{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualization with *yt* and Napari Part 1: fixed resolution regions\n", "\n", "In this notebook series, we'll explore some ways to use [napari](https://napari.org/) to interactively visualize data loaded and sampled with [*yt*](yt-project.org/).\n", "\n", "The inspiration for this series came from watching 'Interactive Image Processing at Scale' from the folks at Coiled and guests Nicholas Sofroniew and Talley Lambert, who presented their work on using napari and dask to load and process multi-resolution images of cells (check out the video [here](https://youtu.be/KG_ye5qzFmk)). In it, Sofroniew and Lambert present some surprisingly simple notebooks that leverage dask's lazy loading and block mapping to define loading and processing functions that get triggered on demand by napari as the user needs it. \n", "\n", "In this notebook, we'll keep things fairly simple and first focus on in-memory visualization in which we use *yt* to generate a 3D image that is then visualized with napari. We'll then look at an example using dask to lazily load *yt* slices.\n", "\n", "## general approach\n", "\n", "Ultimately, loading data into napari is as simple as:\n", "\n", "```\n", "viewer = napari.view_image(image_stack)\n", "```\n", "\n", "where `image_stack` is an array object. We'll start with the simplest case in which `image_stack` is an in-memory image array generated by storing images created from *yt* slices and then demonstrate a lazy loading approach with dask. \n", "\n", "\n", "## required packages\n", "\n", "Running this notebook requires *yt*, napari and dask in addition to stanard packages from the scipy stack (numpy, matplotlib). \n", "\n", "\n", "## imports \n", "\n", "So let's start off by setting up the `qt` environment needed by napari:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ " # for napari, do it first!\n", "%gui qt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and import all the packages we'll use" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "# general imports\n", "import numpy as np\n", "import matplotlib.pyplot as plt \n", "import os\n", "\n", "# yt related\n", "import yt\n", "from yt.units import kpc\n", "\n", "# napari related imports \n", "import napari\n", "from napari.utils import nbscreenshot\n", "import logging\n", "\n", "# dask imports \n", "from dask import delayed\n", "import dask.array as da" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Test data \n", "\n", "We'll use a standard *yt* test ENZO dataset, `IsolatedGalaxy`, (download [here](http://yt-project.org/data/IsolatedGalaxy.tar.gz)).\n", "\n", "So let's first load up the data and slice at a fixed `z` value as usual in *yt*: " ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "yt : [INFO ] 2020-09-08 11:40:42,765 Parameters: current_time = 0.0060000200028298\n", "yt : [INFO ] 2020-09-08 11:40:42,766 Parameters: domain_dimensions = [32 32 32]\n", "yt : [INFO ] 2020-09-08 11:40:42,766 Parameters: domain_left_edge = [0. 0. 0.]\n", "yt : [INFO ] 2020-09-08 11:40:42,767 Parameters: domain_right_edge = [1. 1. 1.]\n", "yt : [INFO ] 2020-09-08 11:40:42,767 Parameters: cosmological_simulation = 0.0\n" ] } ], "source": [ "ds = yt.load(\"IsolatedGalaxy/galaxy0030/galaxy0030\") " ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Parsing Hierarchy : 100%|██████████| 173/173 [00:00<00:00, 18802.20it/s]\n", "yt : [INFO ] 2020-09-08 11:40:42,788 Gathering a field list (this may take a moment.)\n", "yt : [INFO ] 2020-09-08 11:40:43,931 xlim = 0.490001 0.509999\n", "yt : [INFO ] 2020-09-08 11:40:43,932 ylim = 0.490001 0.509999\n", "yt : [INFO ] 2020-09-08 11:40:43,932 xlim = 0.490001 0.509999\n", "yt : [INFO ] 2020-09-08 11:40:43,933 ylim = 0.490001 0.509999\n", "yt : [INFO ] 2020-09-08 11:40:43,934 Making a fixed resolution buffer of (('gas', 'density')) 800 by 800\n" ] }, { "data": { "text/html": [ "
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "slc = yt.SlicePlot(ds, 'z', 'density', center=[0.5, 0.5, 0.5],width=(20,'kpc'))\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading in-memory *yt* Fixed Resolution Region into napari\n", "\n", "In order to load *yt* images into napari, we need to be able to create a numpy array to pass to napari. Perhaps the simplest way is to extract a [Fixed Resolution Region](https://yt-project.org/doc/analyzing/objects.html#selecting-fixed-resolution-regions). \n", "\n", "To extract the log of the density over the whole domain at a resolution of 300x300x300 pixels: " ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(300, 300, 300)\n", "\n" ] } ], "source": [ "cg = ds.r[::300j,::300j,::300j]\n", "dens_log = np.log10(cg['density'])\n", "print(dens_log.shape)\n", "print(type(dens_log))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`dens_log` here is an in-memory numpy array that we can simply load into napari. To do so, we first launch a napari viewer and then add `dens_log` as an image layer:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "viewer = napari.Viewer()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "viewer.add_image(dens_log)\n", "nbscreenshot(viewer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Moving the slider will vary the first dimension of the array that we're slicing along, we can also use napari's color map selector and 3d rendering:\n", "\n", "