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Summaries and plotting: Fix groupby with two bys for pandas 1.0.x

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This commit is contained in:
Tom Brown 2020-03-25 16:42:38 +01:00
parent 62887b7870
commit 0df8fc50cf
2 changed files with 15 additions and 13 deletions
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13
scripts/make_summary.py
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@ -63,7 +63,7 @@ def assign_locations(n):
def calculate_nodal_cfs(n,label,nodal_cfs): def calculate_nodal_cfs(n,label,nodal_cfs):
#Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff #Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components((n.branch_components^{"Line","Transformer"})|n.controllable_one_port_components^{"Load","StorageUnit"}): for c in n.iterate_components((n.branch_components^{"Line","Transformer"})|n.controllable_one_port_components^{"Load","StorageUnit"}):
capacities_c = c.df[opt_name.get(c.name,"p") + "_nom_opt"].groupby((c.df.location,c.df.carrier)).sum() capacities_c = c.df.groupby(["location","carrier"])[opt_name.get(c.name,"p") + "_nom_opt"].sum()
if c.name == "Link": if c.name == "Link":
p = c.pnl.p0.abs().mean() p = c.pnl.p0.abs().mean()
@ -74,7 +74,8 @@ def calculate_nodal_cfs(n,label,nodal_cfs):
else: else:
sys.exit() sys.exit()
p_c = p.groupby((c.df.location,c.df.carrier)).sum() c.df["p"] = p
p_c = c.df.groupby(["location","carrier"])["p"].sum()
cf_c = p_c/capacities_c cf_c = p_c/capacities_c
@ -118,7 +119,8 @@ def calculate_cfs(n,label,cfs):
def calculate_nodal_costs(n,label,nodal_costs): def calculate_nodal_costs(n,label,nodal_costs):
#Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff #Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}): for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
capital_costs = (c.df.capital_cost*c.df[opt_name.get(c.name,"p") + "_nom_opt"]).groupby((c.df.location,c.df.carrier)).sum() 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()]) 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|nodal_costs.index)
nodal_costs.loc[index,label] = capital_costs.values nodal_costs.loc[index,label] = capital_costs.values
@ -139,7 +141,8 @@ def calculate_nodal_costs(n,label,nodal_costs):
items = c.df.index[(c.df.carrier == "co2 stored") & (c.df.marginal_cost <= -100.)] items = c.df.index[(c.df.carrier == "co2 stored") & (c.df.marginal_cost <= -100.)]
c.df.loc[items,"marginal_cost"] = -20. c.df.loc[items,"marginal_cost"] = -20.
marginal_costs = (p*c.df.marginal_cost).groupby((c.df.location,c.df.carrier)).sum() 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()]) 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|nodal_costs.index)
nodal_costs.loc[index,label] = marginal_costs.values nodal_costs.loc[index,label] = marginal_costs.values
@ -206,7 +209,7 @@ def calculate_costs(n,label,costs):
def calculate_nodal_capacities(n,label,nodal_capacities): def calculate_nodal_capacities(n,label,nodal_capacities):
#Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff #Beware this also has extraneous locations for country (e.g. biomass) or continent-wide (e.g. fossil gas/oil) stuff
for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}): for c in n.iterate_components(n.branch_components|n.controllable_one_port_components^{"Load"}):
nodal_capacities_c = c.df[opt_name.get(c.name,"p") + "_nom_opt"].groupby((c.df.location,c.df.carrier)).sum() 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()]) 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|nodal_capacities.index)
nodal_capacities.loc[index,label] = nodal_capacities_c.values nodal_capacities.loc[index,label] = nodal_capacities_c.values

15
scripts/plot_network.py
View File

@ -111,13 +111,17 @@ def plot_map(components=["links","stores","storage_units","generators"],bus_size
costs = pd.DataFrame(index=n.buses.index) costs = pd.DataFrame(index=n.buses.index)
for comp in components: for comp in components:
getattr(n,comp)["nice_group"] = getattr(n,comp).carrier.map(rename_techs_tyndp)
df = getattr(n,comp)
df["nice_group"] = df.carrier.map(rename_techs_tyndp)
attr = "e_nom_opt" if comp == "stores" else "p_nom_opt" attr = "e_nom_opt" if comp == "stores" else "p_nom_opt"
costs = pd.concat((costs,(getattr(n,comp).capital_cost*getattr(n,comp)[attr]).groupby((getattr(n,comp).location,getattr(n,comp).nice_group)).sum().unstack().fillna(0.)),axis=1) df["costs"] = df.capital_cost*df[attr]
costs = pd.concat((costs,df.groupby(by=["location","nice_group"]).sum()["costs"].unstack().fillna(0.)),axis=1)
print(comp,costs)
costs = costs.groupby(costs.columns,axis=1).sum() costs = costs.groupby(costs.columns,axis=1).sum()
@ -127,13 +131,8 @@ def plot_map(components=["links","stores","storage_units","generators"],bus_size
costs = costs[new_columns] costs = costs[new_columns]
#print(costs)
#print(costs.sum())
costs = costs.stack()#.sort_index() costs = costs.stack()#.sort_index()
#print(costs)
fig, ax = plt.subplots(subplot_kw={"projection":ccrs.PlateCarree()}) fig, ax = plt.subplots(subplot_kw={"projection":ccrs.PlateCarree()})
fig.set_size_inches(7,6) fig.set_size_inches(7,6)