To efficiently handle files larger than main memory, LiberTEM never loads the
whole data set into memory. Calling the
function only opens the data set and gives back a handle; running an analysis
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.
Loading through the API¶
In the API, you can use
libertem.api.Context.load(). The general
ctx = Context() ctx.load("typename", path="/path/to/some/file", arg1="val1", arg2=42)
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:
For the full list of supported file formats with links to their reference documentation, see Supported formats below.
Loading using the GUI¶
Using the GUI, mostly the same parameters need to be specified, although some
are only available in the Python API. Tuples (for example for
have to be entered as comma-separated values. We follow
the NumPy convention here and specify the “fast-access” dimension last, so a
"42, 21" would mean the same as specifying
(42, 21) in
the Python API, setting
x=21. Note that the GUI
currently only support 4D data sets, while the scripting API should handle more
general n-dimensional data.
See also the concepts section.
There are some common parameters across data set types:
The name of the data set, for display purposes. Only used in the GUI.
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.
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.