Replace usage of load_all_summary_data() in everest data api

src/everest/api/everest_data_api.py

@@ -1,6 +1,6 @@
 from collections import OrderedDict
 
-import pandas as pd
+import polars as pl
 from seba_sqlite.snapshot import SebaSnapshot
 
 from ert.storage import open_storage
@@ -154,48 +154,49 @@ def gradient_values(self):
     def summary_values(self, batches=None, keys=None):
         if batches is None:
             batches = self.batches
-        simulations = self.simulations
         data_frames = []
         storage = open_storage(self._config.storage_dir, "r")
         experiment = next(storage.experiments)
         for batch_id in batches:
             ensemble = experiment.get_ensemble_by_name(f"batch_{batch_id}")
-            summary = ensemble.load_all_summary_data()
-            if not summary.empty:
-                columns = set(summary.columns)
-                if keys is not None:
-                    columns = columns.intersection(set(keys))
-                    summary = summary[list(columns)]
-                summary = summary.dropna(axis=0, how="all", subset=columns)
-                summary = summary.dropna(axis=1, how="all")
-                summary = summary[
-                    summary.index.get_level_values("Realization").isin(simulations)
-                ]
-                summary.reset_index(inplace=True)
-                summary["batch"] = batch_id
+            try:
+                summary = ensemble.load_responses(
+                    key="summary",
+                    realizations=tuple(self.simulations),
+                )
+            except (ValueError, KeyError):
+                summary = pl.DataFrame()
+
+            if not summary.is_empty():
+                summary = summary.pivot(
+                    on="response_key", index=["realization", "time"], sort_columns=True
+                )
                 # The 'Realization' column exported by ert are
                 # the 'simulations' of everest.
-                summary.rename(
-                    columns={"Realization": "simulation", "Date": "date"}, inplace=True
+                summary = summary.rename({"time": "date", "realization": "simulation"})
+                if keys is not None:
+                    columns = set(summary.columns).intersection(set(keys))
+                    summary = summary[["date", "simulation", *list(columns)]]
+                summary = summary.with_columns(
+                    pl.Series("batch", [batch_id] * summary.shape[0])
                 )
                 # The realization ID as defined by Everest must be
                 # retrieved via the seba snapshot.
                 realization_map = {
-                    str(sim.simulation): sim.realization
+                    sim.simulation: sim.realization
                     for sim in self._snapshot.simulation_data
                     if sim.batch == batch_id
                 }
-                summary["realization"] = (
-                    summary["simulation"].astype(str).map(realization_map)
-                )
-                # If possible, convert the realization id to integer.
-                summary["realization"] = pd.to_numeric(
-                    summary["realization"], errors="ignore", downcast="integer"
+                realizations = pl.Series(
+                    "realization",
+                    [realization_map.get(str(sim)) for sim in summary["simulation"]],
                 )
+                realizations = realizations.cast(pl.Int64, strict=False)
+                summary = summary.with_columns(realizations)
 
             data_frames.append(summary)
         storage.close()
-        return pd.concat(data_frames)
+        return pl.concat(data_frames)
 
     @property
     def output_folder(self):

src/everest/export.py

@@ -336,7 +336,20 @@ def load_batch_by_id():
             experiment = experiments[0]
 
             ensemble = experiment.get_ensemble_by_name(case_name)
-            return ensemble.load_all_summary_data()
+            realizations = ensemble.get_realization_list_with_responses()
+            try:
+                df_pl = ensemble.load_responses("summary", tuple(realizations))
+            except (ValueError, KeyError):
+                return pd.DataFrame()
+            df_pl = df_pl.pivot(
+                on="response_key", index=["realization", "time"], sort_columns=True
+            )
+            df_pl = df_pl.rename({"time": "Date", "realization": "Realization"})
+            return (
+                df_pl.to_pandas()
+                .set_index(["Realization", "Date"])
+                .sort_values(by=["Date", "Realization"])
+            )
 
         batches = {elem[MetaDataColumnNames.BATCH] for elem in metadata}
         batch_data = []

tests/everest/test_api_snapshots.py

@@ -133,7 +133,7 @@ def test_api_summary_snapshot(
             ens.save_response("summary", smry_data.clone(), real)
 
     api = EverestDataAPI(config)
-    dicts = polars.from_pandas(api.summary_values()).to_dicts()
+    dicts = api.summary_values().to_dicts()
     snapshot.assert_match(
         orjson.dumps(dicts, option=orjson.OPT_INDENT_2).decode("utf-8").strip() + "\n",
         "snapshot.json",