def __init__(self, role, filename, hdf5_group, beam_energy_coeff = 1.0,
apply_mask=False, mask_filename=None, mask_hdf5_group=False,
gain_map_correction=False, gain_map_filename=None,
gain_map_hdf5_group=None):
"""Initializes the DarkCal correction algorithm.
Args:
role (str): node role ('worker' or 'master').
filename (str): name of the hdf5 5 with dark calibration.
data
hdf5_group (str): path of the dark calibration data within the
hdf5 file.
apply_mask (Optional[bool]): whether a mask should be applied
(optional, if omitted no mask is applied).
mask_filename (Optional[str]) : if the mask is applied, name of
the hdf5 file with gain_map, otherwise ignored (optional).
mask_hdf5_group (Optional[str]): if the mask is applied,
internal hdf5 path of the data block containing the mask,
otherwise ignored (optional).
gain_map_correction (Optional[bool]): whether a gain_map should be
applied (optional, if omitted no gain map is applied).
gain_map_filename (Optional[str]) : if the gain map is applied,
name of the hdf5 file with gain_map, otherwise ignored (optional).
gain_map_hdf5_group (Optional[str]): if the gain map is applied,
internal hdf5 path of the data block containing the mask,
otherwise ignored (optional).
"""
# Initialized on worker
if role == 'worker':
self.beam_energy_coeff = beam_energy_coeff
# load the darkcals
self.darkcal = load_nparray_from_hdf5_file(filename, hdf5_group)
if apply_mask:
self.mask = load_nparray_from_hdf5_file(mask_filename,
mask_hdf5_group)
else:
self.mask = True
if gain_map_correction:
self.gain_map = load_nparray_from_hdf5_file(
gain_map_filename,
gain_map_hdf5_group)
self.gain_map[numpy.where(self.gain_map==0)] = 1.0
else:
self.gain_map = numpy.ones((352,128,512), dtype=bool)
def __init__(self, monitor_params, rec_ip, rec_port):
super(MainFrame, self).__init__()
self.monitor_params = monitor_params
gen_params = monitor_params['General']
p8pd_params = monitor_params['Peakfinder8PeakDetection']
self.rec_ip, self.rec_port = rec_ip, rec_port
self.data = collections.deque(maxlen=20)
self.data_index = 0
self.image_update_us = 250
self.zeromq_listener_thread = PyQt4.QtCore.QThread()
self.zeromq_listener = ZMQListener(self.rec_ip, self.rec_port,
u'ondarawdata')
self.init_listening_thread()
self.ring_pen = pyqtgraph.mkPen('r', width=2)
self.circle_pen = pyqtgraph.mkPen('b', width=2)
pix_maps = pixel_maps_from_geometry_file(
monitor_params['General']['geometry_file'])
self.pixelmap_radius = pix_maps[2]
self.pixel_maps, self.slab_shape, self.img_shape = pixel_maps_for_image_view(
gen_params['geometry_file'])
self.img_to_draw = numpy.zeros(self.img_shape, dtype=numpy.float32)
self.mask_to_draw = numpy.zeros(self.img_shape + (3, ),
dtype=numpy.int16)
self.max_num_peaks = int(p8pd_params['max_num_peaks'])
self.asic_nx = int(p8pd_params['asics_nx'])
self.asic_ny = int(p8pd_params['asics_ny'])
self.nasics_x = int(p8pd_params['nasics_x'])
self.nasics_y = int(p8pd_params['nasics_y'])
self.adc_thresh = float(p8pd_params['adc_threshold'])
self.minimum_snr = float(p8pd_params['minimum_snr'])
self.min_pixel_count = int(p8pd_params['min_pixel_count'])
self.max_pixel_count = int(p8pd_params['max_pixel_count'])
self.local_bg_radius = int(p8pd_params['local_bg_radius'])
self.mask_filename = p8pd_params['mask_filename']
self.mask_hdf5_path = p8pd_params['mask_hdf5_path']
self.min_res = int(p8pd_params['min_res'])
self.max_res = int(p8pd_params['max_res'])
self.loaded_mask = load_nparray_from_hdf5_file(self.mask_filename,
self.mask_hdf5_path)
self.min_num_peaks_for_hit = int(
monitor_params['General']['min_num_peaks_for_hit'])
self.max_num_peaks_for_hit = int(
monitor_params['General']['max_num_peaks_for_hit'])
self.res_mask = numpy.ones(self.slab_shape, dtype=numpy.int8)
self.res_mask[numpy.where(self.pixelmap_radius < self.min_res)] = 0
self.res_mask[numpy.where(self.pixelmap_radius > self.max_res)] = 0
self.mask = self.loaded_mask * self.res_mask
mask = self.loaded_mask.copy().astype(numpy.float)
mask = mask * 255. / mask.max()
mask = 255. - mask
self.mask_to_draw[self.pixel_maps[0], self.pixel_maps[1],
1] = mask.ravel()
self.mask_image_view = pyqtgraph.ImageItem()
self.peak_canvas = pyqtgraph.ScatterPlotItem()
self.circle_canvas = pyqtgraph.ScatterPlotItem()
self.adc_threshold_label = PyQt4.QtGui.QLabel(self)
self.adc_threshold_label.setText('adc_threshold')
self.adc_threshold_lineedit = PyQt4.QtGui.QLineEdit(self)
self.adc_threshold_lineedit.setText(
str(self.monitor_params['Peakfinder8PeakDetection']
['adc_threshold']))
self.adc_threshold_lineedit.editingFinished.connect(self.update_peaks)
self.hlayout0 = PyQt4.QtGui.QHBoxLayout()
self.hlayout0.addWidget(self.adc_threshold_label)
self.hlayout0.addWidget(self.adc_threshold_lineedit)
self.min_snr_label = PyQt4.QtGui.QLabel(self)
self.min_snr_label.setText('minmum_snr')
self.min_snr_lineedit = PyQt4.QtGui.QLineEdit(self)
self.min_snr_lineedit.setText(
str(self.monitor_params['Peakfinder8PeakDetection']
['minimum_snr']))
self.min_snr_lineedit.editingFinished.connect(self.update_peaks)
self.hlayout1 = PyQt4.QtGui.QHBoxLayout()
self.hlayout1.addWidget(self.min_snr_label)
self.hlayout1.addWidget(self.min_snr_lineedit)
self.min_pixel_count_label = PyQt4.QtGui.QLabel(self)
self.min_pixel_count_label.setText('min_pixel_count')
self.min_pixel_count_lineedit = PyQt4.QtGui.QLineEdit(self)
self.min_pixel_count_lineedit.setText(
str(self.monitor_params['Peakfinder8PeakDetection']
['min_pixel_count']))
self.min_pixel_count_lineedit.editingFinished.connect(
self.update_peaks)
self.hlayout2 = PyQt4.QtGui.QHBoxLayout()
self.hlayout2.addWidget(self.min_pixel_count_label)
self.hlayout2.addWidget(self.min_pixel_count_lineedit)
self.max_pixel_count_label = PyQt4.QtGui.QLabel(self)
self.max_pixel_count_label.setText('max_pixel_count')
self.max_pixel_count_lineedit = PyQt4.QtGui.QLineEdit(self)
self.max_pixel_count_lineedit.setText(
str(self.monitor_params['Peakfinder8PeakDetection']
['max_pixel_count']))
self.max_pixel_count_lineedit.editingFinished.connect(
self.update_peaks)
self.hlayout3 = PyQt4.QtGui.QHBoxLayout()
self.hlayout3.addWidget(self.max_pixel_count_label)
self.hlayout3.addWidget(self.max_pixel_count_lineedit)
self.local_bg_radius_label = PyQt4.QtGui.QLabel(self)
self.local_bg_radius_label.setText('local_bg_raidus')
self.local_bg_radius_lineedit = PyQt4.QtGui.QLineEdit(self)
self.local_bg_radius_lineedit.setText(
str(self.monitor_params['Peakfinder8PeakDetection']
['local_bg_radius']))
self.local_bg_radius_lineedit.editingFinished.connect(
self.update_peaks)
self.hlayout4 = PyQt4.QtGui.QHBoxLayout()
self.hlayout4.addWidget(self.local_bg_radius_label)
self.hlayout4.addWidget(self.local_bg_radius_lineedit)
self.min_res_label = PyQt4.QtGui.QLabel(self)
self.min_res_label.setText('min_res')
self.min_res_lineedit = PyQt4.QtGui.QLineEdit(self)
self.min_res_lineedit.setText(str(self.min_res))
self.min_res_lineedit.editingFinished.connect(self.update_peaks)
self.hlayout5 = PyQt4.QtGui.QHBoxLayout()
self.hlayout5.addWidget(self.min_res_label)
self.hlayout5.addWidget(self.min_res_lineedit)
self.max_res_label = PyQt4.QtGui.QLabel(self)
self.max_res_label.setText('max_res')
self.max_res_lineedit = PyQt4.QtGui.QLineEdit(self)
self.max_res_lineedit.setText(str(self.max_res))
self.max_res_lineedit.editingFinished.connect(self.update_peaks)
self.hlayout6 = PyQt4.QtGui.QHBoxLayout()
self.hlayout6.addWidget(self.max_res_label)
self.hlayout6.addWidget(self.max_res_lineedit)
self.param_label = PyQt4.QtGui.QLabel(self)
self.param_label.setText('<b>Peakfinder Parameters:</b>')
self.ui = GUI.UI.parameter_tweaker_UI.Ui_MainWindow()
self.ui.setupUi(self)
self.init_ui()
self.setWindowTitle('OnDA Live Parameter Tweaker')
self.proxy = pyqtgraph.SignalProxy(
self.ui.imageView.getView().scene().sigMouseClicked,
slot=self.mouse_clicked)
self.update_peaks()
self.draw_things()
self.refresh_timer = PyQt4.QtCore.QTimer()
self.init_timer()
self.show()
def __init__(self, monitor_params, rec_ip, rec_port):
super(MainFrame, self).__init__()
self.monitor_params = monitor_params
gen_params = monitor_params['General']
p8pd_params = monitor_params['Peakfinder8PeakDetection']
self.rec_ip, self.rec_port = rec_ip, rec_port
self.data = collections.deque(maxlen=20)
self.data_index = 0
self.image_update_us = 250
self.zeromq_listener_thread = PyQt4.QtCore.QThread()
self.zeromq_listener = ZMQListener(self.rec_ip, self.rec_port, u'ondarawdata')
self.init_listening_thread()
self.ring_pen = pyqtgraph.mkPen('r', width=2)
self.circle_pen = pyqtgraph.mkPen('b', width=2)
pix_maps = pixel_maps_from_geometry_file(monitor_params['General']['geometry_file'])
self.pixelmap_radius = pix_maps[2]
self.pixel_maps, self.slab_shape, self.img_shape = pixel_maps_for_image_view(gen_params['geometry_file'])
self.img_to_draw = numpy.zeros(self.img_shape, dtype=numpy.float32)
self.mask_to_draw = numpy.zeros(self.img_shape+(3,), dtype=numpy.int16)
self.max_num_peaks = int(p8pd_params['max_num_peaks'])
self.asic_nx = int(p8pd_params['asics_nx'])
self.asic_ny = int(p8pd_params['asics_ny'])
self.nasics_x = int(p8pd_params['nasics_x'])
self.nasics_y = int(p8pd_params['nasics_y'])
self.adc_thresh = float(p8pd_params['adc_threshold'])
self.minimum_snr = float(p8pd_params['minimum_snr'])
self.min_pixel_count = int(p8pd_params['min_pixel_count'])
self.max_pixel_count = int(p8pd_params['max_pixel_count'])
self.local_bg_radius = int(p8pd_params['local_bg_radius'])
self.mask_filename = p8pd_params['mask_filename']
self.mask_hdf5_path = p8pd_params['mask_hdf5_path']
self.min_res = int(p8pd_params['min_res'])
self.max_res = int(p8pd_params['max_res'])
self.loaded_mask = load_nparray_from_hdf5_file(self.mask_filename, self.mask_hdf5_path)
self.min_num_peaks_for_hit = int(monitor_params['General']['min_num_peaks_for_hit'])
self.max_num_peaks_for_hit = int(monitor_params['General']['max_num_peaks_for_hit'])
self.res_mask = numpy.ones(self.slab_shape, dtype=numpy.int8)
self.res_mask[numpy.where(self.pixelmap_radius < self.min_res)] = 0
self.res_mask[numpy.where(self.pixelmap_radius > self.max_res)] = 0
self.mask = self.loaded_mask * self.res_mask
mask = self.loaded_mask.copy().astype(numpy.float)
mask = mask * 255./mask.max()
mask = 255. - mask
self.mask_to_draw[self.pixel_maps[0], self.pixel_maps[1], 1] = mask.ravel()
self.mask_image_view = pyqtgraph.ImageItem()
self.peak_canvas = pyqtgraph.ScatterPlotItem()
self.circle_canvas = pyqtgraph.ScatterPlotItem()
self.adc_threshold_label = PyQt4.QtGui.QLabel(self)
self.adc_threshold_label.setText('adc_threshold')
self.adc_threshold_lineedit = PyQt4.QtGui.QLineEdit(self)
self.adc_threshold_lineedit.setText(str(self.monitor_params['Peakfinder8PeakDetection']['adc_threshold']))
self.adc_threshold_lineedit.editingFinished.connect(self.update_peaks)
self.hlayout0 = PyQt4.QtGui.QHBoxLayout()
self.hlayout0.addWidget(self.adc_threshold_label)
self.hlayout0.addWidget(self.adc_threshold_lineedit)
self.min_snr_label = PyQt4.QtGui.QLabel(self)
self.min_snr_label.setText('minmum_snr')
self.min_snr_lineedit = PyQt4.QtGui.QLineEdit(self)
self.min_snr_lineedit.setText(str(self.monitor_params['Peakfinder8PeakDetection']['minimum_snr']))
self.min_snr_lineedit.editingFinished.connect(self.update_peaks)
self.hlayout1 = PyQt4.QtGui.QHBoxLayout()
self.hlayout1.addWidget(self.min_snr_label)
self.hlayout1.addWidget(self.min_snr_lineedit)
self.min_pixel_count_label = PyQt4.QtGui.QLabel(self)
self.min_pixel_count_label.setText('min_pixel_count')
self.min_pixel_count_lineedit = PyQt4.QtGui.QLineEdit(self)
self.min_pixel_count_lineedit.setText(str(self.monitor_params['Peakfinder8PeakDetection']['min_pixel_count']))
self.min_pixel_count_lineedit.editingFinished.connect(self.update_peaks)
self.hlayout2 = PyQt4.QtGui.QHBoxLayout()
self.hlayout2.addWidget(self.min_pixel_count_label)
self.hlayout2.addWidget(self.min_pixel_count_lineedit)
self.max_pixel_count_label = PyQt4.QtGui.QLabel(self)
self.max_pixel_count_label.setText('max_pixel_count')
self.max_pixel_count_lineedit = PyQt4.QtGui.QLineEdit(self)
self.max_pixel_count_lineedit.setText(str(self.monitor_params['Peakfinder8PeakDetection']['max_pixel_count']))
self.max_pixel_count_lineedit.editingFinished.connect(self.update_peaks)
self.hlayout3 = PyQt4.QtGui.QHBoxLayout()
self.hlayout3.addWidget(self.max_pixel_count_label)
self.hlayout3.addWidget(self.max_pixel_count_lineedit)
self.local_bg_radius_label = PyQt4.QtGui.QLabel(self)
self.local_bg_radius_label.setText('local_bg_raidus')
self.local_bg_radius_lineedit = PyQt4.QtGui.QLineEdit(self)
self.local_bg_radius_lineedit.setText(str(self.monitor_params['Peakfinder8PeakDetection']['local_bg_radius']))
self.local_bg_radius_lineedit.editingFinished.connect(self.update_peaks)
self.hlayout4 = PyQt4.QtGui.QHBoxLayout()
self.hlayout4.addWidget(self.local_bg_radius_label)
self.hlayout4.addWidget(self.local_bg_radius_lineedit)
self.min_res_label = PyQt4.QtGui.QLabel(self)
self.min_res_label.setText('min_res')
self.min_res_lineedit = PyQt4.QtGui.QLineEdit(self)
self.min_res_lineedit.setText(str(self.min_res))
self.min_res_lineedit.editingFinished.connect(self.update_peaks)
self.hlayout5 = PyQt4.QtGui.QHBoxLayout()
self.hlayout5.addWidget(self.min_res_label)
self.hlayout5.addWidget(self.min_res_lineedit)
self.max_res_label = PyQt4.QtGui.QLabel(self)
self.max_res_label.setText('max_res')
self.max_res_lineedit = PyQt4.QtGui.QLineEdit(self)
self.max_res_lineedit.setText(str(self.max_res))
self.max_res_lineedit.editingFinished.connect(self.update_peaks)
self.hlayout6 = PyQt4.QtGui.QHBoxLayout()
self.hlayout6.addWidget(self.max_res_label)
self.hlayout6.addWidget(self.max_res_lineedit)
self.param_label = PyQt4.QtGui.QLabel(self)
self.param_label.setText('<b>Peakfinder Parameters:</b>')
self.ui = GUI.UI.parameter_tweaker_UI.Ui_MainWindow()
self.ui.setupUi(self)
self.init_ui()
self.setWindowTitle('OnDA Live Parameter Tweaker')
self.proxy = pyqtgraph.SignalProxy(self.ui.imageView.getView().scene().sigMouseClicked,
slot=self.mouse_clicked)
self.update_peaks()
self.draw_things()
self.refresh_timer = PyQt4.QtCore.QTimer()
self.init_timer()
self.show()
def __init__(self, role, filter_length, min_filter_length, filter_step,
sigma_factor, streak_elongation_min_steps_count,
streak_elongation_radius_factor, streak_pixel_mask_radius,
num_lines_to_check, background_region_preset,
background_region_dist_from_edge, asic_nx, asic_ny, nasics_x,
nasics_y, pixel_map_x, pixel_map_y, mask_filename,
mask_hdf5_path):
"""Initializes the peakfinder
Args:
role (str): node role ('worker' or 'master')
filter_length (int): length of the radial filter with which the
image is prefiltered
min_filter_length (int): Minimum amount of non-masked pixels in
the radial filter with which the image is prefiltered
float filter_step (float): size of the step through the radial
filter
sigma_factor(float): minimum number of stddev above the mean for
the pixel to be part of a streak.
streak_elongation_min_steps_count (int): number of steps to keep
searching for a streak after it apparently ends
streak_elongation_radius_factor (float): maximum distance in
pixels from the center to keep searching for a streak after it
apparently ends
streak_pixel_mask_radius (int): Radius of the mask around the
streak in pixels
num_lines_to_check (int): maximum distance in pixels from the
inner edge of the inner panels to searching for the beginning of
a streak
background_region_preset (int): preset locations of regions to be
used to estimate the background level
background_region_dist_from_edge (int): distance of background
regions in pixels from the edge of the inner panels
asic_nx (int): fs size of a detector's ASIC.
asic_ny (int): ss size of a detector's ASIC.
nasics_x (int): number of ASICs in the slab in the fs
direction.
nasics_y (int): number of ASICs in the slab in the ss
direction.
pixel_map_x (numpy.ndarray): pixel_map for x coordinate
pixel_map_y (numpy.ndarray): pixel_map for y coordinate
mask_filename (str): filename of the file containing the mask.
mask_hdf5_path (str): internal hdf5 path of the data block
containing the mask.
"""
# Initialized on worker
if role == 'worker':
self.mask = load_nparray_from_hdf5_file(mask_filename,
mask_hdf5_path)
self.background_region_mask = numpy.zeros(self.mask.shape)
self.streak_detection = StreakDetectionClass(
min_filter_length, filter_step, sigma_factor,
streak_elongation_min_steps_count,
streak_elongation_radius_factor, streak_pixel_mask_radius,
num_lines_to_check, 0, background_region_preset,
background_region_dist_from_edge, asic_nx, asic_ny, nasics_x,
nasics_y, pixel_map_x, pixel_map_y, self.mask,
self.background_region_mask)
def __init__(self, role, max_num_peaks, asic_nx, asic_ny, nasics_x,
nasics_y, adc_threshold, minimum_snr, min_pixel_count,
max_pixel_count, local_bg_radius, accumulated_shots, min_res,
max_res, mask_filename, mask_hdf5_path, pixelmap_radius):
"""Initializes the peakfinder
Args:
role (str): node role ('worker' or 'master')
max_num_peaks (int): maximum number of peaks that will be
returned by the algorithm.
asic_nx (int): fs size of a detector's ASIC.
asic_ny (int): ss size of a detector's ASIC.
nasics_x (int): number of ASICs in the slab in the fs
direction.
nasics_y (int): number of ASICs in the slab in the ss
direction.
adc_threshold (float): minimum adc threshold for peak
detection.
minimum_snr (float): minimum signal to noise for peak
detection.
min_pixel_count (int): minimum size of the peak in pixels.
max_pixel_count (int): maximum size of the peak in pixels.
local_bg_radius (int): radius for the estimation of the
local background.
accumulated_shots (int): the number of accumulated shots
before the peak list is returned.
min_res (int): minimum resolution for a peak to be
considered (in pixels).
max_res (int): minimum resolution for a peak to be
considered (in pixels).
mask_filename (str): filename of the file containing the mask.
mask_hdf5_path (str): internal hdf5 path of the data block
containing the mask.
pixelmap_radius (numpy.ndarray): pixelmap in 'slab' format listing
for each pixel the distance from the center of the detector (in
pixels).
"""
self.max_num_peaks = max_num_peaks
self.asic_nx = asic_nx
self.asic_ny = asic_ny
self.nasics_x = nasics_x
self.nasics_y = nasics_y
self.adc_thresh = adc_threshold
self.minimum_snr = minimum_snr
self.min_pixel_count = min_pixel_count
self.max_pixel_count = max_pixel_count
self.local_bg_radius = local_bg_radius
self.pixelmap_radius = pixelmap_radius
if role == 'master':
self.accumulated_shots = accumulated_shots
self.accumulator = ([], [], [])
self.events_in_accumulator = 0
# Initialized on worker
if role == 'worker':
self.mask = load_nparray_from_hdf5_file(mask_filename,
mask_hdf5_path)
self.res_mask = numpy.ones(self.mask.shape, dtype=numpy.int8)
self.res_mask[numpy.where(pixelmap_radius < min_res)] = 0
self.res_mask[numpy.where(pixelmap_radius > max_res)] = 0
self.mask *= self.res_mask
def __init__(self, role, max_num_peaks, asic_nx, asic_ny, nasics_x,
nasics_y, sigma_factor_biggest_pixel, sigma_factor_peak_pixel,
sigma_factor_whole_peak, minimum_sigma,
minimum_peak_oversize_over_neighbours, window_radius,
accumulated_shots, min_res, max_res, mask_filename,
mask_hdf5_path, pixelmap_radius):
"""Initializes the peakfinder
Args:
role (str): node role ('worker' or 'master')
max_num_peaks (int): maximum number of peaks that will be
returned by the algorithm.
asic_nx (int): fs size of a detector's ASIC.
asic_ny (int): ss size of a detector's ASIC.
nasics_x (int): number of ASICs in the slab in the fs
direction.
nasics_y (int): number of ASICs in the slab in the ss
direction.
sigma_factor_biggest_pixel (float): minimum number of stddev
above the mean for the brightest pixel in a peak.
sigma_factor_peak_pixel (float): minimum number of stddev
above the mean for all pixels except the brightest.
Should be equal to or lower than sigma_factor_biggest_pixel.
sigma_factor_whole_peak (float): minimum number of stddev
above the mean for the accumulated inensity of a peak.
Should be equal to or lower than sigma_factor_biggest_pixel.
minimum_sigma (float): minimum number of stddev for a pixel to
be a peak candidate.
minimum_peak_oversize_over_neighbours (float): minimum intensity
difference between the brighest pixel in a peak and the
background pixels (pixels on the border of the background
windows).
window_size (int): radius of the window used to estimate
background.
accumulated_shots (int): the number of accumulated shots
before the peak list is returned.
min_res (int): minimum resolution for a peak to be
considered (in pixels).
max_res (int): minimum resolution for a peak to be
considered (in pixels).
mask_filename (str): filename of the file containing the mask.
mask_hdf5_path (str): internal hdf5 path of the data block
containing the mask.
"""
self.max_num_peaks = max_num_peaks
self.asic_nx = asic_nx
self.asic_ny = asic_ny
self.nasics_x = nasics_x
self.nasics_y = nasics_y
self.sigma_factor_biggest_pixel = sigma_factor_biggest_pixel
self.sigma_factor_peak_pixel = sigma_factor_peak_pixel
self.sigma_factor_whole_peak = sigma_factor_whole_peak
self.minimum_sigma = minimum_sigma
self.minimum_peak_oversize_over_neighbours = minimum_peak_oversize_over_neighbours
self.window_radius = window_radius
if role == 'master':
self.accumulated_shots = accumulated_shots
self.accumulator = ([], [], [])
self.events_in_accumulator = 0
# Initialized on worker
if role == 'worker':
self.mask = load_nparray_from_hdf5_file(mask_filename,
mask_hdf5_path)
self.res_mask = numpy.ones(self.mask.shape, dtype=numpy.int8)
self.res_mask[numpy.where(pixelmap_radius < min_res)] = 0
self.res_mask[numpy.where(pixelmap_radius > max_res)] = 0
self.mask *= self.res_mask
def __init__(self, role, filter_length, min_filter_length, filter_step,
sigma_factor, streak_elongation_min_steps_count,
streak_elongation_radius_factor,
streak_pixel_mask_radius, num_lines_to_check,
background_region_preset,
background_region_dist_from_edge,
asic_nx, asic_ny, nasics_x, nasics_y,
pixel_map_x, pixel_map_y, mask_filename,
mask_hdf5_path):
"""Initializes the peakfinder
Args:
role (str): node role ('worker' or 'master')
filter_length (int): length of the radial filter with which the
image is prefiltered
min_filter_length (int): Minimum amount of non-masked pixels in
the radial filter with which the image is prefiltered
float filter_step (float): size of the step through the radial
filter
sigma_factor(float): minimum number of stddev above the mean for
the pixel to be part of a streak.
streak_elongation_min_steps_count (int): number of steps to keep
searching for a streak after it apparently ends
streak_elongation_radius_factor (float): maximum distance in
pixels from the center to keep searching for a streak after it
apparently ends
streak_pixel_mask_radius (int): Radius of the mask around the
streak in pixels
num_lines_to_check (int): maximum distance in pixels from the
inner edge of the inner panels to searching for the beginning of
a streak
background_region_preset (int): preset locations of regions to be
used to estimate the background level
background_region_dist_from_edge (int): distance of background
regions in pixels from the edge of the inner panels
asic_nx (int): fs size of a detector's ASIC.
asic_ny (int): ss size of a detector's ASIC.
nasics_x (int): number of ASICs in the slab in the fs
direction.
nasics_y (int): number of ASICs in the slab in the ss
direction.
pixel_map_x (numpy.ndarray): pixel_map for x coordinate
pixel_map_y (numpy.ndarray): pixel_map for y coordinate
mask_filename (str): filename of the file containing the mask.
mask_hdf5_path (str): internal hdf5 path of the data block
containing the mask.
"""
# Initialized on worker
if role == 'worker':
self.mask = load_nparray_from_hdf5_file(mask_filename,
mask_hdf5_path)
self.background_region_mask = numpy.zeros(self.mask.shape)
self.streak_detection = StreakDetectionClass(
min_filter_length,
filter_step,
sigma_factor,
streak_elongation_min_steps_count,
streak_elongation_radius_factor,
streak_pixel_mask_radius, num_lines_to_check,
0, background_region_preset,
background_region_dist_from_edge,
asic_nx, asic_ny, nasics_x, nasics_y,
pixel_map_x, pixel_map_y, self.mask,
self.background_region_mask)
def __init__(self, role, max_num_peaks, asic_nx, asic_ny, nasics_x,
nasics_y, adc_threshold, minimum_snr, min_pixel_count,
max_pixel_count, local_bg_radius, accumulated_shots, min_res,
max_res, mask_filename, mask_hdf5_path, pixelmap_radius):
"""Initializes the peakfinder
Args:
role (str): node role ('worker' or 'master')
max_num_peaks (int): maximum number of peaks that will be
returned by the algorithm.
asic_nx (int): fs size of a detector's ASIC.
asic_ny (int): ss size of a detector's ASIC.
nasics_x (int): number of ASICs in the slab in the fs
direction.
nasics_y (int): number of ASICs in the slab in the ss
direction.
adc_threshold (float): minimum adc threshold for peak
detection.
minimum_snr (float): minimum signal to noise for peak
detection.
min_pixel_count (int): minimum size of the peak in pixels.
max_pixel_count (int): maximum size of the peak in pixels.
local_bg_radius (int): radius for the estimation of the
local background.
accumulated_shots (int): the number of accumulated shots
before the peak list is returned.
min_res (int): minimum resolution for a peak to be
considered (in pixels).
max_res (int): minimum resolution for a peak to be
considered (in pixels).
mask_filename (str): filename of the file containing the mask.
mask_hdf5_path (str): internal hdf5 path of the data block
containing the mask.
pixelmap_radius (numpy.ndarray): pixelmap in 'slab' format listing
for each pixel the distance from the center of the detector (in
pixels).
"""
self.max_num_peaks = max_num_peaks
self.asic_nx = asic_nx
self.asic_ny = asic_ny
self.nasics_x = nasics_x
self.nasics_y = nasics_y
self.adc_thresh = adc_threshold
self.minimum_snr = minimum_snr
self.min_pixel_count = min_pixel_count
self.max_pixel_count = max_pixel_count
self.local_bg_radius = local_bg_radius
self.pixelmap_radius = pixelmap_radius
if role == 'master':
self.accumulated_shots = accumulated_shots
self.accumulator = ([], [], [])
self.events_in_accumulator = 0
# Initialized on worker
if role == 'worker':
self.mask = load_nparray_from_hdf5_file(mask_filename, mask_hdf5_path)
self.res_mask = numpy.ones(self.mask.shape, dtype=numpy.int8)
self.res_mask[numpy.where(pixelmap_radius < min_res)] = 0
self.res_mask[numpy.where(pixelmap_radius > max_res)] = 0
self.mask *= self.res_mask
def __init__(self, role, max_num_peaks, asic_nx, asic_ny, nasics_x,
nasics_y, sigma_factor_biggest_pixel, sigma_factor_peak_pixel,
sigma_factor_whole_peak, minimum_sigma,
minimum_peak_oversize_over_neighbours,
window_radius, accumulated_shots, min_res, max_res,
mask_filename, mask_hdf5_path, pixelmap_radius):
"""Initializes the peakfinder
Args:
role (str): node role ('worker' or 'master')
max_num_peaks (int): maximum number of peaks that will be
returned by the algorithm.
asic_nx (int): fs size of a detector's ASIC.
asic_ny (int): ss size of a detector's ASIC.
nasics_x (int): number of ASICs in the slab in the fs
direction.
nasics_y (int): number of ASICs in the slab in the ss
direction.
sigma_factor_biggest_pixel (float): minimum number of stddev
above the mean for the brightest pixel in a peak.
sigma_factor_peak_pixel (float): minimum number of stddev
above the mean for all pixels except the brightest.
Should be equal to or lower than sigma_factor_biggest_pixel.
sigma_factor_whole_peak (float): minimum number of stddev
above the mean for the accumulated inensity of a peak.
Should be equal to or lower than sigma_factor_biggest_pixel.
minimum_sigma (float): minimum number of stddev for a pixel to
be a peak candidate.
minimum_peak_oversize_over_neighbours (float): minimum intensity
difference between the brighest pixel in a peak and the
background pixels (pixels on the border of the background
windows).
window_size (int): radius of the window used to estimate
background.
accumulated_shots (int): the number of accumulated shots
before the peak list is returned.
min_res (int): minimum resolution for a peak to be
considered (in pixels).
max_res (int): minimum resolution for a peak to be
considered (in pixels).
mask_filename (str): filename of the file containing the mask.
mask_hdf5_path (str): internal hdf5 path of the data block
containing the mask.
"""
self.max_num_peaks = max_num_peaks
self.asic_nx = asic_nx
self.asic_ny = asic_ny
self.nasics_x = nasics_x
self.nasics_y = nasics_y
self.sigma_factor_biggest_pixel = sigma_factor_biggest_pixel
self.sigma_factor_peak_pixel = sigma_factor_peak_pixel
self.sigma_factor_whole_peak = sigma_factor_whole_peak
self.minimum_sigma = minimum_sigma
self.minimum_peak_oversize_over_neighbours = minimum_peak_oversize_over_neighbours
self.window_radius = window_radius
if role == 'master':
self.accumulated_shots = accumulated_shots
self.accumulator = ([], [], [])
self.events_in_accumulator = 0
# Initialized on worker
if role == 'worker':
self.mask = load_nparray_from_hdf5_file(mask_filename, mask_hdf5_path)
self.res_mask = numpy.ones(self.mask.shape, dtype=numpy.int8)
self.res_mask[numpy.where(pixelmap_radius < min_res)] = 0
self.res_mask[numpy.where(pixelmap_radius > max_res)] = 0
self.mask *= self.res_mask