To efficiently handle files larger than main memory, LiberTEM never loads the
whole data set at once. Calling the
function only checks that the dataset exists and is value before providing Python
with an object which can be used in later computation. Running an analysis
on this object with
run_udf() then streams the data from mass storage
in optimal-sized chunks, such that even very large datasets can be processed without
saturating the system resources.
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 separated values into the fields. You can hit a comma to jump to
the next field. We follow the NumPy convention here and specify the “fast-access” dimension
last, so a value of
21 would mean the same as specifying
(42, 21) in the Python API, setting
See the GUI usage page for more information on the GUI.
For more general information about how LiberTEM structures data see 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 all the dataset types, you can specify a nav_shape as a tuple (y, x). If the dataset isn’t 4D, the GUI can reshape it to 4D. When using the Python API, you are free to use n-dimensional nav_shape, if the data set and chosen analysis supports it.
In the GUI, you can specify shape of the detector as
width, but when using the Python API, it can be of any dimensionality.
You can specify a sync_offset to handle synchronization or acquisition problems. If it’s positive, sync_offset number of frames will be skipped from the start of the input data. If it’s negative, the dataset will be padded by abs(sync_offset) number of frames at the beginning.
Different methods for I/O are available in LiberTEM, which can influence performance. See I/O Backends for details.
sync_offset or a
nav_shape that exceeds the size of the input data
it is currently not well-defined if zero-filled frames are to be generated or if the missing data is skipped.
Most dataset implementations seem to skip the data. See #1384 for discussion, feedback welcome!
LiberTEM supports the following file formats out of the box, see links for details:
Furthermore, two alternative mechanisms exist for interfacing LiberTEM with data loaded elsewhere in Python via other libraries:
LiberTEM supports a mechanism to efficiently convert any supported dataset
into a Numpy binary file (
.npy), which can then be loaded into memory
independently of LiberTEM (or read as a
npy format dataset as above).
New in version 0.12.0.
To convert a dataset to npy, use the
with lt.Context() as ctx:
As of this time only exporting to the
npy format is supported, but other formats would be
possible as the need arose.
Alternatively, you can create Dask arrays from LiberTEM datasets via the Dask integration. These arrays can then be stored with Dask’s built-in functions or through additional libraries such as RosettaSciIO.