To efficiently handle files larger than main memory, LiberTEM never loads the whole data set into memory. Calling the load() function only opens the data set and gives back a handle; running an analysis with run() or a UDF with run_udf() then streams the data from mass storage.

See Sample Datasets for publicly available datasets for testing.

There are two main ways of opening a data set in LiberTEM: using the GUI, or the Python API.

In the API, you can use libertem.api.Context.load(). The general pattern is:

ctx = Context()


So, you need to specify the data set type, the path, and dataset-specific arguments. These arguments are documented below.

For most file types, it is possible to automatically detect the type and parameters, which you can trigger by using "auto" as file type:

ctx.load("auto", path="/path/to/some/file")


For the full list of supported file formats with links to their reference documentation, see Supported formats below.

Using the GUI, mostly the same parameters need to be specified, although some are only available in the Python API. Tuples (for example for scan_size) have to be entered as comma-separated values. We follow the NumPy convention here and specify the “fast-access” dimension last, so a value of "42, 21" would mean the same as specifying (42, 21) in the Python API, setting y=42 and x=21. Note that the GUI currently only support 4D data sets, while the scripting API should handle more general n-dimensional data.

## Common parameters¶

There are some common parameters across data set types:

name

The name of the data set, for display purposes. Only used in the GUI.

scan_size

In the GUI, we generally support visualizing data containing rectangular 2D scans. For some data set types, you can specify a scan_size as a tuple (y, x). When using the Python API, you are free to use n-dimensional scan_size, if the data set and chosen analysis supports it.

## Supported formats¶

LiberTEM supports the following file formats out of the box, see links for details:

Furthermore, a memory data set can be constructed from a NumPy array for testing purposes. See Memory data set for details.