def build_networks(settings):
name = settings['build_networks']['name']
st = pd.HDFStore(os.path.join(misc.data_dir(), name), "r")
nodes, edges = st.nodes, st.edges
net = pdna.Network(nodes["x"],
nodes["y"],
edges["from"],
edges["to"],
edges[["weight"]],
twoway=True)
net.precompute(settings['build_networks']['max_distance'])
return net
def logsums(settings):
logsums_index = settings.get("logsums_index_col", "taz")
return pd.read_csv(os.path.join(misc.data_dir(), 'logsums.csv'),
index_col=logsums_index)
def household_controls():
df = pd.read_csv(os.path.join(misc.data_dir(), "household_controls.csv"))
return df.set_index('year')
def employment_controls():
df = pd.read_csv(os.path.join(misc.data_dir(), "employment_controls.csv"))
return df.set_index('year')
def zoning_baseline(store, settings, year):
# df = pd.merge(zoning_for_parcels.to_frame(),
# zoning.to_frame(),
# left_on='zoning',
# right_index=True)
df = store['zoning_baseline']
#if os.path.exists(os.path.join(misc.data_dir(), "zoning_parcels.csv")):
# df['parcel_id'] = df.index
# alter = pd.read_csv(os.path.join(misc.data_dir(), "zoning_parcels.csv"), index_col='parcel_id')
# df = pd.merge(df, alter, how='left', left_index=True, right_index=True, suffixes=('','_x'))
# df.max_dua[df.max_dua_x.notnull()] = df.max_dua_x[df.max_dua_x.notnull()]
# df.max_far[df.max_far_x.notnull()] = df.max_far_x[df.max_far_x.notnull()]
# df = df.drop(['max_dua_x','max_far_x'], axis=1)
if os.path.exists(
os.path.join(misc.data_dir(), "scenario_inputs",
settings['scenario'], "zoning_parcels_p.csv")):
update = pd.read_csv(
os.path.join(misc.data_dir(), "scenario_inputs",
settings['scenario'], "zoning_parcels_p.csv"))
update = update[update.year <= year]
update = update.sort_values(by='year', ascending=1)
update = update.drop_duplicates("parcel_id", "last")
if update.empty:
df.max_height = 999
return df
df2 = pd.merge(df,
update,
how='left',
left_index=True,
right_on='parcel_id',
suffixes=('', '_x'))
df2.set_index('parcel_id', inplace=True)
df2.max_dua[df2.max_dua_x.notnull()] = df2.max_dua_x[
df2.max_dua_x.notnull()]
df2.max_dua[df2.max_dua == 0] = np.nan
df2.max_far[df2.max_far_x.notnull()] = df2.max_far_x[
df2.max_far_x.notnull()]
df2.max_far[df2.max_far == 0] = np.nan
df2.type1[df2.type1_x == 1] = 't'
df2.type1[df2.type1_x == 0] = 'f'
df2.type2[df2.type2_x == 1] = 't'
df2.type2[df2.type2_x == 0] = 'f'
df2.type3[df2.type3_x == 1] = 't'
df2.type3[df2.type3_x == 0] = 'f'
df2.type4[df2.type4_x == 1] = 't'
df2.type4[df2.type4_x == 0] = 'f'
df2.type5[df2.type5_x == 1] = 't'
df2.type5[df2.type5_x == 0] = 'f'
df2.type6[df2.type6_x == 1] = 't'
df2.type6[df2.type6_x == 0] = 'f'
df2.type7[df2.type7_x == 1] = 't'
df2.type7[df2.type7_x == 0] = 'f'
df2.type8[df2.type8_x == 1] = 't'
df2.type8[df2.type8_x == 0] = 'f'
df2 = df2.drop([
'year', 'max_dua_x', 'max_far_x', 'type1_x', 'type2_x', 'type3_x',
'type4_x', 'type5_x', 'type6_x', 'type7_x', 'type8_x'
],
axis=1)
df2.max_height = 999
if os.path.exists(
os.path.join(misc.data_dir(), "developableparcels.dbf")):
devbuffer = wfrc_utils.dbf2df(
os.path.join(misc.data_dir(), "developableparcels.dbf"))
undevbuffer = devbuffer.parcel_id.unique()
df2.type1[df2.index.isin(undevbuffer)] = 'f'
df2.type2[df2.index.isin(undevbuffer)] = 'f'
df2.type4[df2.index.isin(undevbuffer)] = 'f'
df2.type5[df2.index.isin(undevbuffer)] = 'f'
return df2
else:
df.max_height = 999
return df