willemjan
9e11dd8d73
AndroidAPS records an automatic SMB micro-bolus as eventType "Correction Bolus" with type "SMB", not as its own eventType. bolus_stats() was grouping by eventType alone, so every SMB was silently counted as a manual correction bolus. Verified against production data: 100% of "Correction Bolus" treatments over 90 days were actually type=SMB. Also adds basal_stats(), computing delivered basal directly from Temp Basal treatments (rate x duration) without needing the pump profile. Closes gitea #20.
134 lines
4.8 KiB
Python
134 lines
4.8 KiB
Python
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 auth import require_auth
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from data_processing import classify_treatments
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from nightscout_client import NightscoutConfigError
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from reporting import (
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basal_stats,
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bolus_stats,
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day_of_week_pattern,
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gmi_percent,
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overnight_daytime_split,
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site_interval_stats,
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)
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from ui_common import (
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get_colors,
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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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)
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st.set_page_config(page_title="Glucose dashboard — Trends", page_icon="📊", layout="wide")
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require_auth()
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st.title("Trends")
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st.caption("Geschat HbA1c (GMI) over tijd en overige statistieken (FR7, FR8).")
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start, end, days = select_period_days(default_label="90 dagen")
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try:
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glucose, treatments = load_range_data(start, end)
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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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except (requests.RequestException, ValueError) as exc:
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st.error(f"Kon geen data ophalen van Nightscout: {exc}")
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st.stop()
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if glucose.empty:
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st.info("Geen glucosemetingen gevonden in deze periode.")
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st.stop()
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render_completeness_warning(glucose, start, end)
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colors = get_colors()
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events = classify_treatments(treatments)
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# --- FR7: geschat HbA1c (GMI) per maand ------------------------------------
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st.subheader("Geschat HbA1c (GMI) per maand")
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monthly = glucose.copy()
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monthly["month"] = monthly["timestamp"].dt.to_period("M").astype(str)
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monthly_gmi = monthly.groupby("month")["mmol"].mean().apply(gmi_percent)
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if len(monthly_gmi) >= 1:
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fig = go.Figure(
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go.Bar(x=monthly_gmi.index, y=monthly_gmi.to_numpy(), marker_color=colors["glucose"])
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)
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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=280,
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margin={"l": 10, "r": 10, "t": 10, "b": 10},
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yaxis={"title": "GMI (%)", "gridcolor": colors["grid"]},
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xaxis={"title": "Maand", "gridcolor": colors["grid"]},
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)
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st.plotly_chart(fig, width="stretch")
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st.caption("GMI is een schatting (Bergenstal et al. 2018), geen labwaarde.")
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# --- FR8: nacht vs. overdag --------------------------------------------------
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st.subheader("Nacht (00:00-06:00) vs. overdag")
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split = overnight_daytime_split(glucose)
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col1, col2 = st.columns(2)
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col1.metric("Tijd in bereik 's nachts", f"{split['overnight']['in_range']:.0f}%")
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col2.metric("Tijd in bereik overdag", f"{split['daytime']['in_range']:.0f}%")
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# --- FR8: patroon per weekdag -------------------------------------------------
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st.subheader("Patroon per weekdag")
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weekday_df = day_of_week_pattern(glucose)
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weekday_plot = weekday_df.dropna(subset=["mean_mmol"])
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if not weekday_plot.empty:
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fig = go.Figure(
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go.Bar(x=weekday_plot["weekday"], y=weekday_plot["mean_mmol"], marker_color=colors["glucose"])
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)
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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=280,
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margin={"l": 10, "r": 10, "t": 10, "b": 10},
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yaxis={"title": "Gem. glucose (mmol/L)", "gridcolor": colors["grid"]},
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xaxis={"gridcolor": colors["grid"]},
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)
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st.plotly_chart(fig, width="stretch")
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# --- FR8: insuline (basaal + bolus-uitsplitsing) ---------------------------
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st.subheader("Insuline")
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st.caption(
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"Basaal berekend uit Temp Basal-treatments (snelheid x duur), zonder pompprofiel "
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"nodig — een ondergrens bij periodes zonder actieve temp basal. Boluses "
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"opgesplitst naar voeding, handmatige correctie en automatische SMB-microbolus "
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"(gitea #20)."
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)
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bolus = bolus_stats(treatments)
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basal = basal_stats(treatments, days)
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total_units = bolus["total_units"] + basal["total_units"]
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col1, col2, col3 = st.columns(3)
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col1.metric("Totaal insuline", f"{total_units:.1f} E")
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col2.metric("Gem. per dag", f"{total_units / days:.1f} E")
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col3.metric("Aantal boluses", bolus["count"])
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breakdown = {"Basaal": basal["total_units"], **bolus["by_type"]}
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if any(breakdown.values()):
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st.table({"Categorie": list(breakdown.keys()), "Eenheden": list(breakdown.values())})
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# --- FR8: pompsite-leeftijd vs. glucose/insuline ---------------------------
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st.subheader("Pompsite-leeftijd vs. glucose en insuline")
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st.caption(
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"Statistieken per interval tussen opeenvolgende pompwissels — een benadering "
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"(zonder basaalinsuline) van of insulinebehoefte samenhangt met de leeftijd "
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"van de pompsite."
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)
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site_stats = site_interval_stats(glucose, treatments, events["site_changes"])
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if site_stats.empty:
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st.info("Geen pompwissels gevonden in deze periode.")
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else:
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display = site_stats.copy()
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display["start"] = display["start"].dt.strftime("%d-%m-%Y")
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display["end"] = display["end"].dt.strftime("%d-%m-%Y")
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st.dataframe(display, width="stretch", hide_index=True)
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st.caption(f"Periode: {start.strftime('%d-%m-%Y')} t/m {end.strftime('%d-%m-%Y')}")
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