merge helper.py into _helpers.py
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parent
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541b83b049
@ -5,12 +5,28 @@
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import urllib
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from pathlib import Path
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import pandas as pd
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from tqdm import tqdm
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import contextlib
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import logging
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import os
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import pytz
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import yaml
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from pypsa.components import component_attrs, components
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from pypsa.descriptors import Dict
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from snakemake.utils import update_config
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logger = logging.getLogger(__name__)
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REGION_COLS = ["geometry", "name", "x", "y", "country"]
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# Define a context manager to temporarily mute print statements
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@contextlib.contextmanager
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def mute_print():
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with open(os.devnull, "w") as devnull:
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with contextlib.redirect_stdout(devnull):
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yield
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def configure_logging(snakemake, skip_handlers=False):
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"""
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@ -340,3 +356,67 @@ def mock_snakemake(rulename, **wildcards):
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os.chdir(script_dir)
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return snakemake
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def override_component_attrs(directory):
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"""Tell PyPSA that links can have multiple outputs by
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overriding the component_attrs. This can be done for
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as many buses as you need with format busi for i = 2,3,4,5,....
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See https://pypsa.org/doc/components.html#link-with-multiple-outputs-or-inputs
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Parameters
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----------
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directory : string
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Folder where component attributes to override are stored
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analogous to ``pypsa/component_attrs``, e.g. `links.csv`.
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Returns
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-------
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Dictionary of overridden component attributes.
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"""
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attrs = Dict({k: v.copy() for k, v in component_attrs.items()})
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for component, list_name in components.list_name.items():
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fn = f"{directory}/{list_name}.csv"
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if os.path.isfile(fn):
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overrides = pd.read_csv(fn, index_col=0, na_values="n/a")
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attrs[component] = overrides.combine_first(attrs[component])
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return attrs
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def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
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"""
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Give a 24*7 long list of weekly hourly profiles, generate this for each
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country for the period dt_index, taking account of time zones and summer
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time.
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"""
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weekly_profile = pd.Series(weekly_profile, range(24 * 7))
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week_df = pd.DataFrame(index=dt_index, columns=nodes)
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for node in nodes:
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timezone = pytz.timezone(pytz.country_timezones[node[:2]][0])
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tz_dt_index = dt_index.tz_convert(timezone)
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week_df[node] = [24 * dt.weekday() + dt.hour for dt in tz_dt_index]
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week_df[node] = week_df[node].map(weekly_profile)
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week_df = week_df.tz_localize(localize)
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return week_df
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def parse(l):
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if len(l) == 1:
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return yaml.safe_load(l[0])
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else:
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return {l.pop(0): parse(l)}
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def update_config_with_sector_opts(config, sector_opts):
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for o in sector_opts.split("-"):
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if o.startswith("CF+"):
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l = o.split("+")[1:]
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update_config(config, parse(l))
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@ -15,7 +15,7 @@ import numpy as np
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import pypsa
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import yaml
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from add_existing_baseyear import add_build_year_to_new_assets
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from helper import override_component_attrs, update_config_with_sector_opts
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from _helpers import override_component_attrs, update_config_with_sector_opts
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def add_brownfield(n, n_p, year):
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@ -17,7 +17,7 @@ import numpy as np
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import pypsa
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import xarray as xr
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import yaml
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from helper import override_component_attrs, update_config_with_sector_opts
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from _helpers import override_component_attrs, update_config_with_sector_opts
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from prepare_sector_network import cluster_heat_buses, define_spatial, prepare_costs
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spatial = SimpleNamespace()
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@ -13,7 +13,7 @@ from functools import partial
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import geopandas as gpd
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import numpy as np
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import pandas as pd
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from helper import mute_print
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from _helpers import mute_print
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from tqdm import tqdm
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idx = pd.IndexSlice
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@ -15,7 +15,7 @@ import multiprocessing as mp
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import numpy as np
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import pandas as pd
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from helper import mute_print
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from _helpers import mute_print
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from tqdm import tqdm
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tj_to_ktoe = 0.0238845
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@ -8,7 +8,7 @@ Build industry sector ratios.
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"""
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import pandas as pd
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from helper import mute_print
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from _helpers import mute_print
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# GWh/ktoe OR MWh/toe
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toe_to_MWh = 11.630
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@ -10,7 +10,7 @@ Build transport demand.
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import numpy as np
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import pandas as pd
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import xarray as xr
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from helper import generate_periodic_profiles
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from _helpers import generate_periodic_profiles
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def build_nodal_transport_data(fn, pop_layout):
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@ -1,91 +0,0 @@
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# -*- coding: utf-8 -*-
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# SPDX-FileCopyrightText: : 2020-2023 The PyPSA-Eur Authors
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#
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# SPDX-License-Identifier: MIT
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import contextlib
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import logging
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import os
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import sys
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from pathlib import Path
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import pandas as pd
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import pytz
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import yaml
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from pypsa.components import component_attrs, components
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from pypsa.descriptors import Dict
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from snakemake.utils import update_config
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logger = logging.getLogger(__name__)
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# Define a context manager to temporarily mute print statements
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@contextlib.contextmanager
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def mute_print():
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with open(os.devnull, "w") as devnull:
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with contextlib.redirect_stdout(devnull):
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yield
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def override_component_attrs(directory):
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"""Tell PyPSA that links can have multiple outputs by
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overriding the component_attrs. This can be done for
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as many buses as you need with format busi for i = 2,3,4,5,....
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See https://pypsa.org/doc/components.html#link-with-multiple-outputs-or-inputs
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Parameters
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----------
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directory : string
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Folder where component attributes to override are stored
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analogous to ``pypsa/component_attrs``, e.g. `links.csv`.
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Returns
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-------
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Dictionary of overridden component attributes.
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"""
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attrs = Dict({k: v.copy() for k, v in component_attrs.items()})
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for component, list_name in components.list_name.items():
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fn = f"{directory}/{list_name}.csv"
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if os.path.isfile(fn):
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overrides = pd.read_csv(fn, index_col=0, na_values="n/a")
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attrs[component] = overrides.combine_first(attrs[component])
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return attrs
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def generate_periodic_profiles(dt_index, nodes, weekly_profile, localize=None):
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"""
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Give a 24*7 long list of weekly hourly profiles, generate this for each
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country for the period dt_index, taking account of time zones and summer
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time.
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"""
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weekly_profile = pd.Series(weekly_profile, range(24 * 7))
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week_df = pd.DataFrame(index=dt_index, columns=nodes)
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for node in nodes:
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timezone = pytz.timezone(pytz.country_timezones[node[:2]][0])
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tz_dt_index = dt_index.tz_convert(timezone)
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week_df[node] = [24 * dt.weekday() + dt.hour for dt in tz_dt_index]
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week_df[node] = week_df[node].map(weekly_profile)
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week_df = week_df.tz_localize(localize)
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return week_df
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def parse(l):
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if len(l) == 1:
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return yaml.safe_load(l[0])
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else:
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return {l.pop(0): parse(l)}
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def update_config_with_sector_opts(config, sector_opts):
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for o in sector_opts.split("-"):
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if o.startswith("CF+"):
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l = o.split("+")[1:]
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update_config(config, parse(l))
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@ -13,7 +13,7 @@ import numpy as np
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import pandas as pd
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import pypsa
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import yaml
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from helper import override_component_attrs
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from _helpers import override_component_attrs
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from prepare_sector_network import prepare_costs
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idx = pd.IndexSlice
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@ -12,7 +12,7 @@ import geopandas as gpd
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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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from helper import override_component_attrs
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from _helpers import override_component_attrs
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from make_summary import assign_carriers
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from plot_summary import preferred_order, rename_techs
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from pypsa.plot import add_legend_circles, add_legend_lines, add_legend_patches
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@ -14,7 +14,7 @@ import pandas as pd
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import pypsa
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import xarray as xr
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from build_energy_totals import build_co2_totals, build_eea_co2, build_eurostat_co2
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from helper import (
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from _helpers import (
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generate_periodic_profiles,
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override_component_attrs,
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update_config_with_sector_opts,
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@ -10,7 +10,7 @@ import logging
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import numpy as np
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import pypsa
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from helper import override_component_attrs, update_config_with_sector_opts
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from _helpers import override_component_attrs, update_config_with_sector_opts
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from vresutils.benchmark import memory_logger
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logger = logging.getLogger(__name__)
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