Merge pull request #37 from chrishavlin/yt_napari_updates

blog post on yt-napari updates

content/posts/yt_napari_updates.md

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+---
+title: Updates from yt-napari
+date: 2023-10-06T11:27:24-05:00
+lastmod: 2023-10-06T11:27:24-05:00
+author: Chris Havlin
+cover: /img/yt_napari_updates/path_3D_measurements.png
+categories:
+  - tutorial
+  - development
+  - releases
+tags:
+  - fun
+  - volume-rendering
+  - time-series
+  - documentation
+draft: false
+---
+
+yt-napari has seen a number of new features, performance improvements and new documentation 
+in the past year or so. Read on to find out more! 
+
+<!--more-->
+
+## Long form video walkthroughs
+
+One of the main efforts of the past year has been improving documentation. This included not just new
+static documentation (e.g., new [example notebooks](https://yt-napari.readthedocs.io/en/latest/notebooks.html)),
+but also the yt-napari Tutorial Series. The series starts with an introduction to napari for yt users, 
+moves on to how to use the yt-napari plugin to load data into napari from yt and ends with some videos showing 
+examples of using **other** napari plugins for analysis and visualization of yt data in napari. The final 
+videos are particularly fun as they apply image analysis methods used by the bio-imaging community to segment 
+yt-data. For example, here's a screenshot of using a watershed transformation via the `napari-simpleitk-image-processing`
+plugin to identify 3D density voids in `enzo_tiny_cosmology` and then the `napari-clusters-plotter` to interactively
+visualize mean field values within the domains:
+
+![](/img/yt_napari_updates/density_watershed.png)
+
+Or, check out [this short clip](https://www.youtube.com/watch?v=lBo8jI52BnM) from a longer video in which the
+`napari-clusters-plotter` plugin is used to interactively visualize how field intensities relate spatially after having 
+used `napari-clusters-plotter` to run a kmeans classification. 
+
+Additionally, you can also use standard napari shapes layers to interactively sample at points or along paths and 
+then use plugins like `napari-line-plot` or `napari-properties-plotter` to visualize how data data varies
+along the path (screen shot from the [Introduction to other plugins video](https://www.youtube.com/watch?v=k1LdEQ_5Gfw)):
+
+![](/img/yt_napari_updates/path_3D_measurements.png)
+
+Check out either of the following to access all the videos:
+
+* [List of video titles and links](https://yt-napari.readthedocs.io/en/latest/tutorials.html)
+* [Full YouTube Playlist](https://www.youtube.com/playlist?list=PLqbhAmYZU5KxuAcnNBIxyBkivUEiKswq1)
+
+
+## New Feature: Sampling Timeseries 
+
+One of the more exciting new features of recent releases is the ability to sample and load
+timeseries for both slices and 3D regions from the napari GUI, a jupyter notebook or via JSON file. 
+This allows you to interactively visualize time-dependent behavior. For example, the following 
+shows a small 3D region centered on the final max density of the `enzo_tiny_cosmology` dataset:
+
+![](/img/yt_napari_updates/yt_napari_timeseries_small.gif)
+
+For higher resolution sampling, you can work from a notebook and leverage dask to lazily-sample 3D 
+regions across a timeseries so that you can load the current timestep on demand:
+
+![](/img/yt_napari_updates/yt_napari_timeseries_regdask_vid.gif)
+
+Check out the sample notebooks and videos for loading timeseries ([notebook](https://yt-napari.readthedocs.io/en/latest/examples/ytnapari_scene_04_timeseries.html), 
+[video](https://youtu.be/uNK33C6nOZU)) and using dask with timeseries
+([notebook](https://yt-napari.readthedocs.io/en/latest/examples/ytnapari_scene_05_timeseries_dask.html), 
+[video](https://www.youtube.com/watch?v=5eeOrcuqvH8))
+
+## The future of yt-napari
+
+One of the exciting features in development upstream in napari is improved on-demand loading of 
+multi-resolution data. Once that work is completed, it will open up the possibility of progressive 
+sampling in yt-napari! Check out this video for a preview of what this might enable in yt-napari: 
+[progressive of the DeeplyNestedZoom dataset](https://www.youtube.com/watch?v=ofoURuz-Cbw). There's 
+also plenty to do with yt-napari as it is now, check out the [Issues page](https://github.com/data-exp-lab/yt-napari/issues)
+to get involved!