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split into functions

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augustin_s
2024-08-09 11:33:55 +02:00
parent 2a12864feb
commit 74e73c1247
1 changed files with 22 additions and 3 deletions
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25
dap/algos/forcesend.py
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@ -18,6 +18,18 @@ def calc_force_send(results, data, pixel_mask_pf, image, data_summed, n_aggregat
data = image data = image
return data, force_send_visualisation, data_summed, n_aggregated_images return data, force_send_visualisation, data_summed, n_aggregated_images
calc_apply_threshold(results, data) # changes data in place
data, force_send_visualisation, data_summed, n_aggregated_images = calc_apply_aggregation(results, data, data_summed, n_aggregated_images)
calc_apply_pixel_mask(data, pixel_mask_pf) # changes data in place
return data, force_send_visualisation, data_summed, n_aggregated_images
def calc_apply_threshold(results, data):
apply_threshold = results.get("apply_threshold", False)
if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")): if apply_threshold and all(k in results for k in ("threshold_min", "threshold_max")):
threshold_min = float(results["threshold_min"]) threshold_min = float(results["threshold_min"])
threshold_max = float(results["threshold_max"]) threshold_max = float(results["threshold_max"])
@ -25,6 +37,11 @@ def calc_force_send(results, data, pixel_mask_pf, image, data_summed, n_aggregat
if threshold_max > threshold_min: if threshold_max > threshold_min:
data[data > threshold_max] = 0.0 data[data > threshold_max] = 0.0
def calc_apply_aggregation(results, data, data_summed, n_aggregated_images):
force_send_visualisation = False
apply_aggregation = results.get("apply_aggregation", False)
if apply_aggregation and "aggregation_max" in results: if apply_aggregation and "aggregation_max" in results:
if data_summed is not None: if data_summed is not None:
data += data_summed data += data_summed
@ -38,10 +55,12 @@ def calc_force_send(results, data, pixel_mask_pf, image, data_summed, n_aggregat
data_summed = None data_summed = None
n_aggregated_images = 1 n_aggregated_images = 1
if pixel_mask_pf is not None:
data[~pixel_mask_pf] = np.nan
return data, force_send_visualisation, data_summed, n_aggregated_images return data, force_send_visualisation, data_summed, n_aggregated_images
def calc_apply_pixel_mask(data, pixel_mask_pf):
if pixel_mask_pf is not None:
data[~pixel_mask_pf] = np.nan