def main():
print "Processing data before loading map"
# Read parcel data
parcels_main = pd.read_csv(parcels_main_dir + r"\inputs\buffered_parcels.dat", delim_whitespace=True)
parcels_alt = pd.read_csv(parcels_alt_dir + r"\inputs\buffered_parcels.dat", delim_whitespace=True)
# calculate accessibility
access_df_main = accessibility_calc(parcels_main, dir_base=parcels_main_dir)
access_df_alt = accessibility_calc(parcels_alt, dir_base=parcels_main_dir)
# Load the logsums data
main_logsum = load_logsum(parcels_main_dir + r"\outputs\aggregate_logsums.1.dat")
alt_logsum = load_logsum(parcels_alt_dir + r"\outputs\aggregate_logsums.1.dat")
# only include results for CCOMP_SHO person type (fewer cars than drivers short access to transit)
main_logsum = main_logsum[["ZONE", "CCOMP_SHO"]]
alt_logsum = alt_logsum[["ZONE", "CCOMP_SHO"]]
# Convert this field to an "Access Score" by normalizing, and rename
main_logsum["CCOMP_SHO"] = main_logsum["CCOMP_SHO"] / main_logsum["CCOMP_SHO"].max()
alt_logsum["CCOMP_SHO"] = alt_logsum["CCOMP_SHO"] / alt_logsum["CCOMP_SHO"].max()
main_logsum["Total Access Score"] = main_logsum["CCOMP_SHO"]
alt_logsum["Total Access Score"] = alt_logsum["CCOMP_SHO"]
main_logsum.drop("CCOMP_SHO", inplace=True, axis=1)
alt_logsum.drop("CCOMP_SHO", inplace=True, axis=1)
# Join to accessibility results df
access_df_main = access_df_main.merge(main_logsum, left_on="TAZ", right_on="ZONE")
access_df_alt = access_df_alt.merge(alt_logsum, left_on="TAZ", right_on="ZONE")
# # Join to parcels data
# parcels_main = parcels_main.merge(main_logsum, left_on='taz_p', right_on='ZONE')
# parcels_alt = parcels_alt.merge(alt_logsum, left_on='taz_p', right_on='ZONE')
# # Rename taz column
# parcels_main = parcels_main.rename(columns = {'taz_p': 'TAZ'})
# parcels_alt = parcels_alt.rename(columns = {'taz_p': 'TAZ'})
# Create dictionary of dataframes to plot on map
d = {"main": access_df_main, "alternative": access_df_alt}
# d = {'main': parcels_main,
# 'alternative': parcels_alt
# }
# Start the dataframe explorer webmap
dframe_explorer.start(
d,
center=[47.614848, -122.3359058],
zoom=11,
shape_json=os.path.join("inputs/", "taz2010.geojson"),
geom_name="TAZ",
join_name="TAZ",
precision=2,
host=socket.gethostbyname(socket.gethostname()), # hosted on machine running the script
)
def main():
print "Processing data before loading map"
# Read parcel data
# parcels_main = pd.read_csv(parcels_main_dir + r'\inputs\buffered_parcels.dat', delim_whitespace=True)
# parcels_alt = pd.read_csv(parcels_alt_dir + r'\inputs\buffered_parcels.dat', delim_whitespace=True)
trip_main = h5trip_to_df(daysim_main)
trip_alt = h5trip_to_df(daysim_alt)
hh_main = h5hh_to_df(daysim_main)
hh_alt = h5hh_to_df(daysim_alt)
pers_main = h5per_to_df(daysim_main)
pers_alt = h5per_to_df(daysim_alt)
# Join household records to person records
pers_hh_main = pd.merge(pers_main, hh_main, on='hhno')
pers_hh_alt = pd.merge(pers_alt, hh_alt, on='hhno')
trip_hh_main = pd.merge(trip_main, pers_hh_main, on=['hhno','pno'])
trip_hh_alt = pd.merge(trip_alt, pers_hh_alt, on=['hhno','pno'])
# Auto ownership calc
hh_main = auto_ownership(hh_main)
hh_alt = auto_ownership(hh_alt)
MAX_INC = 999999999
# Computer daily travel time totals for walk and biking
trip_hh_main = nmt_benefits(trip_hh_main, 1, MAX_INC)
trip_hh_main = nmt_benefits(trip_hh_main, 2, MAX_INC)
trip_hh_alt = nmt_benefits(trip_hh_alt, 1, MAX_INC)
trip_hh_alt = nmt_benefits(trip_hh_alt, 2, MAX_INC)
# Emissions by TAZ location
auto_emissions = calc_emissions_by_taz()
### only have network results for one scenario, so using the same result for main and alt
trip_hh_main = trip_hh_main.merge(auto_emissions,left_on='hhtaz',right_on='TAZ')
trip_hh_alt = trip_hh_main.merge(auto_emissions,left_on='hhtaz',right_on='TAZ')
# Emissions by household location
auto_emissions_by_hh_main = calc_emissions_by_hhtaz(trip_hh_main)
auto_emissions_by_hh_alt = calc_emissions_by_hhtaz(trip_hh_main)
trip_hh_main = trip_hh_main.merge(auto_emissions_by_hh_main,left_on='hhtaz',right_on='hhtaz', suffixes=['_zone_tot','_by_hhtaz'])
trip_hh_alt = trip_hh_main.merge(auto_emissions_by_hh_alt,left_on='hhtaz',right_on='hhtaz', suffixes=['_zone_tot','_by_hhtaz'])
# Only show subset of columns
subset_col = ['hhtaz', 'travcost','travdist','travtime','hhincome','hhvehs',
'1 time','2 time','Carbon Dioxide_zone_tot','Carbon Monoxide_zone_tot',
'Nitrogen Oxide_zone_tot', 'Particulate Matter_zone_tot', 'Volatile Organic Compound_zone_tot',
'Carbon Dioxide_by_hhtaz','Carbon Monoxide_by_hhtaz',
'Nitrogen Oxide_by_hhtaz', 'Particulate Matter_by_hhtaz', 'Volatile Organic Compound_by_hhtaz']
trip_hh_main = trip_hh_main[subset_col]
trip_hh_alt = trip_hh_alt[subset_col]
# Crashes and noise
# These make most sense to be shown at a geographic level
# Would it make sense to show them at a TAZ level?
# Create dictionary of dataframes to plot on map
d = {'main': trip_hh_main,
'alternative': trip_hh_alt
}
# Start the dataframe explorer webmap
dframe_explorer.start(d,
center=[47.614848,-122.3359058],
zoom=11,
shape_json=os.path.join('inputs/', 'taz2010.geojson'),
geom_name='TAZ',
join_name='hhtaz',
precision=2,
host=socket.gethostbyname(socket.gethostname()) # hosted on machine running the script
)
def main():
print "Processing data before loading map"
# Read parcel data
#parcels = pd.read_csv(main_dir + r'/inputs/buffered_parcels.dat', delim_whitespace=True)
#access_df = accessibility_calc(parcels)
# Rename parcel's TAZ column to match geo data
#parcels['TAZ'] = parcels['taz_p']
# Load daysim results
daysim_main = h5py.File(main_dir + r'/outputs/daysim_outputs.h5', "r+")
daysim_alt = h5py.File(map_daysim_alt, 'r+')
trip_main = pd.DataFrame(data={ 'Household ID': daysim_main['Trip']['hhno'][:],
'Travel Time': daysim_main['Trip']['travtime'][:],
'Travel Cost': daysim_main['Trip']['travcost'][:],
'Travel Distance': daysim_main['Trip']['travdist'][:],
'Mode': daysim_main['Trip']['mode'][:],
'Purpose': daysim_main['Trip']['dpurp'][:]})
trip_alt = pd.DataFrame(data={ 'Household ID': daysim_alt['Trip']['hhno'][:],
'Travel Time': daysim_alt['Trip']['travtime'][:],
'Travel Cost': daysim_alt['Trip']['travcost'][:],
'Travel Distance': daysim_alt['Trip']['travdist'][:],
'Mode': daysim_alt['Trip']['mode'][:],
'Purpose': daysim_alt['Trip']['dpurp'][:]})
hh_main = pd.DataFrame(data={ 'TAZ' : daysim_main['Household']['hhtaz'][:],
'Household_Income': daysim_main['Household']['hhincome'][:],
'Household Vehicles': daysim_main['Household']['hhvehs'][:],
'Household Size': daysim_main['Household']['hhsize'][:],
'Household ID': daysim_main['Household']['hhno'][:]})
hh_alt = pd.DataFrame(data={ 'TAZ' : daysim_alt['Household']['hhtaz'][:],
'Household_Income': daysim_alt['Household']['hhincome'][:],
'Household Vehicles': daysim_alt['Household']['hhvehs'][:],
'Household Size': daysim_alt['Household']['hhsize'][:],
'Household ID': daysim_alt['Household']['hhno'][:]})
pers_main = pd.DataFrame(data={'Household ID': daysim_main['Person']['hhno'][:],
'Age': daysim_main['Person']['pagey'][:],
'Gender': daysim_main['Person']['pgend'][:]})
pers_alt = pd.DataFrame(data={'Household ID': daysim_alt['Person']['hhno'][:],
'Age': daysim_alt['Person']['pagey'][:],
'Gender': daysim_alt['Person']['pgend'][:]})
trip_hh_main = pd.merge(trip_main, hh_main[['TAZ', 'Household ID', 'Household_Income']], on='Household ID')
trip_hh_main = pd.merge(trip_hh_main, pers_main, on='Household ID')
trip_hh_alt = pd.merge(trip_alt, hh_alt[['TAZ', 'Household ID', 'Household_Income']], on='Household ID')
trip_hh_alt = pd.merge(trip_hh_alt, pers_alt, on='Household ID')
# Transit share by income class
#transit_share = transit_mode_share(trip_hh)
# Create dictionary of dataframes to plot on map
d = {'main': trip_hh_main,
'alternative': trip_hh_alt
# "Accessibility to Jobs within %d minutes" % max_trav_time : access_df,
# "Land Use": parcels,
# "Households": hh_df,
# "Trips": trip_hh,
# "Transit Mode Share": transit_share
}
# Start the dataframe explorer webmap
dframe_explorer.start(d,
center=[47.614848,-122.3359058],
zoom=11,
shape_json=os.path.join('inputs/', 'taz2010.geojson'),
geom_name='TAZ',
join_name='TAZ',
precision=3,
host=socket.gethostbyname(socket.gethostname()) # hosted on machine running the script
)
def main():
print "Processing data before loading map"
# Read parcel data
#parcels = pd.read_csv(main_dir + r'/inputs/buffered_parcels.dat', delim_whitespace=True)
#access_df = accessibility_calc(parcels)
# Rename parcel's TAZ column to match geo data
#parcels['TAZ'] = parcels['taz_p']
# Load daysim results
daysim_main = h5py.File(main_dir + r'/outputs/daysim_outputs.h5', "r+")
daysim_alt = h5py.File(map_daysim_alt, 'r+')
trip_main = pd.DataFrame(
data={
'Household ID': daysim_main['Trip']['hhno'][:],
'Travel Time': daysim_main['Trip']['travtime'][:],
'Travel Cost': daysim_main['Trip']['travcost'][:],
'Travel Distance': daysim_main['Trip']['travdist'][:],
'Mode': daysim_main['Trip']['mode'][:],
'Purpose': daysim_main['Trip']['dpurp'][:]
})
trip_alt = pd.DataFrame(
data={
'Household ID': daysim_alt['Trip']['hhno'][:],
'Travel Time': daysim_alt['Trip']['travtime'][:],
'Travel Cost': daysim_alt['Trip']['travcost'][:],
'Travel Distance': daysim_alt['Trip']['travdist'][:],
'Mode': daysim_alt['Trip']['mode'][:],
'Purpose': daysim_alt['Trip']['dpurp'][:]
})
hh_main = pd.DataFrame(
data={
'TAZ': daysim_main['Household']['hhtaz'][:],
'Household_Income': daysim_main['Household']['hhincome'][:],
'Household Vehicles': daysim_main['Household']['hhvehs'][:],
'Household Size': daysim_main['Household']['hhsize'][:],
'Household ID': daysim_main['Household']['hhno'][:]
})
hh_alt = pd.DataFrame(
data={
'TAZ': daysim_alt['Household']['hhtaz'][:],
'Household_Income': daysim_alt['Household']['hhincome'][:],
'Household Vehicles': daysim_alt['Household']['hhvehs'][:],
'Household Size': daysim_alt['Household']['hhsize'][:],
'Household ID': daysim_alt['Household']['hhno'][:]
})
pers_main = pd.DataFrame(
data={
'Household ID': daysim_main['Person']['hhno'][:],
'Age': daysim_main['Person']['pagey'][:],
'Gender': daysim_main['Person']['pgend'][:]
})
pers_alt = pd.DataFrame(
data={
'Household ID': daysim_alt['Person']['hhno'][:],
'Age': daysim_alt['Person']['pagey'][:],
'Gender': daysim_alt['Person']['pgend'][:]
})
trip_hh_main = pd.merge(
trip_main,
hh_main[['TAZ', 'Household ID', 'Household_Income']],
on='Household ID')
trip_hh_main = pd.merge(trip_hh_main, pers_main, on='Household ID')
trip_hh_alt = pd.merge(trip_alt,
hh_alt[['TAZ', 'Household ID', 'Household_Income']],
on='Household ID')
trip_hh_alt = pd.merge(trip_hh_alt, pers_alt, on='Household ID')
# Transit share by income class
#transit_share = transit_mode_share(trip_hh)
# Create dictionary of dataframes to plot on map
d = {
'main': trip_hh_main,
'alternative': trip_hh_alt
# "Accessibility to Jobs within %d minutes" % max_trav_time : access_df,
# "Land Use": parcels,
# "Households": hh_df,
# "Trips": trip_hh,
# "Transit Mode Share": transit_share
}
# Start the dataframe explorer webmap
dframe_explorer.start(
d,
center=[47.614848, -122.3359058],
zoom=11,
shape_json=os.path.join('inputs/', 'taz2010.geojson'),
geom_name='TAZ',
join_name='TAZ',
precision=3,
host=socket.gethostbyname(
socket.gethostname()) # hosted on machine running the script
)
