Changelog

0.14.0.dev0

• [Bugfix] Live center of mass regression
• [Bugfix] Fix loading of malformed EMPAD data
• [Bugfix] Manually add MIME type for javascript on Windows
• [Docs] Air-gapped installation
• [Feature] Free resources after inactivity
• [Feature] Valid data masks
• [Feature] Expose y- and x-components in CoMUDF
• [Obsolescence] Remove support for Python 3.7 and Python 3.8
• [Obsolescence] Gridmatching and Fullmatch moving to LiberTEM-blobfinder

0.13.1 / 2023-11-09

The 0.13.1 point-release fixes an issue with loading assets when connecting to the libertem-server web interface via a proxy (#1554).

0.13.0 / 2023-11-09

The 0.13.0 release adds a number of improvements to the libertem-server web interface and its user experience, as well as laying the foundation for descan error compensation in all UDFs based on integration over masked regions of a frame.

Many thanks to everyone who has contributed to this release!

Web Interface

• A progress bar has been added to the web GUI to give feedback on the state of any in-flight computations. (#1500, #1514)

• The cluster launcher in the web interface now allows specification of multiple workers-per-GPU, allowing better utilisation of large GPUs. (#1489, #1499)

• Direct opening of datasets from URL parameters. The web interface now responds to arguments passed to it by URL, namely instructions to load a particular dataset, e.g. #action=open&path=/path/to/your/data/. (#1085, #1518)

• When launching libertem-server additional parameters are now accepted to preload a cluster with a given number of CPU and GPU workers, and also preload data. See the libertem-server documentation for more detail.(#1419, #1535)

Descan compensation

• The backend and interface of ApplyMasksUDF has been extended to allow compensation for descan error in the form of a shifts parameter. The shifts information is used to translate any applied masks relative to the given frame being processed. This lays the groundwork for future support for shifts in all UDFs which rely on the backend of ApplyMasksUDF, including CoMUDF and all virtual imaging analyses. (#1304)

UDF Interface

• The UDF interface was extended to allow a UDF to declare multiple processing methods (process_tile, process_frame etc) and choose at runtime the most appropriate one to use. See the UDF documentation for more detail. (#1508, #1509)

Misc

• A function libertem.contrib.convert_transposed.convert_dm4_transposed() has been added to efficiently convert Gatan Digital Micrograph STEM datasets stored in (sig, nav) ordering to numpy .npy files in (nav, sig) ordering (#1520).

• Several Exception types were moved to libertem.common for MIT license compatibility. They are re-exported to the old import location for backwards compatibility (#1543).

• The Shape class is now hashable allowing it to be used as key in a dict and set. (#1507).

0.12.0 / 2023-08-16

The 0.12 release introduces a dedicated Centre-of-Mass analysis UDF with additional features compared to the existing create_com_analysis() API. The dedicated UDF allows more straightforward live processing support, and can be subclassed as in the LiberTEM-iCoM project.

Also included are two usability improvements aimed at simplifying interaction with LiberTEM:

• ctx.export_dataset() for conversion of any supported LiberTEM dataset to the Numpy binary format .npy.

• lt.Context.make_with(cpus=n, gpus=m) to simplify creation of a LiberTEM Context with a specfic number of workers (rather than using all available resources by default).

This release also contains numerous fixes and backend changes across the codebase, improving the robustness of support for both sparse data and live processing in particular.

Many thanks to everyone who has contributed to this release!

The 0.12.0 release is also the first major release for which LiberTEM is available on conda-forge. See the installation documentation for installation instructions using pip or conda.

Features

• Adds CoMUDF as an extension from create_com_analysis(), with support for automatic correction of flat field effects (beam offset and descan error) (#1392).

• Adds libertem.api.Context.export_dataset() for export of any supported LiberTEM dataset to another format. At this time only exporting to the Numpy binary fomat .npy is supported, but other formats can later be added according to need (#1379).

• The libertem.api.Context.make_with() constructor method has been improved to allow simple specification of the number CPU and GPU workers to use, as well as the type of executor to create (#1443).

• Experimental API for dynamic updates to parameters while UDFs are running, allowing for faster feedback loops during live processing, notably (#1441).

Miscellaneous

• The PipelinedExecutor executor now schedules tasks on the worker with the smallest request queue size (#1451).

• Update PipelinedExecutor to properly match tasks to workers based on resources requested, including GPU workers (#1453).

• Array backend categories are now available in UDF (#1470).

• The UDF runner internals will now raise UDFRunCancelled if a UDF run is cancelled, allowing the user to handle this case (#1448).

• Introduce option for sparse.GCXS in MaskContainer (#1447).

Bugfixes

• We now properly forward the logging level to dask.distributed workers during initialization. This prevents a substantial amount of logging information being printed to stderr at cluster startup. (#1438).

• When processing raw_csr datasets, we now avoid upcasting to float64 when reading an indptr of type uint64 (#1465).

Deployment

• Starting with version 0.12, LiberTEM container images will be available from the GitHub container registry.

0.11.2 / 2023-07-04

This point release works around issue #597 in hdbscan, which is an incompatibility with the latest scikit-learn release 1.3.0.

0.11.1 / 2023-05-11

This point release works around issue #8940 in numba 0.57.0, which can cause crashes under certain circumstances.

0.11.0 / 2023-04-21

This release introduces first-class support for sparse input data and processing using sparse-compatible libraries, primarily oriented towards data from event-based detectors. This support is provided through the new sparseconverter package, which was developed to enable efficient inter-conversion between different sparse formats and dense arrays. Many thanks in particular to Alexander Clausen and Dieter Weber for their extensive work on sparse support. See Sparse arrays for details on how to use it in your UDFs, and Raw binary files in sparse CSR format for a sparse input file format!

This release also marks the end of official support for Python 3.6 (#1369, #1380). At this time the maximum supported Python version remains 3.10 owing to usage of the Numba library within LiberTEM.

Sparse processing

• Datasets and UDFs now support tiles in various array formats, in particular Sparse arrays. (#1205, #1207)

• Add support for raw_csr file format (#1195, #1207).

• ROIs can now now be provided to ctx.run_udf as sparse arrays or coordinate tuples rather than as a dense boolean mask. (#1306)

Other features

• Single-file Gatan Digital Micrograph (DM3/DM4) STEM datasets are now supported in the API and in the web interface. The files must use the newer C-style ordering available in Digital Micrograph to be read correctly. (#1401)

• The information provided by the ctx.run_udf progress bar has been improved with framewise updates, allowing feedback on progress before a partition completes. (#1341)

• HDF5 datasets can now perform on-the-fly reshaping of the scan grid and can be adjusted using the sync_offset parameter. (#441, #1364)

Bugfixes

• Numerous bugs were fixed in the behaviour of the PipelinedExecutor, to avoid crashes and deadlocks throughout the lifecycle of the object, particulary duing live processing. (#1308, #1311, #1316, #1318, #1319, #1342)

• The Context object will now attempt to close itself cleanly at process exit (#1343).

• The handling of sync_offset, nav_shape, sig_shape and sig_dims in libertem.io.dataset.memory.MemoryDataSet is now consistent with other datasets. (#1207)

• Bugs were fixed in libertem.udf.stddev.StdDevUDF for both complex input data and when some partitions are empty. (#1314)

Miscellaneous

• Make self.meta.coordinates available in UDF.get_task_data (#1397).

• The methods libertem.executor.pipelined.PipelinedExecutor.make_spec() and libertem.executor.dask.cluster_spec() both now accept integers for their cpus and cudas arguments, in addition to the existing iterable forms. (#1294, #1336).

• Reduced the overhead of (re-)constructing Slice and Shape objects when slicing tiles, in particular focused on the method BufferWrapper.get_contiguous_view_for_tile(). (#1313, #1321)

Web interface

• Datasets which don’t declare their nav shape will be interpreted as square if their number of frames is a square number (#1309, #1338).

• Raise if user input for numWorker is non-positive integer or other error is encountered in DaskJobExecutor creation. (#1334).

• The dataset loader Reset` button now correctly resets the sync_offset field, if present (#1378).

• The function detect to automatically determine dataset type will now use the file suffix as a hint to choose its search order. This may lead to faster responses in the web client when configuring a new dataset. (#1377)

Breaking changes

• Instead of DataTile objects, an UDF’s processing method will receive plain array objects, such as numpy.ndarray, sparse.SparseArray etc. That means the scheme_idx and tile_slice attributes are not available from the tile anymore, but only from the corresponding libertem.udf.base.UDFMeta.tiling_scheme_idx and libertem.udf.base.UDFMeta.slice. This change makes handling different array types such as sparse arrays or CuPy arrays easier. For CuPy arrays this was already the previous behavior.

0.10.0 / 2022-07-28

This release features the Pipelined executor for parallel live data processing (#1267). This change greatly improves the processing performance for live data, in particular to support detectors with high data rate. Many thanks to Alexander Clausen and Matthew Bryan for their work! The corresponding capabilities in the LiberTEM-live package will be released soon and announced separately.

Other changes:

New features

• Support for Python 3.10.

• NumPy files (NPY) for reading NumPy .npy files (#222, #1249).

• Support for updated EMPAD XML format, including series (#1259, #1260).

• Integrate Tracing using opentelemetry that allows to debug and trace distribted operation of LiberTEM (#691, #1266).

• libertem-server picks a free port if the default is in use and no port was specified (#1184, #1279).

• cluster_spec() now accepts the same CUDA device ID multiple times to spawn multiple workers on the same GPU. This can help increase GPU resource utilisation for some workloads (#1270).

Bugfixes

• Correct type determination in AutoUDF (#1298).

• Fix non-square plots (#1255).

• Disable the Dask profiler due to issues with the DM dataset (#1289).

• Fix GUI glitch in center of mass analysis (#1278).

Documentation

• Example on binning (#1250).

Miscellaneous

• Include tests in PyPI release to prepare release on conda-forge, and exclude unneeded files. (#1271, #1275, #1276).

• Move some code around to make sure that libertem.io and libertem.common only depend on code that is compatible with the MIT license. Moved items are re-imported at the same positions as before to keep backwards compatibility (#1031, #1245).

0.9.2 / 2022-04-28

This is a bugfix release with two small fixes:

• Example notebook: compatibility with HyperSpy 1.7.0 #1242

• Compatibility of CoM auto button with Jupyter server proxy #1220

0.9.0 / 2022-02-17

We are most happy to announce full Dask array integration with this release! Many thanks to Matthew Bryan who implemented major parts of this non-trivial feature. Most notably, HyperSpy lazy signals and LiberTEM can now be combined seamlessly. See Dask integration for details and an example!

This enables the following applications:

• Use HyperSpy file readers and other readers that create Dask arrays for LiberTEM.

• Create an ad-hoc file reader for LiberTEM by just building a Dask array. This is often simpler than implementing a native LiberTEM dataset, at the expense of performance.

• Use LiberTEM file readers for HyperSpy and other software that works with Dask arrays.

• Use the same implementation of an algorithm for live processing with LiberTEM, offline processing with LiberTEM, and offline processing with HyperSpy.

• Simplify implementation of complex processing routines on Dask arrays. That includes, for example, routines that are not purely implemented with NumPy array operations and produce complex output or are not compatible with all Dask array chunking schemes. Here, LiberTEM UDFs offer a more powerful and versatile interface than Dask’s native map_blocks() interface.

• Chain processing steps together using Dask arrays for intermediate results, including using the output of one UDF as input for another UDF. Dask arrays allow working with large intermediate results efficiently since they can remain on the workers.

Specifically, the Dask integration encompasses the following features:

• Create LiberTEM datasets from Dask arrays via the Dask (#1137).

• Create Dask arrays from LiberTEM UDF results using the DelayedJobExecutor. A UDF can define a merge_all() method in addition to the usual merge() to improve performance. See Merge function for Dask array results for details (#1170)!

• Create Dask arrays directly from LiberTEM datasets using libertem.contrib.daskadapter.make_dask_array(), which is already possible since release 0.2.

• Executor options to improve integration, see Scheduler and Executors (#1170, #1146, #922).

Please note that these features are still experimental and cover a large space of possible uses and parameters. Expect the unexpected! Tests, feedback and improvements are highly appreciated.

Other changes in this release:

New features

• Experimental helper function libertem.analysis.com.guess_corrections() to guess parameters for Center of Mass analysis (#1111).

• GUI interface for the COM analysis to call libertem.analysis.com.guess_corrections() and update the GUI parameters from the result (#1172).

• Support for some MIB Quad formats. All integer formats should be supported and were tested with 1x1 and 2x2 layouts. Raw formats with 1x1 and 2x2 layouts using 1 bit, 6 bit, and 12 bit counter depth are supported as well. Support for raw MIB data in other layouts and bit depths can be added on demand (#1169, #1135).

• New attributes libertem.udf.base.UDFMeta.sig_slice and libertem.udf.base.UDFMeta.tiling_scheme_idx. These attributes can be used for performant access to the current signal slice - mostly important for throughput-limited analysis (#1167, #1166).

• New --preload option to libertem-server and libertem-worker. That makes it work as documented in HDF5, following Dask worker preloading (#1151).

• Allow selection of I/O backend in GUI and Python API (#753, #896, #1129).

• Re-add support for direct I/O. It was previously only supported as a special case for raw files on Linux. Now it is supported for all native dataset formats we support on Linux and Windows. Notable exceptions are the OS X platform or HDF5, MRC, and SER formats (#1129, #753).

• Support for reading TVIPS binary files, i.e. *_NNN.tvips files (#1179).

Bugfixes

• Allow running CoM analysis on a linescan dataset by only returning divergence and curl if they are defined (#1138, #1139).

• make_dask_array now works correctly when a roi is specified (#933).

• Correct shape of buffer views in process_tile() when the tile has depth 1 (#1215).

Documentation

• Information on multithreading added to UDF docs in Threading (#1170).

Miscellaneous

• A Docker image with a LiberTEM installation is available on DockerHub now. See Containers for details (#1144, #484).

• Improve performance with large UDF parameters (#1143).

• Start using libertem.preload again and import hdf5plugin if present so that users don’t have to specify this common selection of HDF5 filters as preload themselves (#1160).

0.8.0 / 2021-10-04

This release mainly contains improvements of center of mass / first moment analysis and support for starting the web GUI from JupyterHub or JupyterLab.

New features

• Support for center of mass with annular masks in create_com_analysis(), COMAnalysis and the GUI (#633, #1089).

• Support in the GUI for specifying rotation of scan against detector and flipping the detector y axis (#1087, #31). Previously this was only supported in the Python API.

• Tweaks and instructions for JupyterHub and JupyterLab integration in LiberTEM, see Jupyter integration (#1074). New package LiberTEM/LiberTEM-jupyter-proxy for interfacing.

• In the web API, support was added to re-run visualization only, without re-running UDFs for an analysis. This allows for almost instant feedback for some operations, like changing CoM parameters.

• Added token-based authentication. For now, it is only usable via integrations like Jupyter. It will be extended to local/manual usage later (#1074, #1097). Please comment on #1097 if local/manual use would be beneficial for you so that it is prioritized accordingly.

• SEQ dataset: Added support for loading excluded pixels from XML (#805, #1077). See SEQDataSet for more information. Also support both *.seq.seq and *.seq as extension for the main SEQ file to find files with matching base name that contain correction data (#1120, #1121).

Bugfixes

• Assert that the files argument to DMDataSet is actually a list or tuple, to prevent iterating over a string path (#1058).

• Escape globs to support special characters in file names for multi-file datasets (#1066, #1067).

• Make sure multithreading in the main process still works properly after launching a Context (#1053, #1100).

• Allow custom plots to return RGB as plot data, for example a color wheel for vector fields (#1052, #1101).

• Adjust partition count to match the number of CPU compute workers, not total workers to prevent residual partitions (#1086, #1103).

• Correct partition shape for ROI in UDFMeta (#1109).

• Fix memory leak: Don’t submit dynamically generated callables directly to the distributed cluster, as they are cached in an unbounded cache (#894, #964, #1119).

Documentation

• Note on handling HDF5 files with non-standard compression in H5DataSet (#1059).

• Link to two more public datasets: High-resolution 4D STEM dataset of SrTiO3 and Synthetic 4D STEM dataset based on a SrTiO3 supercell (#1073).

Misc

• Speed up coordinate calculation (#1108, #1109).

• Make sure tasks are scheduled dynamically on available workers if they have uneven run time to benefit more from GPUs (#1107).

• Cache loaded libraries to reduce overhead of setting the thread count (#1117, #1118).

Many thanks to our new contributors Levente Puskás for the excluded pixel loading and to Matthew Bryan for figuring non-standard compression in HDF5 and improving DM input validation. Congratulations to Alex for closing the long-standing CoM issue #31 and for enabling easy and secure access to the web interface on shared IT infrastructure.

0.7.1 / 2021-07-08

This is a bugfix release that ensures compatibility with the upcoming numba 0.54 release.

Our custom numba caching makes some assumptions about numba internals, which have changed in numba 0.54. This fixes compatibility with numba 0.54, and also makes sure we fail gracefully for future changes (#1060, #1061).

0.7.0 / 2021-06-10

This release introduces features that are essential for live data processing, but can be used for offline processing as well: Live plotting, API for bundled execution of several UDFs in one run, iteration over partial UDF results, and asynchronous UDF execution. Features and infrastructure that are specific to live processing are included in the LiberTEM-live package, which will be released soon.

New features

• Support for postprocessing of results on the main node after merging partial results. This adds get_results() and the use parameter to buffer(). See Post-processing after merging for details (#994, #1003, #1001).

• Obtain partial results from each merge step iteratively as a generator using run_udf_iter(). See Partial results and an example for details (#1011)!

• Run multiple UDFs in one pass over a single DataSet by passing a list of UDFs instead of one UDF in run_udf() and run_udf_iter() (#1011).

• Allow usage from an asynchronous context with the new sync=False argument to run_udf() and run_udf_iter(). See Partial results and an example for details (#216, #1011)!

• Live plotting using the new plots parameter for run_udf() and run_udf_iter(), as well as live plotting classes documented in Visualization. Pass plots=True for simple usage. See Live Plotting as well as an example for the various possibilities for advanced usage (#980, #1011).

• Allow some UDF-internal threading. This is mostly interesting for ad-hoc parallelization on top of the InlineJobExecutor and live processing that currently relies on the InlineJobExecutor for simplicity, but could also be used for hybrid multiprocess/multithreaded workloads. Threads for numba, pyfftw, OMP/MKL are automatically controlled. The executor makes the number of allowed threads available as libertem.udf.base.UDFMeta.threads_per_worker for other threading mechanisms that are not controlled automatically (#993).

• K2IS: reshaping, sync offset and time series support. Users can now specify a nav_shape, sig_shape and sync_offset for a K2IS data set, and load time series data (#1019, #911). Many thanks to @AnandBaburajan for implementing this feature!

• Support for Python >=3.9.3, use Python 3.9 in AppImage (#914, #1037, #1039).

Bugfixes

• UDF: Consistently use attribute access in UDF.process_*(), UDF.merge(), UDF.get_results() etc. instead of mixing it with __getitem__() dict-like access. The previous method still works, but triggers a UserWarning (#1000, #1003).

• Also allow non-sliced assignment, for example self.results.res += frame (#1000, #1003).

• Better choice of kind='nav' buffer fill value outside ROI.

  • String : Was 'n', now ''

  • bool : Was True, now False

  • integers : Was smallest possible value, now 0

  • objects : was np.nan, now None (#1011)

• Improve performance for chunked HDF5 files, especially compressed HDF5 files which have a chunking in both navigation dimensions. They were causing excessive read amplification (#984).

• Fix plot range if only zero and one other value are present in the result, most notably boolean values (#944, #1011).

• Fix axes order in COM template: The components in the field are (x, y) while the template had them as (y, x) before (#1023).

Documentation

• Update Gatan Digital Micrograph (GMS) examples to work with the current GMS and LiberTEM releases and demonstrate the new features. (#999, #1002, #1004, #1011). Many thanks to Winnie from Gatan for helping to work around a number of issues!

• Restructure UDF documentation (#1034).

• Document coordinate meta information (#928, #1034).

Obsolescence

• Removed deprecated blobfinder and FeatureVecMakerUDF as previously announced. Blobfinder is available as a separate package at https://github.com/liberTEM/LiberTEM-blobfinder. Instead of FeatureVecMakerUDF, you can use a sparse matrix and ApplyMasksUDF (#979).

• Remove deprecated Job interface as previously announced. The functionality was ported to the more capable UDF interface #978.

0.6.0 / 2021-02-16

We are pleased to announce the latest LiberTEM release, with many improvements since 0.5. We would like to highlight the contributions of our GSoc 2020 students @AnandBaburajan (reshaping and sync offset correction) and @twentyse7en, (Code generation to replicate GUI analyses in Jupyter notebooks) who implemented significant improvements in the areas of I/O and the user interface.

Another highlight of this release is experimental support of NVidia GPUs, both via CuPy and via native libraries. The API is ready to be used, including support in the GUI. Performance optimization is still to be done (#946). GPU support is activated for all mask-based analyses (virtual detector and Radial Fourier) for testing purposes, but will not bring a noticeable improvement of performance yet. GPU-based processing did show significant benefits for computationally heavy applications like the SSB implementation in https://github.com/Ptychography-4-0/ptychography.

A lot of work was done to implement tiled reading, resulting in a new I/O system. This improves performance in many circumstances, especially when dealing with large detector frames. In addition, a correction module was integrated into the new I/O system, which can correct gain, subtract a dark reference, and patch pixel defects on the fly. See below for the full changelog!

New features

• I/O overhaul

  • Implement tiled reading for most file formats (#27, #331, #373, #435).

  • Allow UDFs that implement process_tile to influence the tile shape by overriding libertem.udf.base.UDF.get_tiling_preferences() and make information about the tiling scheme available to the UDF through libertem.udf.base.UDFMeta.tiling_scheme. (#554, #247, #635).

  • Update MemoryDataSet to allow testing with different tile shapes (#634).

  • Added I/O backend selection (#896), which allows users to select the best-performing backend for their circumstance when loading via the new io_backend parameter of Context.load. This fixes a K2IS performance regression (#814) by disabling any readahead hints by default. Additionaly, this fixes a performance regression (#838) on slower media (like HDDs), by adding a buffered reading backend that tries its best to linearize I/O per-worker. GUI integration of backend selection is to be done.

  • For now, direct I/O is no longer supported, please let us know if this is an important use-case for you (#716)!

• Support for specifying logging level from CLI (#758).

• Support for Norpix SEQ files (#153, #767).

• Support for MRC files, as supported by ncempy (#152, #873).

• Support for loading stacks of 3D DM files (#877). GUI integration still to be done.

• GUI: Filebrowser improvements: users can star directories in the file browser for easy navigation (#772).

• Support for running multiple UDFs “at the same time”, not yet exposed in public APIs (#788).

• GUI: Users can add or remove scan size dimensions according to the dataset’s shape (#779).

• GUI: Shutdown button to stop server, useful for example for JupyterHub integration (#786).

• Infrastructure for consistent coordinate transforms are added in libertem.corrections.coordinates and libertem.utils. See also a description of coordinate systems in Concepts.

• create_com_analysis() now allows to specify a flipped y axis and a scan rotation angle to deal with MIB files and scan rotation correctly. (#325, #786).

• Corrections can now be specified by the user when running a UDF (#778, #831, #939).

• Support for loading dark frame and gain map that are sometimes shipped with SEQ data sets.

• GPU support: process data on CPUs, CUDA devices or both (#760, CuPy support).

• Spinning out holography to a separate package is in progress: https://github.com/LiberTEM/LiberTEM-holo/

• Implement CuPy support in HoloReconstructUDF, currently deactivated due to #815 (#760).

• GUI: Allows the user to select the GPUs to use when creating a new local cluster (#812).

• GUI: Support to download Jupyter notebook corresponding to an analysis made by a user in GUI (#801).

• GUI: Copy the Jupyter notebook cells corresponding to the analysis directly from GUI, including cluster connection details (#862, #863)

• Allow reshaping datasets into a custom shape. The DataSet implementations (currently except HDF5 and K2IS) and GUI now allow specifying nav_shape and sig_shape parameters to set a different shape than the layout in the dataset (#441, #793).

• All DataSet implementations handle missing data gracefully (#256, #793).

• The DataSet implementations (except HDF5 and K2IS) and GUI now allow specifying a sync_offset to handle synchronization/acquisition problems (#793).

• Users can access the coordinates of a tile/partition slice through coordinates (#553, #793).

• Cache warmup when opening a data set: Precompiles jit-ed functions on a single process per node, in a controlled manner, preventing CPU oversubscription. This improves further through implementing caching for functions which capture other functions in their closure (#886, #798).

• Allow selecting lin and log scaled visualization for sum, stddev, pick and single mask analyses to handle data with large dynamic range. This adds key intensity_lin to SumResultSet, PickResultSet and the result of SDAnalysis. It adds key intensity_log to SingleMaskResultSet. The new keys are chosen to not affect existing keys (#925, #929).

• Tuples can be added directly to Shape objects. Right addition adds to the signal dimensions of the Shape object while left addition adds to the navigation dimensions (#749)

Bugfixes

• Fix an off-by-one error in sync offset for K2IS data (drive-by change in #706).

• Missing-directory error isn’t thrown if it’s due to last-recent-directory not being available (#748).

• GUI: when cluster connection fails, reopen form with parameters user submitted (#735).

• GUI: Fixed the glitch in file opening dialogue by disallowing parallel browsing before loading is concluded (#752).

• Handle empty ROI and extra_shape with zero. Empty result buffers of the appropriate shape are returned if the ROI is empty or extra_shape has a zero (#765)

• Improve internals of libertem.io.corrections.detector and libertem.io.corrections.corrset to better support correction of many dead pixels. (#890, #889)

• Handle single-frame partitions in combination with aux data. Instead of squeezing the aux buffer, reshape to the correct shape (#791, #902).

• Libertem-server can now be started from Bash on Windows (#731)

• Fix reading without a copy from multi-file datasets. The start offset of the file was not taken account when indexing into the memory maps (#903).

• Improve performance and reduce memory consumption of point analysis. Custom right hand side matrix product to reduce memory consumption and improve performance of sparse masks, such as point analysis. See also scipy/13211 (#917, #920).

• Fix stability issue with multiple dask clients. dd.as_completed needs to specify the loop to work with multiple dask.distributed clients (#921).

• GUI: Snap to pixels in point selection analysis. Consistency between point selection and picking (#926, #927).

• Open datasets with autodetection, positional and keyword arguments. Handle keyword and positional arguments to Context.load('auto', ...) correctly (#936, #938).

Documentation

• Switched to the readthedocs sphinx theme, improving the overall documentation structure. The developer documentation is now in a separate section from the user documentation.

Misc

• Command line options can also be accessed with shorter alternatives (#757).

• Depend on Numba >= 0.49.1 to support setting Numba thread count (#783), bumped to 0.51 to support caching improvements (#886).

• libertem-server: Ask for confirmation if the user press ctrl+c. Can immediately stop using another ctrl+c (#781).

• Included pytest-benchmark to integrate benchmarks in the test infrastructure. See Benchmarking for details (#819).

• The X and Y components for the color wheel visualization in Center of Mass and Radial Fourier Analysis are swapped to match the axis convention in empyre. This just changes the color encoding in the visualization and not the result (#851).

Deprecations

• The tileshape parameter of DataSet implementations is deprecated in favor of tileshape negotiation and will be ignored, if given (#754, #777).

• Remove color wheel code from libertem.viz and replace with imports from empyre. Note that these functions expect three vector components instead of two (#851).

• The new and consistent nav_shape and sig_shape parameters should be used when loading data. The old scan_size and detector_size parameters, where they existed, are still recognized (#793).

0.5.1 / 2020-08-12

Bugfixes

• Allow installation with latest dask distributed on Python 3.6 and 3.7

0.5.0 / 2020-04-23

New features

• In addition to tuples, Shape objects can be used as extra_shape parameter for libertem.udf.base.UDF.buffer() and libertem.udf.base.UDF.aux_data() now. (#694)

• Progress bar support based on tqdm that can be enabled by passing progress=True to libertem.api.Context.run_udf(), libertem.api.Context.run() and libertem.api.Context.map(): Running UDFs. (#613, #670, #655)

• Include explicit support for Direct Electron’s DE5 format based on HDF5. (#704)

• GUI: Downloadable results as HDF5, NPZ, TIFF, and RAW. See Downloading results for details. (#665)

• libertem.api.Context.load() now automatically detects file type and parameters if filetype="auto" is passed. (#610, #621, #734)

• Relocatable GUI: Allow LiberTEM to run from different URL prefixes, allowing integration into, for example, JupyterLab. (#697)

• Run preprocess() also before merge on the main node to allocate or initialize buffers, in addition to running on the workers (#624).

• No need to set thread count environment variables anymore since the thread count for OpenBLAS, OpenMP, Intel MKL and pyFFTW is now set on the workers at run-time. Numba support will be added as soon as Numba 0.49 is released. (#685).

Bugfixes

• A large number of usability improvements (#622, #639, #641, #642, #659, #666, #690, #699, #700, #704). Thanks and credit to many new contributors from GSoC!

• Fixed the buggy “enable Direct I/O” checkbox of the RAW dataset and handle unsupported operating systems gracefully. (#696, #659)

Documentation

• Added screenshots and description of ROI and stddev features in usage docs (#669)

• Improved instructions for installing LiberTEM (general: #664; for development: #598)

• Add information for downloading and generating sample datasets: Sample Datasets. (#650, #670, #707)

Obsolescence

• Parameters crop_detector_to and detector_size_raw of libertem.io.dataset.raw.RawFileDataSet are deprecated and will be removed after 0.6.0. Please specify detector_size instead or use a specialized DataSet, for example for EMPAD.

• libertem.udf.feature_vector_maker.FeatureVecMakerUDF is deprecated and will be removed in 0.6.0. Use ApplyMasksUDF with a sparse stack of single pixel masks or a stack generated by libertem_blobfinder.common.patterns.feature_vector() instead. (#618)

Misc

• Clustering analysis

  • Use a connectivity matrix to only cluster neighboring pixels, reducing memory footprint while improving speed and quality (#618).

  • Use faster ApplyMasksUDF to generate feature vector (#618).

• StdDevUDF

  • About 10x speed-up for large frames (#625, #640)

  • Rename result buffers of StdDevUDF, run_stddev() and consolidate_result() from 'sum_frame' to 'sum', 'num_frame' to 'num_frames' (#640)

  • Resolve ambiguity between variance and sum of variances in result buffer names of StdDevUDF, run_stddev() and consolidate_result(). (#640)

• LiberTEM works with Python 3.8 for experimental use. A context using a remote Dask.Distributed cluster can lead to lock-ups or errors with Python 3.8. The default local Dask.Distributed context works.

• Improve performance with large tiles. (#649)

• SumUDF moved to the libertem.udf folder (#613).

• Make sure the signal dimension of result buffer slices can be flattened without creating an implicit copy (#738, #739)

Many thanks to the contributors to this release: @AnandBaburajan, @twentyse7en, @sayandip18, @bdalevin, @saisunku, @Iamshankhadeep, @abiB27, @sk1p, @uellue

0.4.1 / 2020-02-18

This is a bugfix release, mainly constraining the msgpack dependency, as distributed is not compatible to version 1.0 yet. It also contains important fixes in the HDF5 dataset.

Bugfixes

• Fix HDF5 with automatic tileshape (#608)

• Fix reading from HDF5 with roi beyond the first partition (#606)

• Add version constraint on msgpack

0.4.0 / 2020-02-13

The main points of this release are the Job API deprecation and restructuring of our packaging, namely extracting the blobfinder module.

New features

• dtype support for UDFs Preferred input dtype (#549, #550)

• Dismiss error messages via keyboard: allows pressing the escape key to close all currently open error messages (#437)

• ROI doesn’t have any effect if in pick mode, so we hide the dropdown in that case (#511)

• Make tileshape parameter of HDF5 DataSet optional (#578)

• Open browser after starting the server. Enabled by default, can be disabled using –no-browser (#81, #580)

• Implement libertem.udf.masks.ApplyMasksUDF as a replacement of ApplyMasksJob (#549, #550)

• Implement libertem.udf.raw.PickUDF as a replacement of PickFrameJob (#549, #550)

Bug fixes

• Fix FRMS6 in a distributed setting. We now make sure to only do I/O in methods that are running on worker nodes (#531).

• Fixed loading of nD HDF5 files. Previously the HDF5 DataSet was hardcoded for 4D data - now, arbitraty dimensions should be supported (#574, #567)

• Fix DaskJobExecutor.run_each_host. Need to pass pure=False to ensure multiple runs of the function (#528).

Obsolescence

• Because HDFS support is right now not tested (and to my knowledge also not used) and the upstream hdfs3 project is not actively maintained, remove support for HDFS. ClusterDataSet or CachedDataSet should be used instead (#38, #534).

Misc

• Depend on distributed>=2.2.0 because of an API change. (#577)

• All analyses ported from Job to UDF back-end. The Job-related code remains for now for comparison purposes (#549, #550)

Job API deprecation

The original Job API of LiberTEM is superseded by the new User-defined functions (UDFs) API with release 0.4.0. See #549 for a detailed overview of the changes. The UDF API brings the following advantages:

• Support for regions of interest (ROIs).

• Easier to implement, extend and re-use UDFs compared to Jobs.

• Clean separation between back-end implementation details and application-specific code.

• Facilities to implement non-trivial operations, see User-defined functions: advanced topics.

• Performance is at least on par.

For that reason, the Job API has become obsolete. The existing public interfaces, namely libertem.api.Context.create_mask_job() and libertem.api.Context.create_pick_job(), will be supported in LiberTEM for two more releases after 0.4.0, i.e. including 0.6.0. Using the Job API will trigger deprecation warnings starting with this release. The new ApplyMasksUDF replaces ApplyMasksJob, and PickUDF replaces PickFrameJob.

The Analysis classes that relied on the Job API as a back-end are already ported to the corresponding UDF back-end. The new back-end may lead to minor differences in behavior, such as a change of returned dtype. The legacy code for using a Job back-end will remain until 0.6.0 and can be activated during the transition period by setting analysis.TYPE = 'JOB' before running.

From ApplyMasksJob to ApplyMasksUDF

Main differences:

• ApplyMasksUDF returns the result with the first axes being the dataset’s navigation axes. The last dimension is the mask index. ApplyMasksJob used to return transposed data with flattened navigation dimension.

• Like all UDFs, running an ApplyMasksUDF returns a dictionary. The result data is accessible with key 'intensity' as a BufferWrapper object.

• ROIs are supported now, like in all UDFs.

Previously with ApplyMasksJob:

# Deprecated!
mask_job = ctx.create_mask_job(
  factories=[all_ones, single_pixel],
  dataset=dataset
)
mask_job_result = ctx.run(mask_job)

plt.imshow(mask_job_result[0].reshape(dataset.shape.nav))

Now with ApplyMasksUDF:

mask_udf = libertem.udf.masks.ApplyMasksUDF(
  mask_factories=[all_ones, single_pixel]
)
mask_udf_result = ctx.run_udf(dataset=dataset, udf=mask_udf)

plt.imshow(mask_udf_result['intensity'].data[..., 0])

From PickFrameJob to PickUDF

PickFrameJob allowed to pick arbitrary contiguous slices in both navigation and signal dimension. In practice, however, it was mostly used to extract single complete frames. PickUDF allows to pick the complete signal dimension from an arbitrary non-contiguous region of interest in navigation space by specifying a ROI.

If necessary, more complex subsets of a dataset can be extracted by constructing a suitable subset of an identity matrix for the signal dimension and using it with ApplyMasksUDF and the appropriate ROI for the navigation dimension. Alternatively, it is now easily possible to implement a custom UDF for this purpose. Performing the complete processing through an UDF on the worker nodes instead of loading the data to the central node may be a viable alternative as well.

PickUDF now returns data in the native dtype of the dataset. Previously, PickFrameJob converted to floats.

Using libertem.api.Context.create_pick_analysis() continues to be the recommended convenience function to pick single frames.

Restructuring into sub-packages

We are currently restructuring LiberTEM into packages that can be installed and used independently, see #261. This will be a longer process and changes the import locations.

• Blobfinder is the first module separated in 0.4.0.

• See Package overview for a current overview of sub-packages.

For a transition period, importing from the previous locations is supported but will trigger a FutureWarning. See Show deprecation warnings on how to activate deprecation warning messages, which is strongly recommended while the restructuring is ongoing.

0.3.0 / 2019-12-12

New features

• Make OOP based composition and subclassing easier for CorrelationUDF (#466)

• Introduce plain circular match pattern Circular (#469)

• Distributed sharded dataset ClusterDataSet (#136, #457)

• Support for caching data sets CachedDataSet from slower storage (NFS, spinning metal) on fast local storage (#471)

• Clustering analysis (#401, #408 by @kruzaeva).

• libertem.io.dataset.dm.DMDataSet implementation based on ncempy (#497)

  • Adds a new map() executor primitive. Used to concurrently read the metadata for DM3/DM4 files on initialization.

  • Note: no support for the web GUI yet, as the naming patterns for DM file series varies wildly. Needs changes in the file dialog.

• Speed up of up to 150x for correlation-based peak refinement in libertem.udf.blobfinder.correlation with a Numba-based pipeline (#468)

• Introduce FullFrameCorrelationUDF which correlates a large number (several hundred) of small peaks (10x10) on small frames (256x256) faster than FastCorrelationUDF and SparseCorrelationUDF (#468)

• Introduce UDFPreprocessMixin (#464)

• Implement iterator over AnalysisResultSet (#496)

• Add hologram simulation libertem.utils.generate.hologram_frame() (#475)

• Implement Hologram reconstruction UDF libertem.udf.holography.HoloReconstructUDF (#475)

Bug fixes

• Improved error and validation handling when opening files with GUI (#433, #442)

• Clean-up and improvements of libertem.analysis.fullmatch.FullMatcher (#463)

• Ensure that RAW dataset sizes are calculated as int64 to avoid integer overflows (#495, #493)

• Resolve shape mismatch issue and simplify dominant order calculation in Radial Fourier Analysis (#502)

• Actually pass the enable_direct parameter from web API to the DataSet

Documentation

• Created Authorship policy (#460, #483)

• Change management process (#443, #451, #453)

• Documentation for Crystallinity map and Clustering analysis (#408 by @kruzaeva)

• Instructions for profiling slow tests (#447, #448)

• Improve API reference on Analysis results (#494, #496)

• Restructure and update the API reference for a number of UDFs and other application-specific code (#503, #507, #508)

Obsolescence

• The Job interface is planned to be replaced with an implementation based on UDFs in one of the upcoming releases.

Misc

• Split up the blobfinder code between several files to reduce file size (#468)

0.2.2 / 2019-10-14

Point release to fix a number of minor issues, most notably PR #439 that should have been merged for version 0.2.

Bug fixes

• Trigger a timeout when guessing parameters for HDF5 takes too long (#440 , #449)

• Slightly improved error and validation handling when opening files with GUI (@ec74c13)

• Recognize BLO file type (#432)

• Fixed a glitch where negative peak elevations were possible (#446)

• Update examples to match 0.2 release (#439)

0.2.1 / 2019-10-07

Point release to fix a bug in the Zenodo upload for production releases.

0.2.0 / 2019-10-07

This release constitutes a major update after almost a year of development. Systematic change management starts with this release.

This is the release message:

User-defined functions

LiberTEM 0.2 offers a new API to define a wide range of user-defined reduction functions (UDFs) on distributed data. The interface and implementation offers a number of unique features:

• Reductions are defined as functions that are executed on subsets of the data. That means they are equally suitable for distributed computing, for interactive display of results from a progressing calculation, and for handling live data¹.

• Interfaces adapted to both simple and complex use cases: From a simple map() functionality to complex multi-stage reductions.

• Rich options to define input and output data for the reduction functions, which helps to implement non-trivial operations efficiently within a single pass over the input data.

• Composition and extension through object oriented programming

• Interfaces that allow highly efficient processing: locality of reference, cache efficiency, memory handling

Introduction: https://libertem.github.io/LiberTEM/udf.html

Advanced features: https://libertem.github.io/LiberTEM/udf/advanced.html

A big shoutout to Alex (@sk1p) who developed it! 🏆

¹User-defined functions will work on live data without modification as soon as LiberTEM implements back-end support for live data, expected in 2020.

Support for 4D STEM applications

In parallel to the UDF interface, we have implemented a number of applications that make use of the new facilities:

• Correlation-based peak finding and refinement for CBED (credit: Karina Ruzaeva @kruzaeva)

• Strain mapping

• Clustering

• Fluctuation EM

• Radial Fourier Series (advanced Fluctuation EM)

More details and examples: https://libertem.github.io/LiberTEM/applications.html

Extended documentation

We have greatly improved the coverage of our documentation: https://libertem.github.io/LiberTEM/index.html#documentation

Fully automated release pipeline

Alex (@sk1p) invested a great deal of effort into fully automating our release process. From now on, we will be able to release more often, including service releases. 🚀

Basic dask.distributed array integration

LiberTEM can generate efficient dask.distributed arrays from all supported dataset types with this release. That means it should be possible to use our high-performance file readers in applications outside of LiberTEM.

File formats

Support for various file formats has improved. More details: https://libertem.github.io/LiberTEM/formats.html

0.1.0 / 2018-11-06

Initial release of a minimum viable product and proof of concept.

Support for applying masks with high throughput on distributed systems with interactive web GUI display and scripting capability.