Function analyze_patterns

simple_time_analysis.py:207–256

Analyze programming patterns.

(sessions, daily_data)

def analyze_patterns(sessions, daily_data):
    """Analyze programming patterns."""
    print("PROGRAMMING PATTERNS ANALYSIS")
    print("=" * 40)

    # Hour of day analysis
    hour_counts = defaultdict(int)
    hour_duration = defaultdict(float)

    for session in sessions:
        for commit in session["commits"]:
            hour = commit["datetime"].hour
            hour_counts[hour] += 1
            # Distribute session time across commits
            hour_duration[hour] += session["duration_minutes"] / len(session["commits"])

    print("MOST ACTIVE HOURS (by commits):")
    top_hours = sorted(hour_counts.items(), key=lambda x: x[1], reverse=True)[:5]
    for hour, count in top_hours:
        avg_duration = hour_duration[hour] / count if count > 0 else 0
        print(f"  {hour:2d}:00 - {count:3d} commits ({avg_duration:.1f} min avg)")
    print()

    # Day of week analysis
    weekday_data = defaultdict(lambda: {"duration": 0, "commits": 0, "days": 0})
    weekday_names = [
        "Monday",
        "Tuesday",
        "Wednesday",
        "Thursday",
        "Friday",
        "Saturday",
        "Sunday",
    ]

    for date, data in daily_data.items():
        weekday = date.weekday()
        weekday_data[weekday]["duration"] += data["duration_minutes"]
        weekday_data[weekday]["commits"] += data["commits"]
        weekday_data[weekday]["days"] += 1

    print("PROGRAMMING BY DAY OF WEEK:")
    for i in range(7):
        data = weekday_data[i]
        if data["days"] > 0:
            avg_hours = data["duration"] / 60 / data["days"]
            avg_commits = data["commits"] / data["days"]
            print(
                f"  {weekday_names[i]:<9}: {avg_hours:5.1f}h avg ({avg_commits:4.1f} commits avg, {data['days']} days)"
            )


def main():