Data formats and loader plugins¶

Data loader plugins¶

The erlab.io API contains the loader registry and common loading functions. The following modules describe the available loading interfaces:

Interface	Contents
`erlab.io.plugins`	Built-in data loader plugins
`erlab.io.dataloader`	Loader registry, plugin base class, loading behavior, and scan grouping
`erlab.io.igor`	Igor Pro xarray backend and export functions
`erlab.io.metadata`	Spreadsheet-backed metadata sources
`erlab.io.fitsutils`	FITS conversion functions
`erlab.io.nexusutils`	NeXus conversion functions

Use Data loading and saving for procedures on experimental files. See ARPES data conventions for the coordinate and metadata conventions required by ARPES-specific tools.

When no data loader plugin applies, use the following instructions for file formats to inspect its contents.

Data	Interface
NetCDF and supported HDF5 files	`xarray.open_dataarray()`, `xarray.open_dataset()`, or `xarray.open_datatree()`
HDF5 files with an unknown group structure	`xarray.open_groups()`
CSV and Excel tables	`pandas.read_csv()` or `pandas.read_excel()`, followed by `pandas.DataFrame.to_xarray()` when an xarray object is required
FITS data	`erlab.io.fitsutils.fits_to_xarray()`
NeXus data that requires explicit conversion	`erlab.io.nexusutils`

An xarray object must follow the ARPES data conventions before it is used with an ARPES-specific analysis routine. Use Implementing a data loader plugin to add a new acquisition format or scan-grouping rule.