ENH: Automatically reject bad frames due to cosmic rays #194
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| "[AMICAL] %i unusable frames have been identified in the data cube," | ||
| % (len(bad_frames)) | ||
| + " primarily due to cosmic rays or persistent bad pixels." |
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Suggested change
| "[AMICAL] %i unusable frames have been identified in the data cube," | |
| % (len(bad_frames)) | |
| + " primarily due to cosmic rays or persistent bad pixels." | |
| f"[AMICAL] {len(bad_frames)} unusable frames have been identified in the data cube," | |
| " primarily due to cosmic rays or persistent bad pixels.", | |
| file=sys.stderr, |
| @@ -79,7 +79,7 @@ def find_max(img, filtmed=True, f=3): | |||
| return X, Y | |||
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| def crop_max(img, dim, offx=0, offy=0, filtmed=True, f=3): | |||
| def crop_max(img, dim, iframe=0, offx=0, offy=0, filtmed=True, f=3): | |||
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this is an unwarranted breaking change: new arguments should always be added at the end of an existing signature, and preferably be made keyword-only
| @@ -117,13 +117,22 @@ def select_data(cube, clip_fact=0.5, clip=False, verbose=True, display=True): | |||
| ind_clip = [] | |||
| cube_cleaned_checked = np.array(good_fram) | |||
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| cube_cleaned_checked = np.array(cube_cleaned_checked) | |||
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np.array unconditionally copies data, even if the input variable is already an array. Since the input and output variable are the same, it's clear you don't mean to copy, so np.asarray is better suited here
Suggested change
| cube_cleaned_checked = np.array(cube_cleaned_checked) | |
| cube_cleaned_checked = np.asarray(cube_cleaned_checked) |
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| med_flux = np.median(fluxes_check) | ||
| std_flux = np.std(fluxes_check) | ||
| diffmm = 100 * abs(np.max(fluxes_check) - np.min(fluxes_check)) / med_flux |
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np.ptp(x) does the same as np.max(x) - np.min(x), but in a single data pass
Suggested change
| diffmm = 100 * abs(np.max(fluxes_check) - np.min(fluxes_check)) / med_flux | |
| diffmm = 100 * abs(np.ptp(fluxes_check)) / med_flux |
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In some NIRISS data cases, we've detected problematic pixels (cosmic rays or other anomalous events) that can disrupt the centring step during cleaning, causing errors. These misbehaving pixels can pop up when only a few data groups are involved. To address this, we use a frame index list automatically generated by the centring function to identify and exclude these "bad frames."