From df2874ad3db364df1ad344cbb53ed04e38227a5a Mon Sep 17 00:00:00 2001
From: Eivind Jahren <ejah@equinor.com>
Date: Mon, 18 Nov 2024 07:24:19 +0100
Subject: [PATCH] Add test for mean calculation in dark_storage common

---
 .../unit_tests/dark_storage/test_common.py    | 72 +++++++++++++++++++
 1 file changed, 72 insertions(+)
 create mode 100644 tests/ert/unit_tests/dark_storage/test_common.py

diff --git a/tests/ert/unit_tests/dark_storage/test_common.py b/tests/ert/unit_tests/dark_storage/test_common.py
new file mode 100644
index 00000000000..b05ed558c79
--- /dev/null
+++ b/tests/ert/unit_tests/dark_storage/test_common.py
@@ -0,0 +1,72 @@
+import pandas as pd
+import pytest
+
+from ert.config import SummaryConfig
+from ert.dark_storage.common import data_for_key
+from ert.storage import open_storage
+from tests.ert.unit_tests.config.summary_generator import (
+    Date,
+    Simulator,
+    Smspec,
+    SmspecIntehead,
+    SummaryMiniStep,
+    SummaryStep,
+    UnitSystem,
+    Unsmry,
+)
+
+
+def test_data_for_key_gives_mean_for_duplicate_values(tmp_path):
+    value1 = 1.1
+    value2 = 1.0e19
+    with open_storage(tmp_path / "storage", mode="w") as storage:
+        summary_config = SummaryConfig(name="summary", input_files=["CASE"], keys=["*"])
+        experiment = storage.create_experiment(
+            observations={},
+            parameters=[],
+            responses=[summary_config],
+        )
+        ensemble = experiment.create_ensemble(name="ensemble", ensemble_size=1)
+        unsmry = Unsmry(
+            steps=[
+                SummaryStep(
+                    seqnum=0,
+                    ministeps=[
+                        SummaryMiniStep(mini_step=0, params=[0.0, 5.629901e16]),
+                        SummaryMiniStep(mini_step=1, params=[365.0, value1]),
+                    ],
+                ),
+                SummaryStep(
+                    seqnum=1,
+                    ministeps=[SummaryMiniStep(mini_step=2, params=[365.0, value2])],
+                ),
+            ]
+        )
+        smspec = Smspec(
+            nx=4,
+            ny=4,
+            nz=10,
+            restarted_from_step=0,
+            num_keywords=2,
+            restart="        ",
+            keywords=["TIME    ", "NRPPR"],
+            well_names=[":+:+:+:+", "WELLNAME"],
+            region_numbers=[-32676, 0],
+            units=["HOURS   ", "SM3"],
+            start_date=Date(
+                day=1, month=1, year=2014, hour=0, minutes=0, micro_seconds=0
+            ),
+            intehead=SmspecIntehead(
+                unit=UnitSystem.METRIC,
+                simulator=Simulator.ECLIPSE_100,
+            ),
+        )
+        smspec.to_file(tmp_path / "CASE.SMSPEC")
+        unsmry.to_file(tmp_path / "CASE.UNSMRY")
+        ds = summary_config.read_from_file(tmp_path, 0)
+        ensemble.save_response(summary_config.response_type, ds, 0)
+        df = data_for_key(ensemble, "NRPPR:WELLNAME")
+        assert list(df.columns) == [pd.Timestamp("2014-01-16 05:00:00")]
+        assert df[pd.Timestamp("2014-01-16 05:00:00")][0] == pytest.approx(
+            (value1 + value2) / 2
+        )