moved radial_integration algo out of work function
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@ -1,4 +1,4 @@
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from .radprof import prepare_radial_profile, radial_profile
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from .radprof import calc_radial_integration
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@ -1,6 +1,52 @@
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import numpy as np
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def calc_radial_integration(results, data, keep_pixels, pixel_mask_pf, center_radial_integration, r_radial_integration):
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data_copy_1 = np.copy(data)
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if keep_pixels is None and pixel_mask_pf is not None:
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keep_pixels = (pixel_mask_pf != 0)
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if center_radial_integration is None:
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center_radial_integration = [results["beam_center_x"], results["beam_center_y"]]
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r_radial_integration = None
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if r_radial_integration is None:
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r_radial_integration, nr_radial_integration = prepare_radial_profile(data_copy_1, center_radial_integration, keep_pixels)
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r_min_max = [int(np.min(r_radial_integration)), int(np.max(r_radial_integration)) + 1]
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apply_threshold = results.get("apply_threshold", False)
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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_copy_1[data_copy_1 < threshold_min] = np.nan
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if threshold_max > threshold_min:
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data_copy_1[data_copy_1 > threshold_max] = np.nan
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rp = radial_profile(data_copy_1, r_radial_integration, nr_radial_integration, keep_pixels)
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silent_region_min = results.get("radial_integration_silent_min", None)
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silent_region_max = results.get("radial_integration_silent_max", None)
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if (
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silent_region_min is not None and
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silent_region_max is not None and
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silent_region_max > silent_region_min and
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silent_region_min > r_min_max[0] and
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silent_region_max < r_min_max[1]
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):
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integral_silent_region = np.sum(rp[silent_region_min:silent_region_max])
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rp = rp / integral_silent_region
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results["radint_normalised"] = [silent_region_min, silent_region_max]
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results["radint_I"] = list(rp[r_min_max[0]:])
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results["radint_q"] = r_min_max
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return keep_pixels, center_radial_integration, r_radial_integration
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def radial_profile(data, r, nr, keep_pixels=None):
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if keep_pixels is not None:
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tbin = np.bincount(r, data[keep_pixels].ravel())
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@ -10,7 +10,7 @@ import numpy as np
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import zmq
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from peakfinder8_extension import peakfinder_8
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from algos import prepare_radial_profile, radial_profile
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from algos import calc_radial_integration
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FLAGS = 0
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@ -250,51 +250,12 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
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results["saturated_pixels_x"] = saturated_pixels_coordinates[1].tolist()
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results["saturated_pixels_y"] = saturated_pixels_coordinates[0].tolist()
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# pump probe analysis
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do_radial_integration = results.get("do_radial_integration", False)
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if do_radial_integration:
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keep_pixels, center_radial_integration, r_radial_integration = calc_radial_integration(results, data, keep_pixels, pixel_mask_pf, center_radial_integration, r_radial_integration)
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data_copy_1 = np.copy(data)
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if keep_pixels is None and pixel_mask_pf is not None:
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keep_pixels = pixel_mask_pf!=0
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if center_radial_integration is None:
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center_radial_integration = [results["beam_center_x"], results["beam_center_y"]]
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r_radial_integration = None
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if r_radial_integration is None:
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r_radial_integration, nr_radial_integration = prepare_radial_profile(data_copy_1, center_radial_integration, keep_pixels)
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r_min_max = [int(np.min(r_radial_integration)), int(np.max(r_radial_integration)) + 1]
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apply_threshold = results.get("apply_threshold", False)
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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_copy_1[data_copy_1 < threshold_min] = np.nan
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if threshold_max > threshold_min:
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data_copy_1[data_copy_1 > threshold_max] = np.nan
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rp = radial_profile(data_copy_1, r_radial_integration, nr_radial_integration, keep_pixels)
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silent_region_min = results.get("radial_integration_silent_min", None)
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silent_region_max = results.get("radial_integration_silent_max", None)
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if (
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silent_region_min is not None and
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silent_region_max is not None and
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silent_region_max > silent_region_min and
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silent_region_min > r_min_max[0] and
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silent_region_max < r_min_max[1]
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):
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integral_silent_region = np.sum(rp[silent_region_min:silent_region_max])
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rp = rp / integral_silent_region
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results["radint_normalised"] = [silent_region_min, silent_region_max]
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results["radint_I"] = list(rp[r_min_max[0]:])
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results["radint_q"] = r_min_max
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#copy image to work with peakfinder, just in case
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d = np.copy(data)
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