JP-3827: Remove unused error from ramp data

changes/9109.datamodels.rst

@@ -0,0 +1 @@
+Removed the unused error array from 4D ramp models.

changes/9109.pipeline.rst

@@ -0,0 +1 @@
+Removed references to the unused error array in 4D ramp models in all detector1 pipeline steps.

docs/jwst/dark_current/description.rst

@@ -49,8 +49,6 @@ subtracted from the corresponding first few integrations of the science exposure
 The data in the last integration of the dark reference file is applied to all
 remaining science integrations.
 
-The ERR arrays of the science data are currently not modified by this step.
-
 The DQ flags from the dark reference file are propagated into the science
 exposure PIXELDQ array using a bitwise OR operation.
 

docs/jwst/linearity/description.rst

@@ -63,8 +63,6 @@ Special Handling
    applied to that group. Any groups for that pixel that are not flagged as
    saturated will be corrected.
 
-The ERR array of the input science exposure is not modified.
-
 The flags from the linearity reference file DQ array are propagated into the
 PIXELDQ array of the science exposure using a bitwise OR operation.
 

docs/jwst/pipeline/calwebb_detector1.rst

@@ -127,7 +127,7 @@ input will be in the form of a `~jwst.datamodels.Level1bModel`, which only
 contains the 4D array of detector pixel values, along with some optional
 extensions. When such a file is loaded into the pipeline, it is immediately
 converted into a `~jwst.datamodels.RampModel`, and has all additional data arrays
-for errors and Data Quality flags created and initialized to zero.
+for Data Quality flags created and initialized to zero.
 
 The input can also contain a 3D cube of NIRCam "Frame 0" data, where
 each image plane in the 3D cube is the initial frame for each integration in

jwst/dark_current/dark_current_step.py

@@ -173,5 +173,4 @@ def dark_output_data_2_ramp_model(out_data, out_model):
         out_model.data = out_data.data
         out_model.groupdq = out_data.groupdq
         out_model.pixeldq = out_data.pixeldq
-        out_model.err = out_data.err
         return out_model

jwst/dark_current/tests/test_dark_sub.py

@@ -353,8 +353,12 @@ def test_2_int(make_rampmodel, make_darkmodel):
 
 # Refac done
 def test_frame_avg(make_rampmodel, make_darkmodel):
-    '''Check that if NFRAMES>1 or GROUPGAP>0, the frame-averaged dark data are
-    subtracted group-by-group from science data groups and the ERR arrays are not modified'''
+    """
+    Test frame averaging.
+
+    Check that if NFRAMES>1 or GROUPGAP>0, the frame-averaged dark data are
+    subtracted group-by-group from science data groups.
+    """
 
     # size of integration
     nints = 1
@@ -393,9 +397,6 @@ def test_frame_avg(make_rampmodel, make_darkmodel):
     assert outfile.data[0, 2, 500, 500] == pytest.approx(2.05)
     assert outfile.data[0, 3, 500, 500] == pytest.approx(2.65)
 
-    # check that the error array is not modified.
-    np.testing.assert_array_equal(outfile.err[:, :], 0)
-
 
 # ------------------------------------------------------------------------------
 def test_basic_step(make_rampmodel, make_darkmodel):

jwst/dq_init/tests/test_dq_init.py

@@ -1,8 +1,6 @@
 import numpy as np
 import pytest
-import warnings
 
-from stdatamodels.validate import ValidationWarning
 from stdatamodels.jwst.datamodels import MaskModel, GuiderRawModel, RampModel, dqflags
 
 from jwst.dq_init import DQInitStep
@@ -107,45 +105,6 @@ def test_groupdq():
                                   err_msg='groupdq not initialized to zero')
 
 
-def test_err():
-    """Check that a 4-D ERR array is initialized and all values are zero."""
-
-    # size of integration
-    instrument = 'MIRI'
-    nints = 1
-    ngroups = 5
-    xsize = 1032
-    ysize = 1024
-    xstart = 1
-    ystart = 1
-
-    # create raw input data for step
-    dm_ramp = make_rawramp(instrument, nints, ngroups, ysize, xsize, ystart, xstart)
-
-    # create a MaskModel for the dq input mask
-    dq, dq_def = make_maskmodel(ysize, xsize)
-
-    # write mask model
-    ref_data = MaskModel(dq=dq, dq_def=dq_def)
-    ref_data.meta.instrument.name = instrument
-    ref_data.meta.subarray.xstart = xstart
-    ref_data.meta.subarray.xsize = xsize
-    ref_data.meta.subarray.ystart = ystart
-    ref_data.meta.subarray.ysize = ysize
-
-    # Filter out validation warnings from ref_data
-    warnings.filterwarnings("ignore", category=ValidationWarning)
-
-    # run correction step
-    outfile = do_dqinit(dm_ramp, ref_data)
-
-    # check that ERR array was created and initialized to zero
-    errarr = outfile.err
-
-    assert errarr.ndim == 4  # check that output err array is 4-D
-    assert np.all(errarr == 0)  # check that values are 0
-
-
 def test_dq_subarray():
     """Test that the pipeline properly extracts the subarray from the reference file."""
     # put dq flags in specific pixels and make sure they match in the output subarray file

jwst/linearity/tests/test_linearity.py

@@ -291,36 +291,6 @@ def test_lin_subarray():
     assert (outpixdq[76, 104] == 1)
 
 
-def test_err_array():
-    """Test that the error array is not changed by the linearity step"""
-
-    # size of integration
-    ngroups = 10
-    xsize = 1032
-    ysize = 1024
-
-    # create a JWST datamodel for MIRI data
-    im = RampModel((1, ngroups, ysize, xsize))
-    im.data += 1
-    im.err += 2
-    # set file header values
-    im.meta.instrument.detector = 'MIRIMAGE'
-    im.meta.instrument.name = 'MIRI'
-    im.meta.observation.date = '2018-01-01'
-    im.meta.observation.time = '00:00:00'
-    im.meta.subarray.xstart = 1
-    im.meta.subarray.xsize = xsize
-    im.meta.subarray.ystart = 1
-    im.meta.subarray.ysize = ysize
-
-    # run pipeline
-    outfile = LinearityStep.call(im)
-
-    # check output of error array
-    # test that the science data are not changed
-    np.testing.assert_allclose(im.err, outfile.err)
-
-
 def make_rampmodel(nints, ngroups, ysize, xsize):
     """Function to provide ramp model to tests"""
 

jwst/ramp_fitting/tests/test_ramp_fit.py

@@ -1004,4 +1004,3 @@ def base_print(label, arr):
     arr_str = np.array2string(arr, max_line_width=np.nan, separator=", ")
     print(label)
     print(arr_str)
-

jwst/refpix/irs2_subtract_reference.py

@@ -186,7 +186,7 @@ def correct_model(output_model, irs2_model, scipix_n_default=16, refpix_r_defaul
     else:                       # don't change orientation
         output_model.data = temp_data
 
-    # Strip interleaved ref pixels from the PIXELDQ, GROUPDQ, and ERR extensions.
+    # Strip interleaved ref pixels from the PIXELDQ and GROUPDQ extensions.
     if not preserve_refpix:
         strip_ref_pixels(output_model, irs2_mask)
 
@@ -244,7 +244,7 @@ def make_irs2_mask(nx, ny, scipix_n, refpix_r):
 
 
 def strip_ref_pixels(output_model, irs2_mask):
-    """Copy out the normal pixels from PIXELDQ, GROUPDQ, and ERR arrays.
+    """Copy out the normal pixels from PIXELDQ and GROUPDQ arrays.
 
     Parameters
     ----------
@@ -267,8 +267,6 @@ def strip_ref_pixels(output_model, irs2_mask):
         temp_array = output_model.groupdq
         output_model.groupdq = temp_array[..., irs2_mask, :]
 
-        temp_array = output_model.err
-        output_model.err = temp_array[..., irs2_mask, :]
     elif detector == "NRS2":
         # Reverse the direction of the mask, and select rows.
         temp_mask = irs2_mask[::-1]
@@ -279,8 +277,6 @@ def strip_ref_pixels(output_model, irs2_mask):
         temp_array = output_model.groupdq
         output_model.groupdq = temp_array[..., temp_mask, :]
 
-        temp_array = output_model.err
-        output_model.err = temp_array[..., temp_mask, :]
     else:
         # Select columns.
         temp_array = output_model.pixeldq
@@ -289,9 +285,6 @@ def strip_ref_pixels(output_model, irs2_mask):
         temp_array = output_model.groupdq
         output_model.groupdq = temp_array[..., irs2_mask]
 
-        temp_array = output_model.err
-        output_model.err = temp_array[..., irs2_mask]
-
 
 def clobber_ref(data, output, odd_even, mask, ref_flags, is_irs2,
                 scipix_n=16, refpix_r=4):

jwst/refpix/tests/test_refpix.py

@@ -987,15 +987,12 @@ def test_preserve_refpix(detector, irs2, preserve):
     if not irs2:
         # parameter ignored for non-irs2 data
         assert out.data.shape == (1, ngroups, ysize, xsize)
-        assert out.err.shape == (1, ngroups, ysize, xsize)
         assert out.pixeldq.shape == (ysize, xsize)
     elif preserve:
         # output data shape is the same
         assert out.data.shape == (1, ngroups, ysize, xsize)
-        assert out.err.shape == (1, ngroups, ysize, xsize)
         assert out.pixeldq.shape == (ysize, xsize)
     else:
         # output data is trimmed to remove interleaved refpix
         assert out.data.shape == (1, ngroups, xsize, xsize)
-        assert out.err.shape == (1, ngroups, xsize, xsize)
         assert out.pixeldq.shape == (xsize, xsize)