Merge pull request 'Add pump profile fetching and Insulineprofiel page' (#29) from feature/insulin-profile into main

Reviewed-on: #29

nightscout_client.py

@@ -252,3 +252,21 @@ def fetch_treatments_range(start_date: date, end_date: date) -> pd.DataFrame:
     since = datetime.combine(start_date, time.min, tzinfo=timezone.utc)
     until = datetime.combine(end_date, time.max, tzinfo=timezone.utc)
     return _treatments_since(since, until)
+
+
+def fetch_profile() -> dict:
+    """Haal het meest recent gebruikte pompprofiel op.
+
+    Nightscout's /api/v1/profile.json geeft profieldocumenten terug, nieuwste
+    eerst; met count=1 krijgen we alleen het actief gebruikte document. Een
+    profiel is geen tijdreeks (in tegenstelling tot entries/treatments) dus
+    geen paginering nodig — één klein verzoek volstaat.
+
+    Geeft {} terug als er geen profiel bestaat (bijv. lege Nightscout-instantie).
+    Het resultaat is het rauwe Nightscout-documument; zie profile_data.parse_profile()
+    voor de genormaliseerde vorm (basaalschema, ISF, koolhydraatratio, DIA, streefwaarden).
+    """
+    data = _get("/api/v1/profile.json", {"count": 1})
+    if not data:
+        return {}
+    return data[0]

pages/9_Insulineprofiel.py

@@ -0,0 +1,98 @@
+import pandas as pd
+import plotly.graph_objects as go
+import requests
+import streamlit as st
+
+from auth import require_auth
+from nightscout_client import NightscoutConfigError
+from profile_data import segments_table, segments_to_hourly_bars, total_daily_basal
+from ui_common import get_colors, load_profile
+
+st.set_page_config(page_title="Glucose dashboard — Insulineprofiel", page_icon="📊", layout="wide")
+require_auth()
+
+st.title("Insulineprofiel")
+st.caption("Het meest recent gebruikte pompprofiel: basaalschema, ISF, koolhydraatratio en DIA.")
+
+try:
+    profile = load_profile()
+except NightscoutConfigError as exc:
+    st.error(str(exc))
+    st.stop()
+except (requests.RequestException, ValueError) as exc:
+    st.error(f"Kon geen profiel ophalen van Nightscout: {exc}")
+    st.stop()
+
+if not profile:
+    st.info("Geen pompprofiel gevonden op deze Nightscout-instantie.")
+    st.stop()
+
+colors = get_colors()
+
+col1, col2 = st.columns(2)
+col1.metric("DIA (werkingsduur insuline)", f"{profile['dia']:.1f} uur")
+col2.caption(f"Profielnaam: {profile['name'] or '—'}")
+
+st.subheader("Basaalschema")
+bars = segments_to_hourly_bars(profile["basal"])
+fig = go.Figure(
+    go.Scatter(
+        x=[b["hour"] for b in bars],
+        y=[b["value"] for b in bars],
+        mode="lines",
+        line={"shape": "hv", "color": colors["basal"], "width": 2},
+        fill="tozeroy",
+        fillcolor=colors["basal"],
+        opacity=0.7,
+        name="Basaal",
+        hovertemplate="%{x:.0f}:00 · %{y:.2f} E/uur<extra></extra>",
+    )
+)
+fig.update_layout(
+    paper_bgcolor="rgba(0,0,0,0)",
+    plot_bgcolor="rgba(0,0,0,0)",
+    font_color=colors["text"],
+    height=320,
+    margin={"l": 10, "r": 10, "t": 10, "b": 10},
+    xaxis={"title": "Uur van de dag", "range": [0, 24], "dtick": 2, "gridcolor": colors["grid"]},
+    yaxis={"title": "E/uur", "gridcolor": colors["grid"], "rangemode": "tozero"},
+    showlegend=False,
+)
+st.plotly_chart(fig, width="stretch")
+
+basal_rows = segments_table(profile["basal"])
+st.dataframe(
+    pd.DataFrame(basal_rows).rename(
+        columns={"start": "Van", "end": "Tot", "value": "Basaalsnelheid (E/uur)"}
+    ),
+    width="stretch",
+    hide_index=True,
+)
+
+col_isf, col_ratio = st.columns(2)
+with col_isf:
+    st.subheader("Insulinegevoeligheid (ISF)")
+    isf_rows = segments_table(profile["sens"])
+    st.dataframe(
+        pd.DataFrame(isf_rows).rename(
+            columns={"start": "Van", "end": "Tot", "value": "ISF (mmol/L per E)"}
+        ),
+        width="stretch",
+        hide_index=True,
+    )
+
+with col_ratio:
+    st.subheader("Koolhydraatratio")
+    ratio_rows = segments_table(profile["carbratio"])
+    st.dataframe(
+        pd.DataFrame(ratio_rows).rename(
+            columns={"start": "Van", "end": "Tot", "value": "Ratio (g koolhydraten per E)"}
+        ),
+        width="stretch",
+        hide_index=True,
+    )
+
+st.caption(
+    f"Totale dagelijkse basaal: {total_daily_basal(profile['basal']):.1f} E "
+    "(volgens profielschema, exclusief tijdelijke aanpassingen)."
+)

profile_data.py

@@ -0,0 +1,146 @@
+"""
+Parsing van het Nightscout-pompprofiel (/api/v1/profile): basaalschema,
+insulinegevoeligheid (ISF), koolhydraatratio, DIA en streefwaarden.
+
+Framework-vrije module (geen streamlit-import), zelfde opzet als reporting.py:
+puur functies op dicts, zodat dit los van de UI te unit-testen is. Zie
+nightscout_client.fetch_profile() voor de ruwe fetch; deze module normaliseert
+alleen (uren -> minuten sinds middernacht, mg/dl -> mmol/L waar nodig).
+"""
+
+from __future__ import annotations
+
+from nightscout_client import mgdl_to_mmol
+
+SEGMENT_FIELDS = ("basal", "sens", "carbratio", "target_low", "target_high")
+
+
+def _time_to_minutes(time_str: str) -> int:
+    """Zet 'HH:MM' (Nightscout's segmenttijd-notatie) om naar minuten sinds middernacht."""
+    hours, minutes = time_str.split(":")
+    return int(hours) * 60 + int(minutes)
+
+
+def _parse_segments(raw_segments: list[dict], *, to_mmol: bool = False) -> list[dict]:
+    """Zet Nightscout-segmenten ([{'time': 'HH:MM', 'value': ...}, ...]) om naar
+    [{'minutes': int, 'value': float}, ...], oplopend gesorteerd op tijd."""
+    segments = []
+    for seg in raw_segments:
+        value = float(seg["value"])
+        if to_mmol:
+            value = mgdl_to_mmol(value)
+        segments.append({"minutes": _time_to_minutes(seg["time"]), "value": value})
+    return sorted(segments, key=lambda s: s["minutes"])
+
+
+def parse_profile(raw: dict) -> dict:
+    """Parse één Nightscout-profieldocument (zoals teruggegeven door
+    nightscout_client.fetch_profile()) naar een genormaliseerde dict:
+
+        {
+            "name": str,
+            "units": "mmol",           # altijd genormaliseerd, ongeacht bronformaat
+            "dia": float,               # uur
+            "basal": [{"minutes": int, "value": float}, ...],       # E/uur
+            "sens": [{"minutes": int, "value": float}, ...],        # mmol/L per E (ISF)
+            "carbratio": [{"minutes": int, "value": float}, ...],   # g koolhydraten per E
+            "target_low": [{"minutes": int, "value": float}, ...],  # mmol/L
+            "target_high": [{"minutes": int, "value": float}, ...], # mmol/L
+        }
+
+    Geeft {} terug als er geen (bruikbaar) profiel is.
+    """
+    if not raw:
+        return {}
+
+    default_name = raw.get("defaultProfile")
+    store = raw.get("store", {})
+    profile = store.get(default_name) if default_name else None
+    if profile is None and store:
+        # defaultProfile ontbreekt of matcht niet met een sleutel in store:
+        # val terug op het enige/eerste beschikbare profiel.
+        profile = next(iter(store.values()))
+    if profile is None:
+        return {}
+
+    units = str(profile.get("units", "mg/dl")).strip().lower()
+    to_mmol = units != "mmol"
+
+    return {
+        "name": default_name or "",
+        "units": "mmol",
+        "dia": float(profile.get("dia", 0) or 0),
+        "basal": _parse_segments(profile.get("basal", [])),
+        "sens": _parse_segments(profile.get("sens", []), to_mmol=to_mmol),
+        "carbratio": _parse_segments(profile.get("carbratio", [])),
+        "target_low": _parse_segments(profile.get("target_low", []), to_mmol=to_mmol),
+        "target_high": _parse_segments(profile.get("target_high", []), to_mmol=to_mmol),
+    }
+
+
+def value_at(segments: list[dict], minutes: int) -> float | None:
+    """Geef de waarde terug die actief is op het gegeven tijdstip (minuten
+    sinds middernacht) — het laatste segment met `minutes <= t`. Ligt `t` vóór
+    het eerste segment van de dag, dan geldt het laatste segment (dat loopt
+    door vanaf de vorige dag, want een profiel dekt altijd de volle 24 uur).
+
+    Geeft None terug als `segments` leeg is.
+    """
+    if not segments:
+        return None
+    active = None
+    for seg in segments:
+        if seg["minutes"] <= minutes:
+            active = seg
+        else:
+            break
+    if active is None:
+        active = segments[-1]
+    return active["value"]
+
+
+def _format_time(minutes: int) -> str:
+    """Zet minuten sinds middernacht om naar 'HH:MM'."""
+    return f"{minutes // 60:02d}:{minutes % 60:02d}"
+
+
+def segments_table(segments: list[dict]) -> list[dict]:
+    """Zet segmenten om naar rijen met een start- en eindtijd voor tabelweergave:
+    [{'start': 'HH:MM', 'end': 'HH:MM', 'value': float}, ...]. Het laatste
+    segment loopt door tot 24:00 (een profiel dekt altijd de volle dag)."""
+    rows = []
+    for i, seg in enumerate(segments):
+        end_minutes = segments[i + 1]["minutes"] if i + 1 < len(segments) else 24 * 60
+        rows.append(
+            {
+                "start": _format_time(seg["minutes"]),
+                "end": "24:00" if end_minutes == 24 * 60 else _format_time(end_minutes),
+                "value": seg["value"],
+            }
+        )
+    return rows
+
+
+def total_daily_basal(segments: list[dict]) -> float:
+    """Totale basaaldosis per dag in E: som van elk segment se duur (uur) x snelheid (E/uur)."""
+    if not segments:
+        return 0.0
+    total = 0.0
+    for i, seg in enumerate(segments):
+        end_minutes = segments[i + 1]["minutes"] if i + 1 < len(segments) else 24 * 60
+        total += seg["value"] * (end_minutes - seg["minutes"]) / 60
+    return total
+
+
+def segments_to_hourly_bars(segments: list[dict]) -> list[dict]:
+    """Zet segmenten om naar een lijst geschikt voor een staafgrafiek per uur van
+    de dag: [{'hour': float, 'value': float}, ...], met een extra punt op uur 24
+    (gelijk aan het eerste segment) zodat een staaf/stap-grafiek de volle dag dekt.
+
+    Gebruikt door de basaalgrafiek op de Insulineprofiel-pagina.
+    """
+    if not segments:
+        return []
+    bars = [{"hour": seg["minutes"] / 60, "value": seg["value"]} for seg in segments]
+    bars.append({"hour": 24.0, "value": segments[0]["value"]})
+    return bars

tests/test_nightscout_client.py

@@ -357,6 +357,29 @@ def test_fetch_treatments_range_retrieves_full_history_even_with_a_hidden_server
     assert result["timestamp"].max().date() == end_date
 
 
+def test_fetch_profile_returns_first_document(monkeypatch):
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com")
+    payload = [{"defaultProfile": "Default", "store": {"Default": {"dia": "5"}}}]
+
+    seen_params = []
+    monkeypatch.setattr(
+        ns.requests,
+        "get",
+        lambda *a, **k: (seen_params.append(k["params"]), FakeResponse(payload))[1],
+    )
+
+    result = ns.fetch_profile()
+    assert result == payload[0]
+    assert seen_params[0]["count"] == 1
+
+
+def test_fetch_profile_handles_empty_response(monkeypatch):
+    monkeypatch.setenv("NIGHTSCOUT_URL", "https://example.com")
+    monkeypatch.setattr(ns.requests, "get", lambda *a, **k: FakeResponse([]))
+
+    assert ns.fetch_profile() == {}
+
+
 def test_paginated_get_uses_created_at_field_for_treatments_cursor(monkeypatch):
     """Losstaande, gerichte assertie op het daadwerkelijke queryparameter-veld
     — moet `find[created_at][$lte]` zijn, nooit `find[date][$lte]`, voor het

tests/test_profile_data.py

@@ -0,0 +1,117 @@
+import pytest
+
+import profile_data as pd_
+
+
+def _raw_profile(units="mg/dl", default_name="Default"):
+    return {
+        "defaultProfile": default_name,
+        "store": {
+            "Default": {
+                "units": units,
+                "dia": "5",
+                "basal": [
+                    {"time": "00:00", "value": "0.8"},
+                    {"time": "06:00", "value": "1.2"},
+                    {"time": "22:00", "value": "0.7"},
+                ],
+                "sens": [{"time": "00:00", "value": "50"}],
+                "carbratio": [{"time": "00:00", "value": "10"}],
+                "target_low": [{"time": "00:00", "value": "90"}],
+                "target_high": [{"time": "00:00", "value": "120"}],
+            }
+        },
+    }
+
+
+def test_parse_profile_returns_empty_dict_for_empty_input():
+    assert pd_.parse_profile({}) == {}
+
+
+def test_parse_profile_converts_mgdl_to_mmol():
+    profile = pd_.parse_profile(_raw_profile(units="mg/dl"))
+    assert profile["units"] == "mmol"
+    # 90 mg/dl -> ~5.0 mmol/L, 120 mg/dl -> ~6.7 mmol/L, 50 mg/dl (ISF) -> ~2.8 mmol/L
+    assert profile["target_low"][0]["value"] == pytest.approx(5.0, abs=0.05)
+    assert profile["target_high"][0]["value"] == pytest.approx(6.7, abs=0.05)
+    assert profile["sens"][0]["value"] == pytest.approx(2.8, abs=0.05)
+
+
+def test_parse_profile_leaves_mmol_profiles_unconverted():
+    profile = pd_.parse_profile(_raw_profile(units="mmol"))
+    assert profile["target_low"][0]["value"] == pytest.approx(90.0)
+
+
+def test_parse_profile_does_not_convert_basal_or_carbratio():
+    profile = pd_.parse_profile(_raw_profile(units="mg/dl"))
+    assert profile["basal"][0]["value"] == pytest.approx(0.8)
+    assert profile["carbratio"][0]["value"] == pytest.approx(10.0)
+
+
+def test_parse_profile_sorts_basal_segments_by_time():
+    raw = _raw_profile()
+    raw["store"]["Default"]["basal"] = [
+        {"time": "22:00", "value": "0.7"},
+        {"time": "00:00", "value": "0.8"},
+        {"time": "06:00", "value": "1.2"},
+    ]
+    profile = pd_.parse_profile(raw)
+    assert [seg["minutes"] for seg in profile["basal"]] == [0, 360, 1320]
+
+
+def test_parse_profile_falls_back_to_first_profile_when_default_name_missing():
+    raw = _raw_profile(default_name="DoesNotExist")
+    profile = pd_.parse_profile(raw)
+    assert profile["dia"] == pytest.approx(5.0)
+
+
+def test_parse_profile_dia_and_name():
+    profile = pd_.parse_profile(_raw_profile())
+    assert profile["dia"] == pytest.approx(5.0)
+    assert profile["name"] == "Default"
+
+
+def test_value_at_returns_active_segment():
+    segments = [{"minutes": 0, "value": 0.8}, {"minutes": 360, "value": 1.2}, {"minutes": 1320, "value": 0.7}]
+    assert pd_.value_at(segments, 100) == pytest.approx(0.8)
+    assert pd_.value_at(segments, 360) == pytest.approx(1.2)
+    assert pd_.value_at(segments, 1000) == pytest.approx(1.2)
+    assert pd_.value_at(segments, 1350) == pytest.approx(0.7)
+
+
+def test_value_at_wraps_to_last_segment_before_first():
+    segments = [{"minutes": 360, "value": 1.2}, {"minutes": 1320, "value": 0.7}]
+    assert pd_.value_at(segments, 100) == pytest.approx(0.7)
+
+
+def test_value_at_empty_segments_returns_none():
+    assert pd_.value_at([], 100) is None
+
+
+def test_segments_table_computes_end_times_from_next_segment():
+    segments = [{"minutes": 0, "value": 0.8}, {"minutes": 360, "value": 1.2}]
+    rows = pd_.segments_table(segments)
+    assert rows == [
+        {"start": "00:00", "end": "06:00", "value": 0.8},
+        {"start": "06:00", "end": "24:00", "value": 1.2},
+    ]
+
+
+def test_segments_to_hourly_bars_closes_the_24h_loop():
+    segments = [{"minutes": 0, "value": 0.8}, {"minutes": 360, "value": 1.2}]
+    bars = pd_.segments_to_hourly_bars(segments)
+    assert bars[-1] == {"hour": 24.0, "value": 0.8}
+
+
+def test_segments_to_hourly_bars_empty_input():
+    assert pd_.segments_to_hourly_bars([]) == []
+
+
+def test_total_daily_basal_weights_by_segment_duration():
+    # 6h @ 0.8 + 18h @ 1.2 = 4.8 + 21.6 = 26.4
+    segments = [{"minutes": 0, "value": 0.8}, {"minutes": 360, "value": 1.2}]
+    assert pd_.total_daily_basal(segments) == pytest.approx(26.4)
+
+
+def test_total_daily_basal_empty_input():
+    assert pd_.total_daily_basal([]) == 0.0

ui_common.py

@@ -14,7 +14,8 @@ import pandas as pd
 import plotly.graph_objects as go
 import streamlit as st
 
-from nightscout_client import fetch_entries_range, fetch_treatments_range
+from nightscout_client import fetch_entries_range, fetch_profile, fetch_treatments_range
+from profile_data import parse_profile
 from reporting import (
     HIGH,
     LOW,
@@ -57,6 +58,11 @@ PALETTE = {
         "tir_in_range": "#0ca30c",
         "tir_high": "#fab219",
         "tir_very_high": "#c76f0a",
+        "basal": "#7b5ea3",
+        "bolus_food": "#1baf7a",
+        "bolus_correction": "#2a78d6",
+        "bolus_smb": "#898781",
+        "target": "#52514e",
     },
     "dark": {
         "glucose": "#3987e5",
@@ -76,6 +82,11 @@ PALETTE = {
         "tir_in_range": "#0ca30c",
         "tir_high": "#fab219",
         "tir_very_high": "#d9852e",
+        "basal": "#9b7ec2",
+        "bolus_food": "#199e70",
+        "bolus_correction": "#3987e5",
+        "bolus_smb": "#898781",
+        "target": "#c3c2b7",
     },
 }
 
@@ -129,6 +140,15 @@ def load_range_data(start: date, end: date):
     return glucose, treatments
 
 
+@st.cache_data(ttl=21600, show_spinner="Profiel ophalen van Nightscout...")
+def load_profile() -> dict:
+    """Gecachete profiel-fetch. Langere TTL dan load_range_data (6 uur i.p.v.
+    1 uur): een pompprofiel wijzigt zelden, in tegenstelling tot glucose-/
+    behandeldata van een lopende periode."""
+    raw = fetch_profile()
+    return parse_profile(raw)
+
+
 def add_threshold_lines(fig: go.Figure, colors: dict, row: int | None = None) -> None:
     """Teken de 4 klinische drempellijnen (3.0/3.9/10.0/13.9 mmol/L) op een grafiek.