def test_concat_fail(self):
"""Test failing concat function with fake data."""
with self.assertRaises(TypeError):
Exposures.concat([
self.dummy, self.dummy.gdf, self.dummy.gdf.values, self.dummy
])
def test_concat_pass(self):
"""Test concat function with fake data."""
self.dummy.check()
catexp = Exposures.concat([self.dummy, self.dummy.gdf, pd.DataFrame(self.dummy.gdf.values, columns=self.dummy.gdf.columns), self.dummy])
self.assertEqual(self.dummy.gdf.shape, (10,5))
self.assertEqual(catexp.gdf.shape, (40,5))
self.assertTrue(u_coord.equal_crs(catexp.crs, 'epsg:3395'))
def set_countries(self,
countries,
ref_year=2016,
res_km=None,
from_hr=None,
admin_file='admin_0_countries',
**kwargs):
""" Model countries using values at reference year. If GDP or income
group not available for that year, consider the value of the closest
available year.
Parameters:
countries (list or dict): list of country names (admin0 or subunits)
or dict with key = admin0 name and value = [admin1 names]
ref_year (int, optional): reference year. Default: 2016
res_km (float, optional): approx resolution in km. Default:
nightlights resolution.
from_hr (bool, optional): force to use higher resolution image,
independently of its year of acquisition.
admin_file (str): file name, admin_0_countries or admin_0_map_subunits
kwargs (optional): 'gdp' and 'inc_grp' dictionaries with keys the
country ISO_alpha3 code. 'poly_val' list of polynomial coefficients
[1,x,x^2,...] to apply to nightlight (DEF_POLY_VAL used if not
provided). If provided, these are used.
"""
admin_key_dict = {
'admin_0_countries': ['ADMIN', 'ADM0_A3'],
'admin_0_map_subunits': ['SUBUNIT', 'SU_A3']
}
shp_file = shapereader.natural_earth(resolution='10m',
category='cultural',
name=admin_file)
shp_file = shapereader.Reader(shp_file)
cntry_info, cntry_admin1 = country_iso_geom(countries, shp_file,
admin_key_dict[admin_file])
fill_econ_indicators(ref_year, cntry_info, shp_file, **kwargs)
nightlight, coord_nl, fn_nl, res_fact, res_km = get_nightlight(
ref_year, cntry_info, res_km, from_hr)
tag = Tag(file_name=fn_nl)
bkmrbl_list = []
for cntry_iso, cntry_val in cntry_info.items():
bkmrbl_list.append(
self._set_one_country(cntry_val, nightlight, coord_nl,
res_fact, res_km,
cntry_admin1[cntry_iso], **kwargs).gdf)
tag.description += ("{} {:d} GDP: {:.3e} income group: {:d} \n").\
format(cntry_val[1], cntry_val[3], cntry_val[4], cntry_val[5])
Exposures.__init__(self,
data=Exposures.concat(bkmrbl_list).gdf,
crs=DEF_CRS,
ref_year=ref_year,
tag=tag,
value_unit='USD')
rows, cols, ras_trans = pts_to_raster_meta(
(self.gdf.longitude.min(), self.gdf.latitude.min(),
self.gdf.longitude.max(), self.gdf.latitude.max()),
(coord_nl[0, 1], -coord_nl[0, 1]))
self.meta = {
'width': cols,
'height': rows,
'crs': self.crs,
'transform': ras_trans
}
