scikit-image-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dask and Scikit-Image on a Cluster\n",
    "==================================\n",
    "\n",
    "<img src=\"http://scikit-image.org/_static/img/logo.png\">"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask_gateway import Gateway\n",
    "\n",
    "gateway = Gateway()\n",
    "cluster = gateway.new_cluster()\n",
    "cluster.scale(10)\n",
    "cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask.distributed import Client, progress\n",
    "client = Client(cluster)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from dask import delayed\n",
    "import skimage.io\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read a single image from Google Cloud Storage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sample = skimage.io.imread('https://storage.googleapis.com/pangeo-data/FIB-25/iso.00000.png')\n",
    "\n",
    "fig = plt.figure(figsize=(10, 10))\n",
    "plt.imshow(sample, cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct a Lazy Dask Array from all of the images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dask import delayed\n",
    "import dask.array as da\n",
    "\n",
    "# filenames = ['https://storage.googleapis.com/pangeo-data/FIB-25/iso.%05d.png' % i for i in range(0, 8090)]\n",
    "filenames = ['https://storage.googleapis.com/pangeo-data/FIB-25/iso.%05d.png' % i for i in range(0, 1000)]\n",
    "images = [delayed(skimage.io.imread)(fn) for fn in filenames]\n",
    "arrays = [da.from_delayed(im, shape=sample.shape, dtype=sample.dtype) for im in images]\n",
    "x = da.stack(arrays, axis=0)\n",
    "x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize Lazily with Holoviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import holoviews as hv\n",
    "from holoviews.operation.datashader import regrid\n",
    "hv.extension('bokeh', width=100)\n",
    "\n",
    "dims = tuple([range(d) for d in x.shape][::-1])\n",
    "hv_ds = hv.Dataset(dims + (x,), ['x', 'y', 'sample'], 'z', datatype=['xarray'])\n",
    "im = hv_ds.to(hv.Image, ['x', 'y'], dynamic=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%opts Image [width=800 height=800] (cmap='magma') \n",
    "regrid(im, precompute=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rearrange data by Pixel\n",
    "\n",
    "Currently our data is organized in chunks of size `1x6000x6000`, one chunk for each image.  If we want to do analysis per-pixel then we need to rechunk our data, which we do below into chunks of size `1000x100x100`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.rechunk((1000, 100, 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ask Dask to persist that data in memory\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = x.persist()\n",
    "progress(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Do per-pixel computations, like FFTs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = da.fft.fft(x, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.figure(figsize=(20, 5))\n",
    "plt.plot(y[:, 3000, 3000])\n",
    "plt.show()"
   ]
  },
  {
   "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": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}