def upsample(cntry, group):
"""
Distributes load in country according to population and gdp
"""
l = gegis_load.loc[gegis_load.region_code ==
cntry]["Electricity demand"]
if len(group) == 1:
return pd.DataFrame({group.index[0]: l})
else:
shapes_cntry = shapes.loc[shapes.country == cntry]
transfer = vtransfer.Shapes2Shapes(group,
shapes_cntry.geometry,
normed=False).T.tocsr()
gdp_n = pd.Series(transfer.dot(
shapes_cntry["gdp"].fillna(1.0).values),
index=group.index)
pop_n = pd.Series(transfer.dot(
shapes_cntry["pop"].fillna(1.0).values),
index=group.index)
# relative factors 0.6 and 0.4 have been determined from a linear
# regression on the country to EU continent load data
# (refer to vresutils.load._upsampling_weights)
# TODO: require adjustment for Africa
factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
return pd.DataFrame(
factors.values * l.values[:, np.newaxis],
index=l.index,
columns=factors.index,
)
def upsample(cntry, group):
l = opsd_load[cntry]
if len(group) == 1:
return pd.DataFrame({group.index[0]: l})
else:
nuts3_cntry = nuts3.loc[nuts3.country == cntry]
transfer = vtransfer.Shapes2Shapes(group, nuts3_cntry.geometry, normed=False).T.tocsr()
gdp_n = pd.Series(transfer.dot(nuts3_cntry['gdp'].fillna(1.).values), index=group.index)
pop_n = pd.Series(transfer.dot(nuts3_cntry['pop'].fillna(1.).values), index=group.index)
# relative factors 0.6 and 0.4 have been determined from a linear
# regression on the country to continent load data (refer to vresutils.load._upsampling_weights)
factors = normed(0.6 * normed(gdp_n) + 0.4 * normed(pop_n))
return pd.DataFrame(factors.values * l.values[:,np.newaxis], index=l.index, columns=factors.index)
def build_population_map():
#2014 populations for NUTS3
gdp, pop = vload.gdppop_nuts3()
#pd.Series nuts3 code -> 2-letter country codes
mapping = vmapping.countries_to_nuts3()
countries = mapping.value_counts().index.sort_values()
#Swiss fix
pop["CH040"] = pop["CH04"]
pop["CH070"] = pop["CH07"]
#Separately researched for Montenegro, Albania, Bosnia, Serbia
pop["ME000"] = 650
pop["AL1"] = 2893
pop["BA1"] = 3871
pop["RS1"] = 7210
#pd.Series nuts3 code -> polygon
nuts3 = pd.Series(vshapes.nuts3(tolerance=None, minarea=0.))
#takes 10 minutes
pop_map = pd.DataFrame()
for country in countries:
print(country)
country_nuts = mapping.index[mapping == country]
trans_matrix = vtransfer.Shapes2Shapes(np.asarray(nuts3[country_nuts]),
grid_cells)
#CH has missing bits
country_pop = pop[country_nuts].fillna(0.)
pop_map[country] = np.array(
trans_matrix.multiply(np.asarray(country_pop)).sum(axis=1))[:, 0]
with pd.HDFStore(snakemake.output.pop_map_name, mode='w',
complevel=4) as store:
store['population_gridcell_map'] = pop_map