""" 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 os import numpy as np from operator import attrgetter from six.moves import reduce from itertools import takewhile import pandas as pd import geopandas as gpd from shapely.geometry import MultiPolygon, Polygon from shapely.ops import cascaded_union import pycountry as pyc 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, 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(): cntries = snakemake.config['countries'] if 'RS' in cntries: cntries.append('KV') df = gpd.read_file(snakemake.input.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(cntries) & (df['scalerank'] == 0)] s = df.set_index('name')['geometry'].map(_simplify_polys) if 'RS' in cntries: s['RS'] = s['RS'].union(s.pop('KV')) return s def eez(country_shapes): df = gpd.read_file(snakemake.input.eez) df = df.loc[df['ISO_3digit'].isin([_get_country('alpha_3', alpha_2=c) for c in snakemake.config['countries']])] 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.index.name = "name" return s def country_cover(country_shapes, eez_shapes=None): shapes = list(country_shapes) if eez_shapes is not None: shapes += list(eez_shapes) europe_shape = cascaded_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): df = gpd.read_file(snakemake.input.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(snakemake.input.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(snakemake.input.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'] # Swiss data cantons = pd.read_csv(snakemake.input.ch_cantons) cantons = cantons.set_index(cantons['HASC'].str[3:])['NUTS'] cantons = cantons.str.pad(5, side='right', fillchar='0') swiss = pd.read_excel(snakemake.input.ch_popgdp, skiprows=3, index_col=0) swiss.columns = swiss.columns.to_series().map(cantons) pop = pop.append(pd.to_numeric(swiss.loc['Residents in 1000', 'CH040':])) gdp = gdp.append(pd.to_numeric(swiss.loc['Gross domestic product per capita in Swiss francs', 'CH040':])) 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 = df.append(manual, sort=False) df.loc['ME000', 'pop'] = 650. return df def save_to_geojson(df, fn): if os.path.exists(fn): os.unlink(fn) if not isinstance(df, gpd.GeoDataFrame): df = gpd.GeoDataFrame(dict(geometry=df)) df = df.reset_index() schema = {**gpd.io.file.infer_schema(df), 'geometry': 'Unknown'} df.to_file(fn, driver='GeoJSON', schema=schema) if __name__ == "__main__": # Detect running outside of snakemake and mock snakemake for testing if 'snakemake' not in globals(): from vresutils.snakemake import MockSnakemake, Dict snakemake = MockSnakemake( path='..', wildcards={}, input=Dict( naturalearth='data/bundle/naturalearth/ne_10m_admin_0_countries.shp', eez='data/bundle/eez/World_EEZ_v8_2014.shp', nuts3='data/bundle/NUTS_2013_60M_SH/data/NUTS_RG_60M_2013.shp', nuts3pop='data/bundle/nama_10r_3popgdp.tsv.gz', nuts3gdp='data/bundle/nama_10r_3gdp.tsv.gz', ch_cantons='data/bundle/ch_cantons.csv', ch_popgdp='data/bundle/je-e-21.03.02.xls' ), output=Dict( country_shapes='resources/country_shapes.geojson', offshore_shapes='resource/offshore_shapes.geojson', europe_shape='resources/europe_shape.geojson', nuts3_shapes='resources/nuts3_shapes.geojson' ) ) country_shapes = countries() save_to_geojson(country_shapes, snakemake.output.country_shapes) offshore_shapes = eez(country_shapes) save_to_geojson(offshore_shapes, snakemake.output.offshore_shapes) europe_shape = country_cover(country_shapes, offshore_shapes) save_to_geojson(gpd.GeoSeries(europe_shape), snakemake.output.europe_shape) nuts3_shapes = nuts3(country_shapes) save_to_geojson(nuts3_shapes, snakemake.output.nuts3_shapes)