62 lines
1.5 KiB
Python
62 lines
1.5 KiB
Python
# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2024 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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"""
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Build historical annual ammonia production per country in ktonNH3/a.
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Inputs
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-------
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- ``data/bundle-sector/myb1-2017-nitro.xls``
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Outputs
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-------
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- ``resources/ammonia_production.csv``
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Description
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-------
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This functions takes data from the `Minerals Yearbook <https://www.usgs.gov/centers/national-minerals-information-center/nitrogen-statistics-and-information>`_
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(July 2024) published by the US Geological Survey (USGS) and the National Minerals Information Center and extracts the annual ammonia production per country in ktonN/a. The data is converted to ktonNH3/a.
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"""
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import country_converter as coco
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import pandas as pd
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from _helpers import set_scenario_config
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cc = coco.CountryConverter()
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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
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snakemake = mock_snakemake("build_ammonia_production")
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set_scenario_config(snakemake)
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ammonia = pd.read_excel(
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snakemake.input.usgs,
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sheet_name="T12",
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skiprows=5,
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header=0,
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index_col=0,
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skipfooter=7,
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na_values=["--"],
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)
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ammonia.index = cc.convert(ammonia.index, to="iso2")
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years = [str(i) for i in range(2018, 2023)]
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ammonia = ammonia.rename(columns=lambda x: str(x))[years]
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# convert from ktonN to ktonNH3
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ammonia *= 17 / 14
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ammonia.index.name = "ktonNH3/a"
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ammonia.to_csv(snakemake.output.ammonia_production)
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