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Python Tutorials

Overview

Learn to analyze standardized biomechanical data using Python with our comprehensive tutorial series. These tutorials progress from basic data loading to publication-ready analysis.

Tutorial Series

Loading Data Efficiently

Learn the fundamentals of data loading and memory management - Load phase and time-indexed datasets - Select specific columns to reduce memory usage - Understand data structure and naming conventions - List available subjects, tasks, and variables

Time: 20 minutes | Level: Beginner

Data Filtering

Master the critical skill of data subsetting - Filter by task, subject, and variables - Combine multiple filter conditions - Create reusable filter functions - Save filtered datasets for analysis

Time: 25 minutes | Level: Beginner

Basic Visualization

Create essential biomechanical plots - Compute and plot phase averages - Add standard deviation shading - Create spaghetti plots - Compare multiple conditions

Time: 30 minutes | Level: Intermediate

Cycle Analysis

Analyze individual gait cycles - Extract individual cycles - Calculate ROM and peak values - Detect timing of key events - Identify outlier cycles

Time: 25 minutes | Level: Intermediate

Group Analysis

Aggregate data across subjects - Compute group means and variability - Handle missing data appropriately - Create ensemble averages - Statistical comparisons

Time: 30 minutes | Level: Intermediate

Publication Outputs

Generate publication-ready figures and tables - Create multi-panel figures - Export summary statistics - Format for journal requirements - Ensure reproducibility

Time: 30 minutes | Level: Advanced

Prerequisites

Required Knowledge

  • Basic Python programming
  • Familiarity with pandas DataFrames
  • Understanding of biomechanical concepts

Required Packages

# Core packages
pandas >= 1.3.0
numpy >= 1.20.0
matplotlib >= 3.3.0

# LocoHub library
user_libs.python.locomotion_data

# Optional but recommended
seaborn >= 0.11.0  # Better plot styling
scipy >= 1.7.0     # Statistical functions

Installation

# Install required packages
pip install pandas numpy matplotlib seaborn scipy

# Install locomotion library (from project root)
pip install -e user_libs/python

Learning Path

For Beginners

  1. Start with Tutorial 1 to understand data structure
  2. Master Tutorial 2 on filtering - this is critical
  3. Practice Tutorial 3 visualization techniques
  4. Work through exercises at your own pace

For Experienced Users

  • Jump to specific tutorials as needed
  • Focus on Tutorial 2 (filtering) and Tutorial 3 (visualization)
  • Check Tutorial 6 for publication workflows

For Data Scientists

  • Review Tutorial 1 for data structure
  • Use Tutorial 2 to understand subsetting patterns
  • Apply your own ML/statistical methods to filtered data

Quick Reference

Common Operations

from user_libs.python.locomotion_data import LocomotionData

# Load data
data = LocomotionData('dataset.parquet')

# Filter
level_walking = data[data['task'] == 'level_walking']

# Compute mean
mean = level_walking.groupby('phase_percent')['knee_flexion_angle_ipsi_rad'].mean()

# Plot
import matplotlib.pyplot as plt
plt.plot(mean.index, np.degrees(mean.values))
plt.xlabel('Gait Cycle (%)')
plt.ylabel('Knee Flexion (deg)')
plt.show()

Getting Help

Documentation

Community Support

  • GitHub Issues for bug reports
  • Discussions for questions
  • Example notebooks in repository

Tips for Success

  1. Always filter first - Don't analyze the entire dataset if you only need a subset
  2. Check your units - Data is stored in radians and SI units
  3. Validate your results - Compare with published normal ranges
  4. Save your work - Use scripts, not just notebooks
  5. Document your choices - Record filtering criteria and analysis decisions

Next Steps

Ready to start? Begin with Tutorial 1: Loading Data Efficiently →