added getting data from nightscout

tests/test_data_processing.py

@@ -1,52 +1,74 @@
 import pandas as pd
 import pytest
 
-from data_processing import filter_by_date, hourly_average
+from data_processing import classify_treatments, glucose_status, summarize_time_in_range
 
 
 @pytest.fixture
-def sample_df():
+def sample_glucose():
     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
-                ]
+                ["2026-07-25 08:00", "2026-07-25 09:00", "2026-07-25 10:00", "2026-07-25 11:00"]
             ),
-            "glucose_value": [6.0, 8.0, 7.0, 12.0],
+            "mmol": [3.5, 6.0, 11.0, 7.0],  # laag, in bereik, hoog, in bereik
         }
     )
 
 
-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()
+@pytest.fixture
+def sample_treatments():
+    return pd.DataFrame(
+        {
+            "timestamp": pd.to_datetime(
+                [
+                    "2026-07-25 08:00",
+                    "2026-07-25 12:00",
+                    "2026-07-24 09:00",
+                    "2026-07-20 09:00",
+                ]
+            ),
+            "eventType": ["Meal Bolus", "Correction Bolus", "Site Change", "Sensor Change"],
+            "insulin": [4.0, 1.5, None, None],
+            "carbs": [45.0, None, None, None],
+        }
+    )
 
 
-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_glucose_status_classifies_correctly():
+    assert glucose_status(3.5) == "low"
+    assert glucose_status(6.0) == "in_range"
+    assert glucose_status(11.0) == "high"
 
 
-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_summarize_time_in_range(sample_glucose):
+    result = summarize_time_in_range(sample_glucose)
+    assert result == {"low": 25.0, "in_range": 50.0, "high": 25.0}
 
 
-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
+def test_summarize_time_in_range_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "mmol"])
+    result = summarize_time_in_range(empty)
+    assert result == {"low": 0.0, "in_range": 0.0, "high": 0.0}
+
+
+def test_classify_treatments_splits_into_expected_subsets(sample_treatments):
+    events = classify_treatments(sample_treatments)
+
+    assert len(events["insulin"]) == 2
+    assert set(events["insulin"]["insulin"]) == {4.0, 1.5}
+
+    assert len(events["carbs"]) == 1
+    assert events["carbs"].iloc[0]["carbs"] == 45.0
+
+    assert len(events["site_changes"]) == 1
+    assert events["site_changes"].iloc[0]["eventType"] == "Site Change"
+
+    assert len(events["sensor_changes"]) == 1
+    assert events["sensor_changes"].iloc[0]["eventType"] == "Sensor Change"
+
+
+def test_classify_treatments_handles_empty_input():
+    empty = pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs"])
+    events = classify_treatments(empty)
+    assert all(df.empty for df in events.values())