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address FutureWarning regarding set operations on pd.Index (#103)

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Fabian Neumann 2021-04-29 17:11:10 +02:00 committed by GitHub
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8 changed files with 34 additions and 34 deletions
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4
scripts/build_industrial_energy_demand_per_country_today.py
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@ -98,7 +98,7 @@ for ct in eu28:
for fuel in fuels:
summary.at[fuel,sub] = s[fuels[fuel]].sum()
summary.at['other',sub] = summary.at['all',sub] - summary.loc[summary.index^['all','other'],sub].sum()
summary.at['other',sub] = summary.at['all',sub] - summary.loc[summary.index.symmetric_difference(['all','other']),sub].sum()
summary['Other Industrial Sectors'] = summary[ois_subs].sum(axis=1)
summary.drop(columns=ois_subs,inplace=True)
@ -128,7 +128,7 @@ output = pd.read_csv(snakemake.input.industrial_production_per_country,
eu28_averages = final_summary.groupby(level=1,axis=1).sum().divide(output.loc[eu28].sum(),axis=1)
non_eu28 = output.index^eu28
non_eu28 = output.index.symmetric_difference(eu28)
for ct in non_eu28:
print(ct)

2
scripts/build_industrial_production_per_country.py
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@ -196,7 +196,7 @@ ammonia = pd.read_csv(snakemake.input.ammonia_production,
index_col=0)
there = ammonia.index.intersection(countries_demand.index)
missing = countries_demand.index^there
missing = countries_demand.index.symmetric_difference(there)
print("Following countries have no ammonia demand:", missing)

2
scripts/build_population_layouts.py
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@ -46,7 +46,7 @@ urban_fraction = pd.read_csv(snakemake.input.urban_percent,
#fill missing Balkans values
missing = ["AL","ME","MK"]
reference = ["RS","BA"]
urban_fraction = urban_fraction.reindex(urban_fraction.index|missing)
urban_fraction = urban_fraction.reindex(urban_fraction.index.union(missing))
urban_fraction.loc[missing] = urban_fraction[reference].mean()

4
scripts/build_retro_cost.py
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@ -194,7 +194,7 @@ def prepare_building_stock_data():
area_per_pop = area_tot.unstack().reindex(index=ct_total.index).apply(lambda x: x / ct_total[x.index])
missing_area_ct = ct_total.index.difference(area_tot.index.levels[0])
for ct in (missing_area_ct & ct_total.index):
for ct in missing_area_ct.intersection(ct_total.index):
averaged_data = pd.DataFrame(
area_per_pop.value.reindex(map_for_missings[ct]).mean()
* ct_total[ct],
@ -343,7 +343,7 @@ def calculate_cost_energy_curve(u_values, l_strength, l_weight, average_surface_
res = res.reset_index().set_index(["country", "sector"])
# map missing countries
for ct in pd.Index(map_for_missings.keys()) & countries:
for ct in pd.Index(map_for_missings.keys()).intersection(countries):
averaged_data = res.reindex(index=map_for_missings[ct], level=0).mean(level=1)
index = pd.MultiIndex.from_product([[ct], averaged_data.index.to_list()])
averaged_data.index = index

36
scripts/make_summary.py
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@ -79,7 +79,7 @@ def calculate_nodal_cfs(n,label,nodal_cfs):
cf_c = p_c/capacities_c
index = pd.MultiIndex.from_tuples([(c.list_name,) + t for t in cf_c.index.to_list()])
nodal_cfs = nodal_cfs.reindex(index|nodal_cfs.index)
nodal_cfs = nodal_cfs.reindex(index.union(nodal_cfs.index))
nodal_cfs.loc[index,label] = cf_c.values
return nodal_cfs
@ -106,7 +106,7 @@ def calculate_cfs(n,label,cfs):
cf_c = pd.concat([cf_c], keys=[c.list_name])
cfs = cfs.reindex(cf_c.index|cfs.index)
cfs = cfs.reindex(cf_c.index.union(cfs.index))
cfs.loc[cf_c.index,label] = cf_c
@ -121,7 +121,7 @@ def calculate_nodal_costs(n,label,nodal_costs):
c.df["capital_costs"] = c.df.capital_cost*c.df[opt_name.get(c.name,"p") + "_nom_opt"]
capital_costs = c.df.groupby(["location","carrier"])["capital_costs"].sum()
index = pd.MultiIndex.from_tuples([(c.list_name,"capital") + t for t in capital_costs.index.to_list()])
nodal_costs = nodal_costs.reindex(index|nodal_costs.index)
nodal_costs = nodal_costs.reindex(index.union(nodal_costs.index))
nodal_costs.loc[index,label] = capital_costs.values
if c.name == "Link":
@ -143,7 +143,7 @@ def calculate_nodal_costs(n,label,nodal_costs):
c.df["marginal_costs"] = p*c.df.marginal_cost
marginal_costs = c.df.groupby(["location","carrier"])["marginal_costs"].sum()
index = pd.MultiIndex.from_tuples([(c.list_name,"marginal") + t for t in marginal_costs.index.to_list()])
nodal_costs = nodal_costs.reindex(index|nodal_costs.index)
nodal_costs = nodal_costs.reindex(index.union(nodal_costs.index))
nodal_costs.loc[index,label] = marginal_costs.values
return nodal_costs
@ -158,7 +158,7 @@ def calculate_costs(n,label,costs):
capital_costs_grouped = pd.concat([capital_costs_grouped], keys=["capital"])
capital_costs_grouped = pd.concat([capital_costs_grouped], keys=[c.list_name])
costs = costs.reindex(capital_costs_grouped.index|costs.index)
costs = costs.reindex(capital_costs_grouped.index.union(costs.index))
costs.loc[capital_costs_grouped.index,label] = capital_costs_grouped
@ -185,7 +185,7 @@ def calculate_costs(n,label,costs):
marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=["marginal"])
marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=[c.list_name])
costs = costs.reindex(marginal_costs_grouped.index|costs.index)
costs = costs.reindex(marginal_costs_grouped.index.union(costs.index))
costs.loc[marginal_costs_grouped.index,label] = marginal_costs_grouped
@ -220,7 +220,7 @@ def calculate_nodal_capacities(n,label,nodal_capacities):
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
nodal_capacities_c = c.df.groupby(["location","carrier"])[opt_name.get(c.name,"p") + "_nom_opt"].sum()
index = pd.MultiIndex.from_tuples([(c.list_name,) + t for t in nodal_capacities_c.index.to_list()])
nodal_capacities = nodal_capacities.reindex(index|nodal_capacities.index)
nodal_capacities = nodal_capacities.reindex(index.union(nodal_capacities.index))
nodal_capacities.loc[index,label] = nodal_capacities_c.values
return nodal_capacities
@ -234,7 +234,7 @@ def calculate_capacities(n,label,capacities):
capacities_grouped = c.df[opt_name.get(c.name,"p") + "_nom_opt"].groupby(c.df.carrier).sum()
capacities_grouped = pd.concat([capacities_grouped], keys=[c.list_name])
capacities = capacities.reindex(capacities_grouped.index|capacities.index)
capacities = capacities.reindex(capacities_grouped.index.union(capacities.index))
capacities.loc[capacities_grouped.index,label] = capacities_grouped
@ -267,7 +267,7 @@ def calculate_energy(n,label,energy):
c_energies = pd.concat([c_energies], keys=[c.list_name])
energy = energy.reindex(c_energies.index|energy.index)
energy = energy.reindex(c_energies.index.union(energy.index))
energy.loc[c_energies.index,label] = c_energies
@ -294,7 +294,7 @@ def calculate_supply(n,label,supply):
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply = supply.reindex(s.index|supply.index)
supply = supply.reindex(s.index.union(supply.index))
supply.loc[s.index,label] = s
@ -313,7 +313,7 @@ def calculate_supply(n,label,supply):
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply = supply.reindex(s.index|supply.index)
supply = supply.reindex(s.index.union(supply.index))
supply.loc[s.index,label] = s
return supply
@ -339,7 +339,7 @@ def calculate_supply_energy(n,label,supply_energy):
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply_energy = supply_energy.reindex(s.index|supply_energy.index)
supply_energy = supply_energy.reindex(s.index.union(supply_energy.index))
supply_energy.loc[s.index,label] = s
@ -357,7 +357,7 @@ def calculate_supply_energy(n,label,supply_energy):
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply_energy = supply_energy.reindex(s.index|supply_energy.index)
supply_energy = supply_energy.reindex(s.index.union(supply_energy.index))
supply_energy.loc[s.index,label] = s
@ -366,7 +366,7 @@ def calculate_supply_energy(n,label,supply_energy):
def calculate_metrics(n,label,metrics):
metrics = metrics.reindex(pd.Index(["line_volume","line_volume_limit","line_volume_AC","line_volume_DC","line_volume_shadow","co2_shadow"])|metrics.index)
metrics = metrics.reindex(pd.Index(["line_volume","line_volume_limit","line_volume_AC","line_volume_DC","line_volume_shadow","co2_shadow"]).union(metrics.index))
metrics.at["line_volume_DC",label] = (n.links.length*n.links.p_nom_opt)[n.links.carrier == "DC"].sum()
metrics.at["line_volume_AC",label] = (n.lines.length*n.lines.s_nom_opt).sum()
@ -384,7 +384,7 @@ def calculate_metrics(n,label,metrics):
def calculate_prices(n,label,prices):
prices = prices.reindex(prices.index|n.buses.carrier.unique())
prices = prices.reindex(prices.index.union(n.buses.carrier.unique()))
#WARNING: this is time-averaged, see weighted_prices for load-weighted average
prices[label] = n.buses_t.marginal_price.mean().groupby(n.buses.carrier).mean()
@ -467,7 +467,7 @@ def calculate_market_values(n, label, market_values):
techs = n.generators.loc[generators,"carrier"].value_counts().index
market_values = market_values.reindex(market_values.index | techs)
market_values = market_values.reindex(market_values.index.union(techs))
for tech in techs:
@ -488,7 +488,7 @@ def calculate_market_values(n, label, market_values):
techs = n.links.loc[all_links,"carrier"].value_counts().index
market_values = market_values.reindex(market_values.index | techs)
market_values = market_values.reindex(market_values.index.union(techs))
for tech in techs:
links = all_links[n.links.loc[all_links,"carrier"] == tech]
@ -505,7 +505,7 @@ def calculate_market_values(n, label, market_values):
def calculate_price_statistics(n, label, price_statistics):
price_statistics = price_statistics.reindex(price_statistics.index|pd.Index(["zero_hours","mean","standard_deviation"]))
price_statistics = price_statistics.reindex(price_statistics.index.union(pd.Index(["zero_hours","mean","standard_deviation"])))
buses = n.buses.index[n.buses.carrier == "AC"]

6
scripts/plot_network.py
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@ -130,7 +130,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
costs.drop(list(costs.columns[(costs == 0.).all()]), axis=1, inplace=True)
new_columns = ((preferred_order & costs.columns)
new_columns = (preferred_order.intersection(costs.columns)
.append(costs.columns.difference(preferred_order)))
costs = costs[new_columns]
@ -147,7 +147,7 @@ def plot_map(network, components=["links", "stores", "storage_units", "generator
n.links.carrier != "B2B")], inplace=True)
# drop non-bus
to_drop = costs.index.levels[0] ^ n.buses.index
to_drop = costs.index.levels[0].symmetric_difference(n.buses.index)
if len(to_drop) != 0:
print("dropping non-buses", to_drop)
costs.drop(to_drop, level=0, inplace=True, axis=0)
@ -463,7 +463,7 @@ def plot_series(network, carrier="AC", name="test"):
"battery storage",
"hot water storage"])
new_columns = ((preferred_order & supply.columns)
new_columns = (preferred_order.intersection(supply.columns)
.append(supply.columns.difference(preferred_order)))
supply = supply.groupby(supply.columns, axis=1).sum()

8
scripts/plot_summary.py
View File

@ -82,7 +82,7 @@ def plot_costs():
print(df.sum())
new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))
new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
new_columns = df.sum().sort_values().index
@ -136,7 +136,7 @@ def plot_energy():
print(df)
new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))
new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
new_columns = df.columns.sort_values()
#new_columns = df.sum().sort_values().index
@ -177,7 +177,7 @@ def plot_balances():
balances_df = pd.read_csv(snakemake.input.balances,index_col=list(range(3)),header=list(range(n_header)))
balances = {i.replace(" ","_") : [i] for i in balances_df.index.levels[0]}
balances["energy"] = balances_df.index.levels[0]^co2_carriers
balances["energy"] = balances_df.index.levels[0].symmetric_difference(co2_carriers)
for k,v in balances.items():
@ -205,7 +205,7 @@ def plot_balances():
if df.empty:
continue
new_index = (preferred_order&df.index).append(df.index.difference(preferred_order))
new_index = preferred_order.intersection(df.index).append(df.index.difference(preferred_order))
new_columns = df.columns.sort_values()

6
scripts/prepare_sector_network.py
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@ -240,7 +240,7 @@ def remove_elec_base_techs(n):
for c in n.iterate_components(snakemake.config["pypsa_eur"]):
to_keep = snakemake.config["pypsa_eur"][c.name]
to_remove = pd.Index(c.df.carrier.unique())^to_keep
to_remove = pd.Index(c.df.carrier.unique()).symmetric_difference(to_keep)
print("Removing",c.list_name,"with carrier",to_remove)
names = c.df.index[c.df.carrier.isin(to_remove)]
print(names)
@ -921,7 +921,7 @@ def add_storage(network):
# hydrogen stored overground
h2_capital_cost = costs.at["hydrogen storage tank", "fixed"]
nodes_overground = nodes ^ cavern_nodes.index
nodes_overground = nodes.symmetric_difference(cavern_nodes.index)
network.madd("Store",
nodes_overground + " H2 Store",
@ -1484,7 +1484,7 @@ def create_nodes_for_heat_sector():
else:
nodes[sector + " urban decentral"] = pop_layout.index
# for central nodes, residential and services are aggregated
nodes["urban central"] = pop_layout.index ^ nodes["residential urban decentral"]
nodes["urban central"] = pop_layout.index.symmetric_difference(nodes["residential urban decentral"])
return nodes