Tutorials for APT Data Processing

This section provides step-by-step tutorials for common PyCCAPT calibration and analysis workflows, including data processing, propagation delay correction, visualization, and 3D reconstruction.

Additional widget-based Jupyter workflows are available under pyccapt/calibration/tutorials/jupyter_files for:

• raw detector analysis (RoentDek, Surface Concept, and LEAP/Cameca imports)

• t0 and flight-path estimation

• Cameca RHIT/STR/HITS import

• reflectron detector correction for LEAP EPOS datasets

• TAPSim node/specimen generation

Google Colab support is currently provided for the data_processing.ipynb and visualization.ipynb notebooks.

Dataset Download

• Sample datasets

Tutorial Guides

• Data Processing
• Propagation Delay Calculation
• Visualization
• 3D Reconstruction
• Reflectron Batch Correction (CLI)
  • When to use this instead of the notebook
  • Usage
  • Arguments
  • Running on small-RAM machines
  • Output layout
• Import Ranging from MATLAB Atom-Probe-Toolbox .fig
  • What gets extracted
  • Usage
  • Arguments
  • Limitations
• Working with Big Datasets on Small RAM (Memory-Mapped I/O)
  • The shared abstraction: LazyTable / LazyColumn
  • Backwards-compatible entry points
  • Streaming reflectron correction
  • Raw-data analysis: the “Low memory” toggle
  • Measured impact
  • Tuning tips
  • Extending the lazy paths
• Parallel Execution in Calibration (PYCCAPT_PARALLEL_WORKERS)
  • Environment variables
  • What’s parallelized today
  • Usage
  • Python-side overrides
  • Design notes