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Promotion: Bibliothek aktualisiert und in Netzwerkanalyse prozentualen Anteil hinzugefügt

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Jochen Hanisch-Johannsen
2025-10-19 16:37:27 +02:00
parent 127fc78b81
commit 02ec176582
2 changed files with 2810 additions and 2585 deletions
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5123
Bibliothek/02-01 Suchergebnisse.bib
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272
analyse_netzwerk.py
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@ -314,11 +314,15 @@ def visualize_network(bib_database):
secondary_nodes = []
tertiary_nodes = []
total_fundzahlen = sum(fundzahlen.values())
for node in G.nodes():
color = G.nodes[node]['color']
size = math.log(G.nodes[node].get('size', 10) + 1) * 10
x, y = pos[node]
hovertext = f"{node}<br>Anzahl Funde: {fundzahlen.get(node, 0)}"
count = fundzahlen.get(node, 0)
percentage = (count / total_fundzahlen * 100) if total_fundzahlen else 0
hovertext = f"{node}<br>Anzahl Funde: {count}<br>Anteil: {percentage:.1f}%"
node_data = dict(x=x, y=y, text=node, size=size, hovertext=hovertext)
if color == colors['primaryLine']:
primary_nodes.append(node_data)
@ -359,7 +363,7 @@ def visualize_network(bib_database):
fig = go.Figure(data=[edge_trace, primary_trace, secondary_trace, tertiary_trace])
layout = get_standard_layout(
title=f"Suchbegriff-Netzwerk nach Relevanz und Semantik (n={sum(fundzahlen.values())}, Stand: {current_date})",
title=f"Suchbegriff-Netzwerk nach Relevanz und Semantik (n={total_fundzahlen}, Stand: {current_date})",
x_title="Technologische Dimension",
y_title="Pädagogische Dimension"
)
@ -432,16 +436,25 @@ def visualize_tags(bib_database):
tag_counts[tag] += 1
# Daten für Visualisierung aufbereiten
data = [
{'Tag': tag, 'Count': count, 'Type': tag.split(':')[1].lower()}
data_rows = [
{
'Tag': tag,
'Count': count,
'Type': tag.split(':')[1].lower()
}
for tag, count in tag_counts.items()
if count > 0
]
if not data:
if not data_rows:
print("Warnung: Keine Tags gefunden, die den Suchkriterien entsprechen.")
return
df = pd.DataFrame(data_rows)
df['TypeLabel'] = df['Type'].str.replace('-', ' ').str.title()
total_count = df['Count'].sum()
df['Percentage'] = df['Count'] / total_count * 100 if total_count else 0
# Farbzuordnung
color_map = {
'zeitschriftenartikel': colors['primaryLine'],
@ -453,16 +466,16 @@ def visualize_tags(bib_database):
}
# Visualisierung erstellen
total_count = sum(tag_counts.values())
fig = px.bar(
data,
df,
x='Tag',
y='Count',
title=f'Häufigkeit der Suchbegriffe in der Literaturanalyse (n={total_count}, Stand: {current_date})',
labels={'Tag': 'Tag', 'Count': 'Anzahl der Vorkommen'},
color='Type',
color_discrete_map=color_map,
text_auto=True
text_auto=True,
custom_data=['TypeLabel', 'Percentage']
)
layout = get_standard_layout(
@ -478,6 +491,14 @@ def visualize_tags(bib_database):
layout["xaxis"]["automargin"] = True
layout["autosize"] = True
fig.update_layout(**layout)
fig.update_traces(
hovertemplate=(
"<b>%{x}</b><br>"
"Typ: %{customdata[0]}<br>"
"Anzahl: %{y}<br>"
"Anteil: %{customdata[1]:.1f}%<extra></extra>"
)
)
fig.show(config={"responsive": True})
export_figure_local(fig, "visualize_tags", export_fig_visualize_tags)
@ -508,11 +529,21 @@ def visualize_index(bib_database):
index_data = [{'Index': index, 'Count': count} for index, count in index_counts.items()]
index_data = sorted(index_data, key=lambda x: x['Count'], reverse=True)
total_count = sum(index_counts.values())
index_df = pd.DataFrame(index_data)
total_count = index_df['Count'].sum()
index_df['Percentage'] = index_df['Count'] / total_count * 100 if total_count else 0
print(f"Häufigkeit Indizes (Gesamtanzahl: {total_count}):")
print(tabulate(index_data, headers="keys", tablefmt="grid"))
print(tabulate(index_df.to_dict('records'), headers="keys", tablefmt="grid"))
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)
fig = px.bar(
index_df,
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,
custom_data=['Percentage']
)
layout = get_standard_layout(
title=fig.layout.title.text,
x_title='Index',
@ -527,6 +558,13 @@ def visualize_index(bib_database):
layout["autosize"] = True
fig.update_layout(**layout)
fig.update_traces(marker=plot_styles['balken_primaryLine'])
fig.update_traces(
hovertemplate=(
"<b>%{x}</b><br>"
"Anzahl: %{y}<br>"
"Anteil: %{customdata[0]:.1f}%<extra></extra>"
)
)
fig.show(config={"responsive": True})
export_figure_local(fig, "visualize_index", export_fig_visualize_index)
@ -555,13 +593,22 @@ def visualize_research_questions(bib_database):
rq_data = [{'Research_Question': research_questions[keyword], 'Count': count} for keyword, count in rq_counts.items()]
rq_data = sorted(rq_data, key=lambda x: x['Count'], reverse=True)
rq_data_df = pd.DataFrame(rq_data)
rq_data_df = pd.DataFrame(rq_data, columns=['Research_Question', 'Count'])
total_count = rq_data_df['Count'].sum()
rq_data_df['Percentage'] = rq_data_df['Count'] / total_count * 100 if total_count else 0
print(f"Häufigkeit Forschungsunterfragen (Gesamtanzahl: {total_count}):")
print(tabulate(rq_data, headers="keys", tablefmt="grid"))
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)
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,
custom_data=['Percentage']
)
layout = get_standard_layout(
title=fig.layout.title.text,
x_title='Forschungsunterfrage',
@ -576,6 +623,13 @@ def visualize_research_questions(bib_database):
layout["autosize"] = True
fig.update_layout(**layout)
fig.update_traces(marker=plot_styles['balken_primaryLine'])
fig.update_traces(
hovertemplate=(
"<b>%{x}</b><br>"
"Anzahl: %{y}<br>"
"Anteil: %{customdata[0]:.1f}%<extra></extra>"
)
)
fig.show(config={"responsive": True})
export_figure_local(fig, "visualize_research_questions", export_fig_visualize_research_questions)
@ -599,13 +653,22 @@ def visualize_categories(bib_database):
cat_data = [{'Category': categories[keyword], 'Count': count} for keyword, count in cat_counts.items()]
cat_data = sorted(cat_data, key=lambda x: x['Count'], reverse=True)
cat_data_df = pd.DataFrame(cat_data)
cat_data_df = pd.DataFrame(cat_data, columns=['Category', 'Count'])
total_count = cat_data_df['Count'].sum()
cat_data_df['Percentage'] = cat_data_df['Count'] / total_count * 100 if total_count else 0
print(f"Häufigkeit Kategorien (Gesamtanzahl: {total_count}):")
print(tabulate(cat_data, headers="keys", tablefmt="grid"))
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)
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,
custom_data=['Percentage']
)
layout = get_standard_layout(
title=fig.layout.title.text,
x_title='Kategorie',
@ -620,6 +683,13 @@ def visualize_categories(bib_database):
layout["autosize"] = True
fig.update_layout(**layout)
fig.update_traces(marker=plot_styles['balken_primaryLine'])
fig.update_traces(
hovertemplate=(
"<b>%{x}</b><br>"
"Anzahl: %{y}<br>"
"Anteil: %{customdata[0]:.1f}%<extra></extra>"
)
)
fig.show(config={"responsive": True})
export_figure_local(fig, "visualize_categories", export_fig_visualize_categories)
@ -667,6 +737,7 @@ def plot_relevance_distribution(df, title, x_title, export_flag, filename):
return
total_count = df['Count'].sum()
df['Percentage'] = df['Count'] / total_count * 100 if total_count else 0
fig = px.bar(
df,
x='Kategorie',
@ -676,6 +747,7 @@ def plot_relevance_distribution(df, title, x_title, export_flag, filename):
category_orders={'Relevanzstufe': [RELEVANCE_LEVEL_LABELS[level] for level in RELEVANCE_LEVELS]},
title=f"{title} (n={total_count}, Stand: {current_date})",
labels={'Kategorie': x_title, 'Count': 'Anzahl', 'Relevanzstufe': 'Relevanzstufe'},
custom_data=['Relevanzstufe', 'Percentage']
)
layout = get_standard_layout(
@ -692,6 +764,14 @@ def plot_relevance_distribution(df, title, x_title, export_flag, filename):
layout['xaxis']['automargin'] = True
layout['autosize'] = True
fig.update_layout(**layout)
fig.update_traces(
hovertemplate=(
"<b>%{x}</b><br>"
"Relevanzstufe: %{customdata[0]}<br>"
"Anzahl: %{y}<br>"
"Anteil: %{customdata[1]:.1f}%<extra></extra>"
)
)
fig.show(config={"responsive": True})
export_figure_local(fig, filename, export_flag)
@ -798,13 +878,16 @@ def visualize_time_series(bib_database):
if publication_years:
year_counts = Counter(publication_years)
df = pd.DataFrame(year_counts.items(), columns=['Year', 'Count']).sort_values('Year')
total_publications = df['Count'].sum()
df['Percentage'] = df['Count'] / total_publications * 100 if total_publications else 0
fig = px.line(
df,
x='Year',
y='Count',
title=f'Jährliche Veröffentlichungen in der Literaturanalyse (n={sum(year_counts.values())}, Stand: {current_date})',
labels={'Year': 'Jahr', 'Count': 'Anzahl der Veröffentlichungen'}
labels={'Year': 'Jahr', 'Count': 'Anzahl der Veröffentlichungen'},
custom_data=['Percentage']
)
layout = get_standard_layout(
title=fig.layout.title.text,
@ -821,6 +904,13 @@ def visualize_time_series(bib_database):
layout["autosize"] = True
fig.update_layout(**layout)
fig.update_traces(line=plot_styles['linie_primaryLine'])
fig.update_traces(
hovertemplate=(
"<b>%{x}</b><br>"
"Anzahl: %{y}<br>"
"Anteil: %{customdata[0]:.1f}%<extra></extra>"
)
)
fig.show(config={"responsive": True})
export_figure_local(fig, "visualize_time_series", export_fig_visualize_time_series)
else:
@ -839,8 +929,18 @@ def visualize_top_authors(bib_database):
top_authors = Counter(author_counts).most_common(top_n)
if top_authors:
df = pd.DataFrame(top_authors, columns=['Author', 'Count'])
overall_total = sum(author_counts.values())
df['Percentage'] = df['Count'] / overall_total * 100 if overall_total else 0
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)
fig = px.bar(
df,
x='Author',
y='Count',
title=f'Meistgenannte Autor:innen in der Literaturanalyse (Top {top_n}, n={overall_total}, Stand: {current_date})',
labels={'Author': 'Autor', 'Count': 'Anzahl der Werke'},
text_auto=True,
custom_data=['Percentage']
)
layout = get_standard_layout(
title=fig.layout.title.text,
x_title='Autor',
@ -855,6 +955,13 @@ def visualize_top_authors(bib_database):
layout["autosize"] = True
fig.update_layout(**layout)
fig.update_traces(marker=plot_styles['balken_primaryLine'])
fig.update_traces(
hovertemplate=(
"<b>%{x}</b><br>"
"Anzahl: %{y}<br>"
"Anteil: %{customdata[0]:.1f}%<extra></extra>"
)
)
fig.show(config={"responsive": True})
export_figure_local(fig, "visualize_top_authors", export_fig_visualize_top_authors)
else:
@ -941,6 +1048,7 @@ def create_path_diagram(data):
sources = []
targets = []
values = []
node_counts = Counter()
color_map = {
'zeitschriftenartikel': colors['primaryLine'],
'konferenz-paper': colors['secondaryLine'],
@ -964,8 +1072,19 @@ def create_path_diagram(data):
sources.extend([fu_idx, category_idx, index_idx])
targets.extend([category_idx, index_idx, type_idx])
values.extend([1, 1, 1])
node_counts.update([entry['FU'], entry['Category'], entry['Index'], entry['Type']])
node_colors = [color_map.get(label, colors['primaryLine']) for label in labels]
total_paths = len(data)
total_flows = sum(values)
node_percentages = [
node_counts.get(label, 0) / total_paths * 100 if total_paths else 0
for label in labels
]
link_percentages = [
value / total_flows * 100 if total_flows else 0
for value in values
]
fig = go.Figure(data=[go.Sankey(
node=dict(
@ -973,12 +1092,24 @@ def create_path_diagram(data):
thickness=20,
line=dict(color="black", width=0.5),
label=labels,
color=node_colors
color=node_colors,
customdata=node_percentages,
hovertemplate=(
"%{label}<br>"
"Anzahl: %{value}<br>"
"Anteil der Pfade: %{customdata:.1f}%<extra></extra>"
)
),
link=dict(
source=sources,
target=targets,
value=values
value=values,
customdata=link_percentages,
hovertemplate=(
"%{source.label} → %{target.label}<br>"
"Anzahl: %{value}<br>"
"Anteil der Verbindungen: %{customdata:.1f}%<extra></extra>"
)
)
)])
layout = get_standard_layout(
@ -1096,22 +1227,54 @@ def create_sankey_diagram(bib_database):
colors['positiveHighlight'] # Ausgewählte Quellen
]
node_values = [
initial_sources,
screened_sources,
quality_sources,
relevance_sources,
thematic_sources,
recent_sources,
classic_sources,
selected_sources
]
node_percentages = [
value / initial_sources * 100 if initial_sources else 0
for value in node_values
]
link_percentages = [
value / initial_sources * 100 if initial_sources else 0
for value in values
]
# Sankey-Diagramm erstellen
node_config = {
**plot_styles["sankey_node"],
"label": node_labels,
"color": node_colors
"color": node_colors,
"customdata": node_percentages,
"hovertemplate": (
"%{label}<br>"
"Anzahl: %{value}<br>"
"Anteil an Ausgangsmenge: %{customdata:.1f}%<extra></extra>"
)
}
# Remove any invalid 'font' key if present
node_config.pop("font", None)
link_config = {
**plot_styles["sankey_link"],
"source": sources,
"target": targets,
"value": values,
"customdata": link_percentages,
"hovertemplate": (
"%{source.label} → %{target.label}<br>"
"Anzahl: %{value}<br>"
"Anteil an Ausgangsmenge: %{customdata:.1f}%<extra></extra>"
)
}
fig = go.Figure(go.Sankey(
node=node_config,
link=dict(
**plot_styles["sankey_link"],
source=sources,
target=targets,
value=values
)
link=link_config
))
# Layout anpassen
layout = get_standard_layout(
@ -1224,21 +1387,45 @@ def visualize_sources_status(bib_database):
tablefmt='grid'
))
total_identifiziert = sum(counts["Identifiziert"] for counts in source_data.values())
analysiert_percentages = [
value / total_identifiziert * 100 if total_identifiziert else 0
for value in analysiert_values
]
nicht_analysiert_percentages = [
value / total_identifiziert * 100 if total_identifiziert else 0
for value in nicht_analysiert_values
]
fig = go.Figure()
fig.add_trace(go.Bar(
x=tags,
y=analysiert_values,
name='Analysiert',
marker=dict(color=analysiert_colors)
marker=dict(color=analysiert_colors),
customdata=analysiert_percentages,
hovertemplate=(
"<b>%{x}</b><br>"
"Status: Analysiert<br>"
"Anzahl: %{y}<br>"
"Anteil: %{customdata:.1f}%<extra></extra>"
)
))
fig.add_trace(go.Bar(
x=tags,
y=nicht_analysiert_values,
name='Nicht-Analysiert',
marker=plot_styles['balken_primaryLine']
marker=plot_styles['balken_primaryLine'],
customdata=nicht_analysiert_percentages,
hovertemplate=(
"<b>%{x}</b><br>"
"Status: Nicht-Analysiert<br>"
"Anzahl: %{y}<br>"
"Anteil: %{customdata:.1f}%<extra></extra>"
)
))
layout = get_standard_layout(
title=f'Analyse- und Stichprobenstatus je Suchordner (n={sum(counts["Identifiziert"] for counts in source_data.values())}, Stand: {current_date})',
title=f'Analyse- und Stichprobenstatus je Suchordner (n={total_identifiziert}, Stand: {current_date})',
x_title='Suchbegriffsordner',
y_title='Anzahl der Quellen'
)
@ -1341,8 +1528,8 @@ def visualize_languages(bib_database):
color='Gruppe',
color_discrete_map=color_discrete_map,
title=f'Sprachverteilung der analysierten Quellen (n={sum(norm_counts.values())}, Stand: {current_date})',
hover_data=["Sprache", "Gruppe", "Anzahl", "Anteil (%)"],
barmode="stack"
barmode="stack",
custom_data=['Gruppe', 'Anteil (%)']
)
layout = get_standard_layout(
@ -1357,6 +1544,14 @@ def visualize_languages(bib_database):
# Ergänzung: Y-Achse logarithmisch skalieren
layout["yaxis_type"] = "log"
fig.update_layout(**layout)
fig.update_traces(
hovertemplate=(
"<b>%{x}</b><br>"
"Sprachgruppe: %{customdata[0]}<br>"
"Anzahl: %{y}<br>"
"Anteil: %{customdata[1]:.2f}%<extra></extra>"
)
)
fig.show(config={"responsive": True})
# Tabelle ausgeben
print(tabulate(df.sort_values("Anzahl", ascending=False), headers="keys", tablefmt="grid", showindex=False))
@ -1410,6 +1605,8 @@ def visualize_language_entrytypes(bib_database):
grouped.rename(columns={'ENTRYTYPE': 'Eintragstyp'}, inplace=True)
# Anteil innerhalb Sprache (%)
grouped["Anteil innerhalb Sprache (%)"] = grouped.groupby("Sprache")["Anzahl"].transform(lambda x: (x / x.sum() * 100).round(2))
total_entrytypes = grouped['Anzahl'].sum()
grouped["Anteil Gesamt (%)"] = grouped['Anzahl'] / total_entrytypes * 100 if total_entrytypes else 0
# Mapping Eintragstyp zu Typgruppe
eintragstyp_gruppen = {
@ -1446,7 +1643,8 @@ def visualize_language_entrytypes(bib_database):
barmode="group",
title=f'Verteilung der Eintragstypen pro Sprache (n={len(df)}, Stand: {current_date})',
text='Anzahl',
labels={'Sprache': 'Sprache', 'Eintragstyp': 'Eintragstyp', 'Anzahl': 'Anzahl', 'Typgruppe': 'Typgruppe'}
labels={'Sprache': 'Sprache', 'Eintragstyp': 'Eintragstyp', 'Anzahl': 'Anzahl', 'Typgruppe': 'Typgruppe'},
custom_data=['Eintragstyp', 'Typgruppe', 'Anteil Gesamt (%)', 'Anteil innerhalb Sprache (%)']
)
layout = get_standard_layout(
title=fig.layout.title.text,
@ -1460,6 +1658,16 @@ def visualize_language_entrytypes(bib_database):
# Ergänzung: Y-Achse logarithmisch skalieren
layout["yaxis_type"] = "log"
fig.update_layout(**layout)
fig.update_traces(
hovertemplate=(
"<b>%{x}</b><br>"
"Eintragstyp: %{customdata[0]}<br>"
"Typgruppe: %{customdata[1]}<br>"
"Anzahl: %{y}<br>"
"Anteil gesamt: %{customdata[2]:.2f}%<br>"
"Anteil innerhalb Sprache: %{customdata[3]:.2f}%<extra></extra>"
)
)
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_local(fig, "visualize_language_entrytypes", export_fig_visualize_languages)