iris-ued

Release 5.3.4

• Added new Bragg peak functionality of processed datasets, as well as arbitrary mask loading and generation for pre-processing.

This release brings some major additions:

• The center of diffraction is now calculated and updated as needed automatically.
• Added the DiffractionDataset.mask_apply to modify the diffraction pattern mask.
• Windows installers are now built with pynsist/NSIS instead of PyInstaller (#15).
• Better handling of write permissions.
• Added the MigrationWarning and MigrationError classes. Warnings/errors of these classes tell the user that migration should be performed. This is automatically done by opening a DiffractionDataset with writing permission. The GUI does this automatically.

Some maintenance updates:

• Support for Python 3.6 and NumPy<1.17 has been dropped <https://numpy.org/neps/nep-0029-deprecation_policy.html>_
• Fixed an issue where creating the plug-in directory would rarely fail.

This release brings one major change:

• Parallel operations on datasets (via HDF5 single-writer multiple-reader) is now possible on all platforms. It was previously limited to Windows.

Infrastructure changes listed below have lead to improvements to documentation and correctness:

• Code snippets in documentation are now tested for correctness.
• Migration of test infrastructure to pytest.
• Tests are now included in source distributions.

This release adds support for h5py 3.*, so that you can now use h5py 2.* and 3.*.

This release brings the following changes:

• Re-licensing iris-ued to GPLv3.
• Changed the default colormap for processed datasets, to visually distinguish between raw and processed data viewers
• Added support for Python 3.9

This release sees one major bug-fix and one large distribution change:

• Fixed an issue where a broken plug-in would crash Iris. Instead, broken plug-in will not be loaded.
• iris-ued can now be distributed via conda on Linux.

As well as the usual updates to dependencies, documentation, etc.

Note that this release does not contain Windows installers. In the past, these proved to be too unreliable. If this is problematic for you, please don't hesitate to raise an issue.

This release contains the following changes:

• Added the DiffractionDataset.time_series_selection method, which allows to create time-series integrated across an arbitrary momentum-space selection mask.
  This allows to create time-series from shapes that are not rectangular, at the expense of performance.

• Added a few methods to create selection masks: DiffractionDataset.selection_rect, DiffractionDataset.selection_disk, and DiffractionDataset.selection_ring.

• Added the ability to show/hide dataset control bar;

• Added the ability to export time-series data in CSV format;

• Fixed an issue where calculations of time-series, relative to pre-time-zero, would raise an error.

• Symmetrization dialog is no longer in "beta".

This release sees the following changes:

• Official support for Linux.
• Plug-ins installed via the GUI can now be used right away. No restarts required.
• Added the iris.plugins.load_plugin function to load plug-ins without installing them. Useful for testing.
• Plug-ins can now have the display_name property which will be displayed in the GUI. This is optional and backwards-compatible.
• Siwick Research Group-specific plugins were removed. They can be found here: https://github.com/Siwick-Research-Group/iris-ued-plugins
• Switched to Azure Pipelines for continuous integration builds;
• Added cursor information (position and image value) for processed data view;

Fixes:

• Fixed an issue where very large relative differences in datasets would crash the GUI displays;
• Fixed an issue where time-series fit would not display properly in fractional change mode;

Here are the changes added from version 5.1.2:

• Added logging support for the GUI component. Logs can be reached via the help menu
• Added an update check. You can see whether an update is available via the help menu, as well as via the status bar.
• Added the ability to view time-series dynamics in absolute units AND relative change.

Changes related to the PyPI package:

• Pinned dependency to scikit-ued, to prevent upgrade to scikit-ued 2.0 unless appropriate.
• Pinned dependency to npstreams, to prevent upgrade to npstreams 2.0 unless appropriate.

Users of the PyPI package are strongly encouraged to update.

Unfortunately, a critical error was introduced in 5.1.1 regarding stylesheets. This release fixes this problem.

This release is a bug-fix release mostly for building executables. The changes are:

• Fixed an issue where data reduction would freeze when using more than one CPU;
• Removed the auto-update mechanism. Update checks will run in the background only;
• Fixed an issue where the in-progress indicator would freeze;
• Moved tests outside of source repository;
• Updated GUI stylesheet to QDarkStyle 2.6.6;

No new features in this release. Python 3.7 support is live. Some usability tweaks have been added, such as more visible mask controls.

Windows standalone installers are now available!

Due to new forced image orientation, objects on screens were not properly registered (e.g. diffraction center finder). These have been fixed in this version.

This minor release offers some new UI features, such as

• Ability to fit exponentials to time-series;
• Added region-of-interest text bounds for easier time-series exploration
• Enforced PyQtGraph to use row-major image orientation

On the backend side, datasets are now opened in read-only mode unless absolutely necessary. This should make it safer to handler multiple instances of iris at the same time.

The major change in this version is the ability to guess raw dataset formats using the iris.open_raw function. This allows the possibility to start the GUI and open a dataset at the same time.

The package now only has dependencies that can be installed through conda

This is a minor bug-fix release that also includes user interface niceties (e.g. link to online documentation) and user experience niceties (e.g. confirmation message if you forget pixel masks).

This new version includes a completely rewritten library and GUI front-end. Earlier datasets will need to be re-processed.

New features:

• Faster performance thanks to better data layout in HDF5;
• Plug-in architecture for various raw data formats;
• Faster performance thanks to npstreams package;
• Easier to extend GUI skeleton;
• Online documentation accessible from the GUI;
• Continuous integration.

The package is now available on PyPI as iris-ued:

python -m pip install iris-ued