PeakPacer is a Python-based tool that optimizes cycling performance by providing insights and calculations to help cyclists maximize speed while minimizing energy expenditure. It offers two core functionalities:

1. Power Profile Optimization: Given a GPX file representing a parcours (a GPS route), PeakPacer calculates the optimal power distribution to maintain for maximizing speed while minimizing total power output over the course of the ride.
2. CdA Computation: Given a FIT file containing cycling data, PeakPacer calculates the CdA, which is a crucial aerodynamic factor affecting performance.

• Takes a GPX file as input, which defines a cycling route.
• Analyzes route elevation changes, distances, and slopes.
• Computes the ideal power profile a cyclist should maintain for each segment of the ride to:
  • Maximize speed
  • Minimize power output based on athlete FTP and MAP.

This helps cyclists distribute their energy efficiently during climbs, descents, and flats, leading to better overall performance.

• Takes a FIT file containing real-world cycling data (speed, power, elevation, etc.) as input.
• Analyzes the data to calculate the cyclist and bike CdA, which is a key metric used to measure aerodynamic efficiency.
• Provides insight into how different positions, clothing, or equipment changes might affect overall performance by reflecting changes in CdA during field tests.

1. Clone the repository:

   git clone https://github.com/bgallois/PeakPacer.git
   cd peakpacer

2. Install dependencies:

   pip install -r requirements.txt

1. To start the Flask application:

   flask --app app --debug run

2. Visit the following URLs:

   • For Power Profile Optimization: http://127.0.0.1:5000
   • For CdA Computation: http://127.0.0.1:5000/cda

• For Power Profile Optimization, PeakPacer outputs:

  • Optimal power profile per split or segment.
  • Power output time distribution.

• For CdA Computation, PeakPacer outputs:

  • Calculated CdA by laps.
  • Graphs showing how CdA changes across yaw angles.