def jobs_within_30_min(jobs, travel_data):
from urbansim.utils import misc
j = pd.DataFrame({'zone_id': jobs.zone_id})
td = travel_data.to_frame()
zone_ids = np.unique(td.reset_index().to_zone_id)
return misc.compute_range(td,
j.groupby('zone_id').size().reindex(index=zone_ids).fillna(0),
"am_auto_total_time",
30, agg=np.sum)
def jobs_within_30_min(jobs, travel_data, zone_ids):
travel_data = travel_data.to_frame()
jobs = jobs.to_frame(columns=['zone_id'])
return misc.compute_range(
travel_data,
jobs.groupby('zone_id').size().reindex(index=zone_ids).fillna(0),
"am_single_vehicle_to_work_travel_time",
30,
agg=np.sum)
def population_within_15_min(households, travel_data, zone_ids):
travel_data = travel_data.to_frame()
households = households.to_frame(columns=['persons', 'zone_id'])
return misc.compute_range(
travel_data,
households.groupby('zone_id').persons.sum().reindex(
index=zone_ids).fillna(0),
"am_single_vehicle_to_work_travel_time",
15,
agg=np.sum)
def column_func(zones, beam_skims):
results = misc.compute_range(
beam_skims.to_frame(), zones.get_column(variable_to_summarize),
impedance_measure, distance, agg=np.sum)
if log:
results = results.apply(eval('np.log1p'))
if len(results) < len(zones):
results = results.reindex(zones.index).fillna(0)
return results
def column_func(zones):
df = skims_table.to_frame()
results = misc.compute_range(df,
zones.get_column(variable_to_summarize),
impedance_measure,
distance,
agg=agg)
if len(results) < len(zones):
results = results.reindex(zones.index).fillna(0)
# add vars from orig zone, typically not included in skims
results = results + zones[variable_to_summarize]
if log:
results = results.apply(eval('np.log1p'))
return results
def population_within_15_min(households, travel_data, zone_ids):
travel_data = travel_data.to_frame()
households = households.to_frame(columns = ['persons', 'zone_id'])
return misc.compute_range(travel_data, households.groupby('zone_id').persons.sum().reindex(index = zone_ids).fillna(0),
"am_single_vehicle_to_work_travel_time",
15, agg=np.sum)
def jobs_within_30_min(jobs, travel_data, zone_ids):
travel_data = travel_data.to_frame()
jobs = jobs.to_frame(columns = ['zone_id'])
return misc.compute_range(travel_data, jobs.groupby('zone_id').size().reindex(index = zone_ids).fillna(0),
"am_single_vehicle_to_work_travel_time",
30, agg=np.sum)
