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LiberTEM is an open source platform for high-throughput distributed processing
of large-scale binary data sets and live data streams using a modified
`MapReduce programming model <https://en.wikipedia.org/wiki/MapReduce>`_. The
current focus is `pixelated
<https://en.wikipedia.org/wiki/Scanning_transmission_electron_microscopy#Universal_detectors>`_
scanning transmission electron microscopy (`STEM
<https://en.wikipedia.org/wiki/Scanning_transmission_electron_microscopy>`_)
and scanning electron
beam diffraction data.
MapReduce-like processing allows to specify an algorithm through two functions:
One function that is mapped on portions of the input data, and another function
that merges (reduces) a partial result from this mapping step into the complete
result. A wide range of TEM and 4D STEM processing tasks can be expressed in
this fashion, see `Applications`_.
The UDF interface of LiberTEM offers a standardized, versatile API to decouple
the mathematical core of an algorithm from details of data source, parallelism,
and use of results. Mapping and merging can be performed in any order and with
different subdivisions of the input data, including running parts of the
calculation concurrently. That means the same implementation can be used in a
wide range of modalities, including massive scaling on clusters. Since each
merge step produces an intermediate result, this style of processing is suitable
for displaying live results from a running calculation in a GUI application and
for `processing live data streams <https://github.com/LiberTEM/LiberTEM-live>`_.
A closed-loop feedback between processing and instrument control can be realized
as well. See `User-defined functions
<https://libertem.github.io/LiberTEM/udf.html>`_ for more details on the
LiberTEM UDF interface.
The LiberTEM back-end offers `high throughput and scalability
<https://libertem.github.io/LiberTEM/architecture.html>`_ on PCs, single server
nodes, clusters and cloud services. On clusters it can use fast distributed
local storage on high-performance SSDs. That way it achieves `very high
aggregate IO performance
<https://libertem.github.io/LiberTEM/performance.html>`_ on a compact and
cost-efficient system built from stock components. All CPU cores and CUDA
devices in a system can be used in parallel.
LiberTEM is supported on Linux, Mac OS X and Windows. Other platforms that allow
installation of Python 3.7+ and the required packages will likely work as well. The
GUI is running in a web browser.
Installation
------------
The short version:
.. code-block:: shell
$ virtualenv -p python3 ~/libertem-venv/
$ source ~/libertem-venv/bin/activate
(libertem-venv) $ python -m pip install "libertem[torch]"
# optional for GPU support
# See also https://docs.cupy.dev/en/stable/install.html
(libertem-venv) $ python -m pip install cupy
Please see `our documentation
<https://libertem.github.io/LiberTEM/install.html>`_ for details!
Alternatively, to run the `LiberTEM Docker image
<https://libertem.github.io/LiberTEM/deployment/clustercontainer.html>`_:
.. code-block:: shell
$ docker run -p localhost:9000:9000 --mount type=bind,source=/path/to/your/data/,dst=/data/,ro ghcr.io/libertem/libertem
or
.. code-block:: shell
$ singularity exec docker://ghcr.io/libertem/libertem /venv/bin/libertem-server
Deployment for offline data processing on a single-node system for a local user
is thoroughly tested and can be considered stable. Deployment on a cluster is
experimental and still requires some additional work, see `Issue #105
<https://github.com/LiberTEM/LiberTEM/issues/105>`_. Back-end support for live data processing
is still experimental as well, see https://github.com/LiberTEM/LiberTEM-live.
Applications
------------
Since LiberTEM is programmable through `user-defined functions (UDFs)
<https://libertem.github.io/LiberTEM/udf.html>`_, it can be used for a wide
range of processing tasks on array-like data and data streams. The following
applications have been implemented already:
- Virtual detectors (virtual bright field, virtual HAADF, center of mass ,
custom shapes via masks)
- `Analysis of amorphous materials <https://libertem.github.io/LiberTEM/app/amorphous.html>`_
- `Strain mapping <https://libertem.github.io/LiberTEM-blobfinder/>`_
- `Off-axis electron holography reconstruction <https://libertem.github.io/LiberTEM/app/holography.html>`_
- `Single Side Band ptychography <https://ptychography-4-0.github.io/ptychography/>`_
Some of these applications are available through an `interactive web GUI
<https://libertem.github.io/LiberTEM/usage.html#gui-usage>`_. Please see `the
applications section <https://libertem.github.io/LiberTEM/applications.html>`_
of our documentation for details!
The Python API and user-defined functions (UDFs) can be used for complex
operations such as arbitrary linear operations and other features like data
export. Example Jupyter notebooks are available in the `examples directory
<https://github.com/LiberTEM/LiberTEM/tree/master/examples>`_. If you are having
trouble running the examples, please let us know by filing an issue or
by `joining our Gitter chat <https://gitter.im/LiberTEM/Lobby>`_.
LiberTEM is suitable as a high-performance processing backend for other
applications, including live data streams. `Contact us
<https://gitter.im/LiberTEM/Lobby>`_ if you are interested!
LiberTEM is evolving rapidly and prioritizes features following user demand and
contributions. Currently we are working on `live data processing
<https://github.com/LiberTEM/LiberTEM-live>`_, improving application support for sparse
data and event-based detectors, performance improvements for GPU processing, and implementing
analysis methods for various applications of pixelated
STEM and other large-scale detector data. If you like to influence the direction
this project is taking, or if you'd like to `contribute
<https://libertem.github.io/LiberTEM/contributing.html>`_, please join our
`gitter chat <https://gitter.im/LiberTEM/Lobby>`_ and our `general mailing list
<https://groups.google.com/forum/#!forum/libertem>`_.
File formats
------------
LiberTEM currently opens most file formats used for pixelated STEM. See `our
general information on loading data
<https://libertem.github.io/LiberTEM/formats.html>`_ and `format-specific
documentation
<https://libertem.github.io/LiberTEM/reference/dataset.html#formats>`_ for more
information!
- Raw binary files
- NumPy .npy binary files
- Thermo Fisher EMPAD detector files
- `Quantum Detectors MIB format <https://quantumdetectors.com/products/merlinem/>`_
- Nanomegas .blo block files
- Direct Electron DE5 files (HDF5-based) and Norpix SEQ files for `DE-Series <https://directelectron.com/de-series-cameras/>`_ detectors
- `Gatan K2 IS <https://web.archive.org/web/20180809021832/http://www.gatan.com/products/tem-imaging-spectroscopy/k2-camera>`_ raw format
- Stacks of Gatan DM3 and DM4 files (via `openNCEM <https://github.com/ercius/openNCEM>`_)
- Single-file Gatan DM4 scans when saved using C-ordering
- FRMS6 from PNDetector pnCCD cameras (currently alpha, gain correction still needs UI changes)
- FEI SER files (via `openNCEM <https://github.com/ercius/openNCEM>`_)
- MRC (via `openNCEM <https://github.com/ercius/openNCEM>`_)
- HDF5-based formats such as HyperSpy files, NeXus and EMD
- TVIPS binary files
- Sparse data in Raw CSR (compressed sparse row) format, as is possible
to generate from event-based detectors
- Please contact us if you are interested in support for an additional format!
Live processing and detectors (experimental)
--------------------------------------------
See `LiberTEM-live <https://libertem.github.io/LiberTEM-live/>`_!
License
-------
LiberTEM is licensed under GPLv3. The I/O parts are also available under the MIT
license, please see LICENSE files in the subdirectories for details.
Acknowledgements
----------------
We are very grateful for your continuing support for LiberTEM!
See `the acknowledgement page
<https://libertem.github.io/acknowledgements.html>`_ for a list of authors and
contributors to LiberTEM and its subprojects. See also our info on `funding
<https://libertem.github.io/#funding>`_ and `industry partners
<https://libertem.github.io/#industry-partners>`_.
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"description": "|docs|_ |gitter|_ |azure|_ |github|_ |codeclimate|_ |precommit|_ |joss|_ |zenodo|_ |pypi|_ |condaforge|_\n\n.. |docs| image:: https://img.shields.io/badge/%F0%9F%95%AE-docs-green.svg\n.. _docs: https://libertem.github.io/LiberTEM/\n\n.. |gitter| image:: https://badges.gitter.im/join_chat.svg\n.. _gitter: https://gitter.im/LiberTEM/Lobby\n\n.. |azure| image:: https://dev.azure.com/LiberTEM/LiberTEM/_apis/build/status/LiberTEM.LiberTEM-data?branchName=master\n.. _azure: https://dev.azure.com/LiberTEM/LiberTEM/_build/latest?definitionId=4&branchName=master\n\n.. |zenodo| image:: https://zenodo.org/badge/DOI/10.5281/zenodo.1477847.svg\n.. _zenodo: https://doi.org/10.5281/zenodo.1477847\n\n.. |github| image:: https://img.shields.io/badge/GitHub-GPL--3.0-informational\n.. _github: https://github.com/LiberTEM/LiberTEM/\n\n.. |codeclimate| image:: https://api.codeclimate.com/v1/badges/dee042f64380f64737e5/maintainability\n.. _codeclimate: https://codeclimate.com/github/LiberTEM/LiberTEM\n\n.. |joss| image:: https://joss.theoj.org/papers/10.21105/joss.02006/status.svg\n.. _joss: https://doi.org/10.21105/joss.02006\n\n.. |precommit| image:: https://results.pre-commit.ci/badge/github/LiberTEM/LiberTEM/master.svg\n.. _precommit: https://results.pre-commit.ci/latest/github/LiberTEM/LiberTEM/master\n\n.. |pypi| image:: https://badge.fury.io/py/libertem.svg\n.. _pypi: https://pypi.org/project/libertem/\n\n.. |condaforge| image:: https://anaconda.org/conda-forge/libertem/badges/version.svg\n.. _condaforge: https://anaconda.org/conda-forge/libertem\n\nLiberTEM is an open source platform for high-throughput distributed processing\nof large-scale binary data sets and live data streams using a modified\n`MapReduce programming model <https://en.wikipedia.org/wiki/MapReduce>`_. The\ncurrent focus is `pixelated\n<https://en.wikipedia.org/wiki/Scanning_transmission_electron_microscopy#Universal_detectors>`_\nscanning transmission electron microscopy (`STEM\n<https://en.wikipedia.org/wiki/Scanning_transmission_electron_microscopy>`_)\nand scanning electron\nbeam diffraction data.\n\nMapReduce-like processing allows to specify an algorithm through two functions:\nOne function that is mapped on portions of the input data, and another function\nthat merges (reduces) a partial result from this mapping step into the complete\nresult. A wide range of TEM and 4D STEM processing tasks can be expressed in\nthis fashion, see `Applications`_.\n\nThe UDF interface of LiberTEM offers a standardized, versatile API to decouple\nthe mathematical core of an algorithm from details of data source, parallelism,\nand use of results. Mapping and merging can be performed in any order and with\ndifferent subdivisions of the input data, including running parts of the\ncalculation concurrently. That means the same implementation can be used in a\nwide range of modalities, including massive scaling on clusters. Since each\nmerge step produces an intermediate result, this style of processing is suitable\nfor displaying live results from a running calculation in a GUI application and\nfor `processing live data streams <https://github.com/LiberTEM/LiberTEM-live>`_.\nA closed-loop feedback between processing and instrument control can be realized\nas well. See `User-defined functions\n<https://libertem.github.io/LiberTEM/udf.html>`_ for more details on the\nLiberTEM UDF interface.\n\nThe LiberTEM back-end offers `high throughput and scalability\n<https://libertem.github.io/LiberTEM/architecture.html>`_ on PCs, single server\nnodes, clusters and cloud services. On clusters it can use fast distributed\nlocal storage on high-performance SSDs. That way it achieves `very high\naggregate IO performance\n<https://libertem.github.io/LiberTEM/performance.html>`_ on a compact and\ncost-efficient system built from stock components. All CPU cores and CUDA\ndevices in a system can be used in parallel.\n\nLiberTEM is supported on Linux, Mac OS X and Windows. Other platforms that allow\ninstallation of Python 3.7+ and the required packages will likely work as well. The\nGUI is running in a web browser.\n\nInstallation\n------------\n\nThe short version:\n\n.. code-block:: shell\n\n $ virtualenv -p python3 ~/libertem-venv/\n $ source ~/libertem-venv/bin/activate\n (libertem-venv) $ python -m pip install \"libertem[torch]\"\n\n # optional for GPU support\n # See also https://docs.cupy.dev/en/stable/install.html\n (libertem-venv) $ python -m pip install cupy\n\nPlease see `our documentation\n<https://libertem.github.io/LiberTEM/install.html>`_ for details!\n\nAlternatively, to run the `LiberTEM Docker image\n<https://libertem.github.io/LiberTEM/deployment/clustercontainer.html>`_:\n\n.. code-block:: shell\n\n $ docker run -p localhost:9000:9000 --mount type=bind,source=/path/to/your/data/,dst=/data/,ro ghcr.io/libertem/libertem\n\nor\n\n.. code-block:: shell\n\n $ singularity exec docker://ghcr.io/libertem/libertem /venv/bin/libertem-server\n\nDeployment for offline data processing on a single-node system for a local user\nis thoroughly tested and can be considered stable. Deployment on a cluster is\nexperimental and still requires some additional work, see `Issue #105\n<https://github.com/LiberTEM/LiberTEM/issues/105>`_. Back-end support for live data processing\nis still experimental as well, see https://github.com/LiberTEM/LiberTEM-live.\n\nApplications\n------------\n\nSince LiberTEM is programmable through `user-defined functions (UDFs)\n<https://libertem.github.io/LiberTEM/udf.html>`_, it can be used for a wide\nrange of processing tasks on array-like data and data streams. 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Example Jupyter notebooks are available in the `examples directory\n<https://github.com/LiberTEM/LiberTEM/tree/master/examples>`_. If you are having\ntrouble running the examples, please let us know by filing an issue or\nby `joining our Gitter chat <https://gitter.im/LiberTEM/Lobby>`_.\n\nLiberTEM is suitable as a high-performance processing backend for other\napplications, including live data streams. `Contact us\n<https://gitter.im/LiberTEM/Lobby>`_ if you are interested!\n\nLiberTEM is evolving rapidly and prioritizes features following user demand and\ncontributions. Currently we are working on `live data processing\n<https://github.com/LiberTEM/LiberTEM-live>`_, improving application support for sparse\ndata and event-based detectors, performance improvements for GPU processing, and implementing\nanalysis methods for various applications of pixelated\nSTEM and other large-scale detector data. If you like to influence the direction\nthis project is taking, or if you'd like to `contribute\n<https://libertem.github.io/LiberTEM/contributing.html>`_, please join our\n`gitter chat <https://gitter.im/LiberTEM/Lobby>`_ and our `general mailing list\n<https://groups.google.com/forum/#!forum/libertem>`_.\n\nFile formats\n------------\n\nLiberTEM currently opens most file formats used for pixelated STEM. 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The I/O parts are also available under the MIT\nlicense, please see LICENSE files in the subdirectories for details.\n\nAcknowledgements\n----------------\n\nWe are very grateful for your continuing support for LiberTEM!\n\nSee `the acknowledgement page\n<https://libertem.github.io/acknowledgements.html>`_ for a list of authors and\ncontributors to LiberTEM and its subprojects. See also our info on `funding\n<https://libertem.github.io/#funding>`_ and `industry partners\n<https://libertem.github.io/#industry-partners>`_.\n",
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