def test_validation_switzerland30(self):
"""Validation for Switzerland: two combinations of Lit and Pop,
checking Pearson correlation coefficient and RMSF"""
rho = lp.admin1_validation('CHE', ['LitPop', 'Lit5'], [[1, 1], [5, 0]],\
res_arcsec=30, check_plot=False)[0]
self.assertTrue(np.int(round(rho[0] * 1e12)) == 945416798729)
self.assertTrue(np.int(round(rho[-1] * 1e12)) == 3246081648798)
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)
plt.figure() # Scatter plot per country
lit3_scatter = plt.scatter(adm1[countries[i]]['Lit3'], \
adm0[countries[i]]['Lit3'], c=colors3[0], marker='^')
pop_scatter = plt.scatter(adm1[countries[i]]['Pop'], \
adm0[countries[i]]['Pop'], c=colors3[1])
litpop_scatter = plt.scatter(adm1[countries[i]]['LitPop'], \
adm0[countries[i]]['LitPop'], c=colors3[2])
plt.plot([0, np.max([plt.gca().get_xlim()[1], plt.gca().get_ylim()[1]])],
[0, np.max([plt.gca().get_xlim()[1], plt.gca().get_ylim()[1]])],\
ls="--", c=".3")
plt.legend((litpop_scatter, lit3_scatter, pop_scatter),\
(r'$LitPop$', r'$Lit^3$', r'$Pop$',))
plt.xlabel('Reference nGRP')