def get_val_from_uc_db(query):
try:
result = sql.read_frame(query, conn)
return result.values[0][0]
except:
conn=psycopg2.connect(conn_string)
cur = conn.cursor()
sim.add_injectable('uc_conn', conn)
sim.add_injectable('uc_cur', cur)
result = sql.read_frame(query, conn)
return result.values[0][0]
def get_val_from_uc_db(query):
try:
result = sql.read_frame(query, conn)
return result.values[0][0]
except:
conn = psycopg2.connect(conn_string)
cur = conn.cursor()
sim.add_injectable('uc_conn', conn)
sim.add_injectable('uc_cur', cur)
result = sql.read_frame(query, conn)
return result.values[0][0]
def exec_sql_uc(query):
try:
cur.execute(query)
conn.commit()
except:
conn=psycopg2.connect(conn_string)
cur = conn.cursor()
sim.add_injectable('uc_conn', conn)
sim.add_injectable('uc_cur', cur)
cur.execute(query)
conn.commit()
def build_networks(parcels):
st = pd.HDFStore(os.path.join(misc.data_dir(), "osm_sandag.h5"), "r")
nodes, edges = st.nodes, st.edges
net = pdna.Network(nodes["x"], nodes["y"], edges["from"], edges["to"],
edges[["weight"]])
net.precompute(3000)
sim.add_injectable("net", net)
p = parcels.to_frame(parcels.local_columns)
p['node_id'] = net.get_node_ids(p['x'], p['y'])
sim.add_table("parcels", p)
def build_networks(parcels):
st = pd.HDFStore(os.path.join(misc.data_dir(), "osm_sandag.h5"), "r")
nodes, edges = st.nodes, st.edges
net = pdna.Network(nodes["x"], nodes["y"], edges["from"], edges["to"],
edges[["weight"]])
net.precompute(3000)
sim.add_injectable("net", net)
p = parcels.to_frame(parcels.local_columns)
p['node_id'] = net.get_node_ids(p['x'], p['y'])
sim.add_table("parcels", p)
def exec_sql_uc(query):
try:
cur.execute(query)
conn.commit()
except:
conn = psycopg2.connect(conn_string)
cur = conn.cursor()
sim.add_injectable('uc_conn', conn)
sim.add_injectable('uc_cur', cur)
cur.execute(query)
conn.commit()
def change_scenario(scenario):
assert scenario in sim.get_injectable("scenario_inputs"), \
"Invalid scenario name"
print "Changing scenario to '%s'" % scenario
sim.add_injectable("scenario", scenario)
def change_store(store_name):
sim.add_injectable(
"store",
pd.HDFStore(os.path.join(misc.data_dir(), store_name), mode="r"))
import urbansim.sim.simulation as sim
import os
import pandas as pd
from urbansim.utils import misc
sim.add_injectable("building_sqft_per_job", {
-1: 400,
1: 400,
2: 400,
3: 400,
4: 355,
5: 1161,
6: 470,
7: 661,
8: 960,
9: 825,
10: 445,
11: 445,
12: 383,
13: 383,
14: 383,
})
# this maps building type ids to general building types
# basically just reduces dimensionality
sim.add_injectable("building_type_map", {
1: "Residential",
2: "Residential",
3: "Residential",
def buildings_to_uc(buildings):
year = get_year()
# Export newly predicted buildings (from proforma or Sitespec) to Urban Canvas
b = buildings.to_frame(buildings.local_columns)
new_buildings = b[(b.note=='simulated') | (b.note.str.startswith('Sitespec'))]
new_buildings = new_buildings[new_buildings.year_built == year]
new_buildings = new_buildings.reset_index()
new_buildings = new_buildings.rename(columns = {'development_type_id':'building_type_id'})
new_buildings['building_sqft'] = new_buildings.residential_sqft + new_buildings.non_residential_sqft
new_buildings['sqft_per_unit'] = new_buildings.residential_sqft/new_buildings.residential_units
del new_buildings['res_price_per_sqft']
del new_buildings['nonres_rent_per_sqft']
new_buildings.parcel_id = new_buildings.parcel_id.astype('int32')
new_buildings.residential_units = new_buildings.residential_units.astype('int32')
new_buildings.non_residential_sqft = new_buildings.non_residential_sqft.astype('int32')
new_buildings.stories = new_buildings.stories.astype('int32')
new_buildings.residential_sqft = new_buildings.residential_sqft.astype('int32')
new_buildings.building_sqft = new_buildings.building_sqft.fillna(0).astype('int32')
new_buildings.sqft_per_unit = new_buildings.sqft_per_unit.fillna(0).astype('int32')
# Urban Canvas database connection
conn_string = "host='urbancanvas.cp2xwchuariu.us-west-2.rds.amazonaws.com' dbname='sandag_testing' user='******' password='******' port=5432"
if 'uc_conn' not in sim._INJECTABLES.keys():
conn=psycopg2.connect(conn_string)
cur = conn.cursor()
sim.add_injectable('uc_conn', conn)
sim.add_injectable('uc_cur', cur)
else:
conn = sim.get_injectable('uc_conn')
cur = sim.get_injectable('uc_cur')
def exec_sql_uc(query):
try:
cur.execute(query)
conn.commit()
except:
conn=psycopg2.connect(conn_string)
cur = conn.cursor()
sim.add_injectable('uc_conn', conn)
sim.add_injectable('uc_cur', cur)
cur.execute(query)
conn.commit()
def get_val_from_uc_db(query):
try:
result = sql.read_frame(query, conn)
return result.values[0][0]
except:
conn=psycopg2.connect(conn_string)
cur = conn.cursor()
sim.add_injectable('uc_conn', conn)
sim.add_injectable('uc_cur', cur)
result = sql.read_frame(query, conn)
return result.values[0][0]
max_bid = get_val_from_uc_db("select max(building_id) FROM building where building_id<100000000;")
new_buildings.building_id = np.arange(max_bid+1, max_bid+1+len(new_buildings))
if 'projects_num' not in sim._INJECTABLES.keys():
exec_sql_uc("INSERT INTO scenario(id, name, type) select nextval('scenario_id_seq'), 'Run #' || cast(currval('scenario_id_seq') as character varying), 1;")
nextval = get_val_from_uc_db("SELECT MAX(ID) FROM SCENARIO WHERE ID < 100000;")
sim.add_injectable('projects_num', nextval)
exec_sql_uc("INSERT INTO scenario_project(scenario, project) VALUES(%s, 1);" % nextval)
exec_sql_uc("INSERT INTO scenario_project(scenario, project) VALUES(%s, %s);" % (nextval,nextval))
else:
nextval = sim.get_injectable('projects_num')
nextval = '{' + str(nextval) + '}'
new_buildings['projects'] = nextval
valid_from = '{' + str(year) + '-1-1}'
new_buildings['valid_from'] = valid_from
print 'Exporting %s buildings to Urbancanvas database for project %s and year %s.' % (len(new_buildings),nextval,year)
output = cStringIO.StringIO()
new_buildings.to_csv(output, sep='\t', header=False, index=False)
output.seek(0)
cur.copy_from(output, 'building', columns =tuple(new_buildings.columns.values.tolist()))
conn.commit()
def buildings_to_uc(buildings):
year = get_year()
# Export newly predicted buildings (from proforma or Sitespec) to Urban Canvas
b = buildings.to_frame(buildings.local_columns)
new_buildings = b[(b.note == 'simulated') |
(b.note.str.startswith('Sitespec'))]
new_buildings = new_buildings[new_buildings.year_built == year]
new_buildings = new_buildings.reset_index()
new_buildings = new_buildings.rename(
columns={'development_type_id': 'building_type_id'})
new_buildings[
'building_sqft'] = new_buildings.residential_sqft + new_buildings.non_residential_sqft
new_buildings[
'sqft_per_unit'] = new_buildings.residential_sqft / new_buildings.residential_units
del new_buildings['res_price_per_sqft']
del new_buildings['nonres_rent_per_sqft']
new_buildings.parcel_id = new_buildings.parcel_id.astype('int32')
new_buildings.residential_units = new_buildings.residential_units.astype(
'int32')
new_buildings.non_residential_sqft = new_buildings.non_residential_sqft.astype(
'int32')
new_buildings.stories = new_buildings.stories.astype('int32')
new_buildings.residential_sqft = new_buildings.residential_sqft.astype(
'int32')
new_buildings.building_sqft = new_buildings.building_sqft.fillna(0).astype(
'int32')
new_buildings.sqft_per_unit = new_buildings.sqft_per_unit.fillna(0).astype(
'int32')
# Urban Canvas database connection
conn_string = "host='urbancanvas.cp2xwchuariu.us-west-2.rds.amazonaws.com' dbname='sandag_testing' user='******' password='******' port=5432"
if 'uc_conn' not in sim._INJECTABLES.keys():
conn = psycopg2.connect(conn_string)
cur = conn.cursor()
sim.add_injectable('uc_conn', conn)
sim.add_injectable('uc_cur', cur)
else:
conn = sim.get_injectable('uc_conn')
cur = sim.get_injectable('uc_cur')
def exec_sql_uc(query):
try:
cur.execute(query)
conn.commit()
except:
conn = psycopg2.connect(conn_string)
cur = conn.cursor()
sim.add_injectable('uc_conn', conn)
sim.add_injectable('uc_cur', cur)
cur.execute(query)
conn.commit()
def get_val_from_uc_db(query):
try:
result = sql.read_frame(query, conn)
return result.values[0][0]
except:
conn = psycopg2.connect(conn_string)
cur = conn.cursor()
sim.add_injectable('uc_conn', conn)
sim.add_injectable('uc_cur', cur)
result = sql.read_frame(query, conn)
return result.values[0][0]
max_bid = get_val_from_uc_db(
"select max(building_id) FROM building where building_id<100000000;")
new_buildings.building_id = np.arange(max_bid + 1,
max_bid + 1 + len(new_buildings))
if 'projects_num' not in sim._INJECTABLES.keys():
exec_sql_uc(
"INSERT INTO scenario(id, name, type) select nextval('scenario_id_seq'), 'Run #' || cast(currval('scenario_id_seq') as character varying), 1;"
)
nextval = get_val_from_uc_db(
"SELECT MAX(ID) FROM SCENARIO WHERE ID < 100000;")
sim.add_injectable('projects_num', nextval)
exec_sql_uc(
"INSERT INTO scenario_project(scenario, project) VALUES(%s, 1);" %
nextval)
exec_sql_uc(
"INSERT INTO scenario_project(scenario, project) VALUES(%s, %s);" %
(nextval, nextval))
else:
nextval = sim.get_injectable('projects_num')
nextval = '{' + str(nextval) + '}'
new_buildings['projects'] = nextval
valid_from = '{' + str(year) + '-1-1}'
new_buildings['valid_from'] = valid_from
print 'Exporting %s buildings to Urbancanvas database for project %s and year %s.' % (
len(new_buildings), nextval, year)
output = cStringIO.StringIO()
new_buildings.to_csv(output, sep='\t', header=False, index=False)
output.seek(0)
cur.copy_from(output,
'building',
columns=tuple(new_buildings.columns.values.tolist()))
conn.commit()
def get_run_no():
if 'run_no' not in sim.list_injectables():
sim.add_injectable("run_no", misc.get_run_number())
return sim.get_injectable("run_no")
def change_scenario(scenario):
assert scenario in sim.get_injectable("scenario_inputs"), \
"Invalid scenario name"
print "Changing scenario to '%s'" % scenario
sim.add_injectable("scenario", scenario)
def change_store(store_name):
sim.add_injectable("store",
pd.HDFStore(os.path.join(misc.data_dir(),
store_name), mode="r"))
import urbansim.sim.simulation as sim
import os
import pandas as pd
from urbansim.utils import misc
sim.add_injectable(
"building_sqft_per_job", {
-1: 400,
1: 400,
2: 400,
3: 400,
4: 355,
5: 1161,
6: 470,
7: 661,
8: 960,
9: 825,
10: 445,
11: 445,
12: 383,
13: 383,
14: 383,
})
# this maps building type ids to general building types
# basically just reduces dimensionality
sim.add_injectable(
"building_type_map", {
1: "Residential",
2: "Residential",
3: "Residential",
