Learn mdxplain

mdxplain provides a PipelineManager as the central entry point for all molecular dynamics trajectory analysis. The architecture follows a builder pattern, where complex analyses are constructed step-by-step through a fluent, manager-based interface.

Key Design Principles

• PipelineManager: Single entry point that coordinates all analysis operations

• Manager-based Architecture: Specialized managers for trajectories, features, clustering, decomposition, etc.

• Pipeline Data: Central data structure (pipeline.data) that accumulates all analysis results

• Fluent API: Intuitive, chainable methods like pipeline.feature.add.contacts()

Basic Usage Examples

• Quick Start Example
• Performance Settings (Quick Guide)
• Memory-Efficient Processing
• Trajectory Management
• Feature Computation
• Feature Selection
• Feature Reduction
• Dimensionality Reduction
• Clustering
• Structural Analysis
• Feature Statistics
• Data Selection (Frame/Row Selection)
• Comparative Analysis and Feature Importance
• Plotting and Visualization
• Saving and Loading

Tutorials

Here’s a complete conformational analysis workflow:

• mdxplain Quickstart Tutorial: Introduction to Conformational Analysis with mdxplain
• mdxplain Tutorial: Introduction to Conformational Analysis with mdxplain
• Tutorial 3 - Reusing Sharable Archives
• Tutorial 04: Latrophilin-1 Mutation Analysis

Performance and System Stability

Large KernelPCA and Diffusion Maps runs can overwhelm CPU, memory, and I/O. This short explainer summarizes why hard freezes happen and which mdxplain safeguards prevent them. See What we observed for the full discussion.

• What we observed
• Why this happens
• How mdxplain reduces the risk
• Summary
• References