address deprecation warnings

PyPSA · Jan 2, 2024 · 30c1a1c · 30c1a1c
1 parent e3539b0
commit 30c1a1c
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Showing 7 changed files with 41 additions and 39 deletions.
diff --git a/config/config.default.yaml b/config/config.default.yaml
@@ -158,6 +158,7 @@ renewable:
     resource:
       method: wind
       turbine: Vestas_V112_3MW
+      add_cutout_windspeed: true
     capacity_per_sqkm: 3
     # correction_factor: 0.93
     corine:
@@ -173,6 +174,7 @@ renewable:
     resource:
       method: wind
       turbine: NREL_ReferenceTurbine_5MW_offshore
+      add_cutout_windspeed: true
     capacity_per_sqkm: 2
     correction_factor: 0.8855
     corine: [44, 255]
@@ -188,6 +190,7 @@ renewable:
     resource:
       method: wind
       turbine: NREL_ReferenceTurbine_5MW_offshore
+      add_cutout_windspeed: true
     capacity_per_sqkm: 2
     correction_factor: 0.8855
     corine: [44, 255]

diff --git a/scripts/build_biomass_potentials.py b/scripts/build_biomass_potentials.py
@@ -134,7 +134,7 @@ def disaggregate_nuts0(bio):
     # get population in nuts2
     pop_nuts2 = pop.loc[pop.index.str.len() == 4]
     by_country = pop_nuts2.total.groupby(pop_nuts2.ct).sum()
-    pop_nuts2["fraction"] = pop_nuts2.total / pop_nuts2.ct.map(by_country)
+    pop_nuts2.loc[:, "fraction"] = pop_nuts2.total / pop_nuts2.ct.map(by_country)
 
     # distribute nuts0 data to nuts2 by population
     bio_nodal = bio.loc[pop_nuts2.ct]

diff --git a/scripts/build_energy_totals.py b/scripts/build_energy_totals.py
@@ -189,26 +189,26 @@ def idees_per_country(ct, year, base_dir):
     ct_totals["total residential water"] = df.at["Water heating"]
 
     assert df.index[23] == "Electricity"
-    ct_totals["electricity residential water"] = df[23]
+    ct_totals["electricity residential water"] = df.iloc[23]
 
     ct_totals["total residential cooking"] = df["Cooking"]
 
     assert df.index[30] == "Electricity"
-    ct_totals["electricity residential cooking"] = df[30]
+    ct_totals["electricity residential cooking"] = df.iloc[30]
 
     df = pd.read_excel(fn_residential, "RES_summary", index_col=0)[year]
 
     row = "Energy consumption by fuel - Eurostat structure (ktoe)"
     ct_totals["total residential"] = df[row]
 
     assert df.index[47] == "Electricity"
-    ct_totals["electricity residential"] = df[47]
+    ct_totals["electricity residential"] = df.iloc[47]
 
     assert df.index[46] == "Derived heat"
-    ct_totals["derived heat residential"] = df[46]
+    ct_totals["derived heat residential"] = df.iloc[46]
 
     assert df.index[50] == "Thermal uses"
-    ct_totals["thermal uses residential"] = df[50]
+    ct_totals["thermal uses residential"] = df.iloc[50]
 
     # services
 
@@ -222,26 +222,26 @@ def idees_per_country(ct, year, base_dir):
     ct_totals["total services water"] = df["Hot water"]
 
     assert df.index[24] == "Electricity"
-    ct_totals["electricity services water"] = df[24]
+    ct_totals["electricity services water"] = df.iloc[24]
 
     ct_totals["total services cooking"] = df["Catering"]
 
     assert df.index[31] == "Electricity"
-    ct_totals["electricity services cooking"] = df[31]
+    ct_totals["electricity services cooking"] = df.iloc[31]
 
     df = pd.read_excel(fn_tertiary, "SER_summary", index_col=0)[year]
 
     row = "Energy consumption by fuel - Eurostat structure (ktoe)"
     ct_totals["total services"] = df[row]
 
     assert df.index[50] == "Electricity"
-    ct_totals["electricity services"] = df[50]
+    ct_totals["electricity services"] = df.iloc[50]
 
     assert df.index[49] == "Derived heat"
-    ct_totals["derived heat services"] = df[49]
+    ct_totals["derived heat services"] = df.iloc[49]
 
     assert df.index[53] == "Thermal uses"
-    ct_totals["thermal uses services"] = df[53]
+    ct_totals["thermal uses services"] = df.iloc[53]
 
     # agriculture, forestry and fishing
 
@@ -282,28 +282,28 @@ def idees_per_country(ct, year, base_dir):
     ct_totals["total two-wheel"] = df["Powered 2-wheelers (Gasoline)"]
 
     assert df.index[19] == "Passenger cars"
-    ct_totals["total passenger cars"] = df[19]
+    ct_totals["total passenger cars"] = df.iloc[19]
 
     assert df.index[30] == "Battery electric vehicles"
-    ct_totals["electricity passenger cars"] = df[30]
+    ct_totals["electricity passenger cars"] = df.iloc[30]
 
     assert df.index[31] == "Motor coaches, buses and trolley buses"
-    ct_totals["total other road passenger"] = df[31]
+    ct_totals["total other road passenger"] = df.iloc[31]
 
     assert df.index[39] == "Battery electric vehicles"
-    ct_totals["electricity other road passenger"] = df[39]
+    ct_totals["electricity other road passenger"] = df.iloc[39]
 
     assert df.index[41] == "Light duty vehicles"
-    ct_totals["total light duty road freight"] = df[41]
+    ct_totals["total light duty road freight"] = df.iloc[41]
 
     assert df.index[49] == "Battery electric vehicles"
-    ct_totals["electricity light duty road freight"] = df[49]
+    ct_totals["electricity light duty road freight"] = df.iloc[49]
 
     row = "Heavy duty vehicles (Diesel oil incl. biofuels)"
     ct_totals["total heavy duty road freight"] = df[row]
 
     assert df.index[61] == "Passenger cars"
-    ct_totals["passenger car efficiency"] = df[61]
+    ct_totals["passenger car efficiency"] = df.iloc[61]
 
     df = pd.read_excel(fn_transport, "TrRail_ene", index_col=0)[year]
 
@@ -312,39 +312,39 @@ def idees_per_country(ct, year, base_dir):
     ct_totals["electricity rail"] = df["Electricity"]
 
     assert df.index[15] == "Passenger transport"
-    ct_totals["total rail passenger"] = df[15]
+    ct_totals["total rail passenger"] = df.iloc[15]
 
     assert df.index[16] == "Metro and tram, urban light rail"
     assert df.index[19] == "Electric"
     assert df.index[20] == "High speed passenger trains"
-    ct_totals["electricity rail passenger"] = df[[16, 19, 20]].sum()
+    ct_totals["electricity rail passenger"] = df.iloc[[16, 19, 20]].sum()
 
     assert df.index[21] == "Freight transport"
-    ct_totals["total rail freight"] = df[21]
+    ct_totals["total rail freight"] = df.iloc[21]
 
     assert df.index[23] == "Electric"
-    ct_totals["electricity rail freight"] = df[23]
+    ct_totals["electricity rail freight"] = df.iloc[23]
 
     df = pd.read_excel(fn_transport, "TrAvia_ene", index_col=0)[year]
 
     assert df.index[6] == "Passenger transport"
-    ct_totals["total aviation passenger"] = df[6]
+    ct_totals["total aviation passenger"] = df.iloc[6]
 
     assert df.index[10] == "Freight transport"
-    ct_totals["total aviation freight"] = df[10]
+    ct_totals["total aviation freight"] = df.iloc[10]
 
     assert df.index[7] == "Domestic"
-    ct_totals["total domestic aviation passenger"] = df[7]
+    ct_totals["total domestic aviation passenger"] = df.iloc[7]
 
     assert df.index[8] == "International - Intra-EU"
     assert df.index[9] == "International - Extra-EU"
-    ct_totals["total international aviation passenger"] = df[[8, 9]].sum()
+    ct_totals["total international aviation passenger"] = df.iloc[[8, 9]].sum()
 
     assert df.index[11] == "Domestic and International - Intra-EU"
-    ct_totals["total domestic aviation freight"] = df[11]
+    ct_totals["total domestic aviation freight"] = df.iloc[11]
 
     assert df.index[12] == "International - Extra-EU"
-    ct_totals["total international aviation freight"] = df[12]
+    ct_totals["total international aviation freight"] = df.iloc[12]
 
     ct_totals["total domestic aviation"] = (
         ct_totals["total domestic aviation freight"]
@@ -364,7 +364,7 @@ def idees_per_country(ct, year, base_dir):
     df = pd.read_excel(fn_transport, "TrRoad_act", index_col=0)[year]
 
     assert df.index[85] == "Passenger cars"
-    ct_totals["passenger cars"] = df[85]
+    ct_totals["passenger cars"] = df.iloc[85]
 
     return pd.Series(ct_totals, name=ct)
 

diff --git a/scripts/build_line_rating.py b/scripts/build_line_rating.py
@@ -119,7 +119,7 @@ def calculate_line_rating(n, cutout):
             .apply(lambda x: int(re.findall(r"(\d+)-bundle", x)[0]))
         )
         # Set default number of bundles per line
-        relevant_lines["n_bundle"].fillna(1, inplace=True)
+        relevant_lines["n_bundle"] = relevant_lines["n_bundle"].fillna(1)
         R *= relevant_lines["n_bundle"]
         R = calculate_resistance(T=353, R_ref=R)
     Imax = cutout.line_rating(shapes, R, D=0.0218, Ts=353, epsilon=0.8, alpha=0.8)

diff --git a/scripts/build_retro_cost.py b/scripts/build_retro_cost.py
@@ -836,8 +836,7 @@ def calculate_heat_losses(u_values, data_tabula, l_strength, temperature_factor)
     F_red_temp = map_to_lstrength(l_strength, F_red_temp)
 
     Q_ht = (
-        heat_transfer_perm2.groupby(level=1, axis=1)
-        .sum()
+        heat_transfer_perm2.T.groupby(level=1).sum().T
         .mul(F_red_temp.droplevel(0, axis=1))
         .mul(temperature_factor.reindex(heat_transfer_perm2.index, level=0), axis=0)
     )
@@ -878,7 +877,7 @@ def calculate_gain_utilisation_factor(heat_transfer_perm2, Q_ht, Q_gain):
     Calculates gain utilisation factor nu.
     """
     # time constant of the building tau [h] = c_m [Wh/(m^2K)] * 1 /(H_tr_e+H_tb*H_ve) [m^2 K /W]
-    tau = c_m / heat_transfer_perm2.groupby(level=1, axis=1).sum()
+    tau = c_m / heat_transfer_perm2.T.groupby(axis=1).sum().T
     alpha = alpha_H_0 + (tau / tau_H_0)
     # heat balance ratio
     gamma = (1 / Q_ht).mul(Q_gain.sum(axis=1), axis=0)

diff --git a/scripts/build_ship_raster.py b/scripts/build_ship_raster.py
@@ -64,7 +64,7 @@
     with zipfile.ZipFile(snakemake.input.ship_density) as zip_f:
         zip_f.extract("shipdensity_global.tif")
         with rioxarray.open_rasterio("shipdensity_global.tif") as ship_density:
-            ship_density = ship_density.drop(["band"]).sel(
+            ship_density = ship_density.drop_vars(["band"]).sel(
                 x=slice(min(xs), max(Xs)), y=slice(max(Ys), min(ys))
             )
             ship_density.rio.to_raster(snakemake.output[0])

diff --git a/scripts/prepare_sector_network.py b/scripts/prepare_sector_network.py
@@ -1630,7 +1630,7 @@ def build_heat_demand(n):
     electric_nodes = n.loads.index[n.loads.carrier == "electricity"]
     n.loads_t.p_set[electric_nodes] = (
         n.loads_t.p_set[electric_nodes]
-        - electric_heat_supply.groupby(level=1, axis=1).sum()[electric_nodes]
+        - electric_heat_supply.T.groupby(level=1).sum().T[electric_nodes]
     )
 
     return heat_demand
@@ -1724,15 +1724,15 @@ def add_heat(n, costs):
             if sector in name:
                 heat_load = (
                     heat_demand[[sector + " water", sector + " space"]]
-                    .groupby(level=1, axis=1)
-                    .sum()[nodes[name]]
+                    .T.groupby(level=1)
+                    .sum().T[nodes[name]]
                     .multiply(factor)
                 )
 
         if name == "urban central":
             heat_load = (
-                heat_demand.groupby(level=1, axis=1)
-                .sum()[nodes[name]]
+                heat_demand.T.groupby(level=1)
+                .sum().T[nodes[name]]
                 .multiply(
                     factor * (1 + options["district_heating"]["district_heating_loss"])
                 )