# -*- coding: utf-8 -*- # SPDX-FileCopyrightText: : 2023-2024 The PyPSA-Eur Authors # # SPDX-License-Identifier: MIT """ Build regional demand for international navigation based on outflow volume of ports. """ import json import geopandas as gpd import pandas as pd from _helpers import set_scenario_config if __name__ == "__main__": if "snakemake" not in globals(): from _helpers import mock_snakemake snakemake = mock_snakemake("build_shipping_demand", clusters=48) set_scenario_config(snakemake) scope = gpd.read_file(snakemake.input.scope).geometry[0] regions = gpd.read_file(snakemake.input.regions).set_index("name") demand = pd.read_csv(snakemake.input.demand, index_col=[0, 1])[ "total international navigation" ] demand = demand.xs(snakemake.params.energy_totals_year, level=1) # read port data into GeoDataFrame with open(snakemake.input.ports, "r", encoding="latin_1") as f: ports = json.load(f) ports = pd.json_normalize(ports, "features", sep="_") coordinates = ports.geometry_coordinates geometry = gpd.points_from_xy(coordinates.str[0], coordinates.str[1]) ports = gpd.GeoDataFrame(ports, geometry=geometry, crs=4326) # filter global port data by European ports european_ports = ports[ports.within(scope)] # assign ports to nearest region p = european_ports.to_crs(3857) r = regions.to_crs(3857) outflows = p.sjoin_nearest(r).groupby("name").properties_outflows.sum().div(1e3) # calculate fraction of each country's port outflows countries = outflows.index.str[:2] outflows_per_country = outflows.groupby(countries).sum() fraction = outflows / countries.map(outflows_per_country) # distribute per-country demands to nodes based on these fractions nodal_demand = demand.loc[countries].fillna(0.0) nodal_demand.index = fraction.index nodal_demand = nodal_demand.multiply(fraction, axis=0) nodal_demand = nodal_demand.reindex(regions.index, fill_value=0) # export nodal international navigation demands nodal_demand.to_csv(snakemake.output[0])