs/jwst/outlier_detection/outlier_detection_spec.rst

@@ -10,7 +10,7 @@ Please refer to those docs for more information.
 A :ref:`Stage 3 association <asn-level3-techspecs>`,
 which is loaded into a :py:class:`~jwst.datamodels.ModelContainer` object,
 serves as the input and output to this step, and the :py:class:`~jwst.datamodels.ModelContainer`
-is converted into a :py:class:`~jwst.datamodels.ModelLibrary` object to allow sharing code
+is converted into a :py:class:`~jwst.datamodels.library.ModelLibrary` object to allow sharing code
 with the imaging mode.
 
 This routine performs identical operations to the imaging mode, with the following exceptions:

docs/jwst/pipeline/calwebb_ami3.rst

@@ -40,7 +40,7 @@ Inputs
 3D calibrated images
 ^^^^^^^^^^^^^^^^^^^^
 
-:Data model: `~jwst.datamodels.DataModel`
+:Data model: `~stdatamodels.DataModel`
 :File suffix: _calints
 
 The inputs to ``calwebb_ami3`` need to be in the form of an ASN file that lists

docs/jwst/skymatch/description.rst

@@ -98,7 +98,7 @@ can be grouped together is based on several attributes described in
 `jwst.datamodels.ModelContainer`. This grouping is performed automatically
 in the ``skymatch`` step using the
 `jwst.datamodels.ModelContainer.models_grouped` property or
-:py:meth:`jwst.datamodels.ModelLibrary.group_indices`.
+:py:meth:`jwst.datamodels.library.ModelLibrary.group_indices`.
 
 However, when background across different detectors in a single "exposure"
 (or "group") is dominated by unpredictable background components, we no longer

docs/jwst/stpipe/devel_io_design.rst

@@ -176,7 +176,7 @@ ModelContainer vs ModelLibrary
 ``````````````````````````````
 
 Some steps in the pipeline, namely any steps involved in the Stage 3 Imaging pipeline,
-rely on the :class:`~jwst.datamodels.ModelLibrary` class instead of the
+rely on the :class:`~jwst.datamodels.library.ModelLibrary` class instead of the
 :class:`~jwst.datamodels.ModelContainer` class to process association-type data.
 The `ModelLibrary` class is purpose-built for enabling memory-saving options in the
 image3 pipeline and is only recommended when working with large associations.

jwst/ami/bp_fix.py

@@ -182,7 +182,7 @@ def bad_pixels(data, median_size, median_tres):
 
     Returns
     -------
-    pxdq : np.ndarray[int]
+    pxdq : ndarray[int]
         Bad pixel mask identified by median filtering
     """
     mfil_data = median_filter(data, size=median_size)
@@ -280,7 +280,7 @@ def fix_bad_pixels(data, pxdq0, filt, pxsc, nrm_model):
     -------
     data : numpy array
         Corrected data
-    pxdq : np.ndarray[int]
+    pxdq : ndarray[int]
         Mask of bad pixels, updated if new ones were found
     """
     dq_dnu = pxdq0 & DO_NOT_USE == DO_NOT_USE

jwst/ami/leastsqnrm.py

@@ -548,31 +548,31 @@ class LinearFit:
 
     Attributes
     ----------
-    dependent_variable : np.ndarray (1xN)
+    dependent_variable : ndarray (1xN)
         Dependent_variables of the linear equation system (N equations, M unknown coefficients)
-    inverse_covariance_matrix : np.ndarray (NxN)
+    inverse_covariance_matrix : ndarray (NxN)
         Inverse covariance matrix corresponding to the dependent_variable.
         i.e. data weights proportional to 1/sigma**2 where sigma=uncertainty
-    independent_variable : np.ndarray (MxN)
+    independent_variable : ndarray (MxN)
         The independent_variables that are multiplied by the unknown coefficients
 
     Calculated Attributes
     ----------
-    p : np.ndarray
+    p : ndarray
         Coefficients of the solution
-    p_formal_uncertainty : np.ndarray
+    p_formal_uncertainty : ndarray
         Formal uncertainty of the coefficients
-    p_formal_covariance_matrix : np.ndarray
+    p_formal_covariance_matrix : ndarray
         Formal covariance matrix of the coefficients (not rescaled)
-    p_normalised_uncertainty : np.ndarray
+    p_normalised_uncertainty : ndarray
         Normalised uncertainty (chi2 = 1) of the coefficients
-    p_normalised_covariance_matrix : np.ndarray
+    p_normalised_covariance_matrix : ndarray
         Normalised covariance matrix of the coefficients (rescaled to yield chi2=1)
-    p_correlation_matrix : np.ndarray
+    p_correlation_matrix : ndarray
         Coefficient correlation matrix
-    fit : np.ndarray
+    fit : ndarray
         Values of the best-fit model
-    residuals : np.ndarray
+    residuals : ndarray
         Observed - Calculated (O-C) residuals
     chi2 : float
         Chi-square value of the best fit

jwst/ami/lg_model.py

@@ -52,7 +52,7 @@ def __init__(
             Datamodel containing mask geometry information
         pixscale : float
            Initial estimate of pixel scale in radians
-        bandpass : np.ndarray[float]
+        bandpass : ndarray[float]
             Array of the form: [(weight1, wavl1), (weight2, wavl2), ...]
         mask : str
             Keyword for built-in values
@@ -173,7 +173,7 @@ def make_model(self, fov, psf_offset=(0, 0)):
 
         Returns
         -------
-        model : np.ndarray[float]
+        model : ndarray[float]
             Generated fringe model, shape (fov, fov, N * (N - 1) + 2)
         """
         self.fov = fov