04_Data_Storage.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image Data Storage for the Web"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learning objectives\n",
    "\n",
    "- Become familiar with the design of modern, **cloud storage systems**\n",
    "- Gain experience with the **Zarr data format** for web-based scientific computing \n",
    "- Understand the relationship between **chunked, compressed**, object storage and **parallel processing and multi-scale visualization**\n",
    "- Become familiar with **OME-NGFF**, the next-generation file format for scientific imaging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install package dependencies\n",
    "import sys\n",
    "\n",
    "!{sys.executable} -m pip install --upgrade --pre zarr multiscale-spatial-image matplotlib itk-io tifffile itkwidgets ome-zarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cloud storage\n",
    "\n",
    "**Cloud storage services**, such as:\n",
    "\n",
    "- Amazon Simple Storage Service (AWS S3)\n",
    "- Google Cloud Storage\n",
    "- Microsoft Azure Storage\n",
    "- Minio Cloud Storage\n",
    "\n",
    "**differ from traditional filesystem storage**.\n",
    "\n",
    "In *File Storage*:\n",
    "\n",
    "- Data is organized into files and folders.\n",
    "- There is generally a pool of storage, e.g. a volume, with limited capacity that can be accessed.\n",
    "- Data can be overwritten.\n",
    "- Limited metadata is associated with the file.\n",
    "\n",
    "In cloud, *Object Storage* systems:\n",
    "\n",
    "- Objects, binary blobs, live in a flat structure.\n",
    "- Object have a unique identifier and associated metadata, typically JSON-compatible\n",
    "- Access is possible via simple HTTP requests\n",
    "- Object's cannot be modified"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zarr and n5 formats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Zarr](https://zarr-developers.github.io/about/) and [n5](https://github.com/saalfeldlab/n5/) are file formats with reference implementatinos that map well to cloud Object Storage services. They are also suitable for storage of large bioimages.\n",
    "\n",
    "Together zarr and n5 are formats used by the bioimaging data model of the [Open Microscopy Environment (OME)-Next-generation File Format (NGFF)](https://ngff.openmicroscopy.org/latest/), which represents what was previously stored in [OME-TIFF](http://www.openmicroscopy.org/ome-files/) as *a hierarchy of n-dimensional (dense) arrays with metadata*.\n",
    "\n",
    "Zarr and n5 support:\n",
    "\n",
    "- Group hierarchies\n",
    "- Arbitrary JSON-compatible meta-data\n",
    "- Chunked, n-dimensional binary tensor datasets\n",
    "- Binary component types: [u]int8, [u]int16, [u]int32, [u]int64, float32, float64\n",
    "- Next-generation lossless compression with [blosc](https://blosc.org/pages/blosc-in-depth/) of binary chunks.\n",
    "\n",
    "When combined with a **multiscale image model** such as [OME-NGFF](https://www.nature.com/articles/s41592-021-01326-w), **large image visualization** is possible.\n",
    "\n",
    "- *You do not need to download the entire volume before you can visualization or analyze it.*\n",
    "- *Retrieve a partial region-of-interest at a given scale.*\n",
    "\n",
    "The object storage-compatible model facilitates **parallel processing** because it is conducive to **compressed chunk writes**, even in a cloud storage environment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating an OME-NGFF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from multiscale_spatial_image import to_multiscale\n",
    "from spatial_image import is_spatial_image, to_spatial_image\n",
    "import itk\n",
    "import numpy as np\n",
    "from urllib.request import urlretrieve\n",
    "import os\n",
    "import zarr\n",
    "from itkwidgets import view"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download example image\n",
    "\n",
    "Derived from:\n",
    "\n",
    "https://data.broadinstitute.org/bbbc/BBBC024/\n",
    "\n",
    "Image set BBBC024vl [Svoboda David, Kozubkek Michal, Stejskal Stanislav. Generation of Digital Phantoms of Cell Nuclei and Simulation of Image Formation in 3D Image Cytometry. Cytometry Part A, John Wiley & Sons, Inc., 75A, 6, pp. 494-509, 16 pages. ISSN 1552-4922. 2009.] from the Broad Bioimage Benchmark Collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_name= 'HL50_cell'\n",
    "\n",
    "filename = f'{image_name}.tif'\n",
    "if not os.path.exists(filename):\n",
    "    url = 'https://data.kitware.com/api/v1/file/5b61f16c8d777f06857c1949/download'\n",
    "    urlretrieve(url, filename)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
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      "text/plain": [
       "Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageUS3; pr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Image metadata\n",
    "image = itk.imread(filename)\n",
    "view(image)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
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     "text": [
      "Image (000002080FF6E080)\n",
      "  RTTI typeinfo:   class itk::Image<unsigned short,3>\n",
      "  Reference Count: 1\n",
      "  Modified Time: 417\n",
      "  Debug: Off\n",
      "  Object Name: \n",
      "  Observers: \n",
      "    none\n",
      "  Source: (none)\n",
      "  Source output name: (none)\n",
      "  Release Data: Off\n",
      "  Data Released: False\n",
      "  Global Release Data: Off\n",
      "  PipelineMTime: 232\n",
      "  UpdateMTime: 416\n",
      "  RealTimeStamp: 0 seconds \n",
      "  LargestPossibleRegion: \n",
      "    Dimension: 3\n",
      "    Index: [0, 0, 0]\n",
      "    Size: [363, 212, 129]\n",
      "  BufferedRegion: \n",
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      "    Index: [0, 0, 0]\n",
      "    Size: [363, 212, 129]\n",
      "  RequestedRegion: \n",
      "    Dimension: 3\n",
      "    Index: [0, 0, 0]\n",
      "    Size: [363, 212, 129]\n",
      "  Spacing: [1, 1, 1]\n",
      "  Origin: [0, 0, 0]\n",
      "  Direction: \n",
      "1 0 0\n",
      "0 1 0\n",
      "0 0 1\n",
      "\n",
      "  IndexToPointMatrix: \n",
      "1 0 0\n",
      "0 1 0\n",
      "0 0 1\n",
      "\n",
      "  PointToIndexMatrix: \n",
      "1 0 0\n",
      "0 1 0\n",
      "0 0 1\n",
      "\n",
      "  Inverse Direction: \n",
      "1 0 0\n",
      "0 1 0\n",
      "0 0 1\n",
      "\n",
      "  PixelContainer: \n",
      "    ImportImageContainer (00000208139A4D70)\n",
      "      RTTI typeinfo:   class itk::ImportImageContainer<unsigned __int64,unsigned short>\n",
      "      Reference Count: 3\n",
      "      Modified Time: 413\n",
      "      Debug: Off\n",
      "      Object Name: \n",
      "      Observers: \n",
      "        none\n",
      "      Pointer: 0000020818CCD040\n",
      "      Container manages memory: true\n",
      "      Size: 9927324\n",
      "      Capacity: 9927324\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(image)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Convert the `itk.Image` to an [`xarray.DataArray`](https://docs.xarray.dev/en/stable/generated/xarray.DataArray.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;HL50_cell&#x27; (z: 129, y: 212, x: 363)&gt;\n",
       "array([[[ 95,  96,  84, ...,  89,  93,  97],\n",
       "        [ 94,  89,  92, ...,  93,  95,  85],\n",
       "        [ 95,  93,  92, ...,  90,  88,  92],\n",
       "        ...,\n",
       "        [ 92,  95,  96, ...,  93,  88,  96],\n",
       "        [ 91,  92,  93, ...,  91,  88,  93],\n",
       "        [ 97,  94,  94, ...,  89,  95,  96]],\n",
       "\n",
       "       [[ 86,  88,  92, ...,  95,  93,  97],\n",
       "        [ 96,  96,  93, ...,  91,  93,  94],\n",
       "        [ 85,  95,  82, ...,  91,  86,  92],\n",
       "        ...,\n",
       "        [ 91,  91,  87, ...,  91,  90,  98],\n",
       "        [ 98,  92,  97, ...,  96,  90,  95],\n",
       "        [ 88,  88,  99, ...,  94,  97,  93]],\n",
       "\n",
       "       [[ 92,  92,  95, ...,  92,  92,  89],\n",
       "        [ 96,  94,  93, ...,  95,  96,  89],\n",
       "        [ 92,  93,  93, ...,  95,  84,  90],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ 90,  95,  90, ...,  95,  95, 100],\n",
       "        [ 96,  95,  93, ...,  99,  96,  89],\n",
       "        [ 98,  91,  92, ...,  87,  91,  89]],\n",
       "\n",
       "       [[ 90,  88,  91, ...,  94,  94,  84],\n",
       "        [ 84,  90,  97, ...,  88, 100,  96],\n",
       "        [ 93,  96,  95, ...,  89,  87,  96],\n",
       "        ...,\n",
       "        [ 89, 100,  92, ...,  92,  91,  87],\n",
       "        [ 93,  92,  91, ...,  89,  82,  98],\n",
       "        [ 90,  93,  90, ...,  93,  90,  96]],\n",
       "\n",
       "       [[ 91,  89,  93, ..., 103,  93,  93],\n",
       "        [ 97,  90,  91, ...,  94,  90,  89],\n",
       "        [ 96,  92,  92, ...,  91,  94,  87],\n",
       "        ...,\n",
       "        [ 85,  94,  85, ...,  91,  91,  87],\n",
       "        [ 89,  93,  99, ...,  84,  90,  94],\n",
       "        [ 99,  99,  92, ...,  90,  91,  86]]], dtype=uint16)\n",
       "Coordinates:\n",
       "  * x        (x) float64 0.0 1.0 2.0 3.0 4.0 ... 358.0 359.0 360.0 361.0 362.0\n",
       "  * y        (y) float64 0.0 1.0 2.0 3.0 4.0 ... 207.0 208.0 209.0 210.0 211.0\n",
       "  * z        (z) float64 0.0 1.0 2.0 3.0 4.0 ... 124.0 125.0 126.0 127.0 128.0\n",
       "Attributes:\n",
       "    direction:  [[1. 0. 0.]\\n [0. 1. 0.]\\n [0. 0. 1.]]\n",
       "    Software:   InsightToolkit</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'HL50_cell'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>z</span>: 129</li><li><span class='xr-has-index'>y</span>: 212</li><li><span class='xr-has-index'>x</span>: 363</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-bd9ced80-47df-41fa-a132-f3151c37aaa8' class='xr-array-in' type='checkbox' checked><label for='section-bd9ced80-47df-41fa-a132-f3151c37aaa8' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>95 96 84 89 89 92 95 88 99 96 98 ... 87 94 97 89 96 89 89 86 90 91 86</span></div><div class='xr-array-data'><pre>array([[[ 95,  96,  84, ...,  89,  93,  97],\n",
       "        [ 94,  89,  92, ...,  93,  95,  85],\n",
       "        [ 95,  93,  92, ...,  90,  88,  92],\n",
       "        ...,\n",
       "        [ 92,  95,  96, ...,  93,  88,  96],\n",
       "        [ 91,  92,  93, ...,  91,  88,  93],\n",
       "        [ 97,  94,  94, ...,  89,  95,  96]],\n",
       "\n",
       "       [[ 86,  88,  92, ...,  95,  93,  97],\n",
       "        [ 96,  96,  93, ...,  91,  93,  94],\n",
       "        [ 85,  95,  82, ...,  91,  86,  92],\n",
       "        ...,\n",
       "        [ 91,  91,  87, ...,  91,  90,  98],\n",
       "        [ 98,  92,  97, ...,  96,  90,  95],\n",
       "        [ 88,  88,  99, ...,  94,  97,  93]],\n",
       "\n",
       "       [[ 92,  92,  95, ...,  92,  92,  89],\n",
       "        [ 96,  94,  93, ...,  95,  96,  89],\n",
       "        [ 92,  93,  93, ...,  95,  84,  90],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ 90,  95,  90, ...,  95,  95, 100],\n",
       "        [ 96,  95,  93, ...,  99,  96,  89],\n",
       "        [ 98,  91,  92, ...,  87,  91,  89]],\n",
       "\n",
       "       [[ 90,  88,  91, ...,  94,  94,  84],\n",
       "        [ 84,  90,  97, ...,  88, 100,  96],\n",
       "        [ 93,  96,  95, ...,  89,  87,  96],\n",
       "        ...,\n",
       "        [ 89, 100,  92, ...,  92,  91,  87],\n",
       "        [ 93,  92,  91, ...,  89,  82,  98],\n",
       "        [ 90,  93,  90, ...,  93,  90,  96]],\n",
       "\n",
       "       [[ 91,  89,  93, ..., 103,  93,  93],\n",
       "        [ 97,  90,  91, ...,  94,  90,  89],\n",
       "        [ 96,  92,  92, ...,  91,  94,  87],\n",
       "        ...,\n",
       "        [ 85,  94,  85, ...,  91,  91,  87],\n",
       "        [ 89,  93,  99, ...,  84,  90,  94],\n",
       "        [ 99,  99,  92, ...,  90,  91,  86]]], dtype=uint16)</pre></div></div></li><li class='xr-section-item'><input id='section-66070a5a-8f71-4a44-b2a4-5193028605d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-66070a5a-8f71-4a44-b2a4-5193028605d6' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 360.0 361.0 362.0</div><input id='attrs-06e9d3c0-d597-440d-9419-dc58f6a60e4c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-06e9d3c0-d597-440d-9419-dc58f6a60e4c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bfecfc7d-29ee-465a-9b62-714294502c60' class='xr-var-data-in' type='checkbox'><label for='data-bfecfc7d-29ee-465a-9b62-714294502c60' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.      ,   1.      ,   2.      , ..., 360.000005, 361.000005,\n",
       "       362.000005])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 209.0 210.0 211.0</div><input id='attrs-d3e83ddd-aab0-46e8-9f35-e13a2084cec5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3e83ddd-aab0-46e8-9f35-e13a2084cec5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c766617-3ed1-410c-9964-3211ef0d3732' class='xr-var-data-in' type='checkbox'><label for='data-8c766617-3ed1-410c-9964-3211ef0d3732' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.      ,   1.      ,   2.      , ..., 209.000003, 210.000003,\n",
       "       211.000003])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 126.0 127.0 128.0</div><input id='attrs-fdf4f9ac-8c1d-4f01-a787-36f86469c285' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fdf4f9ac-8c1d-4f01-a787-36f86469c285' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4bb15f7-c5cc-40bd-a4af-92df49736533' class='xr-var-data-in' type='checkbox'><label for='data-a4bb15f7-c5cc-40bd-a4af-92df49736533' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,\n",
       "        12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,\n",
       "        24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,\n",
       "        36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,\n",
       "        48.,  49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,\n",
       "        60.,  61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,\n",
       "        72.,  73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,\n",
       "        84.,  85.,  86.,  87.,  88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,\n",
       "        96.,  97.,  98.,  99., 100., 101., 102., 103., 104., 105., 106., 107.,\n",
       "       108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119.,\n",
       "       120., 121., 122., 123., 124., 125., 126., 127., 128.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-60118781-e6a6-4017-ad97-9b9c11bace72' class='xr-section-summary-in' type='checkbox'  checked><label for='section-60118781-e6a6-4017-ad97-9b9c11bace72' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>direction :</span></dt><dd>[[1. 0. 0.]\n",
       " [0. 1. 0.]\n",
       " [0. 0. 1.]]</dd><dt><span>Software :</span></dt><dd>InsightToolkit</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'HL50_cell' (z: 129, y: 212, x: 363)>\n",
       "array([[[ 95,  96,  84, ...,  89,  93,  97],\n",
       "        [ 94,  89,  92, ...,  93,  95,  85],\n",
       "        [ 95,  93,  92, ...,  90,  88,  92],\n",
       "        ...,\n",
       "        [ 92,  95,  96, ...,  93,  88,  96],\n",
       "        [ 91,  92,  93, ...,  91,  88,  93],\n",
       "        [ 97,  94,  94, ...,  89,  95,  96]],\n",
       "\n",
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       "        [ 96,  96,  93, ...,  91,  93,  94],\n",
       "        [ 85,  95,  82, ...,  91,  86,  92],\n",
       "        ...,\n",
       "        [ 91,  91,  87, ...,  91,  90,  98],\n",
       "        [ 98,  92,  97, ...,  96,  90,  95],\n",
       "        [ 88,  88,  99, ...,  94,  97,  93]],\n",
       "\n",
       "       [[ 92,  92,  95, ...,  92,  92,  89],\n",
       "        [ 96,  94,  93, ...,  95,  96,  89],\n",
       "        [ 92,  93,  93, ...,  95,  84,  90],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ 90,  95,  90, ...,  95,  95, 100],\n",
       "        [ 96,  95,  93, ...,  99,  96,  89],\n",
       "        [ 98,  91,  92, ...,  87,  91,  89]],\n",
       "\n",
       "       [[ 90,  88,  91, ...,  94,  94,  84],\n",
       "        [ 84,  90,  97, ...,  88, 100,  96],\n",
       "        [ 93,  96,  95, ...,  89,  87,  96],\n",
       "        ...,\n",
       "        [ 89, 100,  92, ...,  92,  91,  87],\n",
       "        [ 93,  92,  91, ...,  89,  82,  98],\n",
       "        [ 90,  93,  90, ...,  93,  90,  96]],\n",
       "\n",
       "       [[ 91,  89,  93, ..., 103,  93,  93],\n",
       "        [ 97,  90,  91, ...,  94,  90,  89],\n",
       "        [ 96,  92,  92, ...,  91,  94,  87],\n",
       "        ...,\n",
       "        [ 85,  94,  85, ...,  91,  91,  87],\n",
       "        [ 89,  93,  99, ...,  84,  90,  94],\n",
       "        [ 99,  99,  92, ...,  90,  91,  86]]], dtype=uint16)\n",
       "Coordinates:\n",
       "  * x        (x) float64 0.0 1.0 2.0 3.0 4.0 ... 358.0 359.0 360.0 361.0 362.0\n",
       "  * y        (y) float64 0.0 1.0 2.0 3.0 4.0 ... 207.0 208.0 209.0 210.0 211.0\n",
       "  * z        (z) float64 0.0 1.0 2.0 3.0 4.0 ... 124.0 125.0 126.0 127.0 128.0\n",
       "Attributes:\n",
       "    direction:  [[1. 0. 0.]\\n [0. 1. 0.]\\n [0. 0. 1.]]\n",
       "    Software:   InsightToolkit"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_da = itk.xarray_from_image(image)\n",
    "image_da.name = image_name\n",
    "image_da"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Does the image_da have the characteristics of an xarray.DataArray bioimage with spatial metadata?\n",
    "is_spatial_image(image_da)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OME-NGFF stores a multiscale pyramid for large-scale visualization or analysis. Let's generate it."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  ()\n",
       "Data variables:\n",
       "    *empty*</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>datatree.MultiscaleSpatialImage</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cf5de9db-d8e7-4dff-ba00-29f9d4d886aa' class='xr-section-summary-in' type='checkbox'  ><label for='section-cf5de9db-d8e7-4dff-ba00-29f9d4d886aa' class='xr-section-summary' >Groups: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-sections'><div style='padding-left:2rem;'><ul class='xr-sections'><div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
       "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n",
       "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n",
       "</symbol>\n",
       "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n",
       "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 5.457c0.959 0.959 1.712 1.825 2.268 2.543h-4.811v-4.811c0.718 0.556 1.584 1.309 2.543 2.268zM28 29.5c0 0.271-0.229 0.5-0.5 0.5h-23c-0.271 0-0.5-0.229-0.5-0.5v-27c0-0.271 0.229-0.5 0.5-0.5 0 0 15.499-0 15.5 0v7c0 0.552 0.448 1 1 1h7v19.5z\"></path>\n",
       "<path d=\"M23 26h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 22h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "<path d=\"M23 18h-14c-0.552 0-1-0.448-1-1s0.448-1 1-1h14c0.552 0 1 0.448 1 1s-0.448 1-1 1z\"></path>\n",
       "</symbol>\n",
       "</defs>\n",
       "</svg>\n",
       "<style>/* CSS stylesheet for displaying xarray objects in jupyterlab.\n",
       " *\n",
       " */\n",
       "\n",
       ":root {\n",
       "  --xr-font-color0: var(--jp-content-font-color0, rgba(0, 0, 0, 1));\n",
       "  --xr-font-color2: var(--jp-content-font-color2, rgba(0, 0, 0, 0.54));\n",
       "  --xr-font-color3: var(--jp-content-font-color3, rgba(0, 0, 0, 0.38));\n",
       "  --xr-border-color: var(--jp-border-color2, #e0e0e0);\n",
       "  --xr-disabled-color: var(--jp-layout-color3, #bdbdbd);\n",
       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
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       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (x: 363, y: 212, z: 129)\n",
       "Coordinates:\n",
       "  * x          (x) float64 0.0 1.0 2.0 3.0 4.0 ... 358.0 359.0 360.0 361.0 362.0\n",
       "  * y          (y) float64 0.0 1.0 2.0 3.0 4.0 ... 207.0 208.0 209.0 210.0 211.0\n",
       "  * z          (z) float64 0.0 1.0 2.0 3.0 4.0 ... 124.0 125.0 126.0 127.0 128.0\n",
       "Data variables:\n",
       "    HL50_cell  (z, y, x) uint16 dask.array&lt;chunksize=(64, 64, 64), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    direction:  [[1. 0. 0.]\\n [0. 1. 0.]\\n [0. 0. 1.]]\n",
       "    Software:   InsightToolkit</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>scale0</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8cbf5278-1d53-4cab-b9b5-1a38b3d71b8d' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8cbf5278-1d53-4cab-b9b5-1a38b3d71b8d' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-sections'><div style='padding-left:2rem;'><br></div></ul></div></li><li class='xr-section-item'><input id='section-33ed0624-a0ed-4079-b430-38ec54ddcf59' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-33ed0624-a0ed-4079-b430-38ec54ddcf59' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 363</li><li><span class='xr-has-index'>y</span>: 212</li><li><span class='xr-has-index'>z</span>: 129</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-51342e1e-5ca1-408e-ac11-2b42d57d1bf1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-51342e1e-5ca1-408e-ac11-2b42d57d1bf1' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 360.0 361.0 362.0</div><input id='attrs-69d4cf4e-49ce-42c7-ac0d-dc4410a931d5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-69d4cf4e-49ce-42c7-ac0d-dc4410a931d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a93ac0eb-388a-4965-991a-752631598928' class='xr-var-data-in' type='checkbox'><label for='data-a93ac0eb-388a-4965-991a-752631598928' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.      ,   1.      ,   2.      , ..., 360.000005, 361.000005,\n",
       "       362.000005])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 209.0 210.0 211.0</div><input id='attrs-6f3696c4-0eff-47e5-87d0-213eecbd2abd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6f3696c4-0eff-47e5-87d0-213eecbd2abd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff280135-ce64-42f4-8ff1-85342550aa63' class='xr-var-data-in' type='checkbox'><label for='data-ff280135-ce64-42f4-8ff1-85342550aa63' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.      ,   1.      ,   2.      , ..., 209.000003, 210.000003,\n",
       "       211.000003])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 126.0 127.0 128.0</div><input id='attrs-bcd95121-6316-4b16-adc1-40c1771340c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bcd95121-6316-4b16-adc1-40c1771340c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d679f786-b90a-4544-b11f-e117f6e4ef77' class='xr-var-data-in' type='checkbox'><label for='data-d679f786-b90a-4544-b11f-e117f6e4ef77' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,\n",
       "        12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,\n",
       "        24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,\n",
       "        36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,\n",
       "        48.,  49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,\n",
       "        60.,  61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,\n",
       "        72.,  73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,\n",
       "        84.,  85.,  86.,  87.,  88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,\n",
       "        96.,  97.,  98.,  99., 100., 101., 102., 103., 104., 105., 106., 107.,\n",
       "       108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119.,\n",
       "       120., 121., 122., 123., 124., 125., 126., 127., 128.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-67ef9b24-1122-4ad9-9d43-647d690e65a7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-67ef9b24-1122-4ad9-9d43-647d690e65a7' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>HL50_cell</span></div><div class='xr-var-dims'>(z, y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 64, 64), meta=np.ndarray&gt;</div><input id='attrs-50b51270-c0ff-44f9-ba39-a956709726dd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50b51270-c0ff-44f9-ba39-a956709726dd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c85db94-12bc-4191-a8d9-3337d6156852' class='xr-var-data-in' type='checkbox'><label for='data-3c85db94-12bc-4191-a8d9-3337d6156852' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>direction :</span></dt><dd>[[1. 0. 0.]\n",
       " [0. 1. 0.]\n",
       " [0. 0. 1.]]</dd><dt><span>Software :</span></dt><dd>InsightToolkit</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
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       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 18.93 MiB </td>\n",
       "                        <td> 512.00 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (129, 212, 363) </td>\n",
       "                        <td> (64, 64, 64) </td>\n",
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       "                    <tr>\n",
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       "                        <td> 72 Tasks </td>\n",
       "                        <td> 72 Chunks </td>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> uint16 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (x: 181, y: 106, z: 64)\n",
       "Coordinates:\n",
       "  * x          (x) float64 1.5 3.5 5.5 7.5 9.5 ... 353.5 355.5 357.5 359.5 361.5\n",
       "  * y          (y) float64 0.5 2.5 4.5 6.5 8.5 ... 202.5 204.5 206.5 208.5 210.5\n",
       "  * z          (z) float64 1.5 3.5 5.5 7.5 9.5 ... 119.5 121.5 123.5 125.5 127.5\n",
       "Data variables:\n",
       "    HL50_cell  (z, y, x) uint16 dask.array&lt;chunksize=(64, 64, 64), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    direction:  [[1. 0. 0.]\\n [0. 1. 0.]\\n [0. 0. 1.]]\n",
       "    Software:   InsightToolkit</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>scale1</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-b1fa58f7-bc10-4697-b642-52f70cdd9ea2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-b1fa58f7-bc10-4697-b642-52f70cdd9ea2' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-sections'><div style='padding-left:2rem;'><br></div></ul></div></li><li class='xr-section-item'><input id='section-d8f53608-80d8-4d06-830a-b86e4ac200e4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d8f53608-80d8-4d06-830a-b86e4ac200e4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 181</li><li><span class='xr-has-index'>y</span>: 106</li><li><span class='xr-has-index'>z</span>: 64</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ba96c89b-915e-4fe7-a40d-f16360c46835' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba96c89b-915e-4fe7-a40d-f16360c46835' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5 3.5 5.5 ... 357.5 359.5 361.5</div><input id='attrs-8db0c360-51a6-4d8b-84c1-8406337a4e4f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8db0c360-51a6-4d8b-84c1-8406337a4e4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ba0cc95-daa7-40f9-993a-ad262934666e' class='xr-var-data-in' type='checkbox'><label for='data-7ba0cc95-daa7-40f9-993a-ad262934666e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  1.5     ,   3.5     ,   5.5     ,   7.5     ,   9.5     ,  11.5     ,\n",
       "        13.5     ,  15.5     ,  17.5     ,  19.5     ,  21.5     ,  23.5     ,\n",
       "        25.5     ,  27.5     ,  29.5     ,  31.5     ,  33.500001,  35.500001,\n",
       "        37.500001,  39.500001,  41.500001,  43.500001,  45.500001,  47.500001,\n",
       "        49.500001,  51.500001,  53.500001,  55.500001,  57.500001,  59.500001,\n",
       "        61.500001,  63.500001,  65.500001,  67.500001,  69.500001,  71.500001,\n",
       "        73.500001,  75.500001,  77.500001,  79.500001,  81.500001,  83.500001,\n",
       "        85.500001,  87.500001,  89.500001,  91.500001,  93.500001,  95.500001,\n",
       "        97.500001,  99.500001, 101.500002, 103.500002, 105.500002, 107.500002,\n",
       "       109.500002, 111.500002, 113.500002, 115.500002, 117.500002, 119.500002,\n",
       "       121.500002, 123.500002, 125.500002, 127.500002, 129.500002, 131.500002,\n",
       "       133.500002, 135.500002, 137.500002, 139.500002, 141.500002, 143.500002,\n",
       "       145.500002, 147.500002, 149.500002, 151.500002, 153.500002, 155.500002,\n",
       "       157.500002, 159.500002, 161.500002, 163.500002, 165.500002, 167.500003,\n",
       "       169.500003, 171.500003, 173.500003, 175.500003, 177.500003, 179.500003,\n",
       "       181.500003, 183.500003, 185.500003, 187.500003, 189.500003, 191.500003,\n",
       "       193.500003, 195.500003, 197.500003, 199.500003, 201.500003, 203.500003,\n",
       "       205.500003, 207.500003, 209.500003, 211.500003, 213.500003, 215.500003,\n",
       "       217.500003, 219.500003, 221.500003, 223.500003, 225.500003, 227.500003,\n",
       "       229.500003, 231.500003, 233.500004, 235.500004, 237.500004, 239.500004,\n",
       "       241.500004, 243.500004, 245.500004, 247.500004, 249.500004, 251.500004,\n",
       "       253.500004, 255.500004, 257.500004, 259.500004, 261.500004, 263.500004,\n",
       "       265.500004, 267.500004, 269.500004, 271.500004, 273.500004, 275.500004,\n",
       "       277.500004, 279.500004, 281.500004, 283.500004, 285.500004, 287.500004,\n",
       "       289.500004, 291.500004, 293.500004, 295.500004, 297.500004, 299.500004,\n",
       "       301.500005, 303.500005, 305.500005, 307.500005, 309.500005, 311.500005,\n",
       "       313.500005, 315.500005, 317.500005, 319.500005, 321.500005, 323.500005,\n",
       "       325.500005, 327.500005, 329.500005, 331.500005, 333.500005, 335.500005,\n",
       "       337.500005, 339.500005, 341.500005, 343.500005, 345.500005, 347.500005,\n",
       "       349.500005, 351.500005, 353.500005, 355.500005, 357.500005, 359.500005,\n",
       "       361.500005])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.5 2.5 4.5 ... 206.5 208.5 210.5</div><input id='attrs-f36abd7b-1612-4185-9c79-d77e181c809a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f36abd7b-1612-4185-9c79-d77e181c809a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dfa59847-90e6-4f9f-9ff2-23ca50ee08c3' class='xr-var-data-in' type='checkbox'><label for='data-dfa59847-90e6-4f9f-9ff2-23ca50ee08c3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  0.5     ,   2.5     ,   4.5     ,   6.5     ,   8.5     ,  10.5     ,\n",
       "        12.5     ,  14.5     ,  16.5     ,  18.5     ,  20.5     ,  22.5     ,\n",
       "        24.5     ,  26.5     ,  28.5     ,  30.5     ,  32.5     ,  34.500001,\n",
       "        36.500001,  38.500001,  40.500001,  42.500001,  44.500001,  46.500001,\n",
       "        48.500001,  50.500001,  52.500001,  54.500001,  56.500001,  58.500001,\n",
       "        60.500001,  62.500001,  64.500001,  66.500001,  68.500001,  70.500001,\n",
       "        72.500001,  74.500001,  76.500001,  78.500001,  80.500001,  82.500001,\n",
       "        84.500001,  86.500001,  88.500001,  90.500001,  92.500001,  94.500001,\n",
       "        96.500001,  98.500001, 100.500002, 102.500002, 104.500002, 106.500002,\n",
       "       108.500002, 110.500002, 112.500002, 114.500002, 116.500002, 118.500002,\n",
       "       120.500002, 122.500002, 124.500002, 126.500002, 128.500002, 130.500002,\n",
       "       132.500002, 134.500002, 136.500002, 138.500002, 140.500002, 142.500002,\n",
       "       144.500002, 146.500002, 148.500002, 150.500002, 152.500002, 154.500002,\n",
       "       156.500002, 158.500002, 160.500002, 162.500002, 164.500002, 166.500003,\n",
       "       168.500003, 170.500003, 172.500003, 174.500003, 176.500003, 178.500003,\n",
       "       180.500003, 182.500003, 184.500003, 186.500003, 188.500003, 190.500003,\n",
       "       192.500003, 194.500003, 196.500003, 198.500003, 200.500003, 202.500003,\n",
       "       204.500003, 206.500003, 208.500003, 210.500003])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5 3.5 5.5 ... 123.5 125.5 127.5</div><input id='attrs-0bc2499c-40da-4620-9020-80fb4d21a46a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0bc2499c-40da-4620-9020-80fb4d21a46a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d1d3d24-9102-4c9e-aa82-c0d781013cd7' class='xr-var-data-in' type='checkbox'><label for='data-7d1d3d24-9102-4c9e-aa82-c0d781013cd7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  1.5,   3.5,   5.5,   7.5,   9.5,  11.5,  13.5,  15.5,  17.5,  19.5,\n",
       "        21.5,  23.5,  25.5,  27.5,  29.5,  31.5,  33.5,  35.5,  37.5,  39.5,\n",
       "        41.5,  43.5,  45.5,  47.5,  49.5,  51.5,  53.5,  55.5,  57.5,  59.5,\n",
       "        61.5,  63.5,  65.5,  67.5,  69.5,  71.5,  73.5,  75.5,  77.5,  79.5,\n",
       "        81.5,  83.5,  85.5,  87.5,  89.5,  91.5,  93.5,  95.5,  97.5,  99.5,\n",
       "       101.5, 103.5, 105.5, 107.5, 109.5, 111.5, 113.5, 115.5, 117.5, 119.5,\n",
       "       121.5, 123.5, 125.5, 127.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fbd17221-3f62-4c36-9855-03407884f000' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fbd17221-3f62-4c36-9855-03407884f000' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>HL50_cell</span></div><div class='xr-var-dims'>(z, y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 64, 64), meta=np.ndarray&gt;</div><input id='attrs-773616d4-48c6-4d73-b57f-82a4214af48d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-773616d4-48c6-4d73-b57f-82a4214af48d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3e3e349-1140-45b4-a7c6-4d0b97130357' class='xr-var-data-in' type='checkbox'><label for='data-f3e3e349-1140-45b4-a7c6-4d0b97130357' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>direction :</span></dt><dd>[[1. 0. 0.]\n",
       " [0. 1. 0.]\n",
       " [0. 0. 1.]]</dd><dt><span>Software :</span></dt><dd>InsightToolkit</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 2.34 MiB </td>\n",
       "                        <td> 512.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 106, 181) </td>\n",
       "                        <td> (64, 64, 64) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 846 Tasks </td>\n",
       "                        <td> 6 Chunks </td>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> uint16 </td>\n",
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       "Attributes:\n",
       "    direction:  [[1. 0. 0.]\\n [0. 1. 0.]\\n [0. 0. 1.]]\n",
       "    Software:   InsightToolkit</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>scale2</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-136cc2a2-cce4-4cef-90fd-8e5a55ad00a8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-136cc2a2-cce4-4cef-90fd-8e5a55ad00a8' class='xr-section-summary'  title='Expand/collapse section'>Groups: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-sections'><div style='padding-left:2rem;'><br></div></ul></div></li><li class='xr-section-item'><input id='section-82714db5-0423-4052-9685-c91529ecf6e4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-82714db5-0423-4052-9685-c91529ecf6e4' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>x</span>: 45</li><li><span class='xr-has-index'>y</span>: 26</li><li><span class='xr-has-index'>z</span>: 16</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-d6dd029d-c523-497e-bcef-7826a33941e1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d6dd029d-c523-497e-bcef-7826a33941e1' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>6.5 14.5 22.5 ... 342.5 350.5 358.5</div><input id='attrs-e6519de5-c773-4815-b2d7-4601cbce6b8e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e6519de5-c773-4815-b2d7-4601cbce6b8e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1f583cdb-2ca3-451e-82cf-07af48aca1f7' class='xr-var-data-in' type='checkbox'><label for='data-1f583cdb-2ca3-451e-82cf-07af48aca1f7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  6.5     ,  14.5     ,  22.5     ,  30.5     ,  38.500001,  46.500001,\n",
       "        54.500001,  62.500001,  70.500001,  78.500001,  86.500001,  94.500001,\n",
       "       102.500002, 110.500002, 118.500002, 126.500002, 134.500002, 142.500002,\n",
       "       150.500002, 158.500002, 166.500003, 174.500003, 182.500003, 190.500003,\n",
       "       198.500003, 206.500003, 214.500003, 222.500003, 230.500003, 238.500004,\n",
       "       246.500004, 254.500004, 262.500004, 270.500004, 278.500004, 286.500004,\n",
       "       294.500004, 302.500005, 310.500005, 318.500005, 326.500005, 334.500005,\n",
       "       342.500005, 350.500005, 358.500005])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.5 15.5 23.5 ... 191.5 199.5 207.5</div><input id='attrs-1d5b3880-5653-4490-85dc-03fb0611ea61' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1d5b3880-5653-4490-85dc-03fb0611ea61' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29ceb283-1c88-4ce1-962d-d6c73f9ac37c' class='xr-var-data-in' type='checkbox'><label for='data-29ceb283-1c88-4ce1-962d-d6c73f9ac37c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  7.5     ,  15.5     ,  23.5     ,  31.5     ,  39.500001,  47.500001,\n",
       "        55.500001,  63.500001,  71.500001,  79.500001,  87.500001,  95.500001,\n",
       "       103.500002, 111.500002, 119.500002, 127.500002, 135.500002, 143.500002,\n",
       "       151.500002, 159.500002, 167.500003, 175.500003, 183.500003, 191.500003,\n",
       "       199.500003, 207.500003])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.5 12.5 20.5 ... 108.5 116.5 124.5</div><input id='attrs-dc2b8f5f-c518-4094-83c0-825bd212d5a5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc2b8f5f-c518-4094-83c0-825bd212d5a5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d58048c2-ff69-411f-9fe2-2fe26e163ee1' class='xr-var-data-in' type='checkbox'><label for='data-d58048c2-ff69-411f-9fe2-2fe26e163ee1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([  4.5,  12.5,  20.5,  28.5,  36.5,  44.5,  52.5,  60.5,  68.5,  76.5,\n",
       "        84.5,  92.5, 100.5, 108.5, 116.5, 124.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0da20062-e324-4dac-afd0-9d3c33b4b1f0' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0da20062-e324-4dac-afd0-9d3c33b4b1f0' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>HL50_cell</span></div><div class='xr-var-dims'>(z, y, x)</div><div class='xr-var-dtype'>uint16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(16, 26, 45), meta=np.ndarray&gt;</div><input id='attrs-eacc6b8a-9221-4081-b90e-96be08a915db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eacc6b8a-9221-4081-b90e-96be08a915db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1fd6d68d-91a4-4004-9e38-5609a43d11fe' class='xr-var-data-in' type='checkbox'><label for='data-1fd6d68d-91a4-4004-9e38-5609a43d11fe' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>direction :</span></dt><dd>[[1. 0. 0.]\n",
       " [0. 1. 0.]\n",
       " [0. 0. 1.]]</dd><dt><span>Software :</span></dt><dd>InsightToolkit</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 36.56 kiB </td>\n",
       "                        <td> 36.56 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (16, 26, 45) </td>\n",
       "                        <td> (16, 26, 45) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 903 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> uint16 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-9506f947-27c4-4422-95e2-692dda1a86a1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9506f947-27c4-4422-95e2-692dda1a86a1' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>direction :</span></dt><dd>[[1. 0. 0.]\n",
       " [0. 1. 0.]\n",
       " [0. 0. 1.]]</dd><dt><span>Software :</span></dt><dd>InsightToolkit</dd></dl></div></li></ul></div></div></ul><br></div></ul></div></li><li class='xr-section-item'><input id='section-c785ebb6-ce2b-4b35-8f98-593bbdb457f7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c785ebb6-ce2b-4b35-8f98-593bbdb457f7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fe14f5ff-30dc-48cd-bb94-106052d2473b' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-fe14f5ff-30dc-48cd-bb94-106052d2473b' class='xr-section-summary'  title='Expand/collapse section'>Coordinates: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-dd0b95d0-fc5e-48fc-a526-d842e10bc636' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dd0b95d0-fc5e-48fc-a526-d842e10bc636' class='xr-section-summary'  title='Expand/collapse section'>Data variables: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'></ul></div></li><li class='xr-section-item'><input id='section-19314b0b-518b-4620-8400-46d4675c4cc0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-19314b0b-518b-4620-8400-46d4675c4cc0' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "DataTree('None', parent=None)\n",
       "├── DataTree('scale0')\n",
       "│       Dimensions:    (x: 363, y: 212, z: 129)\n",
       "│       Coordinates:\n",
       "│         * x          (x) float64 0.0 1.0 2.0 3.0 4.0 ... 358.0 359.0 360.0 361.0 362.0\n",
       "│         * y          (y) float64 0.0 1.0 2.0 3.0 4.0 ... 207.0 208.0 209.0 210.0 211.0\n",
       "│         * z          (z) float64 0.0 1.0 2.0 3.0 4.0 ... 124.0 125.0 126.0 127.0 128.0\n",
       "│       Data variables:\n",
       "│           HL50_cell  (z, y, x) uint16 dask.array<chunksize=(64, 64, 64), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           direction:  [[1. 0. 0.]\\n [0. 1. 0.]\\n [0. 0. 1.]]\n",
       "│           Software:   InsightToolkit\n",
       "├── DataTree('scale1')\n",
       "│       Dimensions:    (x: 181, y: 106, z: 64)\n",
       "│       Coordinates:\n",
       "│         * x          (x) float64 1.5 3.5 5.5 7.5 9.5 ... 353.5 355.5 357.5 359.5 361.5\n",
       "│         * y          (y) float64 0.5 2.5 4.5 6.5 8.5 ... 202.5 204.5 206.5 208.5 210.5\n",
       "│         * z          (z) float64 1.5 3.5 5.5 7.5 9.5 ... 119.5 121.5 123.5 125.5 127.5\n",
       "│       Data variables:\n",
       "│           HL50_cell  (z, y, x) uint16 dask.array<chunksize=(64, 64, 64), meta=np.ndarray>\n",
       "│       Attributes:\n",
       "│           direction:  [[1. 0. 0.]\\n [0. 1. 0.]\\n [0. 0. 1.]]\n",
       "│           Software:   InsightToolkit\n",
       "└── DataTree('scale2')\n",
       "        Dimensions:    (x: 45, y: 26, z: 16)\n",
       "        Coordinates:\n",
       "          * x          (x) float64 6.5 14.5 22.5 30.5 38.5 ... 334.5 342.5 350.5 358.5\n",
       "          * y          (y) float64 7.5 15.5 23.5 31.5 39.5 ... 183.5 191.5 199.5 207.5\n",
       "          * z          (z) float64 4.5 12.5 20.5 28.5 36.5 ... 100.5 108.5 116.5 124.5\n",
       "        Data variables:\n",
       "            HL50_cell  (z, y, x) uint16 dask.array<chunksize=(16, 26, 45), meta=np.ndarray>\n",
       "        Attributes:\n",
       "            direction:  [[1. 0. 0.]\\n [0. 1. 0.]\\n [0. 0. 1.]]\n",
       "            Software:   InsightToolkit"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "multiscale = to_multiscale(image_da, [2,4])\n",
    "multiscale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `multiscale` data structure is an [`xarray.Datatree`](https://xarray-datatree.readthedocs.io/en/latest/) composed of [`xarray.Dataset`'s](https://docs.xarray.dev/en/stable/generated/xarray.Dataset.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2081b38ef10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "multiscale['scale0'].ds.HL50_cell.isel(x=150).plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2081c4d1f10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "multiscale['scale0'].ds.HL50_cell.isel(y=100).plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x2081c586160>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "multiscale['scale0'].ds.HL50_cell.isel(z=100).plot.imshow()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "879de961770b482ba1501a8947cbf282",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Viewer(geometries=[], gradient_opacity=0.22, point_sets=[], rendered_image=<itk.itkImagePython.itkImageUS3; pr…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view(multiscale['scale1'].ds.HL50_cell)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the multiscale, chunked image as an OME-NGFF Zarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_path = f'{image_name}.zarr'\n",
    "store = zarr.storage.DirectoryStore(output_path, dimension_separator='/')\n",
    "multiscale.to_zarr(store)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect OME-NGFF Zarr content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\modern-insights-from-microscopy-images\\HL50_cell.zarr [zgroup]\n",
      " - metadata\n",
      "   - Multiscales\n",
      " - data\n",
      "   - (129, 212, 363)\n",
      "   - (64, 106, 181)\n",
      "   - (16, 26, 45)\n"
     ]
    }
   ],
   "source": [
    "# Get metadata on an image\n",
    "!ome_zarr info {output_path}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Examine the contents of the filesystem representation of the OME-Zarr multi-scale image. *What information is stored in each file?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C is Windows-SSD\n",
      " Volume Serial Number is 4666-3801\n",
      "\n",
      " Directory of C:\\modern-insights-from-microscopy-images\\HL50_cell.zarr\n",
      "\n",
      "05/09/2022  11:14 AM    <DIR>          .\n",
      "05/09/2022  11:14 AM    <DIR>          ..\n",
      "05/09/2022  11:14 AM             2,853 .zattrs\n",
      "05/09/2022  11:14 AM                24 .zgroup\n",
      "05/09/2022  11:14 AM            14,696 .zmetadata\n",
      "05/09/2022  11:14 AM    <DIR>          scale0\n",
      "05/09/2022  11:14 AM    <DIR>          scale1\n",
      "05/09/2022  11:14 AM    <DIR>          scale2\n",
      "               3 File(s)         17,573 bytes\n",
      "               5 Dir(s)  879,533,715,456 bytes free\n"
     ]
    }
   ],
   "source": [
    "%ls {output_path}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "OME-NGFF metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pycat {output_path}{os.sep}.zattrs"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Zarr metadata: *.zgroup* identifies a Zarr Group / Path."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pycat {output_path}{os.sep}.zgroup"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataset's hierarchical structure follows the filesystem layout."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C is Windows-SSD\n",
      " Volume Serial Number is 4666-3801\n",
      "\n",
      " Directory of C:\\modern-insights-from-microscopy-images\\HL50_cell.zarr\\scale0\n",
      "\n",
      "05/09/2022  11:14 AM    <DIR>          .\n",
      "05/09/2022  11:14 AM    <DIR>          ..\n",
      "05/09/2022  11:14 AM               274 .zattrs\n",
      "05/09/2022  11:14 AM                24 .zgroup\n",
      "05/09/2022  11:14 AM    <DIR>          HL50_cell\n",
      "05/09/2022  11:14 AM    <DIR>          x\n",
      "05/09/2022  11:14 AM    <DIR>          y\n",
      "05/09/2022  11:14 AM    <DIR>          z\n",
      "               2 File(s)            298 bytes\n",
      "               6 Dir(s)  879,533,744,128 bytes free\n"
     ]
    }
   ],
   "source": [
    "%ls {output_path}{os.sep}scale0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C is Windows-SSD\n",
      " Volume Serial Number is 4666-3801\n",
      "\n",
      " Directory of C:\\modern-insights-from-microscopy-images\\HL50_cell.zarr\\scale0\\HL50_cell\n",
      "\n",
      "05/09/2022  11:14 AM    <DIR>          .\n",
      "05/09/2022  11:14 AM    <DIR>          ..\n",
      "05/09/2022  11:14 AM               397 .zarray\n",
      "05/09/2022  11:14 AM               346 .zattrs\n",
      "05/09/2022  11:14 AM    <DIR>          0\n",
      "05/09/2022  11:14 AM    <DIR>          1\n",
      "05/09/2022  11:14 AM    <DIR>          2\n",
      "               2 File(s)            743 bytes\n",
      "               5 Dir(s)  879,533,617,152 bytes free\n"
     ]
    }
   ],
   "source": [
    "%ls {output_path}{os.sep}scale0{os.sep}HL50_cell"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The *.zarray* files contains metadata about the current multidimensional binary array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "%pycat {output_path}{os.sep}scale0{os.sep}HL50_cell{os.sep}.zarray"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Array chunks are indexed by dimension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C is Windows-SSD\n",
      " Volume Serial Number is 4666-3801\n",
      "\n",
      " Directory of C:\\modern-insights-from-microscopy-images\\HL50_cell.zarr\\scale0\\HL50_cell\\0\n",
      "\n",
      "05/09/2022  11:14 AM    <DIR>          .\n",
      "05/09/2022  11:14 AM    <DIR>          ..\n",
      "05/09/2022  11:14 AM    <DIR>          0\n",
      "05/09/2022  11:14 AM    <DIR>          1\n",
      "05/09/2022  11:14 AM    <DIR>          2\n",
      "05/09/2022  11:14 AM    <DIR>          3\n",
      "               0 File(s)              0 bytes\n",
      "               6 Dir(s)  879,533,293,568 bytes free\n"
     ]
    }
   ],
   "source": [
    "%ls {output_path}{os.sep}scale0{os.sep}HL50_cell{os.sep}0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Array chunks are stored as compressed binary files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Volume in drive C is Windows-SSD\n",
      " Volume Serial Number is 4666-3801\n",
      "\n",
      " Directory of C:\\modern-insights-from-microscopy-images\\HL50_cell.zarr\\scale0\\HL50_cell\\0\\0\n",
      "\n",
      "05/09/2022  11:14 AM    <DIR>          .\n",
      "05/09/2022  11:14 AM    <DIR>          ..\n",
      "05/09/2022  11:14 AM           265,769 0\n",
      "05/09/2022  11:14 AM           270,699 1\n",
      "05/09/2022  11:14 AM           265,796 2\n",
      "05/09/2022  11:14 AM           282,602 3\n",
      "05/09/2022  11:14 AM           272,626 4\n",
      "05/09/2022  11:14 AM           195,033 5\n",
      "               6 File(s)      1,552,525 bytes\n",
      "               2 Dir(s)  879,533,293,568 bytes free\n"
     ]
    }
   ],
   "source": [
    "%ls {output_path}{os.sep}scale0{os.sep}HL50_cell{os.sep}0{os.sep}0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load the OME-NGFF with `zarr`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zarr.hierarchy.Group '/'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import zarr\n",
    "group = zarr.open(f'{output_path}')\n",
    "group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['multiscales'])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.attrs.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'@type': 'ngff:Image',\n",
       "  'axes': [{'name': 'x', 'type': 'space'},\n",
       "   {'name': 'y', 'type': 'space'},\n",
       "   {'name': 'z', 'type': 'space'}],\n",
       "  'datasets': [{'coordinateTransformations': [{'scale': [1.0000000150184933,\n",
       "       1.0000000150184933,\n",
       "       1.0],\n",
       "      'type': 'scale'},\n",
       "     {'translation': [0.0, 0.0, 0.0], 'type': 'translation'}],\n",
       "    'path': 'scale0/HL50_cell'},\n",
       "   {'coordinateTransformations': [{'scale': [2.0000000300369867,\n",
       "       2.000000030036987,\n",
       "       2.0],\n",
       "      'type': 'scale'},\n",
       "     {'translation': [1.5000000225277401, 0.5000000075092467, 1.5],\n",
       "      'type': 'translation'}],\n",
       "    'path': 'scale1/HL50_cell'},\n",
       "   {'coordinateTransformations': [{'scale': [8.000000120147947,\n",
       "       8.000000120147947,\n",
       "       8.0],\n",
       "      'type': 'scale'},\n",
       "     {'translation': [6.500000097620207, 7.5000001126387, 4.5],\n",
       "      'type': 'translation'}],\n",
       "    'path': 'scale2/HL50_cell'}],\n",
       "  'name': 'HL50_cell',\n",
       "  'version': '0.4'}]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "group.attrs['multiscales']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['scale0', 'scale1', 'scale2']"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(group.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "scale0 = group['scale0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zarr.hierarchy.Group '/scale0'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<zarr.core.Array '/scale0/HL50_cell' (129, 212, 363) uint16>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scale0['HL50_cell']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 95,  96,  84, ...,  89,  93,  97],\n",
       "        [ 94,  89,  92, ...,  93,  95,  85],\n",
       "        [ 95,  93,  92, ...,  90,  88,  92],\n",
       "        ...,\n",
       "        [ 92,  95,  96, ...,  93,  88,  96],\n",
       "        [ 91,  92,  93, ...,  91,  88,  93],\n",
       "        [ 97,  94,  94, ...,  89,  95,  96]],\n",
       "\n",
       "       [[ 86,  88,  92, ...,  95,  93,  97],\n",
       "        [ 96,  96,  93, ...,  91,  93,  94],\n",
       "        [ 85,  95,  82, ...,  91,  86,  92],\n",
       "        ...,\n",
       "        [ 91,  91,  87, ...,  91,  90,  98],\n",
       "        [ 98,  92,  97, ...,  96,  90,  95],\n",
       "        [ 88,  88,  99, ...,  94,  97,  93]],\n",
       "\n",
       "       [[ 92,  92,  95, ...,  92,  92,  89],\n",
       "        [ 96,  94,  93, ...,  95,  96,  89],\n",
       "        [ 92,  93,  93, ...,  95,  84,  90],\n",
       "        ...,\n",
       "        [ 92,  93,  91, ...,  92,  95,  93],\n",
       "        [ 94,  94,  83, ...,  89,  89,  86],\n",
       "        [ 90,  90,  92, ...,  86,  92,  96]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[ 85,  89,  92, ...,  97,  97,  88],\n",
       "        [ 95,  92,  92, ...,  88,  96,  90],\n",
       "        [ 81,  81,  92, ...,  98,  94,  86],\n",
       "        ...,\n",
       "        [ 90,  95,  90, ...,  95,  95, 100],\n",
       "        [ 96,  95,  93, ...,  99,  96,  89],\n",
       "        [ 98,  91,  92, ...,  87,  91,  89]],\n",
       "\n",
       "       [[ 90,  88,  91, ...,  94,  94,  84],\n",
       "        [ 84,  90,  97, ...,  88, 100,  96],\n",
       "        [ 93,  96,  95, ...,  89,  87,  96],\n",
       "        ...,\n",
       "        [ 89, 100,  92, ...,  92,  91,  87],\n",
       "        [ 93,  92,  91, ...,  89,  82,  98],\n",
       "        [ 90,  93,  90, ...,  93,  90,  96]],\n",
       "\n",
       "       [[ 91,  89,  93, ..., 103,  93,  93],\n",
       "        [ 97,  90,  91, ...,  94,  90,  89],\n",
       "        [ 96,  92,  92, ...,  91,  94,  87],\n",
       "        ...,\n",
       "        [ 85,  94,  85, ...,  91,  91,  87],\n",
       "        [ 89,  93,  99, ...,  84,  90,  94],\n",
       "        [ 99,  99,  92, ...,  90,  91,  86]]], dtype=uint16)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.asarray(scale0['HL50_cell'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rechunk the multiscale image with a chunk size of 128. *How does the file count change?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121\n"
     ]
    }
   ],
   "source": [
    "file_count = sum(len(files) for _, _, files in os.walk(f'{output_path}'))\n",
    "print(file_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# multiscale_rechunked = multiscale.chunk(128)\n",
    "\n",
    "# .to_zarr\n",
    "\n",
    "# count files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Save the OME-NGFF in a [Zarr ZipStore](https://zarr.readthedocs.io/en/stable/api/storage.html). *How does this file count change?*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from zarr.storage import ZipStore\n",
    "\n",
    "store = ZipStore(f'{output_path}.zip', dimension_separator='/')\n",
    "multiscale.to_zarr(store, mode='w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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