use the same validation logic as in other cases
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@ -3,17 +3,23 @@ import numpy as np
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def calc_apply_threshold(results, data):
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apply_threshold = results.get("apply_threshold", False)
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if not apply_threshold:
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return
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for k in ("threshold_min", "threshold_max"):
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if k not in results:
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return
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threshold_value_choice = results.get("threshold_value", "NaN")
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threshold_value = 0 if threshold_value_choice == "0" else np.nan
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threshold_value = 0 if threshold_value_choice == "0" else np.nan #TODO
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#TODO: this is duplicated in calc_radial_integration
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if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
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threshold_min = float(results["threshold_min"])
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threshold_max = float(results["threshold_max"])
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data[data < threshold_min] = threshold_value
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#TODO: skipping max is a guess, but not obvious/symmetric -- better to ensure the order min < max by switching them if needed
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if threshold_max > threshold_min:
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data[data > threshold_max] = threshold_value
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threshold_min = float(results["threshold_min"])
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threshold_max = float(results["threshold_max"])
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data[data < threshold_min] = threshold_value
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#TODO: skipping max is a guess, but not obvious/symmetric -- better to ensure the order min < max by switching them if needed
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if threshold_max > threshold_min:
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data[data > threshold_max] = threshold_value
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