# merge projected pipelines into one geometry
iter = 0
if selections['crude'] != "":
with fiona.open(crude_utmCA) as source:
with fiona.open(crude_select,
'w',
driver=source.driver,
schema=source.schema,
crs=source.crs) as sink:
for rec in source:
if eval(selections['crude']):
sink.write({
'properties': rec['properties'],
'geometry': rec['geometry']
})
unify_polyline(crude_select, unified)
# Caluclate distances
with fiona.open(unified) as sourceline:
df['km_to_crude'] = dq_to_pipes(df, sourceline) / 1000.0
logging.info("km_to_crude calculated")
iter = iter + 1
if selections['ng'] != "":
with fiona.open(ng_utmCA) as source:
with fiona.open(ng_select,
'w',
driver=source.driver,
schema=source.schema,
crs=source.crs) as sink:
for rec in source:
if eval(selections['ng']):
# merge projected pipelines into one geometry
iter = 0
if selections['crude'] != "":
with fiona.open(crude_utmCA) as source:
with fiona.open(temp_select,
'w',
driver=source.driver,
schema=source.schema,
crs=source.crs) as sink:
for rec in source:
if eval(selections['crude']):
sink.write({
'properties': rec['properties'],
'geometry': rec['geometry']
})
unify_polyline(temp_select, unified)
# Caluclate distances
with fiona.open(unified) as sourceline:
df['km_to_crude'] = dq_to_pipes(df, sourceline) / 1000.0
logging.info("km_to_crude calculated")
iter = iter + 1
if selections['ng'] != "":
with fiona.open(ng_utmCA) as source:
with fiona.open(temp_select,
'w',
driver=source.driver,
schema=source.schema,
crs=source.crs) as sink:
for rec in source:
if eval(selections['ng']):
left_on='sr_property_id', right_on='sr_property_id')
else:
df = dfproj
print('df read')
# merge selected out roads
if selections != "":
with fiona.open(hwy_raw) as source:
with fiona.open(temp_select,'w',
driver = source.driver,
schema = source.schema,
crs = source.crs) as sink:
for rec in source:
if eval(selections):
sink.write({'properties': rec['properties'],
'geometry': rec['geometry']})
# Reproject/unify selected roads
reproject(temp_select,hwy_CA_utmCA,ctm)
unify_polyline(hwy_CA_utmCA,unified)
# Caluclate distances
with fiona.open(unified) as sourceline:
df['km_to_roads'] = dq_to_lines(df,sourceline)/1000.0
logging.info("km_to_roads calculated")
df.drop(['sa_x_coord','sa_y_coord'], inplace=True, axis=1)
df.to_stata(outfile)
logging.info("distance datafile saved")
products_utmCA = r"{}/output/products_utmCA.shp".format(buildPath)
dq_utmCA = r"{}/output/dq_utmCA.dta".format(buildPath)
outfile = r"{}/temp/CA_assess_to_pipes.dta".format(buildPath)
# Temp files
crude_unified = "temp_crude.shp"
ng_unified = "temp_ng.shp"
products_unified = "temp_prod.shp"
# California Transverse Mercator Projection
# (http://spatialreference.org/ref/sr-org/7098/)
# Also known as UTM Zone 10.5
ctm = ctm_proj()
# merge projected pipelines into one geometry
unify_polyline(crude_utmCA, crude_unified)
logging.info("crude done")
unify_polyline(ng_utmCA, ng_unified)
logging.info("ng done")
unify_polyline(products_utmCA, products_unified)
logging.info("products done")
# Now read in reprojected all house locations
df = pd.read_stata(dq_utmCA)
# Caluclate distances
with fiona.open(crude_unified) as sourceline:
df['km_to_crude'] = dq_to_pipes(df, sourceline) / 1000.0