WIP: Jp 3749 none

.github/workflows/ci.yml

@@ -43,6 +43,7 @@ jobs:
       default_python: "3.12"
       envs: |
         - linux: check-dependencies
+        - linux: check-types
   latest_crds_contexts:
     uses: spacetelescope/crds/.github/workflows/contexts.yml@94138b4501c9487535fd6b977492fc1a2c319708  # 12.0.2
   crds_context:

changes/8852.general.rst

@@ -0,0 +1 @@
+Added mypy type checking to CI checks

jwst/__init__.py

@@ -7,6 +7,6 @@
 _regex_git_hash = re.compile(r".*\+g(\w+)")
 __version_commit__ = ""
 if "+" in __version__:
-    commit = _regex_git_hash.match(__version__).groups()
+    commit = _regex_git_hash.match(__version__).groups()  # type: ignore
     if commit:
         __version_commit__ = commit[0]

jwst/ami/ami_analyze.py

@@ -3,7 +3,7 @@
 import numpy as np
 import copy
 
-from jwst.datamodels import CubeModel, ImageModel
+from jwst.datamodels import CubeModel, ImageModel  # type: ignore[attr-defined]
 
 from .find_affine2d_parameters import find_rotation
 from . import instrument_data

jwst/ami/leastsqnrm.py

@@ -613,7 +613,7 @@ def matrix_operations(img, model, flux=None, linfit=False, dqm=None):
 
     if linfit:
         try:
-            from linearfit import linearfit
+            from linearfit import linearfit  # type: ignore[import-not-found]
 
             # dependent variables
             M = np.asmatrix(flatimg)

jwst/ami/matrix_dft.py

@@ -198,7 +198,7 @@ def matrix_idft(*args, **kwargs):
     return matrix_dft(*args, **kwargs)
 
 
-matrix_idft.__doc__ = matrix_dft.__doc__.replace(
+matrix_idft.__doc__ = matrix_dft.__doc__.replace(  # type: ignore
     'Perform a matrix discrete Fourier transform',
     'Perform an inverse matrix discrete Fourier transform'
 )

jwst/assign_wcs/util.py

@@ -99,7 +99,7 @@ def _reproject(x, y):
 
 
 def compute_scale(wcs: WCS, fiducial: Union[tuple, np.ndarray],
-                  disp_axis: int = None, pscale_ratio: float = None) -> float:
+                  disp_axis: int | None = None, pscale_ratio: float | None = None) -> float:
     """Compute scaling transform.
 
     Parameters
@@ -137,8 +137,8 @@ def compute_scale(wcs: WCS, fiducial: Union[tuple, np.ndarray],
     crval_with_offsets = wcs(*crpix_with_offsets, with_bounding_box=False)
 
     coords = SkyCoord(ra=crval_with_offsets[spatial_idx[0]], dec=crval_with_offsets[spatial_idx[1]], unit="deg")
-    xscale = np.abs(coords[0].separation(coords[1]).value)
-    yscale = np.abs(coords[0].separation(coords[2]).value)
+    xscale: float = np.abs(coords[0].separation(coords[1]).value)
+    yscale: float = np.abs(coords[0].separation(coords[2]).value)
 
     if pscale_ratio is not None:
         xscale *= pscale_ratio
@@ -149,7 +149,8 @@ def compute_scale(wcs: WCS, fiducial: Union[tuple, np.ndarray],
         # Assuming disp_axis is consistent with DataModel.meta.wcsinfo.dispersion.direction
         return yscale if disp_axis == 1 else xscale
 
-    return np.sqrt(xscale * yscale)
+    scale: float = np.sqrt(xscale * yscale)
+    return scale
 
 
 def calc_rotation_matrix(roll_ref: float, v3i_yang: float, vparity: int = 1) -> List[float]:

jwst/associations/__init__.py

@@ -9,7 +9,7 @@
 """
 
 # Take version from the upstream package
-from .. import __version__
+from jwst import __version__
 
 
 # Utility

jwst/associations/association.py

@@ -79,12 +79,12 @@ class Association(MutableMapping):
     GLOBAL_CONSTRAINT = None
     """Global constraints"""
 
-    INVALID_VALUES = None
+    INVALID_VALUES: tuple | None = None
     """Attribute values that indicate the
     attribute is not specified.
     """
 
-    ioregistry = IORegistry()
+    ioregistry: IORegistry = IORegistry()
     """The association IO registry"""
 
     def __init__(

jwst/associations/association_io.py

@@ -14,7 +14,7 @@
 logger = logging.getLogger(__name__)
 logger.addHandler(logging.NullHandler())
 
-__all__ = []
+__all__: list = []
 
 
 # Define JSON encoder to convert `Member` to `dict`

jwst/associations/lib/constraint.py

@@ -56,7 +56,7 @@ class SimpleConstraintABC(abc.ABC):
     """
 
     # Attributes to show in the string representation.
-    _str_attrs = ('name', 'value')
+    _str_attrs: tuple = ('name', 'value')
 
     def __new__(cls, *args, **kwargs):
         """Force creation of the constraint attribute dict before anything else."""

jwst/associations/lib/rules_level3_base.py

@@ -97,7 +97,7 @@ class DMS_Level3_Base(DMSBaseMixin, Association):
     INVALID_VALUES = _EMPTY
 
     # Make sequences type-dependent
-    _sequences = defaultdict(Counter)
+    _sequences: defaultdict = defaultdict(Counter)
 
     def __init__(self, *args, **kwargs):
 

jwst/associations/tests/helpers.py

@@ -56,11 +56,11 @@ class BasePoolRule():
 
     # Define the pools and testing parameters related to them.
     # Each entry is a tuple starting with the path of the pool.
-    pools = []
+    pools: list = []
 
     # Define the rules that SHOULD be present.
     # Each entry is the class name of the rule.
-    valid_rules = []
+    valid_rules: list = []
 
     def test_rules_exist(self):
         rules = registry_level3_only()

jwst/badpix_selfcal/badpix_selfcal.py

@@ -3,7 +3,7 @@
 
 import numpy as np
 
-import jwst.datamodels as dm
+from jwst.datamodels import IFUImageModel  # type: ignore[attr-defined]
 from stcal.outlier_detection.utils import medfilt
 from stdatamodels.jwst.datamodels.dqflags import pixel
 
@@ -12,7 +12,7 @@ def badpix_selfcal(minimg: np.ndarray,
                    flagfrac_lower: float = 0.001,
                    flagfrac_upper: float = 0.001,
                    kernel_size: int = 15,
-                   dispaxis=None) -> np.ndarray:
+                   dispaxis=None) -> tuple:
     """
     Flag residual artifacts as bad pixels in the DQ array of a JWST exposure
 
@@ -59,26 +59,25 @@ def badpix_selfcal(minimg: np.ndarray,
     flag_low, flag_high = np.nanpercentile(minimg_hpf, [flagfrac_lower * 100, (1 - flagfrac_upper) * 100])
     bad = (minimg_hpf > flag_high) | (minimg_hpf < flag_low)
     flagged_indices = np.where(bad)
-
     return flagged_indices
 
 
-def apply_flags(input_model: dm.IFUImageModel, flagged_indices: np.ndarray) -> dm.IFUImageModel:
+def apply_flags(input_model: IFUImageModel, flagged_indices: np.ndarray) -> IFUImageModel:
     """
     Apply the flagged indices to the input model. Sets the flagged pixels to NaN
     and the DQ flag to DO_NOT_USE + OTHER_BAD_PIXEL
 
     Parameters
     ----------
-    input_model : dm.IFUImageModel
+    input_model : IFUImageModel
         Input science data to be corrected
     flagged_indices : np.ndarray
         Indices of the flagged pixels,
         shaped like output from np.where
 
     Returns
     -------
-    output_model : dm.IFUImageModel
+    output_model : IFUImageModel
         Flagged data model
     """
 

jwst/cube_skymatch/cube_skymatch_step.py

@@ -52,7 +52,7 @@ class CubeSkyMatchStep(Step):
         binwidth = float(min=0.0, default=0.1) # Bin width for 'mode' and 'midpt' `skystat`, in sigma
     """
 
-    reference_file_types = []
+    reference_file_types: list = []
 
     def process(self, input1, input2):
         cube_models = ModelContainer(input1)

jwst/datamodels/__init__.py

@@ -34,7 +34,7 @@
 _deprecated_modules = ['schema']
 
 # Deprecated models in stdatamodels
-_deprecated_models = []
+_deprecated_models: list[str] = []
 
 # Import all submodules from stdatamodels.jwst.datamodels
 for attr in dir(stdatamodels.jwst.datamodels):

jwst/dq_init/dq_initialization.py

@@ -5,7 +5,7 @@
 from stdatamodels.jwst import datamodels
 
 from ..lib import reffile_utils
-from jwst.datamodels import dqflags
+from jwst.datamodels import dqflags  # type: ignore[attr-defined]
 
 log = logging.getLogger(__name__)
 log.setLevel(logging.DEBUG)

jwst/dq_init/tests/test_dq_init.py

@@ -259,12 +259,12 @@ def test_dq_add1_groupdq():
 
 # Set parameters for multiple runs of guider data
 args = "xstart, ystart, xsize, ysize, nints, ngroups, instrument, exp_type, detector"
-test_data = [(1, 1, 2048, 2048, 2, 2, 'FGS', 'FGS_ID-IMAGE', 'GUIDER1'),
+test_data_multiple = [(1, 1, 2048, 2048, 2, 2, 'FGS', 'FGS_ID-IMAGE', 'GUIDER1'),
              (1, 1, 1032, 1024, 1, 5, 'MIRI', 'MIR_IMAGE', 'MIRIMAGE')]
 ids = ["GuiderRawModel-Image", "RampModel"]
 
 
-@pytest.mark.parametrize(args, test_data, ids=ids)
+@pytest.mark.parametrize(args, test_data_multiple, ids=ids)
 def test_fullstep(xstart, ystart, xsize, ysize, nints, ngroups, instrument, exp_type, detector):
     """Test that the full step runs"""
 

jwst/extract_1d/apply_apcorr.py

@@ -1,9 +1,6 @@
 import abc
 
-from typing import Tuple, Union, Type
 from scipy.interpolate import RectBivariateSpline, interp1d
-from astropy.io import fits
-from stdatamodels import DataModel
 from stdatamodels.jwst.datamodels import MultiSlitModel
 
 from ..assign_wcs.util import compute_scale
@@ -55,10 +52,8 @@ class ApCorrBase(abc.ABC):
         }
     }
 
-    size_key = None
-
-    def __init__(self, input_model: DataModel, apcorr_table: fits.FITS_rec, sizeunit: str,
-                 location: Tuple[float, float, float] = None, slit_name: str = None, **match_kwargs):
+    def __init__(self, input_model, apcorr_table, sizeunit,
+                 location = None, slit_name = None, **match_kwargs):
         self.correction = None
 
         self.model = input_model
@@ -75,6 +70,10 @@ def __init__(self, input_model: DataModel, apcorr_table: fits.FITS_rec, sizeunit
         self.apcorr_func = self.approximate()
         self.tabulated_correction = None
 
+    @property
+    def size_key(self):
+        return None
+
     def _convert_size_units(self):
         """If the SIZE or Radius column is in units of arcseconds, convert to pixels."""
         if self.apcorr_sizeunits.startswith('arcsec'):
@@ -102,7 +101,7 @@ def _convert_size_units(self):
                     'pixels.'
                 )
 
-    def _get_match_keys(self) -> dict:
+    def _get_match_keys(self):
         """Get column keys needed for reducing the reference table based on input."""
         instrument = self.model.meta.instrument.name.upper()
         exptype = self.model.meta.exposure.type.upper()
@@ -113,7 +112,7 @@ def _get_match_keys(self) -> dict:
             if key in exptype:
                 return relevant_pars[key]
 
-    def _get_match_pars(self) -> dict:
+    def _get_match_pars(self):
         """Get meta parameters required for reference table row-selection."""
         match_pars = {}
 
@@ -125,7 +124,7 @@ def _get_match_pars(self) -> dict:
 
         return match_pars
 
-    def _reduce_reftable(self) -> fits.FITS_record:
+    def _reduce_reftable(self):
         """Reduce full reference table to a single matched row."""
         table = self._reference_table.copy()
 
@@ -145,7 +144,7 @@ def approximate(self):
         """Generate an approximate aperture correction function based on input data."""
         pass
 
-    def apply(self, spec_table: fits.FITS_rec):
+    def apply(self, spec_table):
         """Apply interpolated aperture correction value to source-related extraction results in-place.
 
         Parameters
@@ -181,14 +180,14 @@ class ApCorrPhase(ApCorrBase):
     """
     size_key = 'size'
 
-    def __init__(self, *args, pixphase: float = 0.5, **kwargs):
+    def __init__(self, *args, pixphase = 0.5, **kwargs):
         self.phase = pixphase  # In the future we'll attempt to measure the pixel phase from inputs.
 
         super().__init__(*args, **kwargs)
 
     def approximate(self):
         """Generate an approximate function for interpolating apcorr values to input wavelength and size."""
-        def _approx_func(wavelength: float, size: float, pixel_phase: float) -> RectBivariateSpline:
+        def _approx_func(wavelength, size, pixel_phase):
             """Create a 'custom' approximation function that approximates the aperture correction in two stages based on
             input data.
 
@@ -228,7 +227,7 @@ def _approx_func(wavelength: float, size: float, pixel_phase: float) -> RectBiva
     def measure_phase(self):  # Future method in determining pixel phase
         pass
 
-    def tabulate_correction(self, spec_table: fits.FITS_rec):
+    def tabulate_correction(self, spec_table):
         """Tabulate the interpolated aperture correction value.
 
          This will save time when applying it later, especially if it is to be applied to multiple integrations.  
@@ -255,7 +254,7 @@ def tabulate_correction(self, spec_table: fits.FITS_rec):
 
         self.tabulated_correction = np.asarray(coefs)
 
-    def apply(self, spec_table: fits.FITS_rec, use_tabulated=False):
+    def apply(self, spec_table, use_tabulated=False):
         """Apply interpolated aperture correction value to source-related extraction results in-place.
 
         Parameters
@@ -297,8 +296,8 @@ def apply(self, spec_table: fits.FITS_rec, use_tabulated=False):
 class ApCorrRadial(ApCorrBase):
     """Aperture correction class used with spectral data produced from an extraction aperture radius."""
 
-    def __init__(self, input_model: DataModel, apcorr_table,
-                 location: Tuple[float, float, float] = None):
+    def __init__(self, input_model, apcorr_table,
+                 location = None):
 
         self.correction = None
         self.model = input_model
@@ -329,7 +328,7 @@ def _convert_size_units(self):
                     'pixels.'
                 )
 
-    def apply(self, spec_table: fits.FITS_rec):
+    def apply(self, spec_table):
         """Apply interpolated aperture correction value to source-related extraction results in-place.
 
         Parameters
@@ -410,7 +409,7 @@ def approximate(self):
         return RectBivariateSpline(size, wavelength, apcorr.T, ky=1, kx=1)
 
 
-def select_apcorr(input_model: DataModel) -> Union[Type[ApCorr], Type[ApCorrPhase], Type[ApCorrRadial]]:
+def select_apcorr(input_model):
     """Select appropriate Aperture correction class based on input DataModel.
 
     Parameters