add biomass potentials docstrings
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@ -41,6 +41,23 @@ def build_nuts_population_data(year=2013):
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def enspreso_biomass_potentials(year=2020, scenario="ENS_Low"):
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def enspreso_biomass_potentials(year=2020, scenario="ENS_Low"):
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"""
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Loads the JRC ENSPRESO biomass potentials.
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Parameters
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----------
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year : int
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The year for which potentials are to be taken.
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Can be {2010, 2020, 2030, 2040, 2050}.
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scenario : str
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The scenario. Can be {"ENS_Low", "ENS_Med", "ENS_High"}.
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Returns
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-------
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pd.DataFrame
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Biomass potentials for given year and scenario
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in TWh/a by commodity and NUTS2 region.
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"""
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glossary = pd.read_excel(
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glossary = pd.read_excel(
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str(snakemake.input.enspreso_biomass),
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str(snakemake.input.enspreso_biomass),
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@ -84,6 +101,20 @@ def enspreso_biomass_potentials(year=2020, scenario="ENS_Low"):
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def disaggregate_nuts0(bio):
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def disaggregate_nuts0(bio):
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"""
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Some commodities are only given on NUTS0 level.
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These are disaggregated here using the NUTS2
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population as distribution key.
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Parameters
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----------
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bio : pd.DataFrame
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from enspreso_biomass_potentials()
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Returns
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-------
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pd.DataFrame
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"""
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pop = build_nuts_population_data()
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pop = build_nuts_population_data()
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@ -125,6 +156,21 @@ def area(gdf):
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def convert_nuts2_to_regions(bio_nuts2, regions):
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def convert_nuts2_to_regions(bio_nuts2, regions):
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"""
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Converts biomass potentials given in NUTS2 to PyPSA-Eur regions based on the
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overlay of both GeoDataFrames in proportion to the area.
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Parameters
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----------
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bio_nuts2 : gpd.GeoDataFrame
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JRC ENSPRESO biomass potentials indexed by NUTS2 shapes.
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regions : gpd.GeoDataFrame
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PyPSA-Eur clustered onshore regions
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Returns
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-------
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gpd.GeoDataFrame
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"""
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# calculate area of nuts2 regions
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# calculate area of nuts2 regions
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bio_nuts2["area_nuts2"] = area(bio_nuts2)
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bio_nuts2["area_nuts2"] = area(bio_nuts2)
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