# print '%d units were filled' %(new_building_count - old_building_count)
#buildings = buildings.drop(new_homes)
#temp_count += 1
if (temp_count > 50):
break
#out_table.to_csv('C:/users/jmartinez/documents/households_simulation_test.csv')
dset.households.loc[out_table.index] = out_table
#homeless.to_csv('C:/users/jmartinez/documents/homeless.csv')
if __name__ == '__main__':
from drcog.models import dataset
from drcog.variables import variable_library
import os
import cProfile
dset = dataset.DRCOGDataset(os.path.join(misc.data_dir(), 'drcog.h5'))
#Load estimated coefficients
coeff_store = pd.HDFStore(os.path.join(misc.data_dir(), 'coeffs.h5'))
dset.coeffs = coeff_store.coeffs.copy()
coeff_store.close()
coeff_store = pd.HDFStore(os.path.join(misc.data_dir(), 'coeffs_res.h5'))
dset.coeffs_res = coeff_store.coeffs_res.copy()
coeff_store.close()
variable_library.calculate_variables(dset)
alternatives = dset.buildings[(dset.buildings.residential_units > 0)]
sim_year = 2011
fnc = "simulate(dset, year=sim_year,depvar = 'building_id',alternatives=alternatives,simulation_table = 'households',output_names = ('drcog-coeff-hlcm-%s.csv','DRCOG HOUSEHOLD LOCATION CHOICE MODELS (%s)','hh_location_%s','household_building_ids')," +\
"agents_groupby= ['income_3_tenure',],transition_config = {'Enabled':True,'control_totals_table':'annual_household_control_totals','scaling_factor':1.0}," +\
def run(self, name=None, export_buildings_to_urbancanvas=False, base_year=2010, forecast_year=None, fixed_seed=True, random_seed=1, export_indicators=True, indicator_output_directory='C:/opus/data/drcog2/runs', core_components_to_run=None, household_transition=None,household_relocation=None,employment_transition=None, elcm_configuration=None, developer_configuration=None, calibration_configuration=None, hh_targets=None, ru_targets=None, emp_targets=None, nrsqft_targets=None):
"""Runs an UrbanSim2 scenario
"""
##Calibration targets
#resunit_targets = np.array([.198,.205,.105,.032,.002,.165,.142,.014,.002,.099,.037])
#hh_targets = np.array([.198,.205,.105,.032,.002,.165,.142,.014,.002,.099,.037])
#emp_targets = np.array([0.1511,0.2232,0.0737,0.0473,0.0001,0.2435,0.1094,0.0139,0.0005,0.1178,0.0197])
#nonres_targets = np.array([0.1511,0.2232,0.0737,0.0473,0.0001,0.2435,0.1094,0.0139,0.0005,0.1178,0.0197])
hh_targets = np.array([hh_targets['hh_8001_target'],hh_targets['hh_8005_target'],hh_targets['hh_8013_target'],hh_targets['hh_8014_target'],hh_targets['hh_8019_target'],hh_targets['hh_8031_target'],hh_targets['hh_8035_target'],hh_targets['hh_8039_target'],hh_targets['hh_8047_target'],hh_targets['hh_8059_target'],hh_targets['hh_8123_target']])
resunit_targets = np.array([ru_targets['ru_8001_target'],ru_targets['ru_8005_target'],ru_targets['ru_8013_target'],ru_targets['ru_8014_target'],ru_targets['ru_8019_target'],ru_targets['ru_8031_target'],ru_targets['ru_8035_target'],ru_targets['ru_8039_target'],ru_targets['ru_8047_target'],ru_targets['ru_8059_target'],ru_targets['ru_8123_target']])
emp_targets = np.array([emp_targets['emp_8001_target'],emp_targets['emp_8005_target'],emp_targets['emp_8013_target'],emp_targets['emp_8014_target'],emp_targets['emp_8019_target'],emp_targets['emp_8031_target'],emp_targets['emp_8035_target'],emp_targets['emp_8039_target'],emp_targets['emp_8047_target'],emp_targets['emp_8059_target'],emp_targets['emp_8123_target']])
nonres_targets = np.array([nrsqft_targets['nr_8001_target'],nrsqft_targets['nr_8005_target'],nrsqft_targets['nr_8013_target'],nrsqft_targets['nr_8014_target'],nrsqft_targets['nr_8019_target'],nrsqft_targets['nr_8031_target'],nrsqft_targets['nr_8035_target'],nrsqft_targets['nr_8039_target'],nrsqft_targets['nr_8047_target'],nrsqft_targets['nr_8059_target'],nrsqft_targets['nr_8123_target']])
county_id = np.array([8001,8005,8013,8014,8019,8031,8035,8039,8047,8059,8123])
targets = pd.DataFrame({'county_id':county_id,'resunit_target':resunit_targets,'hh_target':hh_targets,'emp_target':emp_targets,'nonres_target':nonres_targets})
delta = calibration_configuration['coefficient_step_size']
margin = calibration_configuration['match_target_within']
iterations = calibration_configuration['iterations']
for it in range(iterations):
logger.log_status('Calibration iteration: ' + str(it))
logger.log_status('Starting UrbanSim2 run.')
dset = dataset.DRCOGDataset(os.path.join(misc.data_dir(),'drcog.h5'))
seconds_start = time.time()
if fixed_seed:
logger.log_status('Running with fixed random seed.')
np.random.seed(random_seed)
#Load estimated coefficients
coeff_store = pd.HDFStore(os.path.join(misc.data_dir(),'coeffs.h5'))
dset.coeffs = coeff_store.coeffs.copy()
coeff_store.close()
coeff_store = pd.HDFStore(os.path.join(misc.data_dir(),'coeffs_res.h5'))
dset.coeffs_res = coeff_store.coeffs_res.copy()
coeff_store.close()
#Keep track of unplaced agents by year
unplaced_hh = []
unplaced_emp = []
for sim_year in range(base_year,forecast_year+1):
print 'Simulating year ' + str(sim_year)
logger.log_status(sim_year)
##Variable Library calculations
variable_library.calculate_variables(dset)
#Record pre-demand model zone-level household/job totals
hh_zone1 = dset.fetch('households').groupby('zone_id').size()
emp_zone1 = dset.fetch('establishments').groupby('zone_id').employees.sum()
############ ELCM SIMULATION
if core_components_to_run['ELCM']:
logger.log_status('ELCM simulation.')
alternatives = dset.buildings[(dset.buildings.non_residential_sqft>0)]
elcm_simulation.simulate(dset, year=sim_year,depvar = 'building_id',alternatives=alternatives,simulation_table = 'establishments',output_names = ("drcog-coeff-elcm-%s.csv","DRCOG EMPLOYMENT LOCATION CHOICE MODELS (%s)","emp_location_%s","establishment_building_ids"),
agents_groupby= ['sector_id_retail_agg',],transition_config = {'Enabled':True,'control_totals_table':'annual_employment_control_totals','scaling_factor':1.0})
################# HLCM simulation
if core_components_to_run['HLCM']:
logger.log_status('HLCM simulation.')
alternatives = dset.buildings[(dset.buildings.residential_units>0)]
new_hlcm_simulation.simulate(dset, year=sim_year,depvar = 'building_id',alternatives=alternatives,simulation_table = 'households',output_names = ("drcog-coeff-hlcm-%s.csv","DRCOG HOUSEHOLD LOCATION CHOICE MODELS (%s)","hh_location_%s","household_building_ids"),
agents_groupby= ['income_3_tenure',],transition_config = {'Enabled':True,'control_totals_table':'annual_household_control_totals','scaling_factor':1.0},
relocation_config = {'Enabled':True,'relocation_rates_table':'annual_household_relocation_rates','scaling_factor':1.0},)
############ REPM SIMULATION
if core_components_to_run['Price']:
logger.log_status('REPM simulation.')
#Residential
#Residential
census_model_simulation.simulate_residential(dset, 'unit_price_res_sqft', 'school_district_id', 10, sim_year)
#Non-residential
regression_model_simulation.simulate(dset, year=sim_year,output_varname='unit_price_non_residential', simulation_table='buildings', output_names = ["drcog-coeff-nrhedonic-%s.csv","DRCOG NRHEDONIC MODEL (%s)","nrprice_%s"],
agents_groupby = 'building_type_id', segment_ids = [5,8,11,16,17,18,21,23,9,22])
############ DEVELOPER SIMULATION
if core_components_to_run['Developer']:
logger.log_status('Proforma simulation.')
buildings, newbuildings = proforma_developer_model.run(dset,hh_zone1,emp_zone1,developer_configuration,sim_year)
dset.d['buildings'] = pd.concat([buildings,newbuildings])
########### Indicators
# if export_indicators:
# unplaced_hh.append((dset.households.building_id==-1).sum())
# unplaced_emp.append(dset.establishments[dset.establishments.building_id==-1].employees.sum())
# if sim_year == forecast_year:
# logger.log_status('Exporting indicators')
# indicators.run(dset, indicator_output_directory, forecast_year)
########### TRAVEL MODEL
# if travel_model_configuration['export_to_tm']:
# if sim_year in travel_model_configuration['years_to_run']:
# logger.log_status('Exporting to TM')
# export_zonal_file.export_zonal_file_to_tm(dset,sim_year,tm_input_dir=travel_model_configuration['tm_input_dir'])
elapsed = time.time() - seconds_start
print "TOTAL elapsed time: " + str(elapsed) + " seconds."
########### Calibration
logger.log_status('Calibration coefficient updating')
import math
hh_submodels = []
for col in dset.coeffs.columns:
if col[0].startswith('hh_') and col[1]=='fnames':
hh_submodels.append(col[0])
emp_submodels = []
for col in dset.coeffs.columns:
if col[0].startswith('emp_') and col[1]=='fnames':
emp_submodels.append(col[0])
#Record base values for temporal comparison
hh = dset.store.households
e = dset.store.establishments
b = dset.store.buildings
p = dset.store.parcels.set_index('parcel_id')
if p.index.name != 'parcel_id':
p=p.set_index(p['parcel_id'])
b['county_id'] = p.county_id[b.parcel_id].values
hh['county_id'] = b.county_id[hh.building_id].values
e['county_id'] = b.county_id[e.building_id].values
base_hh_county = hh.groupby('county_id').size()
base_emp_county = e.groupby('county_id').employees.sum()
base_ru_county = b.groupby('county_id').residential_units.sum()
base_nr_county = b.groupby('county_id').non_residential_sqft.sum()
#Calibration indicators
b = dset.fetch('buildings')
e = dset.fetch('establishments')
hh = dset.fetch('households')
p = dset.parcels
if p.index.name != 'parcel_id':
p = p.set_index(p['parcel_id'])
b['county_id'] = p.county_id[b.parcel_id].values
hh['county_id'] = b.county_id[hh.building_id].values
e['county_id'] = b.county_id[e.building_id].values
sim_hh_county = hh.groupby('county_id').size()
sim_emp_county = e.groupby('county_id').employees.sum()
sim_ru_county = b.groupby('county_id').residential_units.sum()
sim_nr_county = b.groupby('county_id').non_residential_sqft.sum()
hh_diff_county = sim_hh_county - base_hh_county
emp_diff_county = sim_emp_county - base_emp_county
ru_diff_county = sim_ru_county - base_ru_county
nr_diff_county = sim_nr_county - base_nr_county
prop_growth_emp = emp_diff_county*1.0/emp_diff_county.sum()
prop_growth_hh = hh_diff_county*1.0/hh_diff_county.sum()
prop_growth_ru = ru_diff_county*1.0/ru_diff_county.sum()
prop_growth_nr = nr_diff_county*1.0/nr_diff_county.sum()
county_args = pd.read_csv(os.path.join(misc.data_dir(),'county_calib.csv')).set_index('county_id')
i = 0;j = 0;k = 0;m = 0
for x in targets.county_id.values:
cid = int(x)
print cid
prop_ru = prop_growth_ru[cid]
prop_hh = prop_growth_hh[cid]
prop_emp = prop_growth_emp[cid]
prop_nonres = prop_growth_nr[cid]
print 'ru prop is ' + str(prop_ru)
print 'nsqft prop is ' + str(prop_nonres)
print 'hh prop is ' + str(prop_hh)
print 'emp prop is ' + str(prop_emp)
logger.log_status('ru prop is ' + str(prop_ru))
logger.log_status('nsqft prop is ' + str(prop_nonres))
logger.log_status('hh prop is ' + str(prop_hh))
logger.log_status('emp prop is ' + str(prop_emp))
target_ru = targets.resunit_target[targets.county_id==cid].values[0]
target_hh = targets.hh_target[targets.county_id==cid].values[0]
target_emp = targets.emp_target[targets.county_id==cid].values[0]
target_nonres = targets.nonres_target[targets.county_id==cid].values[0]
print 'ru target is ' + str(target_ru)
print 'nsqft target is ' + str(target_nonres)
print 'hh target is ' + str(target_hh)
print 'emp target is ' + str(target_emp)
logger.log_status('ru target is ' + str(target_ru))
logger.log_status('nsqft target is ' + str(target_nonres))
logger.log_status('hh target is ' + str(target_hh))
logger.log_status('emp target is ' + str(target_emp))
varname = 'county%s' % (cid)
print varname
if (prop_ru > (target_ru - margin)) and (prop_ru < (target_ru + margin)):
print 'NO ru action.'
logger.log_status('NO ru action.')
i = i + 1
elif math.isnan(prop_ru) or (prop_ru < target_ru):
county_args.chh_demand_factor[cid] = county_args.chh_demand_factor[cid].astype(float) + 0.01
county_args.cres_price_factor[cid] = county_args.cres_price_factor[cid].astype(float) + 0.01
print 'ru action is PLUS'
logger.log_status('ru action is PLUS')
elif prop_ru > target_ru:
county_args.chh_demand_factor[cid] = county_args.chh_demand_factor[cid].astype(float) - 0.01
county_args.cres_price_factor[cid] = county_args.cres_price_factor[cid].astype(float) - 0.01
print 'ru action is MINUS'
logger.log_status('ru action is MINUS')
if (prop_hh > (target_hh - margin)) and (prop_hh < (target_hh + margin)):
print 'NO hh action.'
logger.log_status('NO hh action.')
j = j + 1
elif math.isnan(prop_hh) or (prop_hh < target_hh):
for submodel in hh_submodels:
dset.coeffs[(submodel, 'coeffs')][dset.coeffs[(submodel,'fnames')]==varname] = dset.coeffs[(submodel, 'coeffs')][dset.coeffs[(submodel,'fnames')]==varname] + delta
print 'hh action is PLUS'
logger.log_status('hh action is PLUS')
elif prop_hh > target_hh:
for submodel in hh_submodels:
dset.coeffs[(submodel, 'coeffs')][dset.coeffs[(submodel,'fnames')]==varname] = dset.coeffs[(submodel, 'coeffs')][dset.coeffs[(submodel,'fnames')]==varname] - delta
print 'hh action is MINUS'
logger.log_status('hh action is MINUS')
if (prop_emp > (target_emp - margin)) and (prop_emp < (target_emp + margin)):
print 'NO emp action.'
logger.log_status('NO emp action.')
k = k + 1
elif math.isnan(prop_emp) or (prop_emp < target_emp):
for submodel in emp_submodels:
dset.coeffs[(submodel, 'coeffs')][dset.coeffs[(submodel,'fnames')]==varname] = dset.coeffs[(submodel, 'coeffs')][dset.coeffs[(submodel,'fnames')]==varname] + delta
print 'emp action is PLUS'
logger.log_status('emp action is PLUS')
elif prop_emp > target_emp:
for submodel in emp_submodels:
dset.coeffs[(submodel, 'coeffs')][dset.coeffs[(submodel,'fnames')]==varname] = dset.coeffs[(submodel, 'coeffs')][dset.coeffs[(submodel,'fnames')]==varname] - delta
print 'emp action is MINUS'
logger.log_status('emp action is MINUS')
if (prop_nonres > (target_nonres - margin)) and (prop_nonres < (target_nonres + margin)):
print 'NO nonres action.'
logger.log_status('NO nonres action.')
m = m + 1
elif math.isnan(prop_nonres) or (prop_nonres < target_nonres):
county_args.cemp_demand_factor[cid] = county_args.cemp_demand_factor[cid].astype(float) + 0.01
county_args.cnonres_price_factor[cid] = county_args.cnonres_price_factor[cid].astype(float) + 0.01
print county_args.cnonres_price_factor[cid]
print 'nonres action is PLUS'
logger.log_status('nonres action is PLUS')
elif prop_nonres > target_nonres:
county_args.cemp_demand_factor[cid] = county_args.cemp_demand_factor[cid].astype(float) - 0.01
county_args.cnonres_price_factor[cid] = county_args.cnonres_price_factor[cid].astype(float) - 0.01
print 'nonres action is MINUS'
print county_args.cnonres_price_factor[cid]
logger.log_status('nonres action is MINUS')
print i,j,k,m
logger.log_status('Number of hh county targets met: %s' % j)
logger.log_status('Number of emp county targets met: %s' % k)
logger.log_status('Number of ru county targets met: %s' % i)
logger.log_status('Number of nr county targets met: %s' % m)
###Save calibrated coefficients at the end of each iteration
coeff_store_path = os.path.join(misc.data_dir(),'coeffs.h5')
coeff_store = pd.HDFStore(coeff_store_path)
coeff_store['coeffs'] = dset.coeffs
coeff_store.close()
county_args.to_csv(os.path.join(misc.data_dir(),'county_calib.csv'))
new_parcel_dict['parcel_id'] = new_parcel_id
new_parcel_dict['county_id'] = county_id
new_parcel_dict['parcel_sqft'] = 43560
new_parcel_dict['land_value'] = 0
new_parcel_dict['zone_id'] = zone_id
new_parcel_dict['centroid_x'] = x
new_parcel_dict['centroid_y'] = y
new_parcel_dict['dist_bus'] = 6000
new_parcel_dict['dist_rail'] = 6000
new_parcel_dict['in_ugb'] = 1
new_parcel_data = new_parcel_dict.values()
new_parcel_frame = pd.DataFrame(columns=dset.parcels.columns)
new_parcel_frame.loc[0] = new_parcel_data
#dset.parcels.loc[pid] = new_parcel_data
dset.parcels = pd.concat([dset.parcels, new_parcel_frame])
print "added new parcel with id %d" % new_parcel_id
return new_parcel_id
if __name__ == '__main__':
from drcog.models import dataset
dset = dataset.DRCOGDataset('C:\urbansim\data\drcog.h5')
add_res_buildings(dset)
def run(self, name=None, export_buildings_to_urbancanvas=False, base_year=2010, forecast_year=None, fixed_seed=True, random_seed=1, indicator_configuration=None, core_components_to_run=None, household_transition=None,household_relocation=None,employment_transition=None, elcm_configuration=None, developer_configuration=None, table_swapping=None, travel_model_configuration1=None, travel_model_configuration2=None, travel_model_configuration3=None, travel_model_configuration4=None, travel_model_configuration5=None, travel_model_configuration6=None):
"""Runs an UrbanSim2 scenario
"""
logger.log_status('Starting UrbanSim2 run.')
dset = dataset.DRCOGDataset(os.path.join(misc.data_dir(),'drcog.h5'))
seconds_start = time.time()
if fixed_seed:
logger.log_status('Running with fixed random seed.')
np.random.seed(random_seed)
#Load estimated coefficients
coeff_store = pd.HDFStore(os.path.join(misc.data_dir(),'coeffs.h5'))
dset.coeffs = coeff_store.coeffs.copy()
coeff_store.close()
coeff_store = pd.HDFStore(os.path.join(misc.data_dir(),'coeffs_res.h5'))
dset.coeffs_res = coeff_store.coeffs_res.copy()
coeff_store.close()
#Keep track of unplaced agents by year
unplaced_hh = []
unplaced_emp = []
#UrbanCanvas scenario id, replaced by db-retrieved value during export step
urbancanvas_scenario_id = 0
#####Residential Buildings#####
new_refiner.add_res_buildings(dset)
#####Non-Residential Buildings#####
new_refiner.add_non_res_buildings(dset)
for sim_year in range(base_year,forecast_year+1):
print 'Simulating year ' + str(sim_year)
logger.log_status(sim_year)
##Variable Library calculations
variable_library.calculate_variables(dset)
#Record pre-demand model zone-level household/job totals
hh_zone1 = dset.fetch('households').groupby('zone_id').size()
emp_zone1 = dset.fetch('establishments').groupby('zone_id').employees.sum()
############ ELCM SIMULATION
if core_components_to_run['ELCM']:
logger.log_status('ELCM simulation.')
alternatives = dset.buildings[(dset.buildings.non_residential_sqft>0)]
new_elcm_model.simulate(dset, year=sim_year,depvar = 'building_id',alternatives=alternatives,simulation_table = 'establishments',output_names = ("drcog-coeff-elcm-%s.csv","DRCOG EMPLOYMENT LOCATION CHOICE MODELS (%s)","emp_location_%s","establishment_building_ids"),
agents_groupby= ['sector_id_retail_agg',],transition_config = {'Enabled':True,'control_totals_table':'annual_employment_control_totals','scaling_factor':1.0})
################# HLCM SIMULATION
if core_components_to_run['HLCM']:
logger.log_status('HLCM simulation.')
alternatives = dset.buildings[(dset.buildings.residential_units>0)]
new_hlcm_simulation.simulate(dset, year=sim_year,depvar = 'building_id',alternatives=alternatives,simulation_table = 'households',output_names = ("drcog-coeff-hlcm-%s.csv","DRCOG HOUSEHOLD LOCATION CHOICE MODELS (%s)","hh_location_%s","household_building_ids"),
agents_groupby= ['income_3_tenure',],transition_config = {'Enabled':True,'control_totals_table':'annual_household_control_totals','scaling_factor':1.0},
relocation_config = {'Enabled':True,'relocation_rates_table':'annual_household_relocation_rates','scaling_factor':1.0},)
############ DEMAND-SIDE REFINEMENT
#refiner.run(dset, sim_year)
# refiner_fnc = "refiner.run(dset, sim_year)"
#cProfile.runctx(refiner_fnc, locals={'dset':dset, 'sim_year':sim_year}, globals={'refiner': refiner}, filename='c:/users/jmartinez/documents/refiner_time')
############ REPM SIMULATION
if core_components_to_run['Price']:
logger.log_status('REPM simulation.')
#Residential
census_model_simulation.simulate_residential(dset, 'unit_price_res_sqft', 'school_district_id', 10, sim_year)
#Non-residential
regression_model_simulation.simulate(dset, year=sim_year,output_varname='unit_price_non_residential', simulation_table='buildings', output_names = ["drcog-coeff-nrhedonic-%s.csv","DRCOG NRHEDONIC MODEL (%s)","nrprice_%s"],
agents_groupby = 'building_type_id', segment_ids = [5,8,11,16,17,18,21,23,9,22])
############ DEVELOPER SIMULATION
if core_components_to_run['Developer']:
logger.log_status('Proforma simulation.')
buildings, newbuildings = proforma_developer_model.run(dset,hh_zone1,emp_zone1,developer_configuration,sim_year)
#import pdb; pdb.set_trace()
dset.d['buildings'] = pd.concat([buildings,newbuildings])
dset.buildings.index.name = 'building_id'
############ INDICATORS
if indicator_configuration['export_indicators']:
unplaced_hh.append((dset.households.building_id==-1).sum())
unplaced_emp.append(dset.establishments[dset.establishments.building_id==-1].employees.sum())
if sim_year in indicator_configuration['years_to_run']:
logger.log_status('Exporting indicators')
indicators.run(dset, indicator_configuration['indicator_output_directory'], sim_year)
logger.log_status('unplaced hh')
logger.log_status(unplaced_hh)
logger.log_status('unplaced emp')
logger.log_status(unplaced_emp)
############ TRAVEL MODEL
export_zonal_file.export_zonal_file_to_tm(dset,sim_year,logger,tm_config=[travel_model_configuration1,travel_model_configuration2,travel_model_configuration3,travel_model_configuration4,travel_model_configuration5,travel_model_configuration6])
############ SWAPPER
if sim_year == table_swapping['year']:
if table_swapping['swap_skims']:
logger.log_status('Swapping skims')
td2 = pd.read_csv(table_swapping['new_skim_file'], index_col=['from_zone_id','to_zone_id'])
dset.d['travel_data'] = td2
if table_swapping['swap_dist_rail']:
logger.log_status('Swapping parcel distance to rail')
p2 = pd.read_csv(table_swapping['new_dist_rail_file'], index_col=['parcel_id'])
dset.d['parcels']['dist_rail'] = p2.dist_rail
############ URBANCANVAS
if export_buildings_to_urbancanvas:
logger.log_status('Exporting %s buildings to Urbancanvas database for project %s and year %s.' % (newbuildings.index.size,urbancanvas_scenario_id,sim_year))
urbancanvas_scenario_id = urbancanvas_export.export_to_urbancanvas(newbuildings, sim_year, urbancanvas_scenario_id)
elapsed = time.time() - seconds_start
print "TOTAL elapsed time: " + str(elapsed) + " seconds."