Merge pull request 'Add basal/bolus/carb/target overlays to Live and Dagoverzicht charts' (#30) from feature/treatment-overlays into feature/insulin-profile
Reviewed-on: #30
This commit was merged in pull request #30.
This commit is contained in:
+78
-31
@@ -1,6 +1,8 @@
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import pandas as pd
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import plotly.graph_objects as go
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import requests
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import streamlit as st
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from plotly.subplots import make_subplots
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from auth import require_auth
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from data_processing import (
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@@ -10,7 +12,15 @@ from data_processing import (
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summarize_time_in_range,
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)
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from nightscout_client import NightscoutConfigError, fetch_entries, fetch_treatments
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from ui_common import MMOL_HOVERTEMPLATE, get_colors
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from reporting import classify_bolus, effective_basal_series, target_glucose_series
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from ui_common import (
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MMOL_HOVERTEMPLATE,
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add_basal_area_trace,
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add_bolus_marker_traces,
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add_target_line_trace,
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get_colors,
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load_profile,
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)
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st.set_page_config(page_title="Glucose dashboard — Live", page_icon="📊", layout="wide")
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require_auth()
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@@ -27,7 +37,28 @@ def load_nightscout_data(hours: int):
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return glucose, treatments
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def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -> go.Figure:
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def _basal_and_target_series(profile: dict, treatments: pd.DataFrame, window_start, window_end):
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"""Basaal- en streefwaarde-reeksen over een rollend venster dat meerdere
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kalenderdagen kan overspannen: per dag berekend (reporting.effective_basal_series/
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target_glucose_series zijn dag-begrensd) en daarna aan elkaar geplakt en
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geclipt tot het venster."""
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dates = pd.date_range(window_start.normalize(), window_end.normalize(), freq="D")
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basal_parts, target_parts = [], []
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for day_start in dates:
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day_end = day_start + pd.Timedelta(1, unit="D")
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basal_parts.append(effective_basal_series(profile, treatments, day_start, day_end))
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target_parts.append(target_glucose_series(profile, day_start, day_end))
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basal = pd.concat(basal_parts, ignore_index=True) if basal_parts else pd.DataFrame(columns=["timestamp", "rate"])
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target = (
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pd.concat(target_parts, ignore_index=True) if target_parts else pd.DataFrame(columns=["timestamp", "target"])
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)
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basal = basal[(basal["timestamp"] >= window_start) & (basal["timestamp"] <= window_end)]
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target = target[(target["timestamp"] >= window_start) & (target["timestamp"] <= window_end)]
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return basal.reset_index(drop=True), target.reset_index(drop=True)
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def build_chart(glucose, bolus, carbs, basal, target, site_changes, sensor_changes, colors) -> go.Figure:
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glucose_min = min(glucose["mmol"].min(), TARGET_LOW)
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glucose_max = max(glucose["mmol"].max(), TARGET_HIGH)
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y_top = glucose_max + 1.0
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@@ -36,7 +67,7 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
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insulin_lane_y = lane_base + 1.5
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carbs_lane_y = lane_base + 0.7
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fig = go.Figure()
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fig = make_subplots(specs=[[{"secondary_y": True}]])
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# Streefbereik als achtergrondband, geen losse legenda-entry nodig.
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fig.add_hrect(
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@@ -45,8 +76,13 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
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fillcolor=colors["band"],
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line_width=0,
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layer="below",
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row=1,
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col=1,
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)
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add_basal_area_trace(fig, basal, colors, row=1, secondary_y=True)
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add_target_line_trace(fig, target, colors, row=1)
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fig.add_trace(
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go.Scatter(
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x=glucose["timestamp"],
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@@ -55,7 +91,9 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
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name="Glucose",
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line={"color": colors["glucose"], "width": 2, "shape": "spline"},
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hovertemplate=MMOL_HOVERTEMPLATE,
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)
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),
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row=1,
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col=1,
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)
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low_points = glucose[glucose["mmol"] < TARGET_LOW]
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@@ -69,7 +107,9 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
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name=f"Laag (<{TARGET_LOW})",
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marker={"color": colors["low"], "size": 6},
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hovertemplate=MMOL_HOVERTEMPLATE,
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)
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),
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row=1,
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col=1,
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)
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if not high_points.empty:
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fig.add_trace(
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@@ -80,21 +120,12 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
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name=f"Hoog (>{TARGET_HIGH})",
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marker={"color": colors["high"], "size": 6},
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hovertemplate=MMOL_HOVERTEMPLATE,
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)
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),
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row=1,
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col=1,
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)
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if not insulin.empty:
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fig.add_trace(
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go.Scatter(
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x=insulin["timestamp"],
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y=[insulin_lane_y] * len(insulin),
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mode="markers",
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name="Insuline",
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marker={"color": colors["insulin"], "size": 10, "symbol": "triangle-up"},
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customdata=insulin["insulin"],
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hovertemplate="%{x|%d-%m %H:%M} · %{customdata:.1f} E<extra></extra>",
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)
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)
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add_bolus_marker_traces(fig, bolus, colors, insulin_lane_y, row=1)
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if not carbs.empty:
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fig.add_trace(
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@@ -106,14 +137,16 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
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marker={"color": colors["carbs"], "size": 10, "symbol": "circle"},
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customdata=carbs["carbs"],
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hovertemplate="%{x|%d-%m %H:%M} · %{customdata:.0f} g<extra></extra>",
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)
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),
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row=1,
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col=1,
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)
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# Let op: annotation_text/-position via add_vline() zelf triggert een bug
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# in plotly's shapeannotation-helper met pandas Timestamp-x-waarden, dus
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# de lijn en het label worden hier los toegevoegd.
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for _, row in site_changes.iterrows():
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fig.add_vline(x=row["timestamp"], line_dash="dot", line_color=colors["muted"], opacity=0.6)
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fig.add_vline(x=row["timestamp"], line_dash="dot", line_color=colors["muted"], opacity=0.6, row=1, col=1)
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fig.add_annotation(
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x=row["timestamp"],
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y=y_top,
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@@ -121,9 +154,11 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
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text="Pomp",
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showarrow=False,
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font={"color": colors["muted"], "size": 10},
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row=1,
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col=1,
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)
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for _, row in sensor_changes.iterrows():
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fig.add_vline(x=row["timestamp"], line_dash="dash", line_color=colors["muted"], opacity=0.6)
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fig.add_vline(x=row["timestamp"], line_dash="dash", line_color=colors["muted"], opacity=0.6, row=1, col=1)
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fig.add_annotation(
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x=row["timestamp"],
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y=y_top,
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@@ -131,6 +166,8 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
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text="Sensor",
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showarrow=False,
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font={"color": colors["muted"], "size": 10},
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row=1,
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col=1,
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)
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fig.update_layout(
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@@ -140,14 +177,18 @@ def build_chart(glucose, insulin, carbs, site_changes, sensor_changes, colors) -
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legend={"orientation": "h", "yanchor": "bottom", "y": 1.02, "xanchor": "left", "x": 0},
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margin={"l": 10, "r": 10, "t": 10, "b": 10},
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height=480,
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yaxis={
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"title": "mmol/L",
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"range": [y_bottom, y_top],
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"gridcolor": colors["grid"],
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"zeroline": False,
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},
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xaxis={"gridcolor": colors["grid"]},
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)
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fig.update_yaxes(
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title_text="mmol/L",
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range=[y_bottom, y_top],
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gridcolor=colors["grid"],
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zeroline=False,
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row=1,
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col=1,
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secondary_y=False,
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)
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fig.update_yaxes(title_text="Basaal (E/uur)", showgrid=False, row=1, col=1, secondary_y=True)
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fig.update_xaxes(gridcolor=colors["grid"], row=1, col=1)
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return fig
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@@ -160,6 +201,7 @@ with st.sidebar:
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try:
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glucose, treatments = load_nightscout_data(hours)
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profile = load_profile()
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except NightscoutConfigError as exc:
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st.error(str(exc))
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st.stop()
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@@ -172,7 +214,9 @@ if glucose.empty:
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st.stop()
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events = classify_treatments(treatments)
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bolus = classify_bolus(treatments)
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tir = summarize_time_in_range(glucose)
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basal, target = _basal_and_target_series(profile, treatments, glucose["timestamp"].min(), glucose["timestamp"].max())
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latest = glucose.iloc[-1]
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previous = glucose.iloc[-2] if len(glucose) > 1 else None
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@@ -192,14 +236,17 @@ colors = get_colors()
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fig = build_chart(
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glucose,
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events["insulin"],
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bolus,
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events["carbs"],
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basal,
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target,
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events["site_changes"],
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events["sensor_changes"],
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colors,
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)
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st.plotly_chart(fig, width="stretch")
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st.caption(
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"Driehoekjes = insuline · cirkels = koolhydraten · stippellijn = pompwissel · "
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"streepjeslijn = sensorwissel"
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"Stippen = bolusinsuline (voeding/correctie/SMB, grootte naar dosis) · cirkels = koolhydraten · "
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"vlak = basaal · streepstippellijn = streefwaarde uit pompprofiel · "
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"stippellijn = pompwissel · streepjeslijn = sensorwissel"
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)
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+97
-11
@@ -1,14 +1,21 @@
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import pandas as pd
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import plotly.graph_objects as go
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import requests
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import streamlit as st
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from plotly.subplots import make_subplots
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from auth import require_auth
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from data_processing import TARGET_HIGH, TARGET_LOW
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from data_processing import TARGET_HIGH, TARGET_LOW, classify_treatments
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from nightscout_client import NightscoutConfigError
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from reporting import classify_bolus, effective_basal_series, target_glucose_series
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from ui_common import (
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add_basal_area_trace,
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add_bolus_marker_traces,
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add_carb_bars_trace,
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add_target_line_trace,
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add_threshold_lines,
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get_colors,
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load_profile,
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load_range_data,
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render_completeness_warning,
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select_period_days,
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@@ -18,7 +25,7 @@ st.set_page_config(page_title="Glucose dashboard — Dagoverzicht", page_icon="
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require_auth()
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st.title("Dagoverzicht")
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st.caption("Eén glucosegrafiek per kalenderdag (FR4).")
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st.caption("Eén glucosegrafiek per kalenderdag, met basaal/bolus/koolhydraten-overlay (FR4).")
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start, end, days = select_period_days(default_label="14 dagen")
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if days > 31:
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@@ -28,7 +35,8 @@ if days > 31:
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)
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try:
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glucose, _treatments = load_range_data(start, end)
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glucose, treatments = load_range_data(start, end)
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profile = load_profile()
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except NightscoutConfigError as exc:
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st.error(str(exc))
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st.stop()
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@@ -47,44 +55,122 @@ glucose = glucose.copy()
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glucose["date"] = glucose["timestamp"].dt.date
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glucose["hour"] = glucose["timestamp"].dt.hour + glucose["timestamp"].dt.minute / 60
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carbs_events = classify_treatments(treatments)["carbs"].copy()
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if not carbs_events.empty:
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carbs_events["date"] = carbs_events["timestamp"].dt.date
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carbs_events["hour"] = carbs_events["timestamp"].dt.hour + carbs_events["timestamp"].dt.minute / 60
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bolus_events = classify_bolus(treatments)
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if not bolus_events.empty:
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bolus_events["date"] = bolus_events["timestamp"].dt.date
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bolus_events["hour"] = bolus_events["timestamp"].dt.hour + bolus_events["timestamp"].dt.minute / 60
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def _close_step_to_day_end(df: pd.DataFrame, value_col: str) -> pd.DataFrame:
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"""Voeg een laatste punt op hour=24 toe (zelfde waarde als het laatste
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segment) zodat een stapvlak/-lijn de volle dag doortekent i.p.v. te
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stoppen bij het laatste breekpunt."""
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if df.empty:
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return df
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last_value = df.iloc[-1][value_col]
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closing_row = pd.DataFrame([{"hour": 24.0, value_col: last_value}])
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return pd.concat([df, closing_row], ignore_index=True)
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dates = sorted(glucose["date"].unique())
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row_height = 150
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row_height = 190
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fig = make_subplots(
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rows=len(dates),
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cols=1,
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shared_xaxes=True,
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subplot_titles=[d.strftime("%A %d-%m-%Y") for d in dates],
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vertical_spacing=min(0.4 / max(len(dates) - 1, 1), 0.05),
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specs=[[{"secondary_y": True}]] * len(dates),
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)
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for i, day in enumerate(dates, start=1):
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day_df = glucose[glucose["date"] == day]
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day_start = pd.Timestamp(day)
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day_end = day_start + pd.Timedelta(1, unit="D")
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fig.add_hrect(
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y0=TARGET_LOW, y1=TARGET_HIGH, fillcolor=colors["band"], line_width=0, layer="below", row=i, col=1
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)
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# Handmatige titel-annotatie t.o.v. het domein van déze rij i.p.v.
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# make_subplots' subplot_titles (die t.o.v. de hele figuur positioneert en
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# daardoor bij veel dagen de gedeelde legenda bovenaan kan overlappen).
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fig.add_annotation(
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text=day.strftime("%A %d-%m-%Y"),
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xref="x domain",
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yref="y domain",
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x=0.0,
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y=1.15,
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xanchor="left",
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yanchor="bottom",
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showarrow=False,
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font={"size": 13, "color": colors["text"]},
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row=i,
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col=1,
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)
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basal_day = effective_basal_series(profile, treatments, day_start, day_end)
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if not basal_day.empty:
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basal_day["hour"] = (basal_day["timestamp"] - day_start).dt.total_seconds() / 3600
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basal_day = _close_step_to_day_end(basal_day, "rate")
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add_basal_area_trace(fig, basal_day, colors, row=i, secondary_y=True, x_col="hour")
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target_day = target_glucose_series(profile, day_start, day_end)
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if not target_day.empty:
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target_day["hour"] = (target_day["timestamp"] - day_start).dt.total_seconds() / 3600
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target_day = _close_step_to_day_end(target_day, "target")
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add_target_line_trace(fig, target_day, colors, row=i, x_col="hour")
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add_carb_bars_trace(fig, carbs_events[carbs_events["date"] == day], colors, row=i, secondary_y=True, x_col="hour")
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lane_y = day_df["mmol"].min() - 1.0 if not day_df.empty else TARGET_LOW - 1.0
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add_bolus_marker_traces(fig, bolus_events[bolus_events["date"] == day], colors, lane_y, row=i, x_col="hour")
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fig.add_trace(
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go.Scatter(
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x=day_df["hour"],
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y=day_df["mmol"],
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mode="lines",
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line={"color": colors["glucose"], "width": 2},
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showlegend=False,
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name="Glucose",
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hovertemplate="%{y:.1f} mmol/L<extra></extra>",
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),
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row=i,
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col=1,
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secondary_y=False,
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)
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fig.update_yaxes(title_text="mmol/L", gridcolor=colors["grid"], row=i, col=1, secondary_y=False)
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fig.update_yaxes(
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title_text="Basaal (E/uur) · KH (g)", showgrid=False, row=i, col=1, secondary_y=True
|
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)
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fig.update_yaxes(title_text="mmol/L", gridcolor=colors["grid"], row=i, col=1)
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add_threshold_lines(fig, colors, row=i)
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# Elke dag herhaalt dezelfde spoornamen (Glucose/Basaal/Koolhydraten/...) — zonder
|
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# deduplicatie zou de legenda 7x "Glucose" etc. tonen. Alleen de eerste keer per
|
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# naam blijft in de legenda staan.
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seen_names = set()
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for trace in fig.data:
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if trace.name in seen_names:
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trace.showlegend = False
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else:
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seen_names.add(trace.name)
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fig.update_xaxes(title_text="Uur van de dag", range=[0, 24], dtick=4, gridcolor=colors["grid"])
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fig.update_layout(
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paper_bgcolor="rgba(0,0,0,0)",
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plot_bgcolor="rgba(0,0,0,0)",
|
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font_color=colors["text"],
|
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height=row_height * len(dates),
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showlegend=False,
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||||
margin={"l": 10, "r": 10, "t": 30, "b": 10},
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height=row_height * len(dates) + 40,
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||||
showlegend=True,
|
||||
legend={"orientation": "h", "yanchor": "bottom", "y": 1.0, "xanchor": "left", "x": 0},
|
||||
margin={"l": 10, "r": 10, "t": 40, "b": 10},
|
||||
)
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||||
st.plotly_chart(fig, width="stretch")
|
||||
st.caption(f"{len(dates)} dagen · {len(glucose)} metingen")
|
||||
st.caption(
|
||||
f"{len(dates)} dagen · {len(glucose)} metingen · stippen = bolusinsuline "
|
||||
"(voeding/correctie/SMB, grootte naar dosis) · vlak = basaal · staafjes = koolhydraten · "
|
||||
"streepstippellijn = streefwaarde uit pompprofiel"
|
||||
)
|
||||
|
||||
+161
@@ -15,6 +15,7 @@ import numpy as np
|
||||
import pandas as pd
|
||||
|
||||
from nightscout_client import INSULIN_EVENT_TYPES
|
||||
from profile_data import value_at
|
||||
|
||||
# Klinische drempels in mmol/L (Battelino et al. 2019, Diabetes Care 42(8):1593-1603).
|
||||
VERY_LOW = 3.0
|
||||
@@ -440,6 +441,166 @@ def bolus_stats(treatments: pd.DataFrame) -> dict:
|
||||
}
|
||||
|
||||
|
||||
BOLUS_CATEGORY_FOOD = "food"
|
||||
BOLUS_CATEGORY_CORRECTION = "correction"
|
||||
BOLUS_CATEGORY_SMB = "smb"
|
||||
|
||||
BOLUS_CATEGORY_COLUMNS = ["timestamp", "eventType", "insulin", "carbs", "category"]
|
||||
|
||||
|
||||
def classify_bolus(treatments: pd.DataFrame) -> pd.DataFrame:
|
||||
"""Splits bolus-insuline op in 'food'/'correction'/'smb', voor grafiekoverlays
|
||||
(gitea insulineprofiel-werk, vervolg op bolus_stats()'s SMB-detectie).
|
||||
|
||||
Herkenningsregels, in volgorde:
|
||||
- SMB: `eventType == "SMB"`, of `eventType == "Correction Bolus"` met
|
||||
`type == "SMB"` (zie bolus_stats()) — automatische AndroidAPS-microbolus.
|
||||
- food: `eventType == "Meal Bolus"`, of een andere bolus-eventType
|
||||
(`Bolus`/`Combo Bolus`) met koolhydraten op dezelfde treatment-rij (een
|
||||
sterke aanwijzing dat het om een maaltijdbolus gaat).
|
||||
- correction: alle overige bolus-rijen (typisch `Correction Bolus` zonder
|
||||
SMB-markering, of een generieke `Bolus`/`Combo Bolus` zonder koolhydraten).
|
||||
|
||||
Geeft een DataFrame terug met kolommen 'timestamp', 'eventType', 'insulin',
|
||||
'carbs', 'category' — alleen de rijen die daadwerkelijk bolusinsuline zijn.
|
||||
"""
|
||||
if treatments.empty:
|
||||
return pd.DataFrame(columns=BOLUS_CATEGORY_COLUMNS)
|
||||
|
||||
bolus = treatments[treatments["eventType"].isin(INSULIN_EVENT_TYPES) & treatments["insulin"].notna()].copy()
|
||||
if bolus.empty:
|
||||
return pd.DataFrame(columns=BOLUS_CATEGORY_COLUMNS)
|
||||
|
||||
is_smb = (bolus["eventType"] == SMB_TYPE) | (
|
||||
(bolus["eventType"] == "Correction Bolus") & (bolus.get("type", "") == SMB_TYPE)
|
||||
)
|
||||
has_carbs = pd.to_numeric(bolus["carbs"], errors="coerce").fillna(0) > 0
|
||||
is_food = ~is_smb & ((bolus["eventType"] == "Meal Bolus") | has_carbs)
|
||||
|
||||
bolus["category"] = BOLUS_CATEGORY_CORRECTION
|
||||
bolus.loc[is_food, "category"] = BOLUS_CATEGORY_FOOD
|
||||
bolus.loc[is_smb, "category"] = BOLUS_CATEGORY_SMB
|
||||
return bolus[BOLUS_CATEGORY_COLUMNS].sort_values("timestamp").reset_index(drop=True)
|
||||
|
||||
|
||||
def glucose_min_max(glucose: pd.DataFrame) -> tuple[float, float]:
|
||||
"""Minimum en maximum glucosewaarde (mmol/L) in de gegeven data."""
|
||||
if glucose.empty:
|
||||
return (0.0, 0.0)
|
||||
return (round(glucose["mmol"].min(), 1), round(glucose["mmol"].max(), 1))
|
||||
|
||||
|
||||
def _temp_basal_intervals(treatments: pd.DataFrame, day_start, day_end) -> list[tuple]:
|
||||
"""Temp Basal-intervallen (start, eind, rate) die (deels) binnen [day_start, day_end) vallen,
|
||||
geclipt aan de daggrenzen."""
|
||||
if treatments is None or treatments.empty:
|
||||
return []
|
||||
temp_basal = treatments[
|
||||
(treatments["eventType"] == BASAL_EVENT_TYPE)
|
||||
& treatments["absolute"].notna()
|
||||
& treatments["duration"].notna()
|
||||
]
|
||||
intervals = []
|
||||
for _, row in temp_basal.iterrows():
|
||||
start = row["timestamp"]
|
||||
end = start + pd.Timedelta(float(row["duration"]), unit="min")
|
||||
if end <= day_start or start >= day_end:
|
||||
continue
|
||||
intervals.append((max(start, day_start), min(end, day_end), row["absolute"]))
|
||||
return intervals
|
||||
|
||||
|
||||
def _basal_breakpoints(basal_segments: list[dict], temp_intervals: list[tuple], day_start, day_end) -> list:
|
||||
"""Alle tijdstippen waarop de effectieve basaalsnelheid kan wijzigen: daggrens,
|
||||
profielsegment-grenzen, en temp-basal start/eind — oplopend gesorteerd."""
|
||||
breakpoints = {day_start}
|
||||
for seg in basal_segments:
|
||||
ts = day_start + pd.Timedelta(float(seg["minutes"]), unit="min")
|
||||
if day_start <= ts < day_end:
|
||||
breakpoints.add(ts)
|
||||
for start, end, _ in temp_intervals:
|
||||
breakpoints.add(start)
|
||||
breakpoints.add(end)
|
||||
return sorted(t for t in breakpoints if day_start <= t < day_end)
|
||||
|
||||
|
||||
def _rate_at(ts, basal_segments: list[dict], temp_intervals: list[tuple], day_start) -> float:
|
||||
"""Effectieve basaalsnelheid op tijdstip `ts`: een actieve Temp Basal wint van het profieltempo."""
|
||||
for start, end, rate in temp_intervals:
|
||||
if start <= ts < end:
|
||||
return rate
|
||||
minutes_of_day = int((ts - day_start).total_seconds() / 60) % MINUTES_PER_DAY
|
||||
value = value_at(basal_segments, minutes_of_day)
|
||||
return value if value is not None else 0.0
|
||||
|
||||
|
||||
def effective_basal_series(profile: dict | None, treatments: pd.DataFrame, day_start, day_end) -> pd.DataFrame:
|
||||
"""Basaalsnelheid (E/uur) als stapfunctie over [day_start, day_end).
|
||||
|
||||
Basislijn is het pompprofiel se basaalschema (`profile["basal"]`); waar een
|
||||
Temp Basal-treatment actief is (rate/duur uit de treatment) overschrijft
|
||||
die het profieltempo voor de duur van die tijdelijke aanpassing — dit is de
|
||||
daadwerkelijk afgeleverde basaal, in tegenstelling tot basal_stats() (die
|
||||
alleen temp-basal-periodes optelt en periodes zonder actieve temp basal
|
||||
mist omdat het profiel daar niet bij betrokken wordt).
|
||||
|
||||
Zonder profiel (`profile` is None/leeg) is de basislijn 0 — dan toont dit
|
||||
alleen de temp-basal-periodes, net als basal_stats().
|
||||
|
||||
Geeft een DataFrame terug met kolommen 'timestamp' en 'rate': elke rij
|
||||
geldt tot de volgende rij (stapfunctie), geschikt voor een
|
||||
step-area-plot (line_shape='hv', fill='tozeroy').
|
||||
"""
|
||||
day_start = pd.Timestamp(day_start)
|
||||
day_end = pd.Timestamp(day_end)
|
||||
basal_segments = (profile or {}).get("basal", [])
|
||||
temp_intervals = _temp_basal_intervals(treatments, day_start, day_end)
|
||||
breakpoints = _basal_breakpoints(basal_segments, temp_intervals, day_start, day_end)
|
||||
|
||||
rows = [
|
||||
{"timestamp": ts, "rate": _rate_at(ts, basal_segments, temp_intervals, day_start)} for ts in breakpoints
|
||||
]
|
||||
return pd.DataFrame(rows, columns=["timestamp", "rate"])
|
||||
|
||||
|
||||
def _target_midpoint_at(minutes_of_day: int, target_low: list[dict], target_high: list[dict]) -> float | None:
|
||||
low = value_at(target_low, minutes_of_day) if target_low else None
|
||||
high = value_at(target_high, minutes_of_day) if target_high else None
|
||||
if low is not None and high is not None:
|
||||
return (low + high) / 2
|
||||
return low if low is not None else high
|
||||
|
||||
|
||||
def target_glucose_series(profile: dict | None, day_start, day_end) -> pd.DataFrame:
|
||||
"""Streefglucose (mmol/L) uit het profiel als stapfunctie over [day_start, day_end) —
|
||||
het midden van target_low/target_high per tijdsegment (de pomp kent vaak één
|
||||
streefwaarde per segment; met losse low/high-segmenten is dat gewoon het
|
||||
gemiddelde). Geeft een lege DataFrame terug als het profiel geen streefwaarden
|
||||
bevat.
|
||||
|
||||
Zelfde stapfunctie-vorm als effective_basal_series(): elke rij geldt tot de
|
||||
volgende rij.
|
||||
"""
|
||||
day_start = pd.Timestamp(day_start)
|
||||
day_end = pd.Timestamp(day_end)
|
||||
target_low = (profile or {}).get("target_low", [])
|
||||
target_high = (profile or {}).get("target_high", [])
|
||||
if not target_low and not target_high:
|
||||
return pd.DataFrame(columns=["timestamp", "target"])
|
||||
|
||||
breakpoints = _basal_breakpoints(target_low + target_high, [], day_start, day_end)
|
||||
rows = [
|
||||
{
|
||||
"timestamp": ts,
|
||||
"target": _target_midpoint_at(
|
||||
int((ts - day_start).total_seconds() / 60) % MINUTES_PER_DAY, target_low, target_high
|
||||
),
|
||||
}
|
||||
for ts in breakpoints
|
||||
]
|
||||
return pd.DataFrame(rows, columns=["timestamp", "target"])
|
||||
|
||||
|
||||
def basal_stats(treatments: pd.DataFrame, period_days: float) -> dict:
|
||||
"""Totale basaalinsuline uit Temp Basal-treatments (rate x duur), zonder pompprofiel.
|
||||
|
||||
|
||||
@@ -8,10 +8,13 @@ from reporting import (
|
||||
agp_percentile_bands,
|
||||
basal_stats,
|
||||
bolus_stats,
|
||||
classify_bolus,
|
||||
completeness_warning,
|
||||
daily_gmi,
|
||||
day_of_week_pattern,
|
||||
detect_events,
|
||||
effective_basal_series,
|
||||
glucose_min_max,
|
||||
gmi_mmol_per_mol,
|
||||
gmi_percent,
|
||||
gmi_trend,
|
||||
@@ -21,6 +24,7 @@ from reporting import (
|
||||
sensor_active_percent,
|
||||
site_interval_stats,
|
||||
summarize_events,
|
||||
target_glucose_series,
|
||||
tir_breakdown_5band,
|
||||
)
|
||||
|
||||
@@ -480,6 +484,140 @@ def test_basal_stats_handles_empty_input():
|
||||
assert result == {"total_units": 0.0, "avg_units_per_day": 0.0}
|
||||
|
||||
|
||||
def test_glucose_min_max_returns_min_and_max():
|
||||
glucose = _series([("2026-01-01 00:00", 6.5), ("2026-01-01 01:00", 3.2), ("2026-01-01 02:00", 14.1)])
|
||||
assert glucose_min_max(glucose) == (3.2, 14.1)
|
||||
|
||||
|
||||
def test_glucose_min_max_handles_empty_input():
|
||||
empty = pd.DataFrame(columns=["timestamp", "mmol"])
|
||||
assert glucose_min_max(empty) == (0.0, 0.0)
|
||||
|
||||
|
||||
def _bolus_treatment(dt, event_type, insulin=1.0, carbs=None, bolus_type=""):
|
||||
return {
|
||||
"timestamp": pd.Timestamp(dt),
|
||||
"eventType": event_type,
|
||||
"insulin": insulin,
|
||||
"carbs": carbs,
|
||||
"type": bolus_type,
|
||||
}
|
||||
|
||||
|
||||
def test_classify_bolus_recognizes_meal_bolus_as_food():
|
||||
treatments = pd.DataFrame([_bolus_treatment("2026-01-01 08:00", "Meal Bolus", insulin=4.0)])
|
||||
result = classify_bolus(treatments)
|
||||
assert result.iloc[0]["category"] == "food"
|
||||
|
||||
|
||||
def test_classify_bolus_recognizes_smb_via_type_field():
|
||||
treatments = pd.DataFrame(
|
||||
[_bolus_treatment("2026-01-01 08:00", "Correction Bolus", insulin=0.3, bolus_type="SMB")]
|
||||
)
|
||||
result = classify_bolus(treatments)
|
||||
assert result.iloc[0]["category"] == "smb"
|
||||
|
||||
|
||||
def test_classify_bolus_recognizes_smb_via_event_type():
|
||||
treatments = pd.DataFrame([_bolus_treatment("2026-01-01 08:00", "SMB", insulin=0.3)])
|
||||
result = classify_bolus(treatments)
|
||||
assert result.iloc[0]["category"] == "smb"
|
||||
|
||||
|
||||
def test_classify_bolus_treats_correction_bolus_without_carbs_as_correction():
|
||||
treatments = pd.DataFrame([_bolus_treatment("2026-01-01 08:00", "Correction Bolus", insulin=1.5)])
|
||||
result = classify_bolus(treatments)
|
||||
assert result.iloc[0]["category"] == "correction"
|
||||
|
||||
|
||||
def test_classify_bolus_treats_generic_bolus_with_carbs_as_food():
|
||||
treatments = pd.DataFrame([_bolus_treatment("2026-01-01 08:00", "Bolus", insulin=3.0, carbs=40.0)])
|
||||
result = classify_bolus(treatments)
|
||||
assert result.iloc[0]["category"] == "food"
|
||||
|
||||
|
||||
def test_classify_bolus_ignores_non_bolus_rows():
|
||||
treatments = pd.DataFrame(
|
||||
[
|
||||
_bolus_treatment("2026-01-01 08:00", "Meal Bolus", insulin=4.0),
|
||||
{"timestamp": pd.Timestamp("2026-01-01 08:05"), "eventType": "Site Change", "insulin": None, "carbs": None, "type": ""},
|
||||
]
|
||||
)
|
||||
result = classify_bolus(treatments)
|
||||
assert len(result) == 1
|
||||
|
||||
|
||||
def test_classify_bolus_handles_empty_input():
|
||||
empty = pd.DataFrame(columns=["timestamp", "eventType", "insulin", "carbs", "type"])
|
||||
result = classify_bolus(empty)
|
||||
assert result.empty
|
||||
assert list(result.columns) == ["timestamp", "eventType", "insulin", "carbs", "category"]
|
||||
|
||||
|
||||
def test_effective_basal_series_uses_profile_baseline_without_temp_basal():
|
||||
profile = {"basal": [{"minutes": 0, "value": 0.8}, {"minutes": 360, "value": 1.2}]}
|
||||
empty_treatments = pd.DataFrame(columns=["timestamp", "eventType", "absolute", "duration"])
|
||||
result = effective_basal_series(
|
||||
profile, empty_treatments, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02")
|
||||
)
|
||||
assert result.iloc[0]["rate"] == pytest.approx(0.8)
|
||||
assert result[result["timestamp"] == pd.Timestamp("2026-01-01 06:00")].iloc[0]["rate"] == pytest.approx(1.2)
|
||||
|
||||
|
||||
def test_effective_basal_series_temp_basal_overrides_profile_rate():
|
||||
profile = {"basal": [{"minutes": 0, "value": 0.8}]}
|
||||
treatments = pd.DataFrame(
|
||||
[{"timestamp": pd.Timestamp("2026-01-01 08:00"), "eventType": "Temp Basal", "absolute": 2.0, "duration": 30}]
|
||||
)
|
||||
result = effective_basal_series(profile, treatments, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
|
||||
during_temp = result[result["timestamp"] == pd.Timestamp("2026-01-01 08:00")].iloc[0]
|
||||
after_temp = result[result["timestamp"] == pd.Timestamp("2026-01-01 08:30")].iloc[0]
|
||||
assert during_temp["rate"] == pytest.approx(2.0)
|
||||
assert after_temp["rate"] == pytest.approx(0.8) # terug naar het profieltempo
|
||||
|
||||
|
||||
def test_effective_basal_series_without_profile_defaults_to_zero_baseline():
|
||||
empty_treatments = pd.DataFrame(columns=["timestamp", "eventType", "absolute", "duration"])
|
||||
result = effective_basal_series(None, empty_treatments, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
|
||||
assert result.iloc[0]["rate"] == pytest.approx(0.0)
|
||||
|
||||
|
||||
def test_effective_basal_series_clips_temp_basal_to_day_boundaries():
|
||||
profile = {"basal": [{"minutes": 0, "value": 0.8}]}
|
||||
# Temp basal begint de dag ervoor en loopt door tot 01:00 van de dag zelf.
|
||||
treatments = pd.DataFrame(
|
||||
[{"timestamp": pd.Timestamp("2025-12-31 23:00"), "eventType": "Temp Basal", "absolute": 2.0, "duration": 120}]
|
||||
)
|
||||
result = effective_basal_series(profile, treatments, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
|
||||
assert result.iloc[0]["timestamp"] == pd.Timestamp("2026-01-01")
|
||||
assert result.iloc[0]["rate"] == pytest.approx(2.0)
|
||||
|
||||
|
||||
def test_target_glucose_series_averages_low_and_high():
|
||||
profile = {
|
||||
"target_low": [{"minutes": 0, "value": 4.5}],
|
||||
"target_high": [{"minutes": 0, "value": 8.5}],
|
||||
}
|
||||
result = target_glucose_series(profile, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
|
||||
assert result.iloc[0]["target"] == pytest.approx(6.5)
|
||||
|
||||
|
||||
def test_target_glucose_series_falls_back_to_single_bound():
|
||||
profile = {"target_low": [{"minutes": 0, "value": 5.0}], "target_high": []}
|
||||
result = target_glucose_series(profile, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
|
||||
assert result.iloc[0]["target"] == pytest.approx(5.0)
|
||||
|
||||
|
||||
def test_target_glucose_series_empty_without_targets_in_profile():
|
||||
result = target_glucose_series({}, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
|
||||
assert result.empty
|
||||
|
||||
|
||||
def test_target_glucose_series_handles_none_profile():
|
||||
result = target_glucose_series(None, pd.Timestamp("2026-01-01"), pd.Timestamp("2026-01-02"))
|
||||
assert result.empty
|
||||
|
||||
|
||||
def test_site_interval_stats_splits_by_site_change():
|
||||
glucose = _series(
|
||||
[
|
||||
|
||||
@@ -1,8 +1,13 @@
|
||||
import pandas as pd
|
||||
import plotly.graph_objects as go
|
||||
|
||||
from ui_common import (
|
||||
PALETTE,
|
||||
THRESHOLD_LINES,
|
||||
add_basal_area_trace,
|
||||
add_bolus_marker_traces,
|
||||
add_carb_bars_trace,
|
||||
add_target_line_trace,
|
||||
add_threshold_lines,
|
||||
build_tir_stacked_bar_figure,
|
||||
hex_to_rgba,
|
||||
@@ -36,3 +41,99 @@ def test_build_tir_stacked_bar_figure_has_one_bar_trace_per_band():
|
||||
assert len(fig.data) == 5
|
||||
assert [trace.y[0] for trace in fig.data] == [1.0, 3.0, 80.0, 12.0, 4.0]
|
||||
assert fig.layout.barmode == "stack"
|
||||
|
||||
|
||||
def test_add_basal_area_trace_adds_one_filled_step_trace():
|
||||
basal = pd.DataFrame(
|
||||
{"timestamp": pd.to_datetime(["2026-01-01 00:00", "2026-01-01 06:00"]), "rate": [0.8, 1.2]}
|
||||
)
|
||||
fig = go.Figure()
|
||||
add_basal_area_trace(fig, basal, PALETTE["light"])
|
||||
assert len(fig.data) == 1
|
||||
assert fig.data[0].name == "Basaal"
|
||||
assert fig.data[0].fill == "tozeroy"
|
||||
assert fig.data[0].line.shape == "hv"
|
||||
|
||||
|
||||
def test_add_basal_area_trace_skips_empty_input():
|
||||
fig = go.Figure()
|
||||
add_basal_area_trace(fig, pd.DataFrame(columns=["timestamp", "rate"]), PALETTE["light"])
|
||||
assert len(fig.data) == 0
|
||||
|
||||
|
||||
def test_add_carb_bars_trace_adds_one_bar_trace():
|
||||
carbs = pd.DataFrame({"timestamp": pd.to_datetime(["2026-01-01 08:00"]), "carbs": [40.0]})
|
||||
fig = go.Figure()
|
||||
add_carb_bars_trace(fig, carbs, PALETTE["light"])
|
||||
assert len(fig.data) == 1
|
||||
assert fig.data[0].name == "Koolhydraten"
|
||||
assert isinstance(fig.data[0], go.Bar)
|
||||
|
||||
|
||||
def test_add_bolus_marker_traces_adds_one_trace_per_category_present():
|
||||
bolus = pd.DataFrame(
|
||||
{
|
||||
"timestamp": pd.to_datetime(["2026-01-01 08:00", "2026-01-01 12:00"]),
|
||||
"insulin": [4.0, 0.3],
|
||||
"category": ["food", "smb"],
|
||||
}
|
||||
)
|
||||
fig = go.Figure()
|
||||
add_bolus_marker_traces(fig, bolus, PALETTE["light"], lane_y=2.0)
|
||||
names = {trace.name for trace in fig.data}
|
||||
assert names == {"Bolus (voeding)", "SMB"}
|
||||
|
||||
|
||||
def test_add_bolus_marker_traces_labels_food_and_correction_but_not_smb():
|
||||
bolus = pd.DataFrame(
|
||||
{
|
||||
"timestamp": pd.to_datetime(["2026-01-01 08:00", "2026-01-01 12:00"]),
|
||||
"insulin": [4.0, 0.3],
|
||||
"category": ["food", "smb"],
|
||||
}
|
||||
)
|
||||
fig = go.Figure()
|
||||
add_bolus_marker_traces(fig, bolus, PALETTE["light"], lane_y=2.0)
|
||||
by_name = {trace.name: trace for trace in fig.data}
|
||||
assert by_name["Bolus (voeding)"].mode == "markers+text"
|
||||
assert by_name["SMB"].mode == "markers"
|
||||
|
||||
|
||||
def test_add_bolus_marker_traces_skips_empty_input():
|
||||
fig = go.Figure()
|
||||
add_bolus_marker_traces(fig, pd.DataFrame(columns=["timestamp", "insulin", "category"]), PALETTE["light"], 2.0)
|
||||
assert len(fig.data) == 0
|
||||
|
||||
|
||||
def test_add_target_line_trace_adds_one_line_trace():
|
||||
target = pd.DataFrame({"timestamp": pd.to_datetime(["2026-01-01 00:00"]), "target": [6.5]})
|
||||
fig = go.Figure()
|
||||
add_target_line_trace(fig, target, PALETTE["light"])
|
||||
assert len(fig.data) == 1
|
||||
assert fig.data[0].name == "Streefwaarde (pomp)"
|
||||
|
||||
|
||||
def test_add_target_line_trace_skips_empty_input():
|
||||
fig = go.Figure()
|
||||
add_target_line_trace(fig, pd.DataFrame(columns=["timestamp", "target"]), PALETTE["light"])
|
||||
assert len(fig.data) == 0
|
||||
|
||||
|
||||
def test_overlay_traces_support_an_hour_based_x_column_for_dagoverzicht():
|
||||
"""Dagoverzicht plot elke dag op een gedeelde 0-24u-as ('hour') i.p.v. echte
|
||||
datums; de hover mag daar dan ook geen datumformattering tonen."""
|
||||
basal = pd.DataFrame({"hour": [0.0, 6.0], "rate": [0.8, 1.2]})
|
||||
carbs = pd.DataFrame({"hour": [8.0], "carbs": [40.0]})
|
||||
bolus = pd.DataFrame({"hour": [8.0], "insulin": [4.0], "category": ["food"]})
|
||||
target = pd.DataFrame({"hour": [0.0], "target": [6.5]})
|
||||
|
||||
fig = go.Figure()
|
||||
add_basal_area_trace(fig, basal, PALETTE["light"], x_col="hour")
|
||||
add_carb_bars_trace(fig, carbs, PALETTE["light"], x_col="hour")
|
||||
add_bolus_marker_traces(fig, bolus, PALETTE["light"], lane_y=2.0, x_col="hour")
|
||||
add_target_line_trace(fig, target, PALETTE["light"], x_col="hour")
|
||||
|
||||
assert len(fig.data) == 4
|
||||
bolus_trace = next(t for t in fig.data if t.name == "Bolus (voeding)")
|
||||
assert list(bolus_trace.x) == [8.0]
|
||||
assert "%d-%m" not in bolus_trace.hovertemplate
|
||||
|
||||
+154
@@ -209,6 +209,160 @@ def build_tir_stacked_bar_figure(tir: dict, colors: dict) -> go.Figure:
|
||||
return fig
|
||||
|
||||
|
||||
BOLUS_CATEGORY_LABELS = {
|
||||
"food": "Bolus (voeding)",
|
||||
"correction": "Bolus (correctie)",
|
||||
"smb": "SMB",
|
||||
}
|
||||
BOLUS_CATEGORY_COLOR_KEYS = {
|
||||
"food": "bolus_food",
|
||||
"correction": "bolus_correction",
|
||||
"smb": "bolus_smb",
|
||||
}
|
||||
# Tekenvolgorde van bolus-categorieën: SMB eerst (onderop, meestal talrijker en
|
||||
# kleiner), voeding/correctie erbovenop (grotere, gelabelde stippen blijven
|
||||
# zichtbaar in plaats van bedolven te raken onder de SMB-stippen).
|
||||
BOLUS_CATEGORY_DRAW_ORDER = ["smb", "correction", "food"]
|
||||
|
||||
|
||||
def add_basal_area_trace(
|
||||
fig: go.Figure,
|
||||
basal: pd.DataFrame,
|
||||
colors: dict,
|
||||
*,
|
||||
row: int | None = None,
|
||||
secondary_y: bool = False,
|
||||
x_col: str = "timestamp",
|
||||
) -> None:
|
||||
"""Basaal-insuline als semi-transparant stapvlak (line_shape='hv'), met alpha
|
||||
zodat onderliggende data zichtbaar blijft. Verwacht kolommen 'rate' en
|
||||
`x_col` (standaard 'timestamp'; de Dagoverzicht-pagina geeft hier 'hour'
|
||||
aan omdat elke dag daar op een gedeelde 0-24u-as staat i.p.v. echte
|
||||
datums — zie reporting.effective_basal_series). Bedoeld voor een
|
||||
secundaire y-as (E/uur past niet op de mmol/L-schaal van de glucoselijn).
|
||||
"""
|
||||
if basal.empty:
|
||||
return
|
||||
kwargs: dict = {"row": row, "col": 1} if row is not None else {}
|
||||
if secondary_y:
|
||||
kwargs["secondary_y"] = True
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=basal[x_col],
|
||||
y=basal["rate"],
|
||||
mode="lines",
|
||||
line={"shape": "hv", "color": colors["basal"], "width": 1},
|
||||
fill="tozeroy",
|
||||
fillcolor=hex_to_rgba(colors["basal"], 0.3),
|
||||
opacity=0.8,
|
||||
name="Basaal",
|
||||
hovertemplate="%{y:.2f} E/uur<extra></extra>",
|
||||
),
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
def add_carb_bars_trace(
|
||||
fig: go.Figure,
|
||||
carbs: pd.DataFrame,
|
||||
colors: dict,
|
||||
*,
|
||||
row: int | None = None,
|
||||
secondary_y: bool = False,
|
||||
x_col: str = "timestamp",
|
||||
) -> None:
|
||||
"""Koolhydraten als staafjes op het moment van invoer. Verwacht kolommen
|
||||
'carbs' en `x_col` (zie add_basal_area_trace voor waarom dit configureerbaar
|
||||
is). Zelfde secundaire as als de basaal-area (allebei geen mmol/L-grootheid),
|
||||
met een eigen kleur en alpha zodat beide onderscheidbaar blijven."""
|
||||
if carbs.empty:
|
||||
return
|
||||
kwargs: dict = {"row": row, "col": 1} if row is not None else {}
|
||||
if secondary_y:
|
||||
kwargs["secondary_y"] = True
|
||||
fig.add_trace(
|
||||
go.Bar(
|
||||
x=carbs[x_col],
|
||||
y=carbs["carbs"],
|
||||
marker_color=colors["carbs"],
|
||||
opacity=0.55,
|
||||
name="Koolhydraten",
|
||||
hovertemplate="%{y:.0f} g<extra></extra>",
|
||||
),
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
def add_bolus_marker_traces(
|
||||
fig: go.Figure,
|
||||
bolus: pd.DataFrame,
|
||||
colors: dict,
|
||||
lane_y: float,
|
||||
*,
|
||||
row: int | None = None,
|
||||
x_col: str = "timestamp",
|
||||
) -> None:
|
||||
"""Eén Scatter-trace per boluscategorie (food/correction/smb), gepositioneerd
|
||||
op een vaste 'lane'-hoogte op de primaire (mmol/L-)as — net als de bestaande
|
||||
insuline/carbs-lanes op de Live-pagina. De hoogte zelf draagt geen
|
||||
kwantitatieve betekenis; de markergrootte (geschaald naar toegediende
|
||||
eenheden) en hoverwaarde wel. Voeding/correctie krijgen een tekstlabel met
|
||||
de dosis; SMB's niet (te talrijk/te klein voor leesbare labels). Verwacht
|
||||
kolommen 'insulin'/'category' en `x_col` (zie reporting.classify_bolus).
|
||||
"""
|
||||
# Datumformattering in de hover werkt alleen op een echte datetime-as; de
|
||||
# Dagoverzicht-pagina geeft hier 'hour' (een kaal getal 0-24) aan, dus
|
||||
# daar laten we het tijdstip weg en toont de hover alleen de dosis.
|
||||
hover_prefix = "%{x|%d-%m %H:%M} · " if x_col == "timestamp" else ""
|
||||
kwargs: dict = {"row": row, "col": 1} if row is not None else {}
|
||||
for category in BOLUS_CATEGORY_DRAW_ORDER:
|
||||
subset = bolus[bolus["category"] == category] if not bolus.empty else bolus
|
||||
if subset.empty:
|
||||
continue
|
||||
show_label = category != "smb"
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=subset[x_col],
|
||||
y=[lane_y] * len(subset),
|
||||
mode="markers+text" if show_label else "markers",
|
||||
marker={
|
||||
"size": (subset["insulin"] * 8).clip(lower=6, upper=30),
|
||||
"color": colors[BOLUS_CATEGORY_COLOR_KEYS[category]],
|
||||
"opacity": 0.75,
|
||||
},
|
||||
text=[f"{v:.1f}E" for v in subset["insulin"]] if show_label else None,
|
||||
textposition="top center",
|
||||
textfont={"size": 9, "color": colors["text"]},
|
||||
name=BOLUS_CATEGORY_LABELS[category],
|
||||
customdata=subset["insulin"],
|
||||
hovertemplate=f"{hover_prefix}%{{customdata:.2f}} E<extra></extra>",
|
||||
),
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
def add_target_line_trace(
|
||||
fig: go.Figure, target: pd.DataFrame, colors: dict, *, row: int | None = None, x_col: str = "timestamp"
|
||||
) -> None:
|
||||
"""Streefglucose uit het pompprofiel als lijn op de primaire (mmol/L-)as.
|
||||
Verwacht kolommen 'target' en `x_col` (zie reporting.target_glucose_series)."""
|
||||
if target.empty:
|
||||
return
|
||||
kwargs: dict = {"row": row, "col": 1} if row is not None else {}
|
||||
fig.add_trace(
|
||||
go.Scatter(
|
||||
x=target[x_col],
|
||||
y=target["target"],
|
||||
mode="lines",
|
||||
line={"shape": "hv", "color": colors["target"], "width": 1.5, "dash": "dashdot"},
|
||||
opacity=0.9,
|
||||
name="Streefwaarde (pomp)",
|
||||
hovertemplate="%{y:.1f} mmol/L<extra></extra>",
|
||||
),
|
||||
**kwargs,
|
||||
)
|
||||
|
||||
|
||||
def render_completeness_warning(glucose: pd.DataFrame, start: date, end: date) -> None:
|
||||
"""Toon reporting.completeness_warning()'s oordeel als st.warning, indien nodig.
|
||||
|
||||
|
||||
Reference in New Issue
Block a user