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clean up unused arrays #277

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clean up unused arrays #277

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3a832fe
clean up unused arrays
tapastro Jul 30, 2024
5e6b65a
Merge branch 'main' into jump_memory_usage
penaguerrero Aug 20, 2024
e7400b6
move comment with associated code
tapastro Aug 23, 2024
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16 changes: 10 additions & 6 deletions src/stcal/jump/twopoint_difference.py
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Original file line number Diff line number Diff line change
Expand Up @@ -190,12 +190,6 @@ def find_crs(
# reset sigma so pxels with 0 readnoise are not flagged as jumps
sigma[np.where(sigma == 0.)] = np.nan

# compute 'ratio' for each group. this is the value that will be
# compared to 'threshold' to classify jumps. subtract the median of
# first_diffs from first_diffs, take the absolute value and divide by sigma.
e_jump_4d = first_diffs - median_diffs[np.newaxis, :, :]
ratio_all = np.abs(first_diffs - median_diffs[np.newaxis, np.newaxis, :, :]) / \
sigma[np.newaxis, np.newaxis, :, :]
# Test to see if there are enough groups to use sigma clipping
if (only_use_ints and nints >= minimum_sigclip_groups) or \
(not only_use_ints and total_groups >= minimum_sigclip_groups):
Expand Down Expand Up @@ -259,6 +253,7 @@ def find_crs(
jump_mask = np.logical_and(masked_ratio.mask, np.logical_not(first_diffs_masked.mask))
gdq[:, 1:, :, :] = np.bitwise_or(gdq[:, 1:, :, :], jump_mask *
np.uint8(dqflags["JUMP_DET"]))
del e_jump, ratio, masked_ratio
else: # low number of diffs requires iterative flagging
# calculate the differences between adjacent groups (first diffs)
# use mask on data, so the results will have sat/donotuse groups masked
Expand Down Expand Up @@ -354,9 +349,18 @@ def find_crs(
gdq[:, 1:, all_crs_row[j], all_crs_col[j]],
dqflags["JUMP_DET"] * np.invert(pix_cr_mask),
)
del e_jump, ratio, masked_ratio

cr_integ, cr_group, cr_row, cr_col = np.where(np.bitwise_and(gdq, jump_flag))
num_primary_crs = len(cr_group)
if flag_4_neighbors: # iterate over each 'jump' pixel
# compute 'ratio' for each group. this is the value that will be
# compared to 'threshold' to classify jumps. subtract the median of
# first_diffs from first_diffs, take the absolute value and divide by sigma.

e_jump_4d = first_diffs - median_diffs[np.newaxis, :, :]
ratio_all = np.abs(first_diffs - median_diffs[np.newaxis, np.newaxis, :, :]) / \
sigma[np.newaxis, np.newaxis, :, :]
for j in range(len(cr_group)):
ratio_this_pix = ratio_all[cr_integ[j], cr_group[j] - 1, cr_row[j], cr_col[j]]

Expand Down
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