for i in election_names:
vars()["{}part".format(i)] = np.array(part[i].percents("Dem"))
vars()["{}".format(i)].append(sorted(vars()["{}part".format(i)]))
vars()["{}wins".format(i)].append(part[i].wins("Dem"))
vars()["{}comp".format(i)].append(
subset_list(vars()["{}part".format(i)], comp_lower, comp_higher))
accumulating_election = accumulating_election + vars()["{}part".format(
i)]
avg = accumulating_election / num_elections
AVG.append(sorted(avg))
avgwins.append(subset_list(avg, 0.5, 1))
avgcomp.append(subset_list(avg, comp_lower, comp_higher))
avg_elect = avg[0]
PB.append(gm.partisan_bias(avg_elect))
MM.append(gm.mean_median(avg_elect))
dem_vote_wins = avg_elect[avg_elect > 0.5]
rep_vote_wins = avg_elect[avg_elect < 0.5]
DemWinPerc.append(np.average(dem_vote_wins))
RepWinPerc.append(1 - np.average(rep_vote_wins))
print("end")
time_elapsed = (time.time() - start_time) / 60
print("--- %s minutes ---" % time_elapsed)
act_accumulating_election = np.zeros((1, num_dist))
for i in election_names:
vars()["{}winsfreq".format(i)] = {
value: len(list(freq))
for value, freq in groupby(sorted(vars()["{}wins".format(i)]))
}
dem_voteshares_elections = total_dem_votes / (total_dem_votes +
total_rep_votes)
dem_voteshare = np.average(dem_voteshares_elections, axis=0)
# ---- Partisan Fairness Metrics ----
dem_voteshares = avg_election_votes[avg_election_votes > 0.5]
rep_voteshares = avg_election_votes[avg_election_votes < 0.5]
avg_dem_win = np.average(dem_voteshares)
avg_rep_win = 1 - np.average(rep_voteshares)
# EG assumes equal turnout -- gets a different result than gerrychain
EG = gm.EG(avg_election_votes)
partisan_bias = gm.partisan_bias(avg_election_votes)
mean_median = gm.mean_median(avg_election_votes)
avgactWins = subset_list(avg_election_votes, 0.5, 1)
# ---- Competitiveness -----
competitive_vote_shares = avg_election_dict
avgactcomp = subset_list(avg_election_votes, comp_lower, comp_higher)
# ---- Export to JSON ------
plan = {
"state": state,
"planName": district_assignment_col,
"numDists": num_dist,
"countySplits": num_splits,
"lowerBoundCountySplits": lower_county_splits,
"upperBoundCountySplits": upper_county_splits,
# AVG Election vote shares
vote_shares = [
np.array(sorted(prop_partition[election_names[i]].percents("Dem")))
for i in range(num_elections)
]
avg_election = np.average(vote_shares, axis=0)
# ---- Partisan Fairness Metrics ----
# EG assumes equal turnout -- gets a different result than gerrychain
EG = gm.EG(avg_election)
partisan_bias = gm.partisan_bias(avg_election)
mean_median = gm.mean_median(avg_election)
# ---- Competitiveness -----
competitive_vote_shares = avg_election
# ---- Export to JSON ------
chain_obj = {
"numDists": num_dist,
"countySplits": num_county_splits,
"lowerBoundCountySplits": lower_county,
"upperBoundCountySplits": upper_county,
"minPolsby-Popper": min_pp,
"avgPolsby-Popper": avg_pp,
"minReock": min_reock,
"avgReock": avg_reock,