JP-3242: Proper Handling of Timing for Final Slope Computation for only 0th Good Group Ramps

CHANGES.rst

@@ -138,6 +138,12 @@ ramp_fitting
   of groups in a segment are computed when applying optimal weighting to line
   fit segments. [#7560, spacetelescope/stcal#163]
 
+- Updated the CI tests due to change in STCAL, which fixed bug for using the
+  correct timing for slope computation.  Since there are now special cases that
+  use ZEROFRAME data, as well as ramps that have only good data in the 0th
+  group, the timing for these ramps is not group time.  These adjusted times
+  are now used. [#7612, spacetelescope/stcal#173]
+
 residual_fringe
 ---------------
 

jwst/ramp_fitting/tests/test_ramp_fit.py

@@ -744,10 +744,10 @@ def test_twenty_groups_two_segments():
     np.testing.assert_allclose(oslope[wh_data], 10. / deltatime, rtol=1E-4)
     np.testing.assert_allclose(oyint[0, 0, 0, :], model1.data[0, 0, 0, :], rtol=1E-5)
 
-    np.testing.assert_allclose(
-        opedestal[0, 0, :],
-        model1.data[0, 0, 0, :] - 10.,
-        rtol=1E-5)
+    check = model1.data[0, 0, 0, :] - oslope
+    tol = 1E-5
+    # Pixel 1 has zero slope, so ignore it.
+    np.testing.assert_allclose(opedestal[0, 0, 1:], check[0, 0, 0, 1:], tol)
 
 
 def test_miri_all_sat():
@@ -897,7 +897,7 @@ def test_zeroframe_usage():
     # Check slopes information
     sdata, sdq, svp, svr, serr = slopes
 
-    check = np.array([[20.709406, 0.46572033, 4.6866207]])
+    check = np.array([[41.003822, 0.46572033, 21.655062]])
     np.testing.assert_allclose(sdata, check, tol, tol)
 
     # Since the second integration is GOOD the final DQ is GOOD.
@@ -916,8 +916,8 @@ def test_zeroframe_usage():
     # Check slopes information
     cdata, cdq, cvp, cvr, cerr = cube
 
-    check = np.array([[[186.28912, np.nan, 93.14456]],
-                      [[0.46572027, 0.46572033, 0.46572033]]])
+    check = np.array([[[3.7257825e+02,        np.nan, 4.6572281e+02]],
+                      [[4.6572030e-01, 4.6572030e-01, 4.6572030e-01]]])
     np.testing.assert_allclose(cdata, check, tol, tol)
 
     # Column 2 in the first integration is marked GOOD because there
@@ -1041,8 +1041,8 @@ def setup_small_cube(ngroups=10, nints=1, nrows=2, ncols=2, deltatime=10.,
 
 
 # Need test for multi-ints near zero with positive and negative slopes
-def setup_inputs(ngroups=10, readnoise=10, nints=1,
-                 nrows=103, ncols=102, nframes=1, grouptime=1.0, gain=1, deltatime=1):
+def setup_inputs(ngroups=10, readnoise=10, nints=1, nrows=103, ncols=102,
+                 nframes=1, grouptime=1.0, gain=1, deltatime=1):
 
     data = np.zeros(shape=(nints, ngroups, nrows, ncols), dtype=np.float32)
     err = np.ones(shape=(nints, ngroups, nrows, ncols), dtype=np.float32)

jwst/ramp_fitting/tests/test_ramp_fit_cases.py

@@ -5,6 +5,7 @@
 
 from stdatamodels.jwst.datamodels import RampModel, GainModel, ReadnoiseModel, dqflags
 
+
 #
 # The first 12 tests are for a single ramp in a single integration. The ramps
 #  have a variety of GROUPDQ vectors, with 1 or more segments in each ramp.  The
@@ -54,7 +55,7 @@ def test_pix_0():
     # Set truth values for OPTIONAL results:
     # [slope, sigslope, var_poisson, var_rnoise, yint, sigyint, ped, weights]
     o_true = [1.0117551, 4.874572, 0.0020202, 0.00647973,
-              15.911023, 27.789335, 4.882449, 13841.038]
+              15.911023, 27.789335, 13.988245, 13841.038]
 
     assert_pri(p_true, new_mod, 0)
     assert_opt(o_true, opt_mod, 0)
@@ -92,7 +93,7 @@ def test_pix_1():
 
     # Set truth values for OPTIONAL results:
     o_true = [1.9, 56.870003, 0.03454545, 1.0691562, -3., 56.870003,
-              -3.999998, 0.82091206]
+              13.1, 0.82091206]
 
     assert_pri(p_true, new_mod, 0)
     assert_opt(o_true, opt_mod, 0)
@@ -129,7 +130,7 @@ def test_pix_2():
               [0.26728904, 1.0691562, 1.0691562],   # var_rnoise
               [14.999998, 51., 15.],                # yint
               [36.709427, 56.870003, 56.870003],     # sigyint
-              [6.5166273],                          # pedestal
+              [14.151663],                          # pedestal
               [13.091425, 0.84580624, 0.84580624],   # weights
               ]
 
@@ -169,7 +170,7 @@ def test_pix_3():
               [0.01272805, 1.0691562],
               [14.504965, 15.],
               [27.842508, 56.870003],
-              [4.253134],
+              [13.925313],
               [4.2576841e+03, 8.458062e-01],
               ]
 
@@ -202,7 +203,7 @@ def test_pix_4():
     p_true = [1.5, GOOD, 1.047105, 0.02727273, 1.0691562]
 
     # Set truth values for OPTIONAL results:
-    o_true = [1.5, 0., 0.02727273, 1.0691562, 0., 0., 0., 0.8318386]
+    o_true = [1.5, 0., 0.02727273, 1.0691562, 0., 0., 13.5, 0.8318386]
 
     assert_pri(p_true, new_mod, 0)
     assert_opt(o_true, opt_mod, 0)
@@ -241,7 +242,7 @@ def test_pix_5():
               [0.10691562, 0.03054732],
               [13.537246, 2015.0737],
               [35.301933, 67.10882],
-              [4.2391253],
+              [13.923912],
               [78.34764, 855.78046]
               ]
 
@@ -282,7 +283,7 @@ def test_pix_6():
               [1.0691562, 0.01909207],
               [15., -143.2391],
               [56.870003, 58.76999],
-              [-45.92052],
+              [8.907948],
               [8.4580624e-01, 2.0433204e+03]
               ]
 
@@ -315,7 +316,7 @@ def test_pix_7():
 
     # Set truth values for OPTIONAL results:
     o_true = [1.0757396, 6.450687, 0.0025974, 0.01272805, 14.504951,
-              27.842508, 4.2426033, 4257.684]
+              27.842508, 13.92426, 4257.684]
 
     assert_pri(p_true, new_mod, 0)
     assert_opt(o_true, opt_mod, 0)
@@ -347,7 +348,7 @@ def test_pix_8():
 
     # Set truth values for OPTIONAL results:
     o_true = [0.98561335, 9.920554, 0.00363636, 0.03054732, 16.508228,
-              39.383667, 5.1438665, 855.78046]
+              39.383667, 14.014386, 855.78046]
 
     assert_pri(p_true, new_mod, 0)
     assert_opt(o_true, opt_mod, 0)
@@ -385,7 +386,7 @@ def test_pix_9():
               [1.0691562, 0.05345781, 1.0691562],
               [15., 20.119896, 15.],
               [56.870003, 68.618195, 56.870003],
-              [5.000005],
+              [14.],
               [0.84580624, 297.23172, 0.84580624]
               ]
 
@@ -425,7 +426,7 @@ def test_pix_10():
               [1.0691562, 0.26728904, 0.05345781],
               [15., 17.999956, 15.000029],
               [56.870003, 88.40799, 93.73906],
-              [5.],
+              [14.],
               [0.84580624, 13.091425, 297.23172]
               ]
 
@@ -457,7 +458,7 @@ def test_pix_11():
     p_true = [1., GOOD, 1.042755, 0.01818182, 1.0691562]
 
     # Set truth values for OPTIONAL results:
-    o_true = [1., 56.870003, 0.01818182, 1.0691562, 15., 56.870003, 5.,
+    o_true = [1., 56.870003, 0.01818182, 1.0691562, 15., 56.870003, 14.,
               0.84580624]
 
     assert_pri(p_true, new_mod, 0)
@@ -498,7 +499,7 @@ def test_pix_12():
     # slope, sig_slope, var_p, var_r, yint, sig_yint, pedestal, weights
     # slope = group1 / deltatime = 15 / 10 = 1.5
     # sig_slope, yint, sig_yint, and pedestal are all zero, because only 1 good group
-    o_true = [1.5, 0., 0.027273, 1.069156, 0., 0., 0., 0.831839]
+    o_true = [1.5, 0., 0.027273, 1.069156, 0., 0., 13.5, 0.831839]
 
     assert_pri(p_true, new_mod, 0)
     assert_opt(o_true, opt_mod, 0)
@@ -544,7 +545,7 @@ def test_miri_0():
 
     # Set truth values for OPTIONAL results:
     o_true = [1.025854, 6.450687, 0.0025974, 0.01272805, 26.439266, 27.842508,
-              14.74146, 4257.684]
+              23.974146, 4257.684]
 
     assert_pri(p_true, new_mod, 0)
     assert_opt(o_true, opt_mod, 0)
@@ -576,7 +577,7 @@ def test_miri_1():
 
     # Set truth values for OPTIONAL results:
     o_true = [1.1996487, 6.450687, 0.0025974, 0.01272805, 126.110214,
-              27.842508, 113.00351, 4257.684]
+              27.842508, 123.800354, 4257.684]
 
     assert_pri(p_true, new_mod, 0)
     assert_opt(o_true, opt_mod, 0)
@@ -608,7 +609,7 @@ def test_miri_2():
 
     # Set truth values for OPTIONAL results:
     o_true = [1.025854, 6.450687, 0.0025974, 0.01272805, 26.439266, 27.842508,
-              14.74146, 4257.684]
+              23.974146, 4257.684]
 
     assert_pri(p_true, new_mod, 0)
     assert_opt(o_true, opt_mod, 0)
@@ -640,7 +641,7 @@ def test_miri_3():
 
     # Set truth values for OPTIONAL results:
     o_true = [1.025854, 6.450687, 0.0025974, 0.01272805, 26.439266,
-              27.842508, 14.74146, 4257.684]
+              27.842508, 23.974146, 4257.684]
 
     assert_pri(p_true, new_mod, 0)
     assert_opt(o_true, opt_mod, 0)

jwst/ramp_fitting/tests/test_ramp_fit_step.py

@@ -330,7 +330,7 @@ def test_one_group_not_suppressed_one_integration(setup_inputs):
     tol = 1e-5
 
     # Check slopes information
-    check = np.array([[np.nan, 1., 1.0000002]])
+    check = np.array([[np.nan, .2, 0.2]])
     np.testing.assert_allclose(slopes.data, check, tol)
 
     check = np.array([[DNU | SAT, GOOD, GOOD]])
@@ -346,7 +346,7 @@ def test_one_group_not_suppressed_one_integration(setup_inputs):
     np.testing.assert_allclose(slopes.err, check, tol)
 
     # Check slopes information
-    check = np.array([[[np.nan, 1., 1.0000001]]])
+    check = np.array([[[np.nan, .2, .2]]])
     np.testing.assert_allclose(cube.data, check, tol)
 
     check = np.array([[[DNU | SAT, GOOD, GOOD]]])
@@ -377,7 +377,7 @@ def test_one_group_suppressed_one_integration(setup_inputs):
     tol = 1e-5
 
     # Check slopes information
-    check = np.array([[np.nan, np.nan, 1.0000002]])
+    check = np.array([[np.nan, np.nan, .2]])
     np.testing.assert_allclose(slopes.data, check, tol)
 
     check = np.array([[DNU | SAT, DNU, GOOD]])
@@ -393,7 +393,7 @@ def test_one_group_suppressed_one_integration(setup_inputs):
     np.testing.assert_allclose(slopes.err, check, tol)
 
     # Check slopes information
-    check = np.array([[[np.nan, np.nan, 1.0000001]]])
+    check = np.array([[[np.nan, np.nan, .2]]])
     np.testing.assert_allclose(cube.data, check, tol)
 
     check = np.array([[[DNU | SAT, DNU, 0]]])
@@ -426,7 +426,7 @@ def test_one_group_not_suppressed_two_integrations(setup_inputs):
     tol = 1e-5
 
     # Check slopes information
-    check = np.array([[1.0000001, 1.0000002, 1.0000002]])
+    check = np.array([[.2, .2, .2]])
     np.testing.assert_allclose(slopes.data, check, tol)
 
     check = np.array([[GOOD, GOOD, GOOD]])
@@ -442,8 +442,8 @@ def test_one_group_not_suppressed_two_integrations(setup_inputs):
     np.testing.assert_allclose(slopes.err, check, tol)
 
     # Check slopes information
-    check = np.array([[[np.nan,        1.,        1.0000001]],
-                      [[1.0000001, 1.0000001, 1.0000001]]])
+    check = np.array([[[np.nan, .2, .2]],
+                      [[.2,     .2, .2]]])
     np.testing.assert_allclose(cube.data, check, tol)
 
     check = np.array([[[SAT | DNU, GOOD, GOOD]],
@@ -480,7 +480,7 @@ def test_one_group_suppressed_two_integrations(setup_inputs):
     tol = 1e-5
 
     # Check slopes information
-    check = np.array([[1.0000001, 1.0000001, 1.0000002]])
+    check = np.array([[.2, .2, .2]])
     np.testing.assert_allclose(slopes.data, check, tol)
 
     check = np.array([[GOOD, GOOD, GOOD]])
@@ -496,8 +496,8 @@ def test_one_group_suppressed_two_integrations(setup_inputs):
     np.testing.assert_allclose(slopes.err, check, tol)
 
     # Check slopes information
-    check = np.array([[[np.nan, np.nan, 1.0000001]],
-                      [[1.0000001, 1.0000001, 1.0000001]]])
+    check = np.array([[[np.nan, np.nan, .2]],
+                      [[.2, .2, .2]]])
     np.testing.assert_allclose(cube.data, check, tol)
 
     check = np.array([[[DNU | SAT, DNU, GOOD]],