Advanced Workflows: Automating Tasks in CrystalViewer

Advanced Workflows: Automating Tasks in CrystalViewer

Overview

This guide explains how to automate repetitive tasks in CrystalViewer to save time, reduce errors, and scale complex visualization or analysis workflows.

Common automation goals

• Batch import and preprocess multiple datasets
• Apply consistent visualization styles (colors, lighting, camera angles)
• Run parameter sweeps and export results (images, animations, data)
• Integrate CrystalViewer into larger pipelines (data sources, analysis tools, CI/CD)
• Schedule routine reports or exports

Tools & interfaces typically used

• Built-in macro or scripting engine (e.g., JavaScript, Python)
• Command-line interface (CLI) for headless runs and scheduling
• API or plugin hooks for third-party integration
• Template projects or configuration files (JSON/YAML) for reproducible setups

Example automation patterns

1. Batch processing (headless)
   • Use CLI to load files, apply a saved scene/template, render images, and export metrics.
2. Scripted parameter sweep
   • Script loops over parameter values, updates scene parameters, renders outputs, and logs results.
3. Triggered pipeline step
   • Use webhooks or file-system watchers to kick off processing when new data arrives.
4. Scheduled reports
   • Combine CLI with cron or a scheduler to produce daily/weekly visual summaries.
5. Error-handling & retries
   • Wrap tasks with logging, timeouts, and retry policies to handle transient failures.

Practical example (pseudocode)

• Load dataset → apply preprocessing → set camera & style → render high-res image → save image + metadata → notify on completion.

Best practices

• Version-control scripts and configuration files.
• Store reusable scene templates and presets.
• Keep processing idempotent (same inputs → same outputs).
• Log inputs, parameters, and outputs for reproducibility.
• Start with small test runs before scaling to full datasets.

When to automate vs. manual

• Automate when tasks are repetitive, time-consuming, or error-prone.
• Keep manual workflows for exploratory analysis or one-off visual tweaks.

If you want, I can provide: a sample Python/CLI script for CrystalViewer automation, a checklist for production workflows, or a template JSON config—tell me which.