def test_single_flip_contiguity_equals_contiguity():
import random
random.seed(1887)
def equality_validator(partition):
val = partition["contiguous"] == partition["flip_check"]
assert val
return partition["contiguous"]
df = gp.read_file("rundmcmc/testData/mo_cleaned_vtds.shp")
with open("rundmcmc/testData/MO_graph.json") as f:
graph_json = json.load(f)
graph = networkx.readwrite.json_graph.adjacency_graph(graph_json)
assignment = get_assignment_dict(df, "GEOID10", "CD")
validator = Validator([equality_validator])
updaters = {"contiguous": contiguous, "cut_edges": cut_edges,
"flip_check": single_flip_contiguous}
initial_partition = Partition(graph, assignment, updaters)
accept = lambda x: True
chain = MarkovChain(propose_random_flip, validator, accept, initial_partition, total_steps=100)
list(chain)
def example_partition():
df = gp.read_file(os.path.join(TEST_DATA_PATH, "mo_cleaned_vtds.shp"))
with open(os.path.join(TEST_DATA_PATH, "MO_graph.json")) as f:
graph_json = json.load(f)
graph = networkx.readwrite.json_graph.adjacency_graph(graph_json)
assignment = get_assignment_dict(df, "GEOID10", "CD")
add_data_to_graph(
df,
graph, ['PR_DV08', 'PR_RV08', 'POP100', 'ALAND10', 'COUNTYFP10'],
id_col='GEOID10')
updaters = {
**votes_updaters(['PR_DV08', 'PR_RV08'], election_name='08'), 'population':
Tally('POP100', alias='population'),
'counties':
county_splits('counties', 'COUNTYFP10'),
'cut_edges':
cut_edges,
'cut_edges_by_part':
cut_edges_by_part
}
return Partition(graph, assignment, updaters)
def main():
# Sketch:
# 1. Load dataframe.
# 2. Construct neighbor information.
# 3. Make a graph from this.
# 4. Throw attributes into graph.
df = gp.read_file("./testData/mo_cleaned_vtds.shp")
graph = networkx.readwrite.read_gpickle('example_graph.gpickle')
add_data_to_graph(df, graph, ["PR_DV08", "PR_RV08", "P_08"], "GEOID10")
assignment = get_assignment_dict(df, "GEOID10", "CD")
updaters = {
'd_votes': statistic_factory('PR_DV08', alias='d_votes'),
'r_votes': statistic_factory('PR_RV08', alias='r_votes'),
'cut_edges': cut_edges
}
initial_partition = Partition(graph, assignment, updaters)
validator = Validator([contiguous])
accept = lambda x: True
chain = MarkovChain(propose_random_flip,
validator,
accept,
initial_partition,
total_steps=100)
mm = []
mt = []
#eg=[]
for state in chain:
mm.append(
mean_median2(state, data_column1='d_votes',
data_column2='r_votes'))
mt.append(
mean_thirdian2(state,
data_column1='d_votes',
data_column2='r_votes'))
#eg.append(efficiency_gap(state, data_column1='d_votes',data_column2='r_votes))
#print(graph.nodes(data=True))
mm_outs = [mm] #,eg]
mt_outs = [mt]
#eg_outs=[eg]
with open('mm_chain_out', "w") as output:
writer = csv.writer(output, lineterminator='\n')
writer.writerows(mm_outs)
with open('mt_chain_out', "w") as output:
writer = csv.writer(output, lineterminator='\n')
writer.writerows(mt_outs)
def example_partition():
df = gp.read_file("./testData/mo_cleaned_vtds.shp")
with open("./testData/MO_graph.json") as f:
graph_json = json.load(f)
graph = networkx.readwrite.json_graph.adjacency_graph(graph_json)
assignment = get_assignment_dict(df, "GEOID10", "CD")
add_data_to_graph(
df,
graph, ['PR_DV08', 'PR_RV08', 'POP100', 'ALAND10', 'COUNTYFP10'],
id_col='GEOID10')
updaters = {
**votes_updaters(['PR_DV08', 'PR_RV08'], election_name='08'), 'population':
Tally('POP100', alias='population'),
'areas':
Tally('ALAND10', alias='areas'),
'counties':
county_splits('counties', 'COUNTYFP10'),
'perimeters':
perimeters,
'exterior_boundaries':
exterior_boundaries,
'boundary_nodes':
boundary_nodes,
'polsby_popper':
polsby_popper,
'cut_edges':
cut_edges,
'cut_edges_by_part':
cut_edges_by_part
}
return Partition(graph, assignment, updaters)