promotion/literaturanalyse
Archived
1
1
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Promotion: Verbesserung Hover in der Korrelationsanalyse

Browse Source
This commit is contained in:
Jochen Hanisch-Johannsen
2025-10-11 18:21:17 +02:00
parent 4e7e479304
commit 835f58de3f
1 changed files with 21 additions and 4 deletions
Download Patch File Download Diff File Expand all files Collapse all files

25
analyse_korrelation.py
View File

@ -288,6 +288,7 @@ def visualize_bivariate_correlation(df, x_terms, y_terms, title, x_label, y_labe
abs_corr = abs(corr)
significance = 'Signifikant' if p_value < 0.05 else 'Nicht signifikant'
hover_color = colors['brightArea'] if p_value < 0.05 else colors['depthArea']
cooccurrence_count = int(((df[x_term] == 1) & (df[y_term] == 1)).sum())
correlations.append({
'x_term': x_term,
'y_term': y_term,
@ -296,6 +297,8 @@ def visualize_bivariate_correlation(df, x_terms, y_terms, title, x_label, y_labe
'p_value': p_value,
'significance': significance,
'hover_color': hover_color,
'n_observations': int(len(x_valid)),
'cooccurrence_count': cooccurrence_count,
'interpretation': (
f"Die Korrelation zwischen '{x_term}' und '{y_term}' beträgt {corr:.2f}. "
f"p-Wert: {p_value:.3e} ({significance})"
@ -361,12 +364,25 @@ def visualize_bivariate_correlation(df, x_terms, y_terms, title, x_label, y_labe
line=dict(width=1, color=colors['background'])
),
hovertemplate=(
'<b>%{customdata[0]}</b><br>'
'<b>%{customdata[0]}</b> ↔ <b>%{customdata[1]}</b><br>'
'Korrelation: %{marker.color:.2f}<br>'
'p-Wert: %{customdata[1]:.3e}<br>'
'Signifikanz: %{customdata[2]}'
'p-Wert: %{customdata[3]:.3e}<br>'
'Signifikanz: %{customdata[4]}<br>'
'Stichprobe (n): %{customdata[5]}<br>'
'Gemeinsame Treffer: %{customdata[6]}<br>'
'%{customdata[7]}'
'<extra></extra>'
),
customdata=correlation_df[['x_term', 'p_value', 'significance']].to_numpy()
customdata=np.array(list(zip(
correlation_df['x_term'],
correlation_df['y_term'],
correlation_df['correlation'],
correlation_df['p_value'],
correlation_df['significance'],
correlation_df['n_observations'],
correlation_df['cooccurrence_count'],
correlation_df['interpretation']
)), dtype=object)
)
# Standardlayout verwenden und ggf. ergänzen, Margin dynamisch für Responsivität
@ -376,6 +392,7 @@ def visualize_bivariate_correlation(df, x_terms, y_terms, title, x_label, y_labe
x_title=x_label,
y_title=y_label
),
hovermode='closest',
xaxis=dict(
tickangle=-45,
automargin=True