def run(self):
# Create an array the size of the roi list and initialized to zero.
num_rois = len(self._roi_list)
num_items = len(self._file_list)
roi_sums = numpy.zeros((num_rois, num_items), dtype=numpy.float32)
#! start_time = time()
# Create two lists. The first holds the starting values of each roi,
# the second holds the ending values of each roi. Do this because the
# roi_list object can't be pickled, which causes difficulties for multiprocessing.
roi_start_list = []
roi_end_list = []
for i in range(num_rois):
roi_start_list.append(self._roi_list[i].GetStart())
roi_end_list.append(self._roi_list[i].GetEnd())
roi_sums = mproc.distribute_jobs(
roi_sums,
func=self._func,
update_func=self._update_func,
num_files=num_items,
args=(roi_start_list, roi_end_list, self._file_list, self._file_path),
ncore=None,
nchunk=None)
###############################################################################################################
## This section of code works, but it runs in a single thread, so it is very slow for large numbers of files.
# For each file, open the file, create an array of the values in the file,
# then create a 2D array that holds the sums of the values that are in each
# ROI.
#! for i in range(num_items):
#! self._data.CreateArrays(os.path.join(self._file_path, self._file_list[i]))
#! for j in range(num_rois):
#! roi_sums[j][i] = roi_sums[j][i] + self._data.SumROIData(self._roi_list[j].GetStart(), self._roi_list[j].GetEnd())
# Cause the progress gauge on the main frame to update
#! evt = UpdateProgressEvent(myEVT_UPDATE_PROGRESS, -1, i)
#! wx.PostEvent(self._parent, evt)
###############################################################################################################
#! end_time = time()
#! elapsed_time = end_time - start_time
#! print "Time to caluclate roi sums=%f" % elapsed_time
# Alert the main application that the data is ready to be plotted.
evt = DataSummationEvent(myEVT_SUM_DATA, -1, roi_sums)
wx.PostEvent(self._parent, evt)
return
def run(self):
# Create an array the size of the roi list and initialized to zero.
num_items = len(self._file_list)
pixel_sums = numpy.zeros((self._height, self._width), dtype=numpy.float32)
pixel_sums = mproc.distribute_jobs(
pixel_sums,
func=self._func,
update_func=self._update_func,
num_files=num_items,
args=(self._file_list, self._file_path),
ncore=None,
nchunk=None)
# Alert the main application that the data is ready to be plotted.
evt = ImageSummationEvent(myEVT_PLOT_IMAGE_SUM, -1, pixel_sums)
wx.PostEvent(self._parent, evt)
return
def run(self):
# Create an array the size of the roi list and initialized to zero.
num_rois = len(self._roi_list)
num_items = len(self._file_list)
roi_sums = numpy.zeros((num_rois, num_items), dtype=numpy.float32)
# Create two lists. The first holds the starting values of each roi,
# the second holds the ending values of each roi. Do this because the
# roi_list object can't be pickled, which causes difficulties for multiprocessing.
roi_start_list = []
roi_end_list = []
for roi in self._roi_list:
points = roi.GetDimensions()
x0 = points[0]
y0 = points[1]
x1 = points[2]
y1 = points[3]
if x1 > x0:
x0 = int(floor(x0))
x1 = int(ceil(x1))
else:
x1 = int(floor(x1))
x0 = int(ceil(x0))
temp = x1
x1 = x0
x0 = temp
if y0 > y1:
y1 = int(floor(y1))
y0 = int(ceil(y0))
temp = y1
y1 = y0
y0 = temp
else:
y0 = int(floor(y0))
y1 = int(ceil(y1))
print (x0,y0,x1,y1)
roi_start_list.append((x0, y0))
roi_end_list.append((x1, y1))
# for i in range(num_items):
# self._data.CreateArrays(os.path.join(self._file_path, self._file_list[i]))
# for j in range(num_rois):
# roi_sums[j][i] = roi_sums[j][i] + self._data.SumROIData(roi_start_list[j], roi_end_list[j])
# print i
roi_sums = mproc.distribute_jobs(
roi_sums,
func=self._func,
update_func=self._update_func,
num_files=num_items,
args=(roi_start_list, roi_end_list, self._file_list, self._file_path),
ncore=None,
nchunk=None)
# Alert the main application that the data is ready to be plotted.
evt = DataSummationEvent(myEVT_SUM_DATA, -1, roi_sums)
wx.PostEvent(self._parent, evt)
return
