Promotion: Verbesserung der Visualiserungen und Exportfunktionen in der Systematischen Literaturrecherche; Fehlerbehebung

analyse_korrelation.py

@@ -6,6 +6,7 @@ from ci_template.plotly_template import export_figure
 from config_korrelation import (
     theme,
     export_fig_visual,
+    export_fig_png,
     export_fig_clusteranalyse,
     export_fig_correlation_suchbegriffe_kategorien,
     export_fig_correlation_fu_kategorien,
@@ -61,6 +62,7 @@ def prepare_figure_export(fig, name):
 
 def export_and_transfer_figure(fig, function_name, export_flag):
     export_figure(fig, function_name, export_flag, export_fig_png)
+    fig.show()
 
 # BibTeX-Datei laden
 bib_path = os.path.join("Research", "Charité - Universitätsmedizin Berlin", "Systematische Literaturrecherche", "Bibliothek", bib_filename)
@@ -436,7 +438,6 @@ def visualize_indizes_vs_indizes(export_flag):
         export_flag
     )
 
-
 #======================================
 
 cluster_colors = {
@@ -723,3 +724,7 @@ visualize_suchbegriffe_vs_suchbegriffe(export_flag=export_fig_correlation_suchbe
 visualize_kategorien_vs_kategorien(export_flag=export_fig_correlation_kategorien_kategorien)
 visualize_indizes_vs_indizes(export_flag=export_fig_correlation_indizes_indizes)
 plot_average_correlation_plotly(summary_df)
+
+# Visualisierungsoption für Plotly: Immer im Browser öffnen
+import plotly.io as pio
+pio.renderers.default = 'browser'

analyse_netzwerk.py

@@ -1,7 +1,7 @@
 
 from config_netzwerk import theme, export_fig_visual, bib_filename
 
-from ci_template.plotly_template import export_figure
+from ci_template.plotly_template import export_figure as export_figure_ci
 
 import os
 
@@ -63,8 +63,10 @@ from config_netzwerk import (
     export_fig_visualize_languages,
 )
 
-def export_figure(fig, name, flag, bib_filename=None):
+from config_netzwerk import export_fig_png
-    export_figure(fig, name, flag, export_fig_png)
+
+def export_figure_local(fig, name, flag, bib_filename=None):
+    export_figure_ci(fig, name, flag, export_fig_png)
 
 from ci_template.plotly_template import get_colors, get_plot_styles, get_standard_layout
 
@@ -322,7 +324,7 @@ def visualize_network(bib_database):
     fig.update_layout(**layout)
 
     fig.show(config={"responsive": True})
-    export_figure(fig, "visualize_network", export_fig_visualize_network, bib_filename)
+    export_figure_local(fig, "visualize_network", export_fig_visualize_network, bib_filename)
 
     # Einfache Pfadanalyse nach dem Anzeigen der Figur
     if 'e-learning' in G and 'online:lernen' in G:
@@ -429,7 +431,7 @@ def visualize_tags(bib_database):
     fig.update_layout(**layout)
 
     fig.show(config={"responsive": True})
-    export_figure(fig, "visualize_tags", export_fig_visualize_tags, bib_filename)
+    export_figure_local(fig, "visualize_tags", export_fig_visualize_tags, bib_filename)
 
  # Visualisierung 3: Häufigkeit Index
 def visualize_index(bib_database):
@@ -477,7 +479,7 @@ def visualize_index(bib_database):
     fig.update_layout(**layout)
     fig.update_traces(marker=plot_styles['balken_primaryLine'])
     fig.show(config={"responsive": True})
-    export_figure(fig, "visualize_index", export_fig_visualize_index, bib_filename)
+    export_figure_local(fig, "visualize_index", export_fig_visualize_index, bib_filename)
 
  # Visualisierung 4: Häufigkeit Forschungsunterfragen
 def visualize_research_questions(bib_database):
@@ -526,7 +528,7 @@ def visualize_research_questions(bib_database):
     fig.update_layout(**layout)
     fig.update_traces(marker=plot_styles['balken_primaryLine'])
     fig.show(config={"responsive": True})
-    export_figure(fig, "visualize_research_questions", export_fig_visualize_research_questions, bib_filename)
+    export_figure_local(fig, "visualize_research_questions", export_fig_visualize_research_questions, bib_filename)
 
  # Visualisierung 5: Häufigkeit spezifischer Kategorien
 def visualize_categories(bib_database):
@@ -570,7 +572,7 @@ def visualize_categories(bib_database):
     fig.update_layout(**layout)
     fig.update_traces(marker=plot_styles['balken_primaryLine'])
     fig.show(config={"responsive": True})
-    export_figure(fig, "visualize_categories", export_fig_visualize_categories, bib_filename)
+    export_figure_local(fig, "visualize_categories", export_fig_visualize_categories, bib_filename)
 
  # Zeitreihenanalyse der Veröffentlichungen
 def visualize_time_series(bib_database):
@@ -617,7 +619,7 @@ def visualize_time_series(bib_database):
         fig.update_layout(**layout)
         fig.update_traces(line=plot_styles['linie_primaryLine'])
         fig.show(config={"responsive": True})
-        export_figure(fig, "visualize_time_series", export_fig_visualize_time_series, bib_filename)
+        export_figure_local(fig, "visualize_time_series", export_fig_visualize_time_series, bib_filename)
     else:
         print("Keine gültigen Veröffentlichungsjahre gefunden.")
 
@@ -651,7 +653,7 @@ def visualize_top_authors(bib_database):
         fig.update_layout(**layout)
         fig.update_traces(marker=plot_styles['balken_primaryLine'])
         fig.show(config={"responsive": True})
-        export_figure(fig, "visualize_top_authors", export_fig_visualize_top_authors, bib_filename)
+        export_figure_local(fig, "visualize_top_authors", export_fig_visualize_top_authors, bib_filename)
     else:
         print("Keine Autoren gefunden.")
 
@@ -697,7 +699,7 @@ def visualize_top_publications(bib_database):
     fig.update_layout(**layout)
     fig.update_traces(marker=plot_styles['balken_primaryLine'])
     fig.show(config={"responsive": True})
-    export_figure(fig, "visualize_top_publications", export_fig_visualize_top_publications, bib_filename)
+    export_figure_local(fig, "visualize_top_publications", export_fig_visualize_top_publications, bib_filename)
 
 ##########
 
@@ -829,7 +831,7 @@ def create_path_diagram(data):
     layout["autosize"] = True
     fig.update_layout(**layout)
     fig.show(config={"responsive": True})
-    export_figure(fig, "create_path_diagram", export_fig_create_path_diagram, bib_filename)
+    export_figure_local(fig, "create_path_diagram", export_fig_create_path_diagram, bib_filename)
 
 #############
 
@@ -962,7 +964,7 @@ def create_sankey_diagram(bib_database):
     layout["autosize"] = True
     fig.update_layout(**layout)
     fig.show(config={"responsive": True})
-    export_figure(fig, "create_sankey_diagram", export_fig_create_sankey_diagram, bib_filename)
+    export_figure_local(fig, "create_sankey_diagram", export_fig_create_sankey_diagram, bib_filename)
 
 ##########
 
@@ -1090,7 +1092,7 @@ def visualize_sources_status(bib_database):
     layout["autosize"] = True
     fig.update_layout(**layout)
     fig.show(config={"responsive": True})
-    export_figure(fig, "visualize_sources_status", export_fig_visualize_sources_status, bib_filename)
+    export_figure_local(fig, "visualize_sources_status", export_fig_visualize_sources_status, bib_filename)
 
 #############
 
@@ -1195,7 +1197,7 @@ def visualize_languages(bib_database):
     fig.show(config={"responsive": True})
     # Tabelle ausgeben
     print(tabulate(df.sort_values("Anzahl", ascending=False), headers="keys", tablefmt="grid", showindex=False))
-    export_figure(fig, "visualize_languages", export_fig_visualize_languages, bib_filename)
+    export_figure_local(fig, "visualize_languages", export_fig_visualize_languages, bib_filename)
 
 # Visualisierung der Verteilung von ENTRYTYPE innerhalb jeder Sprache
 def visualize_language_entrytypes(bib_database):
@@ -1297,7 +1299,7 @@ def visualize_language_entrytypes(bib_database):
     fig.update_layout(**layout)
     fig.show(config={"responsive": True})
     print(tabulate(grouped.sort_values(["Sprache", "Eintragstyp"]), headers=["Sprache", "Eintragstyp", "Anzahl", "Anteil innerhalb Sprache (%)", "Typgruppe"], tablefmt="grid", showindex=False))
-    export_figure(fig, "visualize_language_entrytypes", export_fig_visualize_languages, bib_filename)
+    export_figure_local(fig, "visualize_language_entrytypes", export_fig_visualize_languages, bib_filename)
 
 #############
 

deskriptive-literaturauswahl.py

@@ -9,6 +9,7 @@ import matplotlib.pyplot as plt
 import plotly.graph_objects as go
 from datetime import date
 from scipy.stats.mstats import winsorize
+from scipy.stats import iqr
 
 # --- CI-Template & Konfiguration ---
 from ci_template.plotly_template import (
@@ -18,6 +19,7 @@ from ci_template.plotly_template import (
 )
 from ci_template.plotly_template import export_figure
 from config_deskriptive_literaturauswahl import theme, export_fig_visual
+from config_deskriptive_literaturauswahl import export_fig_png, export_fig_silhouette_plot
 
 # --- Initialisierung ---
 os.system('cls' if os.name == 'nt' else 'clear')
@@ -40,65 +42,110 @@ n_values = np.array([
 ])
 
 # --- Berechnungen ---
-q1 = np.percentile(sc_values, 25)
+# Berechne IQR und automatische untere/obere Grenzen
-q2 = np.percentile(sc_values, 50)
+sc_iqr = iqr(sc_values)
-q3 = np.percentile(sc_values, 75)
+q1_val = np.percentile(sc_values, 25)
+q3_val = np.percentile(sc_values, 75)
+lower_bound = q1_val - 1.5 * sc_iqr
+upper_bound = q3_val + 1.5 * sc_iqr
+sc_winsorized = np.clip(sc_values, lower_bound, upper_bound)
+median_winsorized = np.median(sc_winsorized)
+
+# Quartile
+q1 = q1_val
+q2 = np.median(sc_values)
+q3 = q3_val
 max_value = np.max(sc_values)
 min_value = np.min(sc_values)
 
-sc_winsorized = winsorize(sc_values, limits=[0.1, 0.1])
+# Schwellenwerte datenbasiert
-median_winsorized = np.median(sc_winsorized)
+fatigue_threshold = q1  # oder eine alternative datenbasierte Schwelle
-
+circadian_optimum = q3  # oder np.percentile(sc_values, 90)
-fatigue_threshold = 0.96
-circadian_optimum = 0.99
 
 # --- Visualisierung ---
 fig = go.Figure()
 
-# SC-Linie
+from ci_template.plotly_template import get_plot_styles
-fig.add_trace(go.Scatter(x=years, y=sc_values, name='Silhouette-Scores',
+styles = get_plot_styles()
-                         mode='lines+markers', yaxis='y1'))
 
-# n-Balken
+fig.add_trace(go.Scatter(
-fig.add_trace(go.Bar(x=years, y=n_values, name='n-Werte (Fallzahlen)',
+    x=years,
-                     yaxis='y2', opacity=0.3, marker_color=colors["depthArea"]))
+    y=n_values,
+    name='Fallzahlen (n)',
+    yaxis='y2',
+    mode='lines+markers',
+    line=dict(color=colors["secondaryLine"], width=1),
+    marker=dict(size=16, color=colors["secondaryLine"], symbol="square"),
+    showlegend=True
+))
 
 # Quartile & Bezugslinien
-fig.add_trace(go.Scatter(x=years, y=[q1]*len(years), mode='lines', name=f'SC Q1 = {q1:.4f}',
+fig.add_trace(go.Scatter(x=years, y=[q1]*len(years), mode='lines', name='SC Q1',
-                         line=dict(dash='dot', color='green'), yaxis='y1'))
+                         line=dict(dash='dot', color=colors["brightArea"]), yaxis='y1'))
-fig.add_trace(go.Scatter(x=years, y=[q2]*len(years), mode='lines', name=f'SC Median (Q2) = {q2:.4f}',
+fig.add_trace(go.Scatter(x=years, y=[q2]*len(years), mode='lines', name='SC Median (Q2)',
-                         line=dict(dash='dot', color='blue'), yaxis='y1'))
+                         line=dict(dash='dot', color=colors["depthArea"]), yaxis='y1'))
-fig.add_trace(go.Scatter(x=years, y=[q3]*len(years), mode='lines', name=f'SC Q3 = {q3:.4f}',
+fig.add_trace(go.Scatter(x=years, y=[q3]*len(years), mode='lines', name='SC Q3',
-                         line=dict(dash='dot', color='purple'), yaxis='y1'))
+                         line=dict(dash='dot', color=colors["accent"]), yaxis='y1'))
-fig.add_trace(go.Scatter(x=years, y=[min_value]*len(years), mode='lines', name=f'SC Min = {min_value:.4f}',
+fig.add_trace(go.Scatter(
-                         line=dict(dash='dash', color='red'), yaxis='y1'))
+    x=years,
-fig.add_trace(go.Scatter(x=years, y=[max_value]*len(years), mode='lines', name=f'SC Max = {max_value:.4f}',
+    y=[min_value]*len(years),
-                         line=dict(dash='dash', color='orange'), yaxis='y1'))
+    mode='lines',
-fig.add_trace(go.Scatter(x=years, y=[fatigue_threshold]*len(years), mode='lines',
+    name='SC Min',
-                         name=f'Schwelle: Erschöpfungs-Bias = {fatigue_threshold:.4f}',
+    line=dict(dash='dash', color=colors["negativeHighlight"]),
-                         line=dict(dash='dashdot', color='firebrick'), yaxis='y1'))
+    yaxis='y1'
-fig.add_trace(go.Scatter(x=years, y=[circadian_optimum]*len(years), mode='lines',
+))
-                         name=f'Idealer Wert (circadian) = {circadian_optimum:.4f}',
+fig.add_trace(go.Scatter(
-                         line=dict(dash='dashdot', color='darkcyan'), yaxis='y1'))
+    x=years,
-fig.add_trace(go.Scatter(x=years, y=[median_winsorized]*len(years), mode='lines',
+    y=[max_value]*len(years),
-                         name=f'Winsorisierter Median = {median_winsorized:.4f}',
+    mode='lines',
-                         line=dict(dash='dot', color='black'), yaxis='y1'))
+    name='SC Max',
+    line=dict(dash='dash', color=colors["positiveHighlight"]),
+    yaxis='y1'
+))
+
+fig.add_trace(go.Scatter(
+    x=years,
+    y=sc_values,
+    name='Silhouette-Scores',
+    yaxis='y1',
+    mode='lines+markers',
+    line=dict(color=colors["primaryLine"], width=1),
+    marker=dict(size=16, color=colors["primaryLine"], symbol="circle"),
+    showlegend=True
+))
 
 # Layout
-fig.update_layout(
+layout = get_standard_layout(
-    **get_standard_layout(
+    title="Silhouette-Scores und Fallzahlen pro Jahr",
-        title=f"Silhouette-Scores und Fallzahlen (n={sum(n_values)}, Stand: {current_date})",
+    x_title='Jahr',
-        x_title="Jahr",
+    y_title='Silhouette-Score',
-        y_title="Silhouette-Score",
+    yaxis2=dict(
-        yaxis2=dict(
+        title="Fallzahlen (n)",
-            title="Fallzahlen (n)",
+        showgrid=False,
-            showgrid=False
+        title_standoff=20
-        )
     )
 )
-
+layout["font"] = {"size": 14, "color": colors['text']}
-# Anzeige
+layout["title"] = dict(text="Silhouette-Scores und Fallzahlen pro Jahr", font=dict(color=colors["text"]))
-fig.show(config={"responsive": True})
+layout["margin"] = dict(b=80, t=120, l=60, r=60)
+layout["xaxis"] = layout.get("xaxis", {})
+layout["xaxis"]["automargin"] = True
+layout["autosize"] = True
+layout["legend"] = dict(
+    x=1.05,
+    y=1.0,
+    xanchor="left",
+    yanchor="top",
+    orientation="v",
+    traceorder="normal",
+    itemclick="toggleothers",
+    itemdoubleclick="toggle"
+)
+fig.update_layout(**layout)
 
 # --- Export ---
 export_figure(fig, "silhouette_scores_und_fallzahlen", export_fig_silhouette_plot, export_fig_png)
+
+# (Hinweis: Balkenfarbe wird direkt im Bar-Trace gesetzt)
+
+fig.show(config={"responsive": True})