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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  ...

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