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
if not os.path.exists(RES_DIR):
os.makedirs(RES_DIR)
files = [i for i in os.listdir(ENTITY_DIR) if os.path.isfile(os.path.join(ENTITY_DIR,i)) and \
filename_start in i]
files = np.unique(files)
print('Number of country exposure files: %i' % (len(files)))
#
exposure_data = LitPop()
if try_read_from_hdf5 and os.path.exists(
os.path.join(ENTITY_DIR, 'LitPop_pc_%iarcsec_000_all.hdf5' %
(RES_ARCSEC))):
exposure_data.read_hdf5(
os.path.join(ENTITY_DIR,
'LitPop_pc_%iarcsec_000_all.hdf5' % (RES_ARCSEC)))
else:
grid_stats = pd.DataFrame(
index=np.arange(0, len(files)),
columns=['country', 'grid_count', 'sum', 'max', 'mean', 'median'])
for idx, fi in enumerate(files):
print('Loading: %s ...' % (fi))
exposure_tmp = LitPop()
exposure_tmp = exposure_tmp.from_csv(os.path.join(ENTITY_DIR, fi),
index_col=None)
exposure_data = exposure_data.append(exposure_tmp)
# print('Max. grid cell value: USD %1.0f' %(exposure_tmp.value.max()))
grid_stats.loc[idx, 'country'] = fi[-7:-4]
grid_stats.loc[idx, 'grid_count'] = exposure_tmp.value[
exposure_tmp.value > 0].count()
# res = minimize(specific_calib, x0,
# constraints=cons,
# method='SLSQP',
# options={'xtol': 1e-5, 'disp': True, 'maxiter': 50})
#
# return param_dict_result
if __name__ == "__main__":
# yearly
## tryout calib_all
hazard = TropCyclone()
hazard.read_hdf5('C:/Users/ThomasRoosli/tc_NA_hazard.hdf5')
exposure = LitPop()
exposure.read_hdf5('C:/Users/ThomasRoosli/DOM_LitPop.hdf5')
if_name_or_instance = 'emanuel'
param_full_dict = {'v_thresh': [25.7, 20], 'v_half': [70], 'scale': [1, 0.8]}
impact_data_source = {'emdat':('D:/Documents_DATA/EM-DAT/'
'20181031_disaster_list_all_non-technological/'
'ThomasRoosli_2018-10-31.csv')}
year_range = [2004, 2017]
yearly_impact = True
df_result = calib_all(hazard,exposure,if_name_or_instance,param_full_dict,
impact_data_source, year_range, yearly_impact)
reference_year=REF_YEAR)
# ## tryout calib_optimize
# hazard = TropCyclone()
# hazard.read_hdf5('C:/Users/ThomasRoosli/tc_NA_hazard.hdf5')