def fill_econ_indicators(ref_year, cntry_info, shp_file, **kwargs):
"""Get GDP and income group per country in reference year, or it closest
one. Source: world bank. Natural earth repository used when missing data.
Modifies country info with values [country id, country name,
country geometry, ref_year, gdp, income_group].
Parameters:
ref_year (int): reference year
cntry_info (dict): key = ISO alpha_3 country, value = [country id,
country name, country geometry]
kwargs (optional): 'gdp' and 'inc_grp' dictionaries with keys the
country ISO_alpha3 code. If provided, these are used
"""
for cntry_iso, cntry_val in cntry_info.items():
cntry_val.append(ref_year)
if 'gdp' in kwargs and kwargs['gdp'][cntry_iso] != '':
gdp_val = kwargs['gdp'][cntry_iso]
else:
_, gdp_val = gdp(cntry_iso, ref_year, shp_file)
cntry_val.append(gdp_val)
if 'inc_grp' in kwargs and kwargs['inc_grp'][cntry_iso] != '':
inc_grp = kwargs['inc_grp'][cntry_iso]
else:
_, inc_grp = income_group(cntry_iso, ref_year, shp_file)
cntry_val.append(inc_grp)
def test_switzerland300_pass(self):
"""Create LitPop entity for Switzerland on 300 arcsec:"""
country_name = ['CHE']
resolution = 300
fin_mode = 'income_group'
with self.assertLogs('climada.entity.exposures.litpop',
level='INFO') as cm:
ent = lp.LitPop.from_countries(country_name,
res_arcsec=resolution,
fin_mode=fin_mode,
reference_year=2016)
self.assertIn('LitPop: Init Exposure for country: CHE', cm.output[0])
self.assertEqual(ent.gdf.region_id.min(), 756)
self.assertEqual(ent.gdf.region_id.max(), 756)
# confirm that the total value is equal to GDP * (income_group+1):
self.assertAlmostEqual(ent.gdf.value.sum() / gdp('CHE', 2016)[1],
(income_group('CHE', 2016)[1] + 1))
self.assertIn("LitPop Exposure for ['CHE'] at 300 as, year: 2016",
ent.tag.description)
self.assertIn('income_group', ent.tag.description)
self.assertIn('1, 1', ent.tag.description)
self.assertTrue(u_coord.equal_crs(ent.crs, 'epsg:4326'))
self.assertEqual(ent.meta['width'], 54)
self.assertEqual(ent.meta['height'], 23)
self.assertTrue(u_coord.equal_crs(ent.meta['crs'], '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], 47.75)
def test_income_grp_sxm_1999_pass(self):
""" Test income_group function Sint Maarten."""
ref_year = 1999
with self.assertLogs('climada.util.finance', level='INFO') as cm:
res_year, res_val = income_group('SXM', ref_year, SHP_FILE)
ref_year = 2010
ref_val = 4
self.assertIn('Income group SXM 2010: 4.', cm.output[0])
self.assertEqual(res_year, ref_year)
self.assertEqual(res_val, ref_val)
for coeff in coeff_types:
all_coeffs.append(coeff + '_' + meth)
cc = cc + 1
# indicies of each skill metric:
rp_i = np.arange(0, cc, len(coeff_types))
rs_i = np.arange(1, cc, len(coeff_types))
rmse_i = np.arange(2, cc, len(coeff_types))
rmsf_i = np.arange(3, cc, len(coeff_types))
colors3 = ['#1b9e77', '#7570b3', '#d95f02']
c3_10 = [0, 0, 0, 0, 0, 1, 1, 2, 2, 2]
income_groups = list()
for cntry in countries:
income_groups.append(income_group(cntry, 2016)[1])
if compute_validation:
"""computation of normalized Gross Regional Product nGRP, skill metrics,
and make scatter plots"""
rho = dict()
adm0 = dict()
adm1 = dict()
# loop over countries, computing nGRP and skill
for i in countries_sel:
print('*** ' + countries[i] + ' *** ')
start_time_c = time.time()
rho[countries[i]], adm0[countries[i]], adm1[countries[i]] =\
lp.admin1_validation(countries[i], methods, exponents_list, \
res_arcsec=resolution, check_plot=False)