how-tos/using-s3-storage.ipynb

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    "---\n",
    "title: \"Using S3 Bucket Storage in NASA-Openscapes Hub\"\n",
    "---"
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    "# Overview\n",
    "\n",
    "When you are working in the NASA Openscapes Hub, there are strategies we can use to manage our storage both in terms of cost and performance. The default storage location is the `HOME` directory (`/home/jovyan/`) mounted to the compute instance (the cloud computer that is doing the computations). The Hub uses an [EC2](https://aws.amazon.com/ec2/) compute instance, with the `HOME` directory mounted to [AWS Elastic File System (EFS)](https://aws.amazon.com/efs/) storage. This drive is really handy because it is persistent across server restarts and is a great place to store your code. However the `HOME` directory is not a great place to store data, as it is very expensive, and can also be quite slow to read from and write to. \n",
    "\n",
    "To that end, the hub provides every user access to two [AWS S3](https://aws.amazon.com/s3/) buckets - a \"scratch\" bucket for short-term storage, and a \"persistent\" bucket for longer-term storage. S3 buckets have fast read/write, and storage costs are relatively inexpensive compared to storing in your `HOME` directory. A useful way to think of S3 buckets in relation to your compute instance is like attaching a cheap but fast external hard drive to your expensive laptop. \n",
    "\n",
    "One other thing to note about these buckets is that all hub users can access each other's user directories. These buckets are accessible only when you are working inside the hub; you can access them using the environment variables:\n",
    "\n",
    "- `$SCRATCH_BUCKET` pointing to `s3://openscapeshub-scratch/[your-username]`\n",
    "    - Scratch buckets are designed for storage of temporary files, e.g. intermediate results. Objects stored in a scratch bucket are removed after 7 days from their creation.\n",
    "- `$PERSISTENT_BUCKET` pointing to `s3://openscapeshub-persistent/[your-username]`\n",
    "    - Persistent buckets are designed for storing data that is consistently used throughout the lifetime of a project. There is no automatic purging of objects in persistent buckets, so it is the responsibility of the hub admin and/or hub users to delete objects when they are no longer needed to minimize cloud billing costs.\n",
    "\n",
    "We can interact with these directories in Python using the packages `boto3` and/or `s3fs`, or in a terminal with the `awsv2` cli tool. This tutorial will focus on using the `s3fs` package. [See this page](https://docs.2i2c.org/admin/howto/manage-object-storage-aws/) for more information on using S3 buckets in a 2i2c hub, and tips on using the `aws` cli tool.\n",
    "\n",
    "## Reading and writing to the `$SCRATCH_BUCKET`\n",
    "\n",
    "We will start by accessing the same data we did in the [Earthdata Cloud Clinic](/tutorials/Earthdata-cloud-clinic.ipynb) - reading it into memory as an xarray object and subsetting it."
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       "      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
       "        comm_manager.register_target(comm_id, function(comm) {\n",
       "          comm.on_msg(msg_handler);\n",
       "        });\n",
       "      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n",
       "        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n",
       "          comm.onMsg = msg_handler;\n",
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       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
       "        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n",
       "          var messages = comm.messages[Symbol.asyncIterator]();\n",
       "          function processIteratorResult(result) {\n",
       "            var message = result.value;\n",
       "            console.log(message)\n",
       "            var content = {data: message.data, comm_id};\n",
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       "\n",
       "    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n",
       "      if (comm_id in window.PyViz.comms) {\n",
       "        return window.PyViz.comms[comm_id];\n",
       "      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n",
       "        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n",
       "        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n",
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       "        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n",
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       "      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n",
       "        var comm_promise = google.colab.kernel.comms.open(comm_id)\n",
       "        comm_promise.then((comm) => {\n",
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       "        var sendClosure = (data, metadata, buffers, disposeOnDone) => {\n",
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       "        };\n",
       "        var comm = {\n",
       "          send: sendClosure\n",
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       "      }\n",
       "      window.PyViz.comms[comm_id] = comm;\n",
       "      return comm;\n",
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       "    window.PyViz.comm_manager = new JupyterCommManager();\n",
       "    \n",
       "\n",
       "\n",
       "var JS_MIME_TYPE = 'application/javascript';\n",
       "var HTML_MIME_TYPE = 'text/html';\n",
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       "    html_node.innerHTML = output.data[HTML_MIME_TYPE];\n",
       "    var scripts = [];\n",
       "    var nodelist = html_node.querySelectorAll(\"script\");\n",
       "    for (var i in nodelist) {\n",
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       "    if (JS_MIME_TYPE in output.data) {\n",
       "      toinsert[nchildren-1].children[1].textContent = output.data[JS_MIME_TYPE];\n",
       "    }\n",
       "    output_area._hv_plot_id = id;\n",
       "    if ((window.Bokeh !== undefined) && (id in Bokeh.index)) {\n",
       "      window.PyViz.plot_index[id] = Bokeh.index[id];\n",
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       "  var id = handle.cell.output_area._hv_plot_id;\n",
       "  var server_id = handle.cell.output_area._bokeh_server_id;\n",
       "  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n",
       "  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n",
       "  if (server_id !== null) {\n",
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       "    return;\n",
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       "    comm.send({event_type: 'delete', 'id': id});\n",
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       "  delete PyViz.plot_index[id];\n",
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       "    var doc = window.Bokeh.index[id].model.document\n",
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       "\n",
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       "    // create a DOM node to render to\n",
       "    var toinsert = this.create_output_subarea(\n",
       "    metadata,\n",
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       "    );\n",
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       "    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n",
       "    render(props, toinsert[0]);\n",
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       "    return toinsert\n",
       "  }\n",
       "\n",
       "  events.on('output_added.OutputArea', handle_add_output);\n",
       "  events.on('output_updated.OutputArea', handle_update_output);\n",
       "  events.on('clear_output.CodeCell', handle_clear_output);\n",
       "  events.on('delete.Cell', handle_clear_output);\n",
       "  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n",
       "\n",
       "  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n",
       "    safe: true,\n",
       "    index: 0\n",
       "  });\n",
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       "\n",
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       "    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n",
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      ],
      "application/vnd.holoviews_load.v0+json": "\nif ((window.PyViz === undefined) || (window.PyViz instanceof HTMLElement)) {\n  window.PyViz = {comms: {}, comm_status:{}, kernels:{}, receivers: {}, plot_index: []}\n}\n\n\n    function JupyterCommManager() {\n    }\n\n    JupyterCommManager.prototype.register_target = function(plot_id, comm_id, msg_handler) {\n      if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        comm_manager.register_target(comm_id, function(comm) {\n          comm.on_msg(msg_handler);\n        });\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        window.PyViz.kernels[plot_id].registerCommTarget(comm_id, function(comm) {\n          comm.onMsg = msg_handler;\n        });\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        google.colab.kernel.comms.registerTarget(comm_id, (comm) => {\n          var messages = comm.messages[Symbol.asyncIterator]();\n          function processIteratorResult(result) {\n            var message = result.value;\n            console.log(message)\n            var content = {data: message.data, comm_id};\n            var buffers = []\n            for (var buffer of message.buffers || []) {\n              buffers.push(new DataView(buffer))\n            }\n            var metadata = message.metadata || {};\n            var msg = {content, buffers, metadata}\n            msg_handler(msg);\n            return messages.next().then(processIteratorResult);\n          }\n          return messages.next().then(processIteratorResult);\n        })\n      }\n    }\n\n    JupyterCommManager.prototype.get_client_comm = function(plot_id, comm_id, msg_handler) {\n      if (comm_id in window.PyViz.comms) {\n        return window.PyViz.comms[comm_id];\n      } else if (window.comm_manager || ((window.Jupyter !== undefined) && (Jupyter.notebook.kernel != null))) {\n        var comm_manager = window.comm_manager || Jupyter.notebook.kernel.comm_manager;\n        var comm = comm_manager.new_comm(comm_id, {}, {}, {}, comm_id);\n        if (msg_handler) {\n          comm.on_msg(msg_handler);\n        }\n      } else if ((plot_id in window.PyViz.kernels) && (window.PyViz.kernels[plot_id])) {\n        var comm = window.PyViz.kernels[plot_id].connectToComm(comm_id);\n        comm.open();\n        if (msg_handler) {\n          comm.onMsg = msg_handler;\n        }\n      } else if (typeof google != 'undefined' && google.colab.kernel != null) {\n        var comm_promise = google.colab.kernel.comms.open(comm_id)\n        comm_promise.then((comm) => {\n          window.PyViz.comms[comm_id] = comm;\n          if (msg_handler) {\n            var messages = comm.messages[Symbol.asyncIterator]();\n            function processIteratorResult(result) {\n              var message = 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document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\n    }\n    // store reference to server id on output_area\n    output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n  }\n}\n\n/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } 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     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1002"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import earthaccess \n",
    "import xarray as xr\n",
    "import hvplot.xarray #plot\n",
    "import os\n",
    "import tempfile\n",
    "import s3fs # aws s3 access"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "85163eed-9829-4464-a058-1fcbce505739",
   "metadata": {},
   "outputs": [],
   "source": [
    "auth = earthaccess.login()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d1067f3a-0866-4b79-aa22-f8f0ee9cfd61",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Granules found: 18\n"
     ]
    }
   ],
   "source": [
    "data_name = \"SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205\"\n",
    "\n",
    "results = earthaccess.search_data(\n",
    "    short_name=data_name,\n",
    "    cloud_hosted=True,\n",
    "    temporal=(\"2021-07-01\", \"2021-09-30\"),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c8081124-bcd6-49c6-a8f6-9de519fa484d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Opening 18 granules, approx size: 0.16 GB\n",
      "using endpoint: https://archive.podaac.earthdata.nasa.gov/s3credentials\n"
     ]
    },
    {
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       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "QUEUEING TASKS | :   0%|          | 0/18 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
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    {
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       "version_minor": 0
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      "text/plain": [
       "PROCESSING TASKS | :   0%|          | 0/18 [00:00<?, ?it/s]"
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     "output_type": "display_data"
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    {
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      "text/plain": [
       "COLLECTING RESULTS | :   0%|          | 0/18 [00:00<?, ?it/s]"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
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       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-index-data-in:checked ~ .xr-index-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-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-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",
       ".xr-no-icon {\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; Size: 299MB\n",
       "Dimensions:      (Time: 18, Longitude: 2160, nv: 2, Latitude: 960)\n",
       "Coordinates:\n",
       "  * Longitude    (Longitude) float32 9kB 0.08333 0.25 0.4167 ... 359.8 359.9\n",
       "  * Latitude     (Latitude) float32 4kB -79.92 -79.75 -79.58 ... 79.75 79.92\n",
       "  * Time         (Time) datetime64[ns] 144B 2021-07-05T12:00:00 ... 2021-09-2...\n",
       "Dimensions without coordinates: nv\n",
       "Data variables:\n",
       "    Lon_bounds   (Time, Longitude, nv) float32 311kB dask.array&lt;chunksize=(1, 2160, 2), meta=np.ndarray&gt;\n",
       "    Lat_bounds   (Time, Latitude, nv) float32 138kB dask.array&lt;chunksize=(1, 960, 2), meta=np.ndarray&gt;\n",
       "    Time_bounds  (Time, nv) datetime64[ns] 288B dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;\n",
       "    SLA          (Time, Latitude, Longitude) float32 149MB dask.array&lt;chunksize=(1, 960, 2160), meta=np.ndarray&gt;\n",
       "    SLA_ERR      (Time, Latitude, Longitude) float32 149MB dask.array&lt;chunksize=(1, 960, 2160), meta=np.ndarray&gt;\n",
       "Attributes: (12/21)\n",
       "    Conventions:            CF-1.6\n",
       "    ncei_template_version:  NCEI_NetCDF_Grid_Template_v2.0\n",
       "    Institution:            Jet Propulsion Laboratory\n",
       "    geospatial_lat_min:     -79.916664\n",
       "    geospatial_lat_max:     79.916664\n",
       "    geospatial_lon_min:     0.083333336\n",
       "    ...                     ...\n",
       "    version_number:         2205\n",
       "    Data_Pnts_Each_Sat:     {&quot;16&quot;: 743215, &quot;1007&quot;: 674076}\n",
       "    source_version:         commit 58c7da13c0c0069ae940c33a82bf1544b7d991bf\n",
       "    SLA_Global_MEAN:        0.06428374482174487\n",
       "    SLA_Global_STD:         0.0905195660534004\n",
       "    latency:                final</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-c3bcdb44-586e-45d6-9e3f-55418d7a4754' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-c3bcdb44-586e-45d6-9e3f-55418d7a4754' 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>: 18</li><li><span class='xr-has-index'>Longitude</span>: 2160</li><li><span>nv</span>: 2</li><li><span class='xr-has-index'>Latitude</span>: 960</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-14678b4a-140c-4456-8cf9-460d16ec1181' class='xr-section-summary-in' type='checkbox'  checked><label for='section-14678b4a-140c-4456-8cf9-460d16ec1181' 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'>Longitude</span></div><div class='xr-var-dims'>(Longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.08333 0.25 0.4167 ... 359.8 359.9</div><input id='attrs-0a1dfc40-48f4-4dd1-9bea-e651a3a5f4ed' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0a1dfc40-48f4-4dd1-9bea-e651a3a5f4ed' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-743d247f-42eb-4834-b62f-847cf01db1be' class='xr-var-data-in' type='checkbox'><label for='data-743d247f-42eb-4834-b62f-847cf01db1be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>Lon_bounds</dd></dl></div><div class='xr-var-data'><pre>array([8.333334e-02, 2.500000e-01, 4.166667e-01, ..., 3.595833e+02,\n",
       "       3.597500e+02, 3.599167e+02], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Latitude</span></div><div class='xr-var-dims'>(Latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-79.92 -79.75 ... 79.75 79.92</div><input id='attrs-0bfd258b-54a1-4170-ac7a-ee2a7cee1a83' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bfd258b-54a1-4170-ac7a-ee2a7cee1a83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ccc8969-8d02-4657-b545-5340cc1d96ed' class='xr-var-data-in' type='checkbox'><label for='data-1ccc8969-8d02-4657-b545-5340cc1d96ed' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>Lat_bounds</dd></dl></div><div class='xr-var-data'><pre>array([-79.916664, -79.75    , -79.583336, ...,  79.583336,  79.75    ,\n",
       "        79.916664], dtype=float32)</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-07-05T12:00:00 ... 2021-09-...</div><input id='attrs-25b192cc-7008-4fdb-a01f-573b45b880a7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-25b192cc-7008-4fdb-a01f-573b45b880a7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-464a0f6a-6c73-460d-a1a6-1d89adc43ac3' class='xr-var-data-in' type='checkbox'><label for='data-464a0f6a-6c73-460d-a1a6-1d89adc43ac3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>bounds :</span></dt><dd>Time_bounds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-07-05T12:00:00.000000000&#x27;, &#x27;2021-07-10T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-07-15T12:00:00.000000000&#x27;, &#x27;2021-07-20T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-07-25T12:00:00.000000000&#x27;, &#x27;2021-07-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-04T12:00:00.000000000&#x27;, &#x27;2021-08-09T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-14T12:00:00.000000000&#x27;, &#x27;2021-08-19T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-24T12:00:00.000000000&#x27;, &#x27;2021-08-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-03T12:00:00.000000000&#x27;, &#x27;2021-09-08T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-13T12:00:00.000000000&#x27;, &#x27;2021-09-18T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-23T12:00:00.000000000&#x27;, &#x27;2021-09-28T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-8c2bdc24-afbc-4de6-ac49-c1af0553ceb7' class='xr-section-summary-in' type='checkbox'  checked><label for='section-8c2bdc24-afbc-4de6-ac49-c1af0553ceb7' class='xr-section-summary' >Data variables: <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>Lon_bounds</span></div><div class='xr-var-dims'>(Time, Longitude, nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2160, 2), meta=np.ndarray&gt;</div><input id='attrs-354dfd71-d03c-4705-885e-f9a583227241' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-354dfd71-d03c-4705-885e-f9a583227241' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a66da510-9f8e-4fb7-82f4-5bd65ed04508' class='xr-var-data-in' type='checkbox'><label for='data-a66da510-9f8e-4fb7-82f4-5bd65ed04508' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>comment :</span></dt><dd>longitude values at the west and east bounds of each pixel.</dd></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> 303.75 kiB </td>\n",
       "                        <td> 16.88 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18, 2160, 2) </td>\n",
       "                        <td> (1, 2160, 2) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 18 chunks in 55 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Lat_bounds</span></div><div class='xr-var-dims'>(Time, Latitude, nv)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 960, 2), meta=np.ndarray&gt;</div><input id='attrs-70e5f792-ff9f-4534-bd47-4e403ceda9c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70e5f792-ff9f-4534-bd47-4e403ceda9c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f6c2763-4bb1-4653-97f8-116ef4fbc6e4' class='xr-var-data-in' type='checkbox'><label for='data-7f6c2763-4bb1-4653-97f8-116ef4fbc6e4' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>comment :</span></dt><dd>latitude values at the north and south bounds of each pixel.</dd></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> 135.00 kiB </td>\n",
       "                        <td> 7.50 kiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <th> Dask graph </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Time_bounds</span></div><div class='xr-var-dims'>(Time, nv)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 2), meta=np.ndarray&gt;</div><input id='attrs-a4861ace-2f6d-4cbd-a9d8-722ab0f7c523' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4861ace-2f6d-4cbd-a9d8-722ab0f7c523' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b1907fa2-50e8-4ad2-b70f-6806744cfc13' class='xr-var-data-in' type='checkbox'><label for='data-b1907fa2-50e8-4ad2-b70f-6806744cfc13' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>comment :</span></dt><dd>Time bounds for each time value, same value as time variable. The time variable is defined on points instead of on bounding boxes.</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 288 B </td>\n",
       "                        <td> 16 B </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>SLA</span></div><div class='xr-var-dims'>(Time, Latitude, Longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1, 960, 2160), meta=np.ndarray&gt;</div><input id='attrs-218833a9-7b88-4e9c-b18f-d9fae78e5783' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-218833a9-7b88-4e9c-b18f-d9fae78e5783' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02d6c909-cd06-45ed-bfdb-bb0719bf526f' class='xr-var-data-in' type='checkbox'><label for='data-02d6c909-cd06-45ed-bfdb-bb0719bf526f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Sea Level Anomaly Estimate</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>alias :</span></dt><dd>sea_surface_height_above_sea_level</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 142.38 MiB </td>\n",
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       "       1.5833333730697632,\n",
       "       ...\n",
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       "        359.9166564941406],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;Longitude&#x27;, length=2160))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-42a5e95a-927f-4c49-9cea-e717cd30baa7' class='xr-index-data-in' type='checkbox'/><label for='index-42a5e95a-927f-4c49-9cea-e717cd30baa7' 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(Index([-79.91666412353516,             -79.75, -79.58333587646484,\n",
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       "       -78.91666412353516,             -78.75, -78.58333587646484,\n",
       "       -78.41666412353516,\n",
       "       ...\n",
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       "               &#x27;2021-07-15 12:00:00&#x27;, &#x27;2021-07-20 12:00:00&#x27;,\n",
       "               &#x27;2021-07-25 12:00:00&#x27;, &#x27;2021-07-30 12:00:00&#x27;,\n",
       "               &#x27;2021-08-04 12:00:00&#x27;, &#x27;2021-08-09 12:00:00&#x27;,\n",
       "               &#x27;2021-08-14 12:00:00&#x27;, &#x27;2021-08-19 12:00:00&#x27;,\n",
       "               &#x27;2021-08-24 12:00:00&#x27;, &#x27;2021-08-29 12:00:00&#x27;,\n",
       "               &#x27;2021-09-03 12:00:00&#x27;, &#x27;2021-09-08 12:00:00&#x27;,\n",
       "               &#x27;2021-09-13 12:00:00&#x27;, &#x27;2021-09-18 12:00:00&#x27;,\n",
       "               &#x27;2021-09-23 12:00:00&#x27;, &#x27;2021-09-28 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-221e7da8-dd18-4174-b07a-5a551e13f290' class='xr-section-summary-in' type='checkbox'  ><label for='section-221e7da8-dd18-4174-b07a-5a551e13f290' class='xr-section-summary' >Attributes: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.6</dd><dt><span>ncei_template_version :</span></dt><dd>NCEI_NetCDF_Grid_Template_v2.0</dd><dt><span>Institution :</span></dt><dd>Jet Propulsion Laboratory</dd><dt><span>geospatial_lat_min :</span></dt><dd>-79.916664</dd><dt><span>geospatial_lat_max :</span></dt><dd>79.916664</dd><dt><span>geospatial_lon_min :</span></dt><dd>0.083333336</dd><dt><span>geospatial_lon_max :</span></dt><dd>359.91666</dd><dt><span>time_coverage_start :</span></dt><dd>2021-07-05</dd><dt><span>time_coverage_end :</span></dt><dd>2021-07-05</dd><dt><span>date_created :</span></dt><dd>2022-10-30T20:58:16.297288</dd><dt><span>title :</span></dt><dd>Sea Level Anomaly Estimate based on Altimeter Data, final product (replaced interim version).</dd><dt><span>short_name :</span></dt><dd>SEA_SURFACE_HEIGHT_ALT_GRIDS_L4_2SATS_5DAY_6THDEG_V_JPL2205</dd><dt><span>long_name :</span></dt><dd>MEaSUREs Gridded Sea Surface Height Anomalies Version 2205</dd><dt><span>summary :</span></dt><dd>Sea level anomaly grids from altimeter data using Kriging interpolation, which gives best linear prediction based upon prior knowledge of covariance. </dd><dt><span>DOI :</span></dt><dd>10.5067/SLREF-CDRV3</dd><dt><span>version_number :</span></dt><dd>2205</dd><dt><span>Data_Pnts_Each_Sat :</span></dt><dd>{&quot;16&quot;: 743215, &quot;1007&quot;: 674076}</dd><dt><span>source_version :</span></dt><dd>commit 58c7da13c0c0069ae940c33a82bf1544b7d991bf</dd><dt><span>SLA_Global_MEAN :</span></dt><dd>0.06428374482174487</dd><dt><span>SLA_Global_STD :</span></dt><dd>0.0905195660534004</dd><dt><span>latency :</span></dt><dd>final</dd></dl></div></li></ul></div></div>"
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       "Coordinates:\n",
       "  * Longitude    (Longitude) float32 9kB 0.08333 0.25 0.4167 ... 359.8 359.9\n",
       "  * Latitude     (Latitude) float32 4kB -79.92 -79.75 -79.58 ... 79.75 79.92\n",
       "  * Time         (Time) datetime64[ns] 144B 2021-07-05T12:00:00 ... 2021-09-2...\n",
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       "    SLA_ERR      (Time, Latitude, Longitude) float32 149MB dask.array<chunksize=(1, 960, 2160), meta=np.ndarray>\n",
       "Attributes: (12/21)\n",
       "    Conventions:            CF-1.6\n",
       "    ncei_template_version:  NCEI_NetCDF_Grid_Template_v2.0\n",
       "    Institution:            Jet Propulsion Laboratory\n",
       "    geospatial_lat_min:     -79.916664\n",
       "    geospatial_lat_max:     79.916664\n",
       "    geospatial_lon_min:     0.083333336\n",
       "    ...                     ...\n",
       "    version_number:         2205\n",
       "    Data_Pnts_Each_Sat:     {\"16\": 743215, \"1007\": 674076}\n",
       "    source_version:         commit 58c7da13c0c0069ae940c33a82bf1544b7d991bf\n",
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       "    SLA_Global_STD:         0.0905195660534004\n",
       "    latency:                final"
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     "execution_count": 4,
     "metadata": {},
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   "source": [
    "ds = xr.open_mfdataset(earthaccess.open(results))\n",
    "ds"
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  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "893d2d8f-278d-4439-9698-9420d295bd6b",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;SLA&#x27; (Time: 18, Latitude: 120, Longitude: 156)&gt; Size: 1MB\n",
       "dask.array&lt;getitem, shape=(18, 120, 156), dtype=float32, chunksize=(1, 120, 156), chunktype=numpy.ndarray&gt;\n",
       "Coordinates:\n",
       "  * Longitude  (Longitude) float32 624B 234.6 234.8 234.9 ... 260.1 260.2 260.4\n",
       "  * Latitude   (Latitude) float32 480B 15.92 16.08 16.25 ... 35.42 35.58 35.75\n",
       "  * Time       (Time) datetime64[ns] 144B 2021-07-05T12:00:00 ... 2021-09-28T...\n",
       "Attributes:\n",
       "    units:          m\n",
       "    long_name:      Sea Level Anomaly Estimate\n",
       "    standard_name:  sea_surface_height_above_sea_level\n",
       "    alias:          sea_surface_height_above_sea_level</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'>'SLA'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>Time</span>: 18</li><li><span class='xr-has-index'>Latitude</span>: 120</li><li><span class='xr-has-index'>Longitude</span>: 156</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-fe82cd19-a5e3-4fa3-8c7d-9de373f15d0f' class='xr-array-in' type='checkbox' checked><label for='section-fe82cd19-a5e3-4fa3-8c7d-9de373f15d0f' 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=(1, 120, 156), meta=np.ndarray&gt;</span></div><div class='xr-array-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> 1.29 MiB </td>\n",
       "                        <td> 73.12 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18, 120, 156) </td>\n",
       "                        <td> (1, 120, 156) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 18 chunks in 38 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "       235.58333, 235.75   , 235.91667, 236.08333, 236.25   , 236.41667,\n",
       "       236.58333, 236.75   , 236.91667, 237.08333, 237.25   , 237.41667,\n",
       "       237.58333, 237.75   , 237.91667, 238.08333, 238.25   , 238.41667,\n",
       "       238.58333, 238.75   , 238.91667, 239.08333, 239.25   , 239.41667,\n",
       "       239.58333, 239.75   , 239.91667, 240.08333, 240.25   , 240.41667,\n",
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       "       241.58333, 241.75   , 241.91667, 242.08333, 242.25   , 242.41667,\n",
       "       242.58333, 242.75   , 242.91667, 243.08333, 243.25   , 243.41667,\n",
       "       243.58333, 243.75   , 243.91667, 244.08333, 244.25   , 244.41667,\n",
       "       244.58333, 244.75   , 244.91667, 245.08333, 245.25   , 245.41667,\n",
       "       245.58333, 245.75   , 245.91667, 246.08333, 246.25   , 246.41667,\n",
       "       246.58333, 246.75   , 246.91667, 247.08333, 247.25   , 247.41667,\n",
       "       247.58333, 247.75   , 247.91667, 248.08333, 248.25   , 248.41667,\n",
       "       248.58333, 248.75   , 248.91667, 249.08333, 249.25   , 249.41667,\n",
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       "       250.58333, 250.75   , 250.91667, 251.08333, 251.25   , 251.41667,\n",
       "       251.58333, 251.75   , 251.91667, 252.08333, 252.25   , 252.41667,\n",
       "       252.58333, 252.75   , 252.91667, 253.08333, 253.25   , 253.41667,\n",
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       "       257.58334, 257.75   , 257.91666, 258.08334, 258.25   , 258.41666,\n",
       "       258.58334, 258.75   , 258.91666, 259.08334, 259.25   , 259.41666,\n",
       "       259.58334, 259.75   , 259.91666, 260.08334, 260.25   , 260.41666],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Latitude</span></div><div class='xr-var-dims'>(Latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>15.92 16.08 16.25 ... 35.58 35.75</div><input id='attrs-e88a7ed5-0e17-491e-aaab-eaa45b28b508' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e88a7ed5-0e17-491e-aaab-eaa45b28b508' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc6213b6-13a2-4f6f-a238-10301e54022c' class='xr-var-data-in' type='checkbox'><label for='data-dc6213b6-13a2-4f6f-a238-10301e54022c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>Lat_bounds</dd></dl></div><div class='xr-var-data'><pre>array([15.916667, 16.083334, 16.25    , 16.416666, 16.583334, 16.75    ,\n",
       "       16.916666, 17.083334, 17.25    , 17.416666, 17.583334, 17.75    ,\n",
       "       17.916666, 18.083334, 18.25    , 18.416666, 18.583334, 18.75    ,\n",
       "       18.916666, 19.083334, 19.25    , 19.416666, 19.583334, 19.75    ,\n",
       "       19.916666, 20.083334, 20.25    , 20.416666, 20.583334, 20.75    ,\n",
       "       20.916666, 21.083334, 21.25    , 21.416666, 21.583334, 21.75    ,\n",
       "       21.916666, 22.083334, 22.25    , 22.416666, 22.583334, 22.75    ,\n",
       "       22.916666, 23.083334, 23.25    , 23.416666, 23.583334, 23.75    ,\n",
       "       23.916666, 24.083334, 24.25    , 24.416666, 24.583334, 24.75    ,\n",
       "       24.916666, 25.083334, 25.25    , 25.416666, 25.583334, 25.75    ,\n",
       "       25.916666, 26.083334, 26.25    , 26.416666, 26.583334, 26.75    ,\n",
       "       26.916666, 27.083334, 27.25    , 27.416666, 27.583334, 27.75    ,\n",
       "       27.916666, 28.083334, 28.25    , 28.416666, 28.583334, 28.75    ,\n",
       "       28.916666, 29.083334, 29.25    , 29.416666, 29.583334, 29.75    ,\n",
       "       29.916666, 30.083334, 30.25    , 30.416666, 30.583334, 30.75    ,\n",
       "       30.916666, 31.083334, 31.25    , 31.416666, 31.583334, 31.75    ,\n",
       "       31.916666, 32.083332, 32.25    , 32.416668, 32.583332, 32.75    ,\n",
       "       32.916668, 33.083332, 33.25    , 33.416668, 33.583332, 33.75    ,\n",
       "       33.916668, 34.083332, 34.25    , 34.416668, 34.583332, 34.75    ,\n",
       "       34.916668, 35.083332, 35.25    , 35.416668, 35.583332, 35.75    ],\n",
       "      dtype=float32)</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-07-05T12:00:00 ... 2021-09-...</div><input id='attrs-960fac31-f48b-492a-8417-a7fea9efc145' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-960fac31-f48b-492a-8417-a7fea9efc145' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80724335-cdc0-4573-ae07-4bfa60a4daf6' class='xr-var-data-in' type='checkbox'><label for='data-80724335-cdc0-4573-ae07-4bfa60a4daf6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>bounds :</span></dt><dd>Time_bounds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-07-05T12:00:00.000000000&#x27;, &#x27;2021-07-10T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-07-15T12:00:00.000000000&#x27;, &#x27;2021-07-20T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-07-25T12:00:00.000000000&#x27;, &#x27;2021-07-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-04T12:00:00.000000000&#x27;, &#x27;2021-08-09T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-14T12:00:00.000000000&#x27;, &#x27;2021-08-19T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-24T12:00:00.000000000&#x27;, &#x27;2021-08-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-03T12:00:00.000000000&#x27;, &#x27;2021-09-08T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-13T12:00:00.000000000&#x27;, &#x27;2021-09-18T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-23T12:00:00.000000000&#x27;, &#x27;2021-09-28T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-bbf193ed-17b6-4b10-a3c8-144d0591117f' class='xr-section-summary-in' type='checkbox'  ><label for='section-bbf193ed-17b6-4b10-a3c8-144d0591117f' class='xr-section-summary' >Indexes: <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-index-name'><div>Longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d6db5492-8303-46ab-a314-e6485e1e65f9' class='xr-index-data-in' type='checkbox'/><label for='index-d6db5492-8303-46ab-a314-e6485e1e65f9' 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(Index([234.5833282470703,            234.75, 234.9166717529297,\n",
       "       235.0833282470703,            235.25, 235.4166717529297,\n",
       "       235.5833282470703,            235.75, 235.9166717529297,\n",
       "       236.0833282470703,\n",
       "       ...\n",
       "       258.9166564941406, 259.0833435058594,            259.25,\n",
       "       259.4166564941406, 259.5833435058594,            259.75,\n",
       "       259.9166564941406, 260.0833435058594,            260.25,\n",
       "       260.4166564941406],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;Longitude&#x27;, length=156))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-706947ff-34d3-4334-8ece-0aff15933726' class='xr-index-data-in' type='checkbox'/><label for='index-706947ff-34d3-4334-8ece-0aff15933726' 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(Index([15.916666984558105,  16.08333396911621,              16.25,\n",
       "        16.41666603088379,  16.58333396911621,              16.75,\n",
       "        16.91666603088379,  17.08333396911621,              17.25,\n",
       "        17.41666603088379,\n",
       "       ...\n",
       "                    34.25,  34.41666793823242,  34.58333206176758,\n",
       "                    34.75,  34.91666793823242,  35.08333206176758,\n",
       "                    35.25,  35.41666793823242,  35.58333206176758,\n",
       "                    35.75],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;Latitude&#x27;, length=120))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7d743cf3-c152-4895-98a9-1cc72c9c4410' class='xr-index-data-in' type='checkbox'/><label for='index-7d743cf3-c152-4895-98a9-1cc72c9c4410' 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(DatetimeIndex([&#x27;2021-07-05 12:00:00&#x27;, &#x27;2021-07-10 12:00:00&#x27;,\n",
       "               &#x27;2021-07-15 12:00:00&#x27;, &#x27;2021-07-20 12:00:00&#x27;,\n",
       "               &#x27;2021-07-25 12:00:00&#x27;, &#x27;2021-07-30 12:00:00&#x27;,\n",
       "               &#x27;2021-08-04 12:00:00&#x27;, &#x27;2021-08-09 12:00:00&#x27;,\n",
       "               &#x27;2021-08-14 12:00:00&#x27;, &#x27;2021-08-19 12:00:00&#x27;,\n",
       "               &#x27;2021-08-24 12:00:00&#x27;, &#x27;2021-08-29 12:00:00&#x27;,\n",
       "               &#x27;2021-09-03 12:00:00&#x27;, &#x27;2021-09-08 12:00:00&#x27;,\n",
       "               &#x27;2021-09-13 12:00:00&#x27;, &#x27;2021-09-18 12:00:00&#x27;,\n",
       "               &#x27;2021-09-23 12:00:00&#x27;, &#x27;2021-09-28 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-80a2fb33-2d75-431e-baad-de7e7bfa75f4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-80a2fb33-2d75-431e-baad-de7e7bfa75f4' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Sea Level Anomaly Estimate</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>alias :</span></dt><dd>sea_surface_height_above_sea_level</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'SLA' (Time: 18, Latitude: 120, Longitude: 156)> Size: 1MB\n",
       "dask.array<getitem, shape=(18, 120, 156), dtype=float32, chunksize=(1, 120, 156), chunktype=numpy.ndarray>\n",
       "Coordinates:\n",
       "  * Longitude  (Longitude) float32 624B 234.6 234.8 234.9 ... 260.1 260.2 260.4\n",
       "  * Latitude   (Latitude) float32 480B 15.92 16.08 16.25 ... 35.42 35.58 35.75\n",
       "  * Time       (Time) datetime64[ns] 144B 2021-07-05T12:00:00 ... 2021-09-28T...\n",
       "Attributes:\n",
       "    units:          m\n",
       "    long_name:      Sea Level Anomaly Estimate\n",
       "    standard_name:  sea_surface_height_above_sea_level\n",
       "    alias:          sea_surface_height_above_sea_level"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_subset = ds['SLA'].sel(Latitude=slice(15.8, 35.9), Longitude=slice(234.5,260.5)) \n",
    "ds_subset"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84f20657-a04a-45cd-a252-340d7b0a0c2a",
   "metadata": {},
   "source": [
    "## Home directory\n",
    "\n",
    "Imagining this `ds_subset` object is now an important intermediate dataset, or the result of a complex analysis and we want to save it. Our default action might be to just save it to our `HOME` directory. This is simple, but we want to avoid this as it incurs significant storage costs, and using this data later will be slow."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1547aaca-cc37-49b4-8379-7186f83fdcec",
   "metadata": {},
   "source": [
    "```python\n",
    "ds_subset.to_netcdf(\"test.nc\") # avoid writing to home directory like this\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2527d864-7297-4a98-9a6b-a0712a35a753",
   "metadata": {},
   "source": [
    "## Use the s3fs package to interact with our S3 bucket.\n",
    "\n",
    "[s3fs](https://s3fs.readthedocs.io/en/latest/) is a Python library that allows us to interact with S3 objects in a file-system like manner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "b9ded82a-9041-4f31-8eab-707e7d151dbf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3://openscapeshub-scratch/ateucher\n",
      "s3://openscapeshub-persistent/ateucher\n"
     ]
    }
   ],
   "source": [
    "# Create a S3FileSystem class\n",
    "s3 = s3fs.S3FileSystem()\n",
    "\n",
    "# Get scratch and persistent buckets\n",
    "scratch = os.environ[\"SCRATCH_BUCKET\"]\n",
    "persistent = os.environ[\"PERSISTENT_BUCKET\"]\n",
    "\n",
    "print(scratch)\n",
    "print(persistent)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d173e6ae-ada8-497c-b395-9b68407d0f01",
   "metadata": {},
   "source": [
    "Our user-specific directories in the two buckets aren't actually created until we put something in them, so if we try to check\n",
    "for their existence or list their contents before they are created, we will get an error. We will use the `S3FileSystem.touch()` method to place a simple empty file called `.placeholder` in each one to bring them into existence."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6b4de62d-e6bf-46d8-9aed-28d214ee31e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['openscapeshub-scratch/ateucher/.placeholder']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3.touch(f\"{scratch}/.placeholder\")\n",
    "\n",
    "s3.ls(scratch)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c96c523a-8ce9-4d18-a0b1-d776b661334b",
   "metadata": {},
   "source": [
    "and in our persistent bucket:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "460dfc00-9dfc-4d5a-ac9f-9cb452b4fde9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['openscapeshub-persistent/ateucher/.placeholder']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3.touch(f\"{persistent}/.placeholder\")\n",
    "\n",
    "s3.ls(persistent)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa5dbcae-b45f-472b-acd1-33b91945e63c",
   "metadata": {},
   "source": [
    "(Note that adding these placeholders isn't strictly necessary, as the first time you write anything to these buckets they will be created.)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "686b39a3-345e-44ab-a122-87b24060f7cf",
   "metadata": {},
   "source": [
    "## Save dataset as netcdf file in SCRATCH bucket\n",
    "\n",
    "Next we can save `ds_subset` as a netcdf file in our scratch bucket. This involves writing to a temporary directory first, and then moving that to the `SCRATCH` bucket:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "36bf4cee-e487-4917-a0de-46ace731ac03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['openscapeshub-scratch/ateucher/.placeholder',\n",
       " 'openscapeshub-scratch/ateucher/test123.nc']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Where we want to store it:\n",
    "scratch_nc_file_path = f\"{scratch}/test123.nc\"\n",
    "\n",
    "# Create a temporary intermediate file and save it to the bucket\n",
    "with tempfile.NamedTemporaryFile(suffix = \".nc\") as tmp:\n",
    "    ds_subset.to_netcdf(tmp.name) # save it to a temporary file\n",
    "    s3.put(tmp.name, scratch_nc_file_path) # move that file to the scratch bucket\n",
    "\n",
    "# Ensure the file is there\n",
    "s3.ls(scratch)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "213aefbc-9bd0-41a0-9a9e-b4c4f60cc960",
   "metadata": {},
   "source": [
    "And we can open it to ensure it worked:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "99d836c3-2045-4961-b6ff-d7c7bd4df688",
   "metadata": {},
   "outputs": [
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;SLA&#x27; (Time: 18, Latitude: 120, Longitude: 156)&gt; Size: 1MB\n",
       "[336960 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * Longitude  (Longitude) float32 624B 234.6 234.8 234.9 ... 260.1 260.2 260.4\n",
       "  * Latitude   (Latitude) float32 480B 15.92 16.08 16.25 ... 35.42 35.58 35.75\n",
       "  * Time       (Time) datetime64[ns] 144B 2021-07-05T12:00:00 ... 2021-09-28T...\n",
       "Attributes:\n",
       "    units:          m\n",
       "    long_name:      Sea Level Anomaly Estimate\n",
       "    standard_name:  sea_surface_height_above_sea_level\n",
       "    alias:          sea_surface_height_above_sea_level</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'>'SLA'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>Time</span>: 18</li><li><span class='xr-has-index'>Latitude</span>: 120</li><li><span class='xr-has-index'>Longitude</span>: 156</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-215ea845-6734-49bf-8584-6e46c2af8df8' class='xr-array-in' type='checkbox' checked><label for='section-215ea845-6734-49bf-8584-6e46c2af8df8' 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>...</span></div><div class='xr-array-data'><pre>[336960 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-b896dbb8-f43a-4ffb-ae8e-42e392fd16c1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b896dbb8-f43a-4ffb-ae8e-42e392fd16c1' 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'>Longitude</span></div><div class='xr-var-dims'>(Longitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>234.6 234.8 234.9 ... 260.2 260.4</div><input id='attrs-b310dbe5-6660-4234-8340-e008971e554d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b310dbe5-6660-4234-8340-e008971e554d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cca60921-3bb7-4181-a6a9-7ede397e2ee9' class='xr-var-data-in' type='checkbox'><label for='data-cca60921-3bb7-4181-a6a9-7ede397e2ee9' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>bounds :</span></dt><dd>Lon_bounds</dd></dl></div><div class='xr-var-data'><pre>array([234.58333, 234.75   , 234.91667, 235.08333, 235.25   , 235.41667,\n",
       "       235.58333, 235.75   , 235.91667, 236.08333, 236.25   , 236.41667,\n",
       "       236.58333, 236.75   , 236.91667, 237.08333, 237.25   , 237.41667,\n",
       "       237.58333, 237.75   , 237.91667, 238.08333, 238.25   , 238.41667,\n",
       "       238.58333, 238.75   , 238.91667, 239.08333, 239.25   , 239.41667,\n",
       "       239.58333, 239.75   , 239.91667, 240.08333, 240.25   , 240.41667,\n",
       "       240.58333, 240.75   , 240.91667, 241.08333, 241.25   , 241.41667,\n",
       "       241.58333, 241.75   , 241.91667, 242.08333, 242.25   , 242.41667,\n",
       "       242.58333, 242.75   , 242.91667, 243.08333, 243.25   , 243.41667,\n",
       "       243.58333, 243.75   , 243.91667, 244.08333, 244.25   , 244.41667,\n",
       "       244.58333, 244.75   , 244.91667, 245.08333, 245.25   , 245.41667,\n",
       "       245.58333, 245.75   , 245.91667, 246.08333, 246.25   , 246.41667,\n",
       "       246.58333, 246.75   , 246.91667, 247.08333, 247.25   , 247.41667,\n",
       "       247.58333, 247.75   , 247.91667, 248.08333, 248.25   , 248.41667,\n",
       "       248.58333, 248.75   , 248.91667, 249.08333, 249.25   , 249.41667,\n",
       "       249.58333, 249.75   , 249.91667, 250.08333, 250.25   , 250.41667,\n",
       "       250.58333, 250.75   , 250.91667, 251.08333, 251.25   , 251.41667,\n",
       "       251.58333, 251.75   , 251.91667, 252.08333, 252.25   , 252.41667,\n",
       "       252.58333, 252.75   , 252.91667, 253.08333, 253.25   , 253.41667,\n",
       "       253.58333, 253.75   , 253.91667, 254.08333, 254.25   , 254.41667,\n",
       "       254.58333, 254.75   , 254.91667, 255.08333, 255.25   , 255.41667,\n",
       "       255.58333, 255.75   , 255.91667, 256.08334, 256.25   , 256.41666,\n",
       "       256.58334, 256.75   , 256.91666, 257.08334, 257.25   , 257.41666,\n",
       "       257.58334, 257.75   , 257.91666, 258.08334, 258.25   , 258.41666,\n",
       "       258.58334, 258.75   , 258.91666, 259.08334, 259.25   , 259.41666,\n",
       "       259.58334, 259.75   , 259.91666, 260.08334, 260.25   , 260.41666],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>Latitude</span></div><div class='xr-var-dims'>(Latitude)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>15.92 16.08 16.25 ... 35.58 35.75</div><input id='attrs-07730f86-61b9-44d1-9b4e-d8c77e956273' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-07730f86-61b9-44d1-9b4e-d8c77e956273' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-acd1c5da-b136-4e5d-9a39-e06378cc0704' class='xr-var-data-in' type='checkbox'><label for='data-acd1c5da-b136-4e5d-9a39-e06378cc0704' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>point_spacing :</span></dt><dd>even</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>bounds :</span></dt><dd>Lat_bounds</dd></dl></div><div class='xr-var-data'><pre>array([15.916667, 16.083334, 16.25    , 16.416666, 16.583334, 16.75    ,\n",
       "       16.916666, 17.083334, 17.25    , 17.416666, 17.583334, 17.75    ,\n",
       "       17.916666, 18.083334, 18.25    , 18.416666, 18.583334, 18.75    ,\n",
       "       18.916666, 19.083334, 19.25    , 19.416666, 19.583334, 19.75    ,\n",
       "       19.916666, 20.083334, 20.25    , 20.416666, 20.583334, 20.75    ,\n",
       "       20.916666, 21.083334, 21.25    , 21.416666, 21.583334, 21.75    ,\n",
       "       21.916666, 22.083334, 22.25    , 22.416666, 22.583334, 22.75    ,\n",
       "       22.916666, 23.083334, 23.25    , 23.416666, 23.583334, 23.75    ,\n",
       "       23.916666, 24.083334, 24.25    , 24.416666, 24.583334, 24.75    ,\n",
       "       24.916666, 25.083334, 25.25    , 25.416666, 25.583334, 25.75    ,\n",
       "       25.916666, 26.083334, 26.25    , 26.416666, 26.583334, 26.75    ,\n",
       "       26.916666, 27.083334, 27.25    , 27.416666, 27.583334, 27.75    ,\n",
       "       27.916666, 28.083334, 28.25    , 28.416666, 28.583334, 28.75    ,\n",
       "       28.916666, 29.083334, 29.25    , 29.416666, 29.583334, 29.75    ,\n",
       "       29.916666, 30.083334, 30.25    , 30.416666, 30.583334, 30.75    ,\n",
       "       30.916666, 31.083334, 31.25    , 31.416666, 31.583334, 31.75    ,\n",
       "       31.916666, 32.083332, 32.25    , 32.416668, 32.583332, 32.75    ,\n",
       "       32.916668, 33.083332, 33.25    , 33.416668, 33.583332, 33.75    ,\n",
       "       33.916668, 34.083332, 34.25    , 34.416668, 34.583332, 34.75    ,\n",
       "       34.916668, 35.083332, 35.25    , 35.416668, 35.583332, 35.75    ],\n",
       "      dtype=float32)</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'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2021-07-05T12:00:00 ... 2021-09-...</div><input id='attrs-7244582d-0a3b-49af-9fcb-83b1b068d237' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7244582d-0a3b-49af-9fcb-83b1b068d237' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-639e7950-bf50-4ecc-88a0-7658dacec7ce' class='xr-var-data-in' type='checkbox'><label for='data-639e7950-bf50-4ecc-88a0-7658dacec7ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>long_name :</span></dt><dd>Time</dd><dt><span>bounds :</span></dt><dd>Time_bounds</dd><dt><span>axis :</span></dt><dd>T</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2021-07-05T12:00:00.000000000&#x27;, &#x27;2021-07-10T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-07-15T12:00:00.000000000&#x27;, &#x27;2021-07-20T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-07-25T12:00:00.000000000&#x27;, &#x27;2021-07-30T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-04T12:00:00.000000000&#x27;, &#x27;2021-08-09T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-14T12:00:00.000000000&#x27;, &#x27;2021-08-19T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-08-24T12:00:00.000000000&#x27;, &#x27;2021-08-29T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-03T12:00:00.000000000&#x27;, &#x27;2021-09-08T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-13T12:00:00.000000000&#x27;, &#x27;2021-09-18T12:00:00.000000000&#x27;,\n",
       "       &#x27;2021-09-23T12:00:00.000000000&#x27;, &#x27;2021-09-28T12:00:00.000000000&#x27;],\n",
       "      dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1f7af0e7-118d-4316-9acb-b7f0b082d8a1' class='xr-section-summary-in' type='checkbox'  ><label for='section-1f7af0e7-118d-4316-9acb-b7f0b082d8a1' class='xr-section-summary' >Indexes: <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-index-name'><div>Longitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9b7d98bc-c723-4217-a3ba-2beeb7812c1e' class='xr-index-data-in' type='checkbox'/><label for='index-9b7d98bc-c723-4217-a3ba-2beeb7812c1e' 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(Index([234.5833282470703,            234.75, 234.9166717529297,\n",
       "       235.0833282470703,            235.25, 235.4166717529297,\n",
       "       235.5833282470703,            235.75, 235.9166717529297,\n",
       "       236.0833282470703,\n",
       "       ...\n",
       "       258.9166564941406, 259.0833435058594,            259.25,\n",
       "       259.4166564941406, 259.5833435058594,            259.75,\n",
       "       259.9166564941406, 260.0833435058594,            260.25,\n",
       "       260.4166564941406],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;Longitude&#x27;, length=156))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Latitude</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-f152b5ee-a8d8-407e-85bf-025d89c51e28' class='xr-index-data-in' type='checkbox'/><label for='index-f152b5ee-a8d8-407e-85bf-025d89c51e28' 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(Index([15.916666984558105,  16.08333396911621,              16.25,\n",
       "        16.41666603088379,  16.58333396911621,              16.75,\n",
       "        16.91666603088379,  17.08333396911621,              17.25,\n",
       "        17.41666603088379,\n",
       "       ...\n",
       "                    34.25,  34.41666793823242,  34.58333206176758,\n",
       "                    34.75,  34.91666793823242,  35.08333206176758,\n",
       "                    35.25,  35.41666793823242,  35.58333206176758,\n",
       "                    35.75],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;Latitude&#x27;, length=120))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>Time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b9ad6c01-2e54-4079-803e-e2953a5d62c2' class='xr-index-data-in' type='checkbox'/><label for='index-b9ad6c01-2e54-4079-803e-e2953a5d62c2' 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(DatetimeIndex([&#x27;2021-07-05 12:00:00&#x27;, &#x27;2021-07-10 12:00:00&#x27;,\n",
       "               &#x27;2021-07-15 12:00:00&#x27;, &#x27;2021-07-20 12:00:00&#x27;,\n",
       "               &#x27;2021-07-25 12:00:00&#x27;, &#x27;2021-07-30 12:00:00&#x27;,\n",
       "               &#x27;2021-08-04 12:00:00&#x27;, &#x27;2021-08-09 12:00:00&#x27;,\n",
       "               &#x27;2021-08-14 12:00:00&#x27;, &#x27;2021-08-19 12:00:00&#x27;,\n",
       "               &#x27;2021-08-24 12:00:00&#x27;, &#x27;2021-08-29 12:00:00&#x27;,\n",
       "               &#x27;2021-09-03 12:00:00&#x27;, &#x27;2021-09-08 12:00:00&#x27;,\n",
       "               &#x27;2021-09-13 12:00:00&#x27;, &#x27;2021-09-18 12:00:00&#x27;,\n",
       "               &#x27;2021-09-23 12:00:00&#x27;, &#x27;2021-09-28 12:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;Time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-56fbb511-7706-4691-bee2-7837c41b3ec1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-56fbb511-7706-4691-bee2-7837c41b3ec1' class='xr-section-summary' >Attributes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd><dt><span>long_name :</span></dt><dd>Sea Level Anomaly Estimate</dd><dt><span>standard_name :</span></dt><dd>sea_surface_height_above_sea_level</dd><dt><span>alias :</span></dt><dd>sea_surface_height_above_sea_level</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'SLA' (Time: 18, Latitude: 120, Longitude: 156)> Size: 1MB\n",
       "[336960 values with dtype=float32]\n",
       "Coordinates:\n",
       "  * Longitude  (Longitude) float32 624B 234.6 234.8 234.9 ... 260.1 260.2 260.4\n",
       "  * Latitude   (Latitude) float32 480B 15.92 16.08 16.25 ... 35.42 35.58 35.75\n",
       "  * Time       (Time) datetime64[ns] 144B 2021-07-05T12:00:00 ... 2021-09-28T...\n",
       "Attributes:\n",
       "    units:          m\n",
       "    long_name:      Sea Level Anomaly Estimate\n",
       "    standard_name:  sea_surface_height_above_sea_level\n",
       "    alias:          sea_surface_height_above_sea_level"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_from_scratch = xr.open_dataarray(s3.open(scratch_nc_file_path))\n",
    "\n",
    "ds_from_scratch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "0f3d6a7e-a72e-4a8b-80de-24604e464683",
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
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       "<script type=\"application/javascript\">(function(root) {\n",
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400px));\\n}\",{\"id\":\"p1007\"},{\"id\":\"p1005\"},{\"id\":\"p1006\"}],\"margin\":0,\"sizing_mode\":\"stretch_width\",\"align\":\"start\"}},{\"type\":\"object\",\"name\":\"panel.models.layout.Column\",\"id\":\"p1070\",\"attributes\":{\"name\":\"WidgetBox00888\",\"css_classes\":[\"panel-widget-box\"],\"stylesheets\":[\"\\n:host(.pn-loading):before, .pn-loading:before {\\n  background-color: #c3c3c3;\\n  mask-size: auto calc(min(50%, 400px));\\n  -webkit-mask-size: auto calc(min(50%, 400px));\\n}\",{\"id\":\"p1007\"},{\"type\":\"object\",\"name\":\"ImportedStyleSheet\",\"id\":\"p1079\",\"attributes\":{\"url\":\"https://cdn.holoviz.org/panel/1.4.1/dist/css/widgetbox.css\"}},{\"id\":\"p1077\"},{\"id\":\"p1005\"},{\"id\":\"p1006\"}],\"margin\":0,\"align\":[\"end\",\"center\"],\"children\":[{\"type\":\"object\",\"name\":\"panel.models.layout.Column\",\"id\":\"p1071\",\"attributes\":{\"name\":\"Column00897\",\"stylesheets\":[\"\\n:host(.pn-loading):before, .pn-loading:before {\\n  background-color: #c3c3c3;\\n  mask-size: auto calc(min(50%, 400px));\\n  -webkit-mask-size: auto calc(min(50%, 400px));\\n}\",{\"id\":\"p1007\"},{\"id\":\"p1077\"},{\"id\":\"p1005\"},{\"id\":\"p1006\"}],\"margin\":0,\"align\":\"start\",\"children\":[{\"type\":\"object\",\"name\":\"Div\",\"id\":\"p1073\",\"attributes\":{\"stylesheets\":[\"\\n:host(.pn-loading):before, .pn-loading:before {\\n  background-color: #c3c3c3;\\n  mask-size: auto calc(min(50%, 400px));\\n  -webkit-mask-size: auto calc(min(50%, 400px));\\n}\",{\"id\":\"p1007\"},{\"id\":\"p1005\"},{\"id\":\"p1006\"}],\"margin\":[5,0,0,10],\"align\":\"start\",\"text\":\"Time: <b>2021-07-05 12:00:00</b>\"}},{\"type\":\"object\",\"name\":\"Slider\",\"id\":\"p1075\",\"attributes\":{\"js_property_callbacks\":{\"type\":\"map\",\"entries\":[[\"change:value\",[{\"type\":\"object\",\"name\":\"CustomJS\",\"id\":\"p1083\",\"attributes\":{\"tags\":[[139652075986160,[null,\"value\"],[null,null]]],\"args\":{\"type\":\"map\",\"entries\":[[\"bidirectional\",false],[\"properties\",{\"type\":\"map\"}],[\"source\",{\"id\":\"p1075\"}],[\"target\",{\"id\":\"p1073\"}]]},\"code\":\"try { \\n    var labels = ['Time: <b>2021-07-05 12:00:00</b>', 'Time: <b>2021-07-10 12:00:00</b>', 'Time: <b>2021-07-15 12:00:00</b>', 'Time: <b>2021-07-20 12:00:00</b>', 'Time: <b>2021-07-25 12:00:00</b>', 'Time: <b>2021-07-30 12:00:00</b>', 'Time: <b>2021-08-04 12:00:00</b>', 'Time: <b>2021-08-09 12:00:00</b>', 'Time: <b>2021-08-14 12:00:00</b>', 'Time: <b>2021-08-19 12:00:00</b>', 'Time: <b>2021-08-24 12:00:00</b>', 'Time: <b>2021-08-29 12:00:00</b>', 'Time: <b>2021-09-03 12:00:00</b>', 'Time: <b>2021-09-08 12:00:00</b>', 'Time: <b>2021-09-13 12:00:00</b>', 'Time: <b>2021-09-18 12:00:00</b>', 'Time: <b>2021-09-23 12:00:00</b>', 'Time: <b>2021-09-28 12:00:00</b>']\\n    target.text = labels[source.value]\\n     } catch(err) { console.log(err) }\"}}]]]},\"stylesheets\":[\"\\n:host(.pn-loading):before, .pn-loading:before {\\n  background-color: #c3c3c3;\\n  mask-size: auto calc(min(50%, 400px));\\n  -webkit-mask-size: auto calc(min(50%, 400px));\\n}\",{\"id\":\"p1007\"},{\"id\":\"p1005\"},{\"id\":\"p1006\"}],\"margin\":[0,10,5,10],\"align\":\"start\",\"show_value\":false,\"tooltips\":false,\"start\":0,\"end\":17,\"value\":0}}]}}]}}]}},{\"type\":\"object\",\"name\":\"panel.models.comm_manager.CommManager\",\"id\":\"p1084\",\"attributes\":{\"plot_id\":\"p1004\",\"comm_id\":\"20ff07df81054d24bb2e120847c004df\",\"client_comm_id\":\"67d11c95f6904ccbb7b64ea9fd59bb5f\"}}],\"defs\":[{\"type\":\"model\",\"name\":\"ReactiveHTML1\"},{\"type\":\"model\",\"name\":\"FlexBox1\",\"properties\":[{\"name\":\"align_content\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"align_items\",\"kind\":\"Any\",\"default\":\"flex-start\"},{\"name\":\"flex_direction\",\"kind\":\"Any\",\"default\":\"row\"},{\"name\":\"flex_wrap\",\"kind\":\"Any\",\"default\":\"wrap\"},{\"name\":\"gap\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"justify_content\",\"kind\":\"Any\",\"default\":\"flex-start\"}]},{\"type\":\"model\",\"name\":\"FloatPanel1\",\"properties\":[{\"name\":\"config\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"contained\",\"kind\":\"Any\",\"default\":true},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"right-top\"},{\"name\":\"offsetx\",\"kind\":\"Any\",\"default\":null},{\"name\":\"offsety\",\"kind\":\"Any\",\"default\":null},{\"name\":\"theme\",\"kind\":\"Any\",\"default\":\"primary\"},{\"name\":\"status\",\"kind\":\"Any\",\"default\":\"normalized\"}]},{\"type\":\"model\",\"name\":\"GridStack1\",\"properties\":[{\"name\":\"mode\",\"kind\":\"Any\",\"default\":\"warn\"},{\"name\":\"ncols\",\"kind\":\"Any\",\"default\":null},{\"name\":\"nrows\",\"kind\":\"Any\",\"default\":null},{\"name\":\"allow_resize\",\"kind\":\"Any\",\"default\":true},{\"name\":\"allow_drag\",\"kind\":\"Any\",\"default\":true},{\"name\":\"state\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"drag1\",\"properties\":[{\"name\":\"slider_width\",\"kind\":\"Any\",\"default\":5},{\"name\":\"slider_color\",\"kind\":\"Any\",\"default\":\"black\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":50}]},{\"type\":\"model\",\"name\":\"click1\",\"properties\":[{\"name\":\"terminal_output\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"debug_name\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"clears\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"FastWrapper1\",\"properties\":[{\"name\":\"object\",\"kind\":\"Any\",\"default\":null},{\"name\":\"style\",\"kind\":\"Any\",\"default\":null}]},{\"type\":\"model\",\"name\":\"NotificationAreaBase1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"NotificationArea1\",\"properties\":[{\"name\":\"js_events\",\"kind\":\"Any\",\"default\":{\"type\":\"map\"}},{\"name\":\"notifications\",\"kind\":\"Any\",\"default\":[]},{\"name\":\"position\",\"kind\":\"Any\",\"default\":\"bottom-right\"},{\"name\":\"_clear\",\"kind\":\"Any\",\"default\":0},{\"name\":\"types\",\"kind\":\"Any\",\"default\":[{\"type\":\"map\",\"entries\":[[\"type\",\"warning\"],[\"background\",\"#ffc107\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-exclamation-triangle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]},{\"type\":\"map\",\"entries\":[[\"type\",\"info\"],[\"background\",\"#007bff\"],[\"icon\",{\"type\":\"map\",\"entries\":[[\"className\",\"fas fa-info-circle\"],[\"tagName\",\"i\"],[\"color\",\"white\"]]}]]}]}]},{\"type\":\"model\",\"name\":\"Notification\",\"properties\":[{\"name\":\"background\",\"kind\":\"Any\",\"default\":null},{\"name\":\"duration\",\"kind\":\"Any\",\"default\":3000},{\"name\":\"icon\",\"kind\":\"Any\",\"default\":null},{\"name\":\"message\",\"kind\":\"Any\",\"default\":\"\"},{\"name\":\"notification_type\",\"kind\":\"Any\",\"default\":null},{\"name\":\"_destroyed\",\"kind\":\"Any\",\"default\":false}]},{\"type\":\"model\",\"name\":\"TemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"BootstrapTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"TemplateEditor1\",\"properties\":[{\"name\":\"layout\",\"kind\":\"Any\",\"default\":[]}]},{\"type\":\"model\",\"name\":\"MaterialTemplateActions1\",\"properties\":[{\"name\":\"open_modal\",\"kind\":\"Any\",\"default\":0},{\"name\":\"close_modal\",\"kind\":\"Any\",\"default\":0}]},{\"type\":\"model\",\"name\":\"copy_to_clipboard1\",\"properties\":[{\"name\":\"fill\",\"kind\":\"Any\",\"default\":\"none\"},{\"name\":\"value\",\"kind\":\"Any\",\"default\":null}]}]}};\n",
       "  var render_items = [{\"docid\":\"cccb64e7-e2bb-4418-bdc8-f6a536a4431c\",\"roots\":{\"p1004\":\"ee8dd22c-3a55-4b9f-9fa9-7b15d1e7a141\"},\"root_ids\":[\"p1004\"]}];\n",
       "  var docs = Object.values(docs_json)\n",
       "  if (!docs) {\n",
       "    return\n",
       "  }\n",
       "  const py_version = docs[0].version.replace('rc', '-rc.').replace('.dev', '-dev.')\n",
       "  function embed_document(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  function get_bokeh(root) {\n",
       "    if (root.Bokeh === undefined) {\n",
       "      return null\n",
       "    } else if (root.Bokeh.version !== py_version) {\n",
       "      if (root.Bokeh.versions === undefined || !root.Bokeh.versions.has(py_version)) {\n",
       "\treturn null\n",
       "      }\n",
       "      return root.Bokeh.versions.get(py_version);\n",
       "    } else if (root.Bokeh.version === py_version) {\n",
       "      return root.Bokeh\n",
       "    }\n",
       "    return null\n",
       "  }\n",
       "  function is_loaded(root) {\n",
       "    var Bokeh = get_bokeh(root)\n",
       "    return (Bokeh != null && Bokeh.Panel !== undefined)\n",
       "  }\n",
       "  if (is_loaded(root)) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (is_loaded(root)) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "\t  var Bokeh = get_bokeh(root)\n",
       "\t  if (Bokeh == null || Bokeh.Panel == null) {\n",
       "            console.warn(\"Panel: ERROR: Unable to run Panel code because Bokeh or Panel library is missing\");\n",
       "\t  } else {\n",
       "\t    console.warn(\"Panel: WARNING: Attempting to render but not all required libraries could be resolved.\")\n",
       "\t    embed_document(root)\n",
       "\t  }\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       ":DynamicMap   [Time]\n",
       "   :Image   [Longitude,Latitude]   (SLA)"
      ]
     },
     "execution_count": 11,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "p1004"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_from_scratch.hvplot.image(x='Longitude', y='Latitude', cmap='RdBu', clim=(-0.5, 0.5), title=\"Sea Level Anomaly Estimate (m)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e844b0f7-8478-4d54-af8c-75e2b831d05e",
   "metadata": {},
   "source": [
    "## Move data to the persistent bucket\n",
    "\n",
    "If we decide this is a file we want to keep around for a longer time period, we can move it to our persistent bucket. We can even make a subdirectory in our persistent bucket to keep us organized:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "e4ef6258-b7cd-4493-9dd4-7591775fa952",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['openscapeshub-scratch/ateucher/.placeholder']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "persistent_dest_dir = f\"{persistent}/my-analysis-data/\"\n",
    "\n",
    "# Make directory in persistent bucket\n",
    "s3.mkdir(persistent_dest_dir)\n",
    "\n",
    "# Move the file\n",
    "s3.mv(scratch_nc_file_path, persistent_dest_dir)\n",
    "\n",
    "# Check the scratch and persistent bucket listings:\n",
    "s3.ls(scratch)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d6a41774-297c-4251-bbda-fb701858961f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['openscapeshub-persistent/ateucher/.placeholder',\n",
       " 'openscapeshub-persistent/ateucher/my-analysis-data']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3.ls(persistent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b4ec6c5e-6bc8-4cc8-85a6-a39778131129",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['openscapeshub-persistent/ateucher/my-analysis-data/test123.nc']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s3.ls(persistent_dest_dir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9bccda9-e106-4983-8d9f-020e429a970d",
   "metadata": {},
   "source": [
    "## Move existing data from `HOME` to `PERSISTENT_BUCKET`\n",
    "\n",
    "You may already have some data in your `HOME` directory that you would like to move out to a persistent bucket. You can do that using the `awsv2 s3` command line tool, which is already installed on the hub. You can open a terminal from the Hub Launcher - it will open in your `HOME` directory. You can then use the `awsv2 s3 mv` command to move a file to your bucket.\n",
    "\n",
    "#### Move a single file from `HOME` to `PERSISTENT_BUCKET`:"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3650259e-0aad-4371-9b97-e9ddbff43e0e",
   "metadata": {},
   "source": [
    "```bash\n",
    "$ awsv2 s3 mv my-big-file.nc $PERSISTENT_BUCKET/ # The trailing slash is important here\n",
    "move: ./my-big-file.nc to s3://openscapeshub-persistent/ateucher/my-big-file.nc\n",
    "``` "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da9ce114-74d2-445b-af41-26a0d6d023cc",
   "metadata": {},
   "source": [
    "#### Move a directory of data from `HOME` to `PERSISTENT`\n",
    "\n",
    "List the contents of the local `results-data` directory:\n",
    "\n",
    "```bash\n",
    "$ ls results-data/\n",
    "my-big-file1.nc  my-big-file2.nc\n",
    "```\n",
    "\n",
    "Use `awsv2 s3 mv` with the `--recursive` flag to move all files in a directory to a new directory in `PERSISTENT_BUCKET`\n",
    "\n",
    "```bash\n",
    "$ awsv2 s3 mv --recursive results-data $PERSISTENT_BUCKET/results-data/\n",
    "move: results-data/my-big-file1.nc to s3://openscapeshub-persistent/ateucher/results-data/my-big-file1.nc\n",
    "move: results-data/my-big-file2.nc to s3://openscapeshub-persistent/ateucher/results-data/my-big-file2.nc\n",
    "```"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
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