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

dot_config/private_Code/User/History/6b1c27e9/entries.json

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+{"version":1,"resource":"file:///home/breadway/Documents/Year%2010/Year%2010/Psychology/run_and_verify.py","entries":[{"id":"qMUb.py","source":"Chat Edit: 'can you ensure the data shows an upward trend in happiness as the study goes on, and in direct correlation with the habits completed by that participant? at the moment, the intervention group is happier after a single day.'","timestamp":1774347459417}]}

dot_config/private_Code/User/History/6b1c27e9/qMUb.py

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+#!/usr/bin/env python3
+"""Generate new data and display sample showing upward trend"""
+import subprocess
+import pandas as pd
+
+# Run data generator
+result = subprocess.run(['python3', 'Data Gen.py'], capture_output=True, text=True)
+print(result.stdout)
+if result.stderr:
+    print("Errors:", result.stderr)
+
+# Load and display trend analysis
+df = pd.read_csv('organization_happiness_study_data.csv')
+df['Habits_Count'] = (
+    (df['Calendar_Adherence'] == 'Yes').astype(int) +
+    (df['Cleanliness_Adherence'] == 'Yes').astype(int) +
+    (df['Punctuality_Adherence'] == 'Yes').astype(int)
+)
+
+intervention = df[df['Group'] == 'Intervention']
+control = df[df['Group'] == 'Control']
+
+print("\n" + "="*70)
+print("UPWARD TREND ANALYSIS")
+print("="*70)
+
+print("\n[INTERVENTION GROUP] - Should show upward trend")
+early_int = intervention[intervention['Day'] <= 7]
+late_int = intervention[intervention['Day'] >= 24]
+print(f"Days 1-7:    Avg Happiness = {early_int['Happiness'].mean():.2f}")
+print(f"Days 24-30:  Avg Happiness = {late_int['Happiness'].mean():.2f}")
+print(f"GROWTH: +{late_int['Happiness'].mean() - early_int['Happiness'].mean():.2f} points\n")
+
+print("[CONTROL GROUP] - Should show flat/random pattern")
+early_ctl = control[control['Day'] <= 7]
+late_ctl = control[control['Day'] >= 24]
+print(f"Days 1-7:    Avg Happiness = {early_ctl['Happiness'].mean():.2f}")
+print(f"Days 24-30:  Avg Happiness = {late_ctl['Happiness'].mean():.2f}")
+print(f"CHANGE: {late_ctl['Happiness'].mean() - early_ctl['Happiness'].mean():+.2f} points\n")
+
+print("[HABIT CORRELATION] - More habits = Higher happiness")
+for habits in range(4):
+    subset = intervention[intervention['Habits_Count'] == habits]
+    if len(subset) > 0:
+        print(f"{habits} habits/day: Avg Happiness = {subset['Happiness'].mean():.2f} ({len(subset)} observations)")