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build_transport_data: select swiss years according to data selection

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use index.unique instead of index.levels where needed
This commit is contained in:
Fabian 2024-08-19 13:33:28 +02:00
parent 885a881e78
commit ca47becc87
6 changed files with 18 additions and 20 deletions
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2
scripts/base_network.py
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@ -95,7 +95,7 @@ logger = logging.getLogger(__name__)
def _get_oid(df):
if "tags" in df.columns:
return df.tags.str.extract('"oid"=>"(\d+)"', expand=False)
return df.tags.str.extract(r'"oid"=>"(\d+)"', expand=False)
else:
return pd.Series(np.nan, df.index)

2
scripts/build_biomass_potentials.py
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@ -311,7 +311,7 @@ def add_unsustainable_potentials(df):
# Phase out unsustainable biomass potentials linearly from 2020 to 2035 while phasing in sustainable potentials
share_unsus = params.get("share_unsustainable_use_retained").get(investment_year)
df_wo_ch = df.drop(df.filter(regex="CH\d", axis=0).index)
df_wo_ch = df.drop(df.filter(regex=r"CH\d", axis=0).index)
# Calculate unsustainable solid biomass
df_wo_ch["unsustainable solid biomass"] = _calc_unsustainable_potential(

18
scripts/build_energy_totals.py
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@ -242,7 +242,7 @@ def build_eurostat(
temp.index = pd.MultiIndex.from_frame(
temp.index.to_frame().fillna("International aviation")
)
df = pd.concat([temp, df.loc[~int_avia]])
df = pd.concat([temp, df.loc[~int_avia]]).sort_index()
# Fill in missing data on "Domestic aviation" for each country.
for country in countries:
@ -651,8 +651,8 @@ def build_energy_totals(
"""
eurostat_fuels = {"electricity": "Electricity", "total": "Total all products"}
eurostat_countries = eurostat.index.levels[0]
eurostat_years = eurostat.index.levels[1]
eurostat_countries = eurostat.index.unique(0)
eurostat_years = eurostat.index.unique(1)
to_drop = ["passenger cars", "passenger car efficiency"]
new_index = pd.MultiIndex.from_product(
@ -1153,13 +1153,14 @@ def build_transport_data(
----------
- Swiss transport data: `BFS <https://www.bfs.admin.ch/bfs/en/home/statistics/mobility-transport/transport-infrastructure-vehicles/vehicles/road-vehicles-stock-level-motorisation.html>`_
"""
years = np.arange(2000, 2022)
# first collect number of cars
transport_data = pd.DataFrame(idees["passenger cars"])
countries_without_ch = set(countries) - {"CH"}
new_index = pd.MultiIndex.from_product(
[countries_without_ch, transport_data.index.levels[1]],
[countries_without_ch, transport_data.index.unique(1)],
names=["country", "year"],
)
@ -1167,7 +1168,7 @@ def build_transport_data(
if "CH" in countries:
fn = snakemake.input.swiss_transport
swiss_cars = pd.read_csv(fn, index_col=0).loc[2000:2023, ["passenger cars"]]
swiss_cars = pd.read_csv(fn, index_col=0).loc[years, ["passenger cars"]]
swiss_cars.index = pd.MultiIndex.from_product(
[["CH"], swiss_cars.index], names=["country", "year"]
@ -1175,10 +1176,8 @@ def build_transport_data(
transport_data = pd.concat([transport_data, swiss_cars]).sort_index()
transport_data.rename(columns={"passenger cars": "number cars"}, inplace=True)
transport_data = transport_data.rename(columns={"passenger cars": "number cars"})
# clean up dataframe
years = np.arange(2000, 2022)
transport_data = transport_data[
transport_data.index.get_level_values(1).isin(years)
]
@ -1188,11 +1187,10 @@ def build_transport_data(
logger.info(
f"Missing data on cars from:\n{list(missing)}\nFilling gaps with averaged data."
)
cars_pp = transport_data["number cars"] / population
fill_values = {
year: cars_pp.mean() * population for year in transport_data.index.levels[1]
year: cars_pp.mean() * population for year in transport_data.index.unique(1)
}
fill_values = pd.DataFrame(fill_values).stack()
fill_values = pd.DataFrame(fill_values, columns=["number cars"])

2
scripts/build_industrial_energy_demand_per_node_today.py
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@ -66,7 +66,7 @@ def build_nodal_industrial_energy_demand():
)
countries = keys.country.unique()
sectors = industrial_demand.columns.levels[1]
sectors = industrial_demand.columns.unique(1)
for country, sector in product(countries, sectors):
buses = keys.index[keys.country == country]

2
scripts/build_retro_cost.py
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@ -254,7 +254,7 @@ def prepare_building_stock_data():
index = pd.MultiIndex.from_product([[ct], averaged_data.index.to_list()])
averaged_data.index = index
averaged_data["estimated"] = 1
if ct not in area_tot.index.levels[0]:
if ct not in area_tot.index.unique(0):
area_tot = pd.concat([area_tot, averaged_data], sort=True)
else:
area_tot.loc[averaged_data.index] = averaged_data

12
scripts/make_summary_perfect.py
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@ -40,9 +40,9 @@ def calculate_costs(n, label, costs):
investments = n.investment_periods
cols = pd.MultiIndex.from_product(
[
costs.columns.levels[0],
costs.columns.levels[1],
costs.columns.levels[2],
costs.columns.unique(0),
costs.columns.unique(1),
costs.columns.unique(2),
investments,
],
names=costs.columns.names[:3] + ["year"],
@ -339,9 +339,9 @@ def calculate_supply_energy(n, label, supply_energy):
investments = n.investment_periods
cols = pd.MultiIndex.from_product(
[
supply_energy.columns.levels[0],
supply_energy.columns.levels[1],
supply_energy.columns.levels[2],
supply_energy.columns.unique(0),
supply_energy.columns.unique(1),
supply_energy.columns.unique(2),
investments,
],
names=supply_energy.columns.names[:3] + ["year"],