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' polynomial transformation
[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()
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))
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, gpd.GeoDataFrame(
pd.concat(bkmrbl_list, ignore_index=True)), crs=DEF_CRS)
# set metadata
self.ref_year = ref_year
self.tag = tag
self.tag.file_name = fn_nl
self.value_unit = 'USD'
rows, cols, ras_trans = pts_to_raster_meta(
(self.longitude.min(), self.latitude.min(),
self.longitude.max(), self.latitude.max()),
(coord_nl[0, 1], -coord_nl[0, 1]))
self.meta = {'width': cols, 'height': rows, 'crs': self.crs, 'transform': ras_trans}
def set_countries(self, countries=[], reg=[], ref_year=2000,
path=None):
"""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): list of country names ISO3
ref_year (int, optional): reference year. Default: 2016
path (string): path to exposure dataset (ISIMIP)
"""
gdp2a_list = []
tag = Tag()
if path is None:
LOGGER.error('No path for exposure data set')
raise NameError
if not os.path.exists(path):
LOGGER.error('Invalid path %s', path)
raise NameError
try:
if not countries:
if reg:
natISO = region2isos(reg)
countries = np.array(natISO)
else:
LOGGER.error('set_countries requires countries or reg')
raise ValueError
for cntr_ind in range(len(countries)):
gdp2a_list.append(self._set_one_country(countries[cntr_ind],
ref_year, path))
tag.description += ("{} GDP2Asset \n").\
format(countries[cntr_ind])
Exposures.__init__(self, gpd.GeoDataFrame(
pd.concat(gdp2a_list, ignore_index=True)))
except KeyError:
LOGGER.error('Exposure countries: %s or reg %s could not be set, check ISO3 or'
' reference year %s', countries, reg, ref_year)
raise KeyError
self.tag = tag
self.ref_year = ref_year
self.value_unit = 'USD'
self.tag.description = 'GDP2Asset ' + str(self.ref_year)
self.crs = DEF_CRS
# set meta
res = 0.0416666
rows, cols, ras_trans = pts_to_raster_meta((self.longitude.min(),
self.latitude.min(),
self.longitude.max(),
self.latitude.max()), res)
self.meta = {'width': cols, 'height': rows, 'crs': self.crs,
'transform': ras_trans}
def set_countries(self,
countries,
ref_year=2016,
res_km=None,
from_hr=None,
**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 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.
kwargs (optional): 'gdp' and 'inc_grp' dictionaries with keys the
country ISO_alpha3 code. 'poly_val' polynomial transformation
[1,x,x^2,...] to apply to nightlight (DEF_POLY_VAL used if not
provided). If provided, these are used.
"""
shp_file = shapereader.natural_earth(resolution='10m',
category='cultural',
name='admin_0_countries')
shp_file = shapereader.Reader(shp_file)
cntry_info, cntry_admin1 = country_iso_geom(countries, shp_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()
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))
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, gpd.GeoDataFrame(pd.concat(bkmrbl_list, ignore_index=True)))
# set metadata
self.ref_year = ref_year
self.tag = tag
self.tag.file_name = fn_nl
self.value_unit = 'USD'
self.crs = {'init': 'epsg:4326'}
def set_countries(self, countries=[], reg=[], ref_year=2000,
path=None):
""" 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): list of country names ISO3
ref_year (int, optional): reference year. Default: 2016
path (string): path to exposure dataset
"""
gdp2a_list = []
tag = Tag()
if path is None:
LOGGER.error('No path for exposure data set')
raise NameError
if not os.path.exists(path):
LOGGER.error('Invalid path ' + path)
raise NameError
try:
if not countries:
if reg:
natID_info = pd.read_csv(NAT_REG_ID)
natISO = natID_info["ISO"][np.isin(natID_info["Reg_name"],
reg)]
countries = np.array(natISO)
else:
LOGGER.error('set_countries requires countries or reg')
raise ValueError
for cntr_ind in range(len(countries)):
gdp2a_list.append(self._set_one_country(countries[cntr_ind],
ref_year, path))
tag.description += ("{} GDP2Asset \n").\
format(countries[cntr_ind])
Exposures.__init__(self, gpd.GeoDataFrame(
pd.concat(gdp2a_list, ignore_index=True)))
except KeyError:
LOGGER.error('Exposure countries: ' + str(countries) + ' or reg ' +
str(reg) + ' could not be set, check ISO3 or' +
' reference year ' + str(ref_year))
raise KeyError
self.ref_year = ref_year
self.value_unit = 'USD'
self.tag = tag
self.crs = DEF_CRS
