Promotion / feat(config): Vereinheitlichung aller Export- und Theme-Konfigurationen
- Refaktorierung aller Konfigurationsdateien (config_korrelation.py, config_netzwerk.py, config_deskriptive_literaturauswahl.py) zur einheitlichen Steuerung von Visualisierungs- und PNG-Export über zentrale Schalter:
- export_fig_visual
- export_fig_png
- Einzel-Flags pro Plot als Ableitung des zentralen Schalters
- Integration der CI-konformen Templatefunktionen (get_standard_layout, get_colors, get_plot_styles) in alle Analyse-Skripte
- Standardisierte Exportlogik mittels `export_figure(...)` aus plotly_template, inkl. PNG-Support
- Titelergänzung mit Quelle (BibTeX-Dateiname), dynamischem Datum und Fallzahl
- Konsequent responsive Darstellung aller Visualisierungen
📁 Neue Datei:
- deskriptive-literaturauswahl.py: Ergänzung einer eigenständigen Analyse für deskriptive SC-Zeitreihenvisualisierung, basierend auf winsorisiertem Median, Quartilen und Bias-Schwellenwerten.
🔧 Hinweis:
- Die Exportdateien (HTML/PNG) sind CI-konform benannt und versionierbar
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deskriptive-literaturauswahl.py
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104
deskriptive-literaturauswahl.py
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# ===============================
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# Deskriptive Analyse: Silhouette-Scores & Fallzahlen
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# ===============================
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# --- System- und Modul-Imports ---
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import os
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import numpy as np
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import matplotlib.pyplot as plt
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import plotly.graph_objects as go
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from datetime import date
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from scipy.stats.mstats import winsorize
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# --- CI-Template & Konfiguration ---
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from ci_template.plotly_template import (
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get_standard_layout,
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get_colors,
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set_theme
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)
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from ci_template.plotly_template import export_figure
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from config_deskriptive_literaturauswahl import theme, export_fig_visual
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# --- Initialisierung ---
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os.system('cls' if os.name == 'nt' else 'clear')
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set_theme(theme)
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colors = get_colors()
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current_date = date.today().isoformat()
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# --- Datenbasis ---
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years = np.array([
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2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017,
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2018, 2019, 2020, 2021, 2022, 2023, 2024, 2025
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])
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sc_values = np.array([
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1.0000, 0.9655, 0.8571, 1.0000, 0.9583, 1.0000, 1.0000, 1.0000,
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0.9895, 1.0000, 0.9968, 0.9854, 0.9916, 0.9702, 0.9208, 0.9696
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])
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n_values = np.array([
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7, 29, 7, 28, 24, 28, 25, 98,
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95, 202, 303, 377, 430, 899, 780, 192
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])
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# --- Berechnungen ---
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q1 = np.percentile(sc_values, 25)
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q2 = np.percentile(sc_values, 50)
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q3 = np.percentile(sc_values, 75)
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max_value = np.max(sc_values)
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min_value = np.min(sc_values)
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sc_winsorized = winsorize(sc_values, limits=[0.1, 0.1])
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median_winsorized = np.median(sc_winsorized)
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fatigue_threshold = 0.96
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circadian_optimum = 0.99
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# --- Visualisierung ---
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fig = go.Figure()
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# SC-Linie
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fig.add_trace(go.Scatter(x=years, y=sc_values, name='Silhouette-Scores',
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mode='lines+markers', yaxis='y1'))
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# n-Balken
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fig.add_trace(go.Bar(x=years, y=n_values, name='n-Werte (Fallzahlen)',
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yaxis='y2', opacity=0.3, marker_color=colors["depthArea"]))
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# Quartile & Bezugslinien
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fig.add_trace(go.Scatter(x=years, y=[q1]*len(years), mode='lines', name=f'SC Q1 = {q1:.4f}',
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line=dict(dash='dot', color='green'), yaxis='y1'))
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fig.add_trace(go.Scatter(x=years, y=[q2]*len(years), mode='lines', name=f'SC Median (Q2) = {q2:.4f}',
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line=dict(dash='dot', color='blue'), yaxis='y1'))
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fig.add_trace(go.Scatter(x=years, y=[q3]*len(years), mode='lines', name=f'SC Q3 = {q3:.4f}',
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line=dict(dash='dot', color='purple'), yaxis='y1'))
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fig.add_trace(go.Scatter(x=years, y=[min_value]*len(years), mode='lines', name=f'SC Min = {min_value:.4f}',
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line=dict(dash='dash', color='red'), yaxis='y1'))
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fig.add_trace(go.Scatter(x=years, y=[max_value]*len(years), mode='lines', name=f'SC Max = {max_value:.4f}',
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line=dict(dash='dash', color='orange'), yaxis='y1'))
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fig.add_trace(go.Scatter(x=years, y=[fatigue_threshold]*len(years), mode='lines',
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name=f'Schwelle: Erschöpfungs-Bias = {fatigue_threshold:.4f}',
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line=dict(dash='dashdot', color='firebrick'), yaxis='y1'))
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fig.add_trace(go.Scatter(x=years, y=[circadian_optimum]*len(years), mode='lines',
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name=f'Idealer Wert (circadian) = {circadian_optimum:.4f}',
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line=dict(dash='dashdot', color='darkcyan'), yaxis='y1'))
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fig.add_trace(go.Scatter(x=years, y=[median_winsorized]*len(years), mode='lines',
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name=f'Winsorisierter Median = {median_winsorized:.4f}',
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line=dict(dash='dot', color='black'), yaxis='y1'))
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# Layout
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fig.update_layout(
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**get_standard_layout(
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title=f"Silhouette-Scores und Fallzahlen (n={sum(n_values)}, Stand: {current_date})",
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x_title="Jahr",
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y_title="Silhouette-Score",
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yaxis2=dict(
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title="Fallzahlen (n)",
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showgrid=False
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)
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)
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)
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# Anzeige
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fig.show(config={"responsive": True})
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# --- Export ---
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export_figure(fig, "silhouette_scores_und_fallzahlen", export_fig_silhouette_plot, export_fig_png)
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