avg_election_votes = np.array(avg_election_voteshares)
total_dem_votes = np.array(total_dem_votes)
total_rep_votes = np.array(total_rep_votes)
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,
# ---- Racial Fairness Metrics -----
num_vra_districts = prop_partition["num_vra_districts"]
num_opp_districts = prop_partition["num_opp_districts"]
# 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,