max_siteID = intersections.siteid.max()
progress_bar = tqdm(range(max_siteID))
progress_bar.set_description("splitting sites on mgras")
for i in progress_bar:
sites.append(find_sites(intersections, i + 1))
# we now have sites as a list of dataframes.
# for each frame
# if there are no entries, skip
# if there is one, put all development on the mgra
# if there are multiple determine split.
progress_bar = tqdm(sites)
progress_bar.set_description('allocating development for each site')
for frame in progress_bar:
if len(frame) == 0:
pass
elif len(frame) == 1:
add_to_mgra(mgras, frame)
else:
distribute_units(mgras, frame)
save_to_file(mgras, output_dir, 'scheduled_development_added.csv')
return
if __name__ == "__main__":
if parameters is not None:
use_database = parameters['use_database']
run(open_mgra_io_file(from_database=use_database),
open_sites_file(from_database=use_database))
# os.path.join(output_dir,"..","scheduled_development_"+str(forecast_year)+".csv"),)
# finish meeting demand as needed
print('developing to meet remaining demand:')
mgra_dataframe = develop(mgra_dataframe)
if mgra_dataframe is None:
print('program terminated early')
return
# save output file
save_to_file(mgra_dataframe, output_dir,
'forecasted_year_{}.csv'.format(forecast_year))
# create aa export if crosswalk is available
print('updating AA floorspace file ...')
update_floorspace(mgra_dataframe, forecast_year)
return
if __name__ == "__main__":
import sys
sys.tracebacklimit = 0
sys.excepthook = exception_handler
if parameters is not None:
# load dataframe(s)
use_database = parameters['use_database']
mgra_dataframe = open_mgra_io_file(
from_database=use_database, filename=parameters['input_filename'])
planned_sites = open_sites_file(from_database=True)
# start simulation
run(mgra_dataframe, planned_sites)
from modeling.develop import develop
from utils.interface import save_to_file, open_mgra_io_file
from utils.parameter_access import parameters
def run(mgra_dataframe):
output_dir = parameters['output_directory']
simulation_begin = parameters['simulation_begin']
forecast_year = simulation_begin + 1
print('simulating year {}'.format(forecast_year))
# develop enough land to meet demand for this year.
mgra_dataframe = develop(mgra_dataframe)
if mgra_dataframe is None:
print('program terminated early')
return
# save output file
save_to_file(mgra_dataframe, output_dir,
'forecasted_year_{}.csv'.format(forecast_year))
return
if __name__ == "__main__":
if parameters is not None:
run(open_mgra_io_file(from_database=parameters['use_database']))
# add scheduled development if available
if planned_sites is not None:
print('adding scheduled development:')
add_scheduled_development(mgra_dataframe,
planned_sites,
year=simulation_begin)
# finish meeting demand as needed
print('developing to meet remaining demand:')
mgra_dataframe = develop(mgra_dataframe)
if mgra_dataframe is None:
print('program terminated early')
return
# save output file
save_to_file(mgra_dataframe, output_dir,
'forecasted_year_{}.csv'.format(forecast_year))
# create aa export if crosswalk is available
print('creating AA commodity export file ...')
export_luz_data(mgra_dataframe)
return
if __name__ == "__main__":
if parameters is not None:
use_database = parameters['use_database']
# load dataframe(s)
mgra_dataframe = open_mgra_io_file(from_database=use_database)
planned_sites = open_sites_file(from_database=use_database)
# start simulation
run(mgra_dataframe, planned_sites)