JP-3900: Tests, clean up, and code style for residual_fringe

.pre-commit-config.yaml

@@ -58,7 +58,6 @@ repos:
             jwst/refpix/.* |
             jwst/resample/.* |
             jwst/reset/.* |
-            jwst/residual_fringe/.* |
             jwst/rscd/.* |
             jwst/saturation/.* |
             jwst/scripts/.* |

.ruff.toml

@@ -40,7 +40,6 @@ exclude = [
     "jwst/refpix/**.py",
     "jwst/resample/**.py",
     "jwst/reset/**.py",
-    "jwst/residual_fringe/**.py",
     "jwst/rscd/**.py",
     "jwst/saturation/**.py",
     "jwst/scripts/**.py",
@@ -140,7 +139,6 @@ ignore-fully-untyped = true  # Turn off annotation checking for fully untyped co
 "jwst/refpix/**.py" = ["D", "N", "A", "ARG", "B", "C4", "ICN", "INP", "ISC", "LOG", "NPY", "PGH", "PTH", "S", "SLF", "SLOT", "T20", "TRY", "UP", "YTT", "E501"]
 "jwst/resample/**.py" = ["D", "N", "A", "ARG", "B", "C4", "ICN", "INP", "ISC", "LOG", "NPY", "PGH", "PTH", "S", "SLF", "SLOT", "T20", "TRY", "UP", "YTT", "E501"]
 "jwst/reset/**.py" = ["D", "N", "A", "ARG", "B", "C4", "ICN", "INP", "ISC", "LOG", "NPY", "PGH", "PTH", "S", "SLF", "SLOT", "T20", "TRY", "UP", "YTT", "E501"]
-"jwst/residual_fringe/**.py" = ["D", "N", "A", "ARG", "B", "C4", "ICN", "INP", "ISC", "LOG", "NPY", "PGH", "PTH", "S", "SLF", "SLOT", "T20", "TRY", "UP", "YTT", "E501"]
 "jwst/rscd/**.py" = ["D", "N", "A", "ARG", "B", "C4", "ICN", "INP", "ISC", "LOG", "NPY", "PGH", "PTH", "S", "SLF", "SLOT", "T20", "TRY", "UP", "YTT", "E501"]
 "jwst/saturation/**.py" = ["D", "N", "A", "ARG", "B", "C4", "ICN", "INP", "ISC", "LOG", "NPY", "PGH", "PTH", "S", "SLF", "SLOT", "T20", "TRY", "UP", "YTT", "E501"]
 "jwst/scripts/**.py" = ["D", "N", "A", "ARG", "B", "C4", "ICN", "INP", "ISC", "LOG", "NPY", "PGH", "PTH", "S", "SLF", "SLOT", "T20", "TRY", "UP", "YTT", "E501"]

changes/9242.residual_fringe.rst

@@ -0,0 +1 @@
+Refactor for maintainability, fix intermediate filenames when input datamodel is read from memory, and stop producing an unused intermediate output file.

jwst/regtest/test_miri_mrs_spec2.py

@@ -45,6 +45,24 @@ def run_spec2(rtdata_module):
     Step.from_cmdline(args)
 
 
+@pytest.fixture(scope='module')
+def run_spec2_with_residual_fringe(rtdata_module):
+    """Run the Spec2Pipeline on a single exposure"""
+    rtdata = rtdata_module
+
+    # Get the input rate file
+    rtdata.get_data(INPUT_PATH + '/' + 'jw01024001001_04101_00001_mirifulong_rate.fits')
+
+    # Run the pipeline
+    args = ["calwebb_spec2", rtdata.input,
+            '--output_file=jw01024001001_04101_00001_mirifulong_rf',
+            '--steps.residual_fringe.skip=false',
+            '--steps.residual_fringe.save_results=true',
+            ]
+
+    Step.from_cmdline(args)
+
+
 @pytest.mark.slow
 @pytest.mark.bigdata
 @pytest.mark.parametrize(
@@ -83,3 +101,12 @@ def test_miri_mrs_wcs(run_spec2, fitsdiff_default_kwargs, rtdata_module):
         xtruth, ytruth = im_truth.meta.wcs.backward_transform(ratruth, dectruth, lamtruth)
         assert_allclose(xtest, xtruth)
         assert_allclose(ytest, ytruth)
+
+
+@pytest.mark.slow
+@pytest.mark.bigdata
+@pytest.mark.parametrize('suffix', ['residual_fringe', 'rf_s3d', 'rf_x1d', 'rf_cal'])
+def test_miri_mrs_spec2_with_rf(run_spec2_with_residual_fringe,
+                                fitsdiff_default_kwargs, suffix, rtdata_module):
+    rtdata = rtdata_module
+    rt.is_like_truth(rtdata, fitsdiff_default_kwargs, suffix, truth_path=TRUTH_PATH)

jwst/residual_fringe/__init__.py

@@ -1,3 +1,5 @@
+"""Correct residual fringes in MIRI MRS data."""
+
 from .residual_fringe_step import ResidualFringeStep
 
-__all__ = ['ResidualFringeStep']
+__all__ = ["ResidualFringeStep"]

jwst/residual_fringe/fitter.py

@@ -1,5 +1,6 @@
-import numpy as np
+import warnings
 
+import numpy as np
 import scipy.interpolate
 
 
@@ -8,6 +9,34 @@ def _lsq_spline(x, y, weights, knots, degree):
 
 
 def spline_fitter(x, y, weights, knots, degree, reject_outliers=False, domain=10, tolerance=0.0001):
+    """
+    Fit a spline function to 1D data.
+
+    Parameters
+    ----------
+    x : ndarray
+        Independent variable. Must be increasing.
+    y : ndarray
+        Dependent variable, matching dimensions of `x`.
+    weights : ndarray
+        Weights for spline fitting.  Must be positive.
+    knots : ndarray
+        Interior knots for the spline.
+    degree : int
+        Degree of the spline to fit, >= 1 and <= 5.
+    reject_outliers : bool, optional
+        If True, iteratively fit the data with outlier rejection.
+    domain : int, optional
+        Factor controlling the outlier threshold when `reject_outliers`
+        is True.
+    tolerance : float, optional
+        Fit convergence tolerance when reject_outliers` is True.
+
+    Returns
+    -------
+    callable
+        The spline function fit to the data.
+    """
     if not reject_outliers:
         return _lsq_spline(x, y, weights, knots, degree)
 
@@ -16,7 +45,6 @@ def spline_fitter(x, y, weights, knots, degree, reject_outliers=False, domain=10
     def chi_sq(spline, weights):
         return np.nansum((y - spline(x)) ** 2 * weights)
 
-
     # initial fit
     spline = _lsq_spline(x, y, weights, knots, degree)
     chi = chi_sq(spline, weights)
@@ -26,11 +54,13 @@ def chi_sq(spline, weights):
     deg_of_freedom = np.sum(weights) - nparams
 
     for _ in range(1000 * nparams):
-        scale = np.sqrt(chi / deg_of_freedom)
+        with warnings.catch_warnings():
+            warnings.simplefilter("ignore", RuntimeWarning)
+            scale = np.sqrt(chi / deg_of_freedom)
 
         # Calculate new weights
         resid = (y - spline(x)) / (scale * domain)
-        new_w = (np.where(resid**2 <= 1, 1 - resid**2, 0.))**2 * weights
+        new_w = (np.where(resid**2 <= 1, 1 - resid**2, 0.0)) ** 2 * weights
 
         # Fit new model and find chi
         spline = _lsq_spline(x, y, new_w, knots, degree)