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

dot_config/private_Code/User/History/6c11eec7/54EK.py

@@ -0,0 +1,93 @@
+df = pd.DataFrame(data, columns=['Participant_ID', 'Day', 'Calendar_Adherence', 'Cleanliness_Adherence', 'Punctuality_Adherence', 'Happiness'])
+import pandas as pd
+import numpy as np
+
+
+np.random.seed(42)  # ensures you get exactly the same data every time
+
+
+N_PARTICIPANTS_PER_GROUP = 20
+DAYS = list(range(1, 31))
+
+
+def clip_yes_prob(prob, ceiling):
+    return min(ceiling, max(0.05, prob))
+
+
+def generate_intervention_group(start_participant_id=1):
+    rows = []
+    for offset in range(N_PARTICIPANTS_PER_GROUP):
+        participant_id = start_participant_id + offset
+        org_bias = np.random.normal(0.7, 0.15)  # each person has their own organization tendency
+
+        for day in DAYS:
+            calendar = np.random.choice(['Yes', 'No'], p=[clip_yes_prob(org_bias + 0.1, 0.95), 1 - clip_yes_prob(org_bias + 0.1, 0.95)])
+            clean = np.random.choice(['Yes', 'No'], p=[clip_yes_prob(org_bias, 0.90), 1 - clip_yes_prob(org_bias, 0.90)])
+            ontime = np.random.choice(['Yes', 'No'], p=[clip_yes_prob(org_bias + 0.05, 0.92), 1 - clip_yes_prob(org_bias + 0.05, 0.92)])
+
+            adherence_count = sum(x == 'Yes' for x in [calendar, clean, ontime])
+            baseline_happiness = np.random.normal(5.5, 1.0)
+            happiness = int(np.clip(np.random.normal(baseline_happiness + adherence_count * 1.1, 1.2), 1, 10))
+
+            rows.append([
+                participant_id,
+                'Intervention',
+                day,
+                calendar,
+                clean,
+                ontime,
+                happiness,
+            ])
+
+    return rows
+
+
+def generate_control_group(start_participant_id):
+    rows = []
+    for offset in range(N_PARTICIPANTS_PER_GROUP):
+        participant_id = start_participant_id + offset
+
+        for day in DAYS:
+            # Control group only records happiness; all habit columns are No.
+            calendar = 'No'
+            clean = 'No'
+            ontime = 'No'
+
+            baseline_happiness = np.random.normal(5.5, 1.0)
+            control_noise = np.random.normal(0.0, 1.1)
+            happiness = int(np.clip(baseline_happiness + control_noise, 1, 10))
+
+            rows.append([
+                participant_id,
+                'Control',
+                day,
+                calendar,
+                clean,
+                ontime,
+                happiness,
+            ])
+
+    return rows
+
+
+data = []
+data.extend(generate_intervention_group(start_participant_id=1))
+data.extend(generate_control_group(start_participant_id=N_PARTICIPANTS_PER_GROUP + 1))
+
+df = pd.DataFrame(
+    data,
+    columns=[
+        'Participant_ID',
+        'Group',
+        'Day',
+        'Calendar_Adherence',
+        'Cleanliness_Adherence',
+        'Punctuality_Adherence',
+        'Happiness',
+    ],
+)
+
+# Save the combined dataset
+df.to_csv('organization_happiness_study_data.csv', index=False)
+print("✅ Full dataset saved as 'organization_happiness_study_data.csv' — open it in Excel!")
+print(df.head(10))  # shows first 10 rows