Section 4: Practical Guides (Weeks 13-16)

Workflows, data management, and production-ready analysis

Section Overview

This section teaches production workflows using Pipeline APIs, data format requirements, data I/O for multiple instrument formats, publication-quality visualization, and batch processing.

Timeline: Weeks 13-16 (12-16 hours)

Prerequisites: Sections 1-2 (Fundamentals and Model Usage)

Learning Objectives

By completing this section, you will be able to:

1. Build fluent analysis pipelines with Pipeline API

2. Use modular API for low-level control

3. Understand data format requirements for all analysis types

4. Load data from TRIOS, Anton Paar, CSV, Excel formats

5. Create publication-quality visualizations

6. Process multiple datasets in batch mode

Section Contents

• Pipeline API Tutorial
  • Why Use the Pipeline API?
  • Quick Start
  • Core Pipeline Operations
  • Specialized Workflows
  • Pipeline Builder
  • Batch Processing
  • Error Handling
  • Best Practices
  • Performance Tips
  • Common Patterns
  • Summary
  • Next Steps
• Modular API Tutorial
  • When to Use the Modular API
  • Core Components
  • Working with Models
  • Fitting Models
  • Working with Transforms
  • Custom Fitting Workflows
  • Advanced Parameter Management
  • Serialization and Persistence
  • Integration with External Libraries
  • Best Practices
  • Common Patterns
  • Summary
  • Next Steps
• Model Inventory System
  • CLI Usage
  • Python API Usage
  • Core Definitions
• Data Format Reference
  • Core Data Container: RheoData
  • Test Modes: 8 Protocols
  • Transform Analyses
  • Model Fitting Data Interface
  • Quick Reference Tables
  • Common Patterns
  • Further Reading
• TA Instruments TRIOS Format
  • Overview
  • File Structure
  • Supported Test Types
  • Loading TRIOS Files
  • Auto-Chunking for Large Files
  • Column Detection
  • Unit Conversions
  • Temperature Extraction
  • Metadata Access
  • Complete Example
  • Troubleshooting
  • See Also
• Data I/O Guide
  • Quick Reference
  • Supported Formats
  • Chunked Reading (Large Files)
  • Summary
  • Further Reading
• Visualization Guide
  • Basic Plotting
  • Bayesian Diagnostics
  • Summary
• Batch Processing
  • Basic Batch Workflow
  • Manual Batch Processing
  • Summary

Section Roadmap

Week 13: Pipeline API

• Pipeline API Tutorial — Fluent workflows (load → fit → plot → save)

Week 14: Data Formats & I/O

• Data Format Reference — Data format requirements for all analyses (reference)

• TA Instruments TRIOS Format — TA Instruments TRIOS file format (detailed)

• Data I/O Guide — Auto-detect file formats, HDF5 output

• Modular API Tutorial — Low-level control

Week 15: Visualization

• Visualization Guide — Publication-ready plots, templates

Week 16: Batch Processing

• Batch Processing — High-throughput analysis

Quick Examples

Pipeline API:

from rheojax.pipeline import Pipeline

(Pipeline()
    .load('data.csv', x_col='time', y_col='stress')
    .fit('maxwell')
    .plot()
    .save('results.hdf5'))

Batch Processing:

from rheojax.pipeline.batch import BatchPipeline

batch = BatchPipeline()
batch.load_directory('experiments/', pattern='*.txt')
batch.fit_all('fractional_zener_ss')
batch.save_summary('summary.xlsx')

Next Steps

After completing practical guides, use:

Section 5: Appendices (Section 5: Appendices)

Quick-reference materials for troubleshooting, experimental design, and glossary.