updated reset-index

notebooks/koppelen.py

@@ -0,0 +1,66 @@
+# %%
+
+import sqlite3
+
+import pandas as pd
+from ribasim_nl import CloudStorage, Model, reset_index
+
+cloud = CloudStorage()
+
+
+def update_database(toml_file):
+    database_gpkg = toml_file.with_name("database.gpkg")
+    conn = sqlite3.connect(database_gpkg)
+
+    # get table into DataFrame
+    table = "Outlet / static"
+    df = pd.read_sql_query(f"SELECT * FROM '{table}'", conn)
+
+    # drop urban runoff column if exists
+    df.rename(columns={"min_crest_level": "min_upstream_level"}, inplace=True)
+
+    #  Write the DataFrame back to the SQLite table
+    df.to_sql(table, conn, if_exists="replace", index=False)
+
+    # # Close the connection
+    conn.close()
+
+
+model_url = cloud.joinurl("AmstelGooienVecht", "modellen", "AmstelGooienVecht_parametrized_2024_8_47")
+model_path = cloud.joinpath("AmstelGooienVecht", "modellen", "AmstelGooienVecht_parametrized_2024_8_47")
+cloud.download_content(model_url)
+
+# %% update RWS-HWS
+model_path = cloud.joinpath("Rijkswaterstaat", "modellen", "hws")
+toml_file = model_path / "hws.toml"
+update_database(toml_file)
+rws_model = Model.read(toml_file)
+
+# some fixes
+node_id = 8413
+level = rws_model.upstream_profile(node_id).level.min() + 0.1
+
+mask = (rws_model.tabulated_rating_curve.static.df.node_id == node_id) & (
+    rws_model.tabulated_rating_curve.static.df.level < level
+)
+rws_model.tabulated_rating_curve.static.df.loc[mask, ["level"]] = level
+
+# %% reset index
+rws_model = reset_index(rws_model)
+
+# write model
+rws_model.write(model_path.with_name("hws_temp") / "hws.toml")
+
+# %% update AGV
+toml_file = model_path / "ribasim.toml"
+update_database(toml_file)
+model_to_couple = Model.read(toml_file)
+
+model_to_couple.write(model_path.with_name("AmstelGooienVecht_temp") / "agv.toml")
+
+
+# %%
+
+node_ids = model_to_couple.level_boundary.static.df[
+    model_to_couple.level_boundary.static.df.meta_to_authority == "Rijkswaterstaat"
+].node_id.to_list()

src/ribasim_nl/ribasim_nl/reset_index.py

@@ -7,37 +7,33 @@
 
 def reset_index(model: Model, node_start=1):
     # only reset if we have to
-    node_id_min = model.node_table().df.node_id.min()
-    node_id_max = model.node_table().df.node_id.max()
+    node_id_min = model.node_table().df.index.min()
+    node_id_max = model.node_table().df.index.max()
     expected_length = node_id_max - node_id_min + 1
     if not ((node_start == node_id_min) and (expected_length == len(model.node_table().df))):
         # make sure column node_id == index
-        node_ids = model.node_table().df.node_id
+        node_ids = model.node_table().df.index
 
         # create a new index for re-indexing all tables
         index = pd.Series(data=[i + node_start for i in range(len(node_ids))], index=node_ids).astype("int32")
 
-        # # re-index node_id and fid
-        # model.network.node.df.index = model.network.node.df["fid"].apply(
-        #     lambda x: index.loc[x]
-        # )
-        # model.network.node.df.index.name = "fid"
-        # model.network.node.df.drop(columns=["fid"], inplace=True)
-        # model.network.node.df.loc[:, "node_id"] = model.network.node.df.index
-
         # renumber edges
-        model.edge.df.loc[:, ["from_node_id"]] = model.edge.df["from_node_id"].apply(lambda x: index.loc[x])
+        model.edge.df.loc[:, ["from_node_id"]] = model.edge.df["from_node_id"].apply(lambda x: index[x])
 
-        model.edge.df.loc[:, ["to_node_id"]] = model.edge.df["to_node_id"].apply(lambda x: index.loc[x])
+        model.edge.df.loc[:, ["to_node_id"]] = model.edge.df["to_node_id"].apply(lambda x: index[x])
 
         # renumber tables
         for node_type in model.node_table().df.node_type.unique():
             ribasim_node = getattr(model, pascal_to_snake_case(node_type))
             for attr in ribasim_node.model_fields.keys():
                 table = getattr(ribasim_node, attr)
                 if table.df is not None:
-                    table.df.loc[:, "node_id"] = table.df["node_id"].apply(lambda x: index.loc[x])
-
+                    if "node_id" in table.df.columns:
+                        table.df.loc[:, "node_id"] = table.df["node_id"].apply(lambda x: index[x])
+                        table.df.index += 1
+                    elif table.df.index.name == "node_id":
+                        table.df.index = table.df.reset_index("node_id")["node_id"].apply(lambda x: index.loc[x])
+                        # table.df.index.name = "node_id"
     return model