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production.py
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production.py
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import math
import os.path
import re
from datetime import date
import pandas as pd
import lac_covid19.const as const
from lac_covid19.daily_pr.update import query_date, update_ts
from lac_covid19.current_stats.scrape import query_live
from lac_covid19.current_stats.citations import CITATIONS
from lac_covid19.population import CSA as CSA_POPULATION
from lac_covid19.daily_pr.time_series import generate_all_ts
from lac_covid19.geo.csa import CSA_BLANK, CSA_REGION_MAP, CSA_OBJECTID_MAP
import lac_covid19.geo.geocoder as geocoder
tz_offset = pd.to_timedelta(7, unit='hours')
DIR_DOCS, DIR_EXPORT = [os.path.join(os.path.dirname(__file__), x)
for x in ('docs', 'export')]
DIR_ARCGIS_UPLOAD, DIR_ARCGIS_APPEND = [os.path.join(DIR_EXPORT, f'arcgis-{x}')
for x in ('upload', 'append')]
DIR_TS, DIR_LIVE = [os.path.join(DIR_DOCS, x) for x in ('time-series', 'live')]
def datetime_input(obj):
if isinstance(obj, pd.Timestamp):
return obj
elif isinstance(obj, str):
return pd.to_datetime(obj)
return pd.to_datetime(date.today())
def choropleth_colors(df_area_day, col, lower, upper):
if df_area_day is None:
df_area_day = generate_all_ts()[const.AREA]
df_area_day = df_area_day[
df_area_day[const.DATE]==df_area_day[const.DATE].max()
]
print(f'{col}: {lower}->{df_area_day[col].quantile(lower).round(1)} / '
f'{upper}->{df_area_day[col].quantile(upper).round(1)}')
def area_data(df_area_live, df_area_ts): #, lower=0.05, upper=0.95):
# Get 14 day average of new cases from area time series
df_area_recent = df_area_ts.loc[
df_area_ts[const.DATE] == df_area_ts[const.DATE].max(),
[const.AREA, const.NEW_CASES_14_DAY_AVG,
const.NEW_CASES_14_DAY_AVG_PER_CAPITA]
].copy()
df_area = df_area_live.merge(df_area_recent, 'left', const.AREA)
# Drop City of Los Angeles
df_area = (df_area[df_area[const.AREA]!=const.LOS_ANGELES]
.reset_index(drop=True).copy())
# Add region and population data
df_area[const.REGION] = (df_area[const.AREA].apply(CSA_REGION_MAP.get)
.convert_dtypes())
df_area[const.POPULATION] = df_area[const.AREA].apply(CSA_POPULATION.get)
# Reorder columns and export
df_area = df_area[
[const.AREA, const.REGION, const.POPULATION, const.CF_OUTBREAK,
const.CASES, const.CASE_RATE,
const.NEW_CASES_14_DAY_AVG, const.NEW_CASES_14_DAY_AVG_PER_CAPITA,
const.DEATHS, const.DEATH_RATE]
]
df_area.to_csv(os.path.join(DIR_LIVE, 'area.csv'), index=False)
return df_area
def arcgis_map(df_area, lower=0.05, upper=0.95):
df_geo = CSA_BLANK.merge(df_area, on=const.AREA)
# Fix GeoPandas GeoJSON driver unable to handle Float64
for col in(const.NEW_CASES_14_DAY_AVG,
const.NEW_CASES_14_DAY_AVG_PER_CAPITA):
df_geo[col] = df_geo[col].astype('float')
# Put area data into a geojson
filename = 'csa-live-map'
df_geo.to_file(os.path.join(DIR_ARCGIS_UPLOAD, f'{filename}.geojson'),
driver='GeoJSON')
# Create append file
df_append = df_area.copy()
df_append[const.OBJECTID] = df_append[const.AREA].apply(CSA_OBJECTID_MAP.get)
df_append = df_append.drop(columns=[const.AREA, const.REGION,
const.POPULATION, const.CF_OUTBREAK])
df_append.to_csv(os.path.join(DIR_ARCGIS_APPEND, f'{filename}.csv'),
index=False)
# Choropleth suggestions
def choropleth_suggestions(column, lower=lower, upper=upper):
return choropleth_colors(df_geo, column, lower, upper)
choropleth_suggestions(const.NEW_CASES_14_DAY_AVG_PER_CAPITA)
choropleth_suggestions(const.CASE_RATE)
choropleth_suggestions(const.DEATH_RATE, 0.1, 0.9)
def arcgis_live_vaccinated(df_vaccinated):
df = CSA_BLANK.merge(df_vaccinated, on=const.AREA)
df = df.drop(columns=[const.VACCINATED_PEOPLE, const.VACCINATED_PERCENT])
filename = 'csa-vaccinated'
df.to_file(os.path.join(DIR_ARCGIS_UPLOAD, f'{filename}.geojson'),
driver='GeoJSON')
def arcgis_live_map_version_two(df_area, df_vaccinated):
df = CSA_BLANK.merge(df_vaccinated, on=const.AREA, how='left')
# df = CSA_BLANK.merge(pd.merge(df_area, df_vaccinated, on=const.AREA), on=const.AREA)
filename = 'csa-vaccinated'
df.to_file(os.path.join(DIR_ARCGIS_UPLOAD, f'{filename}.geojson'),
driver='GeoJSON')
def arcgis_csa_days_back(df_area):
reporting_areas = len(df_area[const.AREA].unique())
days_back = math.ceil(50_000 / reporting_areas)
return df_area[const.DATE].max() - pd.Timedelta(days_back, 'days')
def arcgis_csa_ts(df_area, append_date=None):
df_area = df_area.loc[
((df_area[const.AREA] != const.LOS_ANGELES)
& (df_area[const.DATE] >= arcgis_csa_days_back(df_area))),
[const.DATE, const.AREA, const.CASES, const.NEW_CASES]
].copy()
df_area[const.REGION] = df_area[const.AREA].apply(CSA_REGION_MAP.get)
df_area = df_area[[const.DATE, const.AREA, const.REGION,
const.CASES, const.NEW_CASES]]
filename = 'csa-ts.csv'
df_area.to_csv(os.path.join(DIR_ARCGIS_UPLOAD, filename), index=False)
if append_date is not None:
df_area = df_area[df_area[const.DATE]>=datetime_input(append_date)]
# Correct for ArcGIS append timezone change
df_area[const.DATE] = df_area[const.DATE].apply(lambda x: x + tz_offset)
df_area.to_csv(os.path.join(DIR_ARCGIS_APPEND, filename), index=False)
def arcgis_region_ts(df_region, append_date=None):
filename = 'region-ts.csv'
df_region.to_csv(os.path.join(DIR_ARCGIS_UPLOAD, filename), index=False)
if append_date is not None:
df_region = df_region[
df_region[const.DATE]>=datetime_input(append_date)
].copy()
# Correct for ArcGIS append timezone change
df_region[const.DATE] = df_region[const.DATE].apply(
lambda x: x + tz_offset
)
df_region.to_csv(os.path.join(DIR_ARCGIS_APPEND, filename), index=False)
def arcgis_aggregate_ts(df_aggregate, append_date=None):
filename = 'aggregate-ts.csv'
df_aggregate.to_csv(os.path.join(DIR_ARCGIS_UPLOAD, filename), index=False)
if append_date is not None:
df_aggregate = df_aggregate[
df_aggregate[const.DATE]>=datetime_input(append_date)
].copy()
df_aggregate[const.DATE] = df_aggregate[const.DATE].apply(
lambda x: x+tz_offset
)
df_aggregate.to_csv(os.path.join(DIR_ARCGIS_APPEND, filename),
index=False)
def arcgis_region_snapshot(df_region):
df_region.loc[
df_region[const.DATE]==df_region[const.DATE].max(),
[const.REGION, const.CASES_PER_CAPITA,
const.NEW_CASES_14_DAY_AVG_PER_CAPITA]
].to_csv(os.path.join(DIR_ARCGIS_UPLOAD, 'regions-snapshot.csv'),
index=False)
def arcgis_age_snapshot(df_age):
df_age.loc[
df_age[const.DATE]==df_age[const.DATE].max(),
[const.AGE_GROUP, const.CASES_PER_CAPITA,
const.NEW_CASES_14_DAY_AVG_PER_CAPITA]
].to_csv(os.path.join(DIR_ARCGIS_UPLOAD, 'age-groups-snapshot.csv'))
def apply_coordinates(df):
df = df.copy()
df[const.COORDINATES] = df[const.ADDRESS].apply(geocoder.lookup_address)
df[const.LATITUDE] = df[const.COORDINATES].apply(lambda x: x[0])
df[const.LONGITUDE] = df[const.COORDINATES].apply(lambda x: x[1])
return df.drop(columns=const.COORDINATES)
def arcgis_live_non_res(df_non_res):
apply_coordinates(df_non_res).to_csv(
os.path.join(DIR_ARCGIS_UPLOAD, 'non-residential-outbreaks.csv'),
index=False)
def arcgis_live_edu(df_education):
df_education = df_education[
df_education[const.ADDRESS].apply(lambda x: x.upper())
!= 'LOS ANGELES, CA'
]
apply_coordinates(df_education).to_csv(
os.path.join(DIR_ARCGIS_UPLOAD, 'education-outbreaks.csv'), index=False)
def arcgis_citations():
citation_counts = CITATIONS.value_counts([const.NAME, const.ADDRESS])
df = (
CITATIONS.drop_duplicates('Name')
.rename(columns={const.DATE: 'Last Citation'}).copy()
)
df['Category'] = df['Description'].apply(
lambda x: re.match('[^(]+', x).group(0).rstrip())
df[const.NUM_CITATIONS] = df.apply(
lambda x: citation_counts.loc[(x[const.NAME], x[const.ADDRESS])],
axis='columns'
)
apply_coordinates(df).to_csv(
os.path.join(DIR_ARCGIS_UPLOAD, 'citations.csv'), index=False
)
def export_time_series(ts_dict):
for key in ts_dict:
filename = f"{key.lower().replace('/', '-')}-ts.csv"
ts_dict[key].to_csv(os.path.join(DIR_TS, filename), index=False)
def export_live(live_dict):
for key in live_dict:
if key != const.AREA:
filename = f"{key.lower().replace(' ', '-')}.csv"
live_dict[key].to_csv(os.path.join(DIR_LIVE, filename),
index=False)
def publish(date_=None, update_live=True, ts_cache=False, live_cache=False):
df_area_live = None
if update_live:
live_dict = query_live(live_cache)
export_live(live_dict)
df_area_live = live_dict[const.AREA]
geocoder.prep_addresses()
arcgis_live_non_res(live_dict[const.NON_RESIDENTIAL])
arcgis_live_edu(live_dict[const.EDUCATION])
arcgis_citations()
if ts_cache:
ts_dict = generate_all_ts()
else:
ts_dict = update_ts()
export_time_series(ts_dict)
if date_ is None:
date_ = date.today()
df_area_ts = ts_dict[const.AREA]
df_area = area_data(df_area_live, df_area_ts)
arcgis_map(df_area)
arcgis_csa_ts(ts_dict[const.AREA], date_)
# arcgis_region_ts(ts_dict[const.REGION], date)
# arcgis_aggregate_ts(ts_dict[const.AGGREGATE], date_)
arcgis_region_snapshot(ts_dict[const.REGION])
arcgis_age_snapshot(ts_dict[const.AGE_GROUP])
if __name__ == "__main__":
if False:
ts_dict = generate_all_ts()
df_area_ts = ts_dict[const.AREA]
df_area_live = query_live()[const.AREA]
df_area = area_data(df_area_live, df_area_ts)
# df_region = ts_dict[const.REGION]
# df_age = ts_dict[const.AGE_GROUP]
# df_aggregate = ts_dict[const.AGGREGATE]