# SPDX-FileCopyrightText: : 2017-2022 The PyPSA-Eur Authors # # SPDX-License-Identifier: MIT """ Creates GIS shape files of the countries, exclusive economic zones and `NUTS3 `_ areas. Relevant Settings ----------------- .. code:: yaml countries: .. seealso:: Documentation of the configuration file ``config.yaml`` at :ref:`toplevel_cf` Inputs ------ - ``data/bundle/naturalearth/ne_10m_admin_0_countries.shp``: World country shapes .. image:: ../img/countries.png :scale: 33 % - ``data/bundle/eez/World_EEZ_v8_2014.shp``: World `exclusive economic zones `_ (EEZ) .. image:: ../img/eez.png :scale: 33 % - ``data/bundle/NUTS_2013_60M_SH/data/NUTS_RG_60M_2013.shp``: Europe NUTS3 regions .. image:: ../img/nuts3.png :scale: 33 % - ``data/bundle/nama_10r_3popgdp.tsv.gz``: Average annual population by NUTS3 region (`eurostat `__) - ``data/bundle/nama_10r_3gdp.tsv.gz``: Gross domestic product (GDP) by NUTS 3 regions (`eurostat `__) - ``data/bundle/ch_cantons.csv``: Mapping between Swiss Cantons and NUTS3 regions - ``data/bundle/je-e-21.03.02.xls``: Population and GDP data per Canton (`BFS - Swiss Federal Statistical Office `_ ) Outputs ------- - ``resources/country_shapes.geojson``: country shapes out of country selection .. image:: ../img/country_shapes.png :scale: 33 % - ``resources/offshore_shapes.geojson``: EEZ shapes out of country selection .. image:: ../img/offshore_shapes.png :scale: 33 % - ``resources/europe_shape.geojson``: Shape of Europe including countries and EEZ .. image:: ../img/europe_shape.png :scale: 33 % - ``resources/nuts3_shapes.geojson``: NUTS3 shapes out of country selection including population and GDP data. .. image:: ../img/nuts3_shapes.png :scale: 33 % Description ----------- """ import logging from _helpers import configure_logging import numpy as np from operator import attrgetter from functools import reduce from itertools import takewhile import pandas as pd import geopandas as gpd from shapely.geometry import MultiPolygon, Polygon from shapely.ops import unary_union import pycountry as pyc logger = logging.getLogger(__name__) def _get_country(target, **keys): assert len(keys) == 1 try: return getattr(pyc.countries.get(**keys), target) except (KeyError, AttributeError): return np.nan def _simplify_polys(polys, minarea=0.1, tolerance=0.01, filterremote=True): if isinstance(polys, MultiPolygon): polys = sorted(polys.geoms, key=attrgetter('area'), reverse=True) mainpoly = polys[0] mainlength = np.sqrt(mainpoly.area/(2.*np.pi)) if mainpoly.area > minarea: polys = MultiPolygon([p for p in takewhile(lambda p: p.area > minarea, polys) if not filterremote or (mainpoly.distance(p) < mainlength)]) else: polys = mainpoly return polys.simplify(tolerance=tolerance) def countries(naturalearth, country_list): if 'RS' in country_list: country_list.append('KV') df = gpd.read_file(naturalearth) # Names are a hassle in naturalearth, try several fields fieldnames = (df[x].where(lambda s: s!='-99') for x in ('ISO_A2', 'WB_A2', 'ADM0_A3')) df['name'] = reduce(lambda x,y: x.fillna(y), fieldnames, next(fieldnames)).str[0:2] df = df.loc[df.name.isin(country_list) & ((df['scalerank'] == 0) | (df['scalerank'] == 5))] s = df.set_index('name')['geometry'].map(_simplify_polys) if 'RS' in country_list: s['RS'] = s['RS'].union(s.pop('KV')) return s def eez(country_shapes, eez, country_list): df = gpd.read_file(eez) df = df.loc[df['ISO_3digit'].isin([_get_country('alpha_3', alpha_2=c) for c in country_list])] df['name'] = df['ISO_3digit'].map(lambda c: _get_country('alpha_2', alpha_3=c)) s = df.set_index('name').geometry.map(lambda s: _simplify_polys(s, filterremote=False)) s = gpd.GeoSeries({k:v for k,v in s.iteritems() if v.distance(country_shapes[k]) < 1e-3}) s = s.to_frame("geometry") s.index.name = "name" return s def country_cover(country_shapes, eez_shapes=None): shapes = country_shapes if eez_shapes is not None: shapes = pd.concat([shapes, eez_shapes]) europe_shape = unary_union(shapes) if isinstance(europe_shape, MultiPolygon): europe_shape = max(europe_shape, key=attrgetter('area')) return Polygon(shell=europe_shape.exterior) def nuts3(country_shapes, nuts3, nuts3pop, nuts3gdp, ch_cantons, ch_popgdp): df = gpd.read_file(nuts3) df = df.loc[df['STAT_LEVL_'] == 3] df['geometry'] = df['geometry'].map(_simplify_polys) df = df.rename(columns={'NUTS_ID': 'id'})[['id', 'geometry']].set_index('id') pop = pd.read_table(nuts3pop, na_values=[':'], delimiter=' ?\t', engine='python') pop = (pop .set_index(pd.MultiIndex.from_tuples(pop.pop('unit,geo\\time').str.split(','))).loc['THS'] .applymap(lambda x: pd.to_numeric(x, errors='coerce')) .fillna(method='bfill', axis=1))['2014'] gdp = pd.read_table(nuts3gdp, na_values=[':'], delimiter=' ?\t', engine='python') gdp = (gdp .set_index(pd.MultiIndex.from_tuples(gdp.pop('unit,geo\\time').str.split(','))).loc['EUR_HAB'] .applymap(lambda x: pd.to_numeric(x, errors='coerce')) .fillna(method='bfill', axis=1))['2014'] cantons = pd.read_csv(ch_cantons) cantons = cantons.set_index(cantons['HASC'].str[3:])['NUTS'] cantons = cantons.str.pad(5, side='right', fillchar='0') swiss = pd.read_excel(ch_popgdp, skiprows=3, index_col=0) swiss.columns = swiss.columns.to_series().map(cantons) swiss_pop = pd.to_numeric(swiss.loc['Residents in 1000', 'CH040':]) pop = pd.concat([pop, swiss_pop]) swiss_gdp = pd.to_numeric(swiss.loc['Gross domestic product per capita in Swiss francs', 'CH040':]) gdp = pd.concat([gdp, swiss_gdp]) df = df.join(pd.DataFrame(dict(pop=pop, gdp=gdp))) df['country'] = df.index.to_series().str[:2].replace(dict(UK='GB', EL='GR')) excludenuts = pd.Index(('FRA10', 'FRA20', 'FRA30', 'FRA40', 'FRA50', 'PT200', 'PT300', 'ES707', 'ES703', 'ES704','ES705', 'ES706', 'ES708', 'ES709', 'FI2', 'FR9')) excludecountry = pd.Index(('MT', 'TR', 'LI', 'IS', 'CY', 'KV')) df = df.loc[df.index.difference(excludenuts)] df = df.loc[~df.country.isin(excludecountry)] manual = gpd.GeoDataFrame( [['BA1', 'BA', 3871.], ['RS1', 'RS', 7210.], ['AL1', 'AL', 2893.]], columns=['NUTS_ID', 'country', 'pop'] ).set_index('NUTS_ID') manual['geometry'] = manual['country'].map(country_shapes) manual = manual.dropna() df = pd.concat([df, manual], sort=False) df.loc['ME000', 'pop'] = 650. return df if __name__ == "__main__": if 'snakemake' not in globals(): from _helpers import mock_snakemake snakemake = mock_snakemake('build_shapes') configure_logging(snakemake) country_shapes = countries(snakemake.input.naturalearth, snakemake.config['countries']) country_shapes.reset_index().to_file(snakemake.output.country_shapes) offshore_shapes = eez(country_shapes, snakemake.input.eez, snakemake.config['countries']) offshore_shapes.reset_index().to_file(snakemake.output.offshore_shapes) europe_shape = gpd.GeoDataFrame(geometry=[country_cover(country_shapes, offshore_shapes.geometry)]) europe_shape.reset_index().to_file(snakemake.output.europe_shape) nuts3_shapes = nuts3(country_shapes, snakemake.input.nuts3, snakemake.input.nuts3pop, snakemake.input.nuts3gdp, snakemake.input.ch_cantons, snakemake.input.ch_popgdp) nuts3_shapes.reset_index().to_file(snakemake.output.nuts3_shapes)