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How to use name_pattern method in lisa
Best Python code snippet using lisa_python
linhao_support.py
Source:linhao_support.py 
1import csv2import logging as logger3import os4from _csv import writer as csv_writer5from collections import defaultdict6import numpy as np7import scipy8from matplotlib import pyplot as plt9import imagepipe.raw_functions10import imagepipe.tools.helpers11import imagepipe.wrapped_functions12from imagepipe import core_functions as cf13from imagepipe.core_functions import generator_wrapper14@generator_wrapper(in_dims=(None, 2, 2, 2, 2, 1, 1, None, None, 2, 2), out_dims=(None,))15def linhao_gfp_render(name_pattern, proj_gfp, qual_gfp, cell_labels, average_gfp_pad, average_gfp_list,16 predicted_average, std_err, upper_outliers, gfp_outliers, proj_mch,17 save=False, directory_to_save_to='verification'):18 # To Consider: bind all the x/y axes together.19 plt.figure(figsize=(20, 15))20 plt.suptitle(name_pattern)21 main_ax = plt.subplot(241)22 plt.imshow(proj_gfp, interpolation='nearest')23 plt.contour(cell_labels > 0, [0.5], colors='w')24 plt.subplot(242, sharex=main_ax, sharey=main_ax)25 plt.imshow(np.log(proj_gfp + np.min(proj_gfp[proj_gfp > 0])), cmap='hot', interpolation='nearest')26 plt.contour(cell_labels > 0, [0.5], colors='w')27 plt.subplot(243, sharex=main_ax, sharey=main_ax)28 plt.imshow(qual_gfp, cmap='gray', interpolation='nearest')29 plt.contour(cell_labels > 0, [0.5], colors='w')30 ax = plt.subplot(244, sharex=main_ax, sharey=main_ax)31 plt.imshow(cell_labels, cmap=plt.cm.spectral, interpolation='nearest')32 unique = np.sort(np.unique(cell_labels))[1:]33 for i in unique:34 mask = cell_labels == i35 x, y = scipy.ndimage.measurements.center_of_mass(mask)36 ax.text(y-8, x+8, '%s' % i, fontsize=10)37 plt.subplot(245, sharex=main_ax, sharey=main_ax)38 plt.imshow(average_gfp_pad, cmap='hot', interpolation='nearest')39 plt.colorbar()40 plt.contour(cell_labels > 0, [0.5], colors='w')41 plt.subplot(246)42 plt.plot(np.sort(average_gfp_list), 'ko')43 plt.plot(predicted_average, 'r')44 plt.plot(predicted_average + std_err, 'g')45 plt.plot(predicted_average - std_err, 'g')46 ax = plt.subplot(247, sharex=main_ax, sharey=main_ax)47 plt.imshow(gfp_outliers, cmap=plt.cm.spectral, interpolation='nearest')48 unique = np.unique(gfp_outliers)49 for i in unique:50 mask = gfp_outliers == i51 x, y = scipy.ndimage.measurements.center_of_mass(mask)52 ax.text(y-8, x+8, '%s' % i, fontsize=10)53 plt.subplot(248, sharex=main_ax, sharey=main_ax)54 plt.imshow(proj_gfp, interpolation='nearest')55 plt.contour(cell_labels > 0, [0.5], colors='w')56 if not save:57 plt.show()58 else:59 name_puck = directory_to_save_to+'/'+'gfp_base-'+name_pattern+'.png'60 plt.savefig(name_puck)61 plt.close()62@generator_wrapper(in_dims=(None, 2, 2, 2, 2, 2, 2, 2, 2), out_dims=(None,))63def linhao_mch_render(name_pattern, proj_mCh, mitochondria, skeleton,64 mito_classes, final_classes, cell_labels,65 radius_mask, support_mask,66 save=False, directory_to_save_to='verification'):67 # To Consider: bind all the x/y axes together.68 cell_binary = cell_labels > 069 plt.figure(figsize=(20.0, 15.0))70 plt.suptitle(name_pattern)71 plt.subplot(241)72 plt.imshow(proj_mCh, interpolation='nearest')73 plt.contour(cell_binary, [0.5], colors='k')74 plt.subplot(242)75 plt.imshow(np.log(proj_mCh + np.min(proj_mCh[proj_mCh > 0])), cmap='hot', interpolation='nearest')76 plt.contour(cell_binary, [0.5], colors='w')77 plt.subplot(243)78 plt.imshow(mitochondria, interpolation='nearest', cmap='gray')79 plt.subplot(244)80 plt.imshow(skeleton, interpolation='nearest', cmap=plt.cm.spectral)81 plt.contour(mitochondria, [0.5], colors='w')82 plt.subplot(245)83 plt.imshow(radius_mask, cmap='hot', interpolation='nearest')84 plt.contour(cell_binary, [0.5], colors='w')85 plt.subplot(246)86 plt.imshow(support_mask, cmap='hot', interpolation='nearest')87 plt.contour(cell_binary, [0.5], colors='w')88 plt.subplot(247)89 plt.imshow(mito_classes, interpolation='nearest', cmap='coolwarm')90 plt.contour(cell_binary, [0.5], colors='k')91 plt.subplot(248)92 plt.imshow(final_classes, interpolation='nearest', cmap='coolwarm')93 plt.contour(cell_binary, [0.5], colors='k')94 if not save:95 plt.show()96 else:97 name_puck = directory_to_save_to+'/'+'mCh_base-'+name_pattern+'.png'98 plt.savefig(name_puck)99 plt.close()100@generator_wrapper(in_dims=(None, 2, 2, 2, 2, 2, 2), out_dims=(None,))101def linhao_mqvi_render(name_pattern, mito_outlines, cell_labels,102 projected_gfp, projected_mch,103 gfp_mqvi, mch_mqvi,104 save=False, directory_to_save_to='verification'):105 plt.figure(figsize=(26.0, 15.0))106 plt.suptitle(name_pattern)107 main_ax = plt.subplot(221)108 plt.title('mCherry')109 plt.imshow(np.log(projected_mch+np.min(projected_mch[projected_mch > 0])),110 interpolation='nearest', cmap='hot')111 plt.contour(mito_outlines, [0.5], colors='b')112 plt.contour(cell_labels, [0.5], colors='g')113 plt.subplot(222, sharex=main_ax, sharey=main_ax)114 plt.title('GFP')115 plt.imshow(np.log(projected_gfp+np.min(projected_gfp[projected_gfp > 0])),116 interpolation='nearest', cmap='hot')117 plt.contour(mito_outlines, [0.5], colors='b')118 plt.contour(cell_labels, [0.5], colors='g')119 plt.subplot(224, sharex=main_ax, sharey=main_ax)120 plt.title('GFP MQVI')121 plt.imshow(gfp_mqvi, interpolation='nearest', cmap='hot')122 plt.colorbar()123 plt.subplot(223, sharex=main_ax, sharey=main_ax)124 plt.title('mCherry MQVI')125 plt.imshow(mch_mqvi, interpolation='nearest', cmap='hot')126 plt.colorbar()127 if not save:128 plt.show()129 else:130 name_puck = directory_to_save_to+'/'+'mqvi-'+name_pattern+'.png'131 plt.savefig(name_puck)132 plt.close()133@generator_wrapper134def linhao_summarize(primary_namespace, output):135 with open(output, 'ab') as output_file:136 writer = csv_writer(output_file)137 namespace = primary_namespace['name pattern']138 tag_group = primary_namespace['group id']139 secondary_namespace = primary_namespace['per_cell']140 pre_puck = [namespace] + tag_group141 for key, value in secondary_namespace.iteritems():142 if key != '_pad':143 proper_puck = pre_puck+[key, value['gfp_mqvi'], value['mch_mqvi'], value['final_classification']]144 writer.writerow(proper_puck)145 return primary_namespace146@generator_wrapper147def linhao_secondary_summarize(primary_namespace, output):148 with open(output, 'ab') as output_file:149 writer = csv_writer(output_file)150 namespace = primary_namespace['name pattern']151 tag_group = primary_namespace['group id']152 total_cells = len(np.unique(primary_namespace['pre_cell_labels'])) - 1153 analyzed_cells = len(np.unique(primary_namespace['cell_labels'])) - 1154 writer.writerow([namespace] + tag_group + [total_cells, analyzed_cells])155 return primary_namespace156def Linhao_traverse(main_root,157 matching_rule='c', matching_map=None):158 """159 Traverses the main_root directory, looking for all the '.tif/.TIF' files, performs name matching160 then iterates through the resulting matched dictironary.161 Matching assumption is that except for the matching keys, the names are identical162 :param main_root: folder from which will be traversed in depth163 :param matching_rule: name modification to type mapping. Currently '' for no matching, 'color' for colors164 :param matching_map: {'pattern in the file name': color channel number}165 :return:166 """167 matched_images = defaultdict(lambda: ['']*len(matching_map.keys()))168 tags_dict = defaultdict(lambda: [])169 if matching_rule:170 assert(matching_map is not None)171 for current_location, sub_directories, files in os.walk(main_root):172 if files:173 for img in files:174 if ('.TIF' in img or '.tif' in img) and '_thumb_' not in img and 'w' in img:175 prefix = imagepipe.raw_functions.split_and_trim(current_location, main_root)176 img_codename = img.split(' ')[0].split('_')177 color = matching_map[img_codename[-1]]178 name_pattern = ' - '.join(prefix + img_codename[:-1])179 matched_images[name_pattern][color] = os.path.join(current_location, img)180 time_stamp = prefix[-1]181 if time_stamp == 'HS':182 time = 0183 elif 'HS' in time_stamp:184 time = -30185 else:186 time = time_stamp[3:-3] # int(time_stamp[3:-3])187 tags_dict[name_pattern] = []188 tags_dict[name_pattern].append(time) # time in the times series189 _date = prefix[0][:8]190 tags_dict[name_pattern].append("%s-%s-%s" % (_date[:2], _date[2:4], _date[4:]))191 tags_dict[name_pattern].append(prefix[-2])192 delset = []193 for name_pattern, (color_set) in matched_images.iteritems():194 # debugger.logger.debug(color_set)195 if any([color == '' for color in color_set]):196 logger.info('in %s, colorset is broken:', name_pattern)197 for color_name in color_set:198 logger.info('\t %s', color_name)199 logger.info('name_pattern will be deleted')200 delset.append(name_pattern)201 for name_pattern in delset:202 del matched_images[name_pattern]203 user_input_about_new_csv_file = raw_input("To continue, enter 2. To start the process from the beginning, enter 1")204 if user_input_about_new_csv_file == '1':205 print "Preparing a new CSV file"206 initial_open = open("matched_images.csv", 'wb')207 # this is the file we need to save unless user provides input saying we can override it208 writer = csv.writer(initial_open, delimiter='\t')209 for key in matched_images:210 writer.writerow([key] + matched_images[key] + [0])211 initial_open.close()212 else:213 print "Continuing where the process last left off"214 file_exists = os.path.isfile("matched_images.tmp")215 if file_exists:216 open_tmp = open('matched_images.tmp', 'r')217 read_preexisting_tmp = csv.reader(open_tmp, delimiter = '\t')218 tmp_list = []219 for row in read_preexisting_tmp:220 tmp_list.append(row)221 open_tmp.close()222 open_csv = open('matched_images.csv', 'r')223 read_preexisting_csv = csv.reader(open_csv, delimiter = '\t')224 csv_list = []225 for row in read_preexisting_csv:226 csv_list.append(row)227 open_csv.close()228 for csv_row in csv_list:229 for tmp_row in tmp_list:230 if csv_row[0] == tmp_row[0]:231 csv_row[3] = tmp_row[3]232 open_csv_write = open('matched_images.csv', 'wb')233 override_csv = csv.writer(open_csv_write, delimiter='\t')234 for new_csv_row in csv_list:235 override_csv.writerow(new_csv_row)236 open_updated_csv_to_read = open('matched_images.csv', 'rb')237 csv_reader = csv.reader(open_updated_csv_to_read, delimiter='\t')238 open_tmp_to_write = open("matched_images.tmp", 'wb')239 writer_check_tmp = csv.writer(open_tmp_to_write, delimiter='\t')240 for row in csv_reader:241 name_pattern = row[0]242 color_set = [row[1], row[2]]243 if row[3] == 1:244 writer_check_tmp.writerow(row)245 continue246 channels = []247 for color in color_set:248 channels.append(imagepipe.tools.helpers.tiff_stack_2_np_arr(color))249 print "name pattern", name_pattern250 print 'channels', channels251 yield name_pattern, tags_dict[name_pattern], channels252 row[3] = 1...
traversals.py
Source:traversals.py
1import logging2import os3import csv4from collections import defaultdict5import numpy as np6from imagepipe.raw_functions import split_and_trim7from imagepipe.tools.helpers import tiff_stack_2_np_arr8import sys9logger = logging.getLogger('Default Debug Logger')10logger.setLevel(logging.DEBUG)11fh = logging.FileHandler('debug_log.log')12fh.setLevel(logging.DEBUG)13# create console handler with a higher log level14ch = logging.StreamHandler()15ch.setLevel(logging.DEBUG)16# create formatter and add it to the handlers17formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')18fh.setFormatter(formatter)19ch.setFormatter(formatter)20# add the handlers to the logger21logger.addHandler(fh)22logger.addHandler(ch)23def name_channels(stack_group_generator, channel_names):24 """25 Assigns names to the channel for the future processing and bundles them together26 :param stack_group_generator: generator returning image stack,27 :param channel_names:28 :return:29 """30 for name_pattern, group_ids, channels in stack_group_generator:31 print 'name pattern', name_pattern32 print 'group ids', group_ids33 print 'channels', [chan.shape for chan in channels]34 group_dict = {'name pattern': name_pattern,35 'group id': group_ids}36 group_dict['channel list'] = channel_names37 for chan_name, chan in zip(channel_names, channels):38 group_dict[chan_name] = chan39 yield group_dict40def color_based_traversal(main_root, coding_separator='.', group_anchor=1):41 """42 Traverses the main root directory pulling the data from the images. different layers are assumed43 to be different colors, images are assumed to be 2D.44 :param main_root:45 :param coding_separator: character used to derived codename of the image46 :param group_anchor: position where the group information is stored47 :return:48 """49 matched_images = []50 for current_location, sub_directories, files in os.walk(main_root):51 if files:52 for img in files:53 if ('.TIF' in img or '.tif' in img) and '_thumb_' not in img:54 prefix = split_and_trim(current_location, main_root)55 # print current_location, main_root56 # print prefix57 # print "test"58 # sys.exit()59 img_codename = img[:-4].split(coding_separator)60 print img, img_codename61 name_pattern = ' - '.join(prefix + img_codename)62 # group_by = img_codename[0].split('rpe')[1].split('dapi')[0].strip()63 group_by = img_codename[group_anchor]64 matched_images.append((name_pattern, group_by, os.path.join(current_location, img)))65 for name_pattern, group_by, image_location in matched_images:66 stack = tiff_stack_2_np_arr(image_location)67 # print stack.shape68 # stack = np.rollaxis(stack[0, :, :, :], 2) # on the data where channels have not been split into z stacks69 channels = np.split(stack, stack.shape[0])70 channels = [chan[0, :, :] for chan in channels]71 # print "channel debug"72 # for channel in channels:73 # print '>', channel.shape, channel.dtype74 yield name_pattern, group_by, channels75def z_stack_based_traversal(main_root, matching_rule='c', matching_map=None):76 """77 Traverses the main_root directory, looking for all the '.tif/.TIF' files, performs name matching78 then iterates through the resulting matched dictironary.79 Matching assumption is that except for the matching keys, the names are identical80 :param main_root: folder from which will be traversed in depth81 :param matching_rule: name modification to type mapping. Currently '' for no matching, 'color' for colors82 :param matching_map: {'pattern in the file name': color channel number}83 :return:84 """85 matched_images = defaultdict(lambda: ['']*len(matching_map.keys()))86 tags_dict = defaultdict(lambda: [])87 if matching_rule:88 assert(matching_map is not None)89 for current_location, sub_directories, files in os.walk(main_root):90 if files:91 for img in files:92 if ('.TIF' in img or '.tif' in img) and '_thumb_' not in img and 'w' in img:93 prefix = split_and_trim(current_location, main_root)94 img_codename = img.split(' ')[0].split('_')95 color = matching_map[img_codename[-1]]96 name_pattern = ' - '.join(prefix + img_codename[:-1])97 matched_images[name_pattern][color] = os.path.join(current_location, img)98 time_stamp = prefix[-1]99 if time_stamp == 'HS':100 time = 0101 elif 'HS' in time_stamp:102 time = -30103 else:104 time = time_stamp[3:-3] # int(time_stamp[3:-3])105 tags_dict[name_pattern] = []106 tags_dict[name_pattern].append(time) # time in the times series107 _date = prefix[0][:8]108 tags_dict[name_pattern].append("%s-%s-%s" % (_date[:2], _date[2:4], _date[4:]))109 tags_dict[name_pattern].append(prefix[-2])110 delset = []111 for name_pattern, (color_set) in matched_images.iteritems():112 # debugger.logger.debug(color_set)113 if any([color == '' for color in color_set]):114 logger.info('in %s, colorset is broken:', name_pattern)115 for color_name in color_set:116 logger.info('\t %s', color_name)117 logger.info('name_pattern will be deleted')118 delset.append(name_pattern)119 for name_pattern in delset:120 del matched_images[name_pattern]121 user_input_about_new_csv_file = raw_input("To continue, enter 2. To start the process from the beginning, enter 1")122 if user_input_about_new_csv_file == '1':123 print "Preparing a new CSV file"124 initial_open = open("matched_images.csv", 'wb')125 # this is the file we need to save unless user provides input saying we can override it126 writer = csv.writer(initial_open, delimiter='\t')127 for key in matched_images:128 writer.writerow([key] + matched_images[key] + [0])129 initial_open.close()130 else:131 print "Continuing where the process last left off"132 file_exists = os.path.isfile("matched_images.tmp")133 if file_exists:134 open_tmp = open('matched_images.tmp', 'r')135 read_preexisting_tmp = csv.reader(open_tmp, delimiter = '\t')136 tmp_list = []137 for row in read_preexisting_tmp:138 tmp_list.append(row)139 open_tmp.close()140 open_csv = open('matched_images.csv', 'r')141 read_preexisting_csv = csv.reader(open_csv, delimiter = '\t')142 csv_list = []143 for row in read_preexisting_csv:144 csv_list.append(row)145 open_csv.close()146 for csv_row in csv_list:147 for tmp_row in tmp_list:148 if csv_row[0] == tmp_row[0]:149 csv_row[3] = tmp_row[3]150 open_csv_write = open('matched_images.csv', 'wb')151 override_csv = csv.writer(open_csv_write, delimiter='\t')152 for new_csv_row in csv_list:153 override_csv.writerow(new_csv_row)154 open_updated_csv_to_read = open('matched_images.csv', 'rb')155 csv_reader = csv.reader(open_updated_csv_to_read, delimiter='\t')156 open_tmp_to_write = open("matched_images.tmp", 'wb')157 writer_check_tmp = csv.writer(open_tmp_to_write, delimiter='\t')158 for row in csv_reader:159 name_pattern = row[0]160 color_set = [row[1], row[2]]161 if row[3] == 1:162 writer_check_tmp.writerow(row)163 continue164 channels = []165 for color in color_set:166 channels.append(tiff_stack_2_np_arr(color))167 print "name pattern", name_pattern168 print 'channels', channels169 yield name_pattern, tags_dict[name_pattern], channels170 row[3] = 1...
main.py
Source:main.py
1import re2import csv3with open("phonebook_raw.csv", 'r', encoding='utf-8') as f:4 rows = csv.reader(f, delimiter=",")5 contacts_list = list(rows)6 new_list = []7def names_moving():8 name_pattern = r'([Ð-Я])'9 name_substitution = r' \1'10 for column in contacts_list[1:]:11 line = column[0] + column[1] + column[2]12 if len((re.sub(name_pattern, name_substitution, line).split())) == 3:13 column[0] = re.sub(name_pattern, name_substitution, line).split()[0]14 column[1] = re.sub(name_pattern, name_substitution, line).split()[1]15 column[2] = re.sub(name_pattern, name_substitution, line).split()[2]16 elif len((re.sub(name_pattern, name_substitution, line).split())) == 2:17 column[0] = re.sub(name_pattern, name_substitution, line).split()[0]18 column[1] = re.sub(name_pattern, name_substitution, line).split()[1]19 column[2] = ''20 elif len((re.sub(name_pattern, name_substitution, line).split())) == 1:21 column[0] = re.sub(name_pattern, name_substitution, line).split()[0]22 column[1] = ''23 column[2] = ''24 return25def phone_number_formatting():26 phone_pattern = re.compile(27 r'(\+7|8)?\s*\(?(\d{3})\)?\s*\D?(\d{3})[-\s+]?(\d{2})-?(\d{2})((\s)?\(?(доб.)?\s?(\d+)\)?)?')28 phone_substitution = r'+7(\2)\3-\4-\5\7\8\9'29 for column in contacts_list:30 column[5] = phone_pattern.sub(phone_substitution, column[5])31 return32def duplicates_combining():33 for column in contacts_list[1:]:34 first_name = column[0]35 last_name = column[1]36 for contact in contacts_list:37 new_first_name = contact[0]38 new_last_name = contact[1]39 if first_name == new_first_name and last_name == new_last_name:40 if column[2] == '':41 column[2] = contact[2]42 if column[3] == '':43 column[3] = contact[3]44 if column[4] == '':45 column[4] = contact[4]46 if column[5] == '':47 column[5] = contact[5]48 if column[6] == '':49 column[6] = contact[6]50 for contact in contacts_list:51 if contact not in new_list:52 new_list.append(contact)53 return new_list54if __name__ == '__main__':55 names_moving()56 phone_number_formatting()57 duplicates_combining()58 with open("phonebook.csv", "w", encoding='utf-8') as f:59 datawriter = csv.writer(f, delimiter=',')...
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