Features/#762 too many deprecation warnings

etrago/cluster/gas.py

@@ -417,20 +417,20 @@ def gas_postprocessing(etrago, busmap, medoid_idx=None):
         },
         one_port_strategies={
             "Generator": {
-                "marginal_cost": np.mean,
-                "capital_cost": np.mean,
-                "p_nom_max": np.sum,
-                "p_nom_min": np.sum,
-                "e_nom_max": np.sum,
+                "marginal_cost": "mean",
+                "capital_cost": "mean",
+                "p_nom_max": "sum",
+                "p_nom_min": "sum",
+                "e_nom_max": "sum",
             },
             "Store": {
-                "marginal_cost": np.mean,
-                "capital_cost": np.mean,
-                "e_nom": np.sum,
+                "marginal_cost": "mean",
+                "capital_cost": "mean",
+                "e_nom": "sum",
                 "e_nom_max": sum_with_inf,
             },
             "Load": {
-                "p_set": np.sum,
+                "p_set": "sum",
             },
         },
     )

etrago/cluster/spatial.py

@@ -121,38 +121,38 @@ def strategies_lines():
 def strategies_one_ports():
     return {
         "StorageUnit": {
-            "marginal_cost": np.mean,
-            "capital_cost": np.mean,
-            "efficiency_dispatch": np.mean,
-            "standing_loss": np.mean,
-            "efficiency_store": np.mean,
-            "p_min_pu": np.min,
+            "marginal_cost": "mean",
+            "capital_cost": "mean",
+            "efficiency_dispatch": "mean",
+            "standing_loss": "mean",
+            "efficiency_store": "mean",
+            "p_min_pu": "min",
             "p_nom_extendable": ext_storage,
             "p_nom_max": sum_with_inf,
         },
         "Store": {
-            "marginal_cost": np.mean,
-            "capital_cost": np.mean,
-            "standing_loss": np.mean,
-            "e_nom": np.sum,
-            "e_nom_min": np.sum,
+            "marginal_cost": "mean",
+            "capital_cost": "mean",
+            "standing_loss": "mean",
+            "e_nom": "sum",
+            "e_nom_min": "sum",
             "e_nom_max": sum_with_inf,
-            "e_initial": np.sum,
-            "e_min_pu": np.mean,
-            "e_max_pu": np.mean,
+            "e_initial": "sum",
+            "e_min_pu": "mean",
+            "e_max_pu": "mean",
         },
     }
 
 
 def strategies_generators():
     return {
-        "p_nom_min": np.min,
+        "p_nom_min": "min",
         "p_nom_max": sum_with_inf,
-        "weight": np.sum,
-        "p_nom": np.sum,
-        "p_nom_opt": np.sum,
-        "marginal_cost": np.mean,
-        "capital_cost": np.mean,
+        "weight": "sum",
+        "p_nom": "sum",
+        "p_nom_opt": "sum",
+        "marginal_cost": "mean",
+        "capital_cost": "mean",
         "e_nom_max": sum_with_inf,
     }
 
@@ -163,30 +163,30 @@ def strategies_links():
         "bus0": _make_consense_links,
         "bus1": _make_consense_links,
         "carrier": _make_consense_links,
-        "p_nom": np.sum,
+        "p_nom": "sum",
         "p_nom_extendable": _make_consense_links,
         "p_nom_max": sum_with_inf,
-        "capital_cost": np.mean,
-        "length": np.mean,
+        "capital_cost": "mean",
+        "length": "mean",
         "geom": nan_links,
         "topo": nan_links,
         "type": nan_links,
-        "efficiency": np.mean,
-        "p_nom_min": np.sum,
-        "p_set": np.mean,
-        "p_min_pu": np.min,
-        "p_max_pu": np.max,
-        "marginal_cost": np.mean,
+        "efficiency": "mean",
+        "p_nom_min": "sum",
+        "p_set": "mean",
+        "p_min_pu": "min",
+        "p_max_pu": "max",
+        "marginal_cost": "mean",
         "terrain_factor": _make_consense_links,
-        "p_nom_opt": np.mean,
+        "p_nom_opt": "mean",
         "country": nan_links,
-        "build_year": np.mean,
-        "lifetime": np.mean,
-        "min_up_time": np.mean,
-        "min_down_time": np.mean,
-        "up_time_before": np.mean,
-        "down_time_before": np.mean,
-        "committable": np.all,
+        "build_year": "mean",
+        "lifetime": "mean",
+        "min_up_time": "mean",
+        "min_down_time": "mean",
+        "up_time_before": "mean",
+        "down_time_before": "mean",
+        "committable": "all",
     }
 
 
@@ -255,7 +255,7 @@ def arrange_dc_bus0_bus1(network):
     strategies.pop("topo")
     strategies.pop("geom")
 
-    new_df = links.groupby(grouper, axis=0).agg(strategies)
+    new_df = links.groupby(grouper).agg(strategies)
     new_df.index = flatten_multiindex(new_df.index).rename("name")
     new_df = pd.concat(
         [new_df, network.links.loc[~links_agg_b]], axis=0, sort=False
@@ -275,7 +275,7 @@ def arrange_dc_bus0_bus1(network):
                     df_agg = df_agg.multiply(
                         weighting.loc[df_agg.columns], axis=1
                     )
-                pnl_df = df_agg.groupby(grouper, axis=1).sum()
+                pnl_df = df_agg.T.groupby(grouper).sum().T
                 pnl_df.columns = flatten_multiindex(pnl_df.columns).rename(
                     "name"
                 )
@@ -759,7 +759,7 @@ def kmedoids_dijkstra_clustering(
         kmeans.fit(points)
 
         busmap = pd.Series(
-            data=kmeans.predict(buses.loc[buses_i, ["x", "y"]]),
+            data=kmeans.predict(buses.loc[buses_i, ["x", "y"]].values),
             index=buses_i,
             dtype=object,
         )

etrago/tools/shapes_europe/shapes_europe.cpg

@@ -0,0 +1 @@
+ISO-8859-1

etrago/tools/shapes_europe/shapes_europe.prj

@@ -0,0 +1 @@
+GEOGCS["GCS_WGS_1984",DATUM["D_WGS_1984",SPHEROID["WGS_1984",6378137.0,298.257223563]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]]

etrago/tools/utilities.py

@@ -24,6 +24,7 @@
 
 from collections.abc import Mapping
 from copy import deepcopy
+from pathlib import Path
 import json
 import logging
 import math
@@ -287,9 +288,10 @@ def buses_by_country(self, apply_on="grid_model"):
     con = self.engine
     germany_sh = gpd.read_postgis(query, con, geom_col="geometry")
 
-    path = gpd.datasets.get_path("naturalearth_lowres")
-    shapes = gpd.read_file(path)
-    shapes = shapes[shapes.name.isin([*countries])].set_index(keys="name")
+    # read Europe borders. Original data downloaded from naturalearthdata.com/
+    # under Public Domain license
+    path = Path(".") / "tools" / "shapes_europe"
+    shapes = gpd.read_file(path).set_index("name")
 
     # Use Germany borders from egon-data if not using the SH test case
     if len(germany_sh.gen.unique()) > 1: