def test_pca_DEU_2010_pass(self):
""" Test Processed Capital value Germany 2010."""
ref_year = 2010
cntry_iso = 'DEU'
res_year, res_val, q = world_bank_wealth_account(cntry_iso, ref_year, no_land=0)
res_year_noland, res_val_noland, q = \
world_bank_wealth_account(cntry_iso, ref_year, no_land=1)
ref_val = 17675048450284.9
ref_val_noland = 14254071330874.9
self.assertEqual(res_year, ref_year)
self.assertEqual(q, 1)
self.assertEqual(res_val, ref_val)
self.assertEqual(res_year_noland, ref_year)
self.assertEqual(res_val_noland, ref_val_noland)
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_pca_CUB_2015_pass(self):
""" Test Processed Capital value Cuba 2015 (missing value)."""
ref_year = 2015
cntry_iso = 'CUB'
res_year, res_val, q = world_bank_wealth_account(cntry_iso, ref_year, no_land=1)
ref_val = 108675762920.0
self.assertEqual(q, 0)
self.assertEqual(res_year, ref_year)
self.assertEqual(res_val, ref_val)
def test_pca_DEU_2010_pass(self):
"""Test Processed Capital value Germany 2010."""
ref_year = 2010
cntry_iso = 'DEU'
res_year, res_val, q = world_bank_wealth_account(cntry_iso,
ref_year,
no_land=0)
res_year_noland, res_val_noland, q = world_bank_wealth_account(
cntry_iso, ref_year, no_land=1)
ref_val = [17675048450284.9,
19767982562092.2] # second value as updated by worldbank on
# October 27 2021
ref_val_noland = [14254071330874.9, 15941921421042.1] # dito
self.assertEqual(res_year, ref_year)
self.assertEqual(q, 1)
self.assertIn(res_val, ref_val)
self.assertEqual(res_year_noland, ref_year)
self.assertIn(res_val_noland, ref_val_noland)
def test_tow_IND_1985_pass(self):
""" Test Total Wealth value India 1985 (outside year range)."""
ref_year = 1985
cntry_iso = 'IND'
var_name = 'NW.TOW.TO'
res_year, res_val, _ = world_bank_wealth_account(cntry_iso, ref_year, \
variable_name=var_name)
ref_val = 5415188681948.6
self.assertEqual(res_year, ref_year)
self.assertEqual(res_val, ref_val)
def test_pca_CHE_2008_pass(self):
""" Test Prcoessed Capital per capita Switzerland 2008 (interp.)."""
ref_year = 2008
cntry_iso = 'CHE'
var_name = 'NW.PCA.PC'
res_year, res_val, _ = world_bank_wealth_account(cntry_iso, ref_year, \
variable_name=var_name, no_land=0)
ref_val = 328398.7
self.assertEqual(res_year, ref_year)
self.assertEqual(res_val, ref_val)
def test_pca_CHE_2008_pass(self):
"""Test Prcoessed Capital per capita Switzerland 2008 (interp.)."""
ref_year = 2008
cntry_iso = 'CHE'
var_name = 'NW.PCA.PC'
res_year, res_val, _ = world_bank_wealth_account(
cntry_iso, ref_year, variable_name=var_name, no_land=0)
ref_val = [328398.7, 369081.0
] # second value as updated by worldbank on October 27 2021
self.assertEqual(res_year, ref_year)
self.assertIn(res_val, ref_val)
def test_tow_IND_1985_pass(self):
"""Test Total Wealth value India 1985 (outside year range)."""
ref_year = 1985
cntry_iso = 'IND'
var_name = 'NW.TOW.TO'
res_year, res_val, _ = world_bank_wealth_account(
cntry_iso, ref_year, variable_name=var_name)
ref_val = [5415188681942.5,
5861193808788.2] # second value as updated by worldbank on
# October 27 2021
self.assertEqual(res_year, ref_year)
self.assertIn(res_val, ref_val)
def test_tow_IND_1985_pass(self):
"""Test Total Wealth value India 1985 (outside year range)."""
ref_year = 1985
cntry_iso = 'IND'
var_name = 'NW.TOW.TO'
res_year, res_val, _ = world_bank_wealth_account(
cntry_iso, ref_year, variable_name=var_name)
ref_val = [
5415188681934.5, # values sporadically updated by worldbank
5861193808779.6
] # <- October 27 2021
self.assertEqual(res_year, ref_year)
self.assertIn(res_val, ref_val)
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_reproject_first_false_pass(self):
"""Create LitPop entity for Switzerland 2013 for produced capital
and resampling after combining Lit and Pop:"""
country_name = ['CHE']
fin_mode = 'pc'
resolution = 300
ref_year = 2016
comparison_total_val = world_bank_wealth_account(country_name[0],
ref_year,
no_land=1)[1]
ent = lp.LitPop()
ent.set_countries(country_name,
res_arcsec=resolution,
reference_year=ref_year,
fin_mode=fin_mode,
reproject_first=False)
self.assertEqual(ent.gdf.value.sum(), comparison_total_val)
self.assertEqual(ent.value_unit, 'USD')
def test_switzerland300_admin1_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
comparison_total_val = world_bank_wealth_account(country_name[0],
ref_year,
no_land=1)[1]
ent = lp.LitPop.from_countries(country_name,
res_arcsec=resolution,
reference_year=ref_year,
fin_mode=fin_mode,
admin1_calc=adm1)
self.assertAlmostEqual(np.around(ent.gdf.value.sum() * 1e-9, 0),
np.around(comparison_total_val * 1e-9, 0),
places=0)
self.assertEqual(ent.value_unit, 'USD')
'SUR', 'SWZ', 'SWE', 'SYR', 'TWN', 'TJK', 'TZA', 'THA', \
'TLS', 'TGO', 'TON', 'TTO', 'TUN', 'TKM', 'TCA', 'TUV', \
'UGA', 'UKR', 'ARE', 'GBR', 'URY', 'UZB', 'VUT', 'VEN', \
'VNM', 'VGB', 'VIR', 'WLF', 'YEM', 'ZMB', 'ZWE']
"""
# write countries' metadata to CSV:
countries_metadata = pd.DataFrame(columns=['country_name','iso3',\
'region_id', 'total_value', 'pc'])
if not 'success' in locals():
success = list()
for c in iso_cntry:
print(c.name)
if (c.alpha3 in success) or (c.alpha3 in countries):
countries_metadata = countries_metadata.append({'country_name' : c.name, \
'iso3' : c.alpha3, \
'region_id' : c.numeric, \
'total_value' : world_bank_wealth_account(c.alpha3, 2014)[1] , \
'pc' : world_bank_wealth_account(c.alpha3, 2014)[2]} , \
ignore_index=True)
else:
countries_metadata = countries_metadata.append({'country_name' : c.name, \
'iso3' : c.alpha3, \
'region_id' : c.numeric, \
'total_value' : float('NaN')} , \
ignore_index=True)
countries_metadata.to_csv(os.path.join(output_path, \
'LitPop_countries_META_pc_' + str(yyyy) + \
'_' + version + '.csv'))
