address FutureWarning re set operations on pd.Index (#238)
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@ -337,7 +337,7 @@ def attach_hydro(n, costs, ppl):
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country = ppl['bus'].map(n.buses.country).rename("country")
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inflow_idx = ror.index | hydro.index
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inflow_idx = ror.index.union(hydro.index)
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if not inflow_idx.empty:
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dist_key = ppl.loc[inflow_idx, 'p_nom'].groupby(country).transform(normed)
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@ -71,7 +71,7 @@ opt_name = {"Store": "e", "Line" : "s", "Transformer" : "s"}
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def _add_indexed_rows(df, raw_index):
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new_index = df.index|pd.MultiIndex.from_product(raw_index)
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new_index = df.index.union(pd.MultiIndex.from_product(raw_index))
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if isinstance(new_index, pd.Index):
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new_index = pd.MultiIndex.from_tuples(new_index)
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@ -126,7 +126,7 @@ def calculate_costs(n, label, costs):
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marginal_costs_grouped = marginal_costs.groupby(c.df.carrier).sum()
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costs = costs.reindex(costs.index|pd.MultiIndex.from_product([[c.list_name],["marginal"],marginal_costs_grouped.index]))
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costs = costs.reindex(costs.index.union(pd.MultiIndex.from_product([[c.list_name],["marginal"],marginal_costs_grouped.index])))
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costs.loc[idx[c.list_name,"marginal",list(marginal_costs_grouped.index)],label] = marginal_costs_grouped.values
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@ -222,7 +222,7 @@ def calculate_supply(n, label, supply):
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#lots of sign compensation for direction and to do maximums
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s = (-1)**(1-int(end))*((-1)**int(end)*c.pnl["p"+end][items]).max().groupby(c.df.loc[items,'carrier']).sum()
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supply = supply.reindex(supply.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index]))
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supply = supply.reindex(supply.index.union(pd.MultiIndex.from_product([[i],[c.list_name],s.index])))
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supply.loc[idx[i,c.list_name,list(s.index)],label] = s.values
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return supply
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@ -268,7 +268,7 @@ def calculate_supply_energy(n, label, supply_energy):
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s = (-1)*c.pnl["p"+end][items].sum().groupby(c.df.loc[items,'carrier']).sum()
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supply_energy = supply_energy.reindex(supply_energy.index|pd.MultiIndex.from_product([[i],[c.list_name],s.index]))
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supply_energy = supply_energy.reindex(supply_energy.index.union(pd.MultiIndex.from_product([[i],[c.list_name],s.index])))
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supply_energy.loc[idx[i,c.list_name,list(s.index)],label] = s.values
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return supply_energy
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@ -276,7 +276,7 @@ def calculate_supply_energy(n, label, supply_energy):
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def calculate_metrics(n,label,metrics):
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metrics = metrics.reindex(metrics.index|pd.Index(["line_volume","line_volume_limit","line_volume_AC","line_volume_DC","line_volume_shadow","co2_shadow"]))
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metrics = metrics.reindex(metrics.index.union(pd.Index(["line_volume","line_volume_limit","line_volume_AC","line_volume_DC","line_volume_shadow","co2_shadow"])))
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metrics.at["line_volume_DC",label] = (n.links.length*n.links.p_nom_opt)[n.links.carrier == "DC"].sum()
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metrics.at["line_volume_AC",label] = (n.lines.length*n.lines.s_nom_opt).sum()
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@ -298,7 +298,7 @@ def calculate_prices(n,label,prices):
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bus_type = pd.Series(n.buses.index.str[3:],n.buses.index).replace("","electricity")
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prices = prices.reindex(prices.index|bus_type.value_counts().index)
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prices = prices.reindex(prices.index.union(bus_type.value_counts().index))
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logger.warning("Prices are time-averaged, not load-weighted")
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prices[label] = n.buses_t.marginal_price.mean().groupby(bus_type).mean()
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@ -164,7 +164,7 @@ def plot_map(n, ax=None, attribute='p_nom', opts={}):
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handler_map=make_handler_map_to_scale_circles_as_in(ax))
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ax.add_artist(l2)
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techs = (bus_sizes.index.levels[1]) & pd.Index(opts['vre_techs'] + opts['conv_techs'] + opts['storage_techs'])
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techs = (bus_sizes.index.levels[1]).intersection(pd.Index(opts['vre_techs'] + opts['conv_techs'] + opts['storage_techs']))
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handles = []
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labels = []
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for t in techs:
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