Scipp

https://scipp.github.io/about/release-notes.html#v0-6-0-march-2021

Features
- New profile plotting functionality where one of the slider dimensions can be displayed as a profile in a subplot (1-D and 2-D projections only).
- Sliders have a thickness slider associated with them and can be used to show slices of arbitrary thickness.
- Can hide/show individual masks on plots.
- Can toggle log scale of axes and colorbar with buttons in figure toolbar.
- Add binned data support, replacing “event list” dtypes as well as “realign” support.
- Plotting of event data (binned data) with dynamic resampling with “infinite zoom” functionality.
- Value-based slicing support.
- Possibility to plot Scipp objects using my_data_array.plot() in addition to the classical plot() free function.
- Support for saving and loading scipp data structures to HDF5.
- More functions such as nanmean for better handling of special values such as INF and NaN.
- TBB (multi-threading) support for MacOS.
- scipp.neutron
- Improved instrument view, e.g., with buttons to align camera with an axis.
- Experiment logs (previously using Mantid’s Run) are now represented as native scipp objects, e.g., as scalar attributes holding a data array representing a time-series such as a temperature log.
- Support conversion of mantid.MaskWorkspace.

Breaking changes
- Dataset does not have a masks property any more. Use ds['item'].masks instead.
- Dataset does not support attributes any more.
- DataArray and dataset item attributes are now are now handled as “unaligned” coords. Use ds['item'].coords or array.attrs to access these.
- API for log scale on axes and colors has changed. Use plot(da, scale={'tof': 'log'}) to set a log scale on a coordinate axis, and use plot(da, norm='log') to have a log image colorscale or a log y axis on a 1d plot.
- vmin and vmax now represent absolute values instead of exponents when norm='log'.
- The ipympl matplotlib backend is now required for using inside Jupyter notebooks. This has been added as a dependency. It is also the only interactive backend that works in JupyterLab.
- Removed support for event_list dtype, use binned data instead.
- Removed support for “realigned” data. This is replaced by the more flexible and generic support for “binned” data.

Contributors

Matthew Andrew, Owen Arnold, Thibault Chatel, Simon Heybrock, Matthew D. Jones, Daniel Nixon, Piotr Rozyczko, Neil Vaytet, and Jan-Lukas Wynen

Features

* New realign functionality.
* Support for event-filtering.
* Support for subtraction and addition for (realigned) event data.
* Non-range slicing changed to preserve coords as attrs rather than dropping
* ``scipp.neutron``: Instrument view with advanced geometry support, showing correct pixel shapes.
* Instrument view working on doc pages.
* Made it simpler to add new ``dtype`` and support ``transform`` for all types.
* Comparison functions such as ``less``, ``greater_equal``, ...
* ``all`` and ``any`` can work over all dimensions as well as explicitly provided dimension argument
* It is now possible to convert between Scipp objects and Python dictionaries using ``to_dict`` and ``from_dict``.
* New functions ``collapse`` and ``slices`` can be use to split one or more dimensions of a DataArray to a dict of DataArrays.
* You can now inspect the global object list of via the ``repr`` for scipp showing Datasets, DataArrays and Variables
* Internal cleanup and documentation additions.

Noteable bug fixes

* Several fixes in the plotting (non-regular bins, colorbar limits, axes tick labels from unaligned coordinates, etc...)

Breaking changes

* Coord and attributes names for neutron data have been standardized, now using hyphens instead of underscore, except for subscripts. Affected examples: ``pulse-time`` (previously ``pulse_times``), ``source-position`` (previously ``source_position``), ``sample-position`` (previously ``sample_position``), ``detector-info`` (previously ``detector_info``).
* ``scipp.neutron.load`` must use ``advanced_geometry=True`` option for loading ``detector-info`` and pixel shapes.
* Normalization of event data cannot be done directly any more, must use ``realign``.
* Plotting variances in 2D has been removed, and the API for using ``matplotlib`` axes has been simplified slightly, since we no longer have axes for variances:
* Before: ``plot(..., mpl_axes={"ax": myax0, "cax": myax1})``
* After: ``plot(..., ax=myax0, cax=myax1)``
* Plot with keyword argument ``collapse`` has been removed in favour of two more generic free functions that return a ``dict`` of data arrays that can then directly be passed to the ``plot`` function:
* ``collapse(d, keep='x')`` slices all dimensions away to keep only ``'x'``, thus always returning 1D slices.
* ``slices(d, dim='x')`` slices along dimension ``'x'``, returning slices with ``ndim-1`` dimensions contaiing all dimensions other than ``'x'``.

Contributors

Owen Arnold, David Fairbrother, Simon Heybrock, Daniel Nixon, Pawel Ptasznik, Piotr Rozyczko, and Neil Vaytet

Documentation for this release is available at https://scipp.github.io/release/0.3.0/.