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JP-3164: Removing tests that are already handled in STCAL or are unneeded. #7530

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Apr 10, 2023
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JP-3164: Removing tests that are already handled in STCAL or are unneeded. #7530

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46 changes: 0 additions & 46 deletions jwst/ramp_fitting/tests/test_ramp_fit.py
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Original file line number Diff line number Diff line change
Expand Up @@ -137,18 +137,6 @@ def test_mixed_crs_and_donotuse():
assert max_seg == 3


@pytest.mark.skip(reason="GLS code does not [yet] handle single group integrations.")
def test_one_group_small_buffer_fit_gls():
model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=1, gain=1, readnoise=10)
model1.data[0, 0, 50, 50] = 10.0

slopes, cube, optional, gls_dummy = ramp_fit(
model1, 512, True, rnoise, gain, 'GLS', 'optimal', 'none', dqflags.pixel)

data = slopes[0]
np.testing.assert_allclose(data[50, 50], 10.0, 1e-6)


def test_one_group_two_ints_fit_ols():
model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=1, gain=1, readnoise=10, nints=2)
model1.data[0, 0, 50, 50] = 10.0
Expand All @@ -161,27 +149,6 @@ def test_one_group_two_ints_fit_ols():
np.testing.assert_allclose(data[50, 50], 11.0, 1e-6)


@pytest.mark.skip(reason="GLS does not correctly combine the slopes for integrations into the exposure slope.")
def test_gls_vs_ols_two_ints_ols():
"""
A test to see if GLS is correctly combining integrations. The combination should only use the read noise variance.
The current version of GLS does not work correctly.
"""
model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=11, gain=5, readnoise=1, nints=2)
ramp = np.asarray([x * 100 for x in range(11)])
model1.data[0, :, 50, 50] = ramp
model1.data[1, :, 50, 50] = ramp * 2

slopes = ramp_fit(
model1, 1024 * 30000., True, rnoise, gain, 'OLS', 'optimal', 'none', dqflags.pixel)
np.testing.assert_allclose(slopes[0].data[50, 50], 150.0, 1e-6)

slopes_gls = ramp_fit(
model1, 1024 * 30000., True, rnoise, gain, 'GLS', 'optimal', 'none', dqflags.pixel)
np.testing.assert_allclose(slopes_gls[0].data[50, 50], 150.0, 1e-6)


# @pytest.mark.skip(reason="Jenkins environment does not correctly handle multi-processing.")
def test_multiprocessing():
nints, ngroups, nrows = 3, 25, 100
ncols = nrows # make sure these are the same, so the loops below work
Expand Down Expand Up @@ -219,7 +186,6 @@ def test_multiprocessing():
np.testing.assert_allclose(slopes[0], slopes_multi[0], rtol=1e-5)


# @pytest.mark.skip(reason="Jenkins environment does not correctly handle multi-processing.")
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def test_multiprocessing2():
nints, ngroups, nrows = 1, 25, 100
ncols = nrows # make sure these are the same, so the loops below work
Expand Down Expand Up @@ -254,17 +220,6 @@ def test_multiprocessing2():
np.testing.assert_allclose(slopes[0], slopes_multi[0], rtol=1e-5)


@pytest.mark.xfail(reason="GLS code does not [yet] handle single group integrations.")
def test_one_group_two_ints_fit_gls():
model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=1, gain=1, readnoise=10, nints=2)
model1.data[0, 0, 50, 50] = 10.0
model1.data[1, 0, 50, 50] = 12.0

slopes = ramp_fit(
model1, 1024 * 30000., True, rnoise, gain, 'GLS', 'optimal', 'none', dqflags.pixel)

np.testing.assert_allclose(slopes[0].data[50, 50], 11.0, 1e-6)

# tests that apply to both 'ols' and 'gls' are in the TestMethods class so
# that both can use the parameterized 'method'

Expand Down Expand Up @@ -520,7 +475,6 @@ def test_four_groups_oneCR_orphangroupatend_fit(self, method):
data = slopes[0]
np.testing.assert_allclose(data[50, 50], cds_slope, 1e-6)

# @pytest.mark.skip(reason="not using now")
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def test_four_groups_two_CRs_at_end(self, method):
model1, gdq, rnoise, pixdq, err, gain = setup_inputs(ngroups=4, gain=1, readnoise=10)
model1.data[0, 0, 50, 50] = 10.0
Expand Down