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tests/test_data_processing.py

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+import pandas as pd
+import pytest
+
+from data_processing import filter_by_date, hourly_average
+
+
+@pytest.fixture
+def sample_df():
+    return pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(
+                [
+                    "2026-07-22 08:05",
+                    "2026-07-22 08:40",
+                    "2026-07-22 09:10",
+                    "2026-07-23 08:05",  # andere dag, moet uitgefilterd worden
+                ]
+            ),
+            "glucose_value": [6.0, 8.0, 7.0, 12.0],
+        }
+    )
+
+
+def test_filter_by_date_keeps_only_selected_day(sample_df):
+    result = filter_by_date(sample_df, pd.Timestamp("2026-07-22").date())
+    assert len(result) == 3
+    assert (result["timestamp"].dt.date == pd.Timestamp("2026-07-22").date()).all()
+
+
+def test_hourly_average_computes_mean_per_hour(sample_df):
+    filtered = filter_by_date(sample_df, pd.Timestamp("2026-07-22").date())
+    result = hourly_average(filtered)
+
+    hour_8 = result.loc[result["hour"] == 8, "glucose_value"].iloc[0]
+    hour_9 = result.loc[result["hour"] == 9, "glucose_value"].iloc[0]
+
+    assert hour_8 == pytest.approx(7.0)  # gemiddelde van 6.0 en 8.0
+    assert hour_9 == pytest.approx(7.0)
+
+
+def test_hourly_average_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "glucose_value"])
+    result = hourly_average(empty)
+    assert result.empty
+    assert list(result.columns) == ["hour", "glucose_value"]
+
+
+def test_hourly_average_only_returns_hours_with_data(sample_df):
+    filtered = filter_by_date(sample_df, pd.Timestamp("2026-07-22").date())
+    result = hourly_average(filtered)
+    # alleen uur 8 en 9 hebben data op deze dag, dus precies 2 rijen
+    assert len(result) == 2