xarray_insar_example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# InSAR typical operations in Xarray and Zarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is an example Jupyter notebook to realize the typical InSAR operations with Xarray and Zarr. The following operations are covered:\n",
    "\n",
    "- Writing a pointset in `xarray.DataArray` to `zarr` storage with chunks\n",
    "- Subsetting pointset\n",
    "- Appending saving the new variables\n",
    "- Reconstruct raster grid from the saved raster coordinates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment and testing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we will initiate a mannual defined data cube as a representation of the corregistered InSAR images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set data cube dimension\n",
    "n_azimuth=2000\n",
    "n_range=1000\n",
    "n_time=4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# signal\n",
    "signal = np.zeros((n_azimuth,n_range,n_time))\n",
    "signal[:,:,0] = np.tile(np.linspace(0,np.pi,num=n_range), (n_azimuth,1))\n",
    "for i in range(1,n_time):\n",
    "    signal[:,:,i] = signal[:,:,0]+i\n",
    "\n",
    "# noise\n",
    "np.random.seed(0)\n",
    "noise = np.random.rand(n_azimuth,n_range,n_time)*2*np.pi\n",
    "\n",
    "# obs\n",
    "obs = signal + noise"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f3500ae5ba0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 900x300 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the simulated data\n",
    "fig, ax = plt.subplots(1,n_time)\n",
    "fig.set_size_inches(9,3)\n",
    "for i in range(n_time):\n",
    "    im = ax[i].imshow(obs[:,:,i], vmin=0, vmax=12)\n",
    "plt.colorbar(im)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Export to Zarr storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use the following code to convert the dummy datacube to an `xr.DataSet` object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "\n",
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       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "  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",
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       "}\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:  (azimuth: 2000, range: 1000, time: 4)\n",
       "Coordinates:\n",
       "  * azimuth  (azimuth) int64 0 1 2 3 4 5 6 ... 1994 1995 1996 1997 1998 1999\n",
       "  * range    (range) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999\n",
       "  * time     (time) int64 0 1 2 3\n",
       "Data variables:\n",
       "    obs      (azimuth, range, time) float64 3.448 5.494 5.787 ... 7.007 7.702</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-6f402fd3-3c0e-4964-bb02-32d8d96687c2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-6f402fd3-3c0e-4964-bb02-32d8d96687c2' 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'>azimuth</span>: 2000</li><li><span class='xr-has-index'>range</span>: 1000</li><li><span class='xr-has-index'>time</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0ac9a179-6019-42cb-ad76-bfe0b909e509' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0ac9a179-6019-42cb-ad76-bfe0b909e509' 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'>azimuth</span></div><div class='xr-var-dims'>(azimuth)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-e9a7d763-faaa-4506-81fa-25268ee465d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e9a7d763-faaa-4506-81fa-25268ee465d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4bb73e5-4b8b-4903-a0b1-a952c5bdec91' class='xr-var-data-in' type='checkbox'><label for='data-c4bb73e5-4b8b-4903-a0b1-a952c5bdec91' 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, ..., 1997, 1998, 1999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-3e69f344-e1d1-493c-931e-2c28a5fdc86f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3e69f344-e1d1-493c-931e-2c28a5fdc86f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-74594c73-2007-4672-b345-1848d72f76b0' class='xr-var-data-in' type='checkbox'><label for='data-74594c73-2007-4672-b345-1848d72f76b0' 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, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-614f6635-e65a-4fc4-b9bf-4c3b09067f83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-614f6635-e65a-4fc4-b9bf-4c3b09067f83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-484f4265-fe32-41f6-921a-8e292647a10b' class='xr-var-data-in' type='checkbox'><label for='data-484f4265-fe32-41f6-921a-8e292647a10b' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2468b7cd-f37b-4ff5-8ad7-090ef88ded13' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2468b7cd-f37b-4ff5-8ad7-090ef88ded13' 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>obs</span></div><div class='xr-var-dims'>(azimuth, range, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.448 5.494 5.787 ... 7.007 7.702</div><input id='attrs-8f33b7c2-7972-4fd9-a721-498b643fc629' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8f33b7c2-7972-4fd9-a721-498b643fc629' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b29ae455-5e72-40ab-8743-dea5353259f7' class='xr-var-data-in' type='checkbox'><label for='data-b29ae455-5e72-40ab-8743-dea5353259f7' 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([[[ 3.44829694,  5.49366732,  5.78727399,  6.42360201],\n",
       "        [ 2.66504635,  5.06141714,  4.75258627,  8.60631975],\n",
       "        [ 6.06116117,  3.41552359,  6.9808446 ,  6.32943426],\n",
       "        ...,\n",
       "        [ 5.4999263 ,  4.52347252,  7.55728027,  7.65426647],\n",
       "        [ 4.31824349,  6.68112629,  5.89182637,  8.33529685],\n",
       "        [ 5.87398425,  9.35665497, 11.00258675,  8.07894017]],\n",
       "\n",
       "       [[ 1.83872423,  4.5595393 ,  2.8634003 ,  5.19730646],\n",
       "        [ 0.33751311,  3.38490013,  2.7203788 ,  6.53311937],\n",
       "        [ 4.0375418 ,  7.20836851,  3.23238185,  4.30817897],\n",
       "        ...,\n",
       "        [ 8.95386665,  6.87065511,  8.5951076 ,  7.3235653 ],\n",
       "        [ 7.49541132,  5.47492823,  9.47358319, 11.93898048],\n",
       "        [ 9.05596057,  9.17962773,  8.52913806, 11.40199181]],\n",
       "\n",
       "       [[ 2.87618902,  3.36824377,  6.41290177,  4.30265577],\n",
       "        [ 0.46985685,  3.30295785,  4.14341973,  3.76042001],\n",
       "        [ 5.37566274,  5.20438584,  2.16834013,  6.40021207],\n",
       "        ...,\n",
       "...\n",
       "        ...,\n",
       "        [ 6.31106866,  9.22900852,  6.58630379,  8.57220364],\n",
       "        [ 8.9603695 ,  7.57694661, 10.9119072 , 10.887253  ],\n",
       "        [ 5.02624897,  7.70975186, 10.55679573, 10.56739648]],\n",
       "\n",
       "       [[ 3.74417948,  4.53168709,  5.89472535,  7.10311288],\n",
       "        [ 4.41525201,  6.30521549,  6.58264266,  3.53968919],\n",
       "        [ 4.08423329,  4.34873727,  2.99682423,  3.57845392],\n",
       "        ...,\n",
       "        [ 8.69149461,  4.71308725,  5.7542783 ,  8.73345809],\n",
       "        [ 6.88934038,  7.85751903,  8.82840164,  6.22564467],\n",
       "        [ 4.16337595,  9.23755869,  5.51628921,  6.48912119]],\n",
       "\n",
       "       [[ 1.96228409,  3.05328586,  6.84291353,  6.82229612],\n",
       "        [ 4.46297059,  2.34777088,  2.6785867 ,  5.08459572],\n",
       "        [ 3.45678252,  7.2005013 ,  4.40796016,  7.06653181],\n",
       "        ...,\n",
       "        [ 3.44038925,  5.18133803,  7.30264699,  9.35364653],\n",
       "        [ 9.11459154,  9.7177272 , 10.1418854 , 10.30448531],\n",
       "        [ 5.80238023, 10.28567307,  7.00728965,  7.70171342]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-adf5b6af-08ca-48aa-ab9c-57c1580ffc14' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-adf5b6af-08ca-48aa-ab9c-57c1580ffc14' 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": [
       "<xarray.Dataset>\n",
       "Dimensions:  (azimuth: 2000, range: 1000, time: 4)\n",
       "Coordinates:\n",
       "  * azimuth  (azimuth) int64 0 1 2 3 4 5 6 ... 1994 1995 1996 1997 1998 1999\n",
       "  * range    (range) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999\n",
       "  * time     (time) int64 0 1 2 3\n",
       "Data variables:\n",
       "    obs      (azimuth, range, time) float64 3.448 5.494 5.787 ... 7.007 7.702"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.Dataset(\n",
    "    data_vars=dict(\n",
    "        obs=([\"azimuth\", \"range\", \"time\"], obs),\n",
    "    ),\n",
    "    coords=dict(\n",
    "        azimuth=np.arange(n_azimuth),\n",
    "        range=np.arange(n_range),\n",
    "        time=np.arange(n_time),\n",
    "    ),\n",
    ")\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The datacube is 3D, with two space coordinates and one time coordinates. It can be converted to a pointset for the convenience of processing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 4, points: 2000000)\n",
       "Coordinates:\n",
       "  * time     (time) int64 0 1 2 3\n",
       "  * points   (points) object MultiIndex\n",
       "  * azimuth  (points) int64 0 0 0 0 0 0 0 ... 1999 1999 1999 1999 1999 1999 1999\n",
       "  * range    (points) int64 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    obs      (time, points) float64 3.448 2.665 6.061 3.579 ... 9.354 10.3 7.702</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-f3b26c26-9c1b-43c5-b708-834f24bcf8fa' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-f3b26c26-9c1b-43c5-b708-834f24bcf8fa' 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'>time</span>: 4</li><li><span class='xr-has-index'>points</span>: 2000000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-0fe7fd78-2471-482e-a43b-9da0959314af' class='xr-section-summary-in' type='checkbox'  checked><label for='section-0fe7fd78-2471-482e-a43b-9da0959314af' class='xr-section-summary' >Coordinates: <span>(4)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-9a1a6507-7e7d-47f3-8cf0-cef48347cafe' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9a1a6507-7e7d-47f3-8cf0-cef48347cafe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-82b497c5-4b16-4125-b050-138dc0f135f9' class='xr-var-data-in' type='checkbox'><label for='data-82b497c5-4b16-4125-b050-138dc0f135f9' 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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>points</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-1a135cfd-d0a9-42f9-8853-7a2b6a3e9ca9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a135cfd-d0a9-42f9-8853-7a2b6a3e9ca9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20b5bb29-4cc5-4a7d-8ed4-1cff9897f719' class='xr-var-data-in' type='checkbox'><label for='data-20b5bb29-4cc5-4a7d-8ed4-1cff9897f719' 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, 0), (0, 1), (0, 2), ..., (1999, 997), (1999, 998), (1999, 999)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>azimuth</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 ... 1999 1999 1999 1999</div><input id='attrs-3ce2f3d8-16d8-4048-84c0-ea71bc7951b9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3ce2f3d8-16d8-4048-84c0-ea71bc7951b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c3e476e5-f531-4a04-aeb9-4042338c9463' class='xr-var-data-in' type='checkbox'><label for='data-c3e476e5-f531-4a04-aeb9-4042338c9463' 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,    0,    0, ..., 1999, 1999, 1999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-ee8cb146-5413-4f65-b2cd-167884d91139' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ee8cb146-5413-4f65-b2cd-167884d91139' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f93d2010-4b8e-4871-b707-51b937eed00e' class='xr-var-data-in' type='checkbox'><label for='data-f93d2010-4b8e-4871-b707-51b937eed00e' 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, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-18be5640-eb28-43a6-aa6e-b54b645acb6d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-18be5640-eb28-43a6-aa6e-b54b645acb6d' 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>obs</span></div><div class='xr-var-dims'>(time, points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.448 2.665 6.061 ... 10.3 7.702</div><input id='attrs-4f39f935-ce22-4db2-bb52-766dfb7d75c7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4f39f935-ce22-4db2-bb52-766dfb7d75c7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b326771-25a1-42c9-823f-b2eb5b7518a9' class='xr-var-data-in' type='checkbox'><label for='data-1b326771-25a1-42c9-823f-b2eb5b7518a9' 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([[ 3.44829694,  2.66504635,  6.06116117, ...,  3.44038925,\n",
       "         9.11459154,  5.80238023],\n",
       "       [ 5.49366732,  5.06141714,  3.41552359, ...,  5.18133803,\n",
       "         9.7177272 , 10.28567307],\n",
       "       [ 5.78727399,  4.75258627,  6.9808446 , ...,  7.30264699,\n",
       "        10.1418854 ,  7.00728965],\n",
       "       [ 6.42360201,  8.60631975,  6.32943426, ...,  9.35364653,\n",
       "        10.30448531,  7.70171342]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c42ba51c-3ead-4cc5-a30a-7eadee6e1575' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c42ba51c-3ead-4cc5-a30a-7eadee6e1575' 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": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 4, points: 2000000)\n",
       "Coordinates:\n",
       "  * time     (time) int64 0 1 2 3\n",
       "  * points   (points) object MultiIndex\n",
       "  * azimuth  (points) int64 0 0 0 0 0 0 0 ... 1999 1999 1999 1999 1999 1999 1999\n",
       "  * range    (points) int64 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    obs      (time, points) float64 3.448 2.665 6.061 3.579 ... 9.354 10.3 7.702"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert to pointset by stacking two dimensions\n",
    "ds_pointset = ds.stack(points=('azimuth', 'range'))\n",
    "ds_pointset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we use the `stack` function to reshape the dataset, and create a new coordinate: `points`. As the name of the function suggested, the `points` coordinates is a stacked coordinate, or a `MultiIndex`, created from `azimuth` and `range`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
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       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "  float: left;\n",
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       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
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       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
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       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;points&#x27; (points: 2000000)&gt;\n",
       "array([(0, 0), (0, 1), (0, 2), ..., (1999, 997), (1999, 998), (1999, 999)],\n",
       "      dtype=object)\n",
       "Coordinates:\n",
       "  * points   (points) object MultiIndex\n",
       "  * azimuth  (points) int64 0 0 0 0 0 0 0 ... 1999 1999 1999 1999 1999 1999 1999\n",
       "  * range    (points) int64 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999</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'>'points'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>points</span>: 2000000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-11f15964-4968-4973-9cca-7c4ce04f14e4' class='xr-array-in' type='checkbox' checked><label for='section-11f15964-4968-4973-9cca-7c4ce04f14e4' 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>MultiIndex</span></div><div class='xr-array-data'><pre>array([(0, 0), (0, 1), (0, 2), ..., (1999, 997), (1999, 998), (1999, 999)],\n",
       "      dtype=object)</pre></div></div></li><li class='xr-section-item'><input id='section-7faff272-4857-4aad-b7e8-b058f980c028' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7faff272-4857-4aad-b7e8-b058f980c028' 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'>points</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-42337a42-fcc4-45b3-93d1-149352d30c75' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-42337a42-fcc4-45b3-93d1-149352d30c75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f70889a1-d012-4696-bd16-e7d766e6f499' class='xr-var-data-in' type='checkbox'><label for='data-f70889a1-d012-4696-bd16-e7d766e6f499' 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, 0), (0, 1), (0, 2), ..., (1999, 997), (1999, 998), (1999, 999)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>azimuth</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 ... 1999 1999 1999 1999</div><input id='attrs-cd9e990c-5472-4e18-8bd1-6dc38955b174' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-cd9e990c-5472-4e18-8bd1-6dc38955b174' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddbbde90-d887-414b-a961-d7a3988b1b66' class='xr-var-data-in' type='checkbox'><label for='data-ddbbde90-d887-414b-a961-d7a3988b1b66' 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,    0,    0, ..., 1999, 1999, 1999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-a4380977-5502-406a-9cbe-33591d86773a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a4380977-5502-406a-9cbe-33591d86773a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68ea1b9e-79b1-4d69-994e-9bf369976c93' class='xr-var-data-in' type='checkbox'><label for='data-68ea1b9e-79b1-4d69-994e-9bf369976c93' 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, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0408b482-dd88-4f87-a749-8657f9820d22' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-0408b482-dd88-4f87-a749-8657f9820d22' 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": [
       "<xarray.DataArray 'points' (points: 2000000)>\n",
       "array([(0, 0), (0, 1), (0, 2), ..., (1999, 997), (1999, 998), (1999, 999)],\n",
       "      dtype=object)\n",
       "Coordinates:\n",
       "  * points   (points) object MultiIndex\n",
       "  * azimuth  (points) int64 0 0 0 0 0 0 0 ... 1999 1999 1999 1999 1999 1999 1999\n",
       "  * range    (points) int64 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_pointset.points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (time: 4, points: 2000000)\n",
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       "  * range    (points) int64 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    obs      (points, time) float64 3.448 5.494 5.787 ... 10.29 7.007 7.702</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-06c14c5d-22bf-4f6d-9b9e-aea823fa0bf4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-06c14c5d-22bf-4f6d-9b9e-aea823fa0bf4' 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'>time</span>: 4</li><li><span class='xr-has-index'>points</span>: 2000000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9f50a309-42f3-4e07-9320-8bf29a3145fc' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9f50a309-42f3-4e07-9320-8bf29a3145fc' class='xr-section-summary' >Coordinates: <span>(4)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-ef2df18f-843f-4b06-b4ba-50646d754087' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ef2df18f-843f-4b06-b4ba-50646d754087' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4081cec4-60d0-4f8d-8883-8b08c874bcc1' class='xr-var-data-in' type='checkbox'><label for='data-4081cec4-60d0-4f8d-8883-8b08c874bcc1' 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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>points</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-27630e30-8324-4035-a835-509161e182f9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-27630e30-8324-4035-a835-509161e182f9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec2a2ece-72e7-4967-95d0-1dad38d79e52' class='xr-var-data-in' type='checkbox'><label for='data-ec2a2ece-72e7-4967-95d0-1dad38d79e52' 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, 0), (0, 1), (0, 2), ..., (1999, 997), (1999, 998), (1999, 999)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>azimuth</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 ... 1999 1999 1999 1999</div><input id='attrs-a2916d73-d457-43c6-b6e1-241881cac97b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2916d73-d457-43c6-b6e1-241881cac97b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b34dc060-eb65-447e-9425-34f001b74dd6' class='xr-var-data-in' type='checkbox'><label for='data-b34dc060-eb65-447e-9425-34f001b74dd6' 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,    0,    0, ..., 1999, 1999, 1999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-44a35cca-bded-4a4a-a492-5911da246ae2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-44a35cca-bded-4a4a-a492-5911da246ae2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2124ef9a-c8ea-47cd-830f-f6e1b2e6e7a7' class='xr-var-data-in' type='checkbox'><label for='data-2124ef9a-c8ea-47cd-830f-f6e1b2e6e7a7' 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, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e48c7872-4eb7-4637-a23f-de29d35ab669' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e48c7872-4eb7-4637-a23f-de29d35ab669' 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>obs</span></div><div class='xr-var-dims'>(points, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.448 5.494 5.787 ... 7.007 7.702</div><input id='attrs-4a9ad9ce-9eb5-4f92-bb19-f32a3d690f4f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a9ad9ce-9eb5-4f92-bb19-f32a3d690f4f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-60490e66-8a3e-46d6-91f8-0af8a9144600' class='xr-var-data-in' type='checkbox'><label for='data-60490e66-8a3e-46d6-91f8-0af8a9144600' 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([[ 3.44829694,  5.49366732,  5.78727399,  6.42360201],\n",
       "       [ 2.66504635,  5.06141714,  4.75258627,  8.60631975],\n",
       "       [ 6.06116117,  3.41552359,  6.9808446 ,  6.32943426],\n",
       "       ...,\n",
       "       [ 3.44038925,  5.18133803,  7.30264699,  9.35364653],\n",
       "       [ 9.11459154,  9.7177272 , 10.1418854 , 10.30448531],\n",
       "       [ 5.80238023, 10.28567307,  7.00728965,  7.70171342]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-59bc2ce2-03f2-4685-a173-22eed31dd8ab' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-59bc2ce2-03f2-4685-a173-22eed31dd8ab' 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": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 4, points: 2000000)\n",
       "Coordinates:\n",
       "  * time     (time) int64 0 1 2 3\n",
       "  * points   (points) object MultiIndex\n",
       "  * azimuth  (points) int64 0 0 0 0 0 0 0 ... 1999 1999 1999 1999 1999 1999 1999\n",
       "  * range    (points) int64 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    obs      (points, time) float64 3.448 5.494 5.787 ... 10.29 7.007 7.702"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tranpose for InSAR data convention\n",
    "ds_pointset = ds_pointset.transpose(\"points\", \"time\")\n",
    "ds_pointset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, we cannot directly save a `xr.DataSet` with a `MultiIndex` dimension to `Zarr`. This is currently [an ongoing issue of Xarray](https://github.com/pydata/xarray/issues/1077)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Will fail, because \"to_Zarr\" currently does not support Multi-Indexing: \n",
    "# ds_pointset.to_zarr(\"database.zarr\", mode='w') # For this example, use \"w\" flag to overwrite everytime."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The solution can be replacing the newly created Multi-Index `points` coordinates with a real coordiantes. Since the raster coordinates are stored in `azimuth` and `range` coordinates, we can use these coordinates to restore the raster grid later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (points: 2000000, time: 4)\n",
       "Coordinates:\n",
       "  * time     (time) int64 0 1 2 3\n",
       "    azimuth  (points) int64 0 0 0 0 0 0 0 ... 1999 1999 1999 1999 1999 1999 1999\n",
       "    range    (points) int64 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999\n",
       "  * points   (points) int64 0 1 2 3 4 ... 1999996 1999997 1999998 1999999\n",
       "Data variables:\n",
       "    obs      (points, time) float64 3.448 5.494 5.787 ... 10.29 7.007 7.702</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-129b6ab9-800a-495d-bb6e-c7982bf56494' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-129b6ab9-800a-495d-bb6e-c7982bf56494' 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'>points</span>: 2000000</li><li><span class='xr-has-index'>time</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-df4fdcc8-0a9b-4051-bb00-42fb6c6fb723' class='xr-section-summary-in' type='checkbox'  checked><label for='section-df4fdcc8-0a9b-4051-bb00-42fb6c6fb723' class='xr-section-summary' >Coordinates: <span>(4)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-576a2087-3407-4eb4-b188-34d9b7a28f55' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-576a2087-3407-4eb4-b188-34d9b7a28f55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7996621e-d63b-4e59-89bc-3dc9ac665647' class='xr-var-data-in' type='checkbox'><label for='data-7996621e-d63b-4e59-89bc-3dc9ac665647' 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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>azimuth</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 ... 1999 1999 1999 1999</div><input id='attrs-f8f2eaae-55a3-4408-bf1d-7f6e0909c8d2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f8f2eaae-55a3-4408-bf1d-7f6e0909c8d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3a4711c3-8fb3-4f8f-b64a-636b0f60458e' class='xr-var-data-in' type='checkbox'><label for='data-3a4711c3-8fb3-4f8f-b64a-636b0f60458e' 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,    0,    0, ..., 1999, 1999, 1999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>range</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-2d9fd02a-e4fa-4c12-84a2-c51138f0b885' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d9fd02a-e4fa-4c12-84a2-c51138f0b885' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2ec7df91-f1c8-4c6b-b07c-91194b69889e' class='xr-var-data-in' type='checkbox'><label for='data-2ec7df91-f1c8-4c6b-b07c-91194b69889e' 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, ..., 997, 998, 999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>points</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 1999997 1999998 1999999</div><input id='attrs-dc99d611-e069-4595-ada7-33170038d48a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-dc99d611-e069-4595-ada7-33170038d48a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5f888ef-5584-4b7f-94aa-16c6f71c163d' class='xr-var-data-in' type='checkbox'><label for='data-c5f888ef-5584-4b7f-94aa-16c6f71c163d' 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, ..., 1999997, 1999998, 1999999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d21d292-e35c-4268-8fca-229e7e4446e7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2d21d292-e35c-4268-8fca-229e7e4446e7' 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>obs</span></div><div class='xr-var-dims'>(points, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.448 5.494 5.787 ... 7.007 7.702</div><input id='attrs-d920b14e-bc98-41cf-9cce-93f3600dd746' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d920b14e-bc98-41cf-9cce-93f3600dd746' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d37c6a5-4d75-469f-8f15-825a76a95cd5' class='xr-var-data-in' type='checkbox'><label for='data-4d37c6a5-4d75-469f-8f15-825a76a95cd5' 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([[ 3.44829694,  5.49366732,  5.78727399,  6.42360201],\n",
       "       [ 2.66504635,  5.06141714,  4.75258627,  8.60631975],\n",
       "       [ 6.06116117,  3.41552359,  6.9808446 ,  6.32943426],\n",
       "       ...,\n",
       "       [ 3.44038925,  5.18133803,  7.30264699,  9.35364653],\n",
       "       [ 9.11459154,  9.7177272 , 10.1418854 , 10.30448531],\n",
       "       [ 5.80238023, 10.28567307,  7.00728965,  7.70171342]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf965e40-5ab7-4d9b-8eb5-02f780e4a21e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cf965e40-5ab7-4d9b-8eb5-02f780e4a21e' 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": [
       "<xarray.Dataset>\n",
       "Dimensions:  (points: 2000000, time: 4)\n",
       "Coordinates:\n",
       "  * time     (time) int64 0 1 2 3\n",
       "    azimuth  (points) int64 0 0 0 0 0 0 0 ... 1999 1999 1999 1999 1999 1999 1999\n",
       "    range    (points) int64 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999\n",
       "  * points   (points) int64 0 1 2 3 4 ... 1999996 1999997 1999998 1999999\n",
       "Data variables:\n",
       "    obs      (points, time) float64 3.448 5.494 5.787 ... 10.29 7.007 7.702"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-construct the coordinates for the \"point\" dimension\n",
    "ds_pointset = ds_pointset.reset_index(\"points\")\n",
    "ds_pointset['points'] = xr.DataArray(data=range(ds_pointset.points.size), dims=[\"points\"])\n",
    "ds_pointset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However `ds_pointset` still cannot be stored to Zarr. This is because a strange behavior of the `to_index` function which is used to check the multi-index for `azimuth` and `range`. The following code block should give a single index, but gives a multi-index instead. This could potentially be a caching issue of `xarray`. Need to confirm this later."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "            ...\n",
       "            1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999],\n",
       "           dtype='int64', name='azimuth', length=2000000)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# One suspicious behavior , the following command gives a 2-D multi index but it should be 1-D. This will cause Zarr saving to fail\n",
    "ds_pointset.azimuth.to_index()\n",
    "\n",
    "# # This seem to be an cache issue of the `to_index`` function. The following script gives correct results:\n",
    "# vals = ds_pointset.azimuth.values\n",
    "\n",
    "# ds_test = xr.Dataset(\n",
    "#     data_vars=dict(\n",
    "#         obs=([\"azi\"], vals),\n",
    "#     ),\n",
    "#     coords=dict(\n",
    "#         # azi=np.arange(vals.size)\n",
    "#         azi=vals\n",
    "#     ),\n",
    "# )\n",
    "# ds_test\n",
    "\n",
    "# ds_test.azi.to_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The solution can be replacing the `azimuth` and `range` coordinates by themselves "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
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       "  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:  (time: 4, points: 2000000)\n",
       "Coordinates:\n",
       "  * time     (time) int64 0 1 2 3\n",
       "  * points   (points) int64 0 1 2 3 4 ... 1999996 1999997 1999998 1999999\n",
       "    azimuth  (points) int64 0 0 0 0 0 0 0 ... 1999 1999 1999 1999 1999 1999 1999\n",
       "    range    (points) int64 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    obs      (points, time) float64 3.448 5.494 5.787 ... 10.29 7.007 7.702</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-7ac65542-f749-45c6-aa11-bd56493ff0e6' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7ac65542-f749-45c6-aa11-bd56493ff0e6' 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'>time</span>: 4</li><li><span class='xr-has-index'>points</span>: 2000000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c8c2ac3e-ae76-432c-ba4a-4bf7bf3a25bf' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c8c2ac3e-ae76-432c-ba4a-4bf7bf3a25bf' class='xr-section-summary' >Coordinates: <span>(4)</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'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-860112f6-95b2-4052-a74f-0573c190567d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-860112f6-95b2-4052-a74f-0573c190567d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cff5db77-1651-479d-9bbd-462fedeafd6d' class='xr-var-data-in' type='checkbox'><label for='data-cff5db77-1651-479d-9bbd-462fedeafd6d' 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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>points</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 1999997 1999998 1999999</div><input id='attrs-92ea2510-03e2-42e3-9baa-c6cdd411a7e5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-92ea2510-03e2-42e3-9baa-c6cdd411a7e5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a163f22-42f5-4de9-b98e-1ce90a8223b7' class='xr-var-data-in' type='checkbox'><label for='data-6a163f22-42f5-4de9-b98e-1ce90a8223b7' 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, ..., 1999997, 1999998, 1999999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>azimuth</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 ... 1999 1999 1999 1999</div><input id='attrs-d3074561-6373-4f05-a45a-795e60e7f73f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d3074561-6373-4f05-a45a-795e60e7f73f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec0aa88c-cb44-47ec-abf1-f6134564b544' class='xr-var-data-in' type='checkbox'><label for='data-ec0aa88c-cb44-47ec-abf1-f6134564b544' 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,    0,    0, ..., 1999, 1999, 1999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>range</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-1e84f90e-825a-4653-bab8-2da001b7851c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e84f90e-825a-4653-bab8-2da001b7851c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b574b01-2b3e-493b-828b-0424271ee0e1' class='xr-var-data-in' type='checkbox'><label for='data-1b574b01-2b3e-493b-828b-0424271ee0e1' 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, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d4d808b5-f911-4733-9092-9cfdd0698af4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d4d808b5-f911-4733-9092-9cfdd0698af4' 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>obs</span></div><div class='xr-var-dims'>(points, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>3.448 5.494 5.787 ... 7.007 7.702</div><input id='attrs-54d3d96e-2d4d-48fa-ba50-99a19951ca7d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-54d3d96e-2d4d-48fa-ba50-99a19951ca7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c3f94b7-0260-4bf8-82fa-99fc1c84382b' class='xr-var-data-in' type='checkbox'><label for='data-6c3f94b7-0260-4bf8-82fa-99fc1c84382b' 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([[ 3.44829694,  5.49366732,  5.78727399,  6.42360201],\n",
       "       [ 2.66504635,  5.06141714,  4.75258627,  8.60631975],\n",
       "       [ 6.06116117,  3.41552359,  6.9808446 ,  6.32943426],\n",
       "       ...,\n",
       "       [ 3.44038925,  5.18133803,  7.30264699,  9.35364653],\n",
       "       [ 9.11459154,  9.7177272 , 10.1418854 , 10.30448531],\n",
       "       [ 5.80238023, 10.28567307,  7.00728965,  7.70171342]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-537cd4fa-3e2b-404a-b1b2-10f07cfc50f5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-537cd4fa-3e2b-404a-b1b2-10f07cfc50f5' 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": [
       "<xarray.Dataset>\n",
       "Dimensions:  (time: 4, points: 2000000)\n",
       "Coordinates:\n",
       "  * time     (time) int64 0 1 2 3\n",
       "  * points   (points) int64 0 1 2 3 4 ... 1999996 1999997 1999998 1999999\n",
       "    azimuth  (points) int64 0 0 0 0 0 0 0 ... 1999 1999 1999 1999 1999 1999 1999\n",
       "    range    (points) int64 0 1 2 3 4 5 6 7 ... 992 993 994 995 996 997 998 999\n",
       "Data variables:\n",
       "    obs      (points, time) float64 3.448 5.494 5.787 ... 10.29 7.007 7.702"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Overite the \"azimuth\" and \"range\" coordinates. The coordinates stays the same\n",
    "ds_pointset['azimuth'] = xr.DataArray(data=ds_pointset.azimuth.values, dims=[\"points\"])\n",
    "ds_pointset['range'] = xr.DataArray(data=ds_pointset.range.values, dims=[\"points\"])\n",
    "ds_pointset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No the `to_index` function gives the correct results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   0,    0,    0,    0,    0,    0,    0,    0,    0,    0,\n",
       "            ...\n",
       "            1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999, 1999],\n",
       "           dtype='int64', name='points', length=2000000)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# However the \"to_index\" function gives the correct output now\n",
    "ds_pointset.azimuth.to_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to store the the pointset to a `Zarr` storage. We would like to separate the points in spatial trunks for later concurrent accessing. First we check if there is any existing trunks "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Frozen({})"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the current chunks of dataset, there should be none\n",
    "ds_pointset.chunks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can customly define a trunk size:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Frozen({'points': (450000, 450000, 450000, 450000, 200000), 'time': (4,)})"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Chunk in the \"points\" dimension\n",
    "ds_pointset = ds_pointset.chunk({\"points\":450000})\n",
    "ds_pointset.chunks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then the data can be saved in chunks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x7f34f8866ab0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Export to Zarr\n",
    "ds_pointset.to_zarr(\"database.zarr\", mode='w') # For this example, use \"w\" flag to overwrite everytime."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "55M\tdatabase.zarr\n",
      "\u001b[34;42mdatabase.zarr\u001b[00m\n",
      "├── \u001b[34;42mazimuth\u001b[00m\n",
      "│   ├── \u001b[01;32m0\u001b[00m\n",
      "│   ├── \u001b[01;32m1\u001b[00m\n",
      "│   ├── \u001b[01;32m2\u001b[00m\n",
      "│   ├── \u001b[01;32m3\u001b[00m\n",
      "│   └── \u001b[01;32m4\u001b[00m\n",
      "├── \u001b[34;42mobs\u001b[00m\n",
      "│   ├── \u001b[01;32m0.0\u001b[00m\n",
      "│   ├── \u001b[01;32m1.0\u001b[00m\n",
      "│   ├── \u001b[01;32m2.0\u001b[00m\n",
      "│   ├── \u001b[01;32m3.0\u001b[00m\n",
      "│   └── \u001b[01;32m4.0\u001b[00m\n",
      "├── \u001b[34;42mpoints\u001b[00m\n",
      "│   ├── \u001b[01;32m0\u001b[00m\n",
      "│   ├── \u001b[01;32m1\u001b[00m\n",
      "│   ├── \u001b[01;32m10\u001b[00m\n",
      "│   ├── \u001b[01;32m11\u001b[00m\n",
      "│   ├── \u001b[01;32m12\u001b[00m\n",
      "│   ├── \u001b[01;32m13\u001b[00m\n",
      "│   ├── \u001b[01;32m14\u001b[00m\n",
      "│   ├── \u001b[01;32m15\u001b[00m\n",
      "│   ├── \u001b[01;32m16\u001b[00m\n",
      "│   ├── \u001b[01;32m17\u001b[00m\n",
      "│   ├── \u001b[01;32m18\u001b[00m\n",
      "│   ├── \u001b[01;32m19\u001b[00m\n",
      "│   ├── \u001b[01;32m2\u001b[00m\n",
      "│   ├── \u001b[01;32m20\u001b[00m\n",
      "│   ├── \u001b[01;32m21\u001b[00m\n",
      "│   ├── \u001b[01;32m22\u001b[00m\n",
      "│   ├── \u001b[01;32m23\u001b[00m\n",
      "│   ├── \u001b[01;32m24\u001b[00m\n",
      "│   ├── \u001b[01;32m25\u001b[00m\n",
      "│   ├── \u001b[01;32m26\u001b[00m\n",
      "│   ├── \u001b[01;32m27\u001b[00m\n",
      "│   ├── \u001b[01;32m28\u001b[00m\n",
      "│   ├── \u001b[01;32m29\u001b[00m\n",
      "│   ├── \u001b[01;32m3\u001b[00m\n",
      "│   ├── \u001b[01;32m30\u001b[00m\n",
      "│   ├── \u001b[01;32m31\u001b[00m\n",
      "│   ├── \u001b[01;32m4\u001b[00m\n",
      "│   ├── \u001b[01;32m5\u001b[00m\n",
      "│   ├── \u001b[01;32m6\u001b[00m\n",
      "│   ├── \u001b[01;32m7\u001b[00m\n",
      "│   ├── \u001b[01;32m8\u001b[00m\n",
      "│   └── \u001b[01;32m9\u001b[00m\n",
      "├── \u001b[34;42mrange\u001b[00m\n",
      "│   ├── \u001b[01;32m0\u001b[00m\n",
      "│   ├── \u001b[01;32m1\u001b[00m\n",
      "│   ├── \u001b[01;32m2\u001b[00m\n",
      "│   ├── \u001b[01;32m3\u001b[00m\n",
      "│   └── \u001b[01;32m4\u001b[00m\n",
      "└── \u001b[34;42mtime\u001b[00m\n",
      "    └── \u001b[01;32m0\u001b[00m\n",
      "\n",
      "5 directories, 48 files\n"
     ]
    }
   ],
   "source": [
    "# Check Zarr storage volume and file structure\n",
    "! du -h --max-depth=0 database.zarr\n",
    "! tree database.zarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Subsetting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we are going to give an example of subetting. First we load the saved `Zarr` storage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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       "Data variables:\n",
       "    obs      (points, time) float64 dask.array&lt;chunksize=(450000, 4), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-a7ba3b8b-c1e0-4f39-b77f-52da478128b1' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a7ba3b8b-c1e0-4f39-b77f-52da478128b1' 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'>points</span>: 2000000</li><li><span class='xr-has-index'>time</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-8e39aa83-4587-449c-9924-ba0e423adf1a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8e39aa83-4587-449c-9924-ba0e423adf1a' class='xr-section-summary' >Coordinates: <span>(4)</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>azimuth</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(450000,), meta=np.ndarray&gt;</div><input id='attrs-9b4282f2-e846-4a0d-a07a-e5d6dfa360ff' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9b4282f2-e846-4a0d-a07a-e5d6dfa360ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5065a22f-b5b1-4372-b77e-9b5633e3d6b8' class='xr-var-data-in' type='checkbox'><label for='data-5065a22f-b5b1-4372-b77e-9b5633e3d6b8' 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'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>points</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 1999997 1999998 1999999</div><input id='attrs-13a01366-536d-4931-a93f-cbf517dcdddb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-13a01366-536d-4931-a93f-cbf517dcdddb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28bd7017-2706-47aa-8351-3414eed84342' class='xr-var-data-in' type='checkbox'><label for='data-28bd7017-2706-47aa-8351-3414eed84342' 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, ..., 1999997, 1999998, 1999999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>range</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(450000,), meta=np.ndarray&gt;</div><input id='attrs-706ce8cf-c91e-4121-8735-10c304179b68' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-706ce8cf-c91e-4121-8735-10c304179b68' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39ca865c-46a2-4182-9b67-83f1d510d6eb' class='xr-var-data-in' type='checkbox'><label for='data-39ca865c-46a2-4182-9b67-83f1d510d6eb' 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'><table>\n",
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       "                        <td> 15.26 MiB </td>\n",
       "                        <td> 3.43 MiB </td>\n",
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       "                        <td> 5 Chunks </td>\n",
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       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-f79b9ef0-d83c-44d0-bfbf-57f50994dcf9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f79b9ef0-d83c-44d0-bfbf-57f50994dcf9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01950f6c-7515-422b-9c27-349f57802ac9' class='xr-var-data-in' type='checkbox'><label for='data-01950f6c-7515-422b-9c27-349f57802ac9' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2c3c382f-ddfd-4c36-9f4b-a4b981c36d3b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2c3c382f-ddfd-4c36-9f4b-a4b981c36d3b' 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>obs</span></div><div class='xr-var-dims'>(points, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(450000, 4), meta=np.ndarray&gt;</div><input id='attrs-b7cb806d-97e2-4f4e-b9de-00319daa5b29' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7cb806d-97e2-4f4e-b9de-00319daa5b29' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9820907e-92d4-4e62-b94e-b8dc8e4c85ba' class='xr-var-data-in' type='checkbox'><label for='data-9820907e-92d4-4e62-b94e-b8dc8e4c85ba' 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'><table>\n",
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       "        <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",
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       "                        <th> Bytes </th>\n",
       "                        <td> 61.04 MiB </td>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (points: 2000000, time: 4)\n",
       "Coordinates:\n",
       "    azimuth  (points) int64 dask.array<chunksize=(450000,), meta=np.ndarray>\n",
       "  * points   (points) int64 0 1 2 3 4 ... 1999996 1999997 1999998 1999999\n",
       "    range    (points) int64 dask.array<chunksize=(450000,), meta=np.ndarray>\n",
       "  * time     (time) int64 0 1 2 3\n",
       "Data variables:\n",
       "    obs      (points, time) float64 dask.array<chunksize=(450000, 4), meta=np.ndarray>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_pointset = xr.open_zarr(\"database.zarr\")\n",
    "ds_pointset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we select the pixel with low STD, and make a mask:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;obs&#x27; (points: 2000000)&gt;\n",
       "dask.array&lt;lt, shape=(2000000,), dtype=bool, chunksize=(450000,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "    azimuth  (points) int64 dask.array&lt;chunksize=(450000,), meta=np.ndarray&gt;\n",
       "  * points   (points) int64 0 1 2 3 4 ... 1999996 1999997 1999998 1999999\n",
       "    range    (points) int64 dask.array&lt;chunksize=(450000,), meta=np.ndarray&gt;</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'>'obs'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>points</span>: 2000000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2739786c-d3c0-45ff-8675-bdb1da9c92f7' class='xr-array-in' type='checkbox' checked><label for='section-2739786c-d3c0-45ff-8675-bdb1da9c92f7' 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>dask.array&lt;chunksize=(450000,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "                        <td> 1.91 MiB </td>\n",
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       "                        <td> 15.26 MiB </td>\n",
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       "\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>points</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 1999997 1999998 1999999</div><input id='attrs-7a3e4a02-8e68-4f14-af7c-0d81f515597d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7a3e4a02-8e68-4f14-af7c-0d81f515597d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b20fbe3d-47a1-42b1-b58f-3cc15a2d642b' class='xr-var-data-in' type='checkbox'><label for='data-b20fbe3d-47a1-42b1-b58f-3cc15a2d642b' 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, ..., 1999997, 1999998, 1999999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>range</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(450000,), meta=np.ndarray&gt;</div><input id='attrs-49f28a04-1ae8-4565-9ada-1dc5dcae7d44' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-49f28a04-1ae8-4565-9ada-1dc5dcae7d44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ad72fce-7068-490c-a8b1-bf4b2e6bfffc' class='xr-var-data-in' type='checkbox'><label for='data-9ad72fce-7068-490c-a8b1-bf4b2e6bfffc' 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'><table>\n",
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      ],
      "text/plain": [
       "<xarray.DataArray 'obs' (points: 2000000)>\n",
       "dask.array<lt, shape=(2000000,), dtype=bool, chunksize=(450000,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    azimuth  (points) int64 dask.array<chunksize=(450000,), meta=np.ndarray>\n",
       "  * points   (points) int64 0 1 2 3 4 ... 1999996 1999997 1999998 1999999\n",
       "    range    (points) int64 dask.array<chunksize=(450000,), meta=np.ndarray>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask = ds_pointset.obs.std(axis=1)<0.5\n",
    "mask"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `mask` can be used to select the qualified points:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
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       "html[theme=dark],\n",
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       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
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       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
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       "\n",
       ".xr-text-repr-fallback {\n",
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       "\n",
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       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
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       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
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       "\n",
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       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
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       ".xr-sections {\n",
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       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
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       "\n",
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       "  display: inline-block;\n",
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       "  color: var(--xr-font-color2);\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
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       "\n",
       ".xr-section-inline-details {\n",
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       "\n",
       ".xr-section-details {\n",
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       "\n",
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       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
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       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
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       "\n",
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       "  content: '(';\n",
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       "\n",
       ".xr-dim-list:after {\n",
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       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
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       ".xr-has-index {\n",
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       "\n",
       ".xr-var-list,\n",
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       ".xr-var-item > div,\n",
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       ".xr-var-item > .xr-var-name span {\n",
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       "\n",
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       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       "\n",
       ".xr-var-dtype {\n",
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       "  text-align: right;\n",
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       ".xr-attrs dt:hover {\n",
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       "\n",
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       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data {\n",
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       "\n",
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       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-attrs {\n",
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       "\n",
       ".xr-attrs,\n",
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       ".xr-var-data {\n",
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       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
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       "\n",
       ".xr-attrs dt {\n",
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       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
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       "\n",
       ".xr-attrs dd {\n",
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       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2 {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
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       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (points: 34630, time: 4)\n",
       "Coordinates:\n",
       "    azimuth  (points) int64 dask.array&lt;chunksize=(7819,), meta=np.ndarray&gt;\n",
       "  * points   (points) int64 76 133 151 156 ... 1999867 1999940 1999970 1999998\n",
       "    range    (points) int64 dask.array&lt;chunksize=(7819,), meta=np.ndarray&gt;\n",
       "  * time     (time) int64 0 1 2 3\n",
       "Data variables:\n",
       "    obs      (points, time) float64 dask.array&lt;chunksize=(7819, 4), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-1cd414e2-8f3e-4631-a5c1-98b53adcf1e7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1cd414e2-8f3e-4631-a5c1-98b53adcf1e7' 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'>points</span>: 34630</li><li><span class='xr-has-index'>time</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-edd95e63-3bc1-4da8-9e1e-e25fa3108575' class='xr-section-summary-in' type='checkbox'  checked><label for='section-edd95e63-3bc1-4da8-9e1e-e25fa3108575' class='xr-section-summary' >Coordinates: <span>(4)</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>azimuth</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(7819,), meta=np.ndarray&gt;</div><input id='attrs-47eaf96a-abf7-4bff-acd7-aa7303d67483' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-47eaf96a-abf7-4bff-acd7-aa7303d67483' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37d99f02-59a5-4040-bd9f-4e88f5ec8ee1' class='xr-var-data-in' type='checkbox'><label for='data-37d99f02-59a5-4040-bd9f-4e88f5ec8ee1' 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'><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> 270.55 kiB </td>\n",
       "                        <td> 61.09 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (34630,) </td>\n",
       "                        <td> (7819,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 3 Graph Layers </td>\n",
       "                        <td> 5 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> int64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>points</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>76 133 151 ... 1999970 1999998</div><input id='attrs-e428f29f-3d04-4edc-be91-9786a3de9b16' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e428f29f-3d04-4edc-be91-9786a3de9b16' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20d23e28-85ab-43fd-9dd8-f9448e5924f6' class='xr-var-data-in' type='checkbox'><label for='data-20d23e28-85ab-43fd-9dd8-f9448e5924f6' 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([     76,     133,     151, ..., 1999940, 1999970, 1999998])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>range</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(7819,), meta=np.ndarray&gt;</div><input id='attrs-f355837c-886a-47ba-b38c-39bac6b1e8a8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f355837c-886a-47ba-b38c-39bac6b1e8a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d06539c5-c87a-4d09-91d3-2939c613eb89' class='xr-var-data-in' type='checkbox'><label for='data-d06539c5-c87a-4d09-91d3-2939c613eb89' 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'><table>\n",
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       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 270.55 kiB </td>\n",
       "                        <td> 61.09 kiB </td>\n",
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       "                        <td> 1.06 MiB </td>\n",
       "                        <td> 244.34 kiB </td>\n",
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       "                        <td> (7819, 4) </td>\n",
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       "                        <th> Count </th>\n",
       "                        <td> 9 Graph Layers </td>\n",
       "                        <td> 5 Chunks </td>\n",
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       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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      "text/plain": [
       "<xarray.Dataset>\n",
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     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Select the low std pixels\n",
    "ds_pointset_masked = ds_pointset.where(mask)\n",
    "ds_pointset_masked = ds_pointset_masked.dropna('points', subset=['obs'])\n",
    "ds_pointset_masked"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Append writing to Zarr storage"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we selected a subset of the orginal poinset `ds_pointset`. Now we are going to look into updating. If we compute some new features from the original pointset, we can save it appendingly to the `Zarr` storage.\n",
    "\n",
    "We here add a new variable `selection_flag` to the selected pointset `ds_pointset_masked`, indicating if a point is selected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
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       "Data variables:\n",
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       "    selection_flag  (points) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-004de42b-e1ca-470c-ba41-2e22f9c78c07' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-004de42b-e1ca-470c-ba41-2e22f9c78c07' 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'>points</span>: 34630</li><li><span class='xr-has-index'>time</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ba3cd61e-e981-4db2-85f6-901e72584de9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ba3cd61e-e981-4db2-85f6-901e72584de9' class='xr-section-summary' >Coordinates: <span>(4)</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>azimuth</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(7819,), meta=np.ndarray&gt;</div><input id='attrs-2d1101bb-dd5b-4e05-95f0-f1b375a71a46' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2d1101bb-dd5b-4e05-95f0-f1b375a71a46' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90d7226b-c6b1-4946-9f72-f35c1960a639' class='xr-var-data-in' type='checkbox'><label for='data-90d7226b-c6b1-4946-9f72-f35c1960a639' 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'><table>\n",
       "    <tr>\n",
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       "            <table>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
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       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 270.55 kiB </td>\n",
       "                        <td> 61.09 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (34630,) </td>\n",
       "                        <td> (7819,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 3 Graph Layers </td>\n",
       "                        <td> 5 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> int64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
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       "            </table>\n",
       "        </td>\n",
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       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <line x1=\"53\" y1=\"0\" x2=\"53\" y2=\"25\" />\n",
       "  <line x1=\"80\" y1=\"0\" x2=\"80\" y2=\"25\" />\n",
       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >34630</text>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>points</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>76 133 151 ... 1999970 1999998</div><input id='attrs-4ffc9247-4622-4852-a1e3-bb099d2ab511' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4ffc9247-4622-4852-a1e3-bb099d2ab511' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9da68a96-d6b8-422a-ba89-81570a59001a' class='xr-var-data-in' type='checkbox'><label for='data-9da68a96-d6b8-422a-ba89-81570a59001a' 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([     76,     133,     151, ..., 1999940, 1999970, 1999998])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>range</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(7819,), meta=np.ndarray&gt;</div><input id='attrs-edfa53c2-798d-4542-bddc-ce02983f0738' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-edfa53c2-798d-4542-bddc-ce02983f0738' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ced2a81-118e-4e30-84b8-b012ff6c0bf5' class='xr-var-data-in' type='checkbox'><label for='data-3ced2a81-118e-4e30-84b8-b012ff6c0bf5' 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'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 270.55 kiB </td>\n",
       "                        <td> 61.09 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
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       "                        <td> (34630,) </td>\n",
       "                        <td> (7819,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 3 Graph Layers </td>\n",
       "                        <td> 5 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> int64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-5f112b2c-6adc-42b9-98b8-60b25d89cb27' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5f112b2c-6adc-42b9-98b8-60b25d89cb27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ca0fe56-998d-40a9-86cb-0db5d4783453' class='xr-var-data-in' type='checkbox'><label for='data-8ca0fe56-998d-40a9-86cb-0db5d4783453' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-33c5ad56-a888-4ce1-ba56-a4da0f4e3305' class='xr-section-summary-in' type='checkbox'  checked><label for='section-33c5ad56-a888-4ce1-ba56-a4da0f4e3305' class='xr-section-summary' >Data variables: <span>(2)</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>obs</span></div><div class='xr-var-dims'>(points, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(7819, 4), meta=np.ndarray&gt;</div><input id='attrs-5c1eca91-3c9f-4772-93cf-7c890729be60' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c1eca91-3c9f-4772-93cf-7c890729be60' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-aa296799-577d-4336-8ab4-90f8f8ed577b' class='xr-var-data-in' type='checkbox'><label for='data-aa296799-577d-4336-8ab4-90f8f8ed577b' 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'><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> 1.06 MiB </td>\n",
       "                        <td> 244.34 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (34630, 4) </td>\n",
       "                        <td> (7819, 4) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 9 Graph Layers </td>\n",
       "                        <td> 5 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
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       "\n",
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       "\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "\n",
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       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >4</text>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>selection_flag</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0</div><input id='attrs-6e52fff4-f03a-48a0-996f-d149026c974c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6e52fff4-f03a-48a0-996f-d149026c974c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc431cc6-e548-418d-8f0b-6a5ffa867aca' class='xr-var-data-in' type='checkbox'><label for='data-cc431cc6-e548-418d-8f0b-6a5ffa867aca' 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., 1., 1., ..., 1., 1., 1.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a8f06c15-49e8-4fb4-ba70-63fa573c8bb2' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a8f06c15-49e8-4fb4-ba70-63fa573c8bb2' 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": [
       "<xarray.Dataset>\n",
       "Dimensions:         (points: 34630, time: 4)\n",
       "Coordinates:\n",
       "    azimuth         (points) int64 dask.array<chunksize=(7819,), meta=np.ndarray>\n",
       "  * points          (points) int64 76 133 151 156 ... 1999940 1999970 1999998\n",
       "    range           (points) int64 dask.array<chunksize=(7819,), meta=np.ndarray>\n",
       "  * time            (time) int64 0 1 2 3\n",
       "Data variables:\n",
       "    obs             (points, time) float64 dask.array<chunksize=(7819, 4), meta=np.ndarray>\n",
       "    selection_flag  (points) float64 1.0 1.0 1.0 1.0 1.0 ... 1.0 1.0 1.0 1.0 1.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_pointset_masked = ds_pointset_masked.assign(\n",
    "    selection_flag=(\"points\", np.ones((ds_pointset_masked.points.size)))\n",
    ")\n",
    "ds_pointset_masked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can merge this data to the original DataSet:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "  --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",
       "  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:         (points: 2000000, time: 4)\n",
       "Coordinates:\n",
       "  * points          (points) int64 0 1 2 3 4 ... 1999996 1999997 1999998 1999999\n",
       "  * time            (time) int64 0 1 2 3\n",
       "    azimuth         (points) float64 dask.array&lt;chunksize=(450058,), meta=np.ndarray&gt;\n",
       "    range           (points) float64 dask.array&lt;chunksize=(450058,), meta=np.ndarray&gt;\n",
       "Data variables:\n",
       "    obs             (points, time) float64 dask.array&lt;chunksize=(450058, 4), meta=np.ndarray&gt;\n",
       "    selection_flag  (points) float64 nan nan nan nan nan ... nan nan nan 1.0 nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-8b174825-aead-4ad7-b0ec-4762443ec070' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-8b174825-aead-4ad7-b0ec-4762443ec070' 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'>points</span>: 2000000</li><li><span class='xr-has-index'>time</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b6c84b1b-6513-4f25-8cb6-875e0da0e7c4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b6c84b1b-6513-4f25-8cb6-875e0da0e7c4' class='xr-section-summary' >Coordinates: <span>(4)</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'>points</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 1999997 1999998 1999999</div><input id='attrs-950b0f15-0c10-4a9c-8304-ec5f91c8c15f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-950b0f15-0c10-4a9c-8304-ec5f91c8c15f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1042bdc8-7e13-434b-842e-6a99a7095ae2' class='xr-var-data-in' type='checkbox'><label for='data-1042bdc8-7e13-434b-842e-6a99a7095ae2' 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, ..., 1999997, 1999998, 1999999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-f55c607c-fbd1-4412-9b9e-0092cca60142' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f55c607c-fbd1-4412-9b9e-0092cca60142' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e7f037b0-9d47-4093-ab5f-1c518fa03895' class='xr-var-data-in' type='checkbox'><label for='data-e7f037b0-9d47-4093-ab5f-1c518fa03895' 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])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>azimuth</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(450058,), meta=np.ndarray&gt;</div><input id='attrs-b2d19a54-f2fe-47b0-850d-3ca951ff45bd' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b2d19a54-f2fe-47b0-850d-3ca951ff45bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-86f2b09d-f89a-4d7d-865d-1bb36caac51f' class='xr-var-data-in' type='checkbox'><label for='data-86f2b09d-f89a-4d7d-865d-1bb36caac51f' 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'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
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       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 15.26 MiB </td>\n",
       "                        <td> 3.43 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2000000,) </td>\n",
       "                        <td> (450058,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 14 Graph Layers </td>\n",
       "                        <td> 5 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>range</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(450058,), meta=np.ndarray&gt;</div><input id='attrs-98befc1b-bca2-40c2-a432-262504233544' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-98befc1b-bca2-40c2-a432-262504233544' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-184c26e7-c4bd-461c-8791-0b02839f1f65' class='xr-var-data-in' type='checkbox'><label for='data-184c26e7-c4bd-461c-8791-0b02839f1f65' 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'><table>\n",
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       "                <tbody>\n",
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       "                        <td> 61.04 MiB </td>\n",
       "                        <td> 13.73 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 22 Graph Layers </td>\n",
       "                        <td> 5 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>selection_flag</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... nan nan 1.0 nan</div><input id='attrs-56f0fd3e-1fff-4b1b-b7e4-508791bfd838' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-56f0fd3e-1fff-4b1b-b7e4-508791bfd838' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a82870c8-4adf-43cf-bee4-eb3aaaf962fb' class='xr-var-data-in' type='checkbox'><label for='data-a82870c8-4adf-43cf-bee4-eb3aaaf962fb' 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([nan, nan, nan, ..., nan,  1., nan])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a9132f0b-6ccf-49ee-b159-1724a898e381' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a9132f0b-6ccf-49ee-b159-1724a898e381' 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": [
       "<xarray.Dataset>\n",
       "Dimensions:         (points: 2000000, time: 4)\n",
       "Coordinates:\n",
       "  * points          (points) int64 0 1 2 3 4 ... 1999996 1999997 1999998 1999999\n",
       "  * time            (time) int64 0 1 2 3\n",
       "    azimuth         (points) float64 dask.array<chunksize=(450058,), meta=np.ndarray>\n",
       "    range           (points) float64 dask.array<chunksize=(450058,), meta=np.ndarray>\n",
       "Data variables:\n",
       "    obs             (points, time) float64 dask.array<chunksize=(450058, 4), meta=np.ndarray>\n",
       "    selection_flag  (points) float64 nan nan nan nan nan ... nan nan nan 1.0 nan"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_pointset_update = xr.open_zarr(\"database.zarr\")\n",
    "ds_pointset_update = xr.merge((ds_pointset_update,ds_pointset_masked))\n",
    "ds_pointset_update"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And append it to the Zarr storage:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ouku/miniconda3/envs/mobyle_demo/lib/python3.10/site-packages/xarray/core/dataset.py:2077: SerializationWarning: saving variable None with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  return to_zarr(  # type: ignore\n",
      "/home/ouku/miniconda3/envs/mobyle_demo/lib/python3.10/site-packages/xarray/core/dataset.py:2077: SerializationWarning: saving variable None with floating point data as an integer dtype without any _FillValue to use for NaNs\n",
      "  return to_zarr(  # type: ignore\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<xarray.backends.zarr.ZarrStore at 0x7f34f8866a40>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Update zarr database\n",
    "ds_pointset_update.to_zarr(\"database.zarr\", mode='a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56M\tdatabase.zarr\n"
     ]
    }
   ],
   "source": [
    "#Check size\n",
    "! du -h --max-depth=0 database.zarr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reconstruct the raster grid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will recontruct the original raster grid from the saved raster coordinates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we read the subset pointset selected with the standard deviation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
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       "  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:         (points: 34630, time: 4)\n",
       "Coordinates:\n",
       "    azimuth         (points) int64 dask.array&lt;chunksize=(7819,), meta=np.ndarray&gt;\n",
       "  * points          (points) int64 76 133 151 156 ... 1999940 1999970 1999998\n",
       "    range           (points) int64 dask.array&lt;chunksize=(7819,), meta=np.ndarray&gt;\n",
       "  * time            (time) int64 0 1 2 3\n",
       "Data variables:\n",
       "    obs             (points, time) float64 dask.array&lt;chunksize=(7819, 4), meta=np.ndarray&gt;\n",
       "    selection_flag  (points) float64 dask.array&lt;chunksize=(1153,), meta=np.ndarray&gt;</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-c59f0069-8cf6-4bf1-8ec3-dc88636485cf' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c59f0069-8cf6-4bf1-8ec3-dc88636485cf' 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'>points</span>: 34630</li><li><span class='xr-has-index'>time</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-30adf11a-3ca4-4d9e-b50a-58153287c77d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-30adf11a-3ca4-4d9e-b50a-58153287c77d' class='xr-section-summary' >Coordinates: <span>(4)</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>azimuth</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(7819,), meta=np.ndarray&gt;</div><input id='attrs-fb1ba145-f991-456f-ad07-ac05c8bf7470' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-fb1ba145-f991-456f-ad07-ac05c8bf7470' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cd2921a8-a4c0-42c3-8d60-a573426d7020' class='xr-var-data-in' type='checkbox'><label for='data-cd2921a8-a4c0-42c3-8d60-a573426d7020' 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'><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> 270.55 kiB </td>\n",
       "                        <td> 61.09 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (34630,) </td>\n",
       "                        <td> (7819,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 3 Graph Layers </td>\n",
       "                        <td> 5 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> int64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "  <line x1=\"53\" y1=\"0\" x2=\"53\" y2=\"25\" />\n",
       "  <line x1=\"80\" y1=\"0\" x2=\"80\" y2=\"25\" />\n",
       "  <line x1=\"107\" y1=\"0\" x2=\"107\" y2=\"25\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >34630</text>\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>points</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>76 133 151 ... 1999970 1999998</div><input id='attrs-14adead9-a408-48b5-801d-f971878f71ce' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14adead9-a408-48b5-801d-f971878f71ce' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-66dca1c2-f90e-420b-bd99-65834701bed1' class='xr-var-data-in' type='checkbox'><label for='data-66dca1c2-f90e-420b-bd99-65834701bed1' 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([     76,     133,     151, ..., 1999940, 1999970, 1999998])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>range</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(7819,), meta=np.ndarray&gt;</div><input id='attrs-b072c8c4-53b8-4dfc-aab9-0076548a255f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b072c8c4-53b8-4dfc-aab9-0076548a255f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3442377c-54fc-4b51-9345-48f58b7ee363' class='xr-var-data-in' type='checkbox'><label for='data-3442377c-54fc-4b51-9345-48f58b7ee363' 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'><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> 270.55 kiB </td>\n",
       "                        <td> 61.09 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (34630,) </td>\n",
       "                        <td> (7819,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 3 Graph Layers </td>\n",
       "                        <td> 5 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> int64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
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       "\n",
       "  <!-- Text -->\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3</div><input id='attrs-267eb1d3-8c51-4a0e-8d20-957e31bfe38e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-267eb1d3-8c51-4a0e-8d20-957e31bfe38e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a737ca76-f6ad-4d08-a2c8-b8f13cadf353' class='xr-var-data-in' type='checkbox'><label for='data-a737ca76-f6ad-4d08-a2c8-b8f13cadf353' 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])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e4f79ff4-47d0-417f-9426-8354ebbdf08c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e4f79ff4-47d0-417f-9426-8354ebbdf08c' class='xr-section-summary' >Data variables: <span>(2)</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>obs</span></div><div class='xr-var-dims'>(points, time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(7819, 4), meta=np.ndarray&gt;</div><input id='attrs-35ea30d3-74fc-44b3-b857-0e86fa214ad7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-35ea30d3-74fc-44b3-b857-0e86fa214ad7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-067b1368-8e86-4a90-bc95-16d194b0c7f1' class='xr-var-data-in' type='checkbox'><label for='data-067b1368-8e86-4a90-bc95-16d194b0c7f1' 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'><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> 1.06 MiB </td>\n",
       "                        <td> 244.34 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (34630, 4) </td>\n",
       "                        <td> (7819, 4) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 3 Graph Layers </td>\n",
       "                        <td> 5 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>selection_flag</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1153,), meta=np.ndarray&gt;</div><input id='attrs-572aaab8-f9cc-4574-952f-9962de57c6b0' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-572aaab8-f9cc-4574-952f-9962de57c6b0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbdfb3c4-82d6-42a2-91fb-bd578f2e8478' class='xr-var-data-in' type='checkbox'><label for='data-dbdfb3c4-82d6-42a2-91fb-bd578f2e8478' 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'><table>\n",
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       "            <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> 270.55 kiB </td>\n",
       "                        <td> 9.01 kiB </td>\n",
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       "                        <td> (34630,) </td>\n",
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       "                        <td> 3 Graph Layers </td>\n",
       "                        <td> 32 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
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      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (points: 34630, time: 4)\n",
       "Coordinates:\n",
       "    azimuth         (points) int64 dask.array<chunksize=(7819,), meta=np.ndarray>\n",
       "  * points          (points) int64 76 133 151 156 ... 1999940 1999970 1999998\n",
       "    range           (points) int64 dask.array<chunksize=(7819,), meta=np.ndarray>\n",
       "  * time            (time) int64 0 1 2 3\n",
       "Data variables:\n",
       "    obs             (points, time) float64 dask.array<chunksize=(7819, 4), meta=np.ndarray>\n",
       "    selection_flag  (points) float64 dask.array<chunksize=(1153,), meta=np.ndarray>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_pointset = xr.open_zarr(\"database.zarr\")\n",
    "ds_pointset_masked = ds_pointset.dropna('points', subset=['selection_flag'])\n",
    "ds_pointset_masked"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we take out the `selection_flag` variable as a `DataArray`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>points</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>76 133 151 ... 1999970 1999998</div><input id='attrs-2f33d2be-bb1c-407b-aa97-04ff1761a9cc' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2f33d2be-bb1c-407b-aa97-04ff1761a9cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbda8691-8a07-47f0-92e4-c778ca88fa26' class='xr-var-data-in' type='checkbox'><label for='data-dbda8691-8a07-47f0-92e4-c778ca88fa26' 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([     76,     133,     151, ..., 1999940, 1999970, 1999998])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>range</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(7819,), meta=np.ndarray&gt;</div><input id='attrs-1a652202-6f47-476f-a617-5607e6fd96eb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a652202-6f47-476f-a617-5607e6fd96eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-63537516-8bef-4ccf-96e3-c924f8c4b43a' class='xr-var-data-in' type='checkbox'><label for='data-63537516-8bef-4ccf-96e3-c924f8c4b43a' 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'><table>\n",
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      ],
      "text/plain": [
       "<xarray.DataArray 'selection_flag' (points: 34630)>\n",
       "dask.array<getitem, shape=(34630,), dtype=float64, chunksize=(1153,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "    azimuth  (points) int64 dask.array<chunksize=(7819,), meta=np.ndarray>\n",
       "  * points   (points) int64 76 133 151 156 ... 1999867 1999940 1999970 1999998\n",
       "    range    (points) int64 dask.array<chunksize=(7819,), meta=np.ndarray>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
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    }
   ],
   "source": [
    "mask_selection_flag = ds_pointset_masked.selection_flag\n",
    "mask_selection_flag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can sign a manually created `Multi-Index` to the `points` coodrinates agai, build from `azimuth` and `range`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;selection_flag&#x27; (points: 34630)&gt;\n",
       "dask.array&lt;getitem, shape=(34630,), dtype=float64, chunksize=(1153,), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
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       "  * points   (points) object MultiIndex\n",
       "  * range    (points) int64 76 133 151 156 167 175 ... 850 865 867 940 970 998</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'>'selection_flag'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>points</span>: 34630</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-c9edc886-ed70-4b34-a138-4c5b9a175b8d' class='xr-array-in' type='checkbox' checked><label for='section-c9edc886-ed70-4b34-a138-4c5b9a175b8d' 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>dask.array&lt;chunksize=(1153,), meta=np.ndarray&gt;</span></div><div class='xr-array-data'><table>\n",
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       "                        <td> 270.55 kiB </td>\n",
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       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></div></li><li class='xr-section-item'><input id='section-f0a4160f-3a6f-4575-916b-f18ff984857a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-f0a4160f-3a6f-4575-916b-f18ff984857a' 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'>azimuth</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 0 0 0 0 ... 1999 1999 1999 1999</div><input id='attrs-b7728c1c-3adb-46ac-9cec-5ddd5f450a87' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-b7728c1c-3adb-46ac-9cec-5ddd5f450a87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76b980dd-1916-4541-9dc3-ac808462d300' class='xr-var-data-in' type='checkbox'><label for='data-76b980dd-1916-4541-9dc3-ac808462d300' 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,    0,    0, ..., 1999, 1999, 1999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>points</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>MultiIndex</div><input id='attrs-3c783f0c-3d2d-4249-8949-98ef830aa8e3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-3c783f0c-3d2d-4249-8949-98ef830aa8e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30e92918-d75c-4c5c-9411-23fb7c7c4499' class='xr-var-data-in' type='checkbox'><label for='data-30e92918-d75c-4c5c-9411-23fb7c7c4499' 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, 76), (0, 133), (0, 151), ..., (1999, 940), (1999, 970), (1999, 998)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(points)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>76 133 151 156 ... 867 940 970 998</div><input id='attrs-d98bf81e-7fbe-4693-8f2f-d2f3f9637256' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d98bf81e-7fbe-4693-8f2f-d2f3f9637256' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c77c493-ff5b-42df-a425-cadca65bde8c' class='xr-var-data-in' type='checkbox'><label for='data-0c77c493-ff5b-42df-a425-cadca65bde8c' 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([ 76, 133, 151, ..., 940, 970, 998])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ac4ee050-b464-4be5-a111-c07407e9d5d8' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-ac4ee050-b464-4be5-a111-c07407e9d5d8' 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": [
       "<xarray.DataArray 'selection_flag' (points: 34630)>\n",
       "dask.array<getitem, shape=(34630,), dtype=float64, chunksize=(1153,), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * azimuth  (points) int64 0 0 0 0 0 0 0 ... 1999 1999 1999 1999 1999 1999 1999\n",
       "  * points   (points) object MultiIndex\n",
       "  * range    (points) int64 76 133 151 156 167 175 ... 850 865 867 940 970 998"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Inspired by the recent release: https://github.com/pydata/xarray/issues/6752\n",
    "import pandas\n",
    "def _create_multiindex(**kwargs):\n",
    "    return pandas.MultiIndex.from_arrays(list(kwargs.values()), names=kwargs.keys())\n",
    "\n",
    "# Assigin the multi-index again to the \"points\" coordinates\n",
    "mask_selection_flag.coords[\"points\"] =  _create_multiindex(azimuth=mask_selection_flag.azimuth.values, range=mask_selection_flag.range.values)\n",
    "mask_selection_flag"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then use the `unstack` command to restore the original raster grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
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       "  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.DataArray &#x27;selection_flag&#x27; (azimuth: 2000, range: 1000)&gt;\n",
       "dask.array&lt;reshape, shape=(2000, 1000), dtype=float64, chunksize=(63, 1000), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * azimuth  (azimuth) int64 0 1 2 3 4 5 6 ... 1994 1995 1996 1997 1998 1999\n",
       "  * range    (range) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999</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'>'selection_flag'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>azimuth</span>: 2000</li><li><span class='xr-has-index'>range</span>: 1000</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-2edc470a-eb1d-4dea-83b0-80260ed15fb1' class='xr-array-in' type='checkbox' checked><label for='section-2edc470a-eb1d-4dea-83b0-80260ed15fb1' 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>dask.array&lt;chunksize=(62, 1000), meta=np.ndarray&gt;</span></div><div class='xr-array-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> 15.26 MiB </td>\n",
       "                        <td> 492.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2000, 1000) </td>\n",
       "                        <td> (63, 1000) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 10 Graph Layers </td>\n",
       "                        <td> 32 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float64 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"110\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"60\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"3\" x2=\"60\" y2=\"3\" />\n",
       "  <line x1=\"0\" y1=\"11\" x2=\"60\" y2=\"11\" />\n",
       "  <line x1=\"0\" y1=\"18\" x2=\"60\" y2=\"18\" />\n",
       "  <line x1=\"0\" y1=\"22\" x2=\"60\" y2=\"22\" />\n",
       "  <line x1=\"0\" y1=\"30\" x2=\"60\" y2=\"30\" />\n",
       "  <line x1=\"0\" y1=\"37\" x2=\"60\" y2=\"37\" />\n",
       "  <line x1=\"0\" y1=\"41\" x2=\"60\" y2=\"41\" />\n",
       "  <line x1=\"0\" y1=\"48\" x2=\"60\" y2=\"48\" />\n",
       "  <line x1=\"0\" y1=\"56\" x2=\"60\" y2=\"56\" />\n",
       "  <line x1=\"0\" y1=\"60\" x2=\"60\" y2=\"60\" />\n",
       "  <line x1=\"0\" y1=\"67\" x2=\"60\" y2=\"67\" />\n",
       "  <line x1=\"0\" y1=\"75\" x2=\"60\" y2=\"75\" />\n",
       "  <line x1=\"0\" y1=\"78\" x2=\"60\" y2=\"78\" />\n",
       "  <line x1=\"0\" y1=\"86\" x2=\"60\" y2=\"86\" />\n",
       "  <line x1=\"0\" y1=\"93\" x2=\"60\" y2=\"93\" />\n",
       "  <line x1=\"0\" y1=\"97\" x2=\"60\" y2=\"97\" />\n",
       "  <line x1=\"0\" y1=\"105\" x2=\"60\" y2=\"105\" />\n",
       "  <line x1=\"0\" y1=\"112\" x2=\"60\" y2=\"112\" />\n",
       "  <line x1=\"0\" y1=\"120\" x2=\"60\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"60\" y1=\"0\" x2=\"60\" y2=\"120\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 60.0,0.0 60.0,120.0 0.0,120.0\" style=\"fill:#8B4903A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"30.000000\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >1000</text>\n",
       "  <text x=\"80.000000\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,80.000000,60.000000)\">2000</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></div></li><li class='xr-section-item'><input id='section-207c5c6b-5f01-462b-af60-af50a47b53f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-207c5c6b-5f01-462b-af60-af50a47b53f9' class='xr-section-summary' >Coordinates: <span>(2)</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'>azimuth</span></div><div class='xr-var-dims'>(azimuth)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 ... 1996 1997 1998 1999</div><input id='attrs-f4044842-e308-4080-bf34-0bf65514ae06' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f4044842-e308-4080-bf34-0bf65514ae06' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e5a8821f-1338-41ad-b393-fe3326518315' class='xr-var-data-in' type='checkbox'><label for='data-e5a8821f-1338-41ad-b393-fe3326518315' 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, ..., 1997, 1998, 1999])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>range</span></div><div class='xr-var-dims'>(range)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 ... 995 996 997 998 999</div><input id='attrs-65252349-8c24-4aa8-89a0-ae01a2b0fa08' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65252349-8c24-4aa8-89a0-ae01a2b0fa08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d67fc04e-3863-4d18-b7a8-5de1a50c152c' class='xr-var-data-in' type='checkbox'><label for='data-d67fc04e-3863-4d18-b7a8-5de1a50c152c' 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, ..., 997, 998, 999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5bac19ef-a262-40f8-ab84-dace289e1995' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5bac19ef-a262-40f8-ab84-dace289e1995' 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": [
       "<xarray.DataArray 'selection_flag' (azimuth: 2000, range: 1000)>\n",
       "dask.array<reshape, shape=(2000, 1000), dtype=float64, chunksize=(63, 1000), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * azimuth  (azimuth) int64 0 1 2 3 4 5 6 ... 1994 1995 1996 1997 1998 1999\n",
       "  * range    (range) int64 0 1 2 3 4 5 6 7 8 ... 992 993 994 995 996 997 998 999"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask_selection_flag.unstack()"
   ]
  }
 ],
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