262 lines
7.9 KiB
Python
262 lines
7.9 KiB
Python
# ===============================
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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 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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from scipy.stats import iqr
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import os
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import subprocess
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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 config_deskriptive_literaturauswahl import (
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theme,
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export_fig_visual,
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export_fig_png,
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export_fig_silhouette_plot,
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export_path_html,
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export_path_png
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)
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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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# --- Berechnung der euklidischen Distanz zwischen SC und n ---
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distances = np.sqrt((sc_values - sc_values.mean())**2 + (n_values - n_values.mean())**2)
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# --- Berechnungen ---
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# Berechne IQR und automatische untere/obere Grenzen
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sc_iqr = iqr(sc_values)
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q1_val = np.percentile(sc_values, 25)
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q3_val = np.percentile(sc_values, 75)
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lower_bound = q1_val - 1.5 * sc_iqr
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upper_bound = q3_val + 1.5 * sc_iqr
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sc_winsorized = np.clip(sc_values, lower_bound, upper_bound)
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median_winsorized = np.median(sc_winsorized)
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# Quartile
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q1 = q1_val
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q2 = np.median(sc_values)
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q3 = q3_val
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max_value = np.max(sc_values)
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min_value = np.min(sc_values)
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# Schwellenwerte datenbasiert
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fatigue_threshold = q1 # oder eine alternative datenbasierte Schwelle
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circadian_optimum = q3 # oder np.percentile(sc_values, 90)
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# Neue Berechnung von delta_raw und delta_z
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delta_raw = sc_values - (n_values / np.max(n_values))
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delta_z = (delta_raw - np.mean(delta_raw)) / np.std(delta_raw)
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# --- Quartilsbasierte Schwellenwerte für delta_raw ---
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q1_delta = np.percentile(delta_raw, 25)
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q2_delta = np.median(delta_raw)
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q3_delta = np.percentile(delta_raw, 75)
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# --- Visualisierung ---
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fig = go.Figure()
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from ci_template.plotly_template import get_plot_styles
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styles = get_plot_styles()
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fig.add_trace(go.Scatter(
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x=years,
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y=n_values,
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name='Fallzahlen (n)',
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yaxis='y2',
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mode='lines+markers',
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line=dict(color=colors["primaryLine"], width=1),
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marker=dict(size=16, color=colors["primaryLine"], symbol="square"),
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showlegend=True
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))
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# Quartile & Bezugslinien
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fig.add_trace(go.Scatter(x=years, y=[q1]*len(years), mode='lines', name='SC Q1',
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line=dict(dash='dot', color=colors["brightArea"]), yaxis='y1'))
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fig.add_trace(go.Scatter(x=years, y=[q2]*len(years), mode='lines', name='Q2',
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line=dict(dash='dot', color=colors["depthArea"]), yaxis='y1'))
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fig.add_trace(go.Scatter(x=years, y=[q3]*len(years), mode='lines', name='SC Q3',
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line=dict(dash='dot', color=colors["accent"]), yaxis='y1'))
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fig.add_trace(go.Scatter(
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x=years,
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y=[min_value]*len(years),
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mode='lines',
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name='SC Min',
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line=dict(dash='dash', color=colors["negativeHighlight"]),
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yaxis='y1'
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))
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fig.add_trace(go.Scatter(
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x=years,
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y=[max_value]*len(years),
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mode='lines',
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name='SC Max',
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line=dict(dash='dash', color=colors["positiveHighlight"]),
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yaxis='y1'
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))
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fig.add_trace(go.Scatter(
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x=years,
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y=sc_values,
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name='Silhouette-Scores',
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yaxis='y1',
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mode='lines+markers',
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line=dict(color=colors["primaryLine"], width=1),
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marker=dict(size=16, color=colors["primaryLine"], symbol="circle"),
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showlegend=True
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))
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# 3. Abweichung ΔSCₙ – farbcodiert
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fig.add_trace(go.Scatter(x=[None], y=[None], mode='lines',
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line=dict(color=colors["positiveHighlight"], width=5),
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name='ΔSCₙ: Optimal'
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))
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fig.add_trace(go.Scatter(x=[None], y=[None], mode='lines',
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line=dict(color=colors["secondaryLine"], width=5),
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name='ΔSCₙ: Q2+ Bereich'
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))
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fig.add_trace(go.Scatter(x=[None], y=[None], mode='lines',
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line=dict(color=colors["text"], width=5),
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name='ΔSCₙ: Ambivalent'
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))
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fig.add_trace(go.Scatter(x=[None], y=[None], mode='lines',
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line=dict(color=colors["negativeHighlight"], width=5),
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name='ΔSCₙ: Kritisch'
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))
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# Berechne Quartile für SC und n
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sc_q2 = np.percentile(sc_values, 50)
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sc_q3 = np.percentile(sc_values, 75)
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n_q2 = np.percentile(n_values, 50)
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n_q3 = np.percentile(n_values, 75)
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for year, sc, n in zip(years, sc_values, n_values):
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delta = sc - (n / max(n_values))
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if sc >= sc_q3 and n >= n_q3:
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color = colors["positiveHighlight"]
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label = "Optimal (SC & n ≥ Q3)"
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elif sc < sc_q2 and n < n_q2:
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color = colors["negativeHighlight"]
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label = "Kritisch (SC & n < Q2)"
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elif sc >= sc_q2 and n >= n_q2:
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color = colors["secondaryLine"]
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label = "Pragmatisch gut (Q2 ≤ SC & n < Q3)"
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else:
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color = colors["text"]
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label = "Ambivalent (alle übrigen Fälle)"
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if np.isclose(delta, q2_delta, atol=1e-3):
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line_width = 7 # Bonus: Medianlinie hervorheben
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else:
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line_width = 5
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fig.add_trace(go.Scatter(
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x=[year, year],
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y=[0, delta],
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mode='lines',
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line=dict(color=color, width=line_width),
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hoverinfo='text',
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text=[f"Jahr: {year}, ΔSCₙ: {delta:.4f}, {label}"]*2,
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yaxis='y3',
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showlegend=False
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))
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print(f"Jahr: {year}, SC: {sc:.4f}, n: {n}, Kategorie: {label}")
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# Layout
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layout = get_standard_layout(
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title="Silhouette-Scores und Fallzahlen pro Jahr",
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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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title_standoff=20
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)
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)
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layout["font"] = {"size": 14, "color": colors['text']}
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layout["title"] = dict(text="Silhouette-Scores und Fallzahlen pro Jahr", font=dict(color=colors["text"]))
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layout["margin"] = dict(b=80, t=120, l=60, r=100)
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layout["xaxis"] = layout.get("xaxis", {})
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layout["xaxis"]["automargin"] = True
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layout["autosize"] = True
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layout["legend"] = dict(
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x=1.1,
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y=1.0,
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xanchor="left",
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yanchor="top",
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orientation="v",
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traceorder="normal",
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itemclick="toggleothers",
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itemdoubleclick="toggle"
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)
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layout["yaxis3"] = dict(
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title="Abweichung (ΔSCₙ)",
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overlaying="y",
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side="right",
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showgrid=False,
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zeroline=True,
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zerolinewidth=2,
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zerolinecolor='grey',
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titlefont=dict(color=colors["text"]),
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tickfont=dict(color=colors["text"]),
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anchor="free",
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position=1.0
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)
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fig.update_layout(**layout)
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# --- Export-Funktion ---
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def export_figure(fig, name, export_flag_html, export_flag_png):
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from slugify import slugify
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safe_name = slugify(name)
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html_path = f"/tmp/{safe_name}.html"
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if export_flag_html:
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fig.write_html(html_path, include_plotlyjs='cdn', config={"responsive": True})
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try:
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subprocess.run(["scp", html_path, export_path_html], check=True)
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print(f"✅ HTML-Datei '{html_path}' erfolgreich übertragen.")
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os.remove(html_path)
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print(f"🗑️ Lokale HTML-Datei '{html_path}' wurde gelöscht.")
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except subprocess.CalledProcessError as e:
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print("❌ Fehler beim HTML-Übertragen:")
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print(e.stderr)
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if export_flag_png:
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png_path = os.path.join(export_path_png, f"{safe_name}.png")
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try:
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fig.write_image(png_path, scale=2)
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print(f"✅ PNG-Datei lokal gespeichert: '{png_path}'")
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except Exception as e:
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print("❌ Fehler beim PNG-Export:", str(e))
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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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fig.show()
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