The APyT command line interface

The APyT package is designed in a modular way to provide maximum flexibility. Its core modules can be integrated directly into custom workflows, while still offering ready-to-use command line tools for standard analysis tasks.

APyT is built with SQL database integration in mind:

  • Measurement data are stored centrally in a database.

  • Metadata (e.g. experimental parameters, calibration results) are linked to the measurements and stored in the SQL database.

For users who do not want to set up or manage an SQL database, APyT also provides a local database module. This enables you to store measurement files locally while keeping all associated metadata in a lightweight YAML-based database.

Configuration of both SQL and local databases is managed through the global configuration system. See the global configuration options for details.

Why use the command line interface?

While APyT modules can be imported and used directly in Python, the command line interface (CLI) provides ready-made wrappers that:

  • Streamline the most common analysis steps.

  • Require minimal to no setup.

  • Make it easy to test APyT without writing additional Python code.

Typical workflows covered by the CLI range from processing raw measurement data to performing a full 3D reconstruction of atom probe data.

Available command line scripts

The following command line tools are included with APyT:

Each script is documented individually, including its available options, expected input/output formats, and usage examples. For convenience, a lightweight graphical user interface is provided through Matplotlib plots and widgets.

Note

The Matplotlib interface is designed primarily for ease of use and producing high-quality plots rather than raw performance. As a result, interactions may feel somewhat laggy depending on your hardware.

Example workflow

A typical CLI-based workflow might look like this:

  1. Prepare your measurement files and ensure they are registered in either the SQL or local database.

    Note

    Measurement files are expected in the raw file format. If your data is in ePOS format, you can easily convert it to raw format using a one-liner:

    python3 -c "from apyt.io.conv import epos_to_raw; epos_to_raw('<epos_file>')"

  2. Align the mass spectrum using the apyt_spectrum_align script.

  3. Fit the peaks with the apyt_spectrum_fit script to identify species.

  4. Perform a 3D reconstruction of the dataset with the apyt_reconstruction script.

This provides a fully reproducible analysis path from raw input files to processed, interpretable scientific data.

Hint

For detailed instructions on working with an exemplary measurement dataset, please refer to the example usage instructions.