95 lines
2.3 KiB
Python
95 lines
2.3 KiB
Python
#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2017-2023 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Extracts capacities of HVDC links from `Wikipedia.
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<https://en.wikipedia.org/wiki/List_of_HVDC_projects>`_.
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Relevant Settings
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-----------------
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.. code:: yaml
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enable:
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prepare_links_p_nom:
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.. seealso::
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Documentation of the configuration file ``config/config.yaml`` at
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:ref:`toplevel_cf`
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Inputs
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------
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*None*
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Outputs
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-------
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- ``data/links_p_nom.csv``: A plain download of https://en.wikipedia.org/wiki/List_of_HVDC_projects#Europe plus extracted coordinates.
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Description
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-----------
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*None*
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"""
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import logging
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import pandas as pd
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from _helpers import configure_logging
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logger = logging.getLogger(__name__)
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def multiply(s):
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return s.str[0].astype(float) * s.str[1].astype(float)
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def extract_coordinates(s):
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regex = (
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r"(\d{1,2})°(\d{1,2})′(\d{1,2})″(N|S) " r"(\d{1,2})°(\d{1,2})′(\d{1,2})″(E|W)"
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)
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e = s.str.extract(regex, expand=True)
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lat = (
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e[0].astype(float) + (e[1].astype(float) + e[2].astype(float) / 60.0) / 60.0
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) * e[3].map({"N": +1.0, "S": -1.0})
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lon = (
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e[4].astype(float) + (e[5].astype(float) + e[6].astype(float) / 60.0) / 60.0
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) * e[7].map({"E": +1.0, "W": -1.0})
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return lon, lat
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if __name__ == "__main__":
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if "snakemake" not in globals():
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from _helpers import mock_snakemake # rule must be enabled in config
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snakemake = mock_snakemake("prepare_links_p_nom", simpl="")
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configure_logging(snakemake)
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links_p_nom = pd.read_html(
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"https://en.wikipedia.org/wiki/List_of_HVDC_projects", header=0, match="SwePol"
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)[0]
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mw = "Power (MW)"
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m_b = links_p_nom[mw].str.contains("x").fillna(False)
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links_p_nom.loc[m_b, mw] = links_p_nom.loc[m_b, mw].str.split("x").pipe(multiply)
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links_p_nom[mw] = (
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links_p_nom[mw].str.extract("[-/]?([\d.]+)", expand=False).astype(float)
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)
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links_p_nom["x1"], links_p_nom["y1"] = extract_coordinates(
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links_p_nom["Converterstation 1"]
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)
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links_p_nom["x2"], links_p_nom["y2"] = extract_coordinates(
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links_p_nom["Converterstation 2"]
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)
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links_p_nom.dropna(subset=["x1", "y1", "x2", "y2"]).to_csv(
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snakemake.output[0], index=False
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)
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