added getting data from nightscout

2026-07-25 08:54:08 +02:00
parent c02110a82b
commit 60735de445
12 changed files with 613 additions and 285 deletions
@@ -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())
@@ -0,0 +1,98 @@
+from datetime import datetime, timedelta, timezone
+
+import pytest
+
+import nightscout_client as ns
+
+
+class FakeResponse:
+    def __init__(self, payload):
+        self._payload = payload
+
+    def raise_for_status(self):
+        pass
+
+    def json(self):
+        return self._payload
+
+
+def test_mgdl_to_mmol_converts_correctly():
+    assert ns.mgdl_to_mmol(126.0) == pytest.approx(7.0, abs=0.05)
+
+
+def test_base_url_requires_nightscout_url(monkeypatch):
+    monkeypatch.delenv("NIGHTSCOUT_URL", raising=False)
+    with pytest.raises(ns.NightscoutConfigError):
+        ns._base_url()
+
+
+def test_base_url_strips_trailing_slash(monkeypatch):
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com/")
+    assert ns._base_url() == "https://example.com"
+
+
+def test_auth_prefers_token_over_api_secret(monkeypatch):
+    monkeypatch.setenv("NIGHTSCOUT_TOKEN", "my-token")
+    monkeypatch.setenv("NIGHTSCOUT_API_SECRET", "my-secret")
+    headers, params = ns._auth()
+    assert params == {"token": "my-token"}
+    assert headers == {}
+
+
+def test_auth_hashes_api_secret(monkeypatch):
+    monkeypatch.delenv("NIGHTSCOUT_TOKEN", raising=False)
+    monkeypatch.setenv("NIGHTSCOUT_API_SECRET", "my-secret")
+    headers, params = ns._auth()
+    assert params == {}
+    assert headers["API-SECRET"] != "my-secret"  # nooit in plaintext
+    assert len(headers["API-SECRET"]) == 40  # sha1 hexdigest
+
+
+def test_fetch_entries_converts_and_filters_by_time(monkeypatch):
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com")
+    monkeypatch.delenv("NIGHTSCOUT_TOKEN", raising=False)
+    monkeypatch.delenv("NIGHTSCOUT_API_SECRET", raising=False)
+
+    now = datetime.now(timezone.utc)
+    recent = now - timedelta(hours=1)
+    too_old = now - timedelta(hours=48)
+    payload = [
+        {"date": int(recent.timestamp() * 1000), "sgv": 126},
+        {"date": int(too_old.timestamp() * 1000), "sgv": 90},
+    ]
+
+    monkeypatch.setattr(ns.requests, "get", lambda *a, **k: FakeResponse(payload))
+
+    result = ns.fetch_entries(hours=24)
+    assert len(result) == 1
+    assert result.iloc[0]["mmol"] == pytest.approx(7.0, abs=0.05)
+
+
+def test_fetch_entries_handles_empty_response(monkeypatch):
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com")
+    monkeypatch.setattr(ns.requests, "get", lambda *a, **k: FakeResponse([]))
+
+    result = ns.fetch_entries(hours=24)
+    assert result.empty
+    assert list(result.columns) == ["timestamp", "mmol"]
+
+
+def test_fetch_treatments_fills_missing_columns(monkeypatch):
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com")
+    now = datetime.now(timezone.utc)
+    payload = [
+        {"created_at": now.isoformat(), "eventType": "Site Change"},
+    ]
+    monkeypatch.setattr(ns.requests, "get", lambda *a, **k: FakeResponse(payload))
+
+    result = ns.fetch_treatments(hours=24)
+    assert len(result) == 1
+    assert result.iloc[0]["eventType"] == "Site Change"
+    assert pd_isna(result.iloc[0]["insulin"])
+    assert pd_isna(result.iloc[0]["carbs"])
+
+
+def pd_isna(value):
+    import pandas as pd
+
+    return pd.isna(value)