chrisuberti/pace_yourself

A comprehensive cycling performance analysis and course optimization tool that integrates Strava data, physics-based modeling, and advanced pacing strategies.

• Course Optimization Engine: Physics-based power modeling and race simulation
• W' Energy Dynamics: Advanced anaerobic capacity tracking and recovery modeling
• Optimal Pacing Strategies: Find the optimal constant power for any course
• Performance Metrics: TSS, IF, power-to-weight, and critical segment analysis
• Interactive Streamlit Interface: Real-time optimization with comprehensive visualizations

• Strava Integration: Fetch and analyze course data from Strava segments
• Course Segmentation: Multiple algorithms (equal distance, auto-clustering, manual)
• Physics Modeling: Air resistance, rolling resistance, gradient, and wind effects
• Rider Profiling: Critical power, W', and aerodynamic modeling
• Route Visualization: Interactive maps and elevation profiles

• Python 3.8+
• Strava API credentials (optional, for live data)

1. Clone the repository

   git clone https://.com/your-username/pace-yourself.git
   cd pace-yourself

2. Install dependencies

   pip install -r requirements.txt

3. Set up environment variables (optional)

   # Create .env file with:
   ROUTE_PATH=data/sample_routes
   STRAVA_CLIENT_ID=your_client_id
   STRAVA_CLIENT_SECRET=your_client_secret

4. Run the application

   streamlit run main.py

from app.optimization import CourseOptimizer
from app.course import Course
import pandas as pd

# Load course data
course_data = pd.read_csv('data/sample_routes/example_route.csv')
course = Course(course_data)
course.equal_segmentation(8)

# Create optimizer
optimizer = CourseOptimizer(critical_power=300, w_prime=25000)

# Find optimal power strategy
optimal_power, time, results = optimizer.optimize_constant_power(
    create_course_segments_from_course(course)
)

print(f"Optimal power: {optimal_power:.0f}W")
print(f"Race time: {time/60:.1f} minutes")

# Full performance analysis
analysis = optimizer.analyze_pacing_strategy(course_segments)

print(f"TSS: {analysis['performance_metrics']['tss_estimate']:.0f}")
print(f"W' Utilization: {analysis['w_prime_utilization_percent']:.1f}%")
print(f"Critical segments: {len(analysis['critical_segments'])}")