def main(filename, zip_file, county, party):
# Get absentee
absentee = read_absentee_voter_file(filename)
if county:
absentee = absentee[absentee.county_desc == county]
prefix = county
else:
prefix = 'state'
# Only mail ballots
absentee = absentee[absentee.ballot_req_type == 'MAIL']
absentee.dropna(subset=['ballot_rtn_status'], inplace=True)
absentee \
= absentee[~absentee.ballot_rtn_status.str.contains('ACCEPTED') & (absentee.party == party)]
reasons = {}
for zip_code, group in absentee.groupby(by='voter_zip'):
reasons[zip_code] = group.groupby(
by='ballot_rtn_status').county_desc.count()
reasons = pd.DataFrame(reasons).T.fillna(0)
# Zipcodes
count_by_zip_code = absentee.groupby(by='voter_zip').county_desc.count()
zips = read_zip_codes(zip_file)
zips['total'] = count_by_zip_code
zips.dropna(subset=['total'], inplace=True)
bins = [0, 5, 20, 40, 60, 200]
zips['quantile'] \
= pd.cut(zips['total'], bins, duplicates='drop').astype('str')
zips = zips.join(reasons).fillna(0)
ts = pd.Timestamp.now().isoformat()
prefix = party.lower()
out = zips[['total', 'quantile', 'geometry'] +
list(reasons.columns.values)]
to_kml(out, f'limbo/{prefix}_rejected_zip_{ts}.kml')
def main(filename, zip_file, county, party):
# Get absentee
absentee = read_absentee_voter_file(filename)
if county:
absentee = absentee[absentee.county_desc == county]
prefix = county
else:
prefix = 'state'
zips = read_zip_codes(zip_file)
plot_mail_cancels(zips, absentee, party)
plot_mail_cancels_non_spoiled(zips, absentee, party)
plot_one_stop(zips, absentee, party)
def main(filename, registered_filename, party):
absentee = read_absentee_voter_file(filename)
accepted = absentee[(absentee.ballot_rtn_dt >= '2020-09-01')
& (absentee.ballot_rtn_status == 'ACCEPTED')
& (absentee.ballot_rtn_dt <= pd.datetime.now())]
accepted = accepted[accepted.voter_party_code == party]
by_precinct = get_accepted_by_precinct(accepted)
precinct_shapes = load_precincts_shapes()
registered = load_registered(registered_filename)
ts = pd.Timestamp.now().isoformat()
turnout = registered.join(by_precinct)
turnout['ACTIVE+VOTED(%)'] = 100. * turnout['ACTIVE'] / turnout['TOTAL']
ts = pd.Timestamp.now().isoformat()
print(f'limbo/turnout-per-precinct-{ts}.csv')
turnout.to_csv(f'limbo/turnout-per-precinct-{ts}.csv')
print(f'limbo/turnout-per-precinct-latest.csv')
turnout.to_csv(f'limbo/turnout-per-precinct-latest.csv')
def main(filename):
absentee = read_absentee_voter_file(filename)
abm = absentee[(absentee.ballot_req_type == 'MAIL')
& (absentee['ballot_req_dt'] >= '2020-09-04')]
abm['ballot_send_days'] = (abm['ballot_send_dt'] -
abm['ballot_req_dt']).dt.days
plt.rcParams["figure.figsize"] = [16, 10]
plt.rcParams["axes.titlesize"] = 16
plt.rcParams["axes.labelsize"] = 16
plt.rcParams["xtick.labelsize"] = 16
plt.rcParams["ytick.labelsize"] = 16
for county, results in abm.groupby(by='county_desc'):
name = county.lower()
figure = plot_request_send_latency(county, results)
print(f'assets/images/abm-latency/request-to-mail/{name}.png')
figure.savefig(f'assets/images/abm-latency/request-to-mail/{name}.png',
bbox_inches='tight')
plt.close(figure)
def main(filename):
absentee = read_absentee_voter_file(filename)
one_stop = absentee[absentee.ballot_req_type == 'ONE-STOP']
plt.rcParams["figure.figsize"] = [16, 10]
plt.rcParams["axes.titlesize"] = 16
plt.rcParams["axes.labelsize"] = 16
plt.rcParams["xtick.labelsize"] = 16
plt.rcParams["ytick.labelsize"] = 16
plot_state(one_stop)
for county, results in one_stop.groupby(by='county_desc'):
name = county.lower()
figure = plot_by_party(county, results)
print(f'assets/images/one-stop/county-party-totals/{name}.png')
figure.savefig(
f'assets/images/one-stop/county-party-totals/{name}.png',
bbox_inches='tight')
plt.close(figure)
figure = plot_by_race(county, results)
print(f'assets/images/one-stop/county-race-totals/{name}.png')
figure.savefig(f'assets/images/one-stop/county-race-totals/{name}.png',
bbox_inches='tight')
plt.close(figure)
figure = plot_by_age_group(county, results)
print(f'assets/images/one-stop/county-age-totals/{name}.png')
figure.savefig(f'assets/images/one-stop/county-age-totals/{name}.png',
bbox_inches='tight')
plt.close(figure)
figure = plot_one_stop_counts_by_week(county, results)
print(f'assets/images/one-stop/per-week-totals/{name}.png')
figure.savefig(f'assets/images/one-stop/per-week-totals/{name}.png',
bbox_inches='tight')
plt.close(figure)
def main(filename):
absentee = read_absentee_voter_file(filename)
accepted = absentee[absentee.ballot_rtn_status == 'ACCEPTED']
accepted = accepted[(accepted.ballot_rtn_dt >= '2020-09-01')
& (accepted.ballot_rtn_dt <= pd.datetime.now())]
plt.rcParams["figure.figsize"] = [16, 10]
plt.rcParams["axes.titlesize"] = 16
plt.rcParams["axes.labelsize"] = 16
plt.rcParams["xtick.labelsize"] = 16
plt.rcParams["ytick.labelsize"] = 16
plot_state(accepted)
for county, results in accepted.groupby(by='county_desc'):
name = county.lower()
figure = plot_accepted_types_by_week(county, results)
print(f'assets/images/accepted/type-per-week-totals/{name}.png')
figure.savefig(
f'assets/images/accepted/type-per-week-totals/{name}.png',
bbox_inches='tight')
plt.close(figure)
figure = plot_accepted_demographics_by_week(county, results)
print(f'assets/images/accepted/demographic-per-week-totals/{name}.png')
figure.savefig(
f'assets/images/accepted/demographic-per-week-totals/{name}.png',
bbox_inches='tight')
plt.close(figure)
figure = plot_accepted_demographics_cumulative(county, results)
print(
f'assets/images/accepted/demographic-cumulative-totals/{name}.png')
figure.savefig(
f'assets/images/accepted/demographic-cumulative-totals/{name}.png',
bbox_inches='tight')
plt.close(figure)