pangeo_loading_data_into_workermemory.ipynb

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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import numpy as np\n",
    "from numpy import pi, sin, cos, arccos, clip, deg2rad\n",
    "import numpy.ma as ma\n",
    "from datetime import datetime\n",
    "import dask\n",
    "import time\n",
    "import zarr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gcsfs\n",
    "gcs = gcsfs.GCSFileSystem()\n",
    "import os\n",
    "SCRATCH_BUCKET = os.environ['PANGEO_SCRATCH']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "gcs.rm('pangeo-integration-te-3eea-prod-scratch-bucket/ofk123/dataset_alternative_3.zarr', recursive=True)\n",
    "#gcs.ls('pangeo-integration-te-3eea-prod-scratch-bucket/ofk123/dataset_alternative_3.zarr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = 2105319\n",
    "time = np.random.uniform(13000, 18500, n)\n",
    "latitude = np.random.uniform(-80, 80, n) \n",
    "longitude = np.random.uniform(-180, 180, n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "#### Equally structured datasets stored in one zarr-store"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Storing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 496 ms, sys: 225 ms, total: 722 ms\n",
      "Wall time: 11.8 s\n"
     ]
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    {
     "data": {
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       "<xarray.backends.zarr.ZarrStore at 0x7f80b0c2dc40>"
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     "execution_count": 24,
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   "source": [
    "ds1 = xr.Dataset(\n",
    "    data_vars = dict(\n",
    "        Anomalies = ( [\"i\", \"pressure\"], np.random.uniform(-1, 5, n).reshape(n,1) ),\n",
    "        Mean = ( [\"i\", \"pressure\"], np.random.uniform(0, 2, n).reshape(n,1) ),\n",
    "    ),\n",
    "    coords=dict(\n",
    "        i = ([\"i\"], np.arange(n) ),\n",
    "        time = ( [\"i\"], time),\n",
    "        latitude = ( [\"i\"], latitude ),\n",
    "        longitude = ( [\"i\"], longitude),\n",
    "        pressure = ([\"pressure\"], np.array([5]) ),\n",
    "    ),\n",
    "    attrs=dict(\n",
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    "        pindex = 4,\n",
    "        number_of_harmonics = 2,\n",
    "        window_size = 500e3,\n",
    "        creation_date = str( datetime.now() )\n",
    "    ),\n",
    ")\n",
    "\n",
    "\n",
    "dsc = ds1.chunk()\n",
    "path_to_zarrstore = f'{SCRATCH_BUCKET}/dataset_alternative_3.zarr'\n",
    "mapper_alt3 = gcs.get_mapper(path_to_zarrstore)\n",
    "\n",
    "%time dsc.to_zarr(mapper_alt3, consolidated=True)"
   ]
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   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "CPU times: user 518 ms, sys: 107 ms, total: 625 ms\n",
      "Wall time: 11.1 s\n"
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    "ds2 = xr.Dataset(\n",
    "    data_vars = dict(\n",
    "        Anomalies = ( [\"i\", \"pressure\"], np.random.uniform(-1, 5, n).reshape(n,1) ),\n",
    "        Mean = ( [\"i\", \"pressure\"], np.random.uniform(0, 2, n).reshape(n,1) ),\n",
    "    ),\n",
    "    coords=dict(\n",
    "        i = ([\"i\"], np.arange(n) ),\n",
    "        time = ( [\"i\"], time),\n",
    "        latitude = ( [\"i\"], latitude ),\n",
    "        longitude = ( [\"i\"], longitude),\n",
    "        pressure = ([\"pressure\"], np.array([10]) ),\n",
    "    ),\n",
    "    attrs=dict(\n",
    "        description = 'Description',\n",
    "        pindex = 4,\n",
    "        number_of_harmonics = 2,\n",
    "        window_size = 500e3,\n",
    "        creation_date = str( datetime.now() )\n",
    "    ),\n",
    ")\n",
    "\n",
    "dsc2 = ds2.chunk()\n",
    "path_to_zarrstore = f'{SCRATCH_BUCKET}/dataset_alternative_3.zarr'\n",
    "mapper_alt3 = gcs.get_mapper(path_to_zarrstore)\n",
    "%time dsc2.to_zarr(mapper_alt3, consolidated=True, append_dim=\"pressure\")"
   ]
  },
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   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
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     "name": "stdout",
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     "text": [
      "CPU times: user 493 ms, sys: 109 ms, total: 602 ms\n",
      "Wall time: 11.4 s\n"
     ]
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    "ds3 = xr.Dataset(\n",
    "    data_vars = dict(\n",
    "        Anomalies = ( [\"i\", \"pressure\"], np.random.uniform(-1, 5, n).reshape(n,1) ),\n",
    "        Mean = ( [\"i\", \"pressure\"], np.random.uniform(0, 2, n).reshape(n,1) ),\n",
    "    ),\n",
    "    coords=dict(\n",
    "        i = ([\"i\"], np.arange(n) ),\n",
    "        time = ( [\"i\"], time),\n",
    "        latitude = ( [\"i\"], latitude ),\n",
    "        longitude = ( [\"i\"], longitude),\n",
    "        pressure = ([\"pressure\"], np.array([20]) ),\n",
    "    ),\n",
    "    attrs=dict(\n",
    "        description = 'Description',\n",
    "        pindex = 4,\n",
    "        number_of_harmonics = 2,\n",
    "        window_size = 500e3,\n",
    "        creation_date = str( datetime.now() )\n",
    "    ),\n",
    ")\n",
    "\n",
    "dsc3 = ds3.chunk()\n",
    "path_to_zarrstore = f'{SCRATCH_BUCKET}/dataset_alternative_3.zarr'\n",
    "mapper_alt3 = gcs.get_mapper(path_to_zarrstore)\n",
    "%time dsc3.to_zarr(mapper_alt3, consolidated=True, append_dim=\"pressure\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
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       "Dimensions:    (i: 2105319, pressure: 3)\n",
       "Coordinates:\n",
       "  * i          (i) int64 0 1 2 3 4 5 ... 2105314 2105315 2105316 2105317 2105318\n",
       "    latitude   (i) float64 dask.array&lt;chunksize=(2105319,), meta=np.ndarray&gt;\n",
       "    longitude  (i) float64 dask.array&lt;chunksize=(2105319,), meta=np.ndarray&gt;\n",
       "  * pressure   (pressure) int64 5 10 20\n",
       "    time       (i) float64 dask.array&lt;chunksize=(2105319,), meta=np.ndarray&gt;\n",
       "Data variables:\n",
       "    Anomalies  (i, pressure) float64 dask.array&lt;chunksize=(2105319, 1), meta=np.ndarray&gt;\n",
       "    Mean       (i, pressure) float64 dask.array&lt;chunksize=(2105319, 1), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    creation_date:        2023-08-02 12:23:33.346637\n",
       "    description:          Description\n",
       "    number_of_harmonics:  2\n",
       "    pindex:               4\n",
       "    window_size:          500000.0</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-d6199a93-63b2-43e2-8628-9c31641ac036' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d6199a93-63b2-43e2-8628-9c31641ac036' 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'>i</span>: 2105319</li><li><span class='xr-has-index'>pressure</span>: 3</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-fa4fb119-3bda-4fb0-959e-152b11a439f8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-fa4fb119-3bda-4fb0-959e-152b11a439f8' class='xr-section-summary' >Coordinates: <span>(5)</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'>i</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>0 1 2 3 ... 2105316 2105317 2105318</div><input id='attrs-110ffb6b-a145-4641-9bc4-3afde734fe2b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-110ffb6b-a145-4641-9bc4-3afde734fe2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2bef8bdd-9c0c-4927-adc1-b73992e3f75d' class='xr-var-data-in' type='checkbox'><label for='data-2bef8bdd-9c0c-4927-adc1-b73992e3f75d' 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, ..., 2105316, 2105317, 2105318])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>latitude</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2105319,), meta=np.ndarray&gt;</div><input id='attrs-8e317ff1-bb21-407a-b450-ae3fbf32246e' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-8e317ff1-bb21-407a-b450-ae3fbf32246e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-694d27c0-8fcc-4cea-bb28-f7d872387ed4' class='xr-var-data-in' type='checkbox'><label for='data-694d27c0-8fcc-4cea-bb28-f7d872387ed4' 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 style=\"border-collapse: collapse;\">\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> 16.06 MiB </td>\n",
       "                        <td> 16.06 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2105319,) </td>\n",
       "                        <td> (2105319,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 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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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>longitude</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2105319,), meta=np.ndarray&gt;</div><input id='attrs-591824d3-7b4d-4c83-902f-f0903f85da2d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-591824d3-7b4d-4c83-902f-f0903f85da2d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6f618133-c40c-4dc1-a826-034938c08c38' class='xr-var-data-in' type='checkbox'><label for='data-6f618133-c40c-4dc1-a826-034938c08c38' 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 style=\"border-collapse: collapse;\">\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> 16.06 MiB </td>\n",
       "                        <td> 16.06 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2105319,) </td>\n",
       "                        <td> (2105319,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 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",
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       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >2105319</text>\n",
       "  <text x=\"140.000000\" y=\"12.706308\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,12.706308)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pressure</span></div><div class='xr-var-dims'>(pressure)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>5 10 20</div><input id='attrs-74abc3cd-0f60-4e8d-9a49-404798331252' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-74abc3cd-0f60-4e8d-9a49-404798331252' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c457c681-72c3-4c9d-a1cc-036c58eb437f' class='xr-var-data-in' type='checkbox'><label for='data-c457c681-72c3-4c9d-a1cc-036c58eb437f' 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([ 5, 10, 20])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>(i)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2105319,), meta=np.ndarray&gt;</div><input id='attrs-985b50b4-7eb8-4ee9-9576-4e2bb7298d01' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-985b50b4-7eb8-4ee9-9576-4e2bb7298d01' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79dda9df-9b35-4e1a-a149-8d758014bfb3' class='xr-var-data-in' type='checkbox'><label for='data-79dda9df-9b35-4e1a-a149-8d758014bfb3' 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 style=\"border-collapse: collapse;\">\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> 16.06 MiB </td>\n",
       "                        <td> 16.06 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2105319,) </td>\n",
       "                        <td> (2105319,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 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",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "\n",
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       "\n",
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       "\n",
       "  <!-- Text -->\n",
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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-16713def-552b-4102-a970-5724fb48b89b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-16713def-552b-4102-a970-5724fb48b89b' 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>Anomalies</span></div><div class='xr-var-dims'>(i, pressure)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2105319, 1), meta=np.ndarray&gt;</div><input id='attrs-7399673a-3ea4-41b0-88fa-c47017483f70' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7399673a-3ea4-41b0-88fa-c47017483f70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e3ec16b3-18b6-4439-84fa-1ae17fa25801' class='xr-var-data-in' type='checkbox'><label for='data-e3ec16b3-18b6-4439-84fa-1ae17fa25801' 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 style=\"border-collapse: collapse;\">\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> 48.19 MiB </td>\n",
       "                        <td> 16.06 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2105319, 3) </td>\n",
       "                        <td> (2105319, 1) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 3 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\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>Mean</span></div><div class='xr-var-dims'>(i, pressure)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(2105319, 1), meta=np.ndarray&gt;</div><input id='attrs-87fcf789-d7e0-4321-a786-4200631988ea' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-87fcf789-d7e0-4321-a786-4200631988ea' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ed738b7-4a90-4fe7-81da-9b56d2c43c47' class='xr-var-data-in' type='checkbox'><label for='data-3ed738b7-4a90-4fe7-81da-9b56d2c43c47' 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 style=\"border-collapse: collapse;\">\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> 48.19 MiB </td>\n",
       "                        <td> 16.06 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (2105319, 3) </td>\n",
       "                        <td> (2105319, 1) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 3 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"75\" height=\"170\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "\n",
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       "  <polygon points=\"0.0,0.0 25.412616514582485,0.0 25.412616514582485,120.0 0.0,120.0\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"12.706308\" y=\"140.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >3</text>\n",
       "  <text x=\"45.412617\" y=\"60.000000\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(-90,45.412617,60.000000)\">2105319</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-a0bdf417-ec6d-45e0-be23-f4f882cbe883' class='xr-section-summary-in' type='checkbox'  ><label for='section-a0bdf417-ec6d-45e0-be23-f4f882cbe883' class='xr-section-summary' >Indexes: <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-index-name'><div>i</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5e7f9ab6-2c97-48fe-a316-050b320ef59b' class='xr-index-data-in' type='checkbox'/><label for='index-5e7f9ab6-2c97-48fe-a316-050b320ef59b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([      0,       1,       2,       3,       4,       5,       6,\n",
       "                  7,       8,       9,\n",
       "            ...\n",
       "            2105309, 2105310, 2105311, 2105312, 2105313, 2105314, 2105315,\n",
       "            2105316, 2105317, 2105318],\n",
       "           dtype=&#x27;int64&#x27;, name=&#x27;i&#x27;, length=2105319))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>pressure</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-612889f8-18e9-424b-b1c5-c31fc73bf247' class='xr-index-data-in' type='checkbox'/><label for='index-612889f8-18e9-424b-b1c5-c31fc73bf247' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Int64Index([5, 10, 20], dtype=&#x27;int64&#x27;, name=&#x27;pressure&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5f7b14dc-f9b5-404b-8154-230680a29b91' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5f7b14dc-f9b5-404b-8154-230680a29b91' class='xr-section-summary' >Attributes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>creation_date :</span></dt><dd>2023-08-02 12:23:33.346637</dd><dt><span>description :</span></dt><dd>Description</dd><dt><span>number_of_harmonics :</span></dt><dd>2</dd><dt><span>pindex :</span></dt><dd>4</dd><dt><span>window_size :</span></dt><dd>500000.0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (i: 2105319, pressure: 3)\n",
       "Coordinates:\n",
       "  * i          (i) int64 0 1 2 3 4 5 ... 2105314 2105315 2105316 2105317 2105318\n",
       "    latitude   (i) float64 dask.array<chunksize=(2105319,), meta=np.ndarray>\n",
       "    longitude  (i) float64 dask.array<chunksize=(2105319,), meta=np.ndarray>\n",
       "  * pressure   (pressure) int64 5 10 20\n",
       "    time       (i) float64 dask.array<chunksize=(2105319,), meta=np.ndarray>\n",
       "Data variables:\n",
       "    Anomalies  (i, pressure) float64 dask.array<chunksize=(2105319, 1), meta=np.ndarray>\n",
       "    Mean       (i, pressure) float64 dask.array<chunksize=(2105319, 1), meta=np.ndarray>\n",
       "Attributes:\n",
       "    creation_date:        2023-08-02 12:23:33.346637\n",
       "    description:          Description\n",
       "    number_of_harmonics:  2\n",
       "    pindex:               4\n",
       "    window_size:          500000.0"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds = xr.open_zarr(mapper_alt3, consolidated=True)\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('2022.12.0', '2022.11.0', '2.13.3', '2022.12.0')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xr.__version__, gcsfs.__version__, zarr.__version__,dask.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['pangeo-integration-te-3eea-prod-scratch-bucket/ofk123/dataset_alternative_3.zarr/.zattrs',\n",
       " 'pangeo-integration-te-3eea-prod-scratch-bucket/ofk123/dataset_alternative_3.zarr/.zgroup',\n",
       " 'pangeo-integration-te-3eea-prod-scratch-bucket/ofk123/dataset_alternative_3.zarr/.zmetadata',\n",
       " 'pangeo-integration-te-3eea-prod-scratch-bucket/ofk123/dataset_alternative_3.zarr/Anomalies',\n",
       " 'pangeo-integration-te-3eea-prod-scratch-bucket/ofk123/dataset_alternative_3.zarr/Mean',\n",
       " 'pangeo-integration-te-3eea-prod-scratch-bucket/ofk123/dataset_alternative_3.zarr/i',\n",
       " 'pangeo-integration-te-3eea-prod-scratch-bucket/ofk123/dataset_alternative_3.zarr/latitude',\n",
       " 'pangeo-integration-te-3eea-prod-scratch-bucket/ofk123/dataset_alternative_3.zarr/longitude',\n",
       " 'pangeo-integration-te-3eea-prod-scratch-bucket/ofk123/dataset_alternative_3.zarr/pressure',\n",
       " 'pangeo-integration-te-3eea-prod-scratch-bucket/ofk123/dataset_alternative_3.zarr/time']"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gcs.ls('pangeo-integration-te-3eea-prod-scratch-bucket/ofk123/dataset_alternative_3.zarr')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "##### Size of store, and one column, in megabytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(168.425544, 16.842552)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds.nbytes/1e6, ds.Anomalies.sel(pressure=5).nbytes/1e6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#ds.sortby(\"pressure\").info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Connect to a dask-cluster and set cluster-options"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dask_gateway import GatewayCluster, Gateway\n",
    "from distributed import Client\n",
    "\n",
    "g = Gateway()\n",
    "g.list_clusters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "options = g.cluster_options()\n",
    "options.worker_cores = 2; options.worker_memory = 4\n",
    "# Create a cluster with those options\n",
    "cluster = g.new_cluster(options)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[ClusterReport<name=prod.68beb2378684473a91204ca3b6211000, status=RUNNING>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.list_clusters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "    <div style=\"width: 24px; height: 24px; background-color: #e1e1e1; border: 3px solid #9D9D9D; border-radius: 5px; position: absolute;\"> </div>\n",
       "    <div style=\"margin-left: 48px;\">\n",
       "        <h3 style=\"margin-bottom: 0px;\">Client</h3>\n",
       "        <p style=\"color: #9D9D9D; margin-bottom: 0px;\">Client-b61a1d21-312d-11ee-8250-a2780657dbc6</p>\n",
       "        <table style=\"width: 100%; text-align: left;\">\n",
       "\n",
       "        <tr>\n",
       "        \n",
       "            <td style=\"text-align: left;\"><strong>Connection method:</strong> Cluster object</td>\n",
       "            <td style=\"text-align: left;\"><strong>Cluster type:</strong> dask_gateway.GatewayCluster</td>\n",
       "        \n",
       "        </tr>\n",
       "\n",
       "        \n",
       "            <tr>\n",
       "                <td style=\"text-align: left;\">\n",
       "                    <strong>Dashboard: </strong> <a href=\"/services/dask-gateway/clusters/prod.68beb2378684473a91204ca3b6211000/status\" target=\"_blank\">/services/dask-gateway/clusters/prod.68beb2378684473a91204ca3b6211000/status</a>\n",
       "                </td>\n",
       "                <td style=\"text-align: left;\"></td>\n",
       "            </tr>\n",
       "        \n",
       "\n",
       "        </table>\n",
       "\n",
       "        \n",
       "            <button style=\"margin-bottom: 12px;\" data-commandlinker-command=\"dask:populate-and-launch-layout\" data-commandlinker-args='{\"url\": \"/services/dask-gateway/clusters/prod.68beb2378684473a91204ca3b6211000/status\" }'>\n",
       "                Launch dashboard in JupyterLab\n",
       "            </button>\n",
       "        \n",
       "\n",
       "        \n",
       "            <details>\n",
       "            <summary style=\"margin-bottom: 20px;\"><h3 style=\"display: inline;\">Cluster Info</h3></summary>\n",
       "            <div style='background-color: #f2f2f2; display: inline-block; padding: 10px; border: 1px solid #999999;'>\n",
       "  <h3>GatewayCluster</h3>\n",
       "  <ul>\n",
       "    <li><b>Name: </b>prod.68beb2378684473a91204ca3b6211000\n",
       "    <li><b>Dashboard: </b><a href='/services/dask-gateway/clusters/prod.68beb2378684473a91204ca3b6211000/status' target='_blank'>/services/dask-gateway/clusters/prod.68beb2378684473a91204ca3b6211000/status</a>\n",
       "  </ul>\n",
       "</div>\n",
       "\n",
       "            </details>\n",
       "        \n",
       "\n",
       "    </div>\n",
       "</div>"
      ],
      "text/plain": [
       "<Client: 'tls://10.8.2.5:8786' processes=0 threads=0, memory=0 B>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "client = Client(cluster)\n",
    "client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.scale(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Loading of data from cloud-storage<br>\n",
    "Want to load 4 out of 5 columns of data, into several dask-workers memory, and do embarrasingly parallel computations ( ~1e6  )."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dask.delayed\n",
    "def load_chunksauto(mapper, plevel):\n",
    "    \"\"\"Load data from zarr-store into worker-memory\"\"\"\n",
    "    \n",
    "    ds = xr.open_zarr(mapper, consolidated=True, chunks='auto') \n",
    "    data0 = ds.Anomalies.isel(pressure=plevel).load().values\n",
    "    ii = ~xr.apply_ufunc(np.isnan, data0)\n",
    "    data, time, lat, lon = data0[ii], ds.time.load().values[ii],ds.latitude.load().values[ii], ds.longitude.load().values[ii] \n",
    "    \n",
    "    # Calculation using data-, lat-, lon-, and time-arrays\n",
    "    #\n",
    "    #\n",
    "    #\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dask.delayed\n",
    "def load_zarr(mapper, plevel):\n",
    "    \"\"\"Load data from zarr-store into worker-memory\"\"\"\n",
    "    \n",
    "    z = zarr.open_consolidated(mapper)\n",
    "    data0 = z.Anomalies[:,plevel]\n",
    "    ii = ~np.isnan(data0)\n",
    "    data, time, lat, lon = data0[ii], z.time[:][ii], z.latitude[:][ii], z.longitude[:][ii]\n",
    "    \n",
    "    # Calculation using data-, lat-, lon-, and time-arrays\n",
    "    #\n",
    "    #\n",
    "    #\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dask.delayed\n",
    "def load_zarr_convenience(mapper, plevel): \n",
    "    \"\"\"Load data from zarr-store into worker-memory\"\"\"\n",
    "    \n",
    "    z = zarr.convenience.open_consolidated(mapper)\n",
    "    data0 = z.Anomalies[:,plevel]\n",
    "    ii = ~np.isnan(data0)\n",
    "    data, time, lat, lon = data0[ii], z.time[:][ii], z.latitude[:][ii], z.longitude[:][ii]\n",
    "    \n",
    "    # Calculation using data-, lat-, lon-, and time-arrays\n",
    "    #\n",
    "    #\n",
    "    #\n",
    "    return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "@dask.delayed\n",
    "def load_scattered(data_scattered, time_scattered, lat_scattered, lon_scattered): \n",
    "    \"\"\"Load data from zarr-store into worker-memory\"\"\"\n",
    "\n",
    "    data, time, lat, lon = data_scattered, time_scattered, lat_scattered, lon_scattered\n",
    "    # Calculation using data-, lat-, lon-, and time-arrays\n",
    "    #\n",
    "    #\n",
    "    #\n",
    "    return None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "### Timing loading of data:<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "plevel = 2\n",
    "path_to_zarrstore = f'{SCRATCH_BUCKET}/dataset_alternative_3.zarr'\n",
    "mapper_alt3 = gcs.get_mapper(path_to_zarrstore)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "ds = xr.open_zarr(mapper, consolidated=True, chunks='auto'); ...  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.24 s ± 40.4 ms per loop (mean ± std. dev. of 7 runs, 15 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 15 dask.compute( load_chunksauto(mapper_alt3, plevel) )[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "ds = zarr.open_consolidated(mapper); ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "801 ms ± 39.5 ms per loop (mean ± std. dev. of 7 runs, 15 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 15 dask.compute( load_zarr(mapper_alt3, plevel) )[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "ds = zarr.convenience.open_consolidated(mapper); ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "848 ms ± 250 ms per loop (mean ± std. dev. of 7 runs, 15 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 15 dask.compute( load_zarr_convenience(mapper_alt3, plevel) )[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<br>\n",
    "distributing data to workers with client.scatter(data), before compute; ... "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "ds = xr.open_zarr(mapper_alt3, consolidated=True, chunks='auto') \n",
    "data0 = ds.Anomalies.isel(pressure=plevel).load().values\n",
    "ii = ~xr.apply_ufunc(np.isnan, data0)\n",
    "data_scattered, time_scattered, lat_scattered, lon_scattered = client.scatter(data0[ii]), client.scatter(ds.time.load().values[ii]),client.scatter(ds.latitude.load().values[ii]), client.scatter(ds.longitude.load().values[ii]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18.2 ms ± 517 µs per loop (mean ± std. dev. of 7 runs, 15 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -n 15 dask.compute( load_scattered(data_scattered, time_scattered, lat_scattered, lon_scattered) )[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Scaling down and closing cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cluster.scale(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster.shutdown()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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