JP-3550: Jump multi int (#8304)

Co-authored-by: Howard Bushouse <[email protected]>
Co-authored-by: Ned Molter <[email protected]>

CHANGES.rst

@@ -145,6 +145,11 @@ general
 jump
 ----
 
+- To improve performance an additional parameter to the jump step was added
+  that sets the threshold number of differences above which iterative flagging
+  of one CR at a time is turned off. [#8304]
+
+
 - Removed a unit test in Jump that was moved to STCAL to decrease
   the coupling of the two repos. [#8319]
 
@@ -154,9 +159,6 @@ lib
 - Updated ``set_velocity_aberration`` to use datamodels instead of `astropy.io.fits` for opening
   and manipulating input files. [#8285]
 
-lib
----
-
 - Added new function set_nans_to_donotuse in ``lib.basic_utils`` to
   check the science data array for NaN values and check if they have
   a DQ flag of DO_NOT_USE, or set it if not. [#8292]

docs/jwst/jump/arguments.rst

@@ -103,7 +103,9 @@ is defined as:
 
 * ``--extend_ellipse_expand_ratio``: Multiplicative factor to expand the radius of the ellipse fit to the detected extended emission in MIRI showers
 
-* ``--time_masked_after_showers``: Number of seconds to flag groups as jump after a detected extended emission in MIRI showers
+* ``--time_masked_after_shower``: Number of seconds to flag groups as jump after a detected extended emission in MIRI showers
+
+* ``--min_diffs_single_pass``: The minimum number of differences to switch to flagging all outliers at once
 
 **Parameter that affects both Snowball and Shower flagging**
 

jwst/jump/jump.py

@@ -22,7 +22,7 @@ def run_detect_jumps(input_model, gain_model, readnoise_model,
                      sat_required_snowball=True, sat_expand=2,
                      expand_large_events=False, find_showers=False, edge_size=25, extend_snr_threshold=1.1,
                      extend_min_area=90, extend_inner_radius=1, extend_outer_radius=2.6, extend_ellipse_expand_ratio=1.1,
-                     time_masked_after_shower=30,
+                     time_masked_after_shower=30, min_diffs_single_pass=10,
                      max_extended_radius=200,
                      minimum_groups=3,
                      minimum_sigclip_groups=100,
@@ -81,6 +81,7 @@ def run_detect_jumps(input_model, gain_model, readnoise_model,
                                     extend_outer_radius=extend_outer_radius,
                                     extend_ellipse_expand_ratio=extend_ellipse_expand_ratio,
                                     grps_masked_after_shower=grps_masked_after_shower,
+                                    min_diffs_single_pass=min_diffs_single_pass,
                                     max_extended_radius=max_extended_radius,
                                     minimum_groups=minimum_groups,
                                     minimum_sigclip_groups=minimum_sigclip_groups,

jwst/jump/jump_step.py

@@ -36,15 +36,16 @@ class JumpStep(Step):
         use_ellipses = boolean(default=False) # deprecated
         sat_required_snowball = boolean(default=True) # Require the center of snowballs to be saturated
         min_sat_radius_extend = float(default=2.5) # The min radius of the sat core to trigger the extension of the core
-        sat_expand = integer(default=2) # Number of pixels to add to the radius of the saturated core of snowballs       
-        edge_size = integer(default=25) # Size of region on the edges of NIR detectors where a sat core is not required    
-        find_showers = boolean(default=False) # Turn on shower flagging for MIRI        
+        sat_expand = integer(default=2) # Number of pixels to add to the radius of the saturated core of snowballs
+        find_showers = boolean(default=False) # Turn on shower flagging for MIRI
+        edge_size = integer(default=25) # Size of region on the edges of NIR detectors where a sat core is not required
         extend_snr_threshold = float(default=1.2) # The SNR minimum for detection of extended showers in MIRI
         extend_min_area = integer(default=90) # Min area of emission after convolution for the detection of showers
         extend_inner_radius = float(default=1) # Inner radius of the ring_2D_kernel used for convolution
         extend_outer_radius = float(default=2.6) # Outer radius of the ring_2D_Kernel used for convolution
         extend_ellipse_expand_ratio = float(default=1.1) # Expand the radius of the ellipse fit to the extended emission
         time_masked_after_shower = float(default=15) # Seconds to flag as jump after a detected extended emission
+        min_diffs_single_pass = integer(default=10) # The minimum number of differences needed to skip the iterative flagging of jumps.
         max_extended_radius = integer(default=200) # The maximum radius of an extended snowball or shower
         minimum_groups = integer(default=3) # The minimum number of groups to perform jump detection using sigma clipping
         minimum_sigclip_groups = integer(default=100) # The minimum number of groups to switch to sigma clipping
@@ -121,6 +122,7 @@ def process(self, input):
                                       extend_outer_radius=self.extend_outer_radius,
                                       extend_ellipse_expand_ratio=self.extend_ellipse_expand_ratio,
                                       time_masked_after_shower=self.time_masked_after_shower,
+                                      min_diffs_single_pass=self.min_diffs_single_pass,
                                       max_extended_radius=self.max_extended_radius * 2,
                                       minimum_groups=self.minimum_groups,
                                       minimum_sigclip_groups=self.minimum_sigclip_groups,

jwst/jump/tests/test_detect_jumps.py

@@ -776,12 +776,12 @@ def test_proc(setup_inputs):
     model.data[0, 7, 2, 3] = 160.0
     model.data[0, 8, 2, 3] = 170.0
     model.data[0, 9, 2, 3] = 180.0
-
+    model_b = model.copy()
+    model_c = model.copy()
     out_model_a = run_detect_jumps(model, gain, rnoise, 4.0, 5.0, 6.0, 'none', 200, 4, True)
-    out_model_b = run_detect_jumps(model, gain, rnoise, 4.0, 5.0, 6.0, 'half', 200, 4, True)
+    out_model_b = run_detect_jumps(model_b, gain, rnoise, 4.0, 5.0, 6.0, 'half', 200, 4, True)
     assert_array_equal(out_model_a.groupdq, out_model_b.groupdq)
-
-    out_model_c = run_detect_jumps(model, gain, rnoise, 4.0, 5.0, 6.0, 'all', 200, 4, True)
+    out_model_c = run_detect_jumps(model_c, gain, rnoise, 4.0, 5.0, 6.0, 'all', 200, 4, True)
     assert_array_equal(out_model_a.groupdq, out_model_c.groupdq)