Merge branch 'main' into jp-3677

changes/318.bugfix.rst

@@ -0,0 +1,5 @@
+For `ramp_fitting`, the `CRMAG` element was not originally implemented in
+the C-extension for ramp fitting.  It is now implemented.  A bug in the read
+noise recalculation for CHARGELOSS when using the multiprocessing option has
+been fixed.  Further, in `JWST` regression tests have been added to test for
+multiprocessing to ensure testing for anything that could affect multiprocessing.

changes/319.bugfix.rst

@@ -0,0 +1 @@
+Update weight threshold calculation in outlier detection to work around numpy bug that introduces small numerical differences for a mean of a masked array.

src/stcal/outlier_detection/utils.py

@@ -68,20 +68,15 @@ def compute_weight_threshold(weight, maskpt):
     float
         The weight threshold for this integration.
     '''
-    # necessary in order to assure that mask gets applied correctly
-    if hasattr(weight, '_mask'):
-        del weight._mask
-    mask_zero_weight = np.equal(weight, 0.)
-    mask_nans = np.isnan(weight)
-    # Combine the masks
-    weight_masked = np.ma.array(weight, mask=np.logical_or(
-        mask_zero_weight, mask_nans))
-    # Sigma-clip the unmasked data
-    weight_masked = sigma_clip(weight_masked, sigma=3, maxiters=5)
-    mean_weight = np.mean(weight_masked)
-    # Mask pixels where weight falls below maskpt percent
-    weight_threshold = mean_weight * maskpt
-    return weight_threshold
+    return np.mean(
+        sigma_clip(
+            weight[np.isfinite(weight) & (weight != 0)],
+            sigma=3,
+            maxiters=5,
+            masked=False,
+            copy=False,
+        ),
+    dtype='f8') * maskpt
 
 
 def _abs_deriv(array):

src/stcal/ramp_fitting/ols_fit.py

@@ -46,10 +46,6 @@ def ols_ramp_fit_multi(ramp_data, buffsize, save_opt, readnoise_2d, gain_2d, wei
     gain_2d : ndarray
         gain for all pixels
 
-    algorithm : str
-        'OLS' specifies that ordinary least squares should be used;
-        'GLS' specifies that generalized least squares should be used.
-
     weighting : str
         'optimal' specifies that optimal weighting should be used;
          currently the only weighting supported.
@@ -214,6 +210,11 @@ def assemble_pool_results(ramp_data, save_opt, pool_results, rows_per_slice):
     """
     # Create output arrays for each output tuple.  The input ramp data and
     # slices are needed for this.
+    for result in pool_results:
+        image_slice, integ_slice, opt_slice = result
+        if image_slice is None or integ_slice is None:
+            return None, None, None
+
     image_info, integ_info, opt_info = create_output_info(ramp_data, pool_results, save_opt)
 
     # Loop over the slices and assemble each slice into the main return arrays.
@@ -570,6 +571,14 @@ def slice_ramp_data(ramp_data, start_row, nrows):
     ramp_data_slice.flags_saturated = ramp_data.flags_saturated
     ramp_data_slice.flags_no_gain_val = ramp_data.flags_no_gain_val
     ramp_data_slice.flags_unreliable_slope = ramp_data.flags_unreliable_slope
+    ramp_data_slice.flags_chargeloss = ramp_data.flags_chargeloss
+
+    # For possible CHARGELOSS flagging.
+    if ramp_data.orig_gdq is not None:
+        ogdq = ramp_data.orig_gdq[:, :, start_row : start_row + nrows, :].copy()
+        ramp_data_slice.orig_gdq = ogdq
+    else:
+        ramp_data_slice.orig_gdq = None
 
     # Slice info
     ramp_data_slice.start_row = start_row