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moved calc_apply_threshold out of calc_radial_integration

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augustin_s
2024-08-09 15:41:46 +02:00
parent 48c6bbe7c7
commit 2103583aca
1 changed files with 18 additions and 11 deletions
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29
dap/algos/radprof.py
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@ -16,17 +16,7 @@ def calc_radial_integration(results, data, pixel_mask_pf):
r_min = min(rad) r_min = min(rad)
r_max = max(rad) + 1 r_max = max(rad) + 1
apply_threshold = results.get("apply_threshold", False) data = calc_apply_threshold(results, data)
#TODO: this is duplicated in calc_apply_threshold and calc_force_send
if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
threshold_min = float(results["threshold_min"])
threshold_max = float(results["threshold_max"])
data = data.copy() # do the following in-place changes on a copy
data[data < threshold_min] = np.nan
#TODO: skipping max is a guess, but not obvious/symmetric -- better to ensure the order min < max by switching them if needed
if threshold_max > threshold_min:
data[data > threshold_max] = np.nan
rp = radial_profile(data, rad, norm, pixel_mask_pf) rp = radial_profile(data, rad, norm, pixel_mask_pf)
@ -72,3 +62,20 @@ def radial_profile(data, rad, norm, keep_pixels):
def calc_apply_threshold(results, data):
apply_threshold = results.get("apply_threshold", False)
#TODO: this is duplicated in calc_apply_threshold and calc_force_send
if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
threshold_min = float(results["threshold_min"])
threshold_max = float(results["threshold_max"])
data = data.copy() # do the following in-place changes on a copy
data[data < threshold_min] = np.nan
#TODO: skipping max is a guess, but not obvious/symmetric -- better to ensure the order min < max by switching them if needed
if threshold_max > threshold_min:
data[data > threshold_max] = np.nan
return data