{
"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": [
" "
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""
]
},
"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": {
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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",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Assembling a 3D array from 2D slices\n",
"\n",
"We can also assemble a 3D array to visualize by concatenating 2D slices. The main reason to take this approach is that it is easily extendible to a dask lazy-loading process, which we'll explore later on. So that we have a more interesting view, we'll pull out slices focused on the dataset's center.\n",
"\n",
"Let's start by creating a function to slice the data set at a given `x` location and return an image array:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def return_x_slice(xval,res,c,hwid): \n",
" # return a slice at a single xvalue\n",
" # xval: the xvalue in kpc to slice at \n",
" # res: the resolution of the image array at xval \n",
" # c: the center of the image in kpc\n",
" # hwid: the half-width of the image in kpc\n",
" region = ds.r[(xval,'kpc'), (c-hwid,'kpc'):(c+hwid,'kpc'):res*1j,\n",
" (c-hwid,'kpc'):(c+hwid,'kpc'):res*1j]\n",
" return np.log10(region['density'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and define the range we want to slice over:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"res = 300\n",
"c = 500. # center of slices in kpc\n",
"hwid = 25. # half width of region to slice in kpc \n",
"xvals = np.linspace(c-hwid,c+hwid,res)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"before assembling the array, we'll turn off the yt logging so we're not overwhelmed by output:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"yt.funcs.mylog.setLevel(50)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we'll assemble the array (this is what we'll \"daskify\" in a later notebook):"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CPU times: user 16.6 s, sys: 907 ms, total: 17.5 s\n",
"Wall time: 17.7 s\n"
]
},
{
"data": {
"text/plain": [
"(300, 300, 300)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%time\n",
"dens_log_2 = np.array([return_x_slice(xval,res,c,hwid) for xval in xvals])\n",
"dens_log_2.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and add it to the viewer:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"viewer.add_image(dens_log_2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Lazy-loading with Dask\n",
"\n",
"The most obvious way to leverage dask in this process is to transform the image frame loop into a stack of lazy dask processes, so that napari generates each frame as it's selected. The approach is outlined in napari's documentation [here](https://napari.org/tutorials/applications/dask.html) as well as Lambert's [napari-dask-example notebook](https://github.com/tlambert03/napari-dask-example/blob/master/dask_napari.ipynb).\n",
"\n",
"First, we pull out out a sample frame and wrap the `return_x_slice` function with the `dask.delayed` function:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [],
"source": [
"sample = return_x_slice(xvals[0],res,c,hwid)\n",
"lazy_x_frame = delayed(return_x_slice) # our lazy \"reader\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we need to build up our lazy loaded arrays into an image stack: "
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"lazy_arrays = [lazy_x_frame(xval,res,c,hwid) for xval in xvals]\n",
"dask_arrays = [\n",
" da.from_delayed(lazy_array, shape=sample.shape, dtype=sample.dtype)\n",
" for lazy_array in lazy_arrays\n",
"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"From which we build up a single dask array stack:"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
"
Array
Chunk
\n",
" \n",
" \n",
"
Bytes
216.00 MB
720.00 kB
\n",
"
Shape
(300, 300, 300)
(1, 300, 300)
\n",
"
Count
900 Tasks
300 Chunks
\n",
"
Type
float64
numpy.ndarray
\n",
" \n",
"
\n",
"
\n",
"
\n",
"\n",
"
\n",
"
\n",
"
"
],
"text/plain": [
"dask.array"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dask_stack = da.stack(dask_arrays, axis=0)\n",
"dask_stack"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"And now we can just add the `dask_stack` to a napari viewer. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"try: \n",
" viewer.close()\n",
"except:\n",
" pass\n",
"viewer = napari.Viewer()\n",
"viewer.add_image(dask_stack)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now when we move the slider, napari will traverse the `x` slices, loading them as needed:\n",
"\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"One caveat to this approach is that the we're restricted to 2D slices -- switching to a 3D rendering would require loading the entire cube into memory (this is why we closed any existing napari viewer first). And in the above case, where our total 3D image array would only use 218 Mb, we're probably better off just loading into memory. So let's look at a case where loading into memory would be potentially problematic. We'll also slice along a different dimension, so let's create a new function to wrap:"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"def return_z_slice(zval,res,c,hwid): \n",
" # return a slice at a single zvalue \n",
" # zval: the zvalue in kpc to slice at \n",
" # res: the resolution of the image array at xval \n",
" # c: the center of the image in kpc\n",
" # hwid: the half-width of the image in kpc\n",
" region = ds.r[(c-hwid,'kpc'):(c+hwid,'kpc'):res*1j,\n",
" (c-hwid,'kpc'):(c+hwid,'kpc'):res*1j,\n",
" (zval,'kpc')]\n",
" return np.log10(region['density'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now let's bump up the resolution and width of the image then set up the dask stack as before:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"
\n",
"
\n",
"
\n",
" \n",
"
Array
Chunk
\n",
" \n",
" \n",
"
Bytes
64.00 GB
32.00 MB
\n",
"
Shape
(2000, 2000, 2000)
(1, 2000, 2000)
\n",
"
Count
6000 Tasks
2000 Chunks
\n",
"
Type
float64
numpy.ndarray
\n",
" \n",
"
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"
\n",
"\n",
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\n",
"
\n",
"
"
],
"text/plain": [
"dask.array"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res = 2000\n",
"hwid = 500\n",
"zvals = np.linspace(c-hwid,c+hwid,res)\n",
"sample = return_z_slice(zvals[0],res,c,hwid)\n",
"lazy_z_frame = delayed(return_z_slice) # our lazy \"reader\"\n",
"\n",
"lazy_arrays = [lazy_z_frame(zval,res,c,hwid) for zval in zvals]\n",
"dask_arrays = [\n",
" da.from_delayed(lazy_array, shape=sample.shape, dtype=sample.dtype)\n",
" for lazy_array in lazy_arrays\n",
"]\n",
"\n",
"dask_stack = da.stack(dask_arrays, axis=0)\n",
"dask_stack"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In this case we'd need 64 Gb to load this image array into memory while each frame is only 32 Mb."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
""
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"try: \n",
" viewer.close()\n",
"except:\n",
" pass\n",
"viewer = napari.Viewer()\n",
"viewer.add_image(dask_stack)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now we can see a small lag when we move the slider, but it's still quick enough to be a more than acceptable tradeoff when considering how few of us have computers that would have 64 Gb of memory to spare for loading the dataset into memory. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Extra fun!\n",
"\n",
"As a final section, let's load up a different dataset that has more complex 3D structures, the `Enzo_64` test dataset (download [here](http://yt-project.org/data/Enzo_64.tar.gz), 2.4 Gb): "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"Parsing Hierarchy : 100%|██████████| 752/752 [00:00<00:00, 20682.60it/s]\n"
]
},
{
"data": {
"text/html": [
" "
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ds = yt.load(\"Enzo_64/DD0043/data0043\")\n",
"slc = yt.SlicePlot(ds, 'z', 'density', center=[.5,.5,.5],width=(200,'Mpc'))\n",
"slc.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and extract a fixed resolution region covering the whole dataset with 300 pixels in each dimension:"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"cg = ds.r[::300j,::300j,::300j]\n",
"dens_log = np.log10(cg['density'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"and launch a new viewer:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
""
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"try: \n",
" viewer.close()\n",
"except:\n",
" pass\n",
"viewer = napari.Viewer()\n",
"viewer.add_image(dens_log)\n",
"nbscreenshot(viewer)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Final Thoughts\n",
"\n",
"In this notebook we've demonstrated some simple approaches to loading image arrays from *yt* datasets into napari, providing a quick method for interactive visualization of *yt* datasets. In putting together this notebook, I've been impressed by just how **easy** all of this is. By stringing together 3 different open source projects across the python ecosystem, we have instant support for wider audiences than each package has alone: *yt* users can explore their data in more interactive ways and napari users can essentially use *yt* as a back end data visualizer for any of the data formats supported by *yt*, all beneath the dask umbrella!\n",
"\n",
"In future notebooks, we'll explore some more ways to use napari for visualization of *yt* datasets, so stay tuned! "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 4
}