SHAPEFILE_PATH = "./PA_VTD.json"
POPULATION_COL = "TOT_POP"
ASSIGNMENT_COL = "538GOP_PL"

# Initialize graph and partition
graph = BaseGraph(SHAPEFILE_PATH, POPULATION_COL, ASSIGNMENT_COL)
partition = Partition(graph, ASSIGNMENT_COL)

# Define parameters of chain (number of steps and population constraint)
pop_constraint = PopulationConstraint(graph, POPULATION_COL, 0.02)
num_steps = 10

# Initialize Election of interest
election = Election("SEN10", ["SEN10D", "SEN10R"], graph.num_dists)
# Define election-related metrics and scores
election_metrics = [
    vote_count("count_d", election, "SEN10D"),
    efficiency_gap("efficiency_gap", election, "SEN10D"),
    seats_won("seats_won", election, "SEN10D"),
    mean_median("mean_median", election, "SEN10D")
]
scores = [
        DistrictAggregate("presd", "PRES12D"),
        ElectionTracker(election, election_metrics)
]

# Run the chain
println("Running 10-step ReCom chain...")
chain_data = recom_chain(graph, partition, pop_constraint, num_steps, scores)

# Get values of all scores at the 10th state of the chain 
score_dict = get_scores_at_step(chain_data, 10)
# Get all vote counts for each state of the chain
vote_counts_arr = get_score_values(chain_data, "count_d")