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Copy pathCollect_All_Catchment_Area.py
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Collect_All_Catchment_Area.py
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import argparse
from tqdm import tqdm
from glob import glob
import time
import os
from utils import write_bash_script
import subprocess
import pandas as pd
import geopandas as gpd
import pickle
# multi-processing
from joblib import Parallel, delayed
from datetime import datetime as dt
import shutil
from functools import partial
from typing import Union, List
from tqdm import tqdm
multi_processing = False
today = dt.today().strftime('%m-%d-%Y')
def gen_spatial_zip_file(cancer_center_name_abb, catchment_area_df, data_dictionary):
# make sure you have a folder "data" before calling this function
directory_path = os.path.join(os.getcwd(), cancer_center_name_abb) # folder that will contain csv files
ca_name = os.path.join(os.getcwd(), cancer_center_name_abb, cancer_center_name_abb) # will be used for file_name
os.mkdir(cancer_center_name_abb) #create the folder
cancer_center_fips = catchment_area_df.loc[catchment_area_df.name_short.eq(cancer_center_name_abb),:].FIPS.tolist()
cancer_center_state = catchment_area_df.loc[catchment_area_df.name_short.eq(cancer_center_name_abb),:].State.unique().tolist()
for table_name, df in data_dictionary.items():
if 'tract' in table_name:
df.FIPS = df.FIPS.str.zfill(11)
df = df.loc[df.FIPS.str[:5].isin(cancer_center_fips),:].reset_index(drop = True)
gdf = gpd.GeoDataFrame(df)
gdf = gdf.set_geometry('Shape')
gdf.to_file(ca_name + table_name + '_' + today)
del gdf
else: # county
df.FIPS = df.FIPS.str.zfill(5)
df = df.loc[df.FIPS.isin(cancer_center_fips),:].reset_index(drop = True)
gdf = gpd.GeoDataFrame(df)
gdf = gdf.set_geometry('Shape')
gdf.to_file(ca_name + table_name + '_' + today)
del gdf
shutil.make_archive(os.path.join(os.getcwd(), 'data', cancer_center_name_abb + '_shape'), 'zip', directory_path)
shutil.rmtree(directory_path)
return 1
def gen_zip_file(cancer_center_name_abb, catchment_area_df, data_dictionary):
# make sure you have a folder "data" before calling this function
directory_path = os.path.join(os.getcwd(), cancer_center_name_abb) # folder that will contain csv files
ca_name = os.path.join(os.getcwd(), cancer_center_name_abb, cancer_center_name_abb) # will be used for file_name
os.mkdir(cancer_center_name_abb) #create the folder
catchment_area_df = catchment_area_df.loc[catchment_area_df.name_short.eq(cancer_center_name_abb),:]
cancer_center_fips = catchment_area_df.FIPS.unique().tolist()
cancer_center_state = catchment_area_df.State.unique().tolist()
cdata = {}
for table_name, df in data_dictionary.items():
if 'facilities' in table_name:
cdata[table_name] = df.loc[df.State.isin(cancer_center_state),:]
elif 'tract' in table_name:
cdata[table_name] = df.loc[df.FIPS.str[:5].isin(cancer_center_fips),:]
elif 'shape' in table_name: # shape tables
county_shape = df['county_shape']; tract_shape = df['tract_shape']
county_shape = county_shape.loc[county_shape.FIPS.isin(cancer_center_fips),:].sort_values('FIPS').reset_index(drop = True)
tract_shape = tract_shape.loc[tract_shape.FIPS.str[:5].isin(cancer_center_fips),:].sort_values('FIPS').reset_index(drop = True)
county_shape = gpd.GeoDataFrame(county_shape); county_shape = county_shape.set_geometry('Shape')
tract_shape = gpd.GeoDataFrame(tract_shape); tract_shape = tract_shape.set_geometry('Shape')
cdata['county_shape'] = county_shape
cdata['tract_shape'] = tract_shape
else:
cdata[table_name] = df.loc[df.FIPS.str[:5].isin(cancer_center_fips),:]
cdata['county_shape'].to_file(ca_name + '_county_shape')
cdata['tract_shape'].to_file(ca_name + '_tract_shape')
cdata['cancer_incidence'].to_csv(ca_name + '_cancer_incidence_county_' + today + '.csv', encoding='utf-8', index=True)
cdata['cancer_mortality'].to_csv(ca_name + '_cancer_mortality_county_' + today + '.csv', encoding='utf-8', index=True)
cdata['cancer_incidence_long'].to_csv(ca_name + '_cancer_incidence_county_long_' + today + '.csv', encoding='utf-8', index=False)
cdata['cancer_mortality_long'].to_csv(ca_name + '_cancer_mortality_county_long_' + today + '.csv', encoding='utf-8', index=False)
cdata['economy_county'].to_csv(ca_name + '_economy_county_' + today + '.csv', encoding='utf-8', index=False)
cdata['economy_county_long'].to_csv(ca_name + '_economy_county_long_' + today + '.csv', encoding='utf-8', index=False)
cdata['economy_tract'].to_csv(ca_name + '_economy_tract_' + today + '.csv', encoding='utf-8', index=False)
cdata['economy_tract_long'].to_csv(ca_name + '_economy_tract_long_' + today + '.csv', encoding='utf-8', index=False)
cdata['environment_county'].to_csv(ca_name + '_environment_county_' + today + '.csv', encoding='utf-8', index=False)
cdata['environment_county_long'].to_csv(ca_name + '_environment_county_long_' + today + '.csv', encoding='utf-8', index=False)
cdata['environment_tract'].to_csv(ca_name + '_environment_tract_' + today + '.csv', encoding='utf-8', index=False)
cdata['environment_tract_long'].to_csv(ca_name + '_environment_tract_long_' + today + '.csv', encoding='utf-8', index=False)
cdata['ht_county'].to_csv(ca_name + '_housing_trans_county_' + today + '.csv', encoding='utf-8', index=False)
cdata['ht_county_long'].to_csv(ca_name + '_housing_trans_county_long_' + today + '.csv', encoding='utf-8', index=False)
cdata['ht_tract'].to_csv(ca_name + '_housing_trans_tract_' + today + '.csv', encoding='utf-8', index=False)
cdata['ht_tract_long'].to_csv(ca_name + '_housing_trans_tract_long_' + today + '.csv', encoding='utf-8', index=False)
cdata['rf_and_screening_county'].to_csv(ca_name + '_rf_and_screening_county_' + today + '.csv', encoding='utf-8', index=False)
cdata['rf_and_screening_tract'].to_csv(ca_name + '_rf_and_screening_tract_' + today + '.csv', encoding='utf-8', index=False)
cdata['rf_and_screening_county_long'].to_csv(ca_name + '_rf_and_screening_county_long_' + today + '.csv', encoding='utf-8', index=False)
cdata['rf_and_screening_tract_long'].to_csv(ca_name + '_rf_and_screening_tract_long_' + today + '.csv', encoding='utf-8', index=False)
cdata['sociodemographics_county'].to_csv(ca_name + '_sociodemographics_county_' + today + '.csv', encoding='utf-8', index=False)
cdata['sd_county_long'].to_csv(ca_name + '_sociodemographics_county_long_' + today + '.csv', encoding='utf-8', index=False)
cdata['sociodemographics_tract'].to_csv(ca_name + '_sociodemographics_tract_' + today + '.csv', encoding='utf-8', index=False)
cdata['sd_tract_long'].to_csv(ca_name + '_sociodemographics_tract_long_' + today + '.csv', encoding='utf-8', index=False)
# cdata['broadband_speeds'].to_csv(ca_name + '_broadband_speeds_' + today + '.csv', encoding='utf-8', index=False)
cdata['facilities_and_providers'].to_csv(ca_name + '_facilities_and_providers_' + today + '.csv', encoding='utf-8', index=False)
shutil.make_archive(os.path.join(os.getcwd(), 'data', cancer_center_name_abb), 'zip', directory_path)
shutil.rmtree(directory_path)
return 1
def gen_unique_states(fips):
states = fips.str[:2].unique().tolist()
return states
def gen_shapes(fips):
states = gen_unique_states(fips)
if __name__ == '__main__':
bash_script_kwargs = {
"bash_file_name" : 'all_catchment_areas.sh', #the name of a bash file to run
"catchment_area_name": "all", # the name of the catchment area name
"ca_file_path": "all_catchment_areas.csv",
"query_level" : ['county','tract'],
"acs_year" : 2021,
"download_file_type": ['pickle'],
"census_api_key": 'f1a4c4de1f35fe90fc1ceb60fd97b39c9a96e436',
"generate_zip_file" : False,
"install_packages" : False, # We already installed required packages above,
"socrata_user_name": "[email protected]",
"socrata_password" : "MarkeyCancer123!"
}
write_bash_script(**bash_script_kwargs)
subprocess.run(["bash", "all_catchment_areas.sh"])
ca_path = glob('*/all_catchment_areas.csv')[0]
ca = pd.read_csv(ca_path, dtype = {"FIPS":str})
ca['FIPS'] = ca.FIPS.str.zfill(5)
pickle_path = [x for x in glob('*/all_catchment_data*.pickle') if 'spatial' not in x][0]
with open(pickle_path, 'rb') as f:
data_dictionary = pickle.load(f)
if os.path.exists(os.path.join(os.getcwd(), 'data')):
pass
else:
os.mkdir('data')
gen_zip_file_partial = partial(gen_zip_file, catchment_area_df = ca, data_dictionary = data_dictionary)
if multi_processing:
Parallel(n_jobs = -1)(delayed(gen_zip_file_partial)(abb) for abb in tqdm(ca.name_short.unique().tolist()))
else:
for abb in tqdm(ca.name_short.unique().tolist()):
gen_zip_file_partial(abb)
os.remove('cif_raw_data.pickle');
os.remove('all_catchment_areas.sh')