pypsa-eur/scripts/build_shapes.py

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

try:
    from countrycode.countrycode import countrycode
except ImportError:
    from countrycode import countrycode

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):
    cntries = snakemake.config['countries']
    cntries3 = frozenset(countrycode(cntries, origin='iso2c', target='iso3c'))
    df = gpd.read_file(snakemake.input.eez)
    df = df.loc[df['ISO_3digit'].isin(cntries3)]
    df['name'] = countrycode(df['ISO_3digit'], origin='iso3c', target='iso2c')
    s = df.set_index('name').geometry.map(lambda s: _simplify_polys(s, filterremote=False))
    return gpd.GeoSeries({k:v for k,v in s.iteritems() if v.distance(country_shapes[k]) < 1e-3})

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)

    df.loc['ME000', 'pop'] = 650.

    return df

def save_to_geojson(s, fn):
    if os.path.exists(fn):
        os.unlink(fn)
    if isinstance(s, gpd.GeoDataFrame):
        s = s.reset_index()
    s.to_file(fn, driver='GeoJSON')

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)