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use the same validation logic as in other cases

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augustin_s
2024-08-02 12:39:45 +02:00
parent b1b92e4b52
commit d9b2ae4ac2
1 changed files with 14 additions and 8 deletions
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22
dap/algos/thresh.py
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@ -3,17 +3,23 @@ import numpy as np
def calc_apply_threshold(results, data):
apply_threshold = results.get("apply_threshold", False)
if not apply_threshold:
return
for k in ("threshold_min", "threshold_max"):
if k not in results:
return
threshold_value_choice = results.get("threshold_value", "NaN")
threshold_value = 0 if threshold_value_choice == "0" else np.nan
threshold_value = 0 if threshold_value_choice == "0" else np.nan #TODO
#TODO: this is duplicated in calc_radial_integration
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 < threshold_min] = threshold_value
#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] = threshold_value
threshold_min = float(results["threshold_min"])
threshold_max = float(results["threshold_max"])
data[data < threshold_min] = threshold_value
#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] = threshold_value