def get_entity(country, exponents, description_str, date_str):
file_name = (country + '_litpop_' + description_str + '_' + date_str +
'.hdf5')
exposure_file = os.path.abspath(os.path.join( \
CONFIG['local_data']['save_dir'], file_name))
if os.path.isfile(exposure_file):
exposure = LitPop()
exposure.read_hdf5(exposure_file)
# with open(exposure_file, 'rb') as f:
# exposure = pickle.load(f)
else:
exposure = LitPop()
# The arguments 'exponent' is used to set the power with which Lit and Pop go into LitPop:
exposure.set_country(country, exponents=exponents, reference_year=2014)
exposure.set_geometry_points()
exposure.check()
exposure.tag.description = (exposure.tag.description + '_' +
description_str)
exposure.set_lat_lon()
exposure.write_hdf5(exposure_file)
#pickle.dump(exposure,open(exposure_file, 'wb'))
return exposure
def _get_litpop_bbox(country, highValueArea, **kwargs):
"""get litpop exposure for the bbox area of the queried OSM features
Parameters:
country (str)
highValueArea (GeoDataFrame)
bbox (array)
kwargs (dict): arguments for LitPop set_country method
Returns:
exp_sub (LitPop)
"""
# Load LitPop Exposure for whole country, and High Value Area
exp = LitPop()
exp.set_country(country, **kwargs)
exp.set_geometry_points()
# Crop bbox of High Value Area from Country Exposure
exp.gdf = exp.gdf.cx[min(highValueArea.bounds.minx):max(highValueArea.bounds.maxx),
min(highValueArea.bounds.miny):max(highValueArea.bounds.maxy)]
return exp
grid_stats.loc[idx, 'mean'] = exposure_tmp.value.mean()
grid_stats.loc[idx, 'median'] = exposure_tmp.value.median()
del exposure_tmp
grid_stats.to_csv(
os.path.join(
RES_DIR,
'LitPop_pc_%iarcsec_%i_grid_stats.csv' % (RES_ARCSEC, REF_YEAR)))
grid_stats.to_csv(
os.path.join(
ENTITY_DIR,
'LitPop_pc_%iarcsec_%i_grid_stats.csv' % (RES_ARCSEC, REF_YEAR)))
exposure_data = Exposures(exposure_data)
print('\n' + '\x1b[1;03;30;30m' + 'exposure_data is now an Exposures:',
str(type(exposure_data)) + '\x1b[0m')
exposure_data.set_geometry_points(
) # set geometry attribute (shapely Points) from GeoDataFrame from latitude and longitude
print('\n' + '\x1b[1;03;30;30m' + 'check method logs:' + '\x1b[0m')
exposure_data.check() # puts metadata that has not been assigned
print('\n' + '\x1b[1;03;30;30m' + 'exposure_data looks like:' + '\x1b[0m')
print(exposure_data.head())
print('\n' + '\x1b[1;03;30;30m' + 'plotting global map...' + '\x1b[0m')
print('Global max. grid cell value: USD %1.0f' %
(exposure_data.value.max()))
print('Global mean grid cell value: USD %1.0f' %
(exposure_data.value.mean()))
print('Global median grid cell value: USD %1.0f' %
(exposure_data.value.median()))
if write_to_hdf5:
exposure_data.write_hdf5(
os.path.join(ENTITY_DIR,
