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How to use hierarchy_tags method in Lemoncheesecake
Best Python code snippet using lemoncheesecake
feature_extraction_helpers.py
Source:feature_extraction_helpers.py 
1# Imports2import Cluster3import extract4import glob5import partition6import pickle7import re8import timeit9import Visualiser10import numpy as np11import math12from pysal.esda.getisord import G_Local13from pysal.weights.Distance import DistanceBand14from descartes import PolygonPatch15import matplotlib.pyplot as plt16from tqdm import tqdm17import seaborn as sb18from matplotlib import cm19from matplotlib.ticker import LinearLocator, FormatStrFormatter20from shapely.geometry import Point, Polygon, LineString21from mpl_toolkits.mplot3d import Axes3D22def get_tags(hierarchy_tags):23 """24 Gets the x-y coordinates of the piecewise line defining the invasive margin.25 """26 tags = []27 x_tags, y_tags = [], []28 for tag in hierarchy_tags:29 if "X=" in tag and "Y=" in tag:30 x = tag.split("\"")[1]31 y = tag.split("\"")[3]32 x_tags.append(int(x))33 y_tags.append(int(y))34 for i, j in zip(x_tags, y_tags):35 tags.append((int(i), int(j)))36 return tags37def load_linestring(polygon_file):38 """39 Loads the x-y coordinates and creates a LineString object from them.40 This is useful for establishing the distance of cells from the41 invasive margin.42 """43 poly = None44 with open(polygon_file, "r") as f:45 hierarchy = f.read()46 p = re.compile("<*")47 hierarchy_tags = p.split(hierarchy)48 tags = get_tags(hierarchy_tags)49 poly = LineString(tags)50 if poly is None:51 raise Exception52 else:53 return poly54def load_shape(polygon_file):55 """56 Loads the x-y coordinates and creates a Polygon object from them.57 This is useful for establishing whether a given object is within58 the invasive margin or not.59 """60 poly = None61 with open(polygon_file, "r") as f:62 hierarchy = f.read()63 p = re.compile("<*")64 hierarchy_tags = p.split(hierarchy)65 tags = get_tags(hierarchy_tags)66 poly = Polygon(tags)67 if poly is None:68 raise Exception69 else:70 return poly71def plot_line_and_shape(ax, linestring, shape):72 """73 Given a matplotlib axes object, plots the Shapely LineString and Polygon74 objects onto the axes. Returns the axes.75 """76 patch = PolygonPatch(shape, fc="red", alpha=0.5, zorder=2)77 ax.add_patch(patch)78 xs = [x for (x, y) in linestring.coords]79 ys = [y for (x, y) in linestring.coords]80 ax.plot(xs, ys)81 return ax82def get_margin_cells(all_cells, linestring, my_distance):83 """84 Gets all cells that are within my_distance of the invasive margin.85 This includes TB, CD3, and CD8.86 """87 margin_cell_list = []88 assert (type(all_cells) == np.ndarray and all_cells.shape[1] >= 4), "type of all_cells is {}, all_cells.shape is {}".format(type(all_cells), all_cells.shape)89 x_avgs = (all_cells[:, 0] + all_cells[:, 1]) / 290 y_avgs = (all_cells[:, 2] + all_cells[:, 3]) / 291 xy_avgs = np.array((x_avgs, y_avgs)).transpose()92 for i in range(len(all_cells)):93 p = Point(xy_avgs[i])94 if p.distance(linestring) <= my_distance:95 margin_cell_list.append(all_cells[i])96 return np.asarray(margin_cell_list)97def get_core_cells(all_cells, shape, linestring, my_distance):98 """99 Gets all cells that are inside the invasive margin, but not within my_distance to it.100 This includes TB, CD3, and CD8.101 """102 core_cell_list = []103 assert (type(all_cells) == np.ndarray and all_cells.shape[1] >= 4), "type of all_cells is {}, all_cells.shape is {}".format(type(all_cells), all_cells.shape)104 x_avgs = (all_cells[:, 0] + all_cells[:, 1]) / 2105 y_avgs = (all_cells[:, 2] + all_cells[:, 3]) / 2106 xy_avgs = np.array((x_avgs, y_avgs)).transpose()107 for i in range(len(all_cells)):108 p = Point(xy_avgs[i])109 if p.distance(linestring) > my_distance and p.within(shape):110 core_cell_list.append(all_cells[i])111 return np.asarray(core_cell_list)112def get_margin_core_cells(all_cells, shape, linestring, my_distance):113 """114 Excludes all cells that are more than my_distance outside the115 invasive margin. This includes TB, CD3, and CD8.116 """117 margin_cell_list, core_cell_list = [], []118 assert (type(all_cells) == np.ndarray and all_cells.shape[1] >= 4), "type of all_cells is {}, all_cells.shape is {}".format(type(all_cells), all_cells.shape)119 x_avgs = (all_cells[:, 0] + all_cells[:, 1]) / 2120 y_avgs = (all_cells[:, 2] + all_cells[:, 3]) / 2121 xy_avgs = np.array((x_avgs, y_avgs)).transpose()122 for i in range(len(all_cells)):123 p = Point(xy_avgs[i])124 if p.distance(linestring) <= my_distance:125 margin_cell_list.append(all_cells[i])126 elif p.within(shape):127 core_cell_list.append(all_cells[i])128 return np.asarray(margin_cell_list), np.asarray(core_cell_list)129def get_lymphocytes_on_margin(cd3cd8, annotations_file):130 """131 Gets all lymphocytes (CD3/CD8) within 500um of the invasive margin.132 """133 all_cd3, all_cd8 = cd3cd8134 linestring = load_linestring(annotations_file)135 margin_cd3, margin_cd8 = [], []136 margin_cd3, margin_cd8 = np.asarray(get_margin_cells(all_cd3, linestring, 500)), np.asarray(get_margin_cells(all_cd8, linestring, 500))137 return margin_cd3, margin_cd8138def get_lymphocytes_on_margin_and_core(cd3cd8, annotations_file):139 """140 Gets all lymphocytes (CD3/CD8) except those that are more than 500um outside141 the invasive margin142 """143 all_cd3, all_cd8 = cd3cd8144 shape = load_shape(annotations_file)145 linestring = load_linestring(annotations_file)146 margin_cd3, core_cd3 = get_margin_core_cells(all_cd3, shape, linestring, 500)147 margin_cd8, core_cd8 = get_margin_core_cells(all_cd8, shape, linestring, 500)148 return np.asarray(margin_cd3), np.asarray(core_cd3), np.asarray(margin_cd8), np.asarray(core_cd8)149def partition_lymphocytes_for_heatmap(items, tile_size=886, to_list=True, input_type="clean",150 by_metric=True, scale=1):151 """152 Given appropriate parameters and a list of lymphocytes,153 divide them into a (T x T) grid and take the counts of each type154 of object for each tile. Return these 'heatmaps', rotated accordingly.155 """156 partitioned_items, _, _, _, _, _, _, _ = partition.partition(items, tile_size=tile_size,157 to_list=to_list, input_type=input_type,158 by_metric=by_metric, scale=scale)159 heatmap_cd3 = np.zeros(partitioned_items.shape, dtype=int)160 heatmap_cd8 = np.zeros(partitioned_items.shape, dtype=int)161 heatmap_cd3cd8 = np.zeros(partitioned_items.shape, dtype=int)162 (width, height) = partitioned_items.shape163 for col in range(width):164 for row in range(height):165 cells = partitioned_items[col][row]166 heatmap_cd3cd8[col][row] += len(cells)167 for cell in cells:168 if cell[4] > 0:169 heatmap_cd3[col][row] += 1170 elif cell[5] > 0:171 heatmap_cd8[col][row] += 1172 heatmap_cd3 = np.flip(np.rot90(heatmap_cd3), 0)173 heatmap_cd8 = np.flip(np.rot90(heatmap_cd8), 0)174 heatmap_cd3cd8 = np.flip(np.rot90(heatmap_cd3cd8), 0)175 return heatmap_cd3, heatmap_cd8, heatmap_cd3cd8176def partition_tb_for_heatmap(items, tile_size=886, to_list=True, input_type="clean",177 by_metric=True, scale=1):178 """179 Given appropriate parameters and a list of tumour buds,180 divide them into a (T x T) grid and take the counts of tumour buds181 for each tile. Return this 'heatmap', rotated/flipped accordingly.182 """183 partitioned_tb, _, _, _, _, _, _, _ = partition.partition(items, tile_size=tile_size,184 to_list=to_list, input_type=input_type,185 by_metric=by_metric, scale=scale)186 heatmap_tb = np.zeros(partitioned_tb.shape, dtype=int)187 for i in range(partitioned_tb.shape[0]):188 for j in range(partitioned_tb.shape[1]):189 heatmap_tb[i][j] += len(partitioned_tb[i][j])190 heatmap_tb = np.flip(np.rot90(heatmap_tb), 0)191 return heatmap_tb192def partition_nearby_lymphocytes_tb_for_heatmap(items, tile_size=886, to_list=True, input_type="mixed",193 by_metric=True, scale=1):194 """195 Given appropriate parameters and a list of tumour buds and lymphocytes within 50um of any tumour bud,196 divide them into a (T x T) grid and take the counts of tumour buds and lymphocytes197 for each tile. Return these 'heatmaps', rotated accordingly.198 """199 partitioned_items, tiles, \200 lymph_w, lymph_h, \201 clust_w, clust_h, \202 x_step, y_step = partition.partition(items, tile_size=tile_size,203 to_list=to_list, input_type=input_type,204 by_metric=by_metric, scale=scale)205 counts = Cluster.get_lymphocyte_cluster_density_heatmap(partitioned_items, partitioned_items.shape, tiles,206 lymph_w, lymph_h, clust_w, clust_h, x_step, y_step)207 heatmap_tb = np.zeros(counts.shape, dtype=int)208 heatmap_nearby_cd3 = np.zeros(counts.shape, dtype=int)209 heatmap_nearby_cd8 = np.zeros(counts.shape, dtype=int)210 heatmap_nearby_cd3cd8 = np.zeros(counts.shape, dtype=int)211 for i in range(counts.shape[0]):212 for j in range(counts.shape[1]):213 heatmap_nearby_cd3[i][j] += counts[i][j][0]214 heatmap_nearby_cd8[i][j] += counts[i][j][1]215 heatmap_nearby_cd3cd8[i][j] += counts[i][j][2]216 heatmap_tb[i][j] += counts[i][j][3]217 heatmap_nearby_cd3 = np.flip(np.rot90(heatmap_nearby_cd3), 0)218 heatmap_nearby_cd8 = np.flip(np.rot90(heatmap_nearby_cd8), 0)219 heatmap_nearby_cd3cd8 = np.flip(np.rot90(heatmap_nearby_cd3cd8), 0)220 heatmap_tb = np.flip(np.rot90(heatmap_tb), 0)221 return heatmap_tb, heatmap_nearby_cd3, heatmap_nearby_cd8, heatmap_nearby_cd3cd8222def get_cluster_code(cluster_name):223 """224 Path/to/file/.../ -> split on the slash and get the last token225 SN---.p -> just truncate the last 2 characters226 """227 filename = cluster_name.split('/')[-1]228 return filename[0:-2]229def get_annotations_code(annotations_name):230 """231 SN---_----_job----.annotations -> get the first 5 characters232 """233 filename = annotations_name.split('/')[-1]234 return filename[0:5]235def load_cluster_list(cluster_files):236 """237 Cluster file, load through pickle.238 """239 for cluster_filename in sorted(glob.glob(cluster_files)):240 cluster_data = pickle.load(open(cluster_filename, "rb"))241 yield cluster_filename, cluster_data242def load_lymphocyte_list(lymphocyte_files):243 """244 Lymphocyte file, load through csv.245 """246 for lymphocyte_filename in sorted(glob.glob(lymphocyte_files)):247 lymphocyte_data = extract.load(lymphocyte_filename)248 yield lymphocyte_filename, lymphocyte_data249def load_len_lymphocyte_list(lymphocyte_files):250 """251 Lymphocyte file, load through csv.252 """253 for lymphocyte_filename in sorted(glob.glob(lymphocyte_files)):254 lymphocyte_data = extract.load(lymphocyte_filename)255 yield lymphocyte_filename, len(lymphocyte_data)256def load_cluster_tb_metadata(cluster_metadata_files):257 """258 Cluster metadata file, load through .txt file.259 Get the appropriate line, get the val at the end.260 """261 for metadata_filename in tqdm(sorted(glob.glob(cluster_metadata_files))):262 f = open(metadata_filename, 'r')263 contents = f.readlines()264 cluster_count_val = -1265 tb_count_val = -1266 for line in contents:267 if "CLU" in line:268 cluster_count_val = int(line.split(": ")[-1])269 if "TB" in line:270 tb_count_val = int(line.split(": ")[-1])271 if cluster_count_val != -1 and tb_count_val != -1:272 yield metadata_filename, cluster_count_val, tb_count_val273 else:274 raise Exception("No cluster/TB value associated in file " + metadata_filename)275def load_lymphocyte_metadata(lymphocyte_metadata_files):276 """277 Lymphocyte metadata file, load through .txt file.278 Get the appropriate line, get the val at the end.279 """280 for metadata_filename in tqdm(sorted(glob.glob(lymphocyte_metadata_files))):281 f = open(metadata_filename, 'r')282 contents = f.readlines()283 cd3cd8_count_val = -1284 cd3_count_val = -1285 cd8_count_val = -1286 for line in contents:287 if "CD3&CD8" in line:288 cd3cd8_count_val = int(line.split(": ")[-1])289 continue290 if "CD3" in line:291 cd3_count_val = int(line.split(": ")[-1])292 continue293 if "CD8" in line:294 cd8_count_val = int(line.split(": ")[-1])295 continue296 if cd3cd8_count_val > -1 and cd3_count_val > -1 and cd8_count_val > -1:297 yield metadata_filename, cd3_count_val, cd8_count_val, cd3cd8_count_val298 else:299 raise Exception("No CD3/CD8/CD3CD8 value associated in file " + metadata_filename)300def load_margin(margin_files):301 """302 Margin defining shape of invasive margin, load through helpers.303 """304 for margin_filename in sorted(glob.glob(margin_files)):305 linestring, shape = load_linestring(margin_filename), load_shape(margin_filename)306 yield margin_filename, linestring, shape307def load_meta(obj_type, meta_data_files, code_extract_func):308 """309 meta_data_values = [(meta_code1, meta_val1), (meta_code2, meta_val2), ...]310 """311 meta_data_values = []312 for meta_data_filename in tqdm(sorted(glob.glob(meta_data_files))):313 f = open(meta_data_filename, 'r')314 contents = f.readlines()315 obj_count_val = -1316 for line in contents:317 if obj_type in line:318 obj_count_val = int(line.split(": ")[-1])319 meta_data_values.append((code_extract_func(meta_data_filename), obj_count_val))320 return meta_data_values321def code_extract_func(filename):322 return filename.split('/')[-1].split('.')[0]323def visualisation_block(visualise_func, data, linestring, shape, title, image_filename):324 fig, ax = plt.subplots(1, 1, figsize=(6, 6))325 ax = visualise_func(data, ax, title=title)326 xs = [x for (x, y) in linestring.coords]327 ys = [y for (x, y) in linestring.coords]328 patch = PolygonPatch(shape, fc="red", alpha=0.2, zorder=2)329 ax.add_patch(patch)330 ax.plot(xs, ys)331 fig.savefig(image_filename, dpi=100, bbox_inches="tight")332def visualisation_interaction_block(data, linestring, shape, title, image_filename):333 fig, ax = plt.subplots(1, 1, figsize=(6, 6))334 ax = Visualiser.visualise_clusters_and_lymphocytes_on_ax(data, ax, title=title)335 xs = [x for (x, y) in linestring.coords]336 ys = [y for (x, y) in linestring.coords]337 patch = PolygonPatch(shape, fc="red", alpha=0.2, zorder=2)338 ax.add_patch(patch)339 ax.plot(xs, ys)340 fig.savefig(image_filename, dpi=100, bbox_inches="tight")341def statistic_generation_block(partition_func, statistic_funcs, data, meta_data_value, d, heatmap_filename_func, target_directory_stub, filename):342 heatmap = partition_func(data, meta_data_value, d)343 assert heatmap.shape[2] == 4, "Output of transform should be 3D numpy array with 4 layers" # Layers for TB, CD3, CD8, CD3CD8 respectively344 for (statistic_name, statistic_func, statistic_mask) in statistic_funcs:345 transformed_data = statistic_func(heatmap)346 for i, obj_type in enumerate(["TB_CD3", "TB_CD8", "TB_CD3CD8"]):347 heatmap_filename = heatmap_filename_func(target_directory_stub + obj_type + "_" + str(d) + "/", filename, statistic_name)348 p_file = open(heatmap_filename, "wb")349 pickle.dump(transformed_data[:, :, i+1], p_file)350 p_file.close()351 fig, ax = plt.subplots(1, 1, figsize=(6, 6))352 title = "Heatmap for " + target_directory_stub.split('/')[-1] + obj_type + " with d=" + str(d)353 ax = Visualiser.visualise_heatmaps(transformed_data[:, :, i+1], ax, title, statistic_mask(transformed_data[:, :, i+1]))354 image_filename = heatmap_filename_func(target_directory_stub + obj_type + "_IMAGES/", filename, "_heatmap" + "_" + str(d) + "_" + statistic_name)[0:-2] + ".png"355 fig.savefig(image_filename, dpi=100, bbox_inches="tight")356def get_images_with_margins(obj_type, obj_files, expected_num_files, meta_data_files, load_obj_func, target_directory_stub, convert_func, visualise_func, image_filename_func, partition_func, heatmap_filename_func, margin_files, d):357 meta_data_pairs = load_meta(obj_type, meta_data_files, code_extract_func)358 meta_data_codes = [code for (code, val) in meta_data_pairs]359 meta_data_values = [val for (code, val) in meta_data_pairs]360 num_files = 0361 obj_file_type = obj_type if obj_type != "CD3&CD8" else "CD3CD8"362 for (filename, raw_data), (margin_filename, linestring, shape) in zip(load_obj_func(obj_files), load_margin(margin_files)):363 meta_data_value = 0364 try:365 meta_data_value = meta_data_values[meta_data_codes.index(code_extract_func(filename))]366 except:367 raise Exception("Meta-data does not have a value for file " + filename + "\nwith obj_type " + obj_type)368 all_data = convert_func(raw_data, meta_data_value)369 assert type(all_data) == np.ndarray370 ##Â ALL371 print(code_extract_func(filename) + ": " + "All {}".format(obj_type))372 visualisation_block(visualise_func, all_data, linestring, shape, "All {}".format(obj_type), image_filename_func(target_directory_stub + "ALL_{}/".format(obj_file_type), filename))373 statistic_generation_block(partition_func, [("_density", density)], all_data, meta_data_value, [heatmap_filename_func(target_directory_stub + "ALL_{}/".format(obj_file_type), filename)])374 margin_data, core_data = get_margin_core_cells(all_data, shape, linestring, 500)375 assert margin_data.shape[0] > 0 and margin_data.shape[1] >= 4, "Something wrong with {} margin data".format(obj_type)376 assert core_data.shape[0] > 0 and core_data.shape[1] >= 4, "Something wrong with {} core data".format(obj_type)377 ## MARGIN378 print(code_extract_func(filename) + ": " + "{} (IM)".format(obj_type))379 visualisation_block(visualise_func, margin_data, linestring, shape, "{} (IM)".format(obj_type), image_filename_func(target_directory_stub + "MARGIN_{}/".format(obj_file_type), filename))380 statistic_generation_block(partition_func, [("_density", density)], margin_data, meta_data_value, [heatmap_filename_func(target_directory_stub + "MARGIN_{}/".format(obj_file_type), filename)])381 ## CORE382 print(code_extract_func(filename) + ": " + "{} (CT)".format(obj_type))383 visualisation_block(visualise_func, core_data, linestring, shape, "{} (CT)".format(obj_type), image_filename_func(target_directory_stub + "CORE_{}/".format(obj_file_type), filename))384 statistic_generation_block(partition_func, [("_density", density)], core_data, meta_data_value, [heatmap_filename_func(target_directory_stub + "CORE_{}/".format(obj_file_type), filename)])385 ## IMCT386 print(code_extract_func(filename) + ": " + "{} (IMCT)".format(obj_type))387 imct_data = np.concatenate((margin_data, core_data), axis=0)388 assert imct_data.shape[0] == (margin_data.shape[0] + core_data.shape[0]), "Margin and core cells not added properly"389 del(margin_data) # numpy creates a copy on concat, so safe to delete390 del(core_data) # these two arrays391 visualisation_block(visualise_func, imct_data, linestring, shape, "{} (IMCT)".format(obj_type), image_filename_func(target_directory_stub + "IMCT_{}/".format(obj_file_type), filename))392 statistic_generation_block(partition_func, [("_density", density)], imct_data, meta_data_value, [heatmap_filename_func(target_directory_stub + "IMCT_{}/".format(obj_file_type), filename)])393 num_files += 1394 assert(num_files == expected_num_files)395def get_images_with_interactions_condensed(tb_files, lym_files, expected_num_files, meta_data_files,396 target_directory_stub, heatmap_transform, statistics, image_filename_func,397 heatmap_filename_func, margin_files, d):398 tb_meta_pairs = load_meta("TB", meta_data_files, code_extract_func)399 tb_meta_codes = [code for (code, val) in tb_meta_pairs]400 tb_meta_values = [val for (code, val) in tb_meta_pairs]401 cd3_meta_pairs = load_meta("CD3", meta_data_files, code_extract_func)402 cd8_meta_pairs = load_meta("CD8", meta_data_files, code_extract_func)403 cd3cd8_meta_pairs = load_meta("CD3&CD8", meta_data_files, code_extract_func)404 cd3_meta_codes = [code for (code, val) in cd3_meta_pairs]405 cd8_meta_codes = [code for (code, val) in cd8_meta_pairs]406 cd3cd8_meta_codes = [code for (code, val) in cd3cd8_meta_pairs]407 assert(cd3_meta_codes == cd8_meta_codes and cd8_meta_codes == cd3cd8_meta_codes)408 cd3_meta_values = [val for (code, val) in cd3_meta_pairs]409 cd8_meta_values = [val for (code, val) in cd8_meta_pairs]410 cd3cd8_meta_values = [val for (code, val) in cd3cd8_meta_pairs]411 num_files = 0412 for (tb_filename, cluster_raw_data), (lym_filename, lym_raw_data), (margin_filename, linestring, shape) in zip(load_cluster_list(tb_files),413 load_lymphocyte_list(lym_files),414 load_margin(margin_files)):415 tb_meta_value = 0416 cd3_meta_value, cd8_meta_value, cd3cd8_meta_value = 0, 0, 0417 try:418 tb_meta_value = tb_meta_values[tb_meta_codes.index(code_extract_func(tb_filename))]419 except:420 raise Exception("Meta-data does not have a value for file " + tb_filename + "\nwith obj_type TB")421 try:422 cd3_meta_value = cd3_meta_values[cd3_meta_codes.index(code_extract_func(lym_filename))]423 cd8_meta_value = cd8_meta_values[cd8_meta_codes.index(code_extract_func(lym_filename))]424 cd3cd8_meta_value = cd3cd8_meta_values[cd3cd8_meta_codes.index(code_extract_func(lym_filename))]425 except:426 raise Exception("Meta-data does not have a value for file " + lym_filename)427 all_tb_data = convert_cluster_to_tb(cluster_raw_data, tb_meta_value)428 all_lym_data = convert_lymphocyte_to_cd3cd8(lym_raw_data, cd3cd8_meta_value)429 assert type(all_tb_data) == np.ndarray and type(all_lym_data) == np.ndarray430 #####################431 ### CD3&CD8 BLOCK ###432 #####################433 combined_tb_lym_data = merge_tb_lym(all_tb_data, all_lym_data)434 ##Â ALL - TB AND CD3/CD8435 title = "All TB and CD3/CD8"436 print(code_extract_func(tb_filename) + ": " + title)437 visualisation_interaction_block(combined_tb_lym_data, linestring, shape, title, image_filename_func(target_directory_stub + "ALL_TB_CD3CD8_IMAGES/", tb_filename))438 statistic_generation_block(heatmap_transform, statistics, combined_tb_lym_data, tb_meta_value + cd3cd8_meta_value, 50, heatmap_filename_func, target_directory_stub + "ALL_", tb_filename)439 statistic_generation_block(heatmap_transform, statistics, combined_tb_lym_data, tb_meta_value + cd3cd8_meta_value, 100, heatmap_filename_func, target_directory_stub + "ALL_", tb_filename)440 start_time = timeit.default_timer()441 tb_lym_margin_data, tb_lym_core_data = get_margin_core_cells(combined_tb_lym_data, shape, linestring, 500)442 elapsed = timeit.default_timer() - start_time443 print("get_margin_core_cells() took:", elapsed)444 del(combined_tb_lym_data)445 assert tb_lym_margin_data.shape[0] > 0 and tb_lym_margin_data.shape[1] >= 4, "Something wrong with TB-CD3CD8 margin data"446 assert tb_lym_core_data.shape[0] > 0 and tb_lym_core_data.shape[1] >= 4, "Something wrong with TB-CD3CD8 core data"447 ## MARGIN - TB AND CD3/CD8448 title = "TB and CD3/CD8 (IM)"449 print(code_extract_func(tb_filename) + ": " + title)450 visualisation_interaction_block(tb_lym_margin_data, linestring, shape, title, image_filename_func(target_directory_stub + "MARGIN_TB_CD3CD8_IMAGES/", tb_filename))451 statistic_generation_block(heatmap_transform, statistics, tb_lym_margin_data, len(tb_lym_margin_data), 50, heatmap_filename_func, target_directory_stub + "MARGIN_", tb_filename)452 statistic_generation_block(heatmap_transform, statistics, tb_lym_margin_data, len(tb_lym_margin_data), 100, heatmap_filename_func, target_directory_stub + "MARGIN_", tb_filename)453 ## CORE - TB AND CD3/CD8454 title = "TB and CD3/CD8 (CT)"455 print(code_extract_func(tb_filename) + ": " + title)456 visualisation_interaction_block(tb_lym_core_data, linestring, shape, title, image_filename_func(target_directory_stub + "CORE_TB_CD3CD8_IMAGES/", tb_filename))457 statistic_generation_block(heatmap_transform, statistics, tb_lym_core_data, len(tb_lym_core_data), 50, heatmap_filename_func, target_directory_stub + "CORE_", tb_filename)458 statistic_generation_block(heatmap_transform, statistics, tb_lym_core_data, len(tb_lym_core_data), 100, heatmap_filename_func, target_directory_stub + "CORE_", tb_filename)459 ## IMCT - TB AND CD3/CD8460 title = "TB and CD3/CD8 (IMCT)"461 print(code_extract_func(tb_filename) + ": " + title)462 tb_lym_imct_data = np.concatenate((tb_lym_margin_data, tb_lym_core_data), axis=0)463 assert tb_lym_imct_data.shape[0] == (tb_lym_margin_data.shape[0] + tb_lym_core_data.shape[0]), "Margin and core cells not added properly"464 visualisation_interaction_block(tb_lym_imct_data, linestring, shape, title, image_filename_func(target_directory_stub + "IMCT_TB_CD3CD8_IMAGES/", tb_filename))465 statistic_generation_block(heatmap_transform, statistics, tb_lym_imct_data, len(tb_lym_imct_data), 50, heatmap_filename_func, target_directory_stub + "IMCT_", tb_filename)466 statistic_generation_block(heatmap_transform, statistics, tb_lym_imct_data, len(tb_lym_imct_data), 100, heatmap_filename_func, target_directory_stub + "IMCT_", tb_filename)467 num_files += 1468 assert(num_files == expected_num_files)469# def get_images_with_interactions(tb_files, tb_convert, lym_files, lym_convert, tb_lym_interaction_funcs, expected_num_files, meta_data_files, target_directory_stub, visualise_funcs, title_tree, output_filename_func, margin_files, d):470 """471 TB and do CD3, CD8, CD3&CD8 all at once.472 Four(!) levels of looping:473 for all files:474 meta vals...475 for tb_select, lym_select (must be the same region!):476 clean_tb, clean_lym = select_tb(), select_lym()477 for all interactions between current tb and lym selection (target_directories):478 create visualisation (pointwise-scatter with margin)479 for all statistics of interactions between tb and lym:480 ...481 num_files++482 Titles, target directories either need stubs or need to be hierarichial. Visualisation funcs are fine.483 """484 tb_meta_pairs = load_meta("TB", meta_data_files, code_extract_func)485 tb_meta_codes = [code for (code, val) in tb_meta_pairs]486 tb_meta_values = [val for (code, val) in tb_meta_pairs]487 cd3_meta_pairs = load_meta("CD3", meta_data_files, code_extract_func)488 cd8_meta_pairs = load_meta("CD8", meta_data_files, code_extract_func)489 cd3cd8_meta_pairs = load_meta("CD3&CD8", meta_data_files, code_extract_func)490 cd3_meta_codes = [code for (code, val) in cd3_meta_pairs]491 cd8_meta_codes = [code for (code, val) in cd8_meta_pairs]492 cd3cd8_meta_codes = [code for (code, val) in cd3cd8_meta_pairs]493 assert(cd3_meta_codes == cd8_meta_codes and cd8_meta_codes == cd3cd8_meta_codes)494 cd3_meta_values = [val for (code, val) in cd3_meta_pairs]495 cd8_meta_values = [val for (code, val) in cd8_meta_pairs]496 cd3cd8_meta_values = [val for (code, val) in cd3cd8_meta_pairs]497 num_files = 0498 for (tb_file, tb_raw_data), (lym_file, lym_raw_data) in zip(load_cluster_list(tb_files), load_lymphocyte_list(lym_files)):499 tb_meta_value, lym_meta_value = 0, 0500 try:501 tb_meta_value = tb_meta_values[tb_meta_codes.index(code_extract_func(tb_file))]502 cd3_meta_value = cd3_meta_values[cd3_meta_codes.index(code_extract_func(lym_file))]503 cd8_meta_value = cd8_meta_values[cd8_meta_codes.index(code_extract_func(lym_file))]504 cd3cd8_meta_value = cd3cd8_meta_values[cd3cd8_meta_codes.index(code_extract_func(lym_file))]505 except:506 raise Exception("Meta-data does not have a value for file-pair (" + tb_file + ", " + lym_file + ")")507 tb_data = tb_convert(tb_raw_data, tb_meta_value)508 lym_data = lym_convert(tb_raw_data, lym_meta_value)509 for selection in region_selections:510 selected_tb = selection(tb_data, linestring, shape, d)511 selected_lym = selection(lym_data, linestring, shape, d)512 for interaction_func in tb_lym_interaction_funcs:513 tb_lym_data = interaction_func(selected_tb, selected_lym)514 for feature in feature_funcs:515 feature_map = feature(tb_lym_data)516 tb_data = feature_map["TB"]517 cd3_data = feature_map["CD3"]518 cd8_data = feature_map["CD8"]519 cd3cd8_data = feature_map["CD3&CD8"]520 # for beta_transform, title_list, target_directory_list in zip(beta_transforms, title_tree, target_directory_tree):521 # clean_beta_data = beta_transform(beta_raw_data, beta_meta_value)522 # for alpha_beta_transform, visualise_func, title, target_directory in zip(alpha_beta_transforms, visualise_funcs, title_list, target_directory_list):523 # clean_data = alpha_beta_transform(clean_alpha_data, clean_beta_data)524 # fig, ax = plt.subplots(1, 1, figsize=(6, 6))525 # ax = visualise_func(clean_data, ax, title=title)526 # output_filename = output_filename_func(target_directory, alpha_file) # doesn't matter whether alpha-file or beta-file for the name527 # fig.savefig(output_filename, dpi=100, bbox_inches="tight")528 num_files += 1529 assert(num_files == expected_num_files)530def convert_cluster_to_tb(cluster_data, expected_size):531 data = np.array([[int(row[2]), int(row[1]), int(row[4]), int(row[3])] for row in cluster_data if int(row[0]) > 0 and int(row[0]) < 5])532 assert(len(data) == expected_size or expected_size == -1) # Allow a by-pass in the case of results we can't tell ahead of time.533 return data534def convert_lymphocyte_to_cd3(lymphocyte_data, expected_size):535 data = np.array([[int(row[0]), int(row[1]), int(row[2]), int(row[3]), int(row[4]), int(row[5])] for row in lymphocyte_data if int(row[4]) > 0])536 assert(len(data) == expected_size or expected_size == -1)537 return data538def convert_lymphocyte_to_cd8(lymphocyte_data, expected_size):539 data = np.array([[int(row[0]), int(row[1]), int(row[2]), int(row[3]), int(row[4]), int(row[5])] for row in lymphocyte_data if int(row[5]) > 0])540 assert(len(data) == expected_size or expected_size == -1)541 return data542def convert_lymphocyte_to_cd3cd8(lymphocyte_data, expected_size):543 data = np.array([[int(row[0]), int(row[1]), int(row[2]), int(row[3]), int(row[4]), int(row[5])] for row in lymphocyte_data if int(row[4]) > 0 or int(row[5]) > 0])544 assert(len(data) == expected_size or expected_size == -1)545 return data546def density(heatmap):547 """548 Assumes a non-interacting heatmap of lists.549 """550 densities = np.vectorize(len)(heatmap)551 return densities552def density_values(heatmap):553 return heatmap # it already is the answer554def ratio_values(heatmap):555 w, h = heatmap.shape[0], heatmap.shape[1]556 ratios = np.zeros(heatmap.shape)557 for i in range(w):558 for j in range(h):559 tb_val = heatmap[i][j][0]560 cd3_val = heatmap[i][j][1]561 cd8_val = heatmap[i][j][2]562 cd3cd8_val = heatmap[i][j][3]563 ratios[i][j][0] = 0 # don't care about tb, only the other 3564 ratios[i][j][1] = cd3_val / tb_val if tb_val != 0 else -1565 ratios[i][j][2] = cd8_val / tb_val if tb_val != 0 else -1566 ratios[i][j][3] = cd3cd8_val / tb_val if tb_val != 0 else -1567 return ratios568# def get_num_hotspots(heatmap, p_value_threshold=0.05, Z_score_threshold=1.96):569# hotspots = np.zeros((heatmap.shape[0], heatmap.shape[1], 2)) # layer 0 corresponds to coldspots, layer 1 to hotspots570# for i in range(array_of_heatmaps.shape[0]):571# all_count = 0572# for j in range(array_of_heatmaps.shape[1]):573# num_hot, num_cold = 0, 0574# (n1, n2) = heatmap.shape575# points = [[(x, y) for y in range(n2)] for x in range(n1)]576# dist = math.sqrt(2)577# with sb.axes_style("white"):578# msk = heatmap != 0579# points = [points[r][c] for r in range(n1) for c in range(n2) if msk[r][c]]580# w = DistanceBand(points, threshold=dist)581# lg_star = G_Local(heatmap[msk], w, transform='B', star=True)582# Z_scores = np.zeros(heatmap.shape, dtype=np.float32)583# p_values = np.zeros(heatmap.shape, dtype=np.float32)584# ind = 0585# for r in range(n1):586# for c in range(n2):587# if msk[r][c]:588# Z_scores[r, c] = lg_star.Zs[ind]589# p_values[r, c] = lg_star.p_sim[ind]590# ind += 1591# else:592# Z_scores[r][c] = -100.0 # why -100?593# for ind in range(lg_star.Zs.shape[0]):594# if lg_star.p_sim[ind] < p_value_threshold:595# if lg_star.Zs[ind] > Z_score_threshold:596# num_hot += 1597# elif lg_star.Zs[ind] < -(Z_score_threshold):598# num_cold += 1599# array_of_hotspots[i, all_count] = num_cold600# array_of_hotspots[i, all_count+1] = num_hot601# return heatmap602def get_tb_images(cluster_files, expected_num_files, meta_data_files, target_directory_stub, margin_files, d):603 # All, IM, CT, IM&CT604 obj_type = "TB"605 load_obj_func = load_cluster_list606 convert_func = lambda data, exp_size: convert_cluster_to_tb(data, exp_size)607 visualise_func = lambda data, ax, title: Visualiser.visualise_clusters_on_ax(data, ax, size_included=False, title=title)608 partition_func = lambda entire_list, exp_size: partition.partition(entire_list, tile_size=886, to_list=True, input_type="clean", by_metric=True, scale=1)[0]609 image_filename_func = lambda target_directory, filename: target_directory + code_extract_func(filename) + ".png"610 heatmap_filename_func = lambda target_directory, filename, statistic: target_directory + code_extract_func(filename) + statistic + ".p"611 args = (obj_type, cluster_files, expected_num_files, meta_data_files, load_obj_func,612 target_directory_stub, convert_func, visualise_func, image_filename_func, partition_func,613 heatmap_filename_func, margin_files, d)614 get_images_with_margins(*args)615def get_lym_images(lym_type, lymphocyte_files, expected_num_files, meta_data_files, target_directory_stub, margin_files, d):616 # All, IM, CT, IM&CT, for CD3, CD8, CD3&CD8617 load_obj_func = load_lymphocyte_list618 if lym_type == "CD3":619 convert_func = lambda data, exp_size: convert_lymphocyte_to_cd3(data, exp_size)620 elif lym_type == "CD8":621 convert_func = lambda data, exp_size: convert_lymphocyte_to_cd8(data, exp_size)622 else: # CD3&CD8623 convert_func = lambda data, exp_size: convert_lymphocyte_to_cd3cd8(data, exp_size)624 if lym_type == "CD3&CD8":625 visualise_func = lambda data, ax, title: Visualiser.visualise_lymphocytes_on_ax(data, ax, title=title)626 else:627 visualise_func = lambda data, ax, title: Visualiser.visualise_one_cd_on_ax(data, ax, "CD3", title=title, size=0.1, colour='r')628 partition_func = lambda entire_list, exp_size: partition.partition(entire_list, tile_size=886, to_list=True, input_type="clean", by_metric=True, scale=1)[0]629 image_filename_func = lambda target_directory, filename: target_directory + code_extract_func(filename) + ".png"630 heatmap_filename_func = lambda target_directory, filename, statistic: target_directory + code_extract_func(filename) + statistic + ".p"631 args = (lym_type, lymphocyte_files, expected_num_files, meta_data_files, load_obj_func,632 target_directory_stub, convert_func, visualise_func, image_filename_func,633 partition_func, heatmap_filename_func, margin_files, d)634 get_images_with_margins(*args)635def partition_and_transform_tb_interactions(items, meta_data_value, d):636 """637 Mixed partition, produce heatmap of interaction given some function of interaction.638 """639 init_num_objs = len(items)640 partitioned_items, tiles, \641 lymph_w, lymph_h, \642 clust_w, clust_h, \643 x_step, y_step = partition.partition(items, tile_size=886, to_list=True, input_type="mixed",644 by_metric=True, scale=1)645 num_objs = 0646 for i in range(partitioned_items.shape[0]):647 for j in range(partitioned_items.shape[1]):648 (lymphocytes, cancer_clusters) = partitioned_items[i][j]649 num_objs += (len(lymphocytes) + len(cancer_clusters))650 assert num_objs == init_num_objs and init_num_objs == meta_data_value, "num_objs: {}, init_num_objs: {}, and meta_data_value: {}".format(num_objs, init_num_objs, meta_data_value)651 heatmap = Cluster.get_interacting_object_counts(partitioned_items, d, partitioned_items.shape, tiles,652 lymph_w, lymph_h, clust_w, clust_h, x_step, y_step)653 assert heatmap.shape[0] == partitioned_items.shape[0] and heatmap.shape[1] == partitioned_items.shape[1]654 heatmap = np.flip(np.rot90(heatmap), 0)655 return heatmap656def merge_tb_lym(tb_data, lym_data):657 """658 Assumes tb_data to be np array of form:659 +------+------+------+------+660 | xMin | xMax | yMin | yMax |661 +------+------+------+------+662 | .... | .... | .... | .... |663 +------+------+------+------+664 lym data to be np array of form:665 +------+------+------+------+-----+-----+666 | xMin | xMax | yMin | yMax | CD3 | CD8 | <-- Note that CD3 and CD8 columns are (effectively) binary classifications.667 +------+------+------+------+-----+-----+668 | .... | .... | .... | .... | ... | ... |669 +------+------+------+------+-----+-----+670 With the output to be of form:671 +------+------+------+------+-----+-----+----+672 | xMin | xMax | yMin | yMax | CD3 | CD8 | TB | <-- As with CD3/CD8 above, TB column is for binary classification.673 +------+------+------+------+-----+-----+----+674 | .... | .... | .... | .... | ... | ... | .. |675 +------+------+------+------+-----+-----+----+676 """677 tb_data = np.concatenate((tb_data, np.zeros((tb_data.shape[0], 2)), np.ones((tb_data.shape[0], 1))), axis=1)678 lym_data = np.concatenate((lym_data, np.zeros((lym_data.shape[0], 1))), axis=1)679 mixed_data = np.concatenate((lym_data, tb_data))680 assert len(mixed_data) == len(tb_data) + len(lym_data)681 return mixed_data682# Functions for selections of data683def select_all(data, linestring, shape, d):684 return data685def select_CT(data, linestring, shape, d):686 return np.asarray(get_core_cells(data, shape, linestring, d))687def select_IM(data, linestring, shape, d):688 return np.asarray(get_margin_cells(data, linestring, d))689def select_IMCT(data, linestring, shape, d):690 margin_cells, core_cells = get_margin_core_cells(data, shape, linestring, d)691 margin_cells.extend(core_cells)692 return np.asarray(margin_cells)693def get_tb_cd3_cd8_cd3cd8_images(root_dir, meta_data_dir, target_dir_root, expected_num_files):694 """695 All input dirs should have a terminating slash696 This function assumes that select(data, linestring, shape, d) => return convert(data), i.e. ALL-DATA, no interaction with margin697 """698 # TODO: confirm that d = 500um is the correct distance from margin to allow699 d = 500700 tb_target_directory_stub = target_dir_root+"IMAGES/"701 cd3_target_directory_stub = tb_target_directory_stub702 cd8_target_directory_stub = tb_target_directory_stub703 cd3cd8_target_directory_stub = tb_target_directory_stub704 tb_options = (root_dir+"TB_pickle_files/*", expected_num_files, meta_data_dir+"*", tb_target_directory_stub, root_dir+"margin_files/*", d)705 cd3_options = ("CD3", root_dir+"lymphocyte_csv_files/*", expected_num_files, meta_data_dir+"*", cd3_target_directory_stub, root_dir+"margin_files/*", d)706 cd8_options = ("CD8", root_dir+"lymphocyte_csv_files/*", expected_num_files, meta_data_dir+"*", cd8_target_directory_stub, root_dir+"margin_files/*", d)707 cd3cd8_options = ("CD3&CD8", root_dir+"lymphocyte_csv_files/*", expected_num_files, meta_data_dir+"*", cd3cd8_target_directory_stub, root_dir+"margin_files/*", d)708 get_tb_images(*tb_options) # cluster_files, expected_num_files, meta_data_files, target_directories, margin_files, d709 get_lym_images(*cd3_options) # (lym_type, lymphocyte_files, expected_num_files, meta_data_files, target_directories, margin_files, d)710 get_lym_images(*cd8_options)711 get_lym_images(*cd3cd8_options)712def get_tb_cd3_cd8_cd3cd8_interaction_images(root_dir, target_dir_root, expected_num_files):713 """714 All input dirs should have a terminating slash715 This function assumes that select(data, linestring, shape, d) returns:716 > return convert(data) // ALL-DATA, but display with margin717 > return get_core(convert(data), linestring, shape, d) // CORE TUMOUR (CT)718 > return get_margin(convert(data), linestring, shape, d) // INVASIVE MARGIN (IM)719 > return get_margin_and_core(convert(data), linestring, shape, d) // IM & CT720 """721 # d is the maximum distance between nearby lymphocytes and any TB722 d = 50723 target_directory_stub = target_dir_root+"IMAGES/"724 tb_files = root_dir+"TB_pickle_files/*"725 lym_files = root_dir+"lymphocyte_csv_files/*"726 margin_files = root_dir + "margin_files/*"727 meta_data_files = root_dir+"meta_data/*"728 partition_func = partition_and_transform_tb_interactions729 statistics = [("_density", density_values, lambda heatmap: heatmap == 0), ("_ratio", ratio_values, lambda heatmap: heatmap == -1)]730 image_filename_func = lambda target_directory, filename: target_directory + code_extract_func(filename) + ".png"731 heatmap_filename_func = lambda target_directory, filename, statistic: target_directory + code_extract_func(filename) + statistic + ".p"732 args = (tb_files, lym_files, expected_num_files, meta_data_files, target_directory_stub, partition_func, statistics, image_filename_func, heatmap_filename_func, margin_files, d)733 get_images_with_interactions_condensed(*args)734 # ax = fig.gca(projection='3d')735 # Add margin736 # ax = plot_line_and_shape(ax, linestring, shape)737 # X_3D = np.arange(0, heatmap_tb.shape[1], 1)738 # Y_3D = np.arange(0, heatmap_tb.shape[0], 1)739 # X_3D, Y_3D = np.meshgrid(X_3D, Y_3D)740 # Z = heatmap_tb741 # # print(X_3D.shape, Y_3D.shape, Z.shape)742 # # Plot the surface.743 # surf = ax.plot_surface(X_3D, Y_3D, Z, cmap=cm.hot, linewidth=0, antialiased=False, alpha=1)744 # # # Add a color bar which maps values to colors.745 # fig.colorbar(surf, shrink=0.5, aspect=5)746 # plt.show()...
stats.py
Source:stats.py
1'''2Created on Feb 14, 20173@author: nicolas4'''5from __future__ import print_function6from functools import reduce7from lemoncheesecake.helpers.console import print_table, bold8from lemoncheesecake.cli.command import Command9from lemoncheesecake.cli.utils import load_suites_from_project10from lemoncheesecake.filter import add_test_filter_cli_args, make_test_filter11from lemoncheesecake.testtree import flatten_suites12from lemoncheesecake.project import load_project13class Stats:14 def __init__(self):15 self.tests_nb = 016 self.disabled_tests_nb = 017 self.suites_nb = 018 self.non_empty_suites_nb = 019 self.tags = {}20 self.properties = {}21 self.links = {}22def compute_stats(suites):23 stats = Stats()24 for suite in flatten_suites(suites):25 stats.suites_nb += 126 for test in suite.get_tests():27 stats.tests_nb += 128 if test.is_disabled():29 stats.disabled_tests_nb += 130 for tag in test.hierarchy_tags:31 stats.tags[tag] = stats.tags.get(tag, 0) + 132 for prop, value in test.hierarchy_properties.items():33 if prop not in stats.properties:34 stats.properties[prop] = {}35 if value not in stats.properties[prop]:36 stats.properties[prop][value] = 037 stats.properties[prop][value] += 138 for link in test.hierarchy_links:39 stats.links[link] = stats.links.get(link, 0) + 140 else:41 stats.non_empty_suites_nb += 142 return stats43class StatsCommand(Command):44 def get_name(self):45 return "stats"46 def get_description(self):47 return "Display statistics about the project's tests"48 def add_cli_args(self, cli_parser):49 add_test_filter_cli_args(cli_parser)50 def run_cmd(self, cli_args):51 project = load_project()52 suites = load_suites_from_project(project, make_test_filter(cli_args))53 stats = compute_stats(suites)54 def percent_of_tests(val):55 return "%2d%%" % (float(val) / stats.tests_nb * 100)56 # Show tags57 lines = []58 for tag in sorted(stats.tags.keys(), key=lambda k: stats.tags[k], reverse=True):59 lines.append([bold(tag), stats.tags[tag], percent_of_tests(stats.tags[tag])])60 print_table(bold("Tags"), ["Tag", "Tests", "In %"], lines)61 # Show properties62 lines = []63 prop_names = sorted(64 stats.properties.keys(),65 key=lambda k: reduce(lambda x, y: x + y, stats.properties[k].values(), 0),66 reverse=True67 )68 for prop_name in prop_names:69 prop_values = sorted(70 stats.properties[prop_name].keys(),71 key=lambda k: stats.properties[prop_name][k],72 reverse=True73 )74 for prop_value in prop_values:75 lines.append([76 bold(prop_name), bold(prop_value),77 stats.properties[prop_name][prop_value],78 percent_of_tests(stats.properties[prop_name][prop_value])79 ])80 print_table(bold("Properties"), ["Property", "Value", "Tests", "In %"], lines)81 # Show links82 lines = []83 for link in sorted(stats.links.keys(), key=lambda k: stats.links[k], reverse=True):84 lines.append([85 bold(link[1] or "-"), link[0], stats.links[link], percent_of_tests(stats.links[link])86 ])87 print_table(bold("Links"), ["Name", "URL", "Tests", "In %"], lines)88 tests_info = bold("%d tests" % stats.tests_nb)89 if stats.disabled_tests_nb > 0:90 tests_info += " (among which %s disabled tests)" % stats.disabled_tests_nb91 suites_info = bold("%d suites" % stats.non_empty_suites_nb)92 if stats.suites_nb > stats.non_empty_suites_nb:93 suites_info += " (+ %d empty suites)" % (stats.suites_nb - stats.non_empty_suites_nb)94 print("Total: %s in %s" % (tests_info, suites_info))95 print()...
margin.py
Source:margin.py
1import re2import matplotlib.pyplot as plt3from shapely.geometry import Point, Polygon, MultiPolygon4from descartes import PolygonPatch5# 2016_12_17__0690.annotations6# name = "2016_12_17__0765(Ext).annotations"7name = "2016_12_17__0690.annotations"8hierarchy = open("./Daniel line/" + name, "r").read()9p = re.compile("<*")10hierarchy_tags = p.split(hierarchy)11print(len(hierarchy_tags))12x_tags = []13y_tags = []14for tag in hierarchy_tags:15 if "X=" in tag and "Y=" in tag:16 x = tag.split("\"")[1]17 y = tag.split("\"")[3]18 x_tags.append(int(x))19 y_tags.append(int(y))20tags = []21# We have 4 points22#Â For each of 4 points:23# - is within core tumour? (core_tumour.contains(point)) // inner line24# - if no, then check: tumour_front.contains(point) or invasive_front.contains(point) // second or third lines25#26for i, j in zip(x_tags, y_tags):27 tags.append((i, j))28fig = plt.figure(1)29# 130ax = fig.add_subplot(111)31poly = Polygon(tags)32def plot_line(ax, ob):33 x, y = ob.xy34def construct_shape(poly, distance, delta, update):35 difference = 036 if type(poly.buffer(distance)) is MultiPolygon:37 difference = update(difference, delta)38 while type(poly.buffer(distance + difference)) is MultiPolygon:39 difference = update(difference, delta)40 new_poly = poly.buffer(distance + difference)41 return new_poly.buffer(-difference)42 else:43 return poly.buffer(distance)44def construct_shape_add_on_new_shape(poly, distance, delta, update):45 padding = update(0, delta)46 difference = 047 if type(poly.buffer(distance)) is MultiPolygon:48 print("Boo!")49 shape = poly50 difference = update(difference, delta)51 while type(shape.buffer(distance + padding)) is MultiPolygon:52 shape = shape.buffer(distance + padding)53 difference = update(difference, delta)54 return shape.buffer(-difference)55 else:56 return poly.buffer(distance)57# Iterative increasing approach58dilated = construct_shape(poly, 500, 1, lambda x, y: x + y)59contracted = construct_shape(dilated, -1000, 1, lambda x, y: x - y)60print(type(contracted))61print(MultiPolygon)62if type(contracted) is MultiPolygon:63 print("Uh oh. Contracted for " + name + " invalid.")64 int_x = [i[0] for po in contracted for i in po.exterior.coords]65 int_y = [i[1] for po in contracted for i in po.exterior.coords]66else:67 int_x = [i[0] for i in contracted.exterior.coords]68 int_y = [i[1] for i in contracted.exterior.coords]69if type(dilated) is MultiPolygon:70 print("Uh oh. Dilated for " + name + " invalid.")71 ext_x = [i[0] for po in dilated for i in po.exterior.coords]72 ext_y = [i[1] for po in dilated for i in po.exterior.coords]73else:74 ext_x = [i[0] for i in dilated.exterior.coords]75 ext_y = [i[1] for i in dilated.exterior.coords]76plt.plot(ext_x, ext_y, color="r")77plt.plot(int_x, int_y, color="b")78patch = PolygonPatch(poly, fc="red", alpha=0.5, zorder=2)79ax.add_patch(patch)80#Â plt.plot(x_tags, y_tags)81plt.show()...
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