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pypsa-eur/scripts/build_shapes.py

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# -*- 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 <https://en.wikipedia.org/wiki/Exclusive_economic_zone>`_ (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 <http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3popgdp&lang=en>`__)
- ``data/bundle/nama_10r_3gdp.tsv.gz``: Gross domestic product (GDP) by NUTS 3 regions (`eurostat <http://appsso.eurostat.ec.europa.eu/nui/show.do?dataset=nama_10r_3gdp&lang=en>`__)
- ``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 <https://www.bfs.admin.ch/bfs/en/home/news/whats-new.assetdetail.7786557.html>`_ )
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"],
)
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.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)
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