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Fix bug in ordering of MultiIndex in make_summary.py

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df.loc[idx[a,b,some_list],label] does NOT preserve the ordering of
some_list, but sorts it instead. Therefore the pattern:

df.loc[idx[a,b,s.index],label] = s.values

was mismatching the index and values.
This commit is contained in:
Tom Brown 2019-12-19 11:40:17 +01:00
parent eb2fa1b24e
commit 180846945f
1 changed files with 41 additions and 18 deletions
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59
scripts/make_summary.py
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@ -104,9 +104,11 @@ def calculate_cfs(n,label,cfs):
cf_c = p_c/capacities_c
cfs = cfs.reindex(pd.MultiIndex.from_product([[c.list_name],cf_c.index])|cfs.index)
cf_c = pd.concat([cf_c], keys=[c.list_name])
cfs.loc[idx[c.list_name,list(cf_c.index)],label] = cf_c.values
cfs = cfs.reindex(cf_c.index|cfs.index)
cfs.loc[cf_c.index,label] = cf_c
return cfs
@ -151,9 +153,12 @@ def calculate_costs(n,label,costs):
capital_costs = c.df.capital_cost*c.df[opt_name.get(c.name,"p") + "_nom_opt"]
capital_costs_grouped = capital_costs.groupby(c.df.carrier).sum()
costs = costs.reindex(pd.MultiIndex.from_product([[c.list_name],["capital"],capital_costs_grouped.index])|costs.index)
capital_costs_grouped = pd.concat([capital_costs_grouped], keys=["capital"])
capital_costs_grouped = pd.concat([capital_costs_grouped], keys=[c.list_name])
costs.loc[idx[c.list_name,"capital",list(capital_costs_grouped.index)],label] = capital_costs_grouped.values
costs = costs.reindex(capital_costs_grouped.index|costs.index)
costs.loc[capital_costs_grouped.index,label] = capital_costs_grouped
if c.name == "Link":
p = c.pnl.p0.multiply(n.snapshot_weightings,axis=0).sum()
@ -175,9 +180,12 @@ def calculate_costs(n,label,costs):
marginal_costs_grouped = marginal_costs.groupby(c.df.carrier).sum()
costs = costs.reindex(pd.MultiIndex.from_product([[c.list_name],["marginal"],marginal_costs_grouped.index])|costs.index)
marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=["marginal"])
marginal_costs_grouped = pd.concat([marginal_costs_grouped], keys=[c.list_name])
costs.loc[idx[c.list_name,"marginal",list(marginal_costs_grouped.index)],label] = marginal_costs_grouped.values
costs = costs.reindex(marginal_costs_grouped.index|costs.index)
costs.loc[marginal_costs_grouped.index,label] = marginal_costs_grouped
#add back in costs of links if there is a line volume limit
if label[1] != "opt":
@ -212,10 +220,11 @@ def calculate_capacities(n,label,capacities):
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
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(pd.MultiIndex.from_product([[c.list_name],capacities_grouped.index])|capacities.index)
capacities = capacities.reindex(capacities_grouped.index|capacities.index)
capacities.loc[idx[c.list_name,list(capacities_grouped.index)],label] = capacities_grouped.values
capacities.loc[capacities_grouped.index,label] = capacities_grouped
return capacities
@ -240,9 +249,11 @@ def calculate_energy(n,label,energy):
for port in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
c_energies -= c.pnl["p"+port].multiply(n.snapshot_weightings,axis=0).sum().groupby(c.df.carrier).sum()
energy = energy.reindex(pd.MultiIndex.from_product([[c.list_name],c_energies.index])|energy.index)
c_energies = pd.concat([c_energies], keys=[c.list_name])
energy.loc[idx[c.list_name,list(c_energies.index)],label] = c_energies.values
energy = energy.reindex(c_energies.index|energy.index)
energy.loc[c_energies.index,label] = c_energies
return energy
@ -264,9 +275,11 @@ def calculate_supply(n,label,supply):
continue
s = c.pnl.p[items].max().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'carrier']).sum()
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply = supply.reindex(pd.MultiIndex.from_product([[i],[c.list_name],s.index])|supply.index)
supply.loc[idx[i,c.list_name,list(s.index)],label] = s.values
supply = supply.reindex(s.index|supply.index)
supply.loc[s.index,label] = s
for c in n.iterate_components(n.branch_components):
@ -280,9 +293,12 @@ def calculate_supply(n,label,supply):
#lots of sign compensation for direction and to do maximums
s = (-1)**(1-int(end))*((-1)**int(end)*c.pnl["p"+end][items]).max().groupby(c.df.loc[items,'carrier']).sum()
s.index = s.index+end
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply = supply.reindex(pd.MultiIndex.from_product([[i],[c.list_name],s.index+end])|supply.index)
supply.loc[idx[i,c.list_name,list(s.index+end)],label] = s.values
supply = supply.reindex(s.index|supply.index)
supply.loc[s.index,label] = s
return supply
@ -304,9 +320,11 @@ def calculate_supply_energy(n,label,supply_energy):
continue
s = c.pnl.p[items].multiply(n.snapshot_weightings,axis=0).sum().multiply(c.df.loc[items,'sign']).groupby(c.df.loc[items,'carrier']).sum()
s = pd.concat([s], keys=[c.list_name])
s = pd.concat([s], keys=[i])
supply_energy = supply_energy.reindex(pd.MultiIndex.from_product([[i],[c.list_name],s.index])|supply_energy.index)
supply_energy.loc[idx[i,c.list_name,list(s.index)],label] = s.values
supply_energy = supply_energy.reindex(s.index|supply_energy.index)
supply_energy.loc[s.index,label] = s
for c in n.iterate_components(n.branch_components):
@ -319,9 +337,14 @@ def calculate_supply_energy(n,label,supply_energy):
continue
s = (-1)*c.pnl["p"+end][items].multiply(n.snapshot_weightings,axis=0).sum().groupby(c.df.loc[items,'carrier']).sum()
s.index = s.index+end
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.loc[s.index,label] = s
supply_energy = supply_energy.reindex(pd.MultiIndex.from_product([[i],[c.list_name],s.index+end])|supply_energy.index)
supply_energy.loc[idx[i,c.list_name,list(s.index+end)],label] = s.values
return supply_energy