Announcement: napari 0.2.9 We're happy to announce the release of napari 0.2.9! napari is a fast, interactive, multi-dimensional image viewer for Python. It's designed for browsing, annotating, and analyzing large multi-dimensional images. It's built on top of Qt (for the GUI), vispy (for performant GPU-based rendering), and the scientific Python stack (numpy, scipy). For more information, examples, and documentation, please visit our website: https://github.com/napari/napari Highlights better support for surface timeseries (#831) contrast limits slider popup on right click (#837) better isosurface rendering with colormaps (#840) attenuated MIP mode for better 3D rendering (#846) New Features convert layer properties to dictionary (#686) better support for surface timeseries (#831) make contrast_limits_range public and climSlider popup on right click (#837) attenuated MIP mode for better 3D rendering (#846) Improvements bump numpydoc dependency to 0_9_2 for faster startup (#830) better isosurface rendering with colormaps (#840) add nearest interpolation mode to volume rendering for better labels support (#841) refactor RangeSlider to accept data range and values. (#844) in bindings logic, check if generator, not generator function (#853) Bugfixes fix fullscreen crash for test_viewer (#849) fix RangeSlider.rangeChange emit type bug (#856) API Changes edge_color and face_color now refer to colors of all points and shapes in layer, current_edge_color and current_face_color now refer to the colors currently selected in the GUI (#686) 5 authors added to this release [alphabetical by first name or login] Juan Nunez-Iglesias Kira Evans Nicholas Sofroniew Talley Lambert Tony Tung 4 reviewers added to this release [alphabetical by first name or login] Juan Nunez-Iglesias Nicholas Sofroniew Talley Lambert Tony Tung

Data accompanying publication at https://doi.org/10.7554/eLife.12559 and code at https://doi.org/10.5281/zenodo.2949955. For example usage see the notebooks in the repository. The data is organized according to animal id, `00` - `18`. Animals `00` - `12` are electrophysiology data. Each electrophysiology animal data contains the timestamps of the extracted spikes and various processed tabular data. For usage see the `ephys-traces.ipynb` and `ephys-table.ipynb`. Raw voltage traces are not provided. Animals `13` - `18` are imaging data. Each imaging animal data contains timeseries of extracted calcium transients, pixel-wise regression maps of the field of view and various processed tabular data. For usage see the `imaging-raw.ipynb`, `imaging-traces.ipynb`, and `imaging-traces.ipynb`. Raw imaging movies are not provided.

Eyes may be 'the window to the soul' in humans, but whiskers provide a better path to the inner lives of rodents. The brain has remarkable abilities to focus its limited resources on information that matters, while ignoring a cacophony of distractions. While inspecting a visual scene, primates foveate to multiple salient locations, for example mouths and eyes in images of people, and ignore the rest. Similar processes have now been observed and studied in rodents in the context of whisker-based tactile sensation. Rodents use their mechanosensitive whiskers for a diverse range of tactile behaviors such as navigation, object recognition and social interactions. These animals move their whiskers in a purposive manner to locations of interest. The shapes of whiskers, as well as their movements, are exquisitely adapted for tactile exploration in the dark tight burrows where many rodents live. By studying whisker movements during tactile behaviors, we can learn about the tactile information available to rodents through their whiskers and how rodents direct their attention. In this primer, we focus on how the whisker movements of rats and mice are providing clues about the logic of active sensation and the underlying neural mechanisms.