def assign_matrix(self, matrix: AequilibraeMatrix, result_name: str):
conn = database_connection()
sql = f"select link_id, direction, a_node, b_node, distance, 1 capacity from {DELAUNAY_TABLE}"
df = pd.read_sql(sql, conn)
centroids = np.array(np.unique(np.hstack((df.a_node.values, df.b_node.values))), int)
g = Graph()
g.mode = 'delaunay'
g.network = df
g.prepare_graph(centroids)
g.set_blocked_centroid_flows(True)
tc = TrafficClass('delaunay', g, matrix)
ta = TrafficAssignment()
ta.set_classes([tc])
ta.set_time_field('distance')
ta.set_capacity_field('capacity')
ta.set_vdf('BPR')
ta.set_vdf_parameters({"alpha": 0, "beta": 1.0})
ta.set_algorithm('all-or-nothing')
ta.execute()
report = {"setup": str(ta.info())}
data = [result_name, "Delaunay assignment", self.procedure_id, str(report), ta.procedure_date, '']
conn.execute("""Insert into results(table_name, procedure, procedure_id, procedure_report, timestamp,
description) Values(?,?,?,?,?,?)""", data)
conn.commit()
conn.close()
cols = []
for x in matrix.view_names:
cols.extend([f'{x}_ab', f'{x}_ba', f'{x}_tot'])
df = ta.results()[cols]
conn = sqlite3.connect(join(environ[ENVIRON_VAR], "results_database.sqlite"))
df.to_sql(result_name, conn)
conn.close()
def build_graphs(self, fields: list = None, modes: list = None) -> None:
"""Builds graphs for all modes currently available in the model
When called, it overwrites all graphs previously created and stored in the networks'
dictionary of graphs
Args:
*fields* (:obj:`list`, optional): When working with very large graphs with large number of fields in the
database, it may be useful to specify which fields to use
*modes* (:obj:`list`, optional): When working with very large graphs with large number of fields in the
database, it may be useful to generate only those we need
To use the *fields* parameter, a minimalistic option is the following
::
p = Project()
p.open(nm)
fields = ['distance']
p.network.build_graphs(fields, modes = ['c', 'w'])
"""
curr = self.conn.cursor()
if fields is None:
curr.execute("PRAGMA table_info(links);")
field_names = curr.fetchall()
ignore_fields = ["ogc_fid", "geometry"]
all_fields = [
f[1] for f in field_names if f[1] not in ignore_fields
]
else:
fields.extend(
["link_id", "a_node", "b_node", "direction", "modes"])
all_fields = list(set(fields))
if modes is None:
modes = curr.execute("select mode_id from modes;").fetchall()
modes = [m[0] for m in modes]
elif isinstance(modes, str):
modes = [modes]
sql = f"select {','.join(all_fields)} from links"
df = pd.read_sql(sql, self.conn).fillna(value=np.nan)
valid_fields = list(df.select_dtypes(np.number).columns) + ["modes"]
curr.execute(
"select node_id from nodes where is_centroid=1 order by node_id;")
centroids = np.array([i[0] for i in curr.fetchall()], np.uint32)
data = df[valid_fields]
for m in modes:
net = pd.DataFrame(data, copy=True)
net.loc[~net.modes.str.contains(m),
"b_node"] = net.loc[~net.modes.str.contains(m), "a_node"]
g = Graph()
g.mode = m
g.network = net
g.prepare_graph(centroids)
g.set_blocked_centroid_flows(True)
self.graphs[m] = g
