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How to use abbrev_list method in autotest
Best Python code snippet using autotest_python
gs_svae.py
Source:gs_svae.py 
1import os, sys, json2import time, shutil, copy3import subprocess4import math5import multiprocessing as mp6import numpy as np7import matplotlib.pyplot as plt 8import comet_ml9import torch10import torch.nn as nn11import torch.utils12import torch.utils.data13from torch.backends import cudnn14from torchvision import datasets, transforms15from torch.autograd import Variable16from run_svae import main17from utils import get_gpu_info18 19def gs_main():20 f = open("config.json", "r")21 config = json.load(f)22 f.close()23 24 flatten_config = {}25 for key in config.keys():26 flatten_config.update(config[key])27 28 date = config["train"]["date"]29 number_of_date = config["train"]["number_of_date"]30 parallel_strategy_on = True31 direct_parse_strategy_on = True32 max_parallel_queues = min(6, mp.cpu_count())33 minimum_memory = 150034 gpu_id = config["train"]["gpu"]35 #device = torch.device("cuda", config["train"]["gpu"])36 #torch.cuda.manual_seed(config["train"]["seed"])37 38 gs_dict = {"mix":{"system":["EnKO", "FIVO", "IWAE"], "loss_type":["sumprod", "prodsum", "sumprod"]}, "mix2":{"seed":[2, 3], "gpu":[1,2]}}39# gs_dict = {"mix":{"system":["EnKO", "FIVO", "IWAE"], "loss_type":["sumprod", "prodsum", "sumprod"], "gpu":[0,1,2]},40# "outer_scale":[1.0, 2.0]}41# gs_dict = {"mix":{"system":["EnKO", "FIVO", "IWAE"], "gpu":[0,1,2]}}42# gs_dict = {"mix":{"system":["EnKO", "FIVO", "IWAE"], "loss_type":["sumprod", "prodsum", "sumprod"]},43# "n_particles":[8,16,32], "seed":[1,2,3]}44# gs_dict = {"inflation_method":["RTPP", "RTPS"], "inflation_factor":[0.1, 0.2, 0.3], "n_particles":[8,16,32],45# "mix":{"seed":[1,2,3], "gpu":[0,1,2]}}46# gs_dict = {"inflation_method":["RTPP", "RTPS"], "seed":[1,2,3], "mix":{"inflation_factor":[0.1, 0.2, 0.3], "gpu":[0,1,2]}}47# gs_dict = {"seed":[2,3], "mix":{"inflation_method":["RTPP", "RTPS"], "gpu":[1,2]}, "inflation_factor":[0.1, 0.2, 0.3]}48# gs_dict = {"mix":{"inflation_factor":[0.1, 0.2, 0.3], "gpu":[0,1,2]}}49# gs_dict = {"mix":{"loss_type":["prodsum", "sumprod"], "exclude_filtering_on":[False, True], "gpu":[0,1]}}50# gs_dict = {"mix":{"system":["EnKO", "EnKO", "FIVO", "IWAE"],51# "loss_type":["sumprod", "prodsum", "prodsum", "sumprod"],52# "exclude_filtering_on":[True, False, True, True], "gpu":[1,2,1,2]}}53# gs_dict = {"kld_penalty_weight":[0.1, 1.]}54# gs_dict = {"mix":{"kld_penalty_weight":[0, 1.], "gpu":[1,2]}, "inflation_factor":[0.2, 0.3]}55# gs_dict = {"mix":{"only_outer_learning_epochs":[0,10,0,10], "pretrain_epochs":[0,0,10,20]}}56# gs_dict = {"seed":[1,2,3]}57 gs_key = list(gs_dict.keys()) # list of keys for grid search58 gs_length = len(gs_dict)59 gs_key2 = []60 for key in gs_key:61 # if dictionary has hierarchical structure, add hierarchical keys to gs_key2 list.62 if type(gs_dict[key])==list:63 gs_key2.append(key)64 elif type(gs_dict[key])==dict:65 gs_key2 += list(gs_dict[key].keys())66 67 68 topic_name = ""69 for topic in ["system", "outer_model", "model", "data_name"]:70 if not (topic in gs_key2):71 if not (topic in ["system", "outer_model"] and config ["data"][topic] is None):72 topic_name += config["data"][topic] + "_"73 74 dir_name = "{}_{}gs{}/".format(date, topic_name, number_of_date)75 name_list = []76 config_list = []77 if direct_parse_strategy_on:78 parse_list = []79 initial_parse = []80 for key in gs_key2:81 initial_parse += ["--{}".format(key), None]82 83 84 def generate_queue_flatten_config(old_config, name, depth):85 key = gs_key[depth]86 87 if type(gs_dict[key])==list:88 for i, value in enumerate(gs_dict[key]):89 new_name = name90 new_config = copy.deepcopy(old_config)91 new_config[key] = value92 abbrev_list = key.split("_")93 for abbrev in abbrev_list:94 new_name += abbrev[0]95 new_name += str(value)96 if depth+1 < gs_length:97 generate_queue_flatten_config(new_config, new_name, depth+1)98 else:99 config_list.append(new_config)100 name_list.append(new_name)101 elif type(gs_dict[key])==dict:102 interlocking_key = list(gs_dict[key].keys())103 min_length = 10104 for ikey in interlocking_key:105 min_length = len(gs_dict[key][ikey]) if len(gs_dict[key][ikey]) < min_length else min_length106 for i in range(min_length):107 new_name = name108 new_config = copy.deepcopy(old_config)109 for ikey in interlocking_key:110 new_config[ikey] = gs_dict[key][ikey][i]111 abbrev_list = ikey.split("_")112 for abbrev in abbrev_list:113 new_name += abbrev[0]114 new_name += str(gs_dict[key][ikey][i])115 if depth+1 < gs_length:116 generate_queue_flatten_config(new_config, new_name, depth+1)117 else:118 config_list.append(new_config)119 name_list.append(new_name)120 else:121 raise ValueError("elements must be a list type object or a dict type object")122 123 124 def flatten_config_to_parse(config):125 parse_list = []126 for key in config.keys():127 parse_list.append("--{}".format(key))128 if type(config[key])==list:129 parse_list += [str(value) for value in config[key]]130 else:131 parse_list.append(str(config[key]))132 return parse_list133 134 135 def generate_queue_parse(old_parse, name, depth, total_depth):136 key = gs_key[depth]137 138 if type(gs_dict[key])==list:139 for i, value in enumerate(gs_dict[key]):140 new_name = name141 new_parse = copy.deepcopy(old_parse)142 new_parse[2*total_depth+1] = str(value)143 abbrev_list = key.split("_")144 for abbrev in abbrev_list:145 new_name += abbrev[0]146 new_name += str(value)147 if depth+1 < gs_length:148 generate_queue_parse(new_parse, new_name, depth+1, total_depth+1)149 else:150 parse_list.append(new_parse)151 name_list.append(new_name)152 elif type(gs_dict[key])==dict:153 inner_keys = list(gs_dict[key].keys())154 # arrange length of inner list155 min_length = 10156 for ikey in inner_keys:157 min_length = len(gs_dict[key][ikey]) if len(gs_dict[key][ikey]) < min_length else min_length158 for i in range(min_length):159 new_name = name160 new_parse = copy.deepcopy(old_parse)161 for j, ikey in enumerate(inner_keys):162 new_parse[2*(total_depth+j)+1] = str(gs_dict[key][ikey][i])163 abbrev_list = ikey.split("_")164 for abbrev in abbrev_list:165 new_name += abbrev[0]166 new_name += str(gs_dict[key][ikey][i])167 if depth+1 < gs_length:168 generate_queue_parse(new_parse, new_name, depth+1, total_depth+len(inner_keys))169 else:170 parse_list.append(new_parse)171 name_list.append(new_name)172 else:173 raise ValueError("elements must be a list type object or a dict type object")174 175 176 def generate_queue_config(old_config, name, depth):177 key = gs_key[depth]178 179 if type(gs_dict[key])==list:180 for i, value in enumerate(gs_dict[key]):181 new_name = name182 new_config = copy.deepcopy(old_config)183 for ckey in config.keys():184 if key in config[ckey].keys():185 new_config[ckey][key] = value186 abbrev_list = key.split("_")187 for abbrev in abbrev_list:188 new_name += abbrev[0]189 new_name += str(value)190 break191 if depth+1 < gs_length:192 generate_queue_config(new_config, new_name, depth+1)193 else:194 config_list.append(new_config)195 name_list.append(new_name)196 #main(config, new_name)197 elif type(gs_dict[key])==dict:198 interlocking_key = list(gs_dict[key].keys())199 min_length = 10200 for ikey in interlocking_key:201 min_length = len(gs_dict[key][ikey]) if len(gs_dict[key][ikey]) < min_length else min_length202 for i in range(min_length):203 new_name = name204 new_config = copy.deepcopy(old_config)205 for ikey in interlocking_key:206 for ckey in config.keys():207 if ikey in config[ckey].keys():208 new_config[ckey][ikey] = gs_dict[key][ikey][i]209 abbrev_list = ikey.split("_")210 for abbrev in abbrev_list:211 new_name += abbrev[0]212 new_name += str(gs_dict[key][ikey][i])213 break214 if depth+1 < gs_length:215 generate_queue_config(new_config, new_name, depth+1)216 else:217 config_list.append(new_config)218 name_list.append(new_name)219 #main(config, new_name)220 else:221 raise ValueError("elements must be a list type object or a dict type object")222 223 224 if parallel_strategy_on:225 if direct_parse_strategy_on:226 generate_queue_flatten_config(flatten_config, dir_name, 0)227 total_parse_list = []228 for config_element, name_element in zip(config_list, name_list):229 total_parse_list.append(["python", "parse_svae.py"] + flatten_config_to_parse(config_element) + ["--name", name_element])230 print(total_parse_list)231# generate_queue_parse(initial_parse, dir_name, 0, 0)232# for parse_element, name_element in zip(parse_list, name_list):233# total_parse_list.append(["python", "parse_svae.py"] + parse_element + ["--name", name_element])234 else:235 generate_queue_config(config, dir_name, 0)236 else:237 if direct_parse_strategy_on:238 generate_queue_parse(initial_parse, dir_name, 0, 0)239 else:240 generate_queue_flatten_config(flatten_config, dir_name, 0)241 242 if parallel_strategy_on:243 if direct_parse_strategy_on:244 for i in range((len(name_list)-1)//max_parallel_queues+1):245 p = mp.Pool(max_parallel_queues)246 p.map(subprocess.run, total_parse_list[max_parallel_queues*i:max_parallel_queues*(i+1)])247 p.close()248 if "gpu" in gs_key:249 gpu_ids = gs_dict["gpu"]250 memory_used = [int(get_gpu_info()[gpu_id]["memory.used"]) for gpu_id in gpu_ids]251 while max(memory_used) > minimum_memory:252 print("waiting in {}-th parallel computation".format(i+1))253 time.sleep(10)254 memory_used = [int(get_gpu_info()[gpu_id]["memory.used"]) for gpu_id in gpu_ids]255 else:256 memory_used = int(get_gpu_info()[gpu_id]["memory.used"])257 while memory_used > minimum_memory:258 print("waiting in {}-th parallel computation".format(i+1))259 time.sleep(10)260 memory_used = int(get_gpu_info()[gpu_id]["memory.used"])261 else:262 for i in range((len(name_list)-1)//max_parallel_queues+1):263 p = mp.Pool(min(mp.cpu_count(), max_parallel_queues))264 p.starmap(main, zip(config_list[max_parallel_queues*i:max_parallel_queues*(i+1)], name_list[max_parallel_queues*i:max_parallel_queues*(i+1)]))265 p.close()266 memory_used = int(get_gpu_info()[gpu_id]["memory.used"])267 while memory_used > minimum_memory:268 print("waiting")269 time.sleep(10)270 memory_used = int(get_gpu_info()[gpu_id]["memory.used"])271 #subprocess.run(["nvidia-smi"])272 else:273 if direct_parse_strategy_on:274 for parse_element, name_element in zip(parse_list, name_list):275 #print(name_element, parse_element)276 subprocess.run(["python", "parse_svae.py"] + parse_element + ["--name", name_element])277 else:278 for config_element, name_element in zip(config_list, name_list):279 parse_element = flatten_config_to_parse(config_element)280 #print(name_element, parse_element)281 subprocess.run(["python", "parse_svae.py"] + parse_element + ["--name", name_element])282 283 284 285if __name__ == "__main__":...
twtt.py
Source:twtt.py
1#!/usr/bin/python2# -*- coding: utf-8 -*-3import sys, csv, re, urllib24from HTMLParser import HTMLParser5import NLPlib6tagger = NLPlib.NLPlib()7# ---------------------- Pre-processing Functions ----------------------8# All html tags and attributes (i.e., /<[^>]+>/) are removed9def twtt1(input):10 return html_matcher.sub("", input)11# Html character codes (i.e., &...;) are replaced with an ASCII equivalent12def twtt2(input):13 string = urllib2.unquote(input).decode('utf8','ignore')14 return h.unescape(string).encode(sys.getfilesystemencoding())15# All URLs (i.e., tokens beginning with http or www) are removed16def twtt3(input):17 return url_matcher.sub("", input)18# The first character in Twitter user names and hash tags (i.e., @ and #) are removed19def twtt4(input):20 return tag_matcher.sub("", input)21# Each sentence within a tweet is on its own line22def twtt5(input):23 input = input.strip()24 abbrev_list = [re.sub(r"\n", "", item) for item in list(abbrev.readlines())]25 abbrev_list.extend(['e.g.', 'i.e.'])26 sentence = re.split(r'\s+', input)27 for i in range(len(sentence)):28 if re.search(r"\.",sentence[i]):29 if not(sentence[i] in abbrev_list):30 if re.search("n't.", sentence[i]):31 re.sub("n't.", "n't. \n", sentence[i])32 else: 33 if len(re.split(r"(\W+)(?=\w)", sentence[i])) < 3:34 # sentence[i] = re.split(r"(\W+)(?=\w)", sentence[i])[0] + " \n" + "".join(re.split(r"(\W+)(?=\w)", sentence[i])[1:])35 sentence[i] = sentence[i] + " \n"36 else:37 sentence[i] = "".join(re.split(r"(\W+)(?=\w)", sentence[i])[0:-1]) + " \n" + re.split(r"(\W+)(?=\w)", sentence[i])[-1]38 elif re.search(r"\!|\?",sentence[i]):39 # if sentence[i+1][0].isupper():40 sentence[i] = sentence[i] + " \n"41 if len(sentence) == 1 or len(sentence) == 0:42 pass43 elif sentence[len(sentence)-1][-1] == "\n":44 sentence[len(sentence)-1] = sentence[len(sentence)-1][0:-2]45 return re.sub(r"\n\s+", "\n", " ".join(sentence))46# Ellipsis (i.e., â...â), and other kinds of multiple punctuation (e.g., â!!!â) are not split47def twtt6(input):48 print("Already qualified in twtt5")49# Each token, including punctuation and clitics, is separated by spaces50def twtt7(input):51 abbrev_list = [re.sub(r"\n", "", item) for item in list(abbrev.readlines())]52 abbrev_list.extend(['e.g.', 'i.e.'])53 sentence = re.split(r" ", input)54 for i in range(len(sentence)):55 if re.search(r"\w\W|\W\w",sentence[i]):56 if not(sentence[i] in abbrev_list):57 if re.search(r"n't", sentence[i]):58 if re.search("n't.", sentence[i]):59 sentence[i] = re.sub("n't.", "n't .", sentence[i])60 sentence[i] = re.sub("n't", " n't", sentence[i])61 elif re.search(r"\n", sentence[i]):62 sentence[i] = " ".join(re.split(r"(\W+)(?=\w)", sentence[i]))63 sentence[i] = " ".join(re.split(r"(?<=\w)(\W+)", sentence[i]))64 elif re.search(r"\$\d", sentence[i]):65 sentence[i] = " ".join(re.split(r'(\$)', sentence[i]))66 elif re.search(r"\d\W\d", sentence[i]):67 pass68 else:69 sentence[i] = re.split(r'(\W)', sentence[i])[0] + " " + "".join(re.split(r'(\W)', sentence[i])[1:])70 return " ".join(sentence)71# Each token is tagged with its part-of-speech72def twtt8(input):73 global tagger74 sentence = re.split(r' ', input)75 tags = tagger.tag(sentence)76 result = []77 for word, tag in zip(sentence, tags):78 result.append(word + "/" + tag)79 return re.sub(r"\n/NN ", "\n", re.sub(r" /NN", "", " ".join(result)))80# Before each tweet is demarcation â<A=#>â, which occurs on its own line, where # is the numeric class of the tweet (0 or 4)81def twtt9(input, polarity):82 pol = "<A=" + polarity + ">\n"83 result = pol + input84 return result85# ---------------------- Main Function ----------------------86def main(input_file, student_id, output_file):87 #check if input args are valid88 try:89 id = int(student_id)90 except:91 print("Please input a valid student ID.")92 start_pos1 = id % 80 * 1000093 start_pos2 = 800000 + start_pos194 with open(input_file, 'r') as infile, open(output_file, 'w') as outfile:95 raw = csv.reader(infile)96 data = list(raw)97 if output_file == 'test.twt':98 print(len(data))99 for line in data:100 outfile.write(twtt9(twtt8(twtt7(twtt5(twtt4(twtt3(twtt2(twtt1(line[5].strip()))))))), line[0]))101 outfile.write("\n")102 else:103 for line in data[start_pos1 : start_pos1+10000]:104 outfile.write(twtt9(twtt8(twtt7(twtt5(twtt4(twtt3(twtt2(twtt1(line[5].strip()))))))), line[0]))105 outfile.write("\n")106 for line in data[start_pos2 : start_pos2+10000]:107 outfile.write(twtt9(twtt8(twtt7(twtt5(twtt4(twtt3(twtt2(twtt1(line[5].strip()))))))), line[0]))108 outfile.write("\n")109if __name__ == "__main__":110 111 h = HTMLParser()112 htmlCase = r'<[^>]+>'113 html_matcher = re.compile(htmlCase)114 urlCase = r"http\S+|www\S+|Http\S+|WWW\S+"115 url_matcher = re.compile(urlCase)116 tagCase = r"@|#"117 tag_matcher = re.compile(tagCase)118 with open("../Wordlists/abbrev.english", 'r') as abbrev:119 # check if the number of input args is 3120 try:121 main(sys.argv[1], sys.argv[2], sys.argv[3])122 except:123 print("Valid Format: python twtt.py [input_filename] [Student ID] [output_filename]")124 # test = 'Http://bit.ly/PACattack'125 # # print twtt8(twtt7(twtt5(twtt4(twtt3(twtt2(twtt1(test)))))))126 # print twtt9(twtt8(twtt7(twtt5(twtt4(twtt3(twtt2(twtt1(test))))))), '0')127 # print twtt8("sdjfh jaskfjd , jhj she i 've said, she 's are n't ! $ 100,000. ? dogs ' ' something good ' ??? ... j s")128 # test = 'Meet me today at the FEC in DC at 4. Wear a carnation so I know itâs you. <a href="Http://bit.ly/PACattack" target="_blank" class="tweet-url web" rel="nofollow">Http://bit.ly/PACattack</a>.'129 # print twtt9(twtt8(twtt7(twtt5(twtt4(twtt3(twtt2(twtt1(test))))))), '0')130 #print(twtt1('<p>Http://bit.ly/PACattack</p>'))131 132 ...
get_census_geographies.py
Source:get_census_geographies.py
1"""2download geographies from the 2011 tiger line census files3unzip in folders4https://www2.census.gov/geo/tiger/TIGER2011/PLACE/tl_2011_01_place.zip 5https://www2.census.gov/geo/tiger/TIGER2011/COUSUB/tl_2011_01_cousub.zip6- place (done)7- county subdivisions (done)8- blocks (done)9- state shape (done)10- project all to 3857 (state shape)11- and create difference of state shape (diff) place12"""13# importing required modules14import pandas as pd15import geopandas as gpd16import urllib17import zipfile18import time19import os20# read crosswalk21state_fips = pd.read_csv('D:/GIS data/county_fips/state_fips.csv')22state_fips['fips'] = state_fips['fips'].map(lambda x:str(x).zfill(2))23fips_list = list(state_fips['fips'])24abbrev_list = list(state_fips['abbrev'])25cwd = os.getcwd()26cwd27os.chdir('D:/GIS data/')28#download 2011 places29for i in range(0, len(abbrev_list)):30 print (abbrev_list[i], fips_list[i])31 url = "https://www2.census.gov/geo/tiger/TIGER2011/PLACE/tl_2011_{0}_place.zip".format(fips_list[i])32 extract_dir = "./{0}/Census/tl_2011_{0}_place/".format(abbrev_list[i])33 if not os.path.exists(extract_dir):34 os.makedirs(extract_dir)35 36 zip_path, _ = urllib.request.urlretrieve(url)37 with zipfile.ZipFile(zip_path, "r") as f:38 f.extractall(extract_dir)39 40#download 2011 county subdivisions41for i in range(0, len(abbrev_list)):42 print (abbrev_list[i], fips_list[i])43 url = "https://www2.census.gov/geo/tiger/TIGER2011/COUSUB/tl_2011_{0}_cousub.zip".format(fips_list[i])44 extract_dir = "./{0}/Census/tl_2011_{0}_cousub/".format(abbrev_list[i])45 if not os.path.exists(extract_dir):46 os.makedirs(extract_dir)47 zip_path, _ = urllib.request.urlretrieve(url)48 with zipfile.ZipFile(zip_path, "r") as f:49 f.extractall(extract_dir)50 51#download 2010 counties52for i in range(0, len(abbrev_list)):53 print (abbrev_list[i], fips_list[i])54 url = "https://www2.census.gov/geo/tiger/TIGER2010/COUNTY/2010/tl_2010_{0}_county10.zip".format(fips_list[i])55 extract_dir = "./{0}/Census/tl_2010_{1}_county10/".format(abbrev_list[i], fips_list[i])56 if not os.path.exists(extract_dir):57 os.makedirs(extract_dir)58 zip_path, _ = urllib.request.urlretrieve(url)59 with zipfile.ZipFile(zip_path, "r") as f:60 f.extractall(extract_dir)61#download 2010 states62for i in range(0, len(abbrev_list)):63 print (abbrev_list[i], fips_list[i])64 url = "https://www2.census.gov/geo/tiger/TIGER2010/STATE/2010/tl_2010_{0}_state10.zip".format(fips_list[i])65 extract_dir = "./{0}/Census/tl_2010_{1}_state10/".format(abbrev_list[i], fips_list[i])66 if not os.path.exists(extract_dir):67 os.makedirs(extract_dir)68 zip_path, _ = urllib.request.urlretrieve(url)69 with zipfile.ZipFile(zip_path, "r") as f:70 f.extractall(extract_dir)71#download 2019 block groups72for i in range(0, len(abbrev_list)):73 print (abbrev_list[i], fips_list[i])74 url = "https://www2.census.gov/geo/tiger/TIGER2019/BG/tl_2019_{0}_bg.zip".format(fips_list[i])75 extract_dir = "./{0}/Census/tl_2019_{0}_bg/".format(abbrev_list[i])76 if not os.path.exists(extract_dir):77 os.makedirs(extract_dir)78 zip_path, _ = urllib.request.urlretrieve(url)79 with zipfile.ZipFile(zip_path, "r") as f:80 f.extractall(extract_dir)81 82#download 2020 block groups83for i in range(0, len(abbrev_list)):84 print (abbrev_list[i], fips_list[i])85 url = "https://www2.census.gov/geo/tiger/TIGER2020/BG/tl_2020_{0}_bg.zip".format(fips_list[i])86 extract_dir = "./{0}/Census/tl_2020_{0}_bg/".format(abbrev_list[i])87 if not os.path.exists(extract_dir):88 os.makedirs(extract_dir)89 zip_path, _ = urllib.request.urlretrieve(url)90 with zipfile.ZipFile(zip_path, "r") as f:91 f.extractall(extract_dir)92 93 94t0 = time.time() 95# download 2010 blocks96# averages ~3.5 minutes per state97for i in range(42, len(abbrev_list)): # SD - VA98 print (abbrev_list[i], fips_list[i])99 100 url = "https://www2.census.gov/geo/tiger/TIGER2020/TABBLOCK/tl_2020_{0}_tabblock10.zip".format(fips_list[i])101 extract_dir = "./{0}/Census/tl_2020_{0}_tabblock10/".format(abbrev_list[i])102 if not os.path.exists(extract_dir):103 os.makedirs(extract_dir)104 105 zip_path, _ = urllib.request.urlretrieve(url)106 with zipfile.ZipFile(zip_path, "r") as f:107 f.extractall(extract_dir)108t1 = time.time()109elapsed = t1-t0 110t0 = time.time()111# for each state, project places and county subdivisions and state shapes to EPSG 3857 pseudo mercator112for i in range(41,50) :113 st_name = abbrev_list[i]114 st_fips = fips_list[i]115 116 print(st_name, st_fips)117 118 print("read files")119 state_dir = "D:/GIS data/{0}".format(abbrev_list[i])120 place = gpd.read_file("{0}/tl_2011_{1}_place.shp".format(state_dir,fips_list[i]))121 cousub = gpd.read_file("{0}/tl_2011_{1}_cousub.shp".format(state_dir,fips_list[i]))122 st_shape = gpd.read_file("{0}/{1}_TL_2019_STATE_proj.shp".format(state_dir, abbrev_list[i]))123 124 print("change crs")125 place = place.to_crs(st_shape.crs)126 cousub = cousub.to_crs(st_shape.crs)127 128 place.to_file("{0}/tl_2011_{1}_place_proj.shp".format(state_dir,fips_list[i]))129 cousub.to_file("{0}/tl_2011_{1}_cousub_proj.shp".format(state_dir,fips_list[i]))130 131 print("spatial difference")132 place_out = gpd.overlay(st_shape, place, how="difference")133 place_all = place.append(place_out)134 place_all.to_file("{0}/tl_2011_{1}_place_all.shp".format(state_dir,fips_list[i]))135 136t1 = time.time()137total = t1-t0138 ...
Automation Testing Tutorials
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