def network_aggregations_small(netsmall):
"""
This will be turned into a network aggregation template.
"""
nodessmall = networks.from_yaml(netsmall,
'network_aggregations_small.yaml')
nodessmall = nodessmall.fillna(0)
# new variables
print('compute additional aggregation variables')
nodessmall['pop_jobs_ratio_10000'] = (nodessmall['pop_10000'] /
(nodessmall['jobs_10000'])).fillna(0)
nodessmall['pop_jobs_ratio_25000'] = (nodessmall['pop_25000'] /
(nodessmall['jobs_25000'])).fillna(0)
# fill inf and nan with median
nodessmall['pop_jobs_ratio_10000'] = nodessmall[
'pop_jobs_ratio_10000'].replace([np.inf, -np.inf], np.nan).fillna(
nodessmall['pop_jobs_ratio_10000'].median)
nodessmall['pop_jobs_ratio_25000'] = nodessmall[
'pop_jobs_ratio_25000'].replace([np.inf, -np.inf], np.nan).fillna(
nodessmall['pop_jobs_ratio_25000'].median)
# end of addition
print(nodessmall.describe())
orca.add_table('nodessmall', nodessmall)
def price_vars(net):
nodes2 = networks.from_yaml(net["walk"], "price_vars.yaml")
nodes2 = nodes2.fillna(0)
print nodes2.describe()
nodes = orca.get_table('nodes')
nodes = nodes.to_frame().join(nodes2)
orca.add_table("nodes", nodes)
def price_vars(net):
nodes2 = networks.from_yaml(net, "price_vars.yaml")
nodes2 = nodes2.fillna(0)
print nodes2.describe()
nodes = sim.get_table("nodes")
nodes = nodes.to_frame().join(nodes2)
sim.add_table("nodes", nodes)
def price_vars(net):
nodes2 = networks.from_yaml(net["walk"], "price_vars.yaml")
nodes2 = nodes2.fillna(0)
print nodes2.describe()
nodes = orca.get_table('nodes')
nodes = nodes.to_frame().join(nodes2)
orca.add_table("nodes", nodes)
def neighborhood_vars(net):
nodes = networks.from_yaml(net["walk"], "neighborhood_vars.yaml")
nodes = nodes.replace(-np.inf, np.nan)
nodes = nodes.replace(np.inf, np.nan)
nodes = nodes.fillna(0)
print nodes.describe()
orca.add_table("nodes", nodes)
def neighborhood_vars(net):
nodes = networks.from_yaml(net["walk"], "neighborhood_vars.yaml")
nodes = nodes.replace(-np.inf, np.nan)
nodes = nodes.replace(np.inf, np.nan)
nodes = nodes.fillna(0)
print nodes.describe()
orca.add_table("nodes", nodes)
def network_aggregations_beam(netbeam):
"""
This will be turned into a network aggregation template.
"""
nodesbeam = networks.from_yaml(netbeam, 'network_aggregations_beam.yaml')
nodesbeam = nodesbeam.fillna(0)
print(nodesbeam.describe())
orca.add_table('nodesbeam', nodesbeam)
def network_aggregations_small(netsmall):
"""
This will be turned into a network aggregation template.
"""
nodessmall = networks.from_yaml(
netsmall, 'network_aggregations_small.yaml')
nodessmall = nodessmall.fillna(0)
print(nodessmall.describe())
orca.add_table('nodessmall', nodessmall)
def network_aggregations_walk(netwalk):
"""
This will be turned into a network aggregation template.
"""
nodeswalk = networks.from_yaml(netwalk, 'network_aggregations_walk.yaml')
nodeswalk = nodeswalk.fillna(0)
print(nodeswalk.describe())
orca.add_table('nodeswalk', nodeswalk)
def regional_vars(net):
nodes = networks.from_yaml(net["drive"], "regional_vars.yaml")
nodes = nodes.fillna(0)
nodes2 = pd.read_csv('data/regional_poi_distances.csv',
index_col="tmnode_id")
nodes = pd.concat([nodes, nodes2], axis=1)
print nodes.describe()
orca.add_table("tmnodes", nodes)
def regional_vars(net):
nodes = networks.from_yaml(net["drive"], "regional_vars.yaml")
nodes = nodes.fillna(0)
nodes2 = pd.read_csv('data/regional_poi_distances.csv',
index_col="tmnode_id")
nodes = pd.concat([nodes, nodes2], axis=1)
print nodes.describe()
orca.add_table("tmnodes", nodes)
def neighborhood_vars(net):
nodes = networks.from_yaml(net["walk"], "neighborhood_vars.yaml")
nodes = nodes.replace(-np.inf, np.nan)
nodes = nodes.replace(np.inf, np.nan)
nodes = nodes.fillna(0)
# nodes2 = pd.read_csv('data/local_poi_distances.csv', index_col="node_id")
# nodes = pd.concat([nodes, nodes2], axis=1)
print nodes.describe()
orca.add_table("nodes", nodes)
def network_aggregations_drive(netdrive):
"""
This will be turned into a network aggregation template.
"""
nodesdrive = networks.from_yaml(netdrive,
'network_aggregations_drive.yaml')
nodesdrive = nodesdrive.fillna(0)
print(nodesdrive.describe())
orca.add_table('nodesdrive', nodesdrive)
def neighborhood_vars(net):
nodes = networks.from_yaml(net["walk"], "neighborhood_vars.yaml")
nodes = nodes.replace(-np.inf, np.nan)
nodes = nodes.replace(np.inf, np.nan)
nodes = nodes.fillna(0)
# nodes2 = pd.read_csv('data/local_poi_distances.csv', index_col="node_id")
# nodes = pd.concat([nodes, nodes2], axis=1)
print nodes.describe()
orca.add_table("nodes", nodes)
def neighborhood_vars_preproc(store, net, jobs):
# This exists to save time in other steps by relegating the first
# neighborhood_vars function to preprocessing, since this is a time-
# consuming step
# Ensure we're using the preprocessed jobs table
orca.add_table('jobs', store['jobs_preproc'])
nodes = networks.from_yaml(net["walk"], "neighborhood_vars.yaml")
nodes = nodes.replace(-np.inf, np.nan)
nodes = nodes.replace(np.inf, np.nan)
nodes = nodes.fillna(0)
print nodes.describe()
orca.add_table("nodes", nodes)
store['neighborhood_vars_preproc'] = nodes
def regional_vars_preproc(store, net, parcels):
# This exists to save time in other steps by relegating the first
# regional_vars function to preprocessing, since this is a time-
# consuming step
# Ensure we're using the preprocessed jobs table
orca.add_table('jobs', store['jobs_preproc'])
nodes = networks.from_yaml(net["drive"], "regional_vars.yaml")
nodes = nodes.fillna(0)
# nodes2 = pd.read_csv('coedata/regional_poi_distances.csv',
# index_col="tmnode_id")
# nodes = pd.concat([nodes, nodes2], axis=1)
print nodes.describe()
orca.add_table("tmnodes", nodes)
store['regional_vars_preproc'] = nodes
def network_aggregations_walk(netwalk):
"""
This will be turned into a network aggregation template.
"""
nodeswalk = networks.from_yaml(netwalk, 'network_aggregations_walk.yaml')
nodeswalk = nodeswalk.fillna(0)
# new variables
print('compute additional aggregation variables')
nodeswalk['prop_children_500_walk'] = (
(nodeswalk['children_500_walk'] > 0).astype(int) /
nodeswalk['hh_500_walk']).fillna(0)
nodeswalk['prop_singles_500_walk'] = (nodeswalk['singles_500_walk'] /
nodeswalk['hh_500_walk']).fillna(0)
nodeswalk['prop_elderly_500_walk'] = (nodeswalk['elderly_hh_500_walk'] /
nodeswalk['hh_500_walk']).fillna(0)
nodeswalk['prop_black_500_walk'] = (nodeswalk['pop_black_500_walk'] /
nodeswalk['pop_500_walk']).fillna(0)
nodeswalk['prop_white_500_walk'] = (nodeswalk['pop_white_500_walk'] /
nodeswalk['pop_500_walk']).fillna(0)
nodeswalk['prop_asian_500_walk'] = (nodeswalk['pop_asian_500_walk'] /
nodeswalk['pop_500_walk']).fillna(0)
nodeswalk['prop_hisp_500_walk'] = (nodeswalk['pop_hisp_500_walk'] /
nodeswalk['pop_500_walk']).fillna(0)
nodeswalk['prop_rich_500_walk'] = (nodeswalk['rich_500_walk'] /
nodeswalk['pop_500_walk']).fillna(0)
nodeswalk['prop_poor_500_walk'] = (nodeswalk['poor_500_walk'] /
nodeswalk['pop_500_walk']).fillna(0)
nodeswalk['prop_children_1500_walk'] = (
(nodeswalk['children_1500_walk'] > 0).astype(int) /
nodeswalk['hh_1500_walk']).fillna(0)
nodeswalk['prop_singles_1500_walk'] = (nodeswalk['singles_1500_walk'] /
nodeswalk['hh_1500_walk']).fillna(0)
nodeswalk['prop_elderly_1500_walk'] = (nodeswalk['elderly_hh_1500_walk'] /
nodeswalk['hh_1500_walk']).fillna(0)
nodeswalk['prop_black_1500_walk'] = (nodeswalk['pop_black_1500_walk'] /
nodeswalk['pop_1500_walk']).fillna(0)
nodeswalk['prop_white_1500_walk'] = (nodeswalk['pop_white_1500_walk'] /
nodeswalk['pop_1500_walk']).fillna(0)
nodeswalk['prop_asian_1500_walk'] = (nodeswalk['pop_asian_1500_walk'] /
nodeswalk['pop_1500_walk']).fillna(0)
nodeswalk['prop_hisp_1500_walk'] = (nodeswalk['pop_hisp_1500_walk'] /
nodeswalk['pop_1500_walk']).fillna(0)
nodeswalk['prop_rich_1500_walk'] = (nodeswalk['rich_1500_walk'] /
nodeswalk['pop_1500_walk']).fillna(0)
nodeswalk['prop_poor_1500_walk'] = (nodeswalk['poor_1500_walk'] /
nodeswalk['pop_1500_walk']).fillna(0)
nodeswalk['pop_jobs_ratio_1500_walk'] = (
nodeswalk['pop_1500_walk'] / (nodeswalk['jobs_500_walk'])).fillna(0)
nodeswalk['avg_hhs_500_walk'] = (nodeswalk['pop_500_walk'] /
(nodeswalk['hh_500_walk'])).fillna(0)
nodeswalk['avg_hhs_1500_walk'] = (nodeswalk['pop_1500_walk'] /
(nodeswalk['hh_1500_walk'])).fillna(0)
# end of addition
# fill inf and nan with median
def replace_inf_nan_with_median(col_name):
return nodeswalk[col_name].replace([np.inf, -np.inf], np.nan).fillna(
nodeswalk[col_name].median)
for col_name in [
'prop_children_500_walk', 'prop_singles_500_walk',
'prop_elderly_500_walk', 'prop_black_500_walk',
'prop_white_500_walk', 'prop_asian_500_walk', 'prop_hisp_500_walk',
'prop_rich_500_walk', 'prop_poor_500_walk',
'prop_children_1500_walk', 'prop_singles_1500_walk',
'prop_elderly_1500_walk', 'prop_black_1500_walk',
'prop_white_1500_walk', 'prop_asian_1500_walk',
'prop_hisp_1500_walk', 'prop_rich_1500_walk',
'prop_poor_1500_walk', 'pop_jobs_ratio_1500_walk',
'avg_hhs_500_walk', 'avg_hhs_1500_walk'
]:
nodeswalk[col_name] = replace_inf_nan_with_median(col_name)
print(nodeswalk.describe())
orca.add_table('nodeswalk', nodeswalk)
def neighborhood_vars(net):
nodes = networks.from_yaml(net, "neighborhood_vars.yaml")
nodes = nodes.fillna(0)
print nodes.describe()
orca.add_table("nodes", nodes)
def neighborhood_vars(net):
nodes = networks.from_yaml(net, "neighborhood_vars.yaml")
nodes = nodes.fillna(0)
print nodes.describe()
orca.add_table("nodes", nodes)
