Verbesserungen: Balken und Sankey implementiert
This commit is contained in:
@ -4,6 +4,9 @@ import os
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# Clear the terminal
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os.system('cls' if os.name == 'nt' else 'clear')
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import sys
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sys.path.append('/Users/jochenhanisch-johannsen/Documents/scripte/ci_template')
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import bibtexparser
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import pandas as pd
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import numpy as np
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@ -21,8 +24,16 @@ import math
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import re
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import subprocess
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export_fig_visual = False # Expot der Visualsierungen gesamt
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theme = "dark" # Optionen: "dark" oder "light"
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# Template
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from ci_template import plotly_template
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plotly_template.set_theme(theme)
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pd.set_option('display.max_columns', None)
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pd.set_option('future.no_silent_downcasting', True)
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bib_filename = "Suchergebnisse.bib"
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export_fig_visual = False
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# Optional: slugify-Funktion
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def slugify(value):
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@ -68,19 +79,11 @@ def export_figure(fig, name, flag, bib_filename=None):
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print("❌ Fehler beim Übertragen:")
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print(e.stderr)
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# Farben definieren
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colors = {
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"background": "#003366", # Hintergrundfarbe
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"text": "#333333", # Textfarbe
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"accent": "#663300", # Akzentfarbe
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"primaryLine": "#660066", # Bildungswirkfaktor
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"secondaryLine": "#cc6600", # Bildungswirkindikator
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"depthArea": "#006666", # Kompetenzmessunsicherheit
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"brightArea": "#66CCCC", # Kompetenzentwicklungsunsicherheit
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"positiveHighlight": "#336600", # Positive Hervorhebung
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"negativeHighlight": "#990000", # Negative Hervorhebung
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"white": "#ffffff" # Weiß
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}
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from ci_template.plotly_template import get_colors, get_plot_styles, get_standard_layout
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# Farben und Plot-Styles zentral aus Template laden
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colors = get_colors()
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plot_styles = get_plot_styles()
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# Liste der Farben, die für die Wörter verwendet werden sollen
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word_colors = [
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@ -267,7 +270,7 @@ def visualize_network(bib_database):
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edge_trace = go.Scatter(
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x=edge_x, y=edge_y,
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line=dict(width=0.5, color=colors['white']),
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line=plot_styles['linie_secondaryLine'],
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hoverinfo='none',
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mode='lines')
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@ -290,6 +293,16 @@ def visualize_network(bib_database):
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tertiary_nodes.append(node_data)
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def create_node_trace(nodes, name, color):
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# Wähle Punktstil je nach color
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if color == colors['primaryLine']:
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marker_style = plot_styles['punkt_primaryLine'].copy()
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elif color == colors['secondaryLine']:
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marker_style = plot_styles['punkt_secondaryLine'].copy()
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elif color == colors['brightArea']:
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marker_style = plot_styles['punkt_brightArea'].copy()
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else:
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marker_style = dict(color=color)
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marker_style['size'] = [n['size'] for n in nodes]
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return go.Scatter(
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x=[n['x'] for n in nodes],
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y=[n['y'] for n in nodes],
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@ -297,13 +310,9 @@ def visualize_network(bib_database):
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text=[n['text'] for n in nodes],
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hovertext=[n['hovertext'] for n in nodes],
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hoverinfo='text',
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marker=dict(
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size=[n['size'] for n in nodes],
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color=color,
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line_width=2
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),
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marker=marker_style,
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textposition="top center",
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textfont=dict(size=12),
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textfont=dict(size=10, color=colors['white']),
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name=name
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)
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@ -311,42 +320,15 @@ def visualize_network(bib_database):
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secondary_trace = create_node_trace(secondary_nodes, "Sekundäre Begriffe", colors['secondaryLine'])
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tertiary_trace = create_node_trace(tertiary_nodes, "Tertiäre Begriffe", colors['brightArea'])
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fig = go.Figure(data=[edge_trace, primary_trace, secondary_trace, tertiary_trace],
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layout=go.Layout(
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title=f'Suchbegriff-Netzwerk nach Relevanz und Semantik (n={sum(fundzahlen.values())}, Stand: {current_date}) | Quelle: {bib_filename.replace(".bib", "")}',
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titlefont_size=16,
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showlegend=True,
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legend=dict(
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bgcolor=colors['background'],
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bordercolor=colors['white'],
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borderwidth=1,
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font=dict(color=colors['white']),
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itemsizing='constant'
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),
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hovermode='closest',
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margin=dict(b=20, l=5, r=5, t=40),
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xaxis=dict(
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range=[x_scale_min, x_scale_max + 1],
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showgrid=True,
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zeroline=True,
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tickmode='linear',
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tick0=x_scale_min,
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dtick=(x_scale_max - x_scale_min) / 4,
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title='Technologische Dimension'
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),
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yaxis=dict(
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range=[y_scale_min, y_scale_max + 1],
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showgrid=True,
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zeroline=True,
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tickmode='linear',
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tick0=y_scale_min,
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dtick=(y_scale_max - y_scale_min) / 4,
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title='Pädagogische Dimension'
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),
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plot_bgcolor=colors['background'],
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paper_bgcolor=colors['background'],
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font=dict(color=colors['white'])
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))
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fig = go.Figure(data=[edge_trace, primary_trace, secondary_trace, tertiary_trace])
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layout = get_standard_layout(
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title=f"Suchbegriff-Netzwerk nach Relevanz und Semantik (n={sum(fundzahlen.values())}, Stand: {current_date})",
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x_title="Technologische Dimension",
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y_title="Pädagogische Dimension"
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)
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layout["margin"] = dict(b=20, l=5, r=5, t=40)
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layout["hovermode"] = "closest"
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fig.update_layout(**layout)
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fig.show()
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export_figure(fig, "visualize_network", export_fig_visualize_network, bib_filename)
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@ -434,26 +416,18 @@ def visualize_tags(bib_database):
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data,
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x='Tag',
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y='Count',
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title=f'Häufigkeit der Suchbegriffe in der Literaturanalyse (n={total_count}, Stand: {current_date}) | Quelle: {bib_filename.replace(".bib", "")}',
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title=f'Häufigkeit der Suchbegriffe in der Literaturanalyse (n={total_count}, Stand: {current_date})',
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labels={'Tag': 'Tag', 'Count': 'Anzahl der Vorkommen'},
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color='Type',
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color_discrete_map=color_map,
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text_auto=True
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)
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# Layout anpassen
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fig.update_layout(
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plot_bgcolor=colors['background'],
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paper_bgcolor=colors['background'],
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font=dict(color=colors['white']),
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margin=dict(l=0, r=0, t=40, b=40),
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autosize=True
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)
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fig.update_traces(
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marker_line_color=colors['white'],
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marker_line_width=1.5
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)
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fig.update_layout(**get_standard_layout(
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title=fig.layout.title.text,
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x_title='Tag',
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y_title='Anzahl der Vorkommen'
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))
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fig.show(config={"responsive": True})
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export_figure(fig, "visualize_tags", export_fig_visualize_tags, bib_filename)
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@ -488,18 +462,13 @@ def visualize_index(bib_database):
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print(f"Häufigkeit Indizes (Gesamtanzahl: {total_count}):")
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print(tabulate(index_data, headers="keys", tablefmt="grid"))
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fig = px.bar(index_data, x='Index', y='Count', title=f'Relevanzschlüssel nach Indexkategorien (n={total_count}, Stand: {current_date}) | Quelle: {bib_filename.replace(".bib", "")}', labels={'Index': 'Index', 'Count': 'Anzahl der Vorkommen'}, text_auto=True)
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fig.update_layout(
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plot_bgcolor=colors['background'],
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paper_bgcolor=colors['background'],
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font=dict(color=colors['white']),
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margin=dict(l=0, r=0, t=40, b=40),
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autosize=True
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)
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fig.update_traces(marker_color=colors['primaryLine'], marker_line_color=colors['white'], marker_line_width=1.5)
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fig = px.bar(index_data, x='Index', y='Count', title=f'Relevanzschlüssel nach Indexkategorien (n={total_count}, Stand: {current_date})', labels={'Index': 'Index', 'Count': 'Anzahl der Vorkommen'}, text_auto=True)
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fig.update_layout(**get_standard_layout(
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title=fig.layout.title.text,
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x_title='Index',
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y_title='Anzahl der Vorkommen'
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))
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fig.update_traces(marker=plot_styles['balken_primaryLine'])
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fig.show(config={"responsive": True})
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export_figure(fig, "visualize_index", export_fig_visualize_index, bib_filename)
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@ -534,18 +503,13 @@ def visualize_research_questions(bib_database):
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print(f"Häufigkeit Forschungsunterfragen (Gesamtanzahl: {total_count}):")
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print(tabulate(rq_data, headers="keys", tablefmt="grid"))
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fig = px.bar(rq_data_df, x='Research_Question', y='Count', title=f'Zuordnung der Literatur zu Forschungsunterfragen (n={total_count}, Stand: {current_date}) | Quelle: {bib_filename.replace(".bib", "")}', labels={'Research_Question': 'Forschungsunterfrage', 'Count': 'Anzahl der Vorkommen'}, text_auto=True)
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fig.update_layout(
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plot_bgcolor=colors['background'],
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paper_bgcolor=colors['background'],
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font=dict(color=colors['white']),
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margin=dict(l=0, r=0, t=40, b=40),
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autosize=True
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)
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fig.update_traces(marker_color=colors['primaryLine'], marker_line_color=colors['white'], marker_line_width=1.5)
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fig = px.bar(rq_data_df, x='Research_Question', y='Count', title=f'Zuordnung der Literatur zu Forschungsunterfragen (n={total_count}, Stand: {current_date})', labels={'Research_Question': 'Forschungsunterfrage', 'Count': 'Anzahl der Vorkommen'}, text_auto=True)
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fig.update_layout(**get_standard_layout(
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title=fig.layout.title.text,
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x_title='Forschungsunterfrage',
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y_title='Anzahl der Vorkommen'
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))
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fig.update_traces(marker=plot_styles['balken_primaryLine'])
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fig.show(config={"responsive": True})
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export_figure(fig, "visualize_research_questions", export_fig_visualize_research_questions, bib_filename)
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@ -575,18 +539,13 @@ def visualize_categories(bib_database):
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print(f"Häufigkeit Kategorien (Gesamtanzahl: {total_count}):")
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print(tabulate(cat_data, headers="keys", tablefmt="grid"))
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fig = px.bar(cat_data_df, x='Category', y='Count', title=f'Textsortenzuordnung der analysierten Quellen (n={total_count}, Stand: {current_date}) | Quelle: {bib_filename.replace(".bib", "")}', labels={'Category': 'Kategorie', 'Count': 'Anzahl der Vorkommen'}, text_auto=True)
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fig.update_layout(
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plot_bgcolor=colors['background'],
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paper_bgcolor=colors['background'],
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font=dict(color=colors['white']),
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margin=dict(l=0, r=0, t=40, b=40),
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autosize=True
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)
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fig.update_traces(marker_color=colors['primaryLine'], marker_line_color=colors['white'], marker_line_width=1.5)
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fig = px.bar(cat_data_df, x='Category', y='Count', title=f'Textsortenzuordnung der analysierten Quellen (n={total_count}, Stand: {current_date})', labels={'Category': 'Kategorie', 'Count': 'Anzahl der Vorkommen'}, text_auto=True)
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fig.update_layout(**get_standard_layout(
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title=fig.layout.title.text,
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x_title='Kategorie',
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y_title='Anzahl der Vorkommen'
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))
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fig.update_traces(marker=plot_styles['balken_primaryLine'])
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fig.show(config={"responsive": True})
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export_figure(fig, "visualize_categories", export_fig_visualize_categories, bib_filename)
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@ -616,24 +575,21 @@ def visualize_time_series(bib_database):
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df,
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x='Year',
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y='Count',
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title=f'Jährliche Veröffentlichungen in der Literaturanalyse (n={sum(year_counts.values())}, Stand: {current_date}) | Quelle: {bib_filename.replace(".bib", "")}',
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title=f'Jährliche Veröffentlichungen in der Literaturanalyse (n={sum(year_counts.values())}, Stand: {current_date})',
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labels={'Year': 'Jahr', 'Count': 'Anzahl der Veröffentlichungen'}
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)
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fig.update_layout(
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plot_bgcolor=colors['background'],
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paper_bgcolor=colors['background'],
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font=dict(color=colors['white']),
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xaxis=dict(
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layout = get_standard_layout(
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title=fig.layout.title.text,
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x_title='Jahr',
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y_title='Anzahl der Veröffentlichungen'
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)
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layout["xaxis"] = dict(
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tickmode='linear',
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dtick=2,
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tick0=min(publication_years)
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),
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margin=dict(l=0, r=0, t=40, b=40),
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autosize=True
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)
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fig.update_traces(line=dict(color=colors['secondaryLine'], width=3))
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fig.update_layout(**layout)
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fig.update_traces(line=plot_styles['linie_secondaryLine'])
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fig.show(config={"responsive": True})
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export_figure(fig, "visualize_time_series", export_fig_visualize_time_series, bib_filename)
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else:
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@ -653,16 +609,13 @@ def visualize_top_authors(bib_database):
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if top_authors:
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df = pd.DataFrame(top_authors, columns=['Author', 'Count'])
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fig = px.bar(df, x='Author', y='Count', title=f'Meistgenannte Autor:innen in der Literaturanalyse (Top {top_n}, n={sum(author_counts.values())}, Stand: {current_date}) | Quelle: {bib_filename.replace(".bib", "")}', labels={'Author': 'Autor', 'Count': 'Anzahl der Werke'}, text_auto=True)
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fig.update_layout(
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plot_bgcolor=colors['background'],
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paper_bgcolor=colors['background'],
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font=dict(color=colors['white']),
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margin=dict(l=0, r=0, t=40, b=40),
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autosize=True
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)
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fig.update_traces(marker_color=colors['primaryLine'], marker_line_color=colors['white'], marker_line_width=1.5)
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fig = px.bar(df, x='Author', y='Count', title=f'Meistgenannte Autor:innen in der Literaturanalyse (Top {top_n}, n={sum(author_counts.values())}, Stand: {current_date})', labels={'Author': 'Autor', 'Count': 'Anzahl der Werke'}, text_auto=True)
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fig.update_layout(**get_standard_layout(
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title=fig.layout.title.text,
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x_title='Autor',
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y_title='Anzahl der Werke'
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))
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fig.update_traces(marker=plot_styles['balken_primaryLine'])
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fig.show(config={"responsive": True})
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export_figure(fig, "visualize_top_authors", export_fig_visualize_top_authors, bib_filename)
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else:
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@ -695,19 +648,16 @@ def visualize_top_publications(bib_database):
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df = pd.DataFrame(publication_data)
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fig = px.bar(df, x='Title', y='Count', title=f'Häufig zitierte Publikationen in der Analyse (Top {top_n}, n={sum(publication_counts.values())}, Stand: {current_date}) | Quelle: {bib_filename.replace(".bib", "")}', labels={'Title': 'Titel', 'Count': 'Anzahl der Nennungen'})
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fig.update_layout(
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plot_bgcolor=colors['background'],
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paper_bgcolor=colors['background'],
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font=dict(color=colors['white']),
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xaxis_tickangle=-45,
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margin=dict(l=0, r=0, t=40, b=40),
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autosize=True
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fig = px.bar(df, x='Title', y='Count', title=f'Häufig zitierte Publikationen in der Analyse (Top {top_n}, n={sum(publication_counts.values())}, Stand: {current_date})', labels={'Title': 'Titel', 'Count': 'Anzahl der Nennungen'})
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layout = get_standard_layout(
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title=fig.layout.title.text,
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x_title='Titel',
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y_title='Anzahl der Nennungen'
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)
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fig.update_traces(marker_color=colors['primaryLine'], marker_line_color=colors['white'], marker_line_width=1.5)
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layout["xaxis"] = layout.get("xaxis", {})
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layout["xaxis"]["tickangle"] = -45
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fig.update_layout(**layout)
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fig.update_traces(marker=plot_styles['balken_primaryLine'])
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fig.show(config={"responsive": True})
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export_figure(fig, "visualize_top_publications", export_fig_visualize_top_publications, bib_filename)
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@ -823,7 +773,7 @@ def create_path_diagram(data):
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node=dict(
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pad=15,
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thickness=20,
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line=dict(color=colors['white'], width=0.5),
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line=dict(color="black", width=0.5),
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label=labels,
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color=node_colors
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),
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@ -833,14 +783,13 @@ def create_path_diagram(data):
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value=values
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)
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)])
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fig.update_layout(
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title_text=f'Kategorischer Analysepfad der Literatur (n={len(data)}, Stand: {current_date}) | Quelle: {bib_filename.replace(".bib", "")}',
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font=dict(size=10, color=colors['white']),
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plot_bgcolor=colors['background'],
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paper_bgcolor=colors['background']
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layout = get_standard_layout(
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title=f'Kategorischer Analysepfad der Literatur (n={len(data)}, Stand: {current_date})',
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x_title='',
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y_title=''
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)
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layout["font"] = dict(size=10, color=colors['white'])
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fig.update_layout(**layout)
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fig.show()
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export_figure(fig, "create_path_diagram", export_fig_create_path_diagram, bib_filename)
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@ -950,30 +899,26 @@ def create_sankey_diagram(bib_database):
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# Sankey-Diagramm erstellen
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fig = go.Figure(go.Sankey(
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node=dict(
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pad=15,
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thickness=20,
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line=dict(color="black", width=0.5),
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**plot_styles["sankey_node"],
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||||
label=node_labels,
|
||||
color=node_colors
|
||||
),
|
||||
link=dict(
|
||||
**plot_styles["sankey_link"],
|
||||
source=sources,
|
||||
target=targets,
|
||||
value=values,
|
||||
hoverinfo='all', # Zeigt detaillierte Infos bei Mouseover an
|
||||
color=colors['accent']
|
||||
value=values
|
||||
)
|
||||
))
|
||||
|
||||
# Layout anpassen
|
||||
fig.update_layout(
|
||||
title_text=f"Flussdiagramm der Literaturselektion (Stichprobe: n={sample_size}, Stand: {current_date}) | Quelle: {bib_filename.replace('.bib', '')}",
|
||||
font_size=12, # Größere Schriftgröße für bessere Lesbarkeit
|
||||
plot_bgcolor=colors['background'],
|
||||
paper_bgcolor=colors['background'],
|
||||
font=dict(color=colors['white'])
|
||||
layout = get_standard_layout(
|
||||
title=f"Flussdiagramm der Literaturselektion (Stichprobe: n={sample_size}, Stand: {current_date})",
|
||||
x_title='',
|
||||
y_title=''
|
||||
)
|
||||
|
||||
layout["font"] = layout.get("font", {})
|
||||
layout["font"]["size"] = 12
|
||||
fig.update_layout(**layout)
|
||||
fig.show()
|
||||
export_figure(fig, "create_sankey_diagram", export_fig_create_sankey_diagram, bib_filename)
|
||||
|
||||
@ -1073,35 +1018,29 @@ def visualize_sources_status(bib_database):
|
||||
))
|
||||
|
||||
fig = go.Figure()
|
||||
|
||||
fig.add_trace(go.Bar(
|
||||
x=tags,
|
||||
y=analysiert_values,
|
||||
name='Analysiert',
|
||||
marker=dict(color=analysiert_colors)
|
||||
))
|
||||
|
||||
fig.add_trace(go.Bar(
|
||||
x=tags,
|
||||
y=nicht_analysiert_values,
|
||||
name='Nicht-Analysiert',
|
||||
marker=dict(color=colors['primaryLine'])
|
||||
marker=plot_styles['balken_primaryLine']
|
||||
))
|
||||
|
||||
fig.update_layout(
|
||||
barmode='stack',
|
||||
title=f'Analyse- und Stichprobenstatus je Suchordner (n={sum(counts["Identifiziert"] for counts in source_data.values())}, Stand: {current_date}) | Quelle: {bib_filename.replace(".bib", "")}',
|
||||
xaxis_title='Suchbegriffsordner',
|
||||
yaxis_title='Anzahl der Quellen',
|
||||
plot_bgcolor=colors['background'],
|
||||
paper_bgcolor=colors['background'],
|
||||
font=dict(color=colors['white']),
|
||||
xaxis=dict(
|
||||
layout = get_standard_layout(
|
||||
title=f'Analyse- und Stichprobenstatus je Suchordner (n={sum(counts["Identifiziert"] for counts in source_data.values())}, Stand: {current_date})',
|
||||
x_title='Suchbegriffsordner',
|
||||
y_title='Anzahl der Quellen'
|
||||
)
|
||||
layout["barmode"] = "stack"
|
||||
layout["xaxis"] = dict(
|
||||
categoryorder='array',
|
||||
categoryarray=search_folder_tags
|
||||
)
|
||||
)
|
||||
|
||||
fig.update_layout(**layout)
|
||||
fig.show()
|
||||
export_figure(fig, "visualize_sources_status", export_fig_visualize_sources_status, bib_filename)
|
||||
|
||||
|
||||
Reference in New Issue
Block a user