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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-Ð¯])'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)?\(?(Ð´Ð¾Ð±.)?\s?(\d+)\)?)?')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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