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moved ROI algo out of work function

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augustin_s
2024-07-30 18:55:02 +02:00
parent 56a5f94805
commit 3e3916b786
3 changed files with 32 additions and 23 deletions
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1
dap/algos/__init__.py
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@ -3,5 +3,6 @@ from .radprof import calc_radial_integration
from .addmask import calc_apply_additional_mask
from .peakfind import calc_peakfinder_analysis
from .spiana import calc_spi_analysis
from .roi import calc_roi

29
dap/algos/roi.py Normal file
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@ -0,0 +1,29 @@
import numpy as np
def calc_roi(results, pfdata, roi_x1, roi_x2, roi_y1, roi_y2, pixel_mask_pf, threshold_value_choice):
roi_results = [0] * len(roi_x1)
roi_results_normalised = [0] * len(roi_x1)
if pixel_mask_pf is not None:
results["roi_intensities_x"] = []
results["roi_intensities_proj_x"] = []
for iRoi in range(len(roi_x1)):
data_roi = np.copy(pfdata[roi_y1[iRoi]:roi_y2[iRoi], roi_x1[iRoi]:roi_x2[iRoi]])
roi_results[iRoi] = np.nansum(data_roi)
if threshold_value_choice == "NaN":
roi_results_normalised[iRoi] = roi_results[iRoi] / ((roi_y2[iRoi] - roi_y1[iRoi]) * (roi_x2[iRoi] - roi_x1[iRoi]))
else:
roi_results_normalised[iRoi] = np.nanmean(data_roi)
results["roi_intensities_x"].append([roi_x1[iRoi], roi_x2[iRoi]])
results["roi_intensities_proj_x"].append(np.nansum(data_roi, axis=0).tolist())
results["roi_intensities"] = [float(r) for r in roi_results]
results["roi_intensities_normalised"] = [float(r) for r in roi_results_normalised ]

25
dap/worker.py
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@ -7,7 +7,7 @@ from time import sleep
import jungfrau_utils as ju
import numpy as np
from algos import calc_radial_integration, calc_apply_additional_mask, calc_peakfinder_analysis, calc_spi_analysis
from algos import calc_radial_integration, calc_apply_additional_mask, calc_peakfinder_analysis, calc_spi_analysis, calc_roi
from zmqsocks import ZMQSockets
from utils import json_load, read_bit
@ -202,28 +202,7 @@ def work(backend_address, accumulator_host, accumulator_port, visualisation_host
roi_y2 = results.get("roi_y2", [])
if len(roi_x1) > 0 and len(roi_x1) == len(roi_x2) and len(roi_x1) == len(roi_y1) and len(roi_x1) == len(roi_y2):
roi_results = [0] * len(roi_x1)
roi_results_normalised = [0] * len(roi_x1)
if pixel_mask_pf is not None:
results["roi_intensities_x"] = []
results["roi_intensities_proj_x"] = []
for iRoi in range(len(roi_x1)):
data_roi = np.copy(pfdata[roi_y1[iRoi]:roi_y2[iRoi], roi_x1[iRoi]:roi_x2[iRoi]])
roi_results[iRoi] = np.nansum(data_roi)
if threshold_value_choice == "NaN":
roi_results_normalised[iRoi] = roi_results[iRoi] / ((roi_y2[iRoi] - roi_y1[iRoi]) * (roi_x2[iRoi] - roi_x1[iRoi]))
else:
roi_results_normalised[iRoi] = np.nanmean(data_roi)
results["roi_intensities_x"].append([roi_x1[iRoi], roi_x2[iRoi]])
results["roi_intensities_proj_x"].append(np.nansum(data_roi, axis=0).tolist())
results["roi_intensities"] = [float(r) for r in roi_results]
results["roi_intensities_normalised"] = [float(r) for r in roi_results_normalised ]
calc_roi(results, pfdata, roi_x1, roi_x2, roi_y1, roi_y2, pixel_mask_pf, threshold_value_choice)
# SPI analysis
do_spi_analysis = results.get("do_spi_analysis", False)