196 lines
No EOL
8.6 KiB
Python
196 lines
No EOL
8.6 KiB
Python
import pandas as pd
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import numpy as np
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np.random.seed(42) # ensures you get exactly the same data every time
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N_PARTICIPANTS_PER_GROUP = 20
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DAYS = list(range(1, 31))
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def clip_yes_prob(prob, ceiling):
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return min(ceiling, max(0.05, prob))
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def generate_intervention_group(start_participant_id=1):
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rows = []
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for offset in range(N_PARTICIPANTS_PER_GROUP):
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participant_id = start_participant_id + offset
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org_bias = np.random.normal(0.65, 0.18) # each person has their own organization tendency (persistent)
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org_bias = np.clip(org_bias, 0.1, 0.95)
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# Personal baselines for each habit (people are naturally better/worse at specific habits)
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calendar_ease = org_bias + np.random.normal(0.05, 0.08)
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clean_ease = org_bias + np.random.normal(-0.02, 0.08)
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ontime_ease = org_bias + np.random.normal(0.02, 0.08)
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# Baseline happiness and habit strength for this participant
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person_happiness_baseline = np.random.normal(4.8, 1.1) # Lower starting point for growth
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habit_strength = 0.0 # Cumulative measure of consistent habit completion
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# Track previous day's habits for momentum/habit stacking
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prev_calendar, prev_clean, prev_ontime = 'No', 'No', 'No'
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for day in DAYS:
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# Week effect: Sunday (day % 7 == 0) and Saturday (day % 7 == 6) have lower adherence
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week_difficulty = 1.0 if (day % 7) not in [0, 6] else 0.75 # weekends are harder
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# Habit formation/fatigue: early days harder, then easier, slight decline late
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if day < 7:
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time_factor = 0.85 # Getting started is harder
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elif day < 20:
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time_factor = 1.1 # Momentum builds
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else:
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time_factor = 0.98 # Slight fatigue
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# Momentum effect: If you did a habit yesterday, you're more likely to do it today
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calendar_prob = clip_yes_prob(
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calendar_ease * week_difficulty * time_factor +
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(0.15 if prev_calendar == 'Yes' else 0), 0.95
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)
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clean_prob = clip_yes_prob(
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clean_ease * week_difficulty * time_factor +
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(0.15 if prev_clean == 'Yes' else 0), 0.90
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)
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ontime_prob = clip_yes_prob(
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ontime_ease * week_difficulty * time_factor +
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(0.12 if prev_ontime == 'Yes' else 0), 0.93
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)
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calendar = np.random.choice(['Yes', 'No'], p=[calendar_prob, 1 - calendar_prob])
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clean = np.random.choice(['Yes', 'No'], p=[clean_prob, 1 - clean_prob])
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ontime = np.random.choice(['Yes', 'No'], p=[ontime_prob, 1 - ontime_prob])
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# Count habits completed today
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adherence_count = sum(x == 'Yes' for x in [calendar, clean, ontime])
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# Habit strength: accumulates with consistent completion, decays with non-completion
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# This creates a cumulative effect that drives upward trend
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if adherence_count == 3:
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habit_strength += 0.6 # Strong boost for completing all habits
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elif adherence_count == 2:
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habit_strength += 0.35 # Moderate boost
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elif adherence_count == 1:
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habit_strength += 0.15 # Small boost
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else:
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habit_strength -= 0.2 # Small decay for missing all habits
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# Clip habit_strength to reasonable range (0 to 4)
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habit_strength = np.clip(habit_strength, 0, 4)
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# Happiness is baseline + growth from habit_strength over time
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# As study progresses and habit_strength builds, happiness increases more
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study_progress = day / 30.0 # 0.033 to 1.0 over 30 days
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# Daily random noise (small)
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daily_noise = np.random.normal(0, 0.5)
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# Happiness formula: baseline + cumulative effect that strengthens over time
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happiness_value = (
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person_happiness_baseline + # Starting point
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habit_strength * (0.5 + study_progress) + # Habit benefits grow over time
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daily_noise # Day-to-day variability
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)
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happiness = int(np.clip(np.round(happiness_value), 1, 10))
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rows.append([
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participant_id,
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'Intervention',
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day,
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calendar,
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clean,
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ontime,
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happiness,
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])
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# Update for next iteration
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prev_calendar, prev_clean, prev_ontime = calendar, clean, ontime
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return rows
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def generate_control_group(start_participant_id):
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rows = []
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for offset in range(N_PARTICIPANTS_PER_GROUP):
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participant_id = start_participant_id + offset
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# Even without tracking, some people are naturally more organized
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natural_org = np.random.normal(0.3, 0.15) # Lower baseline than intervention
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natural_org = np.clip(natural_org, 0.05, 0.7)
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# Personal tendencies (but not tracked/reported as habits)
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person_happiness_baseline = np.random.normal(4.8, 1.3) # Same baseline as intervention (no advantage)
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# Since they're not tracking, habits happen at random intervals (not streaky)
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prev_untracked_habits = 0
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for day in DAYS:
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# Week effect: sans the awareness/tracking effect
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week_factor = 1.0 if (day % 7) not in [0, 6] else 0.9
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# Without tracking, unaware of patterns, so less habit formation
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time_factor = 1.0 + (day / 100) * 0.1 # Tiny habituation, but weak
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# Untracked habits - they happen but aren't reported
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calendar_untracked = np.random.choice(['Yes', 'No'],
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p=[clip_yes_prob(natural_org * 0.8 * week_factor * time_factor, 0.4),
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1 - clip_yes_prob(natural_org * 0.8 * week_factor * time_factor, 0.4)])
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clean_untracked = np.random.choice(['Yes', 'No'],
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p=[clip_yes_prob(natural_org * 0.75 * week_factor * time_factor, 0.35),
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1 - clip_yes_prob(natural_org * 0.75 * week_factor * time_factor, 0.35)])
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ontime_untracked = np.random.choice(['Yes', 'No'],
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p=[clip_yes_prob(natural_org * 0.85 * week_factor * time_factor, 0.45),
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1 - clip_yes_prob(natural_org * 0.85 * week_factor * time_factor, 0.45)])
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# They report habits as "No" (not tracking), but untracked habits have minimal effect
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untracked_count = sum(x == 'Yes' for x in [calendar_untracked, clean_untracked, ontime_untracked])
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subtle_boost = untracked_count * 0.1 # Tiny effect since unaware/untracked
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# Control group happiness has day-to-day variability but no systematic growth
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# Without awareness and tracking, there's no cumulative benefit
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daily_noise = np.random.normal(0, 1.2)
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happiness_value = (
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person_happiness_baseline + # Same baseline
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subtle_boost + # Minimal benefit from occasional habits
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daily_noise # Higher variability, no systematic trend
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)
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happiness = int(np.clip(np.round(happiness_value), 1, 10))
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rows.append([
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participant_id,
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'Control',
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day,
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'No', # Reported as "No" - not tracking
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'No', # Reported as "No" - not tracking
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'No', # Reported as "No" - not tracking
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happiness,
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])
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prev_untracked_habits = untracked_count
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return rows
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data = []
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data.extend(generate_intervention_group(start_participant_id=1))
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data.extend(generate_control_group(start_participant_id=N_PARTICIPANTS_PER_GROUP + 1))
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df = pd.DataFrame(
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data,
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columns=[
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'Participant_ID',
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'Group',
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'Day',
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'Calendar_Adherence',
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'Cleanliness_Adherence',
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'Punctuality_Adherence',
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'Happiness',
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],
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)
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# Save the combined dataset
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df.to_csv('organization_happiness_study_data.csv', index=False)
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print("✅ Full dataset saved as 'organization_happiness_study_data.csv' — open it in Excel!")
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print(df.head(10)) # shows first 10 rows |