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cleanup refactoring. this refs #8
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@JeanBilheux
JeanBilheux committed Jul 12, 2021
1 parent 21441f1 commit b717c05
Showing 1 changed file with 38 additions and 48 deletions.
86 changes: 38 additions & 48 deletions NeuNorm/normalization.py
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Original file line number Diff line number Diff line change
Expand Up @@ -440,7 +440,7 @@ def normalization(self, roi=None, force=False, force_mean_ob=False, notebook=Fal
if self.__exec_process_status['normalization']:
return
self.__exec_process_status['normalization'] = True

# make sure we loaded some sample data
if self.data['sample']['data'] is None:
raise IOError("No normalization available as no data have been loaded")
Expand All @@ -449,7 +449,7 @@ def normalization(self, roi=None, force=False, force_mean_ob=False, notebook=Fal
if not use_only_sample:
if self.data['ob']['data'] is None:
raise IOError("No normalization available as no OB have been loaded")

# make sure the data loaded have the same size
if not self.data_loaded_have_matching_shape():
raise ValueError("Data loaded do not have the same shape!")
Expand All @@ -464,29 +464,15 @@ def normalization(self, roi=None, force=False, force_mean_ob=False, notebook=Fal
if not use_only_sample:

if roi:
b_list_roi = self.check_roi_format(b_list_roi, roi)
b_list_roi = self.check_roi_format(roi)

if roi:

if b_list_roi:

_sample_corrected_normalized = self.calculate_corrected_normalized(data_type=DataType.sample,
roi=roi)
_ob_corrected_normalized = self.calculate_corrected_normalized(data_type=DataType.ob,
roi=roi)
# for _ob in self.data['ob']['data']:
# total_counts_of_rois = 0
# total_number_of_pixels = 0
# for _roi in roi:
# _x0 = _roi.x0
# _y0 = _roi.y0
# _x1 = _roi.x1
# _y1 = _roi.y1
# total_number_of_pixels += (_y1 - _y0 + 1) * (_x1 - _x0 + 1)
# total_counts_of_rois += np.sum(_ob[_y0:_y1 + 1, _x0:_x1 + 1])
#
# full_ob_mean = total_counts_of_rois / total_number_of_pixels
# _ob_corrected_normalized.append(_ob / full_ob_mean)

else:
_x0 = roi.x0
Expand All @@ -504,8 +490,8 @@ def normalization(self, roi=None, force=False, force_mean_ob=False, notebook=Fal
_sample_corrected_normalized = copy.deepcopy(self.data['sample']['data'])
_ob_corrected_normalized = copy.deepcopy(self.data['ob']['data'])

self.data['sample']['data'] = _sample_corrected_normalized
self.data['ob']['data'] = _ob_corrected_normalized
self.data[DataType.sample]['data'] = _sample_corrected_normalized
self.data[DataType.ob]['data'] = _ob_corrected_normalized

# if the number of sample and ob do not match, use mean of obs
nbr_sample = len(self.data['sample']['file_name'])
Expand Down Expand Up @@ -563,45 +549,47 @@ def normalization(self, roi=None, force=False, force_mean_ob=False, notebook=Fal
self.data['normalized'] = normalized_data

else: # use_sample_only with ROI
normalized_data = self.calculate_corrected_normalized_without_ob(roi=roi)
self.data['normalized'] = normalized_data

if not roi:
raise ValueError("You need to provide at least 1 ROI using this use_only_sample mode!")
return True

b_list_roi = self.check_roi_format(b_list_roi, roi)
def calculate_corrected_normalized_without_ob(self, roi=None):
if not roi:
raise ValueError("You need to provide at least 1 ROI using this use_only_sample mode!")

if b_list_roi:
b_list_roi = self.check_roi_format(roi)

normalized_data = []
for _sample in self.data['sample']['data']:
total_counts_of_rois = 0
total_number_of_pixels = 0
for _roi in roi:
_x0 = _roi.x0
_y0 = _roi.y0
_x1 = _roi.x1
_y1 = _roi.y1
total_number_of_pixels += (_y1 - _y0 + 1) * (_x1 - _x0 + 1)
total_counts_of_rois += np.sum(_sample[_y0:_y1 + 1, _x0:_x1 + 1])

full_sample_mean = total_counts_of_rois / total_number_of_pixels
normalized_data.append(_sample / full_sample_mean)
if b_list_roi:

else:
normalized_data = []
for _sample in self.data['sample']['data']:
total_counts_of_rois = 0
total_number_of_pixels = 0
for _roi in roi:
_x0 = _roi.x0
_y0 = _roi.y0
_x1 = _roi.x1
_y1 = _roi.y1
total_number_of_pixels += (_y1 - _y0 + 1) * (_x1 - _x0 + 1)
total_counts_of_rois += np.sum(_sample[_y0:_y1 + 1, _x0:_x1 + 1])

_x0 = roi.x0
_y0 = roi.y0
_x1 = roi.x1
_y1 = roi.y1
full_sample_mean = total_counts_of_rois / total_number_of_pixels
normalized_data.append(_sample / full_sample_mean)

normalized_data = [_sample / np.mean(_sample[_y0:_y1+1, _x0:_x1+1])
for _sample in self.data['sample']['data']]
else:

self.data['normalized'] = normalized_data
_x0 = roi.x0
_y0 = roi.y0
_x1 = roi.x1
_y1 = roi.y1

return True
normalized_data = [_sample / np.mean(_sample[_y0:_y1+1, _x0:_x1+1])
for _sample in self.data['sample']['data']]

def calculate_corrected_normalized(self, data_type=DataType.sample, roi=None):
return normalized_data

def calculate_corrected_normalized(self, data_type=DataType.sample, roi=None):
corrected_normalized = []
for _sample in self.data[data_type]['data']:
total_counts_of_rois = 0
Expand All @@ -618,7 +606,8 @@ def calculate_corrected_normalized(self, data_type=DataType.sample, roi=None):
corrected_normalized.append(_sample / full_sample_mean)
return corrected_normalized

def check_roi_format(self, b_list_roi, roi):
def check_roi_format(self, roi):
b_list_roi = False
if type(roi) is list:
for _roi in roi:
if not type(_roi) == ROI:
Expand All @@ -632,6 +621,7 @@ def check_roi_format(self, b_list_roi, roi):
else:
if not self.__roi_fit_into_sample(roi=roi):
raise ValueError("roi does not fit into sample image!")

return b_list_roi

def data_loaded_have_matching_shape(self):
Expand Down

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