[pre-commit.ci] auto fixes from pre-commit.com hooks
for more information, see https://pre-commit.ci
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@ -7,7 +7,7 @@ PRODUCTION_PLOTS = [
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"production_deviation_bar",
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"seasonal_operation_area",
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]
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CROSS_BORDER_PLOTS = ["trade_time_series","cross_border_bar"]
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CROSS_BORDER_PLOTS = ["trade_time_series", "cross_border_bar"]
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PRICES_PLOTS = ["price_bar", "price_line"]
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@ -4,10 +4,10 @@
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#
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# SPDX-License-Identifier: MIT
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import country_converter as coco
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import matplotlib.pyplot as plt
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import pandas as pd
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import pypsa
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import country_converter as coco
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import seaborn as sns
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from _helpers import configure_logging
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@ -51,6 +51,7 @@ color_country = {
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"SK": "#d3e75a",
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}
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def sort_one_country(country, df):
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indices = [link for link in df.columns if country in link]
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df_country = df[indices].copy()
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@ -64,10 +65,9 @@ def sort_one_country(country, df):
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def cross_border_time_series(countries, data):
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fig, ax = plt.subplots(2*len(countries), 1, figsize=(15, 10*len(countries)))
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fig, ax = plt.subplots(2 * len(countries), 1, figsize=(15, 10 * len(countries)))
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axis = 0
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for country in countries:
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ymin = 0
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ymax = 0
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@ -88,7 +88,7 @@ def cross_border_time_series(countries, data):
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title = "Historic"
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else:
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title = "Optimized"
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ax[axis].set_title(
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title + " Import / Export for " + cc.convert(country, to="name_short")
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)
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@ -119,8 +119,8 @@ def cross_border_time_series(countries, data):
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ymax = pos_max
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axis = axis + 1
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for x in range(axis-2,axis):
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for x in range(axis - 2, axis):
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ax[x].set_ylim([neg_min, pos_max])
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fig.savefig(snakemake.output.trade_time_series, bbox_inches="tight")
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@ -154,12 +154,12 @@ def cross_border_bar(countries, data):
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df_negative = pd.concat([df_negative_new, df_negative])
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order = order + 1
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fig, ax = plt.subplots(figsize=(15, 60))
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df_positive.plot.barh(ax=ax, stacked=True, color=color, zorder=2)
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df_negative.plot.barh(ax=ax, stacked=True, color=color, zorder=2)
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plt.grid(axis="x", zorder=0)
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plt.grid(axis="y", zorder=0)
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@ -212,7 +212,7 @@ if __name__ == "__main__":
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# Preparing network data to be shaped similar to ENTSOE datastructure
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optimized_links = n.links_t.p0.rename(
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columns=dict(n.links.bus0.str[:2] + " - " + n.links.bus1.str[:2])
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columns=dict(n.links.bus0.str[:2] + " - " + n.links.bus1.str[:2])
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)
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optimized_lines = n.lines_t.p0.rename(
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columns=dict(n.lines.bus0.str[:2] + " - " + n.lines.bus1.str[:2])
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@ -220,7 +220,9 @@ if __name__ == "__main__":
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optimized = pd.concat([optimized_links, optimized_lines], axis=1)
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# Drop internal country connection
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optimized.drop([c for c in optimized.columns if c[:2] == c[5:]], axis=1, inplace=True)
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optimized.drop(
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[c for c in optimized.columns if c[:2] == c[5:]], axis=1, inplace=True
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)
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# align columns name
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for c1 in optimized.columns:
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@ -230,7 +232,7 @@ if __name__ == "__main__":
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optimized = optimized.groupby(lambda x: x, axis=1).sum()
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cross_border_bar(countries,[historic, optimized])
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cross_border_bar(countries, [historic, optimized])
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cross_border_time_series(countries, [historic, optimized])
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