re-use thresh function
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@ -1,6 +1,7 @@
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import numpy as np
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from .mask import calc_mask_pixels
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from .thresh import threshold
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def calc_force_send(results, data, pixel_mask_pf, image, data_summed, n_aggregated_images):
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@ -42,10 +43,9 @@ def calc_apply_threshold(results, data):
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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] = 0
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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] = 0
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threshold(data, threshold_min, threshold_max, 0)
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def calc_apply_aggregation(results, data, data_summed, n_aggregated_images):
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@ -1,5 +1,6 @@
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import numpy as np
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from .thresh import threshold
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from .utils import npmemo
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@ -76,10 +77,8 @@ def calc_apply_threshold(results, data):
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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] = np.nan
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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] = np.nan
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threshold(data, threshold_min, threshold_max, np.nan)
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return data
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@ -16,10 +16,19 @@ def calc_apply_threshold(results, data):
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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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threshold(data, threshold_min, threshold_max, threshold_value)
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def threshold(data, vmin, vmax, replacement):
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"""
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threshold data in place by replacing values < vmin and values > vmax with replacement
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"""
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data[data < vmin] = replacement
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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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if vmax > vmin:
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data[data > vmin] = replacement
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