168 lines
5.6 KiB
Python
168 lines
5.6 KiB
Python
from logging import getLogger
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from cam_server.pipeline.data_processing import functions
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from cam_server.pipeline.data_processing.functions import chunk_copy
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import json
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import numpy
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import scipy.signal
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import scipy.optimize
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import numba
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numba.set_num_threads(4)
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import threading
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import epics
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_logger = getLogger(__name__)
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output_pv, center_pv, fwhm_pv, ymin_pv, ymax_pv, axis_pv = None, None, None, None, None, None
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roi = [0, 0]
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initialized = False
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epics_lock=threading.RLock()
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_logger.info("Import: " +str(threading.get_ident()))
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@numba.njit(parallel=True)
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def get_spectrum (image, background):
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y = image.shape[0]
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x = image.shape[1]
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profile = numpy.zeros(x, dtype=numpy.uint32)
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#for i in range(y):
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for i in numba.prange(y):
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for j in range(x):
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v = image[i,j]
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b = background[i,j]
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if v > b:
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v -= b
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else:
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v = 0
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profile[j] += v
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return profile
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def initialize(parameters):
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global ymin_pv, ymax_pv, axis_pv, output_pv, center_pv, fwhm_pv
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_logger.info("Initialize: " + str(threading.get_ident()))
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epics_pv_name_prefix = parameters["camera_name"]
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output_pv_name = epics_pv_name_prefix + ":SPECTRUM_Y"
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center_pv_name = epics_pv_name_prefix + ":SPECTRUM_CENTER"
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fwhm_pv_name = epics_pv_name_prefix + ":SPECTRUM_FWHM"
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ymin_pv_name = epics_pv_name_prefix + ":SPC_ROI_YMIN"
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ymax_pv_name = epics_pv_name_prefix + ":SPC_ROI_YMAX"
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axis_pv_name = epics_pv_name_prefix + ":SPECTRUM_X"
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epics.ca.clear_cache()
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output_pv = epics.PV(output_pv_name)
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center_pv = epics.PV(center_pv_name)
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fwhm_pv = epics.PV(fwhm_pv_name)
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ymin_pv = epics.PV(ymin_pv_name)
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ymax_pv = epics.PV(ymax_pv_name)
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axis_pv = epics.PV(axis_pv_name)
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ymin_pv.wait_for_connection()
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ymax_pv.wait_for_connection()
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axis_pv.wait_for_connection()
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def process_image(image, pulse_id, timestamp, x_axis, y_axis, parameters, bsdata=None, background=None):
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global roi, initialized, epics_lock
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global ymin_pv, ymax_pv, axis_pv, output_pv, center_pv, fwhm_pv
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processed_data = dict()
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epics_pv_name_prefix = parameters["camera_name"]
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with epics_lock:
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if not initialized:
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initialize(parameters)
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initialized = True
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if ymin_pv and ymin_pv.connected:
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roi[0] = ymin_pv.value
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if ymax_pv and ymax_pv.connected:
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roi[1] = ymax_pv.value
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if axis_pv and axis_pv.connected:
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axis = axis_pv.value
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else:
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axis = None
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if axis is None:
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_logger.warning("Energy axis not connected");
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return None
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if len(axis) < image.shape[1]:
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_logger.warning("Energy axis length %d < image width %d", len(axis), image.shape[1])
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return None
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# match the energy axis to image width
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axis = axis[:image.shape[1]]
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processing_image = image
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nrows, ncols = processing_image.shape
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# validate background data
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background_image = parameters.pop('background_data', None)
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if isinstance(background_image, numpy.ndarray):
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#background_image = chunk_copy(background_image)
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if background_image.shape != processing_image.shape:
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_logger.info("Invalid background shape: %s instead of %s" % (str(background_image.shape), str(processing_image.shape)))
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background_image = None
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else:
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background_image = None
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processed_data[epics_pv_name_prefix + ":processing_parameters"] = json.dumps(
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{"roi": roi, "background": None if (background_image is None) else parameters.get('image_background')})
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# crop the image in y direction
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ymin, ymax = int(roi[0]), int(roi[1])
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if nrows >= ymax > ymin >= 0:
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if (nrows != ymax) or (ymin != 0):
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processing_image = processing_image[ymin: ymax, :]
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if background_image is not None:
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background_image = background_image[ymin:ymax, :]
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# remove the background and collapse in y direction to get the spectrum
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if background_image is not None:
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spectrum = get_spectrum(processing_image, background_image)
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else:
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spectrum = processing_image.sum(0, 'uint32')
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# smooth the spectrum with savgol filter with 51 window size and 3rd order polynomial
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smoothed_spectrum = scipy.signal.savgol_filter(spectrum, 51, 3)
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# check wether spectrum has only noise. the average counts per pixel at the peak
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# should be larger than 1.5 to be considered as having real signals.
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minimum, maximum = smoothed_spectrum.min(), smoothed_spectrum.max()
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amplitude = maximum - minimum
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skip = True
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if amplitude > nrows * 1.5:
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skip = False
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# gaussian fitting
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offset, amplitude, center, sigma = functions.gauss_fit_psss(smoothed_spectrum[::2], axis[::2],
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offset=minimum, amplitude=amplitude, skip=skip)
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# outputs
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processed_data[epics_pv_name_prefix + ":SPECTRUM_Y"] = spectrum
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processed_data[epics_pv_name_prefix + ":SPECTRUM_X"] = axis
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processed_data[epics_pv_name_prefix + ":SPECTRUM_CENTER"] = center
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processed_data[epics_pv_name_prefix + ":SPECTRUM_FWHM"] = 2.355 * sigma
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with epics_lock:
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if output_pv and output_pv.connected:
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output_pv.put(processed_data[epics_pv_name_prefix + ":SPECTRUM_Y"])
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_logger.debug("caput on %s for pulse_id %s", output_pv, pulse_id)
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if center_pv and center_pv.connected:
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center_pv.put(processed_data[epics_pv_name_prefix + ":SPECTRUM_CENTER"])
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if fwhm_pv and fwhm_pv.connected:
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fwhm_pv.put(processed_data[epics_pv_name_prefix + ":SPECTRUM_FWHM"])
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return processed_data
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