def test_switzerland300_pc2016_pass(self):
"""Create LitPop entity for Switzerland 2016 with admin1 and produced capital:"""
country_name = ['CHE']
fin_mode = 'pc'
resolution = 300
ref_year = 2016
adm1 = True
cons = True
comparison_total_val = world_bank_wealth_account(country_name[0],
ref_year,
no_land=1)[1]
ent = LitPop()
with self.assertLogs('climada.entity.exposures.litpop',
level='INFO') as cm:
ent.set_country(country_name,
res_arcsec=resolution,
reference_year=ref_year,
fin_mode=fin_mode,
conserve_cntrytotal=cons,
calc_admin1=adm1)
# print(cm)
self.assertIn('Generating LitPop data at a resolution of 300 arcsec',
cm.output[0])
self.assertEqual(np.around(ent.gdf.value.sum(), 0),
np.around(comparison_total_val, 0))
self.assertAlmostEqual(ent.gdf.value.sum() / 2225854927260, 1.0)
def test_switzerland300_pass(self):
"""Create LitPop entity for Switzerland on 300 arcsec:"""
country_name = ['CHE']
resolution = 300
fin_mode = 'income_group'
ent = LitPop()
with self.assertLogs('climada.entity.exposures.litpop',
level='INFO') as cm:
ent.set_country(country_name,
res_arcsec=resolution,
fin_mode=fin_mode)
# print(cm)
self.assertIn('Generating LitPop data at a resolution of 300 arcsec',
cm.output[0])
self.assertTrue(ent.region_id.min() == 756)
self.assertTrue(ent.region_id.max() == 756)
self.assertTrue(np.int(ent.value.sum().round()) == 3350905328146)
self.assertIn('LitPop for Switzerland at 300 as, year=2016',
ent.tag.description)
self.assertIn('financial mode=income_group', ent.tag.description)
self.assertIn('GPW-year=2015', ent.tag.description)
self.assertIn('BM-year=2016', ent.tag.description)
self.assertIn('exp=[1, 1]', ent.tag.description)
self.assertTrue(equal_crs(ent.crs['init'], {'init': 'epsg:4326'}))
self.assertEqual(ent.meta['width'], 54)
self.assertEqual(ent.meta['height'], 23)
self.assertTrue(equal_crs(ent.meta['crs'], {'init': 'epsg:4326'}))
self.assertAlmostEqual(ent.meta['transform'][0], 0.08333333333333333)
self.assertAlmostEqual(ent.meta['transform'][1], 0)
self.assertAlmostEqual(ent.meta['transform'][2], 5.9166666666666)
self.assertAlmostEqual(ent.meta['transform'][3], 0)
self.assertAlmostEqual(ent.meta['transform'][4], -0.08333333333333333)
self.assertAlmostEqual(ent.meta['transform'][5], 47.7499999999999)
def test_exposure_set_admin1_BGD(self):
"""Test function exposure_set_admin1 of litpop for Bangladesh"""
country_name = ['BGD']
resolution = 600
ent = LitPop()
ent.set_country(country_name, res_arcsec=resolution)
ent = lp.exposure_set_admin1(ent, 600)
self.assertIn(5492, ent.admin1_ID.values)
self.assertIn('Chittagong', ent.admin1.values)
self.assertEqual(len(np.unique(ent.admin1_ID)), 7)
self.assertEqual(np.min(np.unique(ent.admin1_ID)), 1806.0)
def test_suriname30_nfw_pass(self):
"""Create LitPop entity for Suriname for non-finanical wealth:"""
country_name = ['SUR']
fin_mode = 'nfw'
ent = LitPop()
with self.assertLogs('climada.entity.exposures.litpop',
level='INFO') as cm:
ent.set_country(country_name,
reference_year=2016,
fin_mode=fin_mode)
# print(cm)
self.assertIn('Generating LitPop data at a resolution of 30.0 arcsec',
cm.output[0])
self.assertTrue(ent.region_id.min() == 740)
self.assertTrue(ent.region_id.max() == 740)
self.assertTrue(np.int(ent.value.sum().round()) == 2332806589)
def test_switzerland30normPop_pass(self):
"""Create LitPop entity for Switzerland on 30 arcsec:"""
country_name = ['CHE']
resolution = 30
exp = [0, 1]
fin_mode = 'norm'
ent = LitPop()
with self.assertLogs('climada.entity.exposures.litpop',
level='INFO') as cm:
ent.set_country(country_name, res_arcsec=resolution, exponent=exp,\
fin_mode=fin_mode, reference_year=2015)
# print(cm)
self.assertIn('Generating LitPop data at a resolution of 30 arcsec',
cm.output[0])
self.assertTrue(ent.region_id.min() == 756)
self.assertTrue(ent.region_id.max() == 756)
self.assertTrue(np.int((1000 * ent.value.sum()).round()) == 1000)
def test_switzerland300_pc2013_pass(self):
"""Create LitPop entity for Switzerland 2013 for produced capital:"""
country_name = ['CHE']
fin_mode = 'pc'
resolution = 300
ref_year = 2013
comparison_total_val = world_bank_wealth_account(country_name[0], \
ref_year, no_land=1)[1]
ent = LitPop()
with self.assertLogs('climada.entity.exposures.litpop',
level='INFO') as cm:
ent.set_country(country_name, res_arcsec=resolution, \
reference_year=ref_year, fin_mode=fin_mode)
# print(cm)
self.assertIn('Generating LitPop data at a resolution of 300 arcsec',
cm.output[0])
self.assertTrue(ent.value.sum() == comparison_total_val)
self.assertTrue(np.int(ent.value.sum().round()) == 2296358085749)
def test_switzerland300_pass(self):
"""Create LitPop entity for Switzerland on 300 arcsec:"""
country_name = ['CHE']
resolution = 300
fin_mode = 'income_group'
ent = LitPop()
with self.assertLogs('climada.entity.exposures.litpop',
level='INFO') as cm:
ent.set_country(country_name,
res_arcsec=resolution,
fin_mode=fin_mode)
# print(cm)
self.assertIn('Generating LitPop data at a resolution of 300 arcsec',
cm.output[0])
self.assertTrue(ent.region_id.min() == 756)
self.assertTrue(ent.region_id.max() == 756)
self.assertTrue(np.int(ent.value.sum().round()) == 3350905328146)
self.assertIn('LitPop for Switzerland at 300 as, year=2016',
ent.tag.description)
self.assertIn('financial mode=income_group', ent.tag.description)
self.assertIn('GPW-year=2015', ent.tag.description)
self.assertIn('BM-year=2016', ent.tag.description)
self.assertIn('exp=[1, 1]', ent.tag.description)
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
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
filename_start = 'LitPop_pc_%iarcsec_' % (RES_ARCSEC)
RES_DIR = output_path # '/Users/eberenzs/Documents/Projects/climada_papers/201903_litpop_exposure_data_model/climada_v1.3.1/results'
write_to_hdf5 = True
try_read_from_hdf5 = True
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),
# x0 = list(param_dict.values())
# 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()
