# -*- coding: utf-8 -*- # SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors # # SPDX-License-Identifier: MIT """ Creates GIS shape files of the countries, exclusive economic zones and `NUTS3 < https://en.wikipedia.org/wiki/Nomenclature_of_Territorial_Units_for_Statistics> `_ areas. Relevant Settings ----------------- .. code:: yaml countries: .. seealso:: Documentation of the configuration file ``config/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 functools import reduce from itertools import takewhile from operator import attrgetter import geopandas as gpd import numpy as np import pandas as pd import pycountry as pyc from _helpers import configure_logging from shapely.geometry import MultiPolygon, Polygon 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 / (2.0 * 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")) # cleanup shape union s["RS"] = Polygon(s["RS"].exterior.coords) 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.items() 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 = shapes.unary_union 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", 3871.0], ["RS1", "RS", 7210.0], ["AL1", "AL", 2893.0]], columns=["NUTS_ID", "country", "pop"], geometry=gpd.GeoSeries(), ) manual["geometry"] = manual["country"].map(country_shapes) manual = manual.dropna() manual = manual.set_index("NUTS_ID") manual = manual.set_crs("ETRS89") df = pd.concat([df, manual], sort=False) df.loc["ME000", "pop"] = 650.0 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.params.countries) country_shapes.reset_index().to_file(snakemake.output.country_shapes) offshore_shapes = eez( country_shapes, snakemake.input.eez, snakemake.params.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)