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moved radial_integration algo out of work function

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augustin_s
2024-07-30 13:58:16 +02:00
parent 4daa054bda
commit 15d634bb82
3 changed files with 50 additions and 43 deletions
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2
dap/algos/__init__.py
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@ -1,4 +1,4 @@
from .radprof import prepare_radial_profile, radial_profile
from .radprof import calc_radial_integration

46
dap/algos/radprof.py
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@ -1,6 +1,52 @@
import numpy as np
def calc_radial_integration(results, data, keep_pixels, pixel_mask_pf, center_radial_integration, r_radial_integration):
data_copy_1 = np.copy(data)
if keep_pixels is None and pixel_mask_pf is not None:
keep_pixels = (pixel_mask_pf != 0)
if center_radial_integration is None:
center_radial_integration = [results["beam_center_x"], results["beam_center_y"]]
r_radial_integration = None
if r_radial_integration is None:
r_radial_integration, nr_radial_integration = prepare_radial_profile(data_copy_1, center_radial_integration, keep_pixels)
r_min_max = [int(np.min(r_radial_integration)), int(np.max(r_radial_integration)) + 1]
apply_threshold = results.get("apply_threshold", False)
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_copy_1[data_copy_1 < threshold_min] = np.nan
if threshold_max > threshold_min:
data_copy_1[data_copy_1 > threshold_max] = np.nan
rp = radial_profile(data_copy_1, r_radial_integration, nr_radial_integration, keep_pixels)
silent_region_min = results.get("radial_integration_silent_min", None)
silent_region_max = results.get("radial_integration_silent_max", None)
if (
silent_region_min is not None and
silent_region_max is not None and
silent_region_max > silent_region_min and
silent_region_min > r_min_max[0] and
silent_region_max < r_min_max[1]
):
integral_silent_region = np.sum(rp[silent_region_min:silent_region_max])
rp = rp / integral_silent_region
results["radint_normalised"] = [silent_region_min, silent_region_max]
results["radint_I"] = list(rp[r_min_max[0]:])
results["radint_q"] = r_min_max
return keep_pixels, center_radial_integration, r_radial_integration
def radial_profile(data, r, nr, keep_pixels=None):
if keep_pixels is not None:
tbin = np.bincount(r, data[keep_pixels].ravel())

45
dap/worker.py
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@ -10,7 +10,7 @@ import numpy as np
import zmq
from peakfinder8_extension import peakfinder_8
from algos import prepare_radial_profile, radial_profile
from algos import calc_radial_integration
FLAGS = 0
@ -250,51 +250,12 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
results["saturated_pixels_x"] = saturated_pixels_coordinates[1].tolist()
results["saturated_pixels_y"] = saturated_pixels_coordinates[0].tolist()
# pump probe analysis
do_radial_integration = results.get("do_radial_integration", False)
if do_radial_integration:
keep_pixels, center_radial_integration, r_radial_integration = calc_radial_integration(results, data, keep_pixels, pixel_mask_pf, center_radial_integration, r_radial_integration)
data_copy_1 = np.copy(data)
if keep_pixels is None and pixel_mask_pf is not None:
keep_pixels = pixel_mask_pf!=0
if center_radial_integration is None:
center_radial_integration = [results["beam_center_x"], results["beam_center_y"]]
r_radial_integration = None
if r_radial_integration is None:
r_radial_integration, nr_radial_integration = prepare_radial_profile(data_copy_1, center_radial_integration, keep_pixels)
r_min_max = [int(np.min(r_radial_integration)), int(np.max(r_radial_integration)) + 1]
apply_threshold = results.get("apply_threshold", False)
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_copy_1[data_copy_1 < threshold_min] = np.nan
if threshold_max > threshold_min:
data_copy_1[data_copy_1 > threshold_max] = np.nan
rp = radial_profile(data_copy_1, r_radial_integration, nr_radial_integration, keep_pixels)
silent_region_min = results.get("radial_integration_silent_min", None)
silent_region_max = results.get("radial_integration_silent_max", None)
if (
silent_region_min is not None and
silent_region_max is not None and
silent_region_max > silent_region_min and
silent_region_min > r_min_max[0] and
silent_region_max < r_min_max[1]
):
integral_silent_region = np.sum(rp[silent_region_min:silent_region_max])
rp = rp / integral_silent_region
results["radint_normalised"] = [silent_region_min, silent_region_max]
results["radint_I"] = list(rp[r_min_max[0]:])
results["radint_q"] = r_min_max
#copy image to work with peakfinder, just in case
d = np.copy(data)