Update Fedora state: 2026-04-29 11:50

dot_config/private_Code/User/History/7da6e0fb/SA9R.py

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+import argparse
+import os
+from pathlib import Path
+import logging
+
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from scipy import stats
+import statsmodels.api as sm
+import statsmodels.formula.api as smf
+
+
+logging.basicConfig(level=logging.INFO, format='%(levelname)s: %(message)s')
+
+
+def load_data(path):
+    df = pd.read_csv(path)
+    logging.info("Loaded %d rows from %s", len(df), path)
+    return df
+
+
+def prepare_data(df):
+    # Ensure required columns exist
+    required = {'Participant_ID', 'Happiness', 'Calendar_Adherence', 'Cleanliness_Adherence', 'Punctuality_Adherence'}
+    missing = required - set(df.columns)
+    if missing:
+        raise KeyError(f"Missing required columns: {missing}")
+
+    # Normalize adherence to boolean (Yes/No or True/False)
+    for col in ['Calendar_Adherence', 'Cleanliness_Adherence', 'Punctuality_Adherence']:
+        df[col] = df[col].astype(str).str.strip().str.lower().map({'yes': True, 'no': False, 'true': True, 'false': False})
+
+    # Count habits per row
+    df['Habits_Count'] = (
+        df[['Calendar_Adherence', 'Cleanliness_Adherence', 'Punctuality_Adherence']].fillna(False).astype(int).sum(axis=1)
+    )
+
+    # Coerce Happiness to numeric and drop rows without Happiness
+    df['Happiness'] = pd.to_numeric(df['Happiness'], errors='coerce')
+    before = len(df)
+    df = df.dropna(subset=['Happiness'])
+    logging.info('Dropped %d rows without numeric Happiness', before - len(df))
+
+    return df
+
+
+def descriptive_stats(df):
+    print('Dataset shape:', df.shape)
+    print('\nOverall summary:')
+    print(df['Happiness'].describe())
+
+    print('\nAverage happiness by number of habits completed:')
+    print(df.groupby('Habits_Count')['Happiness'].agg(['mean', 'count', 'std']).round(3))
+
+    print('\nMedian happiness by habits:')
+    print(df.groupby('Habits_Count')['Happiness'].median())
+
+    # Correlations
+    print('\nPearson correlation between Habits_Count and Happiness:')
+    print(df[['Habits_Count', 'Happiness']].corr().round(3))
+
+    print('\nPoint-biserial correlation (each habit vs happiness):')
+    for habit in ['Calendar_Adherence', 'Cleanliness_Adherence', 'Punctuality_Adherence']:
+        mask = ~df[habit].isna()
+        if mask.sum() == 0:
+            print(f'{habit:22}  (no data)')
+            continue
+        r, p = stats.pointbiserialr(df.loc[mask, habit].astype(int), df.loc[mask, 'Happiness'])
+        print(f"{habit:22}  r = {r:.3f}   p = {p:.4f}")
+
+
+def cohen_d(x, y):
+    # Cohen's d for two independent samples
+    nx, ny = len(x), len(y)
+    dof = nx + ny - 2
+    pooled_sd = np.sqrt(((nx - 1) * x.std(ddof=1) ** 2 + (ny - 1) * y.std(ddof=1) ** 2) / dof)
+    return (x.mean() - y.mean()) / pooled_sd
+
+
+def run_ols(df):
+    X = sm.add_constant(df['Habits_Count'])
+    y = df['Happiness']
+    model = sm.OLS(y, X).fit()
+    print('\nSimple OLS regression: Happiness ~ Habits_Count')
+    print(model.summary())
+    return model
+
+
+def run_mixedlm(df):
+    # Random intercept for Participant_ID
+    try:
+        md = smf.mixedlm('Happiness ~ Habits_Count', data=df, groups=df['Participant_ID'])
+        mdf = md.fit(reml=False)
+        print('\nMixed-effects model (random intercept by Participant_ID):')
+        print(mdf.summary())
+        return mdf
+    except Exception as e:
+        logging.warning('MixedLM failed: %s', e)
+        return None
+
+
+def make_plots(df, outdir, show_plots=False):
+    outdir = Path(outdir)
+    outdir.mkdir(parents=True, exist_ok=True)
+    sns.set_style('whitegrid')
+
+    # Boxplot by Habits_Count
+    plt.figure(figsize=(9, 6))
+    sns.boxplot(x='Habits_Count', y='Happiness', data=df, palette='viridis')
+    plt.title('Daily Happiness by Number of Habits Completed')
+    plt.xlabel('Number of habits followed (0–3)')
+    plt.ylabel('Happiness (1–10)')
+    f1 = outdir / 'happiness_by_habits_box.png'
+    plt.tight_layout()
+    plt.savefig(f1)
+    if show_plots:
+        plt.show()
+    plt.close()
+
+    # Violin / jitter + regression
+    plt.figure(figsize=(9, 6))
+    sns.violinplot(x='Habits_Count', y='Happiness', data=df, inner=None, palette='muted')
+    sns.stripplot(x='Habits_Count', y='Happiness', data=df, color='k', alpha=0.3, jitter=0.15)
+    plt.title('Happiness distribution by Habits Completed')
+    f2 = outdir / 'happiness_by_habits_violin.png'
+    plt.tight_layout()
+    plt.savefig(f2)
+    if show_plots:
+        plt.show()
+    plt.close()
+
+    # Participant average bar
+    participant_avg = df.groupby('Participant_ID')['Happiness'].mean().sort_values()
+    plt.figure(figsize=(12, 5))
+    sns.barplot(x=participant_avg.index.astype(str), y=participant_avg.values, palette='coolwarm')
+    plt.axhline(df['Happiness'].mean(), color='black', linestyle='--', alpha=0.6)
+    plt.xticks(rotation=45)
+    plt.title('Average Happiness per Participant (sorted)')
+    f3 = outdir / 'participant_avg_happiness.png'
+    plt.tight_layout()
+    plt.savefig(f3)
+    if show_plots:
+        plt.show()
+    plt.close()
+
+    # Scatter with linear fit
+    plt.figure(figsize=(9, 6))
+    sns.regplot(x='Habits_Count', y='Happiness', data=df, x_jitter=0.18, scatter_kws={'alpha': 0.4})
+    plt.title('Happiness vs Number of Habits Completed (with linear fit)')
+    f4 = outdir / 'happiness_vs_habits_regression.png'
+    plt.tight_layout()
+    plt.savefig(f4)
+    if show_plots:
+        plt.show()
+    plt.close()
+
+    logging.info('Saved plots to %s', outdir)
+
+
+def main(args):
+    df = load_data(args.data)
+    df = prepare_data(df)
+
+    descriptive_stats(df)
+
+    # Effect size example: compare 0 vs 3
+    group0 = df[df['Habits_Count'] == 0]['Happiness']
+    group3 = df[df['Habits_Count'] == 3]['Happiness']
+    if len(group0) > 1 and len(group3) > 1:
+        d = cohen_d(group3, group0)
+        print(f"\nCohen's d (3 habits vs 0 habits) = {d:.3f}")
+
+    # Models
+    run_ols(df)
+    run_mixedlm(df)
+
+    # Plots
+    make_plots(df, args.outdir, show_plots=args.show)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Improved data analysis for organization_happiness_study_data.csv')
+    parser.add_argument('--data', type=str, default='organization_happiness_study_data.csv', help='CSV data path')
+    parser.add_argument('--outdir', type=str, default='plots', help='Directory to save plots')
+    parser.add_argument('--show', action='store_true', help='Show plots interactively')
+    args = parser.parse_args()
+    main(args)