Changelog#

2.0.3#

Bug fixes#

Fix an error that happened when the histogram widget was open, but a layer that doesn’t support histogramming (e.g., a labels layer) was selected.

2.0.2#

Dependencies#

napari-matplotlib now adheres to SPEC 0, and has:

Dropped support for Python 3.9
Added support for Python 3.12
Added a minimum required numpy verison of 1.23
Pinned the maximum napari version to < 0.5. Version 3.0 of napari-matplotlib will introduce support for napari version 0.5.

2.0.1#

Bug fixes#

Fixed using the HistogramWidget with layers containing multiscale data.
Make sure HistogramWidget uses 100 bins (not 99) when floating point data is selected.

2.0.0#

Changes to custom theming#

napari-matplotlib now uses colours from the current napari theme to customise the Matplotlib plots. See the example on creating a new napari theme for a helpful guide on how to create custom napari themes.

This means support for custom Matplotlib styles sheets has been removed.

If you spot any issues with the new theming, please report them at matplotlib/napari-matplotlib#issues.

Other changes#

Histogram bin sizes for integer-type data are now force to be an integer.
The HistogramWidget now has two vertical lines showing the contrast limits used to render the selected layer in the main napari window.
Added an example gallery for the FeaturesHistogramWidget.

1.2.0#

Changes#

Dropped support for Python 3.8, and added support for Python 3.11.
Histogram plots of points and vector layers are now coloured with their napari colourmap.
Added support for Matplotlib 3.8

1.1.0#

Additions#

Added a widget to draw a histogram of features.

Changes#

The slice widget is now limited to slicing along the x/y dimensions. Support for slicing along z has been removed for now to make the code simpler.
The slice widget now uses a slider to select the slice value.

Bug fixes#

Fixed creating 1D slices of 2D images.
Removed the limitation that only the first 99 indices could be sliced using the slice widget.

1.0.2#

Bug fixes#

A full dataset is no longer read into memory when using HistogramWidget. Only the current slice is loaded.
Fixed compatibility with napari 0.4.18.

Changes#

Histogram bin limits are now caclualted from the slice being histogrammed, and not the whole dataset. This is as a result of the above bug fix.

1.0.1#

Bug fixes#

Pinned that maximum version of napari to 0.4.17, since napari-matplotlib does not yet work with napari 0.4.18.

1.0.0#

New features#

Added MPLWidget as a widget containing just a Matplotlib canvas without any association with a napari viewer.
Added text to each widget indicating how many layers need to be selected for the widget to plot something.

Visual improvements#

The background of napari-matplotlib figures and axes is now transparent, and the text and axis colour respects the napari theme.
The icons in the Matplotlib toolbar are now the same size as icons in the napari window.
Custom style sheets can now be set to customise plots. See the user guide for more information.

Changes#

The scatter widgets no longer use a LogNorm() for 2D histogram scaling. This is to move the widget in line with the philosophy of using Matplotlib default settings throughout napari-matplotlib. This still leaves open the option of adding the option to change the normalization in the future. If this is something you would be interested in please open an issue at matplotlib/napari-matplotlib.
Labels plotting with the features scatter widget no longer have underscores replaced with spaces.
NapariMPLWidget.update_layers() has been removed as it is intended to be private API. Use NapariMPLWidget.on_update_layers instead to implement funcitonality when layer selection is changed.
The slice widget now only plots x-ticks at integer locations.

Bug fixes#

Importing napari-matplotlib no longer affects how plots are rendered in Jupyter notebooks.

Other#

napari-matplotlib is now tested on macOS and Windows.
Type annotations have been completed throughout the code base.