adjust summary and plotting

scripts/make_summary_perfect.py

@@ -13,7 +13,6 @@ other metrics.
 import numpy as np
 import pandas as pd
 import pypsa
-from _helpers import override_component_attrs
 from make_summary import (
     assign_carriers,
     assign_locations,
@@ -221,31 +220,43 @@ def calculate_curtailment(n, label, curtailment):
 
 
 def calculate_energy(n, label, energy):
+    investments = n.investment_periods
+    cols = pd.MultiIndex.from_product(
+        [
+            energy.columns.levels[0],
+            energy.columns.levels[1],
+            energy.columns.levels[2],
+            investments,
+        ],
+        names=energy.columns.names[:3] + ["year"],
+    )
+    energy = energy.reindex(cols, axis=1)
+    
     for c in n.iterate_components(n.one_port_components | n.branch_components):
         if c.name in n.one_port_components:
             c_energies = (
-                c.pnl.p.multiply(n.snapshot_weightings, axis=0)
+                c.pnl.p.multiply(n.snapshot_weightings.generators, axis=0)
-                .sum()
+                .groupby(level=0).sum()
                 .multiply(c.df.sign)
-                .groupby(c.df.carrier)
+                .groupby(c.df.carrier, axis=1)
                 .sum()
             )
         else:
-            c_energies = pd.Series(0.0, c.df.carrier.unique())
+            c_energies = pd.DataFrame(0.0, columns=c.df.carrier.unique(), index=n.investment_periods)
             for port in [col[3:] for col in c.df.columns if col[:3] == "bus"]:
-                totals = c.pnl["p" + port].multiply(n.snapshot_weightings, axis=0).sum()
+                totals = c.pnl["p" + port].multiply(n.snapshot_weightings.generators, axis=0).groupby(level=0).sum()
                 # remove values where bus is missing (bug in nomopyomo)
                 no_bus = c.df.index[c.df["bus" + port] == ""]
-                totals.loc[no_bus] = n.component_attrs[c.name].loc[
+                totals[no_bus] = float(n.component_attrs[c.name].loc[
                     "p" + port, "default"
-                ]
+                ])
-                c_energies -= totals.groupby(c.df.carrier).sum()
+                c_energies -= totals.groupby(c.df.carrier, axis=1).sum()
 
-        c_energies = pd.concat([c_energies], keys=[c.list_name])
+        c_energies = pd.concat([c_energies.T], keys=[c.list_name])
 
         energy = energy.reindex(c_energies.index.union(energy.index))
 
-        energy.loc[c_energies.index, label] = c_energies
+        energy.loc[c_energies.index, label] = c_energies.values
 
     return energy
 
@@ -599,7 +610,7 @@ def calculate_co2_emissions(n, label, df):
     if emissions.empty:
         return
 
-    weightings = n.snapshot_weightings.mul(
+    weightings = n.snapshot_weightings.generators.mul(
         n.investment_period_weightings["years"]
         .reindex(n.snapshots)
         .fillna(method="bfill")
@@ -661,11 +672,10 @@ def make_summaries(networks_dict):
     for output in outputs:
         df[output] = pd.DataFrame(columns=columns, dtype=float)
 
-    overrides = override_component_attrs(snakemake.input.overrides)
     for label, filename in iteritems(networks_dict):
         print(label, filename)
         try:
-            n = pypsa.Network(filename, override_component_attrs=overrides)
+            n = pypsa.Network(filename)
         except OSError:
             print(label, " not solved yet.")
             continue
@@ -694,7 +704,6 @@ if __name__ == "__main__":
     # Detect running outside of snakemake and mock snakemake for testing
     if "snakemake" not in globals():
         from _helpers import mock_snakemake
-
         snakemake = mock_snakemake("make_summary_perfect")
 
     networks_dict = {

scripts/plot_summary.py

@@ -49,6 +49,10 @@ def rename_techs(label):
         # "H2 Fuel Cell": "hydrogen storage",
         # "H2 pipeline": "hydrogen storage",
         "battery": "battery storage",
+        "H2 for industry": "H2 for industry",
+        "land transport fuel cell": "land transport fuel cell",
+        "land transport oil": "land transport oil",
+        "oil shipping": "shipping oil",
         # "CC": "CC"
     }
 
@@ -157,11 +161,11 @@ def plot_costs():
         df.index.difference(preferred_order)
     )
 
-    new_columns = df.sum().sort_values().index
+    # new_columns = df.sum().sort_values().index
 
     fig, ax = plt.subplots(figsize=(12, 8))
 
-    df.loc[new_index, new_columns].T.plot(
+    df.loc[new_index].T.plot(
         kind="bar",
         ax=ax,
         stacked=True,
@@ -222,13 +226,13 @@ def plot_energy():
         df.index.difference(preferred_order)
     )
 
-    new_columns = df.columns.sort_values()
+    # new_columns = df.columns.sort_values()
 
     fig, ax = plt.subplots(figsize=(12, 8))
 
-    logger.debug(df.loc[new_index, new_columns])
+    logger.debug(df.loc[new_index])
 
-    df.loc[new_index, new_columns].T.plot(
+    df.loc[new_index].T.plot(
         kind="bar",
         ax=ax,
         stacked=True,
@@ -272,7 +276,7 @@ def plot_balances():
         i for i in balances_df.index.levels[0] if i not in co2_carriers
     ]
 
-    fig, ax = plt.subplots(figsize=(12, 8))
+    
 
     for k, v in balances.items():
         df = balances_df.loc[v]
@@ -318,6 +322,8 @@ def plot_balances():
 
         new_columns = df.columns.sort_values()
         
+        fig, ax = plt.subplots(figsize=(12, 8))
+
         df.loc[new_index, new_columns].T.plot(
             kind="bar",
             ax=ax,
@@ -544,7 +550,7 @@ def plot_carbon_budget_distribution(input_eurostat):
     path_cb_plot = "results/" + snakemake.params.RDIR + "/graphs/"
     plt.savefig(path_cb_plot + "carbon_budget_plot.pdf", dpi=300)
 
-
+#%%
 if __name__ == "__main__":
     if "snakemake" not in globals():
         from _helpers import mock_snakemake