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 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 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_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)