def lambda_handler(event, context):
if 'body' in event.keys():
event = json.loads(event["body"])
bucket = "districtr"
state = event["state"].lower().replace(" ", "_")
units = event["units"].lower().replace(" ", "")
# district = event["dist_id"]
plan_assignment = event["assignment"]
keys = set(plan_assignment.keys())
key = "dual_graphs/{}_{}.json".format(state, units)
try:
data = s3.get_object(Bucket=bucket, Key=key)
g = json_graph.adjacency_graph(json.load(data['Body']))
graph = Graph(g)
assignment = {
n: plan_assignment[n] if n in keys else -1
for n in graph.nodes()
}
part = GeographicPartition(graph, assignment)
return {'statusCode': 200, 'body': json.dumps(plan_evaluation(part))}
except Exception as e:
print(e)
return {
"error":
"This state/units ({}, {}) is not supported".format(
event["state"], event["units"])
}
def set_up_graph(link):
"""
The function used to convert online json file to adjacency graph
Parameters:
link (string): the link for online json file
Returns:
Graph: graph with the information
mean: the mean value used for partition
"""
link = "https://people.csail.mit.edu/ddeford//COUNTY/COUNTY_13.json"
r = requests.get(link)
data = json.loads(r.content)
g = json_graph.adjacency_graph(data)
graph = Graph(g)
graph.issue_warnings()
horizontal = []
node_degree = []
# find the node with degree 1 or 2 and remove it
for node in graph.nodes():
graph.nodes[node]["pos"] = [
graph.node[node]['C_X'], graph.node[node]['C_Y']
]
horizontal.append(graph.node[node]['C_X'])
if graph.degree(node) == 1 or graph.degree(node) == 2:
node_degree.append(node)
# remove node with degree 1 or 2 since it will impact the outcome of graph
for i in node_degree:
graph.remove_node(i)
# calculate mean value for partition
mean = sum(horizontal) / len(horizontal)
return graph, mean
def graph_from_url(link):
r = requests.get(url=link)
data = json.loads(r.content)
g = json_graph.adjacency_graph(data)
graph = Graph(g)
graph.issue_warnings()
pos = {}
for node in graph.nodes():
pos[node] = [graph.node[node]['C_X'], graph.node[node]['C_Y']]
return graph
def graph_from_url_processing(link):
r = requests.get(url=link)
data = json.loads(r.content)
g = json_graph.adjacency_graph(data)
graph = Graph(g)
graph.issue_warnings()
for node in graph.nodes():
graph.nodes[node]["pos"] = [graph.nodes[node]['C_X'], graph.nodes[node]['C_Y'] ]
deg_one_nodes = []
for v in graph:
if graph.degree(v) == 1:
deg_one_nodes.append(v)
for node in deg_one_nodes:
graph.remove_node(node)
return graph
# return distance
# link = input("Put graph link: ")
# link = "https://people.csail.mit.edu/ddeford//COUNTY/COUNTY_23.json"
link = "https://people.csail.mit.edu/ddeford//COUNTY/COUNTY_13.json"
r = requests.get(link)
data = json.loads(r.content)
g = json_graph.adjacency_graph(data)
graph = Graph(g)
graph.issue_warnings()
horizontal = []
node_degree_1 = []
# for x in graph.nodes():
# graph.nodes[x]["pos"] = np.array(x[0], x[1])
for node in graph.nodes():
graph.nodes[node]["pos"] = [
graph.node[node]['C_X'], graph.node[node]['C_Y']
]
horizontal.append(graph.node[node]['C_X'])
if graph.degree(node) == 1 or graph.degree(node) == 2:
node_degree_1.append(node)
for i in node_degree_1:
graph.remove_node(i)
mean = sum(horizontal) / len(horizontal)
# m= 5
# graph = nx.grid_graph([m,m])
# graph.name = "grid_size:" + str(m)