144 lines
No EOL
5.2 KiB
Python
144 lines
No EOL
5.2 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 for this participant
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person_happiness_baseline = np.random.normal(5.5, 1.2)
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current_happiness = person_happiness_baseline
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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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# Habit stacking: completing one habit makes the next easier
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adherence_count = sum(x == 'Yes' for x in [calendar, clean, ontime])
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habit_boost = adherence_count * 1.2 if adherence_count > 0 else 0
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# Happiness has persistence but is also affected by habits
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happiness_noise = np.random.normal(0, 1.3)
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current_happiness = np.clip(
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current_happiness * 0.4 + # Previous day influences today
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person_happiness_baseline * 0.4 +
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habit_boost * 0.9 + # Habits have strong effect
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happiness_noise,
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1, 10
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)
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happiness = int(np.round(current_happiness))
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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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for day in DAYS:
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# Control group only records happiness; all habit columns are No.
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calendar = 'No'
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clean = 'No'
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ontime = 'No'
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baseline_happiness = np.random.normal(5.5, 1.0)
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control_noise = np.random.normal(0.0, 1.1)
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happiness = int(np.clip(baseline_happiness + control_noise, 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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calendar,
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clean,
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ontime,
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happiness,
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])
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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 |