Update the jump detection unittest to check

if jumps are correctly identified
This checks if jumps are correctly identified, resolving some lingering
test issues from #227

tests/test_jump_cas22.py

@@ -22,8 +22,8 @@
 #    that the random process will "accidentally" generate a set of data, which
 #    can trigger jump detection. This makes it easier to cleanly test jump
 #    detection is doing what we expect.
-FLUX = 100
-READ_NOISE = np.float32(5)
+FLUX = 10
+READ_NOISE = np.float32(20)
 
 # Set a value for jumps which makes them obvious relative to the normal flux
 JUMP_VALUE = 1_000
@@ -32,7 +32,7 @@
 #    across all tests to make it easier to isolate the effects of something using
 #    multiple tests.
 N_PIXELS = 100_000
-CHI2_TOL = 0.03
+CHI2_TOL = 0.3
 GOOD_PROB = 0.7
 
 
@@ -353,8 +353,7 @@ def test_fit_ramps(detector_data, use_jump, use_dq):
 
     chi2 = 0
     for fit, use in zip(output.fits, okay):
-        if not use_dq and not use_jump:
-            ##### The not use_jump makes this NOT test for false positives #####
+        if not use_dq:
             # Check that the data generated does not generate any false positives
             #   for jumps as this data is reused for `test_find_jumps` below.
             #   This guarantees that all jumps detected in that test are the
@@ -468,10 +467,6 @@ def test_find_jumps(jump_data):
     assert len(output.fits) == len(jump_reads)  # sanity check that a fit/jump is set for every pixel
 
     chi2 = 0
-    incorrect_too_few = 0
-    incorrect_too_many = 0
-    incorrect_does_not_capture = 0
-    incorrect_other = 0
     for fit, jump_index, resultant_index in zip(output.fits, jump_reads, jump_resultants):
         # Check that the jumps are detected correctly
         if jump_index == 0:
@@ -485,51 +480,35 @@ def test_find_jumps(jump_data):
             assert fit["index"][0]["start"] == 0
             assert fit["index"][0]["end"] == len(read_pattern) - 1
         else:
-            # There should be a single jump detected; however, this results in
-            # two resultants being excluded.
-            if resultant_index not in fit["jumps"]:
-                incorrect_does_not_capture += 1
-                continue
-            if len(fit["jumps"]) < 2:
-                incorrect_too_few += 1
-                continue
-            if len(fit["jumps"]) > 2:
-                incorrect_too_many += 1
-                continue
-
-            # The two resultants excluded should be adjacent
-            jump_correct = [
-                (jump in (resultant_index, resultant_index - 1, resultant_index + 1)) for jump in fit["jumps"]
-            ]
-            if not all(jump_correct):
-                incorrect_other += 1
-                continue
-
-            # Because we do not have a data set with no false positives, we cannot run the below
-            # # Test the correct ramp indexes are recorded
-            # ramp_indices = []
-            # for ramp_index in fit['index']:
-            #     # Note start/end of a ramp_index are inclusive meaning that end
-            #     #    is an index included in the ramp_index so the range is to end + 1
-            #     new_indices = list(range(ramp_index["start"], ramp_index["end"] + 1))
-
-            #     # check that all the ramps are non-overlapping
-            #     assert set(ramp_indices).isdisjoint(new_indices)
-
-            #     ramp_indices.extend(new_indices)
-
-            # # check that no ramp_index is a jump
-            # assert set(ramp_indices).isdisjoint(fit['jumps'])
-
-            # # check that all resultant indices are either in a ramp or listed as a jump
-            # assert set(ramp_indices).union(fit['jumps']) == set(range(len(read_pattern)))
+            # Check that the inserted jump is detected or if the jump occurs in the last resultant
+            #    (there are some unresolved issues with this case)
+            assert resultant_index in fit["jumps"] or resultant_index == resultants.shape[0] - 1
+
+            # Here we map out all of the ramps and make sure they are non-overlapping and that
+            #    they do not overlap with the identified jumps
+            ramp_indices = []
+            for ramp_index in fit["index"]:
+                # Note start/end of a ramp_index are inclusive meaning that end
+                #    is an index included in the ramp_index so the range is to end + 1
+                new_indices = list(range(ramp_index["start"], ramp_index["end"] + 1))
+
+                # check that all the ramps are non-overlapping
+                assert set(ramp_indices).isdisjoint(new_indices)
+
+                ramp_indices.extend(new_indices)
+
+            # check that no ramp_index is a jump
+            assert set(ramp_indices).isdisjoint(fit["jumps"])
+
+            # check that all resultant indices are either in a ramp or listed as a jump
+            assert set(ramp_indices).union(fit["jumps"]) == set(range(len(read_pattern)))
 
         # Compute the chi2 for the fit and add it to a running "total chi2"
         total_var = fit["average"]["read_var"] + fit["average"]["poisson_var"]
         chi2 += (fit["average"]["slope"] - FLUX) ** 2 / total_var
 
     # Check that the average chi2 is ~1.
-    chi2 /= N_PIXELS - incorrect_too_few - incorrect_too_many - incorrect_does_not_capture - incorrect_other
+    chi2 /= N_PIXELS
     assert np.abs(chi2 - 1) < CHI2_TOL