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     ]
    }
   ],
   "source": [
    "!pip install pandas matplotlib zarr fsspec s3fs intake intake_xarray intake_parquet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import intake\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.signal import stft\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import LogNorm\n",
    "\n",
    "plt.rcParams[\"font.family\"] = \"sans\"\n",
    "plt.rcParams[\"font.size\"] = 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Gallery\n",
    "\n",
    "This notebook contains example plots of data from different diagnostics as a demonstration of what sort data is available in the archive. \n",
    "\n",
    "First we need to find the url to a particular shot. Here we are going to use shot 30420 as an example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "shot_id = 30420\n",
    "catalog  = intake.open_catalog('https://mastapp.site/intake/catalog.yml')\n",
    "shot_df = pd.DataFrame(catalog.index.level1.shots().read())\n",
    "url = shot_df.loc[shot_df.shot_id == shot_id].iloc[0].url"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plasma Current Data\n",
    "\n",
    "Data from the `amc` source contains\n",
    " - Plasma Current ($I_p$): Flows within the plasma, providing initial heating and contributing to the poloidal magnetic field for confinement and stability.\n",
    " - PF Coil Currents: Control the poloidal magnetic field, allowing for plasma shaping, vertical stability, and edge magnetic configuration control.\n",
    " - TF Coil Currents: Generate the strong toroidal magnetic field necessary for primary plasma confinement."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rt2549/miniconda3/envs/mast-book/lib/python3.11/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  'dims': dict(self._ds.dims),\n"
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       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
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       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
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       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
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       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
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       "\n",
       ".xr-index-preview {\n",
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       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
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       "  text-overflow: ellipsis;\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
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       "\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",
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       "\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: 5MB\n",
       "Dimensions:            (time: 30000)\n",
       "Coordinates:\n",
       "  * time               (time) float32 120kB -2.0 -2.0 -2.0 ... 3.999 4.0 4.0\n",
       "Data variables: (12/46)\n",
       "    efps_current       (time) float32 120kB dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;\n",
       "    error_field_02     (time) float32 120kB dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;\n",
       "    error_field_05     (time) float32 120kB dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;\n",
       "    p2il_coil_current  (time) float32 120kB dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;\n",
       "    p2il_feed_current  (time) float32 120kB dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;\n",
       "    p2iu_coil_current  (time) float32 120kB dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;\n",
       "    ...                 ...\n",
       "    p6u_current        (time) float32 120kB dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;\n",
       "    plasma_current     (time) float32 120kB dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;\n",
       "    sol_current        (time) float32 120kB dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;\n",
       "    status             float32 4B ...\n",
       "    tf_current         (time) float32 120kB dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;\n",
       "    version            float32 4B ...\n",
       "Attributes:\n",
       "    description:  Plasma Current and PF/TF Coil Currents\n",
       "    file_name:    amc0304.20\n",
       "    format:       IDA3\n",
       "    mds_name:     None\n",
       "    name:         amc\n",
       "    quality:      Not Checked\n",
       "    shot_id:      30420\n",
       "    signal_type:  Analysed\n",
       "    source:       amc\n",
       "    uda_name:     AMC\n",
       "    uuid:         01aad0c4-2a84-59e2-8b1b-168b4bd66aa3\n",
       "    version:      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-33e47763-3497-44e6-ae34-48385b89a259' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-33e47763-3497-44e6-ae34-48385b89a259' 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>: 30000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-aa3baa92-eca9-414e-a1fd-cfda27e4caf4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-aa3baa92-eca9-414e-a1fd-cfda27e4caf4' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-2.0 -2.0 -2.0 ... 3.999 4.0 4.0</div><input id='attrs-59a12608-ba19-45b2-be91-10d23d3d5b03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59a12608-ba19-45b2-be91-10d23d3d5b03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a6a6699-c3eb-4390-b419-6118dcc1a762' class='xr-var-data-in' type='checkbox'><label for='data-5a6a6699-c3eb-4390-b419-6118dcc1a762' 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>s</dd></dl></div><div class='xr-var-data'><pre>array([-2.      , -1.9998  , -1.9996  , ...,  3.999399,  3.9996  ,  3.999799],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-250f7d40-5bd4-4d42-94fc-49a37dbf7d1a' class='xr-section-summary-in' type='checkbox'  ><label for='section-250f7d40-5bd4-4d42-94fc-49a37dbf7d1a' class='xr-section-summary' >Data variables: <span>(46)</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>efps_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-7ea25bbb-72b6-41f3-8144-dc1c7dfd5dbf' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7ea25bbb-72b6-41f3-8144-dc1c7dfd5dbf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-31293c1d-fe0a-4c22-96c4-305364c97ca4' class='xr-var-data-in' type='checkbox'><label for='data-31293c1d-fe0a-4c22-96c4-305364c97ca4' 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>description :</span></dt><dd>EFPS current measured at input to P2 link board</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>EFPS Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:EFPS_CURRENT</dd><dt><span>name :</span></dt><dd>amc/efps_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_EFPS CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>184cb059-3b20-5ff2-87fb-959ce07c42d0</dd><dt><span>version :</span></dt><dd>0</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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 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",
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       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
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       "  <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>error_field_02</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-20add3a7-9055-4379-a682-b90b5dd05d10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-20add3a7-9055-4379-a682-b90b5dd05d10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bf2dcaa-eadb-4950-9fd6-36a636f97467' class='xr-var-data-in' type='checkbox'><label for='data-3bf2dcaa-eadb-4950-9fd6-36a636f97467' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Error Field/02</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.ERROR_FIELD:02</dd><dt><span>name :</span></dt><dd>amc/error_field_02</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_ERROR FIELD/02</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>a48b22d3-dc5b-57cc-805b-e2875e4b4783</dd><dt><span>version :</span></dt><dd>0</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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 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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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>error_field_05</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-8b414b56-8d97-4d2f-a239-4595e5d8eac6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b414b56-8d97-4d2f-a239-4595e5d8eac6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec93d0d4-72b8-4d45-91df-43124544a135' class='xr-var-data-in' type='checkbox'><label for='data-ec93d0d4-72b8-4d45-91df-43124544a135' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Error Field/05</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.ERROR_FIELD:05</dd><dt><span>name :</span></dt><dd>amc/error_field_05</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_ERROR FIELD/05</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>a2a22abc-b6dd-5115-87b9-a3106ff30110</dd><dt><span>version :</span></dt><dd>0</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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p2il_coil_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-373a7deb-46ca-4d8b-a4ad-d3bd23f6cd75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-373a7deb-46ca-4d8b-a4ad-d3bd23f6cd75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-523f0c83-e179-4651-a0c5-1c6f4cc13681' class='xr-var-data-in' type='checkbox'><label for='data-523f0c83-e179-4651-a0c5-1c6f4cc13681' 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>description :</span></dt><dd>P2 inner lower current (pseudo coil current signal used by EFIT)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P2IL Coil Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P2IL_COIL:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p2il_coil_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P2IL COIL CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>63691830-c58d-5f03-a811-5601bca0e0b9</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p2il_feed_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-6cd109e4-195f-4934-821b-af138e0a9297' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6cd109e4-195f-4934-821b-af138e0a9297' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6388e992-d77a-42d3-8720-7af817eb8b05' class='xr-var-data-in' type='checkbox'><label for='data-6388e992-d77a-42d3-8720-7af817eb8b05' 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>description :</span></dt><dd>P2L inner winding current - measured at P2 link board</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P2il Feed Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P2IL_FEED:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p2il_feed_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P2IL FEED CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>af5c935e-ce9a-5b8f-b7be-2e97726f314a</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p2iu_coil_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-87f3f70d-2914-47d3-b94d-4041bc8f6747' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87f3f70d-2914-47d3-b94d-4041bc8f6747' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-936e47b0-f533-444c-b4a6-513a7b7f1942' class='xr-var-data-in' type='checkbox'><label for='data-936e47b0-f533-444c-b4a6-513a7b7f1942' 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>description :</span></dt><dd>P2 inner upper feed current (pseudo coil current signal used by EFIT)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P2IU Coil Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P2IU_COIL:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p2iu_coil_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P2IU COIL CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>26141f74-c0b6-5718-b28f-b40765ca326e</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p2iu_feed_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-e53097c2-61d2-4c8a-b45a-aa3bcd8d42fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e53097c2-61d2-4c8a-b45a-aa3bcd8d42fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a25f828d-c39d-40cb-b5c6-dfa15536d0e9' class='xr-var-data-in' type='checkbox'><label for='data-a25f828d-c39d-40cb-b5c6-dfa15536d0e9' 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>description :</span></dt><dd>P2U inner winding current - measured at P2 link board</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P2iu Feed Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P2IU_FEED:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p2iu_feed_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P2IU FEED CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>6f129101-1a1b-5d41-afa3-01b38587dafb</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p2l_case_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-67d49467-f80e-449c-ac42-8be3379b349a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67d49467-f80e-449c-ac42-8be3379b349a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57ce7254-3d1f-41f1-8b42-6ea75073a833' class='xr-var-data-in' type='checkbox'><label for='data-57ce7254-3d1f-41f1-8b42-6ea75073a833' 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>description :</span></dt><dd>P2 lower case current (pseudo coil current signal used by EFIT)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P2L Case Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P2L_CASE:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p2l_case_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P2L CASE CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>99aad5b5-f691-53fb-9b50-cfca47478c1e</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p2ol_coil_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-4f761473-58be-4b5d-85ec-c78782752b73' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4f761473-58be-4b5d-85ec-c78782752b73' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4b08df42-29f7-4660-af4a-5c0a1b54e649' class='xr-var-data-in' type='checkbox'><label for='data-4b08df42-29f7-4660-af4a-5c0a1b54e649' 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>description :</span></dt><dd>P2 outer lower current (pseudo coil current signal used by EFIT)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P2OL Coil Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P2OL_COIL:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p2ol_coil_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P2OL COIL CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>22c92239-552f-5da4-aed1-4595f9998392</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
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       "                        <td> 117.19 kiB </td>\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>p2ou_coil_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-baa5f480-35b3-4ea2-99ca-2e8fc4b8af3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-baa5f480-35b3-4ea2-99ca-2e8fc4b8af3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-eb3a8408-a7f9-4b7e-b84e-9a9e6dbd8976' class='xr-var-data-in' type='checkbox'><label for='data-eb3a8408-a7f9-4b7e-b84e-9a9e6dbd8976' 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>description :</span></dt><dd>P2 outer upper feed current (pseudo coil current signal used by EFIT)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P2OU Coil Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P2OU_COIL:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p2ou_coil_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P2OU COIL CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>f4963f6c-a084-574a-ad4e-58ab7c977b29</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (30000,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p2ou_feed_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-6b72b112-51f4-4f33-b2a3-4f18a70472c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b72b112-51f4-4f33-b2a3-4f18a70472c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-24c07fe0-0848-4380-a5d7-1ad40ee44de5' class='xr-var-data-in' type='checkbox'><label for='data-24c07fe0-0848-4380-a5d7-1ad40ee44de5' 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>description :</span></dt><dd>P2U outer winding current - measured at P2 link board</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P2ou Feed Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P2OU_FEED:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p2ou_feed_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P2OU FEED CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>14f2d15f-7449-5d82-9269-3543d772d2b3</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
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       "                        <td> (30000,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p2u_case_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-67bbb53d-e51d-41c8-a8f3-4cc210e03f57' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67bbb53d-e51d-41c8-a8f3-4cc210e03f57' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2cc5b5a7-b8f3-47f5-bf09-c27d58ec7c4f' class='xr-var-data-in' type='checkbox'><label for='data-2cc5b5a7-b8f3-47f5-bf09-c27d58ec7c4f' 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>description :</span></dt><dd>P2 upper case current (pseudo coil current signal used by EFIT)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P2U Case Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P2U_CASE:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p2u_case_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P2U CASE CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>dc9cf0aa-73df-5b8a-ae76-28784eda0ffd</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
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       "                        <td> (30000,) </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p2u_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-0de088ce-3f49-4498-8a74-cff7f51bea78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0de088ce-3f49-4498-8a74-cff7f51bea78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42ecdaae-6c04-4dd4-b2cb-f3c15409b07e' class='xr-var-data-in' type='checkbox'><label for='data-42ecdaae-6c04-4dd4-b2cb-f3c15409b07e' 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>description :</span></dt><dd>Internal P2U current</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P2U Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:P2U_CURRENT</dd><dt><span>name :</span></dt><dd>amc/p2u_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P2U CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>1f335921-0caa-5a97-9c20-66979363b26f</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (30000,) </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 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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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p3l_case_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-0f4304bc-8100-4c89-82b4-35271a38473d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0f4304bc-8100-4c89-82b4-35271a38473d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9d16a3d-65e5-4635-ab1b-81fef3e18d5e' class='xr-var-data-in' type='checkbox'><label for='data-b9d16a3d-65e5-4635-ab1b-81fef3e18d5e' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P3L Case Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P3L_CASE:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p3l_case_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P3L CASE CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>23c0924c-2ee4-5cc6-8e2d-03ac857efeef</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p3l_coil_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-413d5ed8-29b2-4b60-816e-672cc5ddac35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-413d5ed8-29b2-4b60-816e-672cc5ddac35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-503d41b7-896c-43e3-978e-9800be3779c8' class='xr-var-data-in' type='checkbox'><label for='data-503d41b7-896c-43e3-978e-9800be3779c8' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P3L Coil Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P3L_COIL:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p3l_coil_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P3L COIL CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>d5f3c8aa-7335-5260-92cd-0392ea4bc035</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p3l_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-3a71c2f4-7477-40ac-aaa8-c3b454915784' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a71c2f4-7477-40ac-aaa8-c3b454915784' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e666484-608c-4756-beaf-510100bcef97' class='xr-var-data-in' type='checkbox'><label for='data-2e666484-608c-4756-beaf-510100bcef97' 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>description :</span></dt><dd>Internal P3L current</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P3L Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:P3L_CURRENT</dd><dt><span>name :</span></dt><dd>amc/p3l_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P3L CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>9a9fb709-4ca7-5ccb-8697-bffe44d9583f</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
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       "        </td>\n",
       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p3l_feed_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-4462fd05-c6e5-4668-b97c-40efcc371a75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4462fd05-c6e5-4668-b97c-40efcc371a75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cb19ac6e-795a-4095-992c-204c7f3d9803' class='xr-var-data-in' type='checkbox'><label for='data-cb19ac6e-795a-4095-992c-204c7f3d9803' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P3l Feed Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P3L_FEED:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p3l_feed_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P3L FEED CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>2378bca5-a554-56b0-838e-b29f99803b2c</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p3u_case_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-427f098a-54c2-46f5-a78b-b03a186188b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-427f098a-54c2-46f5-a78b-b03a186188b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-128b3c7a-c860-4cf8-af16-8cdaa7b87e48' class='xr-var-data-in' type='checkbox'><label for='data-128b3c7a-c860-4cf8-af16-8cdaa7b87e48' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P3U Case Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P3U_CASE:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p3u_case_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P3U CASE CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>ba240fad-6013-5da0-8e24-f416ed793b94</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
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       "                        <td> (30000,) </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p3u_coil_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-f61f8a9c-fa35-4dd2-9b4d-c92a00c9d4a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f61f8a9c-fa35-4dd2-9b4d-c92a00c9d4a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cb78a93-59b0-4ab0-b562-0fd1a9b475f5' class='xr-var-data-in' type='checkbox'><label for='data-5cb78a93-59b0-4ab0-b562-0fd1a9b475f5' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P3U Coil Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P3U_COIL:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p3u_coil_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P3U COIL CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>3b4af51c-ed63-5349-9eed-d913f4ba2afa</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p3u_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-5189a55f-993b-4096-8c10-2280d31a719c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5189a55f-993b-4096-8c10-2280d31a719c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9d418c8e-82e3-4196-b1a1-47f3d134ce30' class='xr-var-data-in' type='checkbox'><label for='data-9d418c8e-82e3-4196-b1a1-47f3d134ce30' 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>description :</span></dt><dd>Internal P3U current</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P3U Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:P3U_CURRENT</dd><dt><span>name :</span></dt><dd>amc/p3u_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P3U CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>4cf1388c-8f2e-5c30-93c6-d2b7d40da40b</dd><dt><span>version :</span></dt><dd>0</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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p3u_feed_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-a01d6e73-d699-48f2-8ec4-81c4e4d61dc8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a01d6e73-d699-48f2-8ec4-81c4e4d61dc8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d7f4255-a7d3-4e2d-ad96-6c3fa501c5fc' class='xr-var-data-in' type='checkbox'><label for='data-0d7f4255-a7d3-4e2d-ad96-6c3fa501c5fc' 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>description :</span></dt><dd>P3U winding current - measured at P3/4/5 link board</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P3u Feed Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P3U_FEED:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p3u_feed_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P3U FEED CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>88fe288b-25c1-59f8-949a-a54e2cb360d7</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 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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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p4l_case_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-f3240fc7-a511-4c36-b464-4af87287f55b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f3240fc7-a511-4c36-b464-4af87287f55b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3bc7ef76-defc-43ed-a578-02a4aa5d5bdf' class='xr-var-data-in' type='checkbox'><label for='data-3bc7ef76-defc-43ed-a578-02a4aa5d5bdf' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P4L Case Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P4L_CASE:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p4l_case_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P4L CASE CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>a28f9810-827c-56d9-8622-7ca19671d44d</dd><dt><span>version :</span></dt><dd>0</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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 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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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p4l_coil_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-11803f3f-10af-4eb5-8960-8df268fc4551' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-11803f3f-10af-4eb5-8960-8df268fc4551' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a23e3143-449e-4865-b502-609cc3395aca' class='xr-var-data-in' type='checkbox'><label for='data-a23e3143-449e-4865-b502-609cc3395aca' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P4L Coil Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P4L_COIL:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p4l_coil_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P4L COIL CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>7ddc00ba-98cc-518e-93d9-50ba85196fc9</dd><dt><span>version :</span></dt><dd>0</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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 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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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p4l_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-251bbbbf-2d7a-4f5a-bfa5-f1884496842d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-251bbbbf-2d7a-4f5a-bfa5-f1884496842d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1ec8d664-74b1-4555-afe3-9c8937e7c075' class='xr-var-data-in' type='checkbox'><label for='data-1ec8d664-74b1-4555-afe3-9c8937e7c075' 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>description :</span></dt><dd>Internal P4L current</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P4L Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:P4L_CURRENT</dd><dt><span>name :</span></dt><dd>amc/p4l_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P4L CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>12619bbb-9d72-5784-82d3-041b1a0678cc</dd><dt><span>version :</span></dt><dd>0</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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p4l_feed_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-49fabe8e-2962-4409-a7c5-6d048b4f2b53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49fabe8e-2962-4409-a7c5-6d048b4f2b53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9cd84589-8da2-47e0-bf2a-1c70cba4bf5c' class='xr-var-data-in' type='checkbox'><label for='data-9cd84589-8da2-47e0-bf2a-1c70cba4bf5c' 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>description :</span></dt><dd>P4L winding current - measured at P3/4/5 link board</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P4l Feed Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P4L_FEED:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p4l_feed_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P4L FEED CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>8cd586d7-3ee9-5eb8-b00f-e1f4431d4f27</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p4u_case_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-1a631a57-5fe5-49ad-80b5-bcd4dc4e3754' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a631a57-5fe5-49ad-80b5-bcd4dc4e3754' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42914699-c8f1-486f-ab22-3cc790e5675b' class='xr-var-data-in' type='checkbox'><label for='data-42914699-c8f1-486f-ab22-3cc790e5675b' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P4U Case Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P4U_CASE:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p4u_case_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P4U CASE CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>bab05664-c0cf-5bbd-a0ee-2bcc19899f4a</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p4u_coil_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-698abfc4-71ae-4a0a-9377-3dbe1c5fbeb5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-698abfc4-71ae-4a0a-9377-3dbe1c5fbeb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4d29207b-35a1-4b07-a39b-f2afb481bc7a' class='xr-var-data-in' type='checkbox'><label for='data-4d29207b-35a1-4b07-a39b-f2afb481bc7a' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P4U Coil Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P4U_COIL:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p4u_coil_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P4U COIL CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>83df05ea-5dd2-5d55-af2c-f6f62880f5b9</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p4u_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-30547405-0f17-42c6-9534-3fb2ad9e13fe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-30547405-0f17-42c6-9534-3fb2ad9e13fe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f007d562-83c1-48f6-a384-bbaaded42fae' class='xr-var-data-in' type='checkbox'><label for='data-f007d562-83c1-48f6-a384-bbaaded42fae' 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>description :</span></dt><dd>Internal P4U current</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P4U Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:P4U_CURRENT</dd><dt><span>name :</span></dt><dd>amc/p4u_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P4U CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>c8019ebe-400c-5846-8233-80b1d08a9e03</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </td>\n",
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       "                        <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\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p4u_feed_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-6efd4f5c-830f-47c4-bcd8-6a82d06b7406' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6efd4f5c-830f-47c4-bcd8-6a82d06b7406' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fe85f70-6217-46f4-b9d8-c3eadb504332' class='xr-var-data-in' type='checkbox'><label for='data-0fe85f70-6217-46f4-b9d8-c3eadb504332' 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>description :</span></dt><dd>P4U winding current - measured at P3/4/5 link board</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P4u Feed Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P4U_FEED:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p4u_feed_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P4U FEED CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>fbfe5693-2fa1-577b-8567-76261174b0c4</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p5l_case_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-897f5cba-3d65-4881-a02d-80b64deccd62' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-897f5cba-3d65-4881-a02d-80b64deccd62' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fcb32469-dcab-44f3-a80a-e52a3eae85d8' class='xr-var-data-in' type='checkbox'><label for='data-fcb32469-dcab-44f3-a80a-e52a3eae85d8' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P5L Case Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P5L_CASE:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p5l_case_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P5L CASE CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>94e8a842-4c13-56c3-b581-1279db9762a0</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p5l_coil_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-33922466-b722-4210-9140-5de99d06d334' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-33922466-b722-4210-9140-5de99d06d334' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-73af1095-0cfc-48cb-b05c-02bd09633a4b' class='xr-var-data-in' type='checkbox'><label for='data-73af1095-0cfc-48cb-b05c-02bd09633a4b' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P5L Coil Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P5L_COIL:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p5l_coil_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P5L COIL CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>1f786b53-7971-54ae-a013-f07405e398ca</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </td>\n",
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       "                        <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\"> float32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p5l_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-3e3c3a3c-724e-445a-a92c-c1a6083bbbb9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e3c3a3c-724e-445a-a92c-c1a6083bbbb9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ff32df7-4ae4-4e61-a3bf-8c1e578d7e58' class='xr-var-data-in' type='checkbox'><label for='data-6ff32df7-4ae4-4e61-a3bf-8c1e578d7e58' 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>description :</span></dt><dd>Internal P5L current</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P5L Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:P5L_CURRENT</dd><dt><span>name :</span></dt><dd>amc/p5l_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P5L CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>7f0ab6cd-61cd-502e-af7e-646dc4e7cf12</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (30000,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p5l_feed_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-698c0f1b-90ec-4b20-b30c-d0ec8dbbc1f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-698c0f1b-90ec-4b20-b30c-d0ec8dbbc1f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cf35f91-5a49-404e-803e-75fa9b5966ce' class='xr-var-data-in' type='checkbox'><label for='data-5cf35f91-5a49-404e-803e-75fa9b5966ce' 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>description :</span></dt><dd>P5L winding current - measured at P3/4/5 link board</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P5l Feed Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P5L_FEED:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p5l_feed_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P5L FEED CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>0d4b200c-35e6-52d0-b759-297842b2dcd3</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                    <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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p5u_case_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-dec49f25-2b90-439e-a380-170fa8edd773' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dec49f25-2b90-439e-a380-170fa8edd773' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a124e6b-56fa-4d36-aa09-8209d021be31' class='xr-var-data-in' type='checkbox'><label for='data-6a124e6b-56fa-4d36-aa09-8209d021be31' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P5U Case Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P5U_CASE:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p5u_case_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P5U CASE CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>2b648bdd-e3a8-53b7-9b10-47cd1208bd4a</dd><dt><span>version :</span></dt><dd>0</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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p5u_coil_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-b4c094e4-39fc-4cc6-9a53-528368ce7d4d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b4c094e4-39fc-4cc6-9a53-528368ce7d4d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3c031739-a456-47cb-847c-dda334528b01' class='xr-var-data-in' type='checkbox'><label for='data-3c031739-a456-47cb-847c-dda334528b01' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P5U Coil Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P5U_COIL:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p5u_coil_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P5U COIL CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>346777d8-9808-5b84-883a-5ed522a96f42</dd><dt><span>version :</span></dt><dd>0</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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p5u_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-10910e24-ef75-47eb-93eb-c2c56ed4413d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-10910e24-ef75-47eb-93eb-c2c56ed4413d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5020d9ac-2e25-4bf5-a20e-5643ece541b9' class='xr-var-data-in' type='checkbox'><label for='data-5020d9ac-2e25-4bf5-a20e-5643ece541b9' 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>description :</span></dt><dd>Internal P5U current</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P5U Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:P5U_CURRENT</dd><dt><span>name :</span></dt><dd>amc/p5u_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P5U CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>ffb66909-326a-5dd9-915c-3257b0afe32f</dd><dt><span>version :</span></dt><dd>0</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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p5u_feed_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-31f05be0-a01e-416e-84b0-492718e6d539' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31f05be0-a01e-416e-84b0-492718e6d539' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68492dca-9eca-4ec6-8cf5-ad8e6d5e9b94' class='xr-var-data-in' type='checkbox'><label for='data-68492dca-9eca-4ec6-8cf5-ad8e6d5e9b94' 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>description :</span></dt><dd>P5U winding current - measured at P3/4/5 link board</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P5u Feed Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.P5U_FEED:CURRENT</dd><dt><span>name :</span></dt><dd>amc/p5u_feed_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P5U FEED CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>919e1597-f986-5556-b427-df2dc2d6d075</dd><dt><span>version :</span></dt><dd>0</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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p6l_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-4bc3a482-c039-478e-a280-5e272e9fc0b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bc3a482-c039-478e-a280-5e272e9fc0b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d7cb50c-c7db-4427-8fc1-279a404de620' class='xr-var-data-in' type='checkbox'><label for='data-7d7cb50c-c7db-4427-8fc1-279a404de620' 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>description :</span></dt><dd>Internal P6L current</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P6L Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:P6L_CURRENT</dd><dt><span>name :</span></dt><dd>amc/p6l_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P6L CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>f2032ccb-441e-5241-9a9e-ba1d37c5f437</dd><dt><span>version :</span></dt><dd>0</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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p6u_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-7d09c479-055a-4b27-87b9-0f1fc0039130' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d09c479-055a-4b27-87b9-0f1fc0039130' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9c22660-86a0-44e6-b638-a3e87018fec2' class='xr-var-data-in' type='checkbox'><label for='data-d9c22660-86a0-44e6-b638-a3e87018fec2' 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>description :</span></dt><dd>Internal P6U current</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P6U Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:P6U_CURRENT</dd><dt><span>name :</span></dt><dd>amc/p6u_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_P6U CURRENT</dd><dt><span>units :</span></dt><dd>kA * turn</dd><dt><span>uuid :</span></dt><dd>e48ade2b-11e4-5793-9ad0-0a754c0aa488</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (30000,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plasma_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-e2c56ca1-250a-4445-bdbe-c4dc2896421f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2c56ca1-250a-4445-bdbe-c4dc2896421f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad505084-2328-4f91-8aeb-97479e108134' class='xr-var-data-in' type='checkbox'><label for='data-ad505084-2328-4f91-8aeb-97479e108134' 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>description :</span></dt><dd>Plasma Current</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Plasma Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC.PLASMA:CURRENT</dd><dt><span>name :</span></dt><dd>amc/plasma_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_PLASMA CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>6327e756-e587-5c74-8aed-4e595d3546cf</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 117.19 kiB </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 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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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sol_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-22da562d-f025-4aa5-b286-7262645f2f69' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22da562d-f025-4aa5-b286-7262645f2f69' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc28751c-4e39-4e28-86e2-a215c2e26649' class='xr-var-data-in' type='checkbox'><label for='data-fc28751c-4e39-4e28-86e2-a215c2e26649' 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>description :</span></dt><dd>P1PS - Solenoid Feed Current</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Sol Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:SOL_CURRENT</dd><dt><span>name :</span></dt><dd>amc/sol_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_SOL CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>b2b6c10b-15cf-5a15-82e8-2a53e5be144d</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3ec1f933-dc29-487f-9bc9-84ba94c179bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ec1f933-dc29-487f-9bc9-84ba94c179bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8523749-0be0-4b26-8e2b-0bf5450a3e0d' class='xr-var-data-in' type='checkbox'><label for='data-c8523749-0be0-4b26-8e2b-0bf5450a3e0d' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd> </dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:STATUS</dd><dt><span>name :</span></dt><dd>amc/status</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_STATUS</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>19528798-cd4d-5799-90e5-528d5b275b0c</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tf_current</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(30000,), meta=np.ndarray&gt;</div><input id='attrs-4469feeb-f772-4a86-aa09-dd560dfe1b76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4469feeb-f772-4a86-aa09-dd560dfe1b76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ca671819-1b04-419d-8762-43bf4e8b636a' class='xr-var-data-in' type='checkbox'><label for='data-ca671819-1b04-419d-8762-43bf4e8b636a' 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>description :</span></dt><dd>TF feed current measured in north duct</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>TF Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:TF_CURRENT</dd><dt><span>name :</span></dt><dd>amc/tf_current</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[30000]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_TF CURRENT</dd><dt><span>units :</span></dt><dd>kA</dd><dt><span>uuid :</span></dt><dd>50cd98f3-8f4d-543d-be7a-bf06e87c477e</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <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> 117.19 kiB </td>\n",
       "                        <td> 117.19 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (30000,) </td>\n",
       "                        <td> (30000,) </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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\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\" >30000</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>version</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c9a9514d-6691-4f0a-bc09-eb8e079cd6fd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c9a9514d-6691-4f0a-bc09-eb8e079cd6fd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ca8cc29-aa16-40b3-9015-7f1caebc8e4b' class='xr-var-data-in' type='checkbox'><label for='data-7ca8cc29-aa16-40b3-9015-7f1caebc8e4b' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd> </dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AMC:VERSION</dd><dt><span>name :</span></dt><dd>amc/version</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AMC_VERSION</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>723bd0c7-5695-5576-8f7f-2203f6797ee4</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-16edc01e-06bf-4117-8292-99224101e5c5' class='xr-section-summary-in' type='checkbox'  ><label for='section-16edc01e-06bf-4117-8292-99224101e5c5' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a59e620f-653a-4036-b689-582fceee9a3e' class='xr-index-data-in' type='checkbox'/><label for='index-a59e620f-653a-4036-b689-582fceee9a3e' 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([ -2.000000238418579,  -1.999800205230713, -1.9996002912521362,\n",
       "         -1.99940025806427, -1.9992002248764038, -1.9990001916885376,\n",
       "        -1.998800277709961, -1.9986002445220947, -1.9984002113342285,\n",
       "       -1.9982002973556519,\n",
       "       ...\n",
       "         3.997999429702759,   3.998199701309204,   3.998399496078491,\n",
       "        3.9985997676849365,  3.9987995624542236,   3.998999834060669,\n",
       "         3.999199628829956,   3.999399423599243,  3.9995996952056885,\n",
       "        3.9997994899749756],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;time&#x27;, length=30000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-221eb1ec-de51-4826-ac15-373a806bbf0b' class='xr-section-summary-in' type='checkbox'  ><label for='section-221eb1ec-de51-4826-ac15-373a806bbf0b' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Plasma Current and PF/TF Coil Currents</dd><dt><span>file_name :</span></dt><dd>amc0304.20</dd><dt><span>format :</span></dt><dd>IDA3</dd><dt><span>mds_name :</span></dt><dd>None</dd><dt><span>name :</span></dt><dd>amc</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>amc</dd><dt><span>uda_name :</span></dt><dd>AMC</dd><dt><span>uuid :</span></dt><dd>01aad0c4-2a84-59e2-8b1b-168b4bd66aa3</dd><dt><span>version :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 5MB\n",
       "Dimensions:            (time: 30000)\n",
       "Coordinates:\n",
       "  * time               (time) float32 120kB -2.0 -2.0 -2.0 ... 3.999 4.0 4.0\n",
       "Data variables: (12/46)\n",
       "    efps_current       (time) float32 120kB dask.array<chunksize=(30000,), meta=np.ndarray>\n",
       "    error_field_02     (time) float32 120kB dask.array<chunksize=(30000,), meta=np.ndarray>\n",
       "    error_field_05     (time) float32 120kB dask.array<chunksize=(30000,), meta=np.ndarray>\n",
       "    p2il_coil_current  (time) float32 120kB dask.array<chunksize=(30000,), meta=np.ndarray>\n",
       "    p2il_feed_current  (time) float32 120kB dask.array<chunksize=(30000,), meta=np.ndarray>\n",
       "    p2iu_coil_current  (time) float32 120kB dask.array<chunksize=(30000,), meta=np.ndarray>\n",
       "    ...                 ...\n",
       "    p6u_current        (time) float32 120kB dask.array<chunksize=(30000,), meta=np.ndarray>\n",
       "    plasma_current     (time) float32 120kB dask.array<chunksize=(30000,), meta=np.ndarray>\n",
       "    sol_current        (time) float32 120kB dask.array<chunksize=(30000,), meta=np.ndarray>\n",
       "    status             float32 4B ...\n",
       "    tf_current         (time) float32 120kB dask.array<chunksize=(30000,), meta=np.ndarray>\n",
       "    version            float32 4B ...\n",
       "Attributes:\n",
       "    description:  Plasma Current and PF/TF Coil Currents\n",
       "    file_name:    amc0304.20\n",
       "    format:       IDA3\n",
       "    mds_name:     None\n",
       "    name:         amc\n",
       "    quality:      Not Checked\n",
       "    shot_id:      30420\n",
       "    signal_type:  Analysed\n",
       "    source:       amc\n",
       "    uda_name:     AMC\n",
       "    uuid:         01aad0c4-2a84-59e2-8b1b-168b4bd66aa3\n",
       "    version:      0"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = catalog.level1.shots(url=url, group='amc')\n",
    "dataset = dataset.to_dask()\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1000x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = dataset.isel(time=(dataset.time > 0) & (dataset.time < .35))\n",
    "fig, axes = plt.subplots(3, 1, figsize=(10, 6))\n",
    "ax1, ax2, ax3 = axes.flatten()\n",
    "\n",
    "ax1.plot(dataset['time'], dataset['plasma_current'])\n",
    "ax1.set_xlabel('Time (s)')\n",
    "ax1.set_ylabel('Plasma Current $I_p$ (kA)')\n",
    "\n",
    "ax2.plot(dataset['time'], dataset['sol_current'])\n",
    "ax2.set_xlabel('Time (s)')\n",
    "ax2.set_ylabel('Solenoid Feed Current (kA)')\n",
    "\n",
    "ax3.plot(dataset['time'], dataset['tf_current'])\n",
    "ax3.set_xlabel('Time (s)')\n",
    "ax3.set_ylabel('TF Feed Current (kA)')\n",
    "\n",
    "for ax in axes:\n",
    "    ax.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Thompson Scattering Data\n",
    "\n",
    "`ayc` source holds the Thomspon Scattering data at the core. Thomson scattering diagnostics provide accurate measurements of electron temperature and density."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rt2549/miniconda3/envs/mast-book/lib/python3.11/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  'dims': dict(self._ds.dims),\n"
     ]
    },
    {
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       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".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: 3MB\n",
       "Dimensions:              (time: 146, arb: 130, radial_index: 130,\n",
       "                          spectral_index: 4)\n",
       "Coordinates:\n",
       "  * arb                  (arb) float32 520B 0.0 1.0 2.0 ... 127.0 128.0 129.0\n",
       "  * radial_index         (radial_index) float32 520B 1.0 2.0 3.0 ... 129.0 130.0\n",
       "  * spectral_index       (spectral_index) float32 16B 1.0 2.0 3.0 4.0\n",
       "  * time                 (time) float32 584B 0.0 0.004166 0.008332 ... 71.0 72.0\n",
       "Data variables: (12/37)\n",
       "    acqiris_time         (time) float32 584B dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;\n",
       "    angle                (arb) float32 520B dask.array&lt;chunksize=(130,), meta=np.ndarray&gt;\n",
       "    aspectra             (time, radial_index, spectral_index) float32 304kB dask.array&lt;chunksize=(73, 130, 4), meta=np.ndarray&gt;\n",
       "    chi2                 (time, radial_index) float32 76kB dask.array&lt;chunksize=(146, 130), meta=np.ndarray&gt;\n",
       "    covariance_ne_te     (time, radial_index) float32 76kB dask.array&lt;chunksize=(146, 130), meta=np.ndarray&gt;\n",
       "    instrument_dr        (time, radial_index) float32 76kB dask.array&lt;chunksize=(146, 130), meta=np.ndarray&gt;\n",
       "    ...                   ...\n",
       "    time_                (time) float32 584B dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;\n",
       "    version_fibre        float32 4B ...\n",
       "    version_poly         float32 4B ...\n",
       "    version_raman        float32 4B ...\n",
       "    xyc_time             (time) float32 584B dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;\n",
       "    yag_nelint           (time) float32 584B dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    description:  Core Thomson scattering data\n",
       "    file_name:    ayc0304.20\n",
       "    format:       IDA3\n",
       "    mds_name:     None\n",
       "    name:         ayc\n",
       "    quality:      Not Checked\n",
       "    shot_id:      30420\n",
       "    signal_type:  Analysed\n",
       "    source:       ayc\n",
       "    uda_name:     AYC\n",
       "    uuid:         8d043ece-8bf8-5af8-87e4-d2a1b01716fa\n",
       "    version:      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-5509b031-f12b-4323-a015-e37e6d9a980e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5509b031-f12b-4323-a015-e37e6d9a980e' 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>: 146</li><li><span class='xr-has-index'>arb</span>: 130</li><li><span class='xr-has-index'>radial_index</span>: 130</li><li><span class='xr-has-index'>spectral_index</span>: 4</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-37381467-598f-4e0f-903a-ceb21e56dc01' class='xr-section-summary-in' type='checkbox'  checked><label for='section-37381467-598f-4e0f-903a-ceb21e56dc01' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>arb</span></div><div class='xr-var-dims'>(arb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 127.0 128.0 129.0</div><input id='attrs-2de635a6-7bfa-4b25-a9ac-26293d4e7c8b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2de635a6-7bfa-4b25-a9ac-26293d4e7c8b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6859c243-6ad5-44bd-8fa7-cf1edada7a60' class='xr-var-data-in' type='checkbox'><label for='data-6859c243-6ad5-44bd-8fa7-cf1edada7a60' 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> arb </dd></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,\n",
       "        12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,\n",
       "        24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,\n",
       "        36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,\n",
       "        48.,  49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,\n",
       "        60.,  61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,\n",
       "        72.,  73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,\n",
       "        84.,  85.,  86.,  87.,  88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,\n",
       "        96.,  97.,  98.,  99., 100., 101., 102., 103., 104., 105., 106., 107.,\n",
       "       108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119.,\n",
       "       120., 121., 122., 123., 124., 125., 126., 127., 128., 129.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>radial_index</span></div><div class='xr-var-dims'>(radial_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 ... 128.0 129.0 130.0</div><input id='attrs-69827690-1fda-475c-9f84-7f32da6158ad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69827690-1fda-475c-9f84-7f32da6158ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b8d64647-ce40-4b89-99dd-fe20af6e8437' class='xr-var-data-in' type='checkbox'><label for='data-b8d64647-ce40-4b89-99dd-fe20af6e8437' 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></dd></dl></div><div class='xr-var-data'><pre>array([  1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,  12.,\n",
       "        13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,  24.,\n",
       "        25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,  36.,\n",
       "        37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,  48.,\n",
       "        49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,  60.,\n",
       "        61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,  72.,\n",
       "        73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,\n",
       "        85.,  86.,  87.,  88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,  96.,\n",
       "        97.,  98.,  99., 100., 101., 102., 103., 104., 105., 106., 107., 108.,\n",
       "       109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119., 120.,\n",
       "       121., 122., 123., 124., 125., 126., 127., 128., 129., 130.],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>spectral_index</span></div><div class='xr-var-dims'>(spectral_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>1.0 2.0 3.0 4.0</div><input id='attrs-a6aef445-044f-485c-a568-c6fbeea5116e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a6aef445-044f-485c-a568-c6fbeea5116e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e70c19e-33df-4802-a21d-92726b9c1910' class='xr-var-data-in' type='checkbox'><label for='data-7e70c19e-33df-4802-a21d-92726b9c1910' 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></dd></dl></div><div class='xr-var-data'><pre>array([1., 2., 3., 4.], 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'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.004166 0.008332 ... 71.0 72.0</div><input id='attrs-7611055c-7c73-44e9-b45c-1b9a1d357a3e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7611055c-7c73-44e9-b45c-1b9a1d357a3e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4392c96d-765a-4059-a6be-06c9b5724b0e' class='xr-var-data-in' type='checkbox'><label for='data-4392c96d-765a-4059-a6be-06c9b5724b0e' 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>s</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 4.166000e-03, 8.331900e-03, 1.249810e-02, 1.666450e-02,\n",
       "       2.082960e-02, 2.499600e-02, 2.916220e-02, 3.333340e-02, 3.749940e-02,\n",
       "       4.166520e-02, 4.583140e-02, 4.999780e-02, 5.416300e-02, 5.832930e-02,\n",
       "       6.249550e-02, 6.666670e-02, 7.083270e-02, 7.499850e-02, 7.916470e-02,\n",
       "       8.333110e-02, 8.749630e-02, 9.166270e-02, 9.582880e-02, 1.000000e-01,\n",
       "       1.041660e-01, 1.083319e-01, 1.124980e-01, 1.166644e-01, 1.208296e-01,\n",
       "       1.249960e-01, 1.291621e-01, 1.333333e-01, 1.374993e-01, 1.416652e-01,\n",
       "       1.458313e-01, 1.499978e-01, 1.541629e-01, 1.583293e-01, 1.624955e-01,\n",
       "       1.666667e-01, 1.708326e-01, 1.749985e-01, 1.791647e-01, 1.833311e-01,\n",
       "       1.874963e-01, 1.916626e-01, 1.958288e-01, 2.000000e-01, 2.041660e-01,\n",
       "       2.083318e-01, 2.124980e-01, 2.166644e-01, 2.208296e-01, 2.249960e-01,\n",
       "       2.291621e-01, 2.333333e-01, 2.374993e-01, 2.416652e-01, 2.458313e-01,\n",
       "       2.499977e-01, 2.541629e-01, 2.583293e-01, 2.624954e-01, 2.666666e-01,\n",
       "       2.708326e-01, 2.749985e-01, 2.791646e-01, 2.833311e-01, 2.874962e-01,\n",
       "       2.916626e-01, 2.958288e-01, 2.999999e-01, 3.041659e-01, 1.000000e+00,\n",
       "       2.000000e+00, 3.000000e+00, 4.000000e+00, 5.000000e+00, 6.000000e+00,\n",
       "       7.000000e+00, 8.000000e+00, 9.000000e+00, 1.000000e+01, 1.100000e+01,\n",
       "       1.200000e+01, 1.300000e+01, 1.400000e+01, 1.500000e+01, 1.600000e+01,\n",
       "       1.700000e+01, 1.800000e+01, 1.900000e+01, 2.000000e+01, 2.100000e+01,\n",
       "       2.200000e+01, 2.300000e+01, 2.400000e+01, 2.500000e+01, 2.600000e+01,\n",
       "       2.700000e+01, 2.800000e+01, 2.900000e+01, 3.000000e+01, 3.100000e+01,\n",
       "       3.200000e+01, 3.300000e+01, 3.400000e+01, 3.500000e+01, 3.600000e+01,\n",
       "       3.700000e+01, 3.800000e+01, 3.900000e+01, 4.000000e+01, 4.100000e+01,\n",
       "       4.200000e+01, 4.300000e+01, 4.400000e+01, 4.500000e+01, 4.600000e+01,\n",
       "       4.700000e+01, 4.800000e+01, 4.900000e+01, 5.000000e+01, 5.100000e+01,\n",
       "       5.200000e+01, 5.300000e+01, 5.400000e+01, 5.500000e+01, 5.600000e+01,\n",
       "       5.700000e+01, 5.800000e+01, 5.900000e+01, 6.000000e+01, 6.100000e+01,\n",
       "       6.200000e+01, 6.300000e+01, 6.400000e+01, 6.500000e+01, 6.600000e+01,\n",
       "       6.700000e+01, 6.800000e+01, 6.900000e+01, 7.000000e+01, 7.100000e+01,\n",
       "       7.200000e+01], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-504a8e5f-228a-44b7-b2f4-a491e7ed8b60' class='xr-section-summary-in' type='checkbox'  ><label for='section-504a8e5f-228a-44b7-b2f4-a491e7ed8b60' class='xr-section-summary' >Data variables: <span>(37)</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>acqiris_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;</div><input id='attrs-26182609-cfa3-4db6-b701-0f4a5703d855' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26182609-cfa3-4db6-b701-0f4a5703d855' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6cf55ba-156d-4885-ab5f-bb42d4ef6e55' class='xr-var-data-in' type='checkbox'><label for='data-d6cf55ba-156d-4885-ab5f-bb42d4ef6e55' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>time</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:ACQIRIS_TIME</dd><dt><span>name :</span></dt><dd>ayc/acqiris_time</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[73]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_ACQIRIS_TIME</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>uuid :</span></dt><dd>424fbc4e-f837-5997-b90d-27da7842ee34</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    </tr>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 584 B </td>\n",
       "                        <td> 584 B </td>\n",
       "                    </tr>\n",
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       "                        <td> (146,) </td>\n",
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       "                        <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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>angle</span></div><div class='xr-var-dims'>(arb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(130,), meta=np.ndarray&gt;</div><input id='attrs-87343dea-9f5e-4fc9-8e0f-dceefd2b5207' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-87343dea-9f5e-4fc9-8e0f-dceefd2b5207' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0fe0e9ca-0f24-4582-86f9-f500f6e6b873' class='xr-var-data-in' type='checkbox'><label for='data-0fe0e9ca-0f24-4582-86f9-f500f6e6b873' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;arb&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>scattering angle</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:ANGLE</dd><dt><span>name :</span></dt><dd>ayc/angle</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 130]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_ANGLE</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>9b6b5033-9c0b-53c6-b940-c4bc29e02421</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> (146, 130) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>covariance_ne_te</span></div><div class='xr-var-dims'>(time, radial_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146, 130), meta=np.ndarray&gt;</div><input id='attrs-457ee800-a74b-4408-8ff9-28f3fef473d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-457ee800-a74b-4408-8ff9-28f3fef473d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc96b2a3-5a27-4fca-9d3d-b5cb68568665' class='xr-var-data-in' type='checkbox'><label for='data-fc96b2a3-5a27-4fca-9d3d-b5cb68568665' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;radial_index&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>fit covariance of ne Te</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC.COVARIANCE:NE_TE</dd><dt><span>name :</span></dt><dd>ayc/covariance_ne_te</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[73, 130]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_COVARIANCE_NE_TE</dd><dt><span>units :</span></dt><dd>eV / m ** 3</dd><dt><span>uuid :</span></dt><dd>6c4c38dd-f19c-5af2-9254-334abf13f088</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <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> 74.14 kiB </td>\n",
       "                        <td> 74.14 kiB </td>\n",
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       "                    <tr>\n",
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       "                        <td> (146, 130) </td>\n",
       "                        <td> (146, 130) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>instrument_dr</span></div><div class='xr-var-dims'>(time, radial_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146, 130), meta=np.ndarray&gt;</div><input id='attrs-fec8df95-fecf-41c3-9321-c7db804eefc1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fec8df95-fecf-41c3-9321-c7db804eefc1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ee79214c-400a-48f7-a9e4-7684aa8a01ab' class='xr-var-data-in' type='checkbox'><label for='data-ee79214c-400a-48f7-a9e4-7684aa8a01ab' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;radial_index&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>instrument_dr</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC.INSTRUMENT:DR</dd><dt><span>name :</span></dt><dd>ayc/instrument_dr</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[20, 130]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_INSTRUMENT_DR</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>aac29b54-4567-5a77-beae-c12bd3317588</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 74.14 kiB </td>\n",
       "                        <td> 74.14 kiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (146, 130) </td>\n",
       "                        <td> (146, 130) </td>\n",
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       "                        <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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>interferometer_corr</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;</div><input id='attrs-fe5f422c-82b7-4af3-b39c-a3c2b437120b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe5f422c-82b7-4af3-b39c-a3c2b437120b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2aae9f95-2489-471a-9d73-4ea57a7bc9bd' class='xr-var-data-in' type='checkbox'><label for='data-2aae9f95-2489-471a-9d73-4ea57a7bc9bd' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Interferometer Correctio</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC.TERFEROMETER:CORR</dd><dt><span>name :</span></dt><dd>ayc/interferometer_corr</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[73]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_INTERFEROMETER_CORR</dd><dt><span>units :</span></dt><dd> - </dd><dt><span>uuid :</span></dt><dd>f0b399ed-75aa-5443-9243-313f2293bd3e</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 584 B </td>\n",
       "                        <td> 584 B </td>\n",
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       "                    <tr>\n",
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       "                        <td> (146,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>laser_energy</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;</div><input id='attrs-b192b1f0-94a4-4032-9f14-8821da017fc7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b192b1f0-94a4-4032-9f14-8821da017fc7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d2e64a55-676c-4904-9d37-d799614a0251' class='xr-var-data-in' type='checkbox'><label for='data-d2e64a55-676c-4904-9d37-d799614a0251' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>laser energy</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:LASER_ENERGY</dd><dt><span>name :</span></dt><dd>ayc/laser_energy</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[73]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_LASER_ENERGY</dd><dt><span>units :</span></dt><dd>J</dd><dt><span>uuid :</span></dt><dd>b90679b3-0109-59fc-9e67-3f78444cb09c</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 584 B </td>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 584 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (146,) </td>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ne</span></div><div class='xr-var-dims'>(time, radial_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146, 130), meta=np.ndarray&gt;</div><input id='attrs-e82d61af-63bc-4514-ac21-6e6246c7ccdb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e82d61af-63bc-4514-ac21-6e6246c7ccdb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ebe6ec51-0675-4f5d-ab6f-d85102758989' class='xr-var-data-in' type='checkbox'><label for='data-ebe6ec51-0675-4f5d-ab6f-d85102758989' 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>description :</span></dt><dd>Electron Density</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;radial_index&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>electron density</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:NE</dd><dt><span>name :</span></dt><dd>ayc/ne</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[73, 130]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_NE</dd><dt><span>units :</span></dt><dd>1 / m ** 3</dd><dt><span>uuid :</span></dt><dd>0141808d-ccda-5bce-917c-2eebd553f22f</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <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> 74.14 kiB </td>\n",
       "                        <td> 74.14 kiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (146, 130) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ne_core</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;</div><input id='attrs-e87f562c-2fdf-40f8-809d-e01c1ae96772' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e87f562c-2fdf-40f8-809d-e01c1ae96772' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96de3a6a-475d-4e55-885e-7086d9d9883a' class='xr-var-data-in' type='checkbox'><label for='data-96de3a6a-475d-4e55-885e-7086d9d9883a' 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>description :</span></dt><dd>Peak electron density (t)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>core density</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:NE_CORE</dd><dt><span>name :</span></dt><dd>ayc/ne_core</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[73]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_NE_CORE</dd><dt><span>units :</span></dt><dd>1 / m ** 3</dd><dt><span>uuid :</span></dt><dd>3a040ddc-6f6f-55ae-ad2e-b0e9489701e1</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 584 B </td>\n",
       "                        <td> 584 B </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ne_error</span></div><div class='xr-var-dims'>(time, radial_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146, 130), meta=np.ndarray&gt;</div><input id='attrs-67f1d10b-7d0d-4e2a-81cd-46145fea6116' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-67f1d10b-7d0d-4e2a-81cd-46145fea6116' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-785265bb-45d5-4953-880f-4cf8c1f3bd1f' class='xr-var-data-in' type='checkbox'><label for='data-785265bb-45d5-4953-880f-4cf8c1f3bd1f' 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></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> 74.14 kiB </td>\n",
       "                        <td> 74.14 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (146, 130) </td>\n",
       "                        <td> (146, 130) </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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>passnumber</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a3aac8a2-7366-418b-83a1-21151ed9754d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3aac8a2-7366-418b-83a1-21151ed9754d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d4678dc8-e50d-498f-87c6-3a9103845bea' class='xr-var-data-in' type='checkbox'><label for='data-d4678dc8-e50d-498f-87c6-3a9103845bea' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd> </dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:PASSNUMBER</dd><dt><span>name :</span></dt><dd>ayc/passnumber</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_PASSNUMBER</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>476690c4-d531-5a96-924c-d2aa75fd8b7c</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pe</span></div><div class='xr-var-dims'>(time, radial_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146, 130), meta=np.ndarray&gt;</div><input id='attrs-e7a27579-ddae-4e6b-b894-881f5275ee6a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7a27579-ddae-4e6b-b894-881f5275ee6a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57bd759e-913b-4bf9-a09c-f6ebb98f9e20' class='xr-var-data-in' type='checkbox'><label for='data-57bd759e-913b-4bf9-a09c-f6ebb98f9e20' 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>description :</span></dt><dd>Electron Pressure</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;radial_index&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>electron pressure</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:PE</dd><dt><span>name :</span></dt><dd>ayc/pe</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[73, 130]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_PE</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>d724d9a7-97ad-59c5-99bd-00f2de46ddf1</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <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> 74.14 kiB </td>\n",
       "                        <td> 74.14 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (146, 130) </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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 74.14 kiB </td>\n",
       "                        <td> 74.14 kiB </td>\n",
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       "                        <td> (146, 130) </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 296.56 kiB </td>\n",
       "                        <td> 148.28 kiB </td>\n",
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       "                        <td> (73, 130, 4) </td>\n",
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       "                    <tr>\n",
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       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <td> (73, 130, 4) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pulse</span></div><div class='xr-var-dims'>(arb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(130,), meta=np.ndarray&gt;</div><input id='attrs-ab8c055c-999a-4f2b-acd2-dc8d6461a006' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab8c055c-999a-4f2b-acd2-dc8d6461a006' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48926e64-15d9-4774-8fc9-001d2afe3966' class='xr-var-data-in' type='checkbox'><label for='data-48926e64-15d9-4774-8fc9-001d2afe3966' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;arb&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>pulse</dd><dt><span>mds_name :</span></dt><dd></dd><dt><span>name :</span></dt><dd>ayc/pulse</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 130]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_PULSE</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>5986d498-7a4f-56fd-9c1d-bd1f7fce9792</dd><dt><span>version :</span></dt><dd>0</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> 520 B </td>\n",
       "                        <td> 520 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (130,) </td>\n",
       "                        <td> (130,) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r</span></div><div class='xr-var-dims'>(time, radial_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146, 130), meta=np.ndarray&gt;</div><input id='attrs-dcee7983-8981-4677-b20f-85ac07dd7ea2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcee7983-8981-4677-b20f-85ac07dd7ea2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fc8e4cf0-1e89-4f19-8c06-f7385f4c5ef9' class='xr-var-data-in' type='checkbox'><label for='data-fc8e4cf0-1e89-4f19-8c06-f7385f4c5ef9' 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>description :</span></dt><dd>Radial basis (x,t)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;radial_index&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>major radius</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:R</dd><dt><span>name :</span></dt><dd>ayc/r</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[73, 130]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_R</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>30d2c0c2-39ee-5a8e-a321-9d87ace7b5b9</dd><dt><span>version :</span></dt><dd>0</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> 74.14 kiB </td>\n",
       "                        <td> 74.14 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (146, 130) </td>\n",
       "                        <td> (146, 130) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r_core</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;</div><input id='attrs-8dfa855f-708a-4fbd-965d-d8afb736cdda' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8dfa855f-708a-4fbd-965d-d8afb736cdda' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6a6f4ec3-4ac4-45e2-845c-89d00ae8f167' class='xr-var-data-in' type='checkbox'><label for='data-6a6f4ec3-4ac4-45e2-845c-89d00ae8f167' 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>description :</span></dt><dd>Peak position (t)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>core radius</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:R_CORE</dd><dt><span>name :</span></dt><dd>ayc/r_core</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[73]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_R_CORE</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>aab4d505-2c5d-5425-9be8-811d64a8cfe7</dd><dt><span>version :</span></dt><dd>0</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> 584 B </td>\n",
       "                        <td> 584 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (146,) </td>\n",
       "                        <td> (146,) </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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>r_error</span></div><div class='xr-var-dims'>(time, radial_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146, 130), meta=np.ndarray&gt;</div><input id='attrs-4add23fe-9c72-40c4-8f75-4d5f6b45eda4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4add23fe-9c72-40c4-8f75-4d5f6b45eda4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f14cd36-a958-43f5-ae05-ccd4ba112de3' class='xr-var-data-in' type='checkbox'><label for='data-4f14cd36-a958-43f5-ae05-ccd4ba112de3' 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></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> 74.14 kiB </td>\n",
       "                        <td> 74.14 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (146, 130) </td>\n",
       "                        <td> (146, 130) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scat_length</span></div><div class='xr-var-dims'>(arb)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(130,), meta=np.ndarray&gt;</div><input id='attrs-a3e91adc-07fa-4c38-b994-9c42debaad4b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3e91adc-07fa-4c38-b994-9c42debaad4b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd812444-e91e-4575-960f-1f23eb3580f3' class='xr-var-data-in' type='checkbox'><label for='data-bd812444-e91e-4575-960f-1f23eb3580f3' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;arb&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>scattering length</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:SCAT_LENGTH</dd><dt><span>name :</span></dt><dd>ayc/scat_length</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 130]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_SCAT_LENGTH</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>3edcd54b-335d-56f6-b942-89cd09684d14</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 520 B </td>\n",
       "                        <td> 520 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (130,) </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\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>segment_number</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;</div><input id='attrs-bbb75764-cc35-4bd9-8b04-bf9906afb56f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbb75764-cc35-4bd9-8b04-bf9906afb56f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-715fa37c-94c2-461a-b457-fd3587e70ff4' class='xr-var-data-in' type='checkbox'><label for='data-715fa37c-94c2-461a-b457-fd3587e70ff4' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>segment number</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC.SEGMENT:NUMBER</dd><dt><span>name :</span></dt><dd>ayc/segment_number</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[73]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_SEGMENT_NUMBER</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>92c061ca-16ef-5736-a5e2-943f19dce455</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 584 B </td>\n",
       "                        <td> 584 B </td>\n",
       "                    </tr>\n",
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       "                    <tr>\n",
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       "                        <td> (146,) </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                    <tr>\n",
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       "                        <td> 148.28 kiB </td>\n",
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       "                        <td> (73, 130, 4) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 2 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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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spectra_error</span></div><div class='xr-var-dims'>(time, radial_index, spectral_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(73, 130, 4), meta=np.ndarray&gt;</div><input id='attrs-cb8f56cf-1dcb-452f-b21e-c18fc876863e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb8f56cf-1dcb-452f-b21e-c18fc876863e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-68992e75-35f5-481c-837f-082d82d412a9' class='xr-var-data-in' type='checkbox'><label for='data-68992e75-35f5-481c-837f-082d82d412a9' 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></dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 148.28 kiB </td>\n",
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       "                        <td> (73, 130, 4) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 2 chunks in 2 graph layers </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>spectra_stray_light</span></div><div class='xr-var-dims'>(time, radial_index, spectral_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(73, 130, 4), meta=np.ndarray&gt;</div><input id='attrs-eedbecb7-095b-4e78-acf8-f8650ea9fa0d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eedbecb7-095b-4e78-acf8-f8650ea9fa0d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d31e2bd6-004d-480c-af3e-e2b107757287' class='xr-var-data-in' type='checkbox'><label for='data-d31e2bd6-004d-480c-af3e-e2b107757287' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;radial_index&#x27;, &#x27;spectral_index&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Raw spectra</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC.SPECTRA_:STRAY_LIGHT</dd><dt><span>name :</span></dt><dd>ayc/spectra_stray_light</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>3</dd><dt><span>shape :</span></dt><dd>[73, 130, 4]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_SPECTRA_STRAY_LIGHT</dd><dt><span>units :</span></dt><dd>V</dd><dt><span>uuid :</span></dt><dd>0cdfb1e6-1318-57c6-8947-2840b427b683</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
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       "                        <td> (73, 130, 4) </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> 2 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6d24695c-1236-4bea-9eca-acebde68159d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6d24695c-1236-4bea-9eca-acebde68159d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27585df9-b773-4869-851e-003fc0f33cef' class='xr-var-data-in' type='checkbox'><label for='data-27585df9-b773-4869-851e-003fc0f33cef' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd> </dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:STATUS</dd><dt><span>name :</span></dt><dd>ayc/status</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_STATUS</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>fc60b479-2d9a-56d3-a028-723152f978c9</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>te</span></div><div class='xr-var-dims'>(time, radial_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146, 130), meta=np.ndarray&gt;</div><input id='attrs-eb360a29-1041-43e7-b421-92553a1149df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb360a29-1041-43e7-b421-92553a1149df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90795d2c-ce25-4ea0-99d9-97b72a1bcb1e' class='xr-var-data-in' type='checkbox'><label for='data-90795d2c-ce25-4ea0-99d9-97b72a1bcb1e' 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>description :</span></dt><dd>Electron Temperature</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;radial_index&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>electron temperature</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:TE</dd><dt><span>name :</span></dt><dd>ayc/te</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[73, 130]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_TE</dd><dt><span>units :</span></dt><dd>eV</dd><dt><span>uuid :</span></dt><dd>54a16683-8517-5f0e-a242-f36d4fbfb9ba</dd><dt><span>version :</span></dt><dd>0</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> 74.14 kiB </td>\n",
       "                        <td> 74.14 kiB </td>\n",
       "                    </tr>\n",
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       "                        <td> (146, 130) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>te_core</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;</div><input id='attrs-991cc72d-e92f-4587-b7b8-305a4f85b6a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-991cc72d-e92f-4587-b7b8-305a4f85b6a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a798a078-524c-472f-8336-55c89ce1bef1' class='xr-var-data-in' type='checkbox'><label for='data-a798a078-524c-472f-8336-55c89ce1bef1' 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>description :</span></dt><dd>Peak electron temperature (t)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>core temperature</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:TE_CORE</dd><dt><span>name :</span></dt><dd>ayc/te_core</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[73]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_TE_CORE</dd><dt><span>units :</span></dt><dd>eV</dd><dt><span>uuid :</span></dt><dd>f5ebb5d8-f799-5a58-816e-613ef60f6e44</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                <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> 584 B </td>\n",
       "                        <td> 584 B </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 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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       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>te_error</span></div><div class='xr-var-dims'>(time, radial_index)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146, 130), meta=np.ndarray&gt;</div><input id='attrs-f792e0da-be35-454a-b10c-54638ae422d2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f792e0da-be35-454a-b10c-54638ae422d2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d3ca7586-0cbd-4575-a373-d179b4a4be4c' class='xr-var-data-in' type='checkbox'><label for='data-d3ca7586-0cbd-4575-a373-d179b4a4be4c' 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></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> 74.14 kiB </td>\n",
       "                        <td> 74.14 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (146, 130) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;</div><input id='attrs-4dc6487c-c6b9-478a-9ce7-547e7a59e4e0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4dc6487c-c6b9-478a-9ce7-547e7a59e4e0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-571c17a0-ff4d-4abb-b392-bdcbcb22c70a' class='xr-var-data-in' type='checkbox'><label for='data-571c17a0-ff4d-4abb-b392-bdcbcb22c70a' 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>description :</span></dt><dd>Time</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>time</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:TIME</dd><dt><span>name :</span></dt><dd>ayc/time_</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[73]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_TIME</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>uuid :</span></dt><dd>f744017c-223f-5bd7-a939-3c0b770a6398</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
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       "                        <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> 584 B </td>\n",
       "                        <td> 584 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
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       "                        <td> (146,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>version_fibre</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0757530e-9c8e-415f-a4a0-9c2b4cb334d9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0757530e-9c8e-415f-a4a0-9c2b4cb334d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-40890b6c-86d9-48fe-88bb-311da89d3f7d' class='xr-var-data-in' type='checkbox'><label for='data-40890b6c-86d9-48fe-88bb-311da89d3f7d' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd> </dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC.VERSION:FIBRE</dd><dt><span>name :</span></dt><dd>ayc/version_fibre</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_VERSION_FIBRE</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>d3fc997f-62ab-5f15-8bc9-5968427f3fd7</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>version_poly</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1408c1e4-1e87-4156-958b-11ea3e4ef318' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1408c1e4-1e87-4156-958b-11ea3e4ef318' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d01e9edc-c0e0-4c60-a9c1-a832b824f6fb' class='xr-var-data-in' type='checkbox'><label for='data-d01e9edc-c0e0-4c60-a9c1-a832b824f6fb' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd> </dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:VERSION_POLY</dd><dt><span>name :</span></dt><dd>ayc/version_poly</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_VERSION_POLY</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>9c215e48-c155-5343-8c29-a4b8d67f4a22</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>version_raman</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0eed9f11-c0ca-496e-96b1-2d4b94569bac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0eed9f11-c0ca-496e-96b1-2d4b94569bac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-007f4fde-56a8-4cde-bcd7-5c775d449549' class='xr-var-data-in' type='checkbox'><label for='data-007f4fde-56a8-4cde-bcd7-5c775d449549' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd> </dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC.VERSION:RAMAN</dd><dt><span>name :</span></dt><dd>ayc/version_raman</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_VERSION_RAMAN</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>d371ccf9-2582-5f6b-81f6-3d6419e874ca</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>xyc_time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;</div><input id='attrs-d0a07ebf-7443-48fd-b723-af0b335a9728' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0a07ebf-7443-48fd-b723-af0b335a9728' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f79e731a-e43a-4cb1-b33c-feac24165e35' class='xr-var-data-in' type='checkbox'><label for='data-f79e731a-e43a-4cb1-b33c-feac24165e35' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>xyc_time</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:XYC_TIME</dd><dt><span>name :</span></dt><dd>ayc/xyc_time</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[73]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_XYC_TIME</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>uuid :</span></dt><dd>8eebc9a7-a49b-57cd-8d1d-b94b6d98f329</dd><dt><span>version :</span></dt><dd>0</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> 584 B </td>\n",
       "                        <td> 584 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (146,) </td>\n",
       "                        <td> (146,) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
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       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >146</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>yag_nelint</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(146,), meta=np.ndarray&gt;</div><input id='attrs-a810db9c-1223-49fd-874a-1fd7f93b26b8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a810db9c-1223-49fd-874a-1fd7f93b26b8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-988847a1-4d78-474c-a4a2-e720dd9ad6cf' class='xr-var-data-in' type='checkbox'><label for='data-988847a1-4d78-474c-a4a2-e720dd9ad6cf' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>yag line integral densit</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.AYC:YAG_NELINT</dd><dt><span>name :</span></dt><dd>ayc/yag_nelint</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[73]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>AYC_YAG_NELINT</dd><dt><span>units :</span></dt><dd>e- / m2</dd><dt><span>uuid :</span></dt><dd>b82681c6-08c5-5207-9343-9215a65b270f</dd><dt><span>version :</span></dt><dd>0</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> 584 B </td>\n",
       "                        <td> 584 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (146,) </td>\n",
       "                        <td> (146,) </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\"> float32 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",
       "  <line x1=\"0\" y1=\"25\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"25\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >146</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></ul></div></li><li class='xr-section-item'><input id='section-24d51e49-7835-44be-ba4e-6e4bda55ca34' class='xr-section-summary-in' type='checkbox'  ><label for='section-24d51e49-7835-44be-ba4e-6e4bda55ca34' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>arb</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ed212ae8-d43a-4c0c-809d-df34f36a3145' class='xr-index-data-in' type='checkbox'/><label for='index-ed212ae8-d43a-4c0c-809d-df34f36a3145' 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([  0.0,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,\n",
       "       ...\n",
       "       120.0, 121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0, 128.0, 129.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;arb&#x27;, length=130))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>radial_index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-09c5d764-7977-48ce-84c0-3d956d3f53fb' class='xr-index-data-in' type='checkbox'/><label for='index-09c5d764-7977-48ce-84c0-3d956d3f53fb' 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([  1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,  10.0,\n",
       "       ...\n",
       "       121.0, 122.0, 123.0, 124.0, 125.0, 126.0, 127.0, 128.0, 129.0, 130.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;radial_index&#x27;, length=130))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>spectral_index</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-94a51642-ca35-4b91-9c3f-d653c298a5ac' class='xr-index-data-in' type='checkbox'/><label for='index-94a51642-ca35-4b91-9c3f-d653c298a5ac' 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([1.0, 2.0, 3.0, 4.0], dtype=&#x27;float32&#x27;, name=&#x27;spectral_index&#x27;))</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-1793dbe4-499c-45d3-9a51-769d0b338625' class='xr-index-data-in' type='checkbox'/><label for='index-1793dbe4-499c-45d3-9a51-769d0b338625' 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([                 0.0, 0.004166000057011843, 0.008331900462508202,\n",
       "         0.0124981002882123, 0.016664499416947365,  0.02082959935069084,\n",
       "       0.024995999410748482, 0.029162200167775154,  0.03333339840173721,\n",
       "        0.03749940171837807,\n",
       "       ...\n",
       "                       63.0,                 64.0,                 65.0,\n",
       "                       66.0,                 67.0,                 68.0,\n",
       "                       69.0,                 70.0,                 71.0,\n",
       "                       72.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;time&#x27;, length=146))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-fc0b2867-8290-4fd3-95cb-3e56025939a2' class='xr-section-summary-in' type='checkbox'  ><label for='section-fc0b2867-8290-4fd3-95cb-3e56025939a2' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Core Thomson scattering data</dd><dt><span>file_name :</span></dt><dd>ayc0304.20</dd><dt><span>format :</span></dt><dd>IDA3</dd><dt><span>mds_name :</span></dt><dd>None</dd><dt><span>name :</span></dt><dd>ayc</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ayc</dd><dt><span>uda_name :</span></dt><dd>AYC</dd><dt><span>uuid :</span></dt><dd>8d043ece-8bf8-5af8-87e4-d2a1b01716fa</dd><dt><span>version :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 3MB\n",
       "Dimensions:              (time: 146, arb: 130, radial_index: 130,\n",
       "                          spectral_index: 4)\n",
       "Coordinates:\n",
       "  * arb                  (arb) float32 520B 0.0 1.0 2.0 ... 127.0 128.0 129.0\n",
       "  * radial_index         (radial_index) float32 520B 1.0 2.0 3.0 ... 129.0 130.0\n",
       "  * spectral_index       (spectral_index) float32 16B 1.0 2.0 3.0 4.0\n",
       "  * time                 (time) float32 584B 0.0 0.004166 0.008332 ... 71.0 72.0\n",
       "Data variables: (12/37)\n",
       "    acqiris_time         (time) float32 584B dask.array<chunksize=(146,), meta=np.ndarray>\n",
       "    angle                (arb) float32 520B dask.array<chunksize=(130,), meta=np.ndarray>\n",
       "    aspectra             (time, radial_index, spectral_index) float32 304kB dask.array<chunksize=(73, 130, 4), meta=np.ndarray>\n",
       "    chi2                 (time, radial_index) float32 76kB dask.array<chunksize=(146, 130), meta=np.ndarray>\n",
       "    covariance_ne_te     (time, radial_index) float32 76kB dask.array<chunksize=(146, 130), meta=np.ndarray>\n",
       "    instrument_dr        (time, radial_index) float32 76kB dask.array<chunksize=(146, 130), meta=np.ndarray>\n",
       "    ...                   ...\n",
       "    time_                (time) float32 584B dask.array<chunksize=(146,), meta=np.ndarray>\n",
       "    version_fibre        float32 4B ...\n",
       "    version_poly         float32 4B ...\n",
       "    version_raman        float32 4B ...\n",
       "    xyc_time             (time) float32 584B dask.array<chunksize=(146,), meta=np.ndarray>\n",
       "    yag_nelint           (time) float32 584B dask.array<chunksize=(146,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    description:  Core Thomson scattering data\n",
       "    file_name:    ayc0304.20\n",
       "    format:       IDA3\n",
       "    mds_name:     None\n",
       "    name:         ayc\n",
       "    quality:      Not Checked\n",
       "    shot_id:      30420\n",
       "    signal_type:  Analysed\n",
       "    source:       ayc\n",
       "    uda_name:     AYC\n",
       "    uuid:         8d043ece-8bf8-5af8-87e4-d2a1b01716fa\n",
       "    version:      0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = catalog.level1.shots(url=url, group='ayc')\n",
    "dataset = dataset.to_dask()\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(2,1)\n",
    "ax1, ax2 = axes\n",
    "\n",
    "ax1.plot(dataset['time'], dataset['te_core'])\n",
    "ax1.set_xlabel('Time (s)')\n",
    "ax1.set_ylabel('Core Temperature (eV)')\n",
    "\n",
    "ax2.plot(dataset['time'], dataset['ne_core'])\n",
    "ax2.set_xlabel('Time (s)')\n",
    "ax2.set_ylabel('Peak Core Electron Density ($1 / m^3$)')\n",
    "\n",
    "for ax in axes:\n",
    "    ax.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CO2 Interferometers\n",
    "\n",
    "CO2 interferometers (`ane`) are used to measure the electron density in the plasma. By measuring the phase shift of the laser beam as it passes through the plasma, the electron density can be inferred with high precision."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rt2549/miniconda3/envs/mast-book/lib/python3.11/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  'dims': dict(self._ds.dims),\n"
     ]
    },
    {
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       "\n",
       ".xr-attrs dt {\n",
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       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
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       "\n",
       ".xr-attrs dd {\n",
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       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 524kB\n",
       "Dimensions:        (time: 32768)\n",
       "Coordinates:\n",
       "  * time           (time) float32 131kB -0.01 -0.00996 -0.00992 ... 1.301 1.301\n",
       "Data variables:\n",
       "    co2            (time) float32 131kB dask.array&lt;chunksize=(32768,), meta=np.ndarray&gt;\n",
       "    density        (time) float32 131kB dask.array&lt;chunksize=(32768,), meta=np.ndarray&gt;\n",
       "    hene           (time) float32 131kB dask.array&lt;chunksize=(32768,), meta=np.ndarray&gt;\n",
       "    passnumber     float32 4B ...\n",
       "    status         float32 4B ...\n",
       "    status_detail  float32 4B ...\n",
       "    version        float32 4B ...\n",
       "Attributes:\n",
       "    description:  CO2 Interferometry\n",
       "    file_name:    ane0304.20\n",
       "    format:       IDA3\n",
       "    mds_name:     None\n",
       "    name:         ane\n",
       "    quality:      Not Checked\n",
       "    shot_id:      30420\n",
       "    signal_type:  Analysed\n",
       "    source:       ane\n",
       "    uda_name:     ANE\n",
       "    uuid:         5f814dd8-7336-56d7-a1ea-bac62e5cdcd3\n",
       "    version:      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-a67c6e2a-ada0-4624-a3f9-5482759ca2c0' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a67c6e2a-ada0-4624-a3f9-5482759ca2c0' 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>: 32768</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-438c6406-649e-4edb-a53a-de85d5233a10' class='xr-section-summary-in' type='checkbox'  checked><label for='section-438c6406-649e-4edb-a53a-de85d5233a10' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-0.01 -0.00996 ... 1.301 1.301</div><input id='attrs-e5e0ece5-364f-4d93-9433-1bfbbc969062' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e5e0ece5-364f-4d93-9433-1bfbbc969062' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4f1e81eb-191e-4fab-85f8-609a67fe24e4' class='xr-var-data-in' type='checkbox'><label for='data-4f1e81eb-191e-4fab-85f8-609a67fe24e4' 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>s</dd></dl></div><div class='xr-var-data'><pre>array([-0.01   , -0.00996, -0.00992, ...,  1.3006 ,  1.30064,  1.30068],\n",
       "      dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4a697ea3-bf76-4a5a-ab4b-ae0aec761187' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4a697ea3-bf76-4a5a-ab4b-ae0aec761187' class='xr-section-summary' >Data variables: <span>(7)</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>co2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(32768,), meta=np.ndarray&gt;</div><input id='attrs-471b7e75-77d2-4904-b2ab-242b7a149d48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-471b7e75-77d2-4904-b2ab-242b7a149d48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8565a854-98a9-46e3-a49a-586c2ed051a4' class='xr-var-data-in' type='checkbox'><label for='data-8565a854-98a9-46e3-a49a-586c2ed051a4' 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>description :</span></dt><dd>integrated electron density  determined from co2 laser interference fringes</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>m^-2</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.ANE:CO2</dd><dt><span>name :</span></dt><dd>ane/co2</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[32768]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ane</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>ANE_CO2</dd><dt><span>units :</span></dt><dd>1 / m ** 2</dd><dt><span>uuid :</span></dt><dd>74853458-cebb-55b9-a92c-9ba9bb367d17</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 128.00 kiB </td>\n",
       "                        <td> 128.00 kiB </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>density</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(32768,), meta=np.ndarray&gt;</div><input id='attrs-2aacf0f2-ba19-4e91-b27a-d3cf21b80528' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2aacf0f2-ba19-4e91-b27a-d3cf21b80528' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ed865e67-19bd-411a-bf6e-fa4a2441e3ac' class='xr-var-data-in' type='checkbox'><label for='data-ed865e67-19bd-411a-bf6e-fa4a2441e3ac' 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>description :</span></dt><dd>integrated electron density including vibration correction</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>m^-2</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.ANE:DENSITY</dd><dt><span>name :</span></dt><dd>ane/density</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[32768]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ane</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>ANE_DENSITY</dd><dt><span>units :</span></dt><dd>1 / m ** 2</dd><dt><span>uuid :</span></dt><dd>16841548-2592-5ac3-ab6d-6abed4f61440</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>hene</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(32768,), meta=np.ndarray&gt;</div><input id='attrs-a4a12645-779d-4559-8eb6-42c148d98a27' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4a12645-779d-4559-8eb6-42c148d98a27' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2e016b2-d4cd-4ed7-9256-dc63db29c7e5' class='xr-var-data-in' type='checkbox'><label for='data-c2e016b2-d4cd-4ed7-9256-dc63db29c7e5' 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>description :</span></dt><dd>electron density from HeNe fringes correspondes to  vibrations in interferometer</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>m^-2</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.ANE:HENE</dd><dt><span>name :</span></dt><dd>ane/hene</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[32768]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ane</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>ANE_HENE</dd><dt><span>units :</span></dt><dd>1 / m ** 2</dd><dt><span>uuid :</span></dt><dd>28e1edee-78c6-546c-bc47-253a87a72067</dd><dt><span>version :</span></dt><dd>0</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> 128.00 kiB </td>\n",
       "                        <td> 128.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (32768,) </td>\n",
       "                        <td> (32768,) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"75\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
       "  <!-- Horizontal lines -->\n",
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       "\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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       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,25.412616514582485 0.0,25.412616514582485\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"45.412617\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >32768</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>passnumber</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1252856a-04a9-4fb7-87c1-30f74585e9a2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1252856a-04a9-4fb7-87c1-30f74585e9a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbe2f827-19d0-41bf-a4ba-eba02f60fa9e' class='xr-var-data-in' type='checkbox'><label for='data-dbe2f827-19d0-41bf-a4ba-eba02f60fa9e' 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>description :</span></dt><dd>Pass number of data</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Passno</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.ANE:PASSNUMBER</dd><dt><span>name :</span></dt><dd>ane/passnumber</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ane</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>ANE_PASSNUMBER</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>963011dc-53cc-5693-898d-b3edc5560c70</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d221301f-07bf-4952-86f8-51a3d6b76707' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d221301f-07bf-4952-86f8-51a3d6b76707' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2820fff8-5cca-4115-97ed-88d602baec8c' class='xr-var-data-in' type='checkbox'><label for='data-2820fff8-5cca-4115-97ed-88d602baec8c' 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>description :</span></dt><dd>status flag</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Status</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.ANE:STATUS</dd><dt><span>name :</span></dt><dd>ane/status</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ane</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>ANE_STATUS</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>810c5f50-1b7a-57e6-bd55-691b9c71a470</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>status_detail</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ad60758f-62c2-44db-a9cd-ccbd96c9a7f3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ad60758f-62c2-44db-a9cd-ccbd96c9a7f3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b56010d9-956b-42de-b94e-1c817ac187c8' class='xr-var-data-in' type='checkbox'><label for='data-b56010d9-956b-42de-b94e-1c817ac187c8' 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>description :</span></dt><dd>status detail</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>status_detail</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.ANE.STATUS_:DETAIL</dd><dt><span>name :</span></dt><dd>ane/status_detail</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ane</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>ANE_STATUS_DETAIL</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>0945c7f5-3c05-5148-bdba-47c525432a53</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>version</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ede4c2a7-d693-4a22-92c5-300683136f76' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ede4c2a7-d693-4a22-92c5-300683136f76' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-97d0fbf1-5dee-4802-b8d1-45fe93bf1b81' class='xr-var-data-in' type='checkbox'><label for='data-97d0fbf1-5dee-4802-b8d1-45fe93bf1b81' 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>description :</span></dt><dd>version of analysis code</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Version</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.ANE:VERSION</dd><dt><span>name :</span></dt><dd>ane/version</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ane</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>ANE_VERSION</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>791e58b4-3e52-5494-8c2a-e13812aa01a1</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-433ea349-99e0-4a5c-9c57-905e0eaddab6' class='xr-section-summary-in' type='checkbox'  ><label for='section-433ea349-99e0-4a5c-9c57-905e0eaddab6' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7139a5d0-29f7-4a98-af10-0de72b239b9b' class='xr-index-data-in' type='checkbox'/><label for='index-7139a5d0-29f7-4a98-af10-0de72b239b9b' 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([-0.009999999776482582, -0.009959999471902847, -0.009920000098645687,\n",
       "       -0.009879999794065952, -0.009839999489486217, -0.009800000116229057,\n",
       "       -0.009759999811649323, -0.009719999507069588, -0.009680000133812428,\n",
       "       -0.009639999829232693,\n",
       "       ...\n",
       "           1.300320029258728,    1.3003599643707275,    1.3004000186920166,\n",
       "          1.3004399538040161,    1.3004800081253052,    1.3005199432373047,\n",
       "          1.3005599975585938,    1.3005999326705933,    1.3006399869918823,\n",
       "          1.3006799221038818],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;time&#x27;, length=32768))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e9f3c933-2ca7-4912-9eda-b7c86b99c437' class='xr-section-summary-in' type='checkbox'  ><label for='section-e9f3c933-2ca7-4912-9eda-b7c86b99c437' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>CO2 Interferometry</dd><dt><span>file_name :</span></dt><dd>ane0304.20</dd><dt><span>format :</span></dt><dd>IDA3</dd><dt><span>mds_name :</span></dt><dd>None</dd><dt><span>name :</span></dt><dd>ane</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>ane</dd><dt><span>uda_name :</span></dt><dd>ANE</dd><dt><span>uuid :</span></dt><dd>5f814dd8-7336-56d7-a1ea-bac62e5cdcd3</dd><dt><span>version :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 524kB\n",
       "Dimensions:        (time: 32768)\n",
       "Coordinates:\n",
       "  * time           (time) float32 131kB -0.01 -0.00996 -0.00992 ... 1.301 1.301\n",
       "Data variables:\n",
       "    co2            (time) float32 131kB dask.array<chunksize=(32768,), meta=np.ndarray>\n",
       "    density        (time) float32 131kB dask.array<chunksize=(32768,), meta=np.ndarray>\n",
       "    hene           (time) float32 131kB dask.array<chunksize=(32768,), meta=np.ndarray>\n",
       "    passnumber     float32 4B ...\n",
       "    status         float32 4B ...\n",
       "    status_detail  float32 4B ...\n",
       "    version        float32 4B ...\n",
       "Attributes:\n",
       "    description:  CO2 Interferometry\n",
       "    file_name:    ane0304.20\n",
       "    format:       IDA3\n",
       "    mds_name:     None\n",
       "    name:         ane\n",
       "    quality:      Not Checked\n",
       "    shot_id:      30420\n",
       "    signal_type:  Analysed\n",
       "    source:       ane\n",
       "    uda_name:     ANE\n",
       "    uuid:         5f814dd8-7336-56d7-a1ea-bac62e5cdcd3\n",
       "    version:      0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = catalog.level1.shots(url=url, group='ane')\n",
    "dataset = dataset.to_dask()\n",
    "\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1)\n",
    "plt.plot(dataset['time'], dataset['density'])\n",
    "ax.set_xlabel('Time (s)')\n",
    "ax.set_ylabel('Integrated Electron Density ($1 / m^2$)')\n",
    "ax.grid(alpha=0.3)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Equillibrium Reconstruction Data\n",
    "\n",
    "The source `efm` contains data from EFIT. EFIT is a computational tool used to reconstruct the magnetic equilibrium configuration of the plasma in a tokamak. It calculates the shape and position of the plasma, as well as the distribution of the current and pressure within it, based on magnetic measurements.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rt2549/miniconda3/envs/mast-book/lib/python3.11/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  'dims': dict(self._ds.dims),\n"
     ]
    },
    {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 4MB\n",
       "Dimensions:            (time: 64, psi_norm: 65, n_iterations: 10,\n",
       "                        fcoil_seg_n: 938, fcoil_n: 101, ffprime_coefs_n: 2,\n",
       "                        mag_probe_n: 78, psi_loop_n: 46, r: 65, z: 65,\n",
       "                        profile_r: 129, lcfs_coords: 147, limiter_n: 37,\n",
       "                        pprime_coefs_n: 2, profile_z: 65)\n",
       "Coordinates: (12/13)\n",
       "  * fcoil_n            (fcoil_n) float32 404B 0.0 1.0 2.0 ... 98.0 99.0 100.0\n",
       "  * ffprime_coefs_n    (ffprime_coefs_n) float32 8B 0.0 1.0\n",
       "  * lcfs_coords        (lcfs_coords) float32 588B 0.0 1.0 2.0 ... 145.0 146.0\n",
       "  * mag_probe_n        (mag_probe_n) float32 312B 0.0 1.0 2.0 ... 75.0 76.0 77.0\n",
       "  * n_iterations       (n_iterations) float32 40B 0.0 1.0 2.0 ... 7.0 8.0 9.0\n",
       "  * pprime_coefs_n     (pprime_coefs_n) float32 8B 0.0 1.0\n",
       "    ...                 ...\n",
       "  * profile_z          (profile_z) float32 260B -2.0 -1.938 -1.875 ... 1.938 2.0\n",
       "  * psi_loop_n         (psi_loop_n) float32 184B 0.0 1.0 2.0 ... 43.0 44.0 45.0\n",
       "  * psi_norm           (psi_norm) float32 260B 0.0 0.01562 ... 0.9844 1.0\n",
       "  * r                  (r) float32 260B 0.06 0.09031 0.1206 ... 1.939 1.97 2.0\n",
       "  * time               (time) float32 256B -0.05 -0.045 -0.04 ... 0.29 0.295 0.3\n",
       "  * z                  (z) float32 260B -2.0 -1.938 -1.875 ... 1.875 1.938 2.0\n",
       "Dimensions without coordinates: fcoil_seg_n, limiter_n\n",
       "Data variables: (12/151)\n",
       "    all_times          (time) float32 256B dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;\n",
       "    areap_c            (time, psi_norm) float32 17kB dask.array&lt;chunksize=(64, 65), meta=np.ndarray&gt;\n",
       "    betan              (time) float32 256B dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;\n",
       "    betap              (time) float32 256B dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;\n",
       "    betapd             (time) float32 256B dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;\n",
       "    betat              (time) float32 256B dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;\n",
       "    ...                 ...\n",
       "    wpol               (time) float32 256B dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;\n",
       "    xpoint1_rc         (time) float32 256B dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;\n",
       "    xpoint1_zc         (time) float32 256B dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;\n",
       "    xpoint2_rc         (time) float32 256B dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;\n",
       "    xpoint2_zc         (time) float32 256B dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;\n",
       "    zbdry              (time) float32 256B dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    description:  Basic EFIT\n",
       "    file_name:    efm0304.20\n",
       "    format:       IDA3\n",
       "    mds_name:     None\n",
       "    name:         efm\n",
       "    quality:      Not Checked\n",
       "    shot_id:      30420\n",
       "    signal_type:  Analysed\n",
       "    source:       efm\n",
       "    uda_name:     EFM\n",
       "    uuid:         1e39c600-8ffb-5f56-900d-2941e352319c\n",
       "    version:      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-2ea487ec-8ce5-45ef-ac66-9db0d5276a71' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2ea487ec-8ce5-45ef-ac66-9db0d5276a71' 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>: 64</li><li><span class='xr-has-index'>psi_norm</span>: 65</li><li><span class='xr-has-index'>n_iterations</span>: 10</li><li><span>fcoil_seg_n</span>: 938</li><li><span class='xr-has-index'>fcoil_n</span>: 101</li><li><span class='xr-has-index'>ffprime_coefs_n</span>: 2</li><li><span class='xr-has-index'>mag_probe_n</span>: 78</li><li><span class='xr-has-index'>psi_loop_n</span>: 46</li><li><span class='xr-has-index'>r</span>: 65</li><li><span class='xr-has-index'>z</span>: 65</li><li><span class='xr-has-index'>profile_r</span>: 129</li><li><span class='xr-has-index'>lcfs_coords</span>: 147</li><li><span>limiter_n</span>: 37</li><li><span class='xr-has-index'>pprime_coefs_n</span>: 2</li><li><span class='xr-has-index'>profile_z</span>: 65</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-2698e19c-0781-40cc-aee1-2e49ed825a1c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2698e19c-0781-40cc-aee1-2e49ed825a1c' class='xr-section-summary' >Coordinates: <span>(13)</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'>fcoil_n</span></div><div class='xr-var-dims'>(fcoil_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 3.0 ... 98.0 99.0 100.0</div><input id='attrs-3c314967-3e3a-4d18-b35c-ab8337968890' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3c314967-3e3a-4d18-b35c-ab8337968890' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-446753e3-5566-4e86-928e-7f09422da14c' class='xr-var-data-in' type='checkbox'><label for='data-446753e3-5566-4e86-928e-7f09422da14c' 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></dd></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,\n",
       "        12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,\n",
       "        24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,\n",
       "        36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,\n",
       "        48.,  49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,\n",
       "        60.,  61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,\n",
       "        72.,  73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,\n",
       "        84.,  85.,  86.,  87.,  88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,\n",
       "        96.,  97.,  98.,  99., 100.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>ffprime_coefs_n</span></div><div class='xr-var-dims'>(ffprime_coefs_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1.0</div><input id='attrs-977b51bb-2a21-45ae-93d8-1a6afdde102f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-977b51bb-2a21-45ae-93d8-1a6afdde102f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-366b8765-3844-45c5-9a20-0268db56bb2e' class='xr-var-data-in' type='checkbox'><label for='data-366b8765-3844-45c5-9a20-0268db56bb2e' 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></dd></dl></div><div class='xr-var-data'><pre>array([0., 1.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lcfs_coords</span></div><div class='xr-var-dims'>(lcfs_coords)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 ... 144.0 145.0 146.0</div><input id='attrs-3631e0b1-0448-40e4-bc59-fd40d99f0265' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3631e0b1-0448-40e4-bc59-fd40d99f0265' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f7e34958-a6c3-4c9e-a6c7-e5fa587a6eb3' class='xr-var-data-in' type='checkbox'><label for='data-f7e34958-a6c3-4c9e-a6c7-e5fa587a6eb3' 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></dd></dl></div><div class='xr-var-data'><pre>array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.,  11.,\n",
       "        12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,  20.,  21.,  22.,  23.,\n",
       "        24.,  25.,  26.,  27.,  28.,  29.,  30.,  31.,  32.,  33.,  34.,  35.,\n",
       "        36.,  37.,  38.,  39.,  40.,  41.,  42.,  43.,  44.,  45.,  46.,  47.,\n",
       "        48.,  49.,  50.,  51.,  52.,  53.,  54.,  55.,  56.,  57.,  58.,  59.,\n",
       "        60.,  61.,  62.,  63.,  64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,\n",
       "        72.,  73.,  74.,  75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,\n",
       "        84.,  85.,  86.,  87.,  88.,  89.,  90.,  91.,  92.,  93.,  94.,  95.,\n",
       "        96.,  97.,  98.,  99., 100., 101., 102., 103., 104., 105., 106., 107.,\n",
       "       108., 109., 110., 111., 112., 113., 114., 115., 116., 117., 118., 119.,\n",
       "       120., 121., 122., 123., 124., 125., 126., 127., 128., 129., 130., 131.,\n",
       "       132., 133., 134., 135., 136., 137., 138., 139., 140., 141., 142., 143.,\n",
       "       144., 145., 146.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>mag_probe_n</span></div><div class='xr-var-dims'>(mag_probe_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 3.0 ... 75.0 76.0 77.0</div><input id='attrs-209be0d7-ff45-43a3-b2c8-0562db403d1a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-209be0d7-ff45-43a3-b2c8-0562db403d1a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d544e5b3-d278-4ea5-86d5-d54d7ab86bc7' class='xr-var-data-in' type='checkbox'><label for='data-d544e5b3-d278-4ea5-86d5-d54d7ab86bc7' 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></dd></dl></div><div class='xr-var-data'><pre>array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,\n",
       "       14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,\n",
       "       28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,\n",
       "       42., 43., 44., 45., 46., 47., 48., 49., 50., 51., 52., 53., 54., 55.,\n",
       "       56., 57., 58., 59., 60., 61., 62., 63., 64., 65., 66., 67., 68., 69.,\n",
       "       70., 71., 72., 73., 74., 75., 76., 77.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>n_iterations</span></div><div class='xr-var-dims'>(n_iterations)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 3.0 ... 6.0 7.0 8.0 9.0</div><input id='attrs-6f4b8e4b-bfc7-4565-b8a0-10436322c1f7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f4b8e4b-bfc7-4565-b8a0-10436322c1f7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ecb579d0-8761-44f3-a04e-17218ed7fa47' class='xr-var-data-in' type='checkbox'><label for='data-ecb579d0-8761-44f3-a04e-17218ed7fa47' 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></dd></dl></div><div class='xr-var-data'><pre>array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pprime_coefs_n</span></div><div class='xr-var-dims'>(pprime_coefs_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1.0</div><input id='attrs-f1322d0c-5c19-4a6e-8dbd-edfcfbbc52d1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f1322d0c-5c19-4a6e-8dbd-edfcfbbc52d1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2449b633-19ab-4f8c-a253-7ff9fe964ffa' class='xr-var-data-in' type='checkbox'><label for='data-2449b633-19ab-4f8c-a253-7ff9fe964ffa' 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></dd></dl></div><div class='xr-var-data'><pre>array([0., 1.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>profile_r</span></div><div class='xr-var-dims'>(profile_r)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.01562 0.03125 ... 1.97 2.0</div><input id='attrs-95dbe5d6-3b0c-47b5-b3d6-c4259bf02d5f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-95dbe5d6-3b0c-47b5-b3d6-c4259bf02d5f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9764bdb-5dcb-4fff-b5b9-be7fc7d2430f' class='xr-var-data-in' type='checkbox'><label for='data-b9764bdb-5dcb-4fff-b5b9-be7fc7d2430f' 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></dd></dl></div><div class='xr-var-data'><pre>array([0.      , 0.015625, 0.03125 , 0.046875, 0.06    , 0.0625  , 0.078125,\n",
       "       0.090312, 0.09375 , 0.109375, 0.120625, 0.125   , 0.140625, 0.150937,\n",
       "       0.15625 , 0.171875, 0.18125 , 0.1875  , 0.203125, 0.211563, 0.21875 ,\n",
       "       0.234375, 0.241875, 0.25    , 0.265625, 0.272188, 0.28125 , 0.296875,\n",
       "       0.3025  , 0.3125  , 0.328125, 0.332813, 0.34375 , 0.359375, 0.363125,\n",
       "       0.375   , 0.390625, 0.393438, 0.40625 , 0.421875, 0.42375 , 0.4375  ,\n",
       "       0.453125, 0.454063, 0.46875 , 0.484375, 0.5     , 0.514687, 0.515625,\n",
       "       0.53125 , 0.545   , 0.546875, 0.5625  , 0.575312, 0.578125, 0.59375 ,\n",
       "       0.605625, 0.609375, 0.625   , 0.635938, 0.640625, 0.65625 , 0.66625 ,\n",
       "       0.671875, 0.6875  , 0.696563, 0.703125, 0.71875 , 0.726875, 0.734375,\n",
       "       0.75    , 0.757188, 0.765625, 0.78125 , 0.7875  , 0.796875, 0.8125  ,\n",
       "       0.817813, 0.828125, 0.84375 , 0.848125, 0.859375, 0.875   , 0.878438,\n",
       "       0.890625, 0.90625 , 0.90875 , 0.921875, 0.9375  , 0.939063, 0.953125,\n",
       "       0.96875 , 0.969375, 0.984375, 0.999688, 1.      , 1.03    , 1.060313,\n",
       "       1.090625, 1.120937, 1.15125 , 1.181562, 1.211875, 1.242188, 1.2725  ,\n",
       "       1.302812, 1.333125, 1.363438, 1.39375 , 1.424062, 1.454375, 1.484687,\n",
       "       1.515   , 1.545313, 1.575625, 1.605937, 1.63625 , 1.666562, 1.696875,\n",
       "       1.727188, 1.7575  , 1.787812, 1.818125, 1.848438, 1.87875 , 1.909063,\n",
       "       1.939375, 1.969687, 2.      ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>profile_z</span></div><div class='xr-var-dims'>(profile_z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-2.0 -1.938 -1.875 ... 1.938 2.0</div><input id='attrs-9741efb3-4619-4f74-afb7-fbf0c8a38af4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9741efb3-4619-4f74-afb7-fbf0c8a38af4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-642820a1-10ac-45a0-8bde-20dafe666cc6' class='xr-var-data-in' type='checkbox'><label for='data-642820a1-10ac-45a0-8bde-20dafe666cc6' 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></dl></div><div class='xr-var-data'><pre>array([-2.    , -1.9375, -1.875 , -1.8125, -1.75  , -1.6875, -1.625 , -1.5625,\n",
       "       -1.5   , -1.4375, -1.375 , -1.3125, -1.25  , -1.1875, -1.125 , -1.0625,\n",
       "       -1.    , -0.9375, -0.875 , -0.8125, -0.75  , -0.6875, -0.625 , -0.5625,\n",
       "       -0.5   , -0.4375, -0.375 , -0.3125, -0.25  , -0.1875, -0.125 , -0.0625,\n",
       "        0.    ,  0.0625,  0.125 ,  0.1875,  0.25  ,  0.3125,  0.375 ,  0.4375,\n",
       "        0.5   ,  0.5625,  0.625 ,  0.6875,  0.75  ,  0.8125,  0.875 ,  0.9375,\n",
       "        1.    ,  1.0625,  1.125 ,  1.1875,  1.25  ,  1.3125,  1.375 ,  1.4375,\n",
       "        1.5   ,  1.5625,  1.625 ,  1.6875,  1.75  ,  1.8125,  1.875 ,  1.9375,\n",
       "        2.    ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>psi_loop_n</span></div><div class='xr-var-dims'>(psi_loop_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 3.0 ... 43.0 44.0 45.0</div><input id='attrs-a0efe2df-3c5a-4b96-8e35-b44547ce643a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0efe2df-3c5a-4b96-8e35-b44547ce643a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0244540d-0ea5-4cd6-8aea-ec62df15bb65' class='xr-var-data-in' type='checkbox'><label for='data-0244540d-0ea5-4cd6-8aea-ec62df15bb65' 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></dd></dl></div><div class='xr-var-data'><pre>array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.,\n",
       "       14., 15., 16., 17., 18., 19., 20., 21., 22., 23., 24., 25., 26., 27.,\n",
       "       28., 29., 30., 31., 32., 33., 34., 35., 36., 37., 38., 39., 40., 41.,\n",
       "       42., 43., 44., 45.], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>psi_norm</span></div><div class='xr-var-dims'>(psi_norm)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.0 0.01562 0.03125 ... 0.9844 1.0</div><input id='attrs-34358f46-ef6f-4a56-b95d-69cfd87958bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34358f46-ef6f-4a56-b95d-69cfd87958bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02da2fc9-f3f3-4a86-b11b-0323b1917377' class='xr-var-data-in' type='checkbox'><label for='data-02da2fc9-f3f3-4a86-b11b-0323b1917377' 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></dd></dl></div><div class='xr-var-data'><pre>array([0.      , 0.015625, 0.03125 , 0.046875, 0.0625  , 0.078125, 0.09375 ,\n",
       "       0.109375, 0.125   , 0.140625, 0.15625 , 0.171875, 0.1875  , 0.203125,\n",
       "       0.21875 , 0.234375, 0.25    , 0.265625, 0.28125 , 0.296875, 0.3125  ,\n",
       "       0.328125, 0.34375 , 0.359375, 0.375   , 0.390625, 0.40625 , 0.421875,\n",
       "       0.4375  , 0.453125, 0.46875 , 0.484375, 0.5     , 0.515625, 0.53125 ,\n",
       "       0.546875, 0.5625  , 0.578125, 0.59375 , 0.609375, 0.625   , 0.640625,\n",
       "       0.65625 , 0.671875, 0.6875  , 0.703125, 0.71875 , 0.734375, 0.75    ,\n",
       "       0.765625, 0.78125 , 0.796875, 0.8125  , 0.828125, 0.84375 , 0.859375,\n",
       "       0.875   , 0.890625, 0.90625 , 0.921875, 0.9375  , 0.953125, 0.96875 ,\n",
       "       0.984375, 1.      ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>r</span></div><div class='xr-var-dims'>(r)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>0.06 0.09031 0.1206 ... 1.97 2.0</div><input id='attrs-24a995d1-916c-4131-9d8d-95bc0248103c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-24a995d1-916c-4131-9d8d-95bc0248103c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5c2fbe8-27e5-457f-b2a0-3e15e78b4341' class='xr-var-data-in' type='checkbox'><label for='data-c5c2fbe8-27e5-457f-b2a0-3e15e78b4341' 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></dl></div><div class='xr-var-data'><pre>array([0.06    , 0.090312, 0.120625, 0.150937, 0.18125 , 0.211563, 0.241875,\n",
       "       0.272188, 0.3025  , 0.332813, 0.363125, 0.393438, 0.42375 , 0.454063,\n",
       "       0.484375, 0.514687, 0.545   , 0.575312, 0.605625, 0.635938, 0.66625 ,\n",
       "       0.696563, 0.726875, 0.757188, 0.7875  , 0.817813, 0.848125, 0.878438,\n",
       "       0.90875 , 0.939063, 0.969375, 0.999688, 1.03    , 1.060313, 1.090625,\n",
       "       1.120937, 1.15125 , 1.181562, 1.211875, 1.242188, 1.2725  , 1.302812,\n",
       "       1.333125, 1.363438, 1.39375 , 1.424062, 1.454375, 1.484687, 1.515   ,\n",
       "       1.545313, 1.575625, 1.605937, 1.63625 , 1.666562, 1.696875, 1.727188,\n",
       "       1.7575  , 1.787812, 1.818125, 1.848438, 1.87875 , 1.909063, 1.939375,\n",
       "       1.969687, 2.      ], 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'>float32</div><div class='xr-var-preview xr-preview'>-0.05 -0.045 -0.04 ... 0.295 0.3</div><input id='attrs-7a2701b7-6f9d-4bb7-87e5-8459ec90c074' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7a2701b7-6f9d-4bb7-87e5-8459ec90c074' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e140413d-33b1-4d6f-9403-0f4a4baf7e47' class='xr-var-data-in' type='checkbox'><label for='data-e140413d-33b1-4d6f-9403-0f4a4baf7e47' 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>S</dd></dl></div><div class='xr-var-data'><pre>array([-0.05 , -0.045, -0.04 , -0.035, -0.03 , -0.025, -0.02 , -0.015, -0.01 ,\n",
       "       -0.005,  0.   ,  0.04 ,  0.045,  0.05 ,  0.055,  0.06 ,  0.065,  0.07 ,\n",
       "        0.075,  0.08 ,  0.085,  0.09 ,  0.095,  0.1  ,  0.105,  0.11 ,  0.115,\n",
       "        0.12 ,  0.125,  0.13 ,  0.135,  0.14 ,  0.145,  0.15 ,  0.155,  0.16 ,\n",
       "        0.165,  0.17 ,  0.175,  0.18 ,  0.185,  0.19 ,  0.195,  0.2  ,  0.205,\n",
       "        0.21 ,  0.215,  0.22 ,  0.225,  0.23 ,  0.235,  0.24 ,  0.245,  0.25 ,\n",
       "        0.255,  0.26 ,  0.265,  0.27 ,  0.275,  0.28 ,  0.285,  0.29 ,  0.295,\n",
       "        0.3  ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>-2.0 -1.938 -1.875 ... 1.938 2.0</div><input id='attrs-ae01c52d-cab6-455d-b289-95bcc8c81b7d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae01c52d-cab6-455d-b289-95bcc8c81b7d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5359aec6-2d04-4aac-8470-3862f11d0aff' class='xr-var-data-in' type='checkbox'><label for='data-5359aec6-2d04-4aac-8470-3862f11d0aff' 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></dl></div><div class='xr-var-data'><pre>array([-2.    , -1.9375, -1.875 , -1.8125, -1.75  , -1.6875, -1.625 , -1.5625,\n",
       "       -1.5   , -1.4375, -1.375 , -1.3125, -1.25  , -1.1875, -1.125 , -1.0625,\n",
       "       -1.    , -0.9375, -0.875 , -0.8125, -0.75  , -0.6875, -0.625 , -0.5625,\n",
       "       -0.5   , -0.4375, -0.375 , -0.3125, -0.25  , -0.1875, -0.125 , -0.0625,\n",
       "        0.    ,  0.0625,  0.125 ,  0.1875,  0.25  ,  0.3125,  0.375 ,  0.4375,\n",
       "        0.5   ,  0.5625,  0.625 ,  0.6875,  0.75  ,  0.8125,  0.875 ,  0.9375,\n",
       "        1.    ,  1.0625,  1.125 ,  1.1875,  1.25  ,  1.3125,  1.375 ,  1.4375,\n",
       "        1.5   ,  1.5625,  1.625 ,  1.6875,  1.75  ,  1.8125,  1.875 ,  1.9375,\n",
       "        2.    ], dtype=float32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a51bf2ce-891a-4e9c-ad3f-f0f92bddd95a' class='xr-section-summary-in' type='checkbox'  ><label for='section-a51bf2ce-891a-4e9c-ad3f-f0f92bddd95a' class='xr-section-summary' >Data variables: <span>(151)</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>all_times</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-e249689d-1cac-46b9-bf38-f9cfffdf3104' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e249689d-1cac-46b9-bf38-f9cfffdf3104' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fad33b99-fd45-4325-aa00-151955742457' class='xr-var-data-in' type='checkbox'><label for='data-fad33b99-fd45-4325-aa00-151955742457' 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>description :</span></dt><dd>All times of attempted reconstruction (time base A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Time of reconstruction</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:ALL_TIMES</dd><dt><span>name :</span></dt><dd>efm/all_times</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[64]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_ALL_TIMES</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>uuid :</span></dt><dd>b7d2df9a-4fb1-5174-9350-8eadc6d3544e</dd><dt><span>version :</span></dt><dd>0</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> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64,) </td>\n",
       "                        <td> (64,) </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\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>areap_c</span></div><div class='xr-var-dims'>(time, psi_norm)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 65), meta=np.ndarray&gt;</div><input id='attrs-e2b50b10-f772-44e6-84bf-a7371b2b3dec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2b50b10-f772-44e6-84bf-a7371b2b3dec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f73753e9-a4af-4108-8e48-1dad2dc46edd' class='xr-var-data-in' type='checkbox'><label for='data-f73753e9-a4af-4108-8e48-1dad2dc46edd' 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>description :</span></dt><dd>Poloidal area enclosed by flux surface; f(psin, B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;psi_norm&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>pol area of surfaces</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:AREAP_C</dd><dt><span>name :</span></dt><dd>efm/areap_c</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 65]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_AREAP_(C)</dd><dt><span>units :</span></dt><dd>m ** 2</dd><dt><span>uuid :</span></dt><dd>cda04872-e742-5145-86c0-e3fb6c3828eb</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                        <td> 16.25 kiB </td>\n",
       "                        <td> 16.25 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 65) </td>\n",
       "                        <td> (64, 65) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>betan</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-4723fed3-2c70-42f6-b705-b32c1e431369' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4723fed3-2c70-42f6-b705-b32c1e431369' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-415686c5-583b-4303-9b95-e494887568cb' class='xr-var-data-in' type='checkbox'><label for='data-415686c5-583b-4303-9b95-e494887568cb' 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>description :</span></dt><dd>Normalised beta, efm_betat * | plasma minor radius (m) *  vacuum toroidal B field at magnetic axis (T) / plasma current (MA) |; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>betat/(I/ a Bvac_geom)</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:BETAN</dd><dt><span>name :</span></dt><dd>efm/betan</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_BETAN</dd><dt><span>units :</span></dt><dd>T</dd><dt><span>uuid :</span></dt><dd>c0db4373-b67d-51c5-8c42-ba1f42d47461</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (64,) </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\"> float32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>betap</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-981f1be4-9718-469b-ba9a-ec7dee05c3ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-981f1be4-9718-469b-ba9a-ec7dee05c3ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f029698-8d06-41f0-b534-d9866d2cede1' class='xr-var-data-in' type='checkbox'><label for='data-9f029698-8d06-41f0-b534-d9866d2cede1' 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>description :</span></dt><dd>Poloidal beta, volume-averaged pressure * 2 * mu_0 / &lt;Bp&gt;^2, Bp = averaged poloidal B field around LCFS (T), mu_0*I_plasma/integral(dl); f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Poloidal Beta</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:BETAP</dd><dt><span>name :</span></dt><dd>efm/betap</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_BETAP</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>509acbb3-d182-5a9d-9a62-e95e36587d07</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <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> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64,) </td>\n",
       "                        <td> (64,) </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\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>betapd</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-4fe8047a-302d-4c00-a952-dd4cecdddae2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4fe8047a-302d-4c00-a952-dd4cecdddae2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5ea59ef5-5cf1-473c-8b23-94610eec307c' class='xr-var-data-in' type='checkbox'><label for='data-5ea59ef5-5cf1-473c-8b23-94610eec307c' 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>description :</span></dt><dd>Poloidal beta computed using diamagnetic flux; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Poloidal Diamagnetic Bet</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:BETAPD</dd><dt><span>name :</span></dt><dd>efm/betapd</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_BETAPD</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>b368f633-dd58-5fc0-b5ae-ad89e1ad3a03</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 256 B </td>\n",
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       "                        <td> (64,) </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>betat</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-97c30d43-2eff-47f4-9c1a-1e811f58f98d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-97c30d43-2eff-47f4-9c1a-1e811f58f98d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d121e8f5-29f9-4768-87df-c3856f1d4a38' class='xr-var-data-in' type='checkbox'><label for='data-d121e8f5-29f9-4768-87df-c3856f1d4a38' 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>description :</span></dt><dd>Toroidal beta, volume-averaged pressure * 2 * mu_0 / Bphi^2, Bphi = vacuum toroidal B field at magnetic axis (T); f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Toroidal Beta</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:BETAT</dd><dt><span>name :</span></dt><dd>efm/betat</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_BETAT</dd><dt><span>units :</span></dt><dd>%</dd><dt><span>uuid :</span></dt><dd>31cb8e3a-3b82-5189-aeac-51afc7d37598</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 256 B </td>\n",
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       "                    \n",
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       "                        <td> (64,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>betatd</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-808e8fdf-c7d0-4eb5-bd56-9888fe33ce95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-808e8fdf-c7d0-4eb5-bd56-9888fe33ce95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-99de534e-d2c2-4ec6-b5f4-1bd6cf27a093' class='xr-var-data-in' type='checkbox'><label for='data-99de534e-d2c2-4ec6-b5f4-1bd6cf27a093' 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>description :</span></dt><dd>Toroidal beta computed using diamagnetic flux; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Toroidal Diamagnetic Bet</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:BETATD</dd><dt><span>name :</span></dt><dd>efm/betatd</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_BETATD</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>cfd99e7d-0996-500a-9ce9-c1049a65cf47</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bphi_rgeom</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-dcc988e1-9c89-4aed-a88f-d3e5f04d051f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dcc988e1-9c89-4aed-a88f-d3e5f04d051f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a9c00ab-21c0-41b8-b669-df67f009f17b' class='xr-var-data-in' type='checkbox'><label for='data-5a9c00ab-21c0-41b8-b669-df67f009f17b' 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>description :</span></dt><dd>Toroidal B field (total) at geometric axis; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Bphi at rgeom</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:BPHI_RGEOM</dd><dt><span>name :</span></dt><dd>efm/bphi_rgeom</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_BPHI_RGEOM</dd><dt><span>units :</span></dt><dd>T</dd><dt><span>uuid :</span></dt><dd>56188c7e-5ed0-58c9-a45e-3956dd976e00</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                    <tr>\n",
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       "                        <td> (64,) </td>\n",
       "                    </tr>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bphi_rmag</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-250660f1-7ced-4e4a-9efc-32f2e7c717a8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-250660f1-7ced-4e4a-9efc-32f2e7c717a8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-05c2a773-8b6d-47b0-9eea-5efb91614c1d' class='xr-var-data-in' type='checkbox'><label for='data-05c2a773-8b6d-47b0-9eea-5efb91614c1d' 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>description :</span></dt><dd>Toroidal B field (total) at magnetic axis; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Bphi at rmag</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:BPHI_RMAG</dd><dt><span>name :</span></dt><dd>efm/bphi_rmag</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_BPHI_RMAG</dd><dt><span>units :</span></dt><dd>T</dd><dt><span>uuid :</span></dt><dd>30855da8-9b93-5f15-8020-15466cfcdfda</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (64,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bphi_squared</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-cea9f6df-2711-4a3a-b951-387c3ad8b051' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cea9f6df-2711-4a3a-b951-387c3ad8b051' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-849a49bc-fe8c-430a-b611-2c2a8ea3446e' class='xr-var-data-in' type='checkbox'><label for='data-849a49bc-fe8c-430a-b611-2c2a8ea3446e' 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>description :</span></dt><dd>plasma volume integral of (total toroidal B field squared); f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Bphi^2 dV</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:BPHI_SQUARED</dd><dt><span>name :</span></dt><dd>efm/bphi_squared</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_BPHI_SQUARED</dd><dt><span>units :</span></dt><dd>T ** 2 * m ** 3</dd><dt><span>uuid :</span></dt><dd>e267f5cf-2189-5678-9421-009da412c98d</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bpol_squared</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-b7a16c4b-4858-459f-9eec-27e587d9221a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7a16c4b-4858-459f-9eec-27e587d9221a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fee3ec45-a54d-4a49-b05c-9220e6d8f29d' class='xr-var-data-in' type='checkbox'><label for='data-fee3ec45-a54d-4a49-b05c-9220e6d8f29d' 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>description :</span></dt><dd>plasma volume integral of (total poloidal B field squared); f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Bpol dV</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:BPOL_SQUARED</dd><dt><span>name :</span></dt><dd>efm/bpol_squared</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_BPOL_SQUARED</dd><dt><span>units :</span></dt><dd>T ** 2 * m ** 3</dd><dt><span>uuid :</span></dt><dd>34fe2d69-9528-538e-a752-95a72a29c716</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (64,) </td>\n",
       "                    </tr>\n",
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       "                        <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\"> float32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bvac_r</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-577a8741-1ffe-42a9-b34a-2325a69712b9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-577a8741-1ffe-42a9-b34a-2325a69712b9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2dc572d7-5df7-448e-89fa-7af337373972' class='xr-var-data-in' type='checkbox'><label for='data-2dc572d7-5df7-448e-89fa-7af337373972' 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>description :</span></dt><dd>Reference radius for efm_bvac_val; f(A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>radius at where B_phi=_b</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:BVAC_R</dd><dt><span>name :</span></dt><dd>efm/bvac_r</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[64]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_BVAC_R</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>8a236221-c3d2-5268-91aa-5055de097af1</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <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> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (64,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bvac_rgeom</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-f2ab093b-3224-48ab-8a8c-5ecd883c4eee' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f2ab093b-3224-48ab-8a8c-5ecd883c4eee' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a1bc2fa6-c6f4-4dac-91ea-0b011ee6aa18' class='xr-var-data-in' type='checkbox'><label for='data-a1bc2fa6-c6f4-4dac-91ea-0b011ee6aa18' 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>description :</span></dt><dd>Vacuum toroidal B field at geometric axis; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Bvac at rgeom</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:BVAC_RGEOM</dd><dt><span>name :</span></dt><dd>efm/bvac_rgeom</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_BVAC_RGEOM</dd><dt><span>units :</span></dt><dd>T</dd><dt><span>uuid :</span></dt><dd>5efaa422-e4b9-5390-b2c4-e6cc58c50f97</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <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> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (64,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bvac_rmag</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-3e3e28cd-a216-4213-be0b-a37d6d170655' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e3e28cd-a216-4213-be0b-a37d6d170655' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d2008de-fed8-48c7-82c6-11c74f59ab1f' class='xr-var-data-in' type='checkbox'><label for='data-0d2008de-fed8-48c7-82c6-11c74f59ab1f' 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>description :</span></dt><dd>Vacuum toroidal B field at magnetic axis; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Bvac at rmag</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:BVAC_RMAG</dd><dt><span>name :</span></dt><dd>efm/bvac_rmag</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_BVAC_RMAG</dd><dt><span>units :</span></dt><dd>T</dd><dt><span>uuid :</span></dt><dd>b3d92487-954b-540a-8bdd-ba31aab20633</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "            <table style=\"border-collapse: collapse;\">\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> (64,) </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>bvac_val</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-6811eea3-379b-4c4e-80d1-ed0f328fdfbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6811eea3-379b-4c4e-80d1-ed0f328fdfbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cf788ec3-48b0-4776-aba2-c53a97625f17' class='xr-var-data-in' type='checkbox'><label for='data-cf788ec3-48b0-4776-aba2-c53a97625f17' 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>description :</span></dt><dd>Reference vacuum toroidal B field at efm_bvac_r; f(A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Vacuum toroidal field at</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:BVAC_VAL</dd><dt><span>name :</span></dt><dd>efm/bvac_val</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[64]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_BVAC_VAL</dd><dt><span>units :</span></dt><dd>T</dd><dt><span>uuid :</span></dt><dd>93aa280a-d1fe-518c-8f09-36bfbabe318e</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 256 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 256 B </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>current_centrd_r</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-63d95726-658c-462c-83e6-2a91ffa532f4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-63d95726-658c-462c-83e6-2a91ffa532f4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dbde7f30-926b-4d7c-8ac6-891665d8a1aa' class='xr-var-data-in' type='checkbox'><label for='data-dbde7f30-926b-4d7c-8ac6-891665d8a1aa' 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>description :</span></dt><dd>R co-ordinate of current centroid; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>radius of current centro</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM.CURRENT:CENTRD_R</dd><dt><span>name :</span></dt><dd>efm/current_centrd_r</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_CURRENT_CENTRD_R</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>36f75534-498b-57bd-a552-d6a46c2525c5</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                        <td> 256 B </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <td> 256 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>cutip</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1975b078-ca87-4df4-8303-56bbe498993b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1975b078-ca87-4df4-8303-56bbe498993b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2d8590be-7afd-424f-8e21-ea79d8722177' class='xr-var-data-in' type='checkbox'><label for='data-2d8590be-7afd-424f-8e21-ea79d8722177' 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>description :</span></dt><dd>Plasma current cut-off; currents below this imply vacuum reconstruction</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Ip threshold current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:CUTIP</dd><dt><span>name :</span></dt><dd>efm/cutip</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_CUTIP</dd><dt><span>units :</span></dt><dd>A</dd><dt><span>uuid :</span></dt><dd>a8ab9ea9-5b86-541d-86b4-54a44a90a82c</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diamag_fluxc</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-9eec5a9e-df1b-413d-b174-689936b89f0f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9eec5a9e-df1b-413d-b174-689936b89f0f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c1794127-e565-42fe-b932-bafa6715bfda' class='xr-var-data-in' type='checkbox'><label for='data-c1794127-e565-42fe-b932-bafa6715bfda' 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>description :</span></dt><dd>Output (computed) diamagnetic flux; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Computed Diamagnetic Flu</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM.DIAMAG_FLUX:C</dd><dt><span>name :</span></dt><dd>efm/diamag_fluxc</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_DIAMAG_FLUX(C)</dd><dt><span>units :</span></dt><dd>Wb</dd><dt><span>uuid :</span></dt><dd>0e65f3a5-c0da-5528-b08b-c50f6328af23</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64,) </td>\n",
       "                        <td> (64,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>diamag_fluxx</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-891d6fe6-4283-4809-a7f1-e498c93f6841' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-891d6fe6-4283-4809-a7f1-e498c93f6841' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7b8f5591-1153-4fb9-8090-e4efc2e1908e' class='xr-var-data-in' type='checkbox'><label for='data-7b8f5591-1153-4fb9-8090-e4efc2e1908e' 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>description :</span></dt><dd>Input (experimental) diamagnetic flux; f(A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Measured Diamagnetic Flu</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM.DIAMAG_FLUX:X</dd><dt><span>name :</span></dt><dd>efm/diamag_fluxx</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_DIAMAG_FLUX(X)</dd><dt><span>units :</span></dt><dd>Wb</dd><dt><span>uuid :</span></dt><dd>fdd4b47d-e12d-561f-b89e-97acf3312b15</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (64,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fcoil_c</span></div><div class='xr-var-dims'>(time, fcoil_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 101), meta=np.ndarray&gt;</div><input id='attrs-e7fa2da9-3294-4685-bd4f-e0c5da5a9d48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e7fa2da9-3294-4685-bd4f-e0c5da5a9d48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7ff38cf3-a2ed-45b3-89c6-55a6225a0856' class='xr-var-data-in' type='checkbox'><label for='data-7ff38cf3-a2ed-45b3-89c6-55a6225a0856' 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>description :</span></dt><dd>Output (computed) fitted toroidal currents; f(nfcoil, A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;fcoil_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Computed f-coil current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:FCOIL_C</dd><dt><span>name :</span></dt><dd>efm/fcoil_c</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[64, 101]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_FCOIL_(C)</dd><dt><span>units :</span></dt><dd>A</dd><dt><span>uuid :</span></dt><dd>88d8d3ed-53e1-512e-b37b-d57d2b097292</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <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> 25.25 kiB </td>\n",
       "                        <td> 25.25 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 101) </td>\n",
       "                        <td> (64, 101) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fcoil_chisq</span></div><div class='xr-var-dims'>(time, fcoil_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 101), meta=np.ndarray&gt;</div><input id='attrs-b45942a4-3ac2-4cdf-b41a-d9340cb7147e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b45942a4-3ac2-4cdf-b41a-d9340cb7147e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff333b26-5360-4207-a13b-d3ed08d5f35a' class='xr-var-data-in' type='checkbox'><label for='data-ff333b26-5360-4207-a13b-d3ed08d5f35a' 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>description :</span></dt><dd>Chi-squared of each fitted f-coil; f(nfcoil, A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;fcoil_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Chi**2 of each f-coil</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:FCOIL_CHISQ</dd><dt><span>name :</span></dt><dd>efm/fcoil_chisq</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[64, 101]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_FCOIL_CHISQ</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>512a9812-48cc-528f-9af9-4b633366ec9d</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 25.25 kiB </td>\n",
       "                        <td> 25.25 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 101) </td>\n",
       "                        <td> (64, 101) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fcoil_circ</span></div><div class='xr-var-dims'>(fcoil_seg_n)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(938,), meta=np.ndarray&gt;</div><input id='attrs-4576c3b4-6c70-4fac-894b-c902e7af587f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4576c3b4-6c70-4fac-894b-c902e7af587f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4e6a9ee4-81d8-403b-bc27-28ddd3952b11' class='xr-var-data-in' type='checkbox'><label for='data-4e6a9ee4-81d8-403b-bc27-28ddd3952b11' 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>description :</span></dt><dd>Circuit number of each f-coil element; f(fcoil_segs_n)</dd><dt><span>dims :</span></dt><dd>[&#x27;fcoil_seg_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>f-Coil circuit</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:FCOIL_CIRC</dd><dt><span>name :</span></dt><dd>efm/fcoil_circ</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 938]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_FCOIL_CIRC</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>55b009a2-d6a4-534c-8da1-a04dd6ecb1eb</dd><dt><span>version :</span></dt><dd>0</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> 3.66 kiB </td>\n",
       "                        <td> 3.66 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (938,) </td>\n",
       "                        <td> (938,) </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\"> int32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fcoil_height</span></div><div class='xr-var-dims'>(fcoil_seg_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(938,), meta=np.ndarray&gt;</div><input id='attrs-3e910401-43ff-4155-9949-35ed32a5e496' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3e910401-43ff-4155-9949-35ed32a5e496' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29757d3d-4392-4e96-9348-11e12a3ad630' class='xr-var-data-in' type='checkbox'><label for='data-29757d3d-4392-4e96-9348-11e12a3ad630' 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>description :</span></dt><dd>Z extent of each f-coil element; f(fcoil_segs_n)</dd><dt><span>dims :</span></dt><dd>[&#x27;fcoil_seg_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>f-Coil Height</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:FCOIL_HEIGHT</dd><dt><span>name :</span></dt><dd>efm/fcoil_height</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 938]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_FCOIL_HEIGHT</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>cda552cd-454d-5b4c-8783-f299242be50d</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 3.66 kiB </td>\n",
       "                        <td> 3.66 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (938,) </td>\n",
       "                        <td> (938,) </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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fcoil_r</span></div><div class='xr-var-dims'>(fcoil_seg_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(938,), meta=np.ndarray&gt;</div><input id='attrs-36f94056-e0b5-4467-a3bc-db6facb79c2e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-36f94056-e0b5-4467-a3bc-db6facb79c2e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-522deedb-707c-4ed8-a5f8-28babc8000be' class='xr-var-data-in' type='checkbox'><label for='data-522deedb-707c-4ed8-a5f8-28babc8000be' 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>description :</span></dt><dd>R centroid of each f-coil element; f(fcoil_segs_n)</dd><dt><span>dims :</span></dt><dd>[&#x27;fcoil_seg_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>f-Coil Location Radius</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:FCOIL_R</dd><dt><span>name :</span></dt><dd>efm/fcoil_r</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 938]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_FCOIL_R</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>0fe95de9-0141-5d40-9b5f-016adba85b5e</dd><dt><span>version :</span></dt><dd>0</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> 3.66 kiB </td>\n",
       "                        <td> 3.66 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (938,) </td>\n",
       "                        <td> (938,) </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\"> float32 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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       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\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>fcoil_turns</span></div><div class='xr-var-dims'>(fcoil_seg_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(938,), meta=np.ndarray&gt;</div><input id='attrs-e2ebeb88-a7e9-4752-b107-1a91b3683265' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e2ebeb88-a7e9-4752-b107-1a91b3683265' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-698b4bc8-48d9-4a71-9fd0-ff8ba1e479b4' class='xr-var-data-in' type='checkbox'><label for='data-698b4bc8-48d9-4a71-9fd0-ff8ba1e479b4' 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>description :</span></dt><dd>Number of turns represented by each f-coil elements; f(fcoil_segs_n)</dd><dt><span>dims :</span></dt><dd>[&#x27;fcoil_seg_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>f-Coil turns</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:FCOIL_TURNS</dd><dt><span>name :</span></dt><dd>efm/fcoil_turns</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 938]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_FCOIL_TURNS</dd><dt><span>units :</span></dt><dd>deg</dd><dt><span>uuid :</span></dt><dd>6baebdf3-512d-552e-bde1-71876bf5885c</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <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> 3.66 kiB </td>\n",
       "                        <td> 3.66 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (938,) </td>\n",
       "                        <td> (938,) </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\"> float32 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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       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"25\" style=\"stroke-width:2\" />\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>fcoil_width</span></div><div class='xr-var-dims'>(fcoil_seg_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(938,), meta=np.ndarray&gt;</div><input id='attrs-a9c81e28-ef95-4cc0-ae1e-16932d6a74a9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9c81e28-ef95-4cc0-ae1e-16932d6a74a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c06de721-ec67-4d26-883c-25195353a80f' class='xr-var-data-in' type='checkbox'><label for='data-c06de721-ec67-4d26-883c-25195353a80f' 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>description :</span></dt><dd>R extent of each f-coil element; f(fcoil_segs_n)</dd><dt><span>dims :</span></dt><dd>[&#x27;fcoil_seg_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>f-Coil Width</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:FCOIL_WIDTH</dd><dt><span>name :</span></dt><dd>efm/fcoil_width</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 938]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_FCOIL_WIDTH</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>f3afe543-951e-5023-a156-164e78d05458</dd><dt><span>version :</span></dt><dd>0</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> 3.66 kiB </td>\n",
       "                        <td> 3.66 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (938,) </td>\n",
       "                        <td> (938,) </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\"> float32 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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       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fcoil_x</span></div><div class='xr-var-dims'>(time, fcoil_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 101), meta=np.ndarray&gt;</div><input id='attrs-69ae68ad-d9ba-4ae7-98f3-3d948f127da2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69ae68ad-d9ba-4ae7-98f3-3d948f127da2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-55ec47af-6c59-4348-98da-0a5a4c519d6b' class='xr-var-data-in' type='checkbox'><label for='data-55ec47af-6c59-4348-98da-0a5a4c519d6b' 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>description :</span></dt><dd>Input (experimental) fitted toroidal currents; f(nfcoil, A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;fcoil_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Measured f-coil current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:FCOIL_X</dd><dt><span>name :</span></dt><dd>efm/fcoil_x</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[64, 101]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_FCOIL_(X)</dd><dt><span>units :</span></dt><dd>A</dd><dt><span>uuid :</span></dt><dd>42b92f1d-3050-5101-a821-a717b44db81f</dd><dt><span>version :</span></dt><dd>0</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> 25.25 kiB </td>\n",
       "                        <td> 25.25 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 101) </td>\n",
       "                        <td> (64, 101) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"126\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>fcoil_xmult</span></div><div class='xr-var-dims'>(fcoil_seg_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(938,), meta=np.ndarray&gt;</div><input id='attrs-09b4a450-bce4-4428-9ba7-48608fff2e30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-09b4a450-bce4-4428-9ba7-48608fff2e30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-70db226c-1db5-45b4-8c44-541e22c15f13' class='xr-var-data-in' type='checkbox'><label for='data-70db226c-1db5-45b4-8c44-541e22c15f13' 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>description :</span></dt><dd>Multiplier for each f-coil element to weight current within coil, e.g. by area; f(fcoil_segs_n)</dd><dt><span>dims :</span></dt><dd>[&#x27;fcoil_seg_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>f-Coil turns multiplier</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:FCOIL_XMULT</dd><dt><span>name :</span></dt><dd>efm/fcoil_xmult</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 938]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_FCOIL_XMULT</dd><dt><span>units :</span></dt><dd>deg</dd><dt><span>uuid :</span></dt><dd>72c999a4-3312-572d-9eab-a7e484696661</dd><dt><span>version :</span></dt><dd>0</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> 3.66 kiB </td>\n",
       "                        <td> 3.66 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (938,) </td>\n",
       "                        <td> (938,) </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",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <td> 16.25 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ip_times</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-c2abc719-bf79-4591-9435-1ee6f029f9ef' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c2abc719-bf79-4591-9435-1ee6f029f9ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8c7240f3-edb2-434f-a69b-3624b69c7088' class='xr-var-data-in' type='checkbox'><label for='data-8c7240f3-edb2-434f-a69b-3624b69c7088' 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>description :</span></dt><dd>All times of attempted reconstruction for which plasma is present (time base C)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Time of reconstruction</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:IP_TIMES</dd><dt><span>name :</span></dt><dd>efm/ip_times</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_IP_TIMES</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>uuid :</span></dt><dd>28913cd6-ffdc-57e1-8e42-9e8fb1f57044</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>irod</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-91c001b7-bc1f-4f0c-9bf2-a925a8dfa9ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-91c001b7-bc1f-4f0c-9bf2-a925a8dfa9ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8764f8f6-300e-4b28-967a-bc5dfe8566b7' class='xr-var-data-in' type='checkbox'><label for='data-8764f8f6-300e-4b28-967a-bc5dfe8566b7' 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>description :</span></dt><dd>Toroidal rod current; f(A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>toroidal rod current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:IROD</dd><dt><span>name :</span></dt><dd>efm/irod</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[64]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_IROD</dd><dt><span>units :</span></dt><dd>A-T</dd><dt><span>uuid :</span></dt><dd>31ffb6f2-be4f-563e-a7bb-1a853f089cd2</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kffcur</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-7926b8ad-62ec-4d9f-b733-f5472f70e7aa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7926b8ad-62ec-4d9f-b733-f5472f70e7aa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c0fef581-55e0-4487-ac60-86bae254856b' class='xr-var-data-in' type='checkbox'><label for='data-c0fef581-55e0-4487-ac60-86bae254856b' 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>description :</span></dt><dd>Number of ff&#x27; coefficients</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Number of FFprime Coefs</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:KFFCUR</dd><dt><span>name :</span></dt><dd>efm/kffcur</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_KFFCUR</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>6aacce3c-9816-5036-8fc1-c612ae201c7d</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kfffnc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d6ef642e-2838-474a-bb1c-978851590ca3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6ef642e-2838-474a-bb1c-978851590ca3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec3a8784-45e9-47b6-a22f-07fa41f65ec1' class='xr-var-data-in' type='checkbox'><label for='data-ec3a8784-45e9-47b6-a22f-07fa41f65ec1' 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>description :</span></dt><dd>ff&#x27; basis function type</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Basis Function Number fo</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:KFFFNC</dd><dt><span>name :</span></dt><dd>efm/kfffnc</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_KFFFNC</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>2e2872a1-a56b-5f26-aeaa-062d86c8f99a</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kppcur</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-6997bb24-bb05-4f53-a751-631a46b04fd3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6997bb24-bb05-4f53-a751-631a46b04fd3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9a5a2d17-a533-4fbe-af16-f2c150f8246e' class='xr-var-data-in' type='checkbox'><label for='data-9a5a2d17-a533-4fbe-af16-f2c150f8246e' 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>description :</span></dt><dd>Number of p&#x27; coefficients</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Number of PPrime Coefs</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:KPPCUR</dd><dt><span>name :</span></dt><dd>efm/kppcur</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_KPPCUR</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>42652d41-1a2b-57a1-96f8-c992dc0a7a1d</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kppfnc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ea6be593-cbb0-4bf7-9ab4-2a692b247eb7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea6be593-cbb0-4bf7-9ab4-2a692b247eb7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5f5bcd9-9d49-495a-8981-381f56d544d9' class='xr-var-data-in' type='checkbox'><label for='data-b5f5bcd9-9d49-495a-8981-381f56d544d9' 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>description :</span></dt><dd>p&#x27; basis function type</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Basis Function Number fo</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:KPPFNC</dd><dt><span>name :</span></dt><dd>efm/kppfnc</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_KPPFNC</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>3956ffcf-a0d9-519b-b3d0-9dae3bc12dc4</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kwwcur</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ed54c614-9df8-49e1-998c-8dc38f32d520' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed54c614-9df8-49e1-998c-8dc38f32d520' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-710042fb-22a4-405f-b011-fcc53feb2008' class='xr-var-data-in' type='checkbox'><label for='data-710042fb-22a4-405f-b011-fcc53feb2008' 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>description :</span></dt><dd>Number of pw&#x27; coefficients</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Number of P(Rot) Coefs</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:KWWCUR</dd><dt><span>name :</span></dt><dd>efm/kwwcur</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_KWWCUR</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>2a44700a-7b6c-549b-8a33-371aebecea0d</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>kwwfnc</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-21fcf57f-bfde-4ec0-bfe0-541b497bb03b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21fcf57f-bfde-4ec0-bfe0-541b497bb03b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c0abfd9-d698-4a76-a280-0035dc5a57e2' class='xr-var-data-in' type='checkbox'><label for='data-2c0abfd9-d698-4a76-a280-0035dc5a57e2' 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>description :</span></dt><dd>pw&#x27; basis function type</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Basis Function Number fo</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:KWWFNC</dd><dt><span>name :</span></dt><dd>efm/kwwfnc</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_KWWFNC</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>00f8b868-d0af-5c1e-b6f3-5764c56b55eb</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lcfs_length</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-bd75ee07-e243-4e23-ab97-18b59eecfe87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bd75ee07-e243-4e23-ab97-18b59eecfe87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-54b2a9bd-f8be-472b-842c-9d0f019e0115' class='xr-var-data-in' type='checkbox'><label for='data-54b2a9bd-f8be-472b-842c-9d0f019e0115' 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>description :</span></dt><dd>Length of poloidal cross-section of LCFS; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>length of lcfs</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:LCFS_LENGTH</dd><dt><span>name :</span></dt><dd>efm/lcfs_length</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_LCFS_LENGTH</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>d663ddf5-7aa2-546f-94b0-ec9dbeb0d439</dd><dt><span>version :</span></dt><dd>0</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> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64,) </td>\n",
       "                        <td> (64,) </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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"77\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lcfs_r</span></div><div class='xr-var-dims'>(time, lcfs_coords)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 147), meta=np.ndarray&gt;</div><input id='attrs-70a23cb1-a96c-4e8f-85c7-967d4c724024' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-70a23cb1-a96c-4e8f-85c7-967d4c724024' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b45417c6-ff36-4fb3-929b-4ce1c390e692' class='xr-var-data-in' type='checkbox'><label for='data-b45417c6-ff36-4fb3-929b-4ce1c390e692' 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>description :</span></dt><dd>LCFS R coordinate values; f(nlcfs, B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;lcfs_coords&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>r-coords of separatrix</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:LCFS_R_C</dd><dt><span>name :</span></dt><dd>efm/lcfsr_c</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 2600]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_LCFS(R)_(C)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>e5dfae60-bbcc-594f-a009-fe925a30cb56</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 36.75 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 147) </td>\n",
       "                        <td> (64, 147) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lcfs_z</span></div><div class='xr-var-dims'>(time, lcfs_coords)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 147), meta=np.ndarray&gt;</div><input id='attrs-4c8c4480-4d50-4db3-a1a7-3ee84fcefa09' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4c8c4480-4d50-4db3-a1a7-3ee84fcefa09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50fcb082-6cab-40b7-bc1b-16e59d430119' class='xr-var-data-in' type='checkbox'><label for='data-50fcb082-6cab-40b7-bc1b-16e59d430119' 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>description :</span></dt><dd>LCFS Z coordinate values; f(nlcfs, B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;lcfs_coords&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>z-coords of separatrix</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:LCFS_Z_C</dd><dt><span>name :</span></dt><dd>efm/lcfsz_c</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 2600]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_LCFS(Z)_(C)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>b7cccb45-5cb3-574e-9a7e-03248a416f2a</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 36.75 kiB </td>\n",
       "                        <td> 36.75 kiB </td>\n",
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       "                        <td> (64, 147) </td>\n",
       "                        <td> (64, 147) </td>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lcfsn_c</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-d1e696f0-5e4a-4c09-bbb4-87e622fabe8a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1e696f0-5e4a-4c09-bbb4-87e622fabe8a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e7b1d50-1727-4f64-9217-c34e06d2b64b' class='xr-var-data-in' type='checkbox'><label for='data-7e7b1d50-1727-4f64-9217-c34e06d2b64b' 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>description :</span></dt><dd>Number of LCFS coordinate points; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>No. of coords on lcfs</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:LCFS_N_C</dd><dt><span>name :</span></dt><dd>efm/lcfsn_c</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_LCFS(N)_(C)</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>40983563-a4d5-5bd9-add4-e96b36795023</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 512 B </td>\n",
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       "                        <td> (64,) </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>li</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-ef8a0861-07e3-4b47-906e-4533bec1962e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ef8a0861-07e3-4b47-906e-4533bec1962e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce254407-d14e-4160-bca9-41ec79d73723' class='xr-var-data-in' type='checkbox'><label for='data-ce254407-d14e-4160-bca9-41ec79d73723' 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>description :</span></dt><dd>Plasma internal inductance, vol avg (Bp^2) / surf avg (Bp)^2; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Plasma Inductance</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:LI</dd><dt><span>name :</span></dt><dd>efm/li</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_LI</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>8477215a-950b-584e-a482-848eb28d6b6a</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 256 B </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 148 B </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>limiterz</span></div><div class='xr-var-dims'>(limiter_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(37,), meta=np.ndarray&gt;</div><input id='attrs-9921a1f3-49c5-4f09-b32f-e0c4cd2e77a1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9921a1f3-49c5-4f09-b32f-e0c4cd2e77a1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb069298-8155-4b1e-945f-1fef128a230b' class='xr-var-data-in' type='checkbox'><label for='data-bb069298-8155-4b1e-945f-1fef128a230b' 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>description :</span></dt><dd>Z co-ordinates of limiter; f(nlimiter)</dd><dt><span>dims :</span></dt><dd>[&#x27;limiter_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Limiter Height</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:LIMITER_Z</dd><dt><span>name :</span></dt><dd>efm/limiterz</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 37]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_LIMITER(Z)</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>2d3888a5-604f-530f-9c1e-f2488b997d0a</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <td> 148 B </td>\n",
       "                        <td> 148 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (37,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>magnetic_axis_r</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-5478a934-4f5c-4c74-8976-596ed0f3bc72' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5478a934-4f5c-4c74-8976-596ed0f3bc72' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-826235bf-3d36-4c80-9fa8-fb1c085e7da2' class='xr-var-data-in' type='checkbox'><label for='data-826235bf-3d36-4c80-9fa8-fb1c085e7da2' 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>description :</span></dt><dd>R co-ordinate of magnetic axis; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Radius of Magnetic Axis</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM.MAGNETIC:AXIS_R</dd><dt><span>name :</span></dt><dd>efm/magnetic_axis_r</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_MAGNETIC_AXIS_R</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>dd081d76-7df6-56a5-a87e-8e30403e5283</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (64,) </td>\n",
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       "                    <tr>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>magnetic_axis_z</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-5cf2df8d-4ea5-4923-a611-6e1ea6b1c6de' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5cf2df8d-4ea5-4923-a611-6e1ea6b1c6de' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-28705dcb-1f05-47f9-8ac0-3249cfdc83c0' class='xr-var-data-in' type='checkbox'><label for='data-28705dcb-1f05-47f9-8ac0-3249cfdc83c0' 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>description :</span></dt><dd>Z co-ordinate of magnetic axis; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Height of Magnetic Axis</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM.MAGNETIC:AXIS_Z</dd><dt><span>name :</span></dt><dd>efm/magnetic_axis_z</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_MAGNETIC_AXIS_Z</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>3ada1264-043f-58e0-94af-3b2210ddf5f8</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 256 B </td>\n",
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       "                        <td> (64,) </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 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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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>magpr_ang</span></div><div class='xr-var-dims'>(mag_probe_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(78,), meta=np.ndarray&gt;</div><input id='attrs-0bb3884d-6cce-4960-9bce-f04f4dfd334f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bb3884d-6cce-4960-9bce-f04f4dfd334f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c42bb142-c301-4904-890d-36b53fd2ffad' class='xr-var-data-in' type='checkbox'><label for='data-c42bb142-c301-4904-890d-36b53fd2ffad' 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>description :</span></dt><dd>Defining angular skew of each magnetic field probe; f(magpr_n_n)</dd><dt><span>dims :</span></dt><dd>[&#x27;mag_probe_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Magnetic Probe Angle</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:MAGPR_ANG</dd><dt><span>name :</span></dt><dd>efm/magpr_ang</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 78]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_MAGPR_ANG</dd><dt><span>units :</span></dt><dd>deg</dd><dt><span>uuid :</span></dt><dd>cf0344ce-f800-54cb-9c04-c45b65509417</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 312 B </td>\n",
       "                        <td> 312 B </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (78,) </td>\n",
       "                        <td> (78,) </td>\n",
       "                    </tr>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>magpr_c</span></div><div class='xr-var-dims'>(time, mag_probe_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 78), meta=np.ndarray&gt;</div><input id='attrs-49c0a0b7-d120-4b10-8d41-34a420bebb78' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-49c0a0b7-d120-4b10-8d41-34a420bebb78' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-636fa555-cbe4-4f1b-b41a-996b339f7f3c' class='xr-var-data-in' type='checkbox'><label for='data-636fa555-cbe4-4f1b-b41a-996b339f7f3c' 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>description :</span></dt><dd>Output (computed) fitted magnetic field probes; f(magpr_n, A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;mag_probe_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Computed magnetic signal</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:MAGPR_C</dd><dt><span>name :</span></dt><dd>efm/magpr_c</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[64, 78]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_MAGPR_(C)</dd><dt><span>units :</span></dt><dd>T</dd><dt><span>uuid :</span></dt><dd>140c8311-4680-5029-98a5-b37771e66280</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 19.50 kiB </td>\n",
       "                        <td> 19.50 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (64, 78) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>magpr_len</span></div><div class='xr-var-dims'>(mag_probe_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(78,), meta=np.ndarray&gt;</div><input id='attrs-9a7f4226-0de9-4b00-a797-fba4b8d07e85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a7f4226-0de9-4b00-a797-fba4b8d07e85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f99f9296-e814-4234-9b77-cebe41bba873' class='xr-var-data-in' type='checkbox'><label for='data-f99f9296-e814-4234-9b77-cebe41bba873' 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>description :</span></dt><dd>Defining length of each magnetic field probe; f(magpr_n_n)</dd><dt><span>dims :</span></dt><dd>[&#x27;mag_probe_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Magnetic Probe Length</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:MAGPR_LEN</dd><dt><span>name :</span></dt><dd>efm/magpr_len</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 78]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_MAGPR_LEN</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>0f4e84d7-9ff5-5641-b9a5-ddfe5b41d99f</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 312 B </td>\n",
       "                        <td> 312 B </td>\n",
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       "                        <td> (78,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>magpr_r</span></div><div class='xr-var-dims'>(mag_probe_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(78,), meta=np.ndarray&gt;</div><input id='attrs-9836972c-455e-49ee-a4b5-3f62be486422' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9836972c-455e-49ee-a4b5-3f62be486422' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce7a0bc1-85ee-4d6f-a202-ad3b9ee27154' class='xr-var-data-in' type='checkbox'><label for='data-ce7a0bc1-85ee-4d6f-a202-ad3b9ee27154' 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>description :</span></dt><dd>R of each magnetic field probe; f(magpr_n)</dd><dt><span>dims :</span></dt><dd>[&#x27;mag_probe_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Magnetic Probe Location</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:MAGPR_R</dd><dt><span>name :</span></dt><dd>efm/magpr_r</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 78]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_MAGPR_R</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>53aed4a5-9f8c-5bc0-b9e6-c8893224ea2c</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 312 B </td>\n",
       "                        <td> 312 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (78,) </td>\n",
       "                        <td> (78,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>magpr_x</span></div><div class='xr-var-dims'>(time, mag_probe_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 78), meta=np.ndarray&gt;</div><input id='attrs-1045830e-37c9-4317-a2bf-693c33ff93e8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1045830e-37c9-4317-a2bf-693c33ff93e8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-50e4bc26-325f-44d0-8488-73b090ee767e' class='xr-var-data-in' type='checkbox'><label for='data-50e4bc26-325f-44d0-8488-73b090ee767e' 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>description :</span></dt><dd>Input (experimental) fitted magnetic field probes; f(magpr_n, A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;mag_probe_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Measured magnetic signal</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:MAGPR_X</dd><dt><span>name :</span></dt><dd>efm/magpr_x</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[64, 78]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_MAGPR_(X)</dd><dt><span>units :</span></dt><dd>T</dd><dt><span>uuid :</span></dt><dd>c0ff6e1d-723f-59df-9257-93b1853e397a</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 19.50 kiB </td>\n",
       "                        <td> 19.50 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 78) </td>\n",
       "                        <td> (64, 78) </td>\n",
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       "                    <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",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>magpr_z</span></div><div class='xr-var-dims'>(mag_probe_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(78,), meta=np.ndarray&gt;</div><input id='attrs-718b8297-8402-431b-9022-aeb758ad62b1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-718b8297-8402-431b-9022-aeb758ad62b1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-29ef0927-e64d-4cb3-8391-8bea5eb84046' class='xr-var-data-in' type='checkbox'><label for='data-29ef0927-e64d-4cb3-8391-8bea5eb84046' 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>description :</span></dt><dd>Z of each magnetic field probe; f(magpr_n)</dd><dt><span>dims :</span></dt><dd>[&#x27;mag_probe_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Magnetic Probe Location</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:MAGPR_Z</dd><dt><span>name :</span></dt><dd>efm/magpr_z</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 78]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_MAGPR_Z</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>d3f6a120-771f-5a5c-b77d-51ba8cf70341</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                        <th> Array </th>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 312 B </td>\n",
       "                        <td> 312 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (78,) </td>\n",
       "                        <td> (78,) </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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>magpri_chisq</span></div><div class='xr-var-dims'>(time, mag_probe_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 78), meta=np.ndarray&gt;</div><input id='attrs-791fbceb-96c1-4ed3-b27b-6ce839ddaa10' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-791fbceb-96c1-4ed3-b27b-6ce839ddaa10' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7f554b27-f796-4cae-953f-7924d6a00c54' class='xr-var-data-in' type='checkbox'><label for='data-7f554b27-f796-4cae-953f-7924d6a00c54' 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>description :</span></dt><dd>Chi-squared of each fitted magnetic field probe; f(magpr_n, A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;mag_probe_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Chi**2 of each magnetic</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:MAGPRI_CHISQ</dd><dt><span>name :</span></dt><dd>efm/magpri_chisq</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[64, 78]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_MAGPRI_CHISQ</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>d75b0d2a-d00b-5708-aa38-12ba81466f52</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 19.50 kiB </td>\n",
       "                        <td> 19.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 78) </td>\n",
       "                        <td> (64, 78) </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\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"148\" 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",
       "  <line x1=\"0\" y1=\"98\" x2=\"120\" y2=\"98\" style=\"stroke-width:2\" />\n",
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       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"98\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"98\" style=\"stroke-width:2\" />\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>minor_radius</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-f4e0426c-c79c-4987-a9db-41beb07e3c70' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4e0426c-c79c-4987-a9db-41beb07e3c70' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5cd71b27-fc9c-4e77-8684-68e53f4da773' class='xr-var-data-in' type='checkbox'><label for='data-5cd71b27-fc9c-4e77-8684-68e53f4da773' 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>description :</span></dt><dd>Minor radius of plasma; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Minor Radius</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:MINOR_RADIUS</dd><dt><span>name :</span></dt><dd>efm/minor_radius</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_MINOR_RADIUS</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>a221f003-19e0-5589-8378-ab4b55407364</dd><dt><span>version :</span></dt><dd>0</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> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64,) </td>\n",
       "                        <td> (64,) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"77\" 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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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nh</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1820da2e-5547-4067-8c67-4a0595607283' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1820da2e-5547-4067-8c67-4a0595607283' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96efdd90-4d46-422d-827e-67f583cfe819' class='xr-var-data-in' type='checkbox'><label for='data-96efdd90-4d46-422d-827e-67f583cfe819' 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>description :</span></dt><dd>Number of vertical grid points</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Number of grid points in</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:NH</dd><dt><span>name :</span></dt><dd>efm/nh</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_NH</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>c68811ac-f71c-57dd-ad68-54d2df6e0075</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>npress</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0ae9e308-a6e4-4238-87fa-b1f5bcf9e036' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0ae9e308-a6e4-4238-87fa-b1f5bcf9e036' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6ea12dc2-397c-4d9f-8eee-99ed7a7a6146' class='xr-var-data-in' type='checkbox'><label for='data-6ea12dc2-397c-4d9f-8eee-99ed7a7a6146' 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>description :</span></dt><dd>Number of pressure constraints</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>No. of pressure constrai</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:NPRESS</dd><dt><span>name :</span></dt><dd>efm/npress</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_NPRESS</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>8c183e04-e4a2-5458-8886-b23cce6fb7d2</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>num_iterations</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-26cf7765-6065-4f26-b669-372faf36f059' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-26cf7765-6065-4f26-b669-372faf36f059' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-986bf7f7-15ef-4360-995e-6678e100d0eb' class='xr-var-data-in' type='checkbox'><label for='data-986bf7f7-15ef-4360-995e-6678e100d0eb' 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>description :</span></dt><dd>Number of iterations; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>number of iterations</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM.NUM:ITERATIONS</dd><dt><span>name :</span></dt><dd>efm/num_iterations</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_NUM_ITERATIONS</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>d34f53c3-011b-5fe4-aea2-bc2865c179b2</dd><dt><span>version :</span></dt><dd>0</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> 512 B </td>\n",
       "                        <td> 512 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64,) </td>\n",
       "                        <td> (64,) </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=\"77\" 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",
       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"27\" style=\"stroke-width:2\" />\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>nw</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-3bb7cd1a-417f-43af-878e-310a1284f65a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3bb7cd1a-417f-43af-878e-310a1284f65a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3089bbb1-bdd0-4ef0-bc8b-c03379ff1922' class='xr-var-data-in' type='checkbox'><label for='data-3089bbb1-bdd0-4ef0-bc8b-c03379ff1922' 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>description :</span></dt><dd>Number of horizontal grid points</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Number of grid points in</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:NW</dd><dt><span>name :</span></dt><dd>efm/nw</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_NW</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>65f6da9a-16a2-56d2-a15b-4b7d100cd164</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p2ar_c</span></div><div class='xr-var-dims'>(time, profile_r)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 129), meta=np.ndarray&gt;</div><input id='attrs-7052cec2-02ac-4a78-930c-d51126bb5f50' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7052cec2-02ac-4a78-930c-d51126bb5f50' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d284a5e9-eef2-48b0-a203-919fc21e509f' class='xr-var-data-in' type='checkbox'><label for='data-d284a5e9-eef2-48b0-a203-919fc21e509f' 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>description :</span></dt><dd>Non-rotational pressure contribution as a function of radius at Z=0; f(nw, B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;profile_r&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>p(r) (non-rotational par</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:P2A_R_C</dd><dt><span>name :</span></dt><dd>efm/p2ar_c</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 65]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_P2A(R)_(C)</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>uuid :</span></dt><dd>976a8e19-f85a-5952-aa6f-5e953a4e8594</dd><dt><span>version :</span></dt><dd>0</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> 32.25 kiB </td>\n",
       "                        <td> 32.25 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 129) </td>\n",
       "                        <td> (64, 129) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"109\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p2br_c</span></div><div class='xr-var-dims'>(time, profile_r)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 129), meta=np.ndarray&gt;</div><input id='attrs-5bcdd10e-c396-4da7-9346-13e9a0ed042f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bcdd10e-c396-4da7-9346-13e9a0ed042f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-93368733-03b0-445a-a965-882f161cca2e' class='xr-var-data-in' type='checkbox'><label for='data-93368733-03b0-445a-a965-882f161cca2e' 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>description :</span></dt><dd>Coefficient of rotational pressure contribution as a function of radius at Z=0; f(nw, B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;profile_r&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>pw(r) (rotational part)</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:P2B_R_C</dd><dt><span>name :</span></dt><dd>efm/p2br_c</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 65]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_P2B(R)_(C)</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>uuid :</span></dt><dd>e339ce02-2da8-5c93-b4de-4db8f03057bf</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                        <td> 32.25 kiB </td>\n",
       "                        <td> 32.25 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
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       "                        <td> (64, 129) </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p2cr_c</span></div><div class='xr-var-dims'>(time, profile_r)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 129), meta=np.ndarray&gt;</div><input id='attrs-a3fec6da-d1e2-432b-8dd5-18dd3f5aa89c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a3fec6da-d1e2-432b-8dd5-18dd3f5aa89c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-02cc1749-f581-4f77-9644-75e5d5fdf965' class='xr-var-data-in' type='checkbox'><label for='data-02cc1749-f581-4f77-9644-75e5d5fdf965' 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>description :</span></dt><dd>Rotational pressure contribution as a function of radius at Z=0; f(nw, B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;profile_r&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>contribution of pw(r) to</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:P2C_R_C</dd><dt><span>name :</span></dt><dd>efm/p2cr_c</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 65]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_P2C(R)_(C)</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>uuid :</span></dt><dd>75054c75-2b83-5320-b11a-d3912e35600f</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> (64, 129) </td>\n",
       "                        <td> (64, 129) </td>\n",
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       "                        <th> Dask graph </th>\n",
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       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>passnumber</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a9571365-de50-49de-9e17-f6b324163962' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9571365-de50-49de-9e17-f6b324163962' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f498310a-a150-4eb3-8cef-717f2225c88d' class='xr-var-data-in' type='checkbox'><label for='data-f498310a-a150-4eb3-8cef-717f2225c88d' 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>description :</span></dt><dd>MAST scheduler pass number</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Pass</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:PASSNUMBER</dd><dt><span>name :</span></dt><dd>efm/passnumber</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_PASSNUMBER</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>8d1335f3-b16c-50ba-8c0a-0e67c75a5dca</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pcurbd</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ce08917e-d6f2-4bdd-bbcc-ccecd9afa743' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce08917e-d6f2-4bdd-bbcc-ccecd9afa743' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-007ee2bf-27c0-4ba3-aca7-11dc9b4a0800' class='xr-var-data-in' type='checkbox'><label for='data-007ee2bf-27c0-4ba3-aca7-11dc9b4a0800' 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>description :</span></dt><dd>p&#x27; polynomial fit boundary condition; 1 for zero at psin=1, 0 for free</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>PPrime Boundary Conditio</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:PCURBD</dd><dt><span>name :</span></dt><dd>efm/pcurbd</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_PCURBD</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>5f570b49-2b37-5290-a3d7-7e3e15b9df24</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plasma_area</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-1e275867-b661-4bff-bce7-4f26ca73d956' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1e275867-b661-4bff-bce7-4f26ca73d956' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a918903-f7c3-477a-a4c5-7f47df067cb9' class='xr-var-data-in' type='checkbox'><label for='data-5a918903-f7c3-477a-a4c5-7f47df067cb9' 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>description :</span></dt><dd>Area of poloidal cross-section of plasma; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Plasma Area</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:PLASMA_AREA</dd><dt><span>name :</span></dt><dd>efm/plasma_area</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_PLASMA_AREA</dd><dt><span>units :</span></dt><dd>m ** 2</dd><dt><span>uuid :</span></dt><dd>cba97a4d-dd14-535f-a073-4ed8c6f8c137</dd><dt><span>version :</span></dt><dd>0</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> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64,) </td>\n",
       "                        <td> (64,) </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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>plasma_current_c</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-50079ea3-b1af-4ef3-bcaa-69c357954be6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-50079ea3-b1af-4ef3-bcaa-69c357954be6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-232ece72-77a2-4d3a-81d0-56d233f16f8e' class='xr-var-data-in' type='checkbox'><label for='data-232ece72-77a2-4d3a-81d0-56d233f16f8e' 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>description :</span></dt><dd>Output (computed) fitted total plasma current; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Computed Plasma Current</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM.PLASMA_CURR:C</dd><dt><span>name :</span></dt><dd>efm/plasma_currc</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_PLASMA_CURR(C)</dd><dt><span>units :</span></dt><dd>A</dd><dt><span>uuid :</span></dt><dd>740b5611-77cc-53be-ad21-de424871e0ce</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (64,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pol_length</span></div><div class='xr-var-dims'>(time, psi_norm)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 65), meta=np.ndarray&gt;</div><input id='attrs-59d674e2-7103-4927-94ff-b72a437d8fa2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-59d674e2-7103-4927-94ff-b72a437d8fa2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-362f42df-cef7-4fd3-b28a-772842e21a2c' class='xr-var-data-in' type='checkbox'><label for='data-362f42df-cef7-4fd3-b28a-772842e21a2c' 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>description :</span></dt><dd>Poloidal lengths of flux surfaces as a function of flux; f(psin, B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;psi_norm&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>pol lengths of flux surf</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:POL_LENGTH</dd><dt><span>name :</span></dt><dd>efm/pol_length</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 65]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_POL_LENGTH</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>455da776-f151-5810-b1d4-4311ab40d837</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pprime_coefs</span></div><div class='xr-var-dims'>(time, pprime_coefs_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 2), meta=np.ndarray&gt;</div><input id='attrs-699ea5a9-8f0f-4cde-af00-612087ffbc2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-699ea5a9-8f0f-4cde-af00-612087ffbc2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-980e18a0-53d2-48dd-a385-168d8dbc9a2c' class='xr-var-data-in' type='checkbox'><label for='data-980e18a0-53d2-48dd-a385-168d8dbc9a2c' 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>description :</span></dt><dd>p&#x27; polynomial coefficients</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;pprime_coefs_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>PPrime Coefs</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:PPRIME_COEFS</dd><dt><span>name :</span></dt><dd>efm/pprime_coefs</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 2]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_PPRIME_COEFS</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>5a98f10c-95be-504a-ba03-750f5793ba43</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>pprimew</span></div><div class='xr-var-dims'>(time, psi_norm)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 65), meta=np.ndarray&gt;</div><input id='attrs-1bfce259-fec1-4f6c-bd7f-2b0d11960f17' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1bfce259-fec1-4f6c-bd7f-2b0d11960f17' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bf07a991-1ce0-4944-b374-a5582f5091ca' class='xr-var-data-in' type='checkbox'><label for='data-bf07a991-1ce0-4944-b374-a5582f5091ca' 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>description :</span></dt><dd>Rotational pressure contribution profile; f(nw, B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;psi_norm&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>prpime(rot) (centre to e</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:PPRIMEW</dd><dt><span>name :</span></dt><dd>efm/pprimew</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 65]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_PPRIMEW</dd><dt><span>units :</span></dt><dd>Pa-ra/Wb</dd><dt><span>uuid :</span></dt><dd>c6ed9eae-b2cd-591c-bd3d-e8cc6633a119</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ppsi_c</span></div><div class='xr-var-dims'>(time, psi_norm)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 65), meta=np.ndarray&gt;</div><input id='attrs-05de65b0-828f-4a00-8711-53e93569f641' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-05de65b0-828f-4a00-8711-53e93569f641' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90228143-75c4-4aef-a01c-add0f9e7c173' class='xr-var-data-in' type='checkbox'><label for='data-90228143-75c4-4aef-a01c-add0f9e7c173' 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>description :</span></dt><dd>Pressure as a function of flux surface; f(psin, B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;psi_norm&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>computed press (centre t</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:P_PSI_C</dd><dt><span>name :</span></dt><dd>efm/ppsi_c</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 65]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_P(PSI)_(C)</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>uuid :</span></dt><dd>0d710474-d92e-5992-a3d1-c4caf144787f</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>psi_axis</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-a0232384-b8f0-4a1c-80ff-fa7b23c1382d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a0232384-b8f0-4a1c-80ff-fa7b23c1382d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b4f5e457-9e20-4516-9ec2-d9cf6f914284' class='xr-var-data-in' type='checkbox'><label for='data-b4f5e457-9e20-4516-9ec2-d9cf6f914284' 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>description :</span></dt><dd>Poloidal magnetic flux per toroidal radian at the magnetic axis; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Poloidal flux on axis</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:PSI_AXIS</dd><dt><span>name :</span></dt><dd>efm/psi_axis</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_PSI_AXIS</dd><dt><span>units :</span></dt><dd>Wb / rad</dd><dt><span>uuid :</span></dt><dd>6cfc8f31-91bf-5b61-bf45-f343591f5f74</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>psi_boundary</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-90a66935-4f74-462f-b2e0-6c7c4b0f8dad' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-90a66935-4f74-462f-b2e0-6c7c4b0f8dad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1537596-ad3a-4d29-bd3f-41909211e1f1' class='xr-var-data-in' type='checkbox'><label for='data-d1537596-ad3a-4d29-bd3f-41909211e1f1' 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>description :</span></dt><dd>Poloidal magnetic flux per toroidal radian at the plasma boundary; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Poloidal flux at boundar</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:PSI_BOUNDARY</dd><dt><span>name :</span></dt><dd>efm/psi_boundary</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_PSI_BOUNDARY</dd><dt><span>units :</span></dt><dd>Wb / rad</dd><dt><span>uuid :</span></dt><dd>26637893-a225-5955-8178-02b9c355d65f</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q=3_radius</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-aef39484-07c1-4638-a621-dec274d7183e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-aef39484-07c1-4638-a621-dec274d7183e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-38665ace-9c85-4283-982e-d3a1a2f3e29e' class='xr-var-data-in' type='checkbox'><label for='data-38665ace-9c85-4283-982e-d3a1a2f3e29e' 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>description :</span></dt><dd>For q=3 surface: (R_max-R_min)/2 at Z=Z_mag; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>radius of q=3 surface</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:Q_3_RADIUS</dd><dt><span>name :</span></dt><dd>efm/q=3_radius</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_Q=3_RADIUS</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>faa09b4a-f9e3-56a8-8bec-379a5821a585</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 256 B </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q_100</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-5bdeca87-23f5-4f27-a098-309253258263' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5bdeca87-23f5-4f27-a098-309253258263' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e2127d52-a45e-4b9a-a6af-45ad6aab70ec' class='xr-var-data-in' type='checkbox'><label for='data-e2127d52-a45e-4b9a-a6af-45ad6aab70ec' 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>description :</span></dt><dd>Safety factor at 100% normalised magnetic flux; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>q at Psi_norm=100%</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:Q_100</dd><dt><span>name :</span></dt><dd>efm/q_100</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_Q_100</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>8041cdce-59ec-54f5-9769-6b4fbccb8e8b</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                        <td> 256 B </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (64,) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q_90</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-a64bb56c-04f9-41fc-ae19-d5347b33ac90' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a64bb56c-04f9-41fc-ae19-d5347b33ac90' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71abae54-53ec-44d0-858f-2c33177fcc8e' class='xr-var-data-in' type='checkbox'><label for='data-71abae54-53ec-44d0-858f-2c33177fcc8e' 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>description :</span></dt><dd>Safety factor at 90% normalised magnetic flux; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>q at Psi_norm=90%</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:Q_90</dd><dt><span>name :</span></dt><dd>efm/q_90</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_Q_90</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>51ab04a6-35f3-5ebb-8cd9-9bc1be4de4e9</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
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       "                    <tr>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>q_95</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-ae671b11-a8f5-4004-97d0-008fbd85ae45' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae671b11-a8f5-4004-97d0-008fbd85ae45' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c38b0095-d5eb-4897-b97b-64839ef41202' class='xr-var-data-in' type='checkbox'><label for='data-c38b0095-d5eb-4897-b97b-64839ef41202' 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>description :</span></dt><dd>Safety factor at 95% normalised magnetic flux; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>q at Psi_norm=95%</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:Q_95</dd><dt><span>name :</span></dt><dd>efm/q_95</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_Q_95</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>18b9439c-e71e-5ce6-b3b4-b9dbe2a08caf</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Array </th>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                        <td> 256 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                <tbody>\n",
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       "                        <th> Shape </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qpsi_c</span></div><div class='xr-var-dims'>(time, psi_norm)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 65), meta=np.ndarray&gt;</div><input id='attrs-08f99efb-573f-42c5-a536-4f086e7a1665' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-08f99efb-573f-42c5-a536-4f086e7a1665' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b9c1f4b-07af-4ff5-99b7-648fe5328ec7' class='xr-var-data-in' type='checkbox'><label for='data-3b9c1f4b-07af-4ff5-99b7-648fe5328ec7' 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>description :</span></dt><dd>Safety factor as a function of magnetic flux; f(npsi, B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;psi_norm&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>q-profile (centre to edg</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:Q_PSI_C</dd><dt><span>name :</span></dt><dd>efm/qpsi_c</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 65]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_Q(PSI)_(C)</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>0387c7e0-76d1-5107-bd2c-6292db62098b</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qr</span></div><div class='xr-var-dims'>(time, profile_r)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 129), meta=np.ndarray&gt;</div><input id='attrs-a75a4998-3bd7-43ba-a19a-6283d85b899b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a75a4998-3bd7-43ba-a19a-6283d85b899b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ce0ca6b5-c8e2-43f5-8f5c-7689fe3ebbf1' class='xr-var-data-in' type='checkbox'><label for='data-ce0ca6b5-c8e2-43f5-8f5c-7689fe3ebbf1' 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>description :</span></dt><dd>Safety factor as a function of radius at Z=0; f(npsi, B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;profile_r&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>q(r) at z=0.</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:Q_R</dd><dt><span>name :</span></dt><dd>efm/qr</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 65]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_Q(R)</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>dc5c59c6-c842-5747-98db-d0495738576a</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 32.25 kiB </td>\n",
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       "                        <td> (64, 129) </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qstar</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-9f69e0c1-16ab-4f90-bff1-ee7d8d4c607f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9f69e0c1-16ab-4f90-bff1-ee7d8d4c607f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57e7395b-f1b2-4386-8342-0f61c1d2a703' class='xr-var-data-in' type='checkbox'><label for='data-57e7395b-f1b2-4386-8342-0f61c1d2a703' 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>description :</span></dt><dd>qstar = fvac*(kappa^2+1)/(4e-7 I_p A^2)*(1.24-0.54*kappa+0.3*(kappa^2+triangn^2)+0.13*triangn )*(1 + (1+0.5*(betap+li/2)^2)/A^2); f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>q-star</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:QSTAR</dd><dt><span>name :</span></dt><dd>efm/qstar</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_QSTAR</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>5d69b017-2bc4-50d9-b998-b30b531dbb48</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rbdry</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-9b2770a4-bace-4e0a-996a-4163c6ac5d58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9b2770a4-bace-4e0a-996a-4163c6ac5d58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3e0b02c8-b2e8-40d7-bcb1-793d9ac79af6' class='xr-var-data-in' type='checkbox'><label for='data-3e0b02c8-b2e8-40d7-bcb1-793d9ac79af6' 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>description :</span></dt><dd>Radius of boundary position constraints; f(nbdry, A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>rbdry</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:RBDRY</dd><dt><span>name :</span></dt><dd>efm/rbdry</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_RBDRY</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>6fcf7493-9ffc-54a5-b8af-00390405aa87</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rpsi100_in</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-668eb3ca-2dd5-4ee6-ba64-b3be17984766' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-668eb3ca-2dd5-4ee6-ba64-b3be17984766' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0890582b-bcf9-4ebd-a836-1d0f13966757' class='xr-var-data-in' type='checkbox'><label for='data-0890582b-bcf9-4ebd-a836-1d0f13966757' 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>description :</span></dt><dd>Inboard radius of 100% normalised magnetic flux; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>i/b Rad of lcfs at Mid</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:R_PSI100_IN</dd><dt><span>name :</span></dt><dd>efm/rpsi100_in</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_R(PSI100)_IN</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>c9d75e73-3990-5c0b-be27-968f224e696c</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
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       "                        <td> (64,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rpsi100_out</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-41238162-7e63-49b8-b33c-eaf7ea943c24' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41238162-7e63-49b8-b33c-eaf7ea943c24' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c40bfbad-4604-48a5-b8ba-f453e734f498' class='xr-var-data-in' type='checkbox'><label for='data-c40bfbad-4604-48a5-b8ba-f453e734f498' 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>description :</span></dt><dd>Outboard radius of 100% normalised magnetic flux; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>o/b Rad of lcfs at Mid</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:R_PSI100_OUT</dd><dt><span>name :</span></dt><dd>efm/rpsi100_out</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_R(PSI100)_OUT</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>5a30e41a-d1a1-5fa5-bada-7367488a5f0e</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (64,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"77\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rpsi90_in</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-2dab5ee0-8f76-452f-b7b5-32a2b40826c0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2dab5ee0-8f76-452f-b7b5-32a2b40826c0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ec7d347-43b4-466a-b7a6-3fd30c17d123' class='xr-var-data-in' type='checkbox'><label for='data-3ec7d347-43b4-466a-b7a6-3fd30c17d123' 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>description :</span></dt><dd>Inboard radius of 90% normalised magnetic flux; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>i/b Rad of 90%Psi at Mid</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:R_PSI90_IN</dd><dt><span>name :</span></dt><dd>efm/rpsi90_in</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_R(PSI90)_IN</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>1bf651e5-0abe-5c57-acba-c8d43c8d9123</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64,) </td>\n",
       "                        <td> (64,) </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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"77\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
       "\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rpsi90_out</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-4bbe3848-5302-4743-9641-1db2c5a13d35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4bbe3848-5302-4743-9641-1db2c5a13d35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-377b0a5f-e233-42f0-a0a9-c8c10139c816' class='xr-var-data-in' type='checkbox'><label for='data-377b0a5f-e233-42f0-a0a9-c8c10139c816' 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>description :</span></dt><dd>Outboard radius of 90% normalised magnetic flux; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>o/b Rad of 90%Psi at Mid</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:R_PSI90_OUT</dd><dt><span>name :</span></dt><dd>efm/rpsi90_out</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_R(PSI90)_OUT</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>55d05ed7-7e40-5c77-b667-44792d2caa43</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <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> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64,) </td>\n",
       "                        <td> (64,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"77\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rpsi95_in</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-ae1ab5cb-53fb-47fb-b48d-6d66e82f157d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ae1ab5cb-53fb-47fb-b48d-6d66e82f157d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5a3451dd-0b60-4e91-88a2-7706a5709bdf' class='xr-var-data-in' type='checkbox'><label for='data-5a3451dd-0b60-4e91-88a2-7706a5709bdf' 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>description :</span></dt><dd>Inboard radius of 95% normalised magnetic flux; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>i/b Rad of 95%Psi at Mid</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:R_PSI95_IN</dd><dt><span>name :</span></dt><dd>efm/rpsi95_in</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_R(PSI95)_IN</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>316e719b-be8e-53a9-b685-1718e5a49277</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                    <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> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64,) </td>\n",
       "                        <td> (64,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"77\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rpsi95_out</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-53ef235d-de8e-45b3-bf48-c70bcafca83e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-53ef235d-de8e-45b3-bf48-c70bcafca83e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-119d111c-6e34-4f93-857e-be4c301545ca' class='xr-var-data-in' type='checkbox'><label for='data-119d111c-6e34-4f93-857e-be4c301545ca' 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>description :</span></dt><dd>Outboard radius of 95% normalised magnetic flux; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>o/b Rad of 95%Psi at Mid</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:R_PSI95_OUT</dd><dt><span>name :</span></dt><dd>efm/rpsi95_out</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_R(PSI95)_OUT</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>3623b161-4b17-53b0-ae72-f753ed33cccc</dd><dt><span>version :</span></dt><dd>0</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",
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       "                    </tr>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "                        <td> 256 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64,) </td>\n",
       "                        <td> (64,) </td>\n",
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       "                    <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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"77\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rt</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-94904707-a3e9-44f7-b467-2e4f07bcd349' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-94904707-a3e9-44f7-b467-2e4f07bcd349' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ecec362-21dc-4e7c-805c-fd5137c0703c' class='xr-var-data-in' type='checkbox'><label for='data-3ecec362-21dc-4e7c-805c-fd5137c0703c' 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>description :</span></dt><dd>Shafranov integral RT</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>shafranov_integral_RT</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:RT</dd><dt><span>name :</span></dt><dd>efm/rt</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_RT</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>fd127bbd-fba3-5805-9261-4cd3039005cb</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
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       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
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       "                        <td> (64,) </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 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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       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"170\" height=\"77\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rvals</span></div><div class='xr-var-dims'>(time, profile_r)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 129), meta=np.ndarray&gt;</div><input id='attrs-4e335352-9842-4176-94d4-93a4cfb9a04d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e335352-9842-4176-94d4-93a4cfb9a04d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-989b4553-9430-4df8-8e4b-d937ac8b6ee9' class='xr-var-data-in' type='checkbox'><label for='data-989b4553-9430-4df8-8e4b-d937ac8b6ee9' 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>description :</span></dt><dd>Radial co-ordinates used for radial profiles; f(nw)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;profile_r&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>R-coords used for radial</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:RVALS</dd><dt><span>name :</span></dt><dd>efm/rvals</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 65]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_RVALS</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>48050b25-9e92-5383-8d90-e4ca0e3e147d</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <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> 32.25 kiB </td>\n",
       "                        <td> 32.25 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 129) </td>\n",
       "                        <td> (64, 129) </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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
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       "\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rvtor</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-86cc889f-e9c8-4929-9cc3-5efa98c4695e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-86cc889f-e9c8-4929-9cc3-5efa98c4695e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c3e92d4-fb01-4e71-a8e9-58efea825672' class='xr-var-data-in' type='checkbox'><label for='data-9c3e92d4-fb01-4e71-a8e9-58efea825672' 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>description :</span></dt><dd>Rotational pressure parameter</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>rotational constraint co</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:RVTOR</dd><dt><span>name :</span></dt><dd>efm/rvtor</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_RVTOR</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>241f7ddc-2c87-50fd-979f-d525aaeb10dc</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>scalepr</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b3a282aa-6f10-4ee3-af9e-a915bc0db90c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3a282aa-6f10-4ee3-af9e-a915bc0db90c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-efa45c25-283d-44aa-8486-287153a9f426' class='xr-var-data-in' type='checkbox'><label for='data-efa45c25-283d-44aa-8486-287153a9f426' 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>description :</span></dt><dd>Constant to scale input pressure by</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>pressure scaling factor</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:SCALEPR</dd><dt><span>name :</span></dt><dd>efm/scalepr</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_SCALEPR</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>59c7da09-4c25-54c9-be48-6484481d2c03</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shaf_integral_1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-f149f72b-420b-4b7a-b156-59bdd291d6d8' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f149f72b-420b-4b7a-b156-59bdd291d6d8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6b427824-cf00-4cb5-bd77-63daff07ab11' class='xr-var-data-in' type='checkbox'><label for='data-6b427824-cf00-4cb5-bd77-63daff07ab11' 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>description :</span></dt><dd>Shafranov integral 1</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>shafranov_integral_1</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM.SHAF:INTEGRAL_1</dd><dt><span>name :</span></dt><dd>efm/shaf_integral_1</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_SHAF_INTEGRAL_1</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>21798555-7f56-5b17-85ce-a5cc45a78c3b</dd><dt><span>version :</span></dt><dd>0</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> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64,) </td>\n",
       "                        <td> (64,) </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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>shaf_integral_2</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-34876fea-027a-46dc-8359-a7bf633c6251' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-34876fea-027a-46dc-8359-a7bf633c6251' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72f2a92e-b4a9-4751-8372-f1a8a9446060' class='xr-var-data-in' type='checkbox'><label for='data-72f2a92e-b4a9-4751-8372-f1a8a9446060' 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>description :</span></dt><dd>Shafranov integral 2</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>shafranov_integral_2</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM.SHAF:INTEGRAL_2</dd><dt><span>name :</span></dt><dd>efm/shaf_integral_2</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_SHAF_INTEGRAL_2</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>dcd63312-5b11-5006-bd8d-ed9e52ae6f50</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 256 B </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>sigbdry</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-58e42ec2-71cc-4e45-ad0d-67d43d546e35' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-58e42ec2-71cc-4e45-ad0d-67d43d546e35' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6410735b-b9fe-45cc-9cd0-0fc18d749016' class='xr-var-data-in' type='checkbox'><label for='data-6410735b-b9fe-45cc-9cd0-0fc18d749016' 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>description :</span></dt><dd>Relative position error on boundary position constraints; f(nbdry, A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>sigbdry</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:SIGBDRY</dd><dt><span>name :</span></dt><dd>efm/sigbdry</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_SIGBDRY</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>b8ac0e0b-7ce3-52e3-a21d-467967091e70</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>silop_chisq</span></div><div class='xr-var-dims'>(time, psi_loop_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 46), meta=np.ndarray&gt;</div><input id='attrs-de0067ef-163b-4f9a-b0c1-31e0f4afc282' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-de0067ef-163b-4f9a-b0c1-31e0f4afc282' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-72ecc17d-a997-4628-ad8c-17276a036302' class='xr-var-data-in' type='checkbox'><label for='data-72ecc17d-a997-4628-ad8c-17276a036302' 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>description :</span></dt><dd>Chi-squared of each fitted magnetic flux probe; f(silop_n, A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;psi_loop_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Chi**2 of each flux loop</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:SILOP_CHISQ</dd><dt><span>name :</span></dt><dd>efm/silop_chisq</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[64, 46]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_SILOP_CHISQ</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>36edf804-c067-5b55-aa93-c9d9ecc58fe8</dd><dt><span>version :</span></dt><dd>0</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> 11.50 kiB </td>\n",
       "                        <td> 11.50 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 46) </td>\n",
       "                        <td> (64, 46) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>silop_dphi</span></div><div class='xr-var-dims'>(psi_loop_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(46,), meta=np.ndarray&gt;</div><input id='attrs-78804a5d-9c50-43cc-ac05-a6da9e24a9db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78804a5d-9c50-43cc-ac05-a6da9e24a9db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a9924359-f8ca-41eb-a5d2-465bdf83dbff' class='xr-var-data-in' type='checkbox'><label for='data-a9924359-f8ca-41eb-a5d2-465bdf83dbff' 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>description :</span></dt><dd>Toroidal angular extent of each magnetic flux probe; f(magpr_n)</dd><dt><span>dims :</span></dt><dd>[&#x27;psi_loop_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Flux Loop Toroidal Exten</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:SILOP_DPHI</dd><dt><span>name :</span></dt><dd>efm/silop_dphi</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 46]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_SILOP_DPHI</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>5cf990ab-ed3f-5d66-a7c6-4132fa2a317a</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 184 B </td>\n",
       "                        <td> 184 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (46,) </td>\n",
       "                        <td> (46,) </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\"> float32 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>silop_r</span></div><div class='xr-var-dims'>(psi_loop_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(46,), meta=np.ndarray&gt;</div><input id='attrs-a07693e6-3851-4912-ba96-bc3fce3561ab' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a07693e6-3851-4912-ba96-bc3fce3561ab' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-742039ce-1015-4b9e-8204-b0944c9da225' class='xr-var-data-in' type='checkbox'><label for='data-742039ce-1015-4b9e-8204-b0944c9da225' 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>description :</span></dt><dd>R of each magnetic flux probe; f(magpr_n)</dd><dt><span>dims :</span></dt><dd>[&#x27;psi_loop_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Flux Loop Location Radiu</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:SILOP_R</dd><dt><span>name :</span></dt><dd>efm/silop_r</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 46]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_SILOP_R</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>856165b2-670a-5ab4-8575-b37ff70e3f1d</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                <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> 184 B </td>\n",
       "                        <td> 184 B </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (46,) </td>\n",
       "                        <td> (46,) </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\"> float32 numpy.ndarray </td>\n",
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       "        </td>\n",
       "        <td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>silop_x</span></div><div class='xr-var-dims'>(time, psi_loop_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 46), meta=np.ndarray&gt;</div><input id='attrs-2593649c-def8-4997-8d23-eae0ddb4e937' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2593649c-def8-4997-8d23-eae0ddb4e937' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-90804822-02e4-41ec-aabe-19af3d7e06ea' class='xr-var-data-in' type='checkbox'><label for='data-90804822-02e4-41ec-aabe-19af3d7e06ea' 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>description :</span></dt><dd>Input (experimental) fitted magnetic flux probes; f(silop_n, A)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;psi_loop_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Measured flux loops sign</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:SILOP_X</dd><dt><span>name :</span></dt><dd>efm/silop_x</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[64, 46]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_SILOP_(X)</dd><dt><span>units :</span></dt><dd>Wb</dd><dt><span>uuid :</span></dt><dd>8791ad66-9c81-5288-b312-2458557608bb</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "        <td>\n",
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       "                    <tr>\n",
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       "                        <th> Array </th>\n",
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       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 11.50 kiB </td>\n",
       "                        <td> 11.50 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (64, 46) </td>\n",
       "                        <td> (64, 46) </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\"> float32 numpy.ndarray </td>\n",
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       "        <td>\n",
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       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>silop_z</span></div><div class='xr-var-dims'>(psi_loop_n)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(46,), meta=np.ndarray&gt;</div><input id='attrs-577f7d6f-d86a-42c3-b1c4-f3f9f68a4cf0' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-577f7d6f-d86a-42c3-b1c4-f3f9f68a4cf0' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f4cf0ce8-dd7b-465f-b5a4-611bad597490' class='xr-var-data-in' type='checkbox'><label for='data-f4cf0ce8-dd7b-465f-b5a4-611bad597490' 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>description :</span></dt><dd>Z of each magnetic flux probe; f(magpr_n)</dd><dt><span>dims :</span></dt><dd>[&#x27;psi_loop_n&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Flux Loop Location Heigh</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:SILOP_Z</dd><dt><span>name :</span></dt><dd>efm/silop_z</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[1, 46]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_SILOP_Z</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>uuid :</span></dt><dd>5d60facb-c6d0-55b7-9548-c0e8be2e5a78</dd><dt><span>version :</span></dt><dd>0</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> 184 B </td>\n",
       "                        <td> 184 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (46,) </td>\n",
       "                        <td> (46,) </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\"> 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>status</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4316c21d-36f0-44e3-8041-8b8501b5b6ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4316c21d-36f0-44e3-8041-8b8501b5b6ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-48420f6f-70f8-4ded-9661-9b81b2eaff21' class='xr-var-data-in' type='checkbox'><label for='data-48420f6f-70f8-4ded-9661-9b81b2eaff21' 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>description :</span></dt><dd>MAST scheduler status</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>MAST Status flag</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:STATUS</dd><dt><span>name :</span></dt><dd>efm/status</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_STATUS</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>255ae97c-f9a9-54a1-8bd7-b9e7c955e23f</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=int32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time_</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-4196f508-ec9d-4121-8dda-58393b18349d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4196f508-ec9d-4121-8dda-58393b18349d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-34e6a4f1-0299-4b0b-a0ae-97c98ff5051e' class='xr-var-data-in' type='checkbox'><label for='data-34e6a4f1-0299-4b0b-a0ae-97c98ff5051e' 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>description :</span></dt><dd>All times of converged reconstruction (time base B) - identical to EFM_CNVRGD_TIMES</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Time of reconstruction</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:TIME</dd><dt><span>name :</span></dt><dd>efm/time_</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[64]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_TIME</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>uuid :</span></dt><dd>e7248f5d-a27a-59ce-ada3-1f56e2e088f7</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 256 B </td>\n",
       "                        <td> 256 B </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>triang_lower</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-6fc9d3cd-518a-4fc9-9b09-a7d59a511177' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6fc9d3cd-518a-4fc9-9b09-a7d59a511177' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-94b74e43-3592-4568-8126-be79314de146' class='xr-var-data-in' type='checkbox'><label for='data-94b74e43-3592-4568-8126-be79314de146' 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>description :</span></dt><dd>Lower plasma triangularity; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Lower Triangularity</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:TRIANG_LOWER</dd><dt><span>name :</span></dt><dd>efm/triang_lower</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_TRIANG_LOWER</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>72bf53f4-5ecb-5a52-bd39-b860fd55ef14</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
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       "                        <td> 256 B </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (64,) </td>\n",
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       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>triang_lpsi_c</span></div><div class='xr-var-dims'>(time, psi_norm)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64, 65), meta=np.ndarray&gt;</div><input id='attrs-ea783ae5-38bf-4a3e-99d8-a7f1b279bf28' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ea783ae5-38bf-4a3e-99d8-a7f1b279bf28' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c01cd5e1-f8db-4bc7-aebd-5f06febb8e90' class='xr-var-data-in' type='checkbox'><label for='data-c01cd5e1-f8db-4bc7-aebd-5f06febb8e90' 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>description :</span></dt><dd>Lower plasma triangularity as a function of magnetic flux; f(npsi, B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;psi_norm&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>lower delta of surfaces</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM.TRIANG_L:PSI_C</dd><dt><span>name :</span></dt><dd>efm/triang_lpsi_c</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>2</dd><dt><span>shape :</span></dt><dd>[53, 65]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_TRIANG_L(PSI)_(C)</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>89187327-4026-5ad4-b2d9-e52fa6d4f243</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 16.25 kiB </td>\n",
       "                        <td> 16.25 kiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (64, 65) </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>triang_upper</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-4aa8df14-9362-4834-adbf-0c3f389e8988' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4aa8df14-9362-4834-adbf-0c3f389e8988' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e0adb2cc-6d72-4680-88d1-4df579aaab0b' class='xr-var-data-in' type='checkbox'><label for='data-e0adb2cc-6d72-4680-88d1-4df579aaab0b' 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>description :</span></dt><dd>Upper plasma triangularity; f(B)</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Upper Triangularity</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:TRIANG_UPPER</dd><dt><span>name :</span></dt><dd>efm/triang_upper</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_TRIANG_UPPER</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>4664260a-ff20-5e3a-bf62-61fd6a476805</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wcurbd</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b3e6b940-d025-4b14-a17a-40cccb04422c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b3e6b940-d025-4b14-a17a-40cccb04422c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c6375c57-2453-4a5c-a5dd-3e32c3a64749' class='xr-var-data-in' type='checkbox'><label for='data-c6375c57-2453-4a5c-a5dd-3e32c3a64749' 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>description :</span></dt><dd>pw&#x27; polynomial fit boundary condition; 1 for zero at psin=1, 0 for free</dd><dt><span>dims :</span></dt><dd>[]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>P(Rot) Boundary Conditio</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:WCURBD</dd><dt><span>name :</span></dt><dd>efm/wcurbd</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_WCURBD</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>0856f852-b925-551c-90ae-4093dc73a02a</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>wplasmd</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(64,), meta=np.ndarray&gt;</div><input id='attrs-a77baff9-8b58-42f6-81ab-09713240d8f1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a77baff9-8b58-42f6-81ab-09713240d8f1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b28ac744-fdb1-4fe4-a4b8-59fb9e944d9b' class='xr-var-data-in' type='checkbox'><label for='data-b28ac744-fdb1-4fe4-a4b8-59fb9e944d9b' 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>description :</span></dt><dd>Plasma energy computed using diamagnetic energy, as in betapd</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Plasma Diamagnetic Energ</dd><dt><span>mds_name :</span></dt><dd>\\TOP.ANALYSED.EFM:WPLASMD</dd><dt><span>name :</span></dt><dd>efm/wplasmd</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[53]</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>EFM_WPLASMD</dd><dt><span>units :</span></dt><dd>J</dd><dt><span>uuid :</span></dt><dd>92ab9f43-0de5-581b-a883-c6bc10b8dfed</dd><dt><span>version :</span></dt><dd>0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "                        <th> Data type </th>\n",
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       "                        <td> (64,) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float32 numpy.ndarray </td>\n",
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       "  <!-- Horizontal lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"120\" y2=\"0\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"0\" y1=\"27\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Vertical lines -->\n",
       "  <line x1=\"0\" y1=\"0\" x2=\"0\" y2=\"27\" style=\"stroke-width:2\" />\n",
       "  <line x1=\"120\" y1=\"0\" x2=\"120\" y2=\"27\" style=\"stroke-width:2\" />\n",
       "\n",
       "  <!-- Colored Rectangle -->\n",
       "  <polygon points=\"0.0,0.0 120.0,0.0 120.0,27.675387197907465 0.0,27.675387197907465\" style=\"fill:#ECB172A0;stroke-width:0\"/>\n",
       "\n",
       "  <!-- Text -->\n",
       "  <text x=\"60.000000\" y=\"47.675387\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >64</text>\n",
       "  <text x=\"140.000000\" y=\"13.837694\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(0,140.000000,13.837694)\">1</text>\n",
       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-d300e831-38ec-4aaf-a0ca-c9e6e4d146bb' class='xr-section-summary-in' type='checkbox'  ><label for='section-d300e831-38ec-4aaf-a0ca-c9e6e4d146bb' class='xr-section-summary' >Indexes: <span>(13)</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>fcoil_n</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c83bc75b-f9ce-47f6-ac28-1b54df2f3f68' class='xr-index-data-in' type='checkbox'/><label for='index-c83bc75b-f9ce-47f6-ac28-1b54df2f3f68' 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([  0.0,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,\n",
       "       ...\n",
       "        91.0,  92.0,  93.0,  94.0,  95.0,  96.0,  97.0,  98.0,  99.0, 100.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;fcoil_n&#x27;, length=101))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>ffprime_coefs_n</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3517fe24-a5f8-46f9-8d5d-978232681678' class='xr-index-data-in' type='checkbox'/><label for='index-3517fe24-a5f8-46f9-8d5d-978232681678' 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([0.0, 1.0], dtype=&#x27;float32&#x27;, name=&#x27;ffprime_coefs_n&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lcfs_coords</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-0df4f686-b872-4209-9a14-b8a9593a7f0f' class='xr-index-data-in' type='checkbox'/><label for='index-0df4f686-b872-4209-9a14-b8a9593a7f0f' 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([  0.0,   1.0,   2.0,   3.0,   4.0,   5.0,   6.0,   7.0,   8.0,   9.0,\n",
       "       ...\n",
       "       137.0, 138.0, 139.0, 140.0, 141.0, 142.0, 143.0, 144.0, 145.0, 146.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;lcfs_coords&#x27;, length=147))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>mag_probe_n</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-b8f10302-1827-4e58-b42a-820d1facb133' class='xr-index-data-in' type='checkbox'/><label for='index-b8f10302-1827-4e58-b42a-820d1facb133' 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([ 0.0,  1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,  9.0, 10.0, 11.0,\n",
       "       12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0,\n",
       "       24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0,\n",
       "       36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0, 46.0, 47.0,\n",
       "       48.0, 49.0, 50.0, 51.0, 52.0, 53.0, 54.0, 55.0, 56.0, 57.0, 58.0, 59.0,\n",
       "       60.0, 61.0, 62.0, 63.0, 64.0, 65.0, 66.0, 67.0, 68.0, 69.0, 70.0, 71.0,\n",
       "       72.0, 73.0, 74.0, 75.0, 76.0, 77.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;mag_probe_n&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>n_iterations</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-76350f16-d380-407a-9046-ff7c95330dfe' class='xr-index-data-in' type='checkbox'/><label for='index-76350f16-d380-407a-9046-ff7c95330dfe' 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([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0], dtype=&#x27;float32&#x27;, name=&#x27;n_iterations&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>pprime_coefs_n</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-544db38d-fcb2-451d-b356-7e752be88c8e' class='xr-index-data-in' type='checkbox'/><label for='index-544db38d-fcb2-451d-b356-7e752be88c8e' 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([0.0, 1.0], dtype=&#x27;float32&#x27;, name=&#x27;pprime_coefs_n&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>profile_r</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fec37624-cb44-4366-bfe4-f5d2511f514c' class='xr-index-data-in' type='checkbox'/><label for='index-fec37624-cb44-4366-bfe4-f5d2511f514c' 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([                0.0,            0.015625,             0.03125,\n",
       "                  0.046875, 0.05999999865889549,              0.0625,\n",
       "                  0.078125, 0.09031249582767487,             0.09375,\n",
       "                  0.109375,\n",
       "       ...\n",
       "        1.7271875143051147,  1.7574999332427979,  1.7878124713897705,\n",
       "        1.8181250095367432,  1.8484375476837158,   1.878749966621399,\n",
       "        1.9090625047683716,  1.9393750429153442,  1.9696874618530273,\n",
       "                       2.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;profile_r&#x27;, length=129))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>profile_z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cf4345ae-2b8b-4c28-8ee3-75da39c085e5' class='xr-index-data-in' type='checkbox'/><label for='index-cf4345ae-2b8b-4c28-8ee3-75da39c085e5' 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([   -2.0, -1.9375,  -1.875, -1.8125,   -1.75, -1.6875,  -1.625, -1.5625,\n",
       "          -1.5, -1.4375,  -1.375, -1.3125,   -1.25, -1.1875,  -1.125, -1.0625,\n",
       "          -1.0, -0.9375,  -0.875, -0.8125,   -0.75, -0.6875,  -0.625, -0.5625,\n",
       "          -0.5, -0.4375,  -0.375, -0.3125,   -0.25, -0.1875,  -0.125, -0.0625,\n",
       "           0.0,  0.0625,   0.125,  0.1875,    0.25,  0.3125,   0.375,  0.4375,\n",
       "           0.5,  0.5625,   0.625,  0.6875,    0.75,  0.8125,   0.875,  0.9375,\n",
       "           1.0,  1.0625,   1.125,  1.1875,    1.25,  1.3125,   1.375,  1.4375,\n",
       "           1.5,  1.5625,   1.625,  1.6875,    1.75,  1.8125,   1.875,  1.9375,\n",
       "           2.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;profile_z&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>psi_loop_n</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d98412bb-774c-4486-9070-155c8845849a' class='xr-index-data-in' type='checkbox'/><label for='index-d98412bb-774c-4486-9070-155c8845849a' 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([ 0.0,  1.0,  2.0,  3.0,  4.0,  5.0,  6.0,  7.0,  8.0,  9.0, 10.0, 11.0,\n",
       "       12.0, 13.0, 14.0, 15.0, 16.0, 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0,\n",
       "       24.0, 25.0, 26.0, 27.0, 28.0, 29.0, 30.0, 31.0, 32.0, 33.0, 34.0, 35.0,\n",
       "       36.0, 37.0, 38.0, 39.0, 40.0, 41.0, 42.0, 43.0, 44.0, 45.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;psi_loop_n&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>psi_norm</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-572363cb-1376-457e-ba08-d253a0114069' class='xr-index-data-in' type='checkbox'/><label for='index-572363cb-1376-457e-ba08-d253a0114069' 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([     0.0, 0.015625,  0.03125, 0.046875,   0.0625, 0.078125,  0.09375,\n",
       "       0.109375,    0.125, 0.140625,  0.15625, 0.171875,   0.1875, 0.203125,\n",
       "        0.21875, 0.234375,     0.25, 0.265625,  0.28125, 0.296875,   0.3125,\n",
       "       0.328125,  0.34375, 0.359375,    0.375, 0.390625,  0.40625, 0.421875,\n",
       "         0.4375, 0.453125,  0.46875, 0.484375,      0.5, 0.515625,  0.53125,\n",
       "       0.546875,   0.5625, 0.578125,  0.59375, 0.609375,    0.625, 0.640625,\n",
       "        0.65625, 0.671875,   0.6875, 0.703125,  0.71875, 0.734375,     0.75,\n",
       "       0.765625,  0.78125, 0.796875,   0.8125, 0.828125,  0.84375, 0.859375,\n",
       "          0.875, 0.890625,  0.90625, 0.921875,   0.9375, 0.953125,  0.96875,\n",
       "       0.984375,      1.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;psi_norm&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>r</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3ed58e47-ae7d-4439-bcd5-5ccf4272423f' class='xr-index-data-in' type='checkbox'/><label for='index-3ed58e47-ae7d-4439-bcd5-5ccf4272423f' 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([0.05999999865889549, 0.09031249582767487, 0.12062500417232513,\n",
       "        0.1509374976158142, 0.18125000596046448, 0.21156251430511475,\n",
       "       0.24187500774860382,  0.2721875011920929, 0.30250000953674316,\n",
       "       0.33281251788139343,  0.3631250262260437,  0.3934375047683716,\n",
       "       0.42375001311302185,  0.4540625214576721,            0.484375,\n",
       "        0.5146874785423279,  0.5450000166893005,  0.5753124952316284,\n",
       "        0.6056250333786011,   0.635937511920929,  0.6662500500679016,\n",
       "        0.6965625286102295,  0.7268750071525574,    0.75718754529953,\n",
       "        0.7875000238418579,  0.8178125023841858,  0.8481250405311584,\n",
       "        0.8784375190734863,  0.9087499976158142,  0.9390625357627869,\n",
       "        0.9693750143051147,  0.9996875524520874,  1.0299999713897705,\n",
       "        1.0603125095367432,  1.0906249284744263,   1.120937466621399,\n",
       "        1.1512500047683716,  1.1815624237060547,  1.2118749618530273,\n",
       "                 1.2421875,  1.2725000381469727,  1.3028124570846558,\n",
       "        1.3331249952316284,   1.363437533378601,  1.3937499523162842,\n",
       "        1.4240624904632568,  1.4543750286102295,  1.4846874475479126,\n",
       "        1.5149999856948853,   1.545312523841858,   1.575624942779541,\n",
       "        1.6059374809265137,  1.6362500190734863,  1.6665624380111694,\n",
       "         1.696874976158142,  1.7271875143051147,  1.7574999332427979,\n",
       "        1.7878124713897705,  1.8181250095367432,  1.8484375476837158,\n",
       "         1.878749966621399,  1.9090625047683716,  1.9393750429153442,\n",
       "        1.9696874618530273,                 2.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;r&#x27;))</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-7af85877-e4f3-4089-bfaa-b1f04ec05db5' class='xr-index-data-in' type='checkbox'/><label for='index-7af85877-e4f3-4089-bfaa-b1f04ec05db5' 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([ -0.04999999701976776, -0.044999998062849045,  -0.03999999910593033,\n",
       "        -0.03500000014901161, -0.029999999329447746,  -0.02499999850988388,\n",
       "       -0.019999999552965164, -0.014999999664723873, -0.009999999776482582,\n",
       "       -0.004999999888241291,                   0.0,   0.03999999910593033,\n",
       "        0.044999998062849045,   0.04999999701976776,  0.054999999701976776,\n",
       "         0.05999999865889549,   0.06499999761581421,   0.07000000029802322,\n",
       "         0.07499999552965164,   0.07999999821186066,   0.08500000089406967,\n",
       "         0.08999999612569809,    0.0949999988079071,   0.09999999403953552,\n",
       "         0.10499999672174454,   0.10999999940395355,   0.11499999463558197,\n",
       "         0.11999999731779099,                 0.125,   0.12999999523162842,\n",
       "         0.13499999046325684,   0.14000000059604645,   0.14499999582767487,\n",
       "         0.14999999105930328,    0.1550000011920929,    0.1599999964237213,\n",
       "         0.16499999165534973,   0.17000000178813934,   0.17499999701976776,\n",
       "         0.17999999225139618,    0.1850000023841858,    0.1899999976158142,\n",
       "         0.19499999284744263,   0.19999998807907104,   0.20499999821186066,\n",
       "         0.20999999344348907,    0.2149999886751175,    0.2199999988079071,\n",
       "         0.22499999403953552,   0.22999998927116394,   0.23499999940395355,\n",
       "         0.23999999463558197,    0.2449999898672104,                  0.25,\n",
       "          0.2549999952316284,   0.25999999046325684,   0.26499998569488525,\n",
       "         0.26999998092651367,    0.2750000059604645,    0.2800000011920929,\n",
       "          0.2849999964237213,   0.28999999165534973,   0.29499998688697815,\n",
       "         0.29999998211860657],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5d0ecc13-4c14-402f-a6ca-8e9c442726fa' class='xr-index-data-in' type='checkbox'/><label for='index-5d0ecc13-4c14-402f-a6ca-8e9c442726fa' 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([   -2.0, -1.9375,  -1.875, -1.8125,   -1.75, -1.6875,  -1.625, -1.5625,\n",
       "          -1.5, -1.4375,  -1.375, -1.3125,   -1.25, -1.1875,  -1.125, -1.0625,\n",
       "          -1.0, -0.9375,  -0.875, -0.8125,   -0.75, -0.6875,  -0.625, -0.5625,\n",
       "          -0.5, -0.4375,  -0.375, -0.3125,   -0.25, -0.1875,  -0.125, -0.0625,\n",
       "           0.0,  0.0625,   0.125,  0.1875,    0.25,  0.3125,   0.375,  0.4375,\n",
       "           0.5,  0.5625,   0.625,  0.6875,    0.75,  0.8125,   0.875,  0.9375,\n",
       "           1.0,  1.0625,   1.125,  1.1875,    1.25,  1.3125,   1.375,  1.4375,\n",
       "           1.5,  1.5625,   1.625,  1.6875,    1.75,  1.8125,   1.875,  1.9375,\n",
       "           2.0],\n",
       "      dtype=&#x27;float32&#x27;, name=&#x27;z&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2742125f-4a0f-4505-b73c-c32d5f77dd3d' class='xr-section-summary-in' type='checkbox'  ><label for='section-2742125f-4a0f-4505-b73c-c32d5f77dd3d' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Basic EFIT</dd><dt><span>file_name :</span></dt><dd>efm0304.20</dd><dt><span>format :</span></dt><dd>IDA3</dd><dt><span>mds_name :</span></dt><dd>None</dd><dt><span>name :</span></dt><dd>efm</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Analysed</dd><dt><span>source :</span></dt><dd>efm</dd><dt><span>uda_name :</span></dt><dd>EFM</dd><dt><span>uuid :</span></dt><dd>1e39c600-8ffb-5f56-900d-2941e352319c</dd><dt><span>version :</span></dt><dd>0</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 4MB\n",
       "Dimensions:            (time: 64, psi_norm: 65, n_iterations: 10,\n",
       "                        fcoil_seg_n: 938, fcoil_n: 101, ffprime_coefs_n: 2,\n",
       "                        mag_probe_n: 78, psi_loop_n: 46, r: 65, z: 65,\n",
       "                        profile_r: 129, lcfs_coords: 147, limiter_n: 37,\n",
       "                        pprime_coefs_n: 2, profile_z: 65)\n",
       "Coordinates: (12/13)\n",
       "  * fcoil_n            (fcoil_n) float32 404B 0.0 1.0 2.0 ... 98.0 99.0 100.0\n",
       "  * ffprime_coefs_n    (ffprime_coefs_n) float32 8B 0.0 1.0\n",
       "  * lcfs_coords        (lcfs_coords) float32 588B 0.0 1.0 2.0 ... 145.0 146.0\n",
       "  * mag_probe_n        (mag_probe_n) float32 312B 0.0 1.0 2.0 ... 75.0 76.0 77.0\n",
       "  * n_iterations       (n_iterations) float32 40B 0.0 1.0 2.0 ... 7.0 8.0 9.0\n",
       "  * pprime_coefs_n     (pprime_coefs_n) float32 8B 0.0 1.0\n",
       "    ...                 ...\n",
       "  * profile_z          (profile_z) float32 260B -2.0 -1.938 -1.875 ... 1.938 2.0\n",
       "  * psi_loop_n         (psi_loop_n) float32 184B 0.0 1.0 2.0 ... 43.0 44.0 45.0\n",
       "  * psi_norm           (psi_norm) float32 260B 0.0 0.01562 ... 0.9844 1.0\n",
       "  * r                  (r) float32 260B 0.06 0.09031 0.1206 ... 1.939 1.97 2.0\n",
       "  * time               (time) float32 256B -0.05 -0.045 -0.04 ... 0.29 0.295 0.3\n",
       "  * z                  (z) float32 260B -2.0 -1.938 -1.875 ... 1.875 1.938 2.0\n",
       "Dimensions without coordinates: fcoil_seg_n, limiter_n\n",
       "Data variables: (12/151)\n",
       "    all_times          (time) float32 256B dask.array<chunksize=(64,), meta=np.ndarray>\n",
       "    areap_c            (time, psi_norm) float32 17kB dask.array<chunksize=(64, 65), meta=np.ndarray>\n",
       "    betan              (time) float32 256B dask.array<chunksize=(64,), meta=np.ndarray>\n",
       "    betap              (time) float32 256B dask.array<chunksize=(64,), meta=np.ndarray>\n",
       "    betapd             (time) float32 256B dask.array<chunksize=(64,), meta=np.ndarray>\n",
       "    betat              (time) float32 256B dask.array<chunksize=(64,), meta=np.ndarray>\n",
       "    ...                 ...\n",
       "    wpol               (time) float32 256B dask.array<chunksize=(64,), meta=np.ndarray>\n",
       "    xpoint1_rc         (time) float32 256B dask.array<chunksize=(64,), meta=np.ndarray>\n",
       "    xpoint1_zc         (time) float32 256B dask.array<chunksize=(64,), meta=np.ndarray>\n",
       "    xpoint2_rc         (time) float32 256B dask.array<chunksize=(64,), meta=np.ndarray>\n",
       "    xpoint2_zc         (time) float32 256B dask.array<chunksize=(64,), meta=np.ndarray>\n",
       "    zbdry              (time) float32 256B dask.array<chunksize=(64,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    description:  Basic EFIT\n",
       "    file_name:    efm0304.20\n",
       "    format:       IDA3\n",
       "    mds_name:     None\n",
       "    name:         efm\n",
       "    quality:      Not Checked\n",
       "    shot_id:      30420\n",
       "    signal_type:  Analysed\n",
       "    source:       efm\n",
       "    uda_name:     EFM\n",
       "    uuid:         1e39c600-8ffb-5f56-900d-2941e352319c\n",
       "    version:      0"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = catalog.level1.shots(url=url, group='efm')\n",
    "dataset = dataset.to_dask()\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below we show how to load and plot the plasma current denisty and with the last closed flux surface (LCFS)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/xr/yr8z575s52b4tbg3fj65qwx00000gp/T/ipykernel_27658/1992647380.py:28: UserWarning: The following kwargs were not used by contour: 'label'\n",
      "  ax.contourf(R, Z, d[index], cmap='magma', levels=20, label='Plasma Current')\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x35770c250>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d = dataset['plasma_current_rz'].dropna(dim='time')\n",
    "r = dataset['r']\n",
    "z = dataset['z']\n",
    "\n",
    "\n",
    "lcfs_R = dataset['lcfs_r'].sel(time=d.time)\n",
    "lcfs_Z = dataset['lcfs_z'].sel(time=d.time)\n",
    "\n",
    "R, Z = np.meshgrid(r, z)\n",
    "\n",
    "index = 50\n",
    "\n",
    "# Get the x-point\n",
    "xpoint_r = dataset['xpoint2_rc'][index]\n",
    "xpoint_z = dataset['xpoint2_zc'][index]\n",
    "\n",
    "# Get the current centre\n",
    "mag_axis_r = dataset['current_centrd_r'][index]\n",
    "mag_axis_z = dataset['current_centrd_z'][index]\n",
    "\n",
    "# Get the last closed flux surface (LCFS)\n",
    "lcfs_r = lcfs_R[index].values\n",
    "lcfs_r = lcfs_r[~np.isnan(lcfs_r)]\n",
    "lcfs_z = lcfs_Z[index].values\n",
    "lcfs_z = lcfs_z[~np.isnan(lcfs_z)]\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "ax.contourf(R, Z, d[index], cmap='magma', levels=20, label='Plasma Current')\n",
    "ax.plot(lcfs_r, lcfs_z, c='red', linestyle='--', label='LCFS')\n",
    "ax.scatter(xpoint_r, xpoint_z, marker='x', color='green', label='X Point')\n",
    "ax.scatter(mag_axis_r, mag_axis_z, marker='o', color='purple', label='Current Centre')\n",
    "\n",
    "plt.title(f'EFIT Plasma Current & LCFS for Shot {d.attrs[\"shot_id\"]}')\n",
    "plt.ylabel('Z (m)')\n",
    "plt.xlabel('R (m)')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mirnov Coils"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Mirnov coils are primarily used to measure magnetic fluctuations in the plasma. These fluctuations can provide important information about various plasma instabilities. \n",
    "\n",
    "They are particularly useful for studying magnetohydrodynamic (MHD) phenomena. MHD activity includes various modes of instabilities, such as kink modes and tearing modes, which can affect plasma confinement and stability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rt2549/miniconda3/envs/mast-book/lib/python3.11/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  'dims': dict(self._ds.dims),\n"
     ]
    },
    {
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       "  --xr-background-color: var(--jp-layout-color0, white);\n",
       "  --xr-background-color-row-even: var(--jp-layout-color1, white);\n",
       "  --xr-background-color-row-odd: var(--jp-layout-color2, #eeeeee);\n",
       "}\n",
       "\n",
       "html[theme=dark],\n",
       "body[data-theme=dark],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: rgba(255, 255, 255, 1);\n",
       "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
       "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
       "  --xr-border-color: #1F1F1F;\n",
       "  --xr-disabled-color: #515151;\n",
       "  --xr-background-color: #111111;\n",
       "  --xr-background-color-row-even: #111111;\n",
       "  --xr-background-color-row-odd: #313131;\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "}\n",
       "\n",
       ".xr-text-repr-fallback {\n",
       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".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: 101MB\n",
       "Dimensions:                        (dim_0: 16, dim_1: 2, time: 1400000)\n",
       "Coordinates:\n",
       "  * dim_0                          (dim_0) int32 64B 0 1 2 3 4 ... 12 13 14 15\n",
       "  * dim_1                          (dim_1) int32 8B 0 1\n",
       "  * time                           (time) float64 11MB -0.1 -0.1 ... 0.6 0.6\n",
       "Data variables: (12/18)\n",
       "    devices_d3_acq216_025_channel  (dim_0) int32 64B dask.array&lt;chunksize=(16,), meta=np.ndarray&gt;\n",
       "    devices_d3_acq216_025_range    (dim_0, dim_1) float32 128B dask.array&lt;chunksize=(16, 2), meta=np.ndarray&gt;\n",
       "    devices_limit                  (dim_0) float64 128B dask.array&lt;chunksize=(16,), meta=np.ndarray&gt;\n",
       "    omaha_1lz                      (time) float32 6MB dask.array&lt;chunksize=(87500,), meta=np.ndarray&gt;\n",
       "    omaha_2lt                      (time) float32 6MB dask.array&lt;chunksize=(87500,), meta=np.ndarray&gt;\n",
       "    omaha_2lz                      (time) float32 6MB dask.array&lt;chunksize=(87500,), meta=np.ndarray&gt;\n",
       "    ...                             ...\n",
       "    omaha_5lz                      (time) float32 6MB dask.array&lt;chunksize=(87500,), meta=np.ndarray&gt;\n",
       "    omaha_5ur                      (time) float32 6MB dask.array&lt;chunksize=(87500,), meta=np.ndarray&gt;\n",
       "    omaha_5ut                      (time) float32 6MB dask.array&lt;chunksize=(87500,), meta=np.ndarray&gt;\n",
       "    omaha_5uz                      (time) float32 6MB dask.array&lt;chunksize=(87500,), meta=np.ndarray&gt;\n",
       "    omaha_6lz                      (time) float32 6MB dask.array&lt;chunksize=(87500,), meta=np.ndarray&gt;\n",
       "    time1                          (time) float64 11MB dask.array&lt;chunksize=(87500,), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    description:  Magnetic Field Measurements: OMAHA high frequency Mirnov co...\n",
       "    file_name:    xmo029790.nc\n",
       "    format:       CDF\n",
       "    mds_name:     None\n",
       "    name:         xmo\n",
       "    quality:      Not Checked\n",
       "    shot_id:      29790\n",
       "    signal_type:  Raw\n",
       "    source:       xmo\n",
       "    uda_name:     XMO\n",
       "    uuid:         ff541ac3-6b1c-5373-8d33-b85fd46bc75b\n",
       "    version:      -1</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-7cf1efcf-b855-4ea8-8572-922f545446b9' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-7cf1efcf-b855-4ea8-8572-922f545446b9' 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'>dim_0</span>: 16</li><li><span class='xr-has-index'>dim_1</span>: 2</li><li><span class='xr-has-index'>time</span>: 1400000</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7a2d01f1-9a9c-4f38-8a10-a975db16771f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7a2d01f1-9a9c-4f38-8a10-a975db16771f' 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'>dim_0</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1 2 3 4 5 6 ... 10 11 12 13 14 15</div><input id='attrs-673dd76d-6cfc-4af2-abc6-cf91167ccefc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-673dd76d-6cfc-4af2-abc6-cf91167ccefc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b2315193-797d-46ba-aec0-c922ec244306' class='xr-var-data-in' type='checkbox'><label for='data-b2315193-797d-46ba-aec0-c922ec244306' 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></dd></dl></div><div class='xr-var-data'><pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>dim_1</span></div><div class='xr-var-dims'>(dim_1)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>0 1</div><input id='attrs-727f5883-0bb5-4f9d-9c59-34d1ee269538' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-727f5883-0bb5-4f9d-9c59-34d1ee269538' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-762215d9-af9a-4b15-82bc-8514cf650be7' class='xr-var-data-in' type='checkbox'><label for='data-762215d9-af9a-4b15-82bc-8514cf650be7' 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></dd></dl></div><div class='xr-var-data'><pre>array([0, 1], dtype=int32)</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'>float64</div><div class='xr-var-preview xr-preview'>-0.1 -0.1 -0.1 -0.1 ... 0.6 0.6 0.6</div><input id='attrs-fae9657e-297a-4986-afc4-fa7ffaf62a66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fae9657e-297a-4986-afc4-fa7ffaf62a66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cceb0086-751f-4f99-81ab-c3320d79a8e8' class='xr-var-data-in' type='checkbox'><label for='data-cceb0086-751f-4f99-81ab-c3320d79a8e8' 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>s</dd></dl></div><div class='xr-var-data'><pre>array([-0.1     , -0.099999, -0.099999, ...,  0.599998,  0.599999,  0.599999])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-37c84c5b-642e-42a0-87f0-5c6573ba8f9f' class='xr-section-summary-in' type='checkbox'  ><label for='section-37c84c5b-642e-42a0-87f0-5c6573ba8f9f' class='xr-section-summary' >Data variables: <span>(18)</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>devices_d3_acq216_025_channel</span></div><div class='xr-var-dims'>(dim_0)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(16,), meta=np.ndarray&gt;</div><input id='attrs-da4d5ceb-57bc-4bc8-9758-724d8fa627e9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-da4d5ceb-57bc-4bc8-9758-724d8fa627e9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-402b61ac-55f3-481e-a5b5-737667034cd4' class='xr-var-data-in' type='checkbox'><label for='data-402b61ac-55f3-481e-a5b5-737667034cd4' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;dim_0&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd></dd><dt><span>mds_name :</span></dt><dd>\\TOP.RAW.XMO.DEVICES_D3.ACQ216_025:CHANNEL</dd><dt><span>name :</span></dt><dd>xmo/devices_d3_acq216_025_channel</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[16]</dd><dt><span>shot_id :</span></dt><dd>29790</dd><dt><span>signal_type :</span></dt><dd>Raw</dd><dt><span>source :</span></dt><dd>xmo</dd><dt><span>time_index :</span></dt><dd>null</dd><dt><span>uda_name :</span></dt><dd>/XMO/DEVICES/D3_ACQ216_025/CHANNEL</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>b8dfcfeb-7532-5744-a051-05b03e011a92</dd><dt><span>version :</span></dt><dd>-1</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> 64 B </td>\n",
       "                        <td> 64 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (16,) </td>\n",
       "                        <td> (16,) </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",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>omaha_6lz</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(87500,), meta=np.ndarray&gt;</div><input id='attrs-fc6a9bba-45b3-46a2-964d-1a536f68d4b6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc6a9bba-45b3-46a2-964d-1a536f68d4b6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc255a9b-f406-427f-9bc5-9d0e6e1dcc12' class='xr-var-data-in' type='checkbox'><label for='data-bc255a9b-f406-427f-9bc5-9d0e6e1dcc12' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>arb</dd><dt><span>mds_name :</span></dt><dd></dd><dt><span>name :</span></dt><dd>xmo/omaha_6lz</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1400000]</dd><dt><span>shot_id :</span></dt><dd>29790</dd><dt><span>signal_type :</span></dt><dd>Raw</dd><dt><span>source :</span></dt><dd>xmo</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>/XMO/OMAHA/6LZ</dd><dt><span>units :</span></dt><dd></dd><dt><span>uuid :</span></dt><dd>d91166e7-867c-58a0-a710-ff010bd00f44</dd><dt><span>version :</span></dt><dd>-1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <thead>\n",
       "                    <tr>\n",
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       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 5.34 MiB </td>\n",
       "                        <td> 341.80 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (1400000,) </td>\n",
       "                        <td> (87500,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 16 chunks in 2 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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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time1</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(87500,), meta=np.ndarray&gt;</div><input id='attrs-1a0d39e3-0bbb-464a-a08c-c4078923af2a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1a0d39e3-0bbb-464a-a08c-c4078923af2a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37672453-a621-4fca-b765-3f0a45a4f13c' class='xr-var-data-in' type='checkbox'><label for='data-37672453-a621-4fca-b765-3f0a45a4f13c' 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>description :</span></dt><dd></dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;]</dd><dt><span>file_name :</span></dt><dd>None</dd><dt><span>format :</span></dt><dd>None</dd><dt><span>label :</span></dt><dd>Time</dd><dt><span>mds_name :</span></dt><dd>\\TOP.RAW.XMO:TIME1</dd><dt><span>name :</span></dt><dd>xmo/time1</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>1</dd><dt><span>shape :</span></dt><dd>[1400000]</dd><dt><span>shot_id :</span></dt><dd>29790</dd><dt><span>signal_type :</span></dt><dd>Raw</dd><dt><span>source :</span></dt><dd>xmo</dd><dt><span>time_index :</span></dt><dd>0</dd><dt><span>uda_name :</span></dt><dd>/XMO/TIME1</dd><dt><span>units :</span></dt><dd>s</dd><dt><span>uuid :</span></dt><dd>bfe58a24-e54f-5239-afb0-4c84aedc3006</dd><dt><span>version :</span></dt><dd>-1</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                <tbody>\n",
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       "                        <td> 10.68 MiB </td>\n",
       "                        <td> 683.59 kiB </td>\n",
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       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "\n",
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       "</svg>\n",
       "        </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-37644e8b-81f6-4c21-8141-056b0ea5d2f7' class='xr-section-summary-in' type='checkbox'  ><label for='section-37644e8b-81f6-4c21-8141-056b0ea5d2f7' 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>dim_0</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-baf79c9a-3dd9-4993-b749-de606488f2bd' class='xr-index-data-in' type='checkbox'/><label for='index-baf79c9a-3dd9-4993-b749-de606488f2bd' 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([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], dtype=&#x27;int32&#x27;, name=&#x27;dim_0&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>dim_1</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c62ff8e3-1946-4379-b215-ced63ae98953' class='xr-index-data-in' type='checkbox'/><label for='index-c62ff8e3-1946-4379-b215-ced63ae98953' 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([0, 1], dtype=&#x27;int32&#x27;, name=&#x27;dim_1&#x27;))</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-5c6b360d-2177-4f67-af6c-9aa1fe8d5d1a' class='xr-index-data-in' type='checkbox'/><label for='index-5c6b360d-2177-4f67-af6c-9aa1fe8d5d1a' 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([-0.09999999999999998, -0.09999949999999998, -0.09999899999999998,\n",
       "       -0.09999849999999998, -0.09999799999999998, -0.09999749999999998,\n",
       "       -0.09999699999999997, -0.09999649999999997, -0.09999599999999997,\n",
       "       -0.09999549999999997,\n",
       "       ...\n",
       "         0.5999949999958225,   0.5999954999958225,   0.5999959999958224,\n",
       "         0.5999964999958224,   0.5999969999958223,   0.5999974999958223,\n",
       "         0.5999979999958223,   0.5999984999958222,   0.5999989999958222,\n",
       "         0.5999994999958221],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=1400000))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e6845cc-e7c7-4a69-8a32-6e393430eba1' class='xr-section-summary-in' type='checkbox'  ><label for='section-6e6845cc-e7c7-4a69-8a32-6e393430eba1' class='xr-section-summary' >Attributes: <span>(12)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>Magnetic Field Measurements: OMAHA high frequency Mirnov coils (OMAHA), Centre Column Halo Current</dd><dt><span>file_name :</span></dt><dd>xmo029790.nc</dd><dt><span>format :</span></dt><dd>CDF</dd><dt><span>mds_name :</span></dt><dd>None</dd><dt><span>name :</span></dt><dd>xmo</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>shot_id :</span></dt><dd>29790</dd><dt><span>signal_type :</span></dt><dd>Raw</dd><dt><span>source :</span></dt><dd>xmo</dd><dt><span>uda_name :</span></dt><dd>XMO</dd><dt><span>uuid :</span></dt><dd>ff541ac3-6b1c-5373-8d33-b85fd46bc75b</dd><dt><span>version :</span></dt><dd>-1</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 101MB\n",
       "Dimensions:                        (dim_0: 16, dim_1: 2, time: 1400000)\n",
       "Coordinates:\n",
       "  * dim_0                          (dim_0) int32 64B 0 1 2 3 4 ... 12 13 14 15\n",
       "  * dim_1                          (dim_1) int32 8B 0 1\n",
       "  * time                           (time) float64 11MB -0.1 -0.1 ... 0.6 0.6\n",
       "Data variables: (12/18)\n",
       "    devices_d3_acq216_025_channel  (dim_0) int32 64B dask.array<chunksize=(16,), meta=np.ndarray>\n",
       "    devices_d3_acq216_025_range    (dim_0, dim_1) float32 128B dask.array<chunksize=(16, 2), meta=np.ndarray>\n",
       "    devices_limit                  (dim_0) float64 128B dask.array<chunksize=(16,), meta=np.ndarray>\n",
       "    omaha_1lz                      (time) float32 6MB dask.array<chunksize=(87500,), meta=np.ndarray>\n",
       "    omaha_2lt                      (time) float32 6MB dask.array<chunksize=(87500,), meta=np.ndarray>\n",
       "    omaha_2lz                      (time) float32 6MB dask.array<chunksize=(87500,), meta=np.ndarray>\n",
       "    ...                             ...\n",
       "    omaha_5lz                      (time) float32 6MB dask.array<chunksize=(87500,), meta=np.ndarray>\n",
       "    omaha_5ur                      (time) float32 6MB dask.array<chunksize=(87500,), meta=np.ndarray>\n",
       "    omaha_5ut                      (time) float32 6MB dask.array<chunksize=(87500,), meta=np.ndarray>\n",
       "    omaha_5uz                      (time) float32 6MB dask.array<chunksize=(87500,), meta=np.ndarray>\n",
       "    omaha_6lz                      (time) float32 6MB dask.array<chunksize=(87500,), meta=np.ndarray>\n",
       "    time1                          (time) float64 11MB dask.array<chunksize=(87500,), meta=np.ndarray>\n",
       "Attributes:\n",
       "    description:  Magnetic Field Measurements: OMAHA high frequency Mirnov co...\n",
       "    file_name:    xmo029790.nc\n",
       "    format:       CDF\n",
       "    mds_name:     None\n",
       "    name:         xmo\n",
       "    quality:      Not Checked\n",
       "    shot_id:      29790\n",
       "    signal_type:  Raw\n",
       "    source:       xmo\n",
       "    uda_name:     XMO\n",
       "    uuid:         ff541ac3-6b1c-5373-8d33-b85fd46bc75b\n",
       "    version:      -1"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = catalog.level1.shots(url='s3://mast/level1/shots/29790.zarr', group='xmo')\n",
    "dataset = dataset.to_dask()\n",
    "dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can first look at the line profile for one of the Mirnov coils:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(1, 1, figsize=(10, 5))\n",
    "ax.plot(dataset['time'], dataset['omaha_3lz'])\n",
    "ax.grid()\n",
    "\n",
    "ax.grid(alpha=0.3)\n",
    "ax.set_ylabel('Volts (V)')\n",
    "ax.set_xlabel('Time (s)')\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the spectrogram of the dataset can show us information about the MHD modes. Here we see several mode instabilities occuring before the plasma is lost."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x374ca1090>"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds = dataset['omaha_3lz']\n",
    "# Parameters to limit the number of frequencies\n",
    "nperseg = 2000  # Number of points per segment\n",
    "nfft = 2000  # Number of FFT points\n",
    "\n",
    "# Compute the Short-Time Fourier Transform (STFT)\n",
    "sample_rate = 1/(ds.time[1] - ds.time[0])\n",
    "f, t, Zxx = stft(ds, fs=int(sample_rate), nperseg=nperseg, nfft=nfft)\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "cax = ax.pcolormesh(t, f/1000, np.abs(Zxx), shading='nearest', cmap='jet', norm=LogNorm(vmin=1e-5))\n",
    "ax.set_ylim(0, 50)\n",
    "ax.set_title(f'XMO/OMAHA/3LZ - Shot {ds.attrs[\"shot_id\"]}')\n",
    "ax.set_ylabel('Frequency [Hz]')\n",
    "ax.set_xlabel('Time [sec]')\n",
    "plt.colorbar(cax, ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Photron Camera Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RBA\n",
    "\n",
    "RBA contains the data from Photron bullet camera A. \n",
    "\n",
    "A Photron Bullet Camera provides high-speed, high-resolution imaging of fast transient events in the plasma. Its ability to capture detailed images of plasma instabilities, turbulence, and disruptions makes it essential for understanding and controlling plasma behavior, ultimately aiding in the pursuit of sustained nuclear fusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/rt2549/miniconda3/envs/mast-book/lib/python3.11/site-packages/intake_xarray/base.py:21: FutureWarning: The return type of `Dataset.dims` will be changed to return a set of dimension names in future, in order to be more consistent with `DataArray.dims`. To access a mapping from dimension names to lengths, please use `Dataset.sizes`.\n",
      "  'dims': dict(self._ds.dims),\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n",
       "<defs>\n",
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       "\n",
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       "\n",
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       "\n",
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       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
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       ".xr-var-item label,\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
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       "\n",
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       "\n",
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       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
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       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
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       "\n",
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       "  padding: 0;\n",
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       "  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: 130MB\n",
       "Dimensions:  (time: 186, height: 912, width: 768)\n",
       "Coordinates:\n",
       "  * time     (time) float64 1kB 0.000256 0.002256 0.004256 ... 0.3093 0.3103\n",
       "Dimensions without coordinates: height, width\n",
       "Data variables:\n",
       "    data     (time, height, width) uint8 130MB dask.array&lt;chunksize=(24, 228, 192), meta=np.ndarray&gt;\n",
       "Attributes: (12/48)\n",
       "    CLASS:           IMAGE\n",
       "    IMAGE_SUBCLASS:  IMAGE_INDEXED\n",
       "    IMAGE_VERSION:   1.2\n",
       "    board_temp:      0.0\n",
       "    bottom:          1024\n",
       "    camera:          \n",
       "    ...              ...\n",
       "    units:           pixels\n",
       "    uuid:            4a073a8f-aa2d-5deb-95df-91f93194c494\n",
       "    vbin:            0\n",
       "    version:         -1\n",
       "    view:            Hl07 floor mount + FFC2 + 25mm lens + CII filter\n",
       "    width:           768</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-242796bc-5460-480a-b11f-6ce302839795' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-242796bc-5460-480a-b11f-6ce302839795' 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>: 186</li><li><span>height</span>: 912</li><li><span>width</span>: 768</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-b8ca422b-0304-4a45-96e1-dec81e27bb6a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b8ca422b-0304-4a45-96e1-dec81e27bb6a' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.000256 0.002256 ... 0.3093 0.3103</div><input id='attrs-c769eb0e-57c2-4769-a234-3cb8a6a77c8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c769eb0e-57c2-4769-a234-3cb8a6a77c8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5abbaddc-41ff-4641-9d30-f9cce667eaf7' class='xr-var-data-in' type='checkbox'><label for='data-5abbaddc-41ff-4641-9d30-f9cce667eaf7' 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>s</dd></dl></div><div class='xr-var-data'><pre>array([2.56000e-04, 2.25600e-03, 4.25600e-03, 6.25600e-03, 8.25600e-03,\n",
       "       1.02560e-02, 1.22560e-02, 1.42560e-02, 1.62560e-02, 1.82560e-02,\n",
       "       2.02560e-02, 2.22560e-02, 2.42560e-02, 2.62560e-02, 2.82560e-02,\n",
       "       3.02560e-02, 3.22560e-02, 3.42560e-02, 3.62560e-02, 3.82560e-02,\n",
       "       4.02560e-02, 4.22560e-02, 4.42560e-02, 4.62560e-02, 4.82560e-02,\n",
       "       5.02560e-02, 5.22560e-02, 5.42560e-02, 5.62560e-02, 5.82560e-02,\n",
       "       6.02560e-02, 6.22560e-02, 6.42560e-02, 6.62560e-02, 6.82560e-02,\n",
       "       7.02560e-02, 7.22560e-02, 7.42560e-02, 7.62560e-02, 7.82560e-02,\n",
       "       8.02560e-02, 8.22560e-02, 8.42560e-02, 8.62560e-02, 8.82560e-02,\n",
       "       9.02560e-02, 9.22560e-02, 9.42560e-02, 9.62560e-02, 9.82560e-02,\n",
       "       1.00256e-01, 1.02256e-01, 1.04256e-01, 1.06256e-01, 1.08256e-01,\n",
       "       1.10256e-01, 1.12256e-01, 1.14256e-01, 1.16256e-01, 1.18256e-01,\n",
       "       1.20256e-01, 1.22256e-01, 1.24256e-01, 1.26256e-01, 1.28256e-01,\n",
       "       1.30256e-01, 1.32256e-01, 1.34256e-01, 1.36256e-01, 1.38256e-01,\n",
       "       1.40256e-01, 1.42256e-01, 1.44256e-01, 1.46256e-01, 1.48256e-01,\n",
       "       1.50256e-01, 1.52256e-01, 1.54256e-01, 1.56256e-01, 1.58256e-01,\n",
       "       1.60256e-01, 1.62256e-01, 1.64256e-01, 1.66256e-01, 1.68256e-01,\n",
       "       1.70256e-01, 1.72256e-01, 1.74256e-01, 1.76256e-01, 1.78256e-01,\n",
       "       1.80256e-01, 1.82256e-01, 1.84256e-01, 1.86256e-01, 1.88256e-01,\n",
       "       1.90256e-01, 1.92256e-01, 1.94256e-01, 1.96256e-01, 1.98256e-01,\n",
       "       2.00256e-01, 2.02256e-01, 2.04256e-01, 2.06256e-01, 2.08256e-01,\n",
       "       2.10256e-01, 2.12256e-01, 2.14256e-01, 2.16256e-01, 2.18256e-01,\n",
       "       2.20256e-01, 2.22256e-01, 2.24256e-01, 2.26256e-01, 2.28256e-01,\n",
       "       2.30256e-01, 2.32256e-01, 2.34256e-01, 2.36256e-01, 2.38256e-01,\n",
       "       2.40256e-01, 2.42256e-01, 2.44256e-01, 2.46256e-01, 2.48256e-01,\n",
       "       2.50256e-01, 2.51256e-01, 2.52256e-01, 2.53256e-01, 2.54256e-01,\n",
       "       2.55256e-01, 2.56256e-01, 2.57256e-01, 2.58256e-01, 2.59256e-01,\n",
       "       2.60256e-01, 2.61256e-01, 2.62256e-01, 2.63256e-01, 2.64256e-01,\n",
       "       2.65256e-01, 2.66256e-01, 2.67256e-01, 2.68256e-01, 2.69256e-01,\n",
       "       2.70256e-01, 2.71256e-01, 2.72256e-01, 2.73256e-01, 2.74256e-01,\n",
       "       2.75256e-01, 2.76256e-01, 2.77256e-01, 2.78256e-01, 2.79256e-01,\n",
       "       2.80256e-01, 2.81256e-01, 2.82256e-01, 2.83256e-01, 2.84256e-01,\n",
       "       2.85256e-01, 2.86256e-01, 2.87256e-01, 2.88256e-01, 2.89256e-01,\n",
       "       2.90256e-01, 2.91256e-01, 2.92256e-01, 2.93256e-01, 2.94256e-01,\n",
       "       2.95256e-01, 2.96256e-01, 2.97256e-01, 2.98256e-01, 2.99256e-01,\n",
       "       3.00256e-01, 3.01256e-01, 3.02256e-01, 3.03256e-01, 3.04256e-01,\n",
       "       3.05256e-01, 3.06256e-01, 3.07256e-01, 3.08256e-01, 3.09256e-01,\n",
       "       3.10256e-01])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-729f4fea-c441-4d20-ad98-07840843b2df' class='xr-section-summary-in' type='checkbox'  checked><label for='section-729f4fea-c441-4d20-ad98-07840843b2df' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>data</span></div><div class='xr-var-dims'>(time, height, width)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(24, 228, 192), meta=np.ndarray&gt;</div><input id='attrs-12b0e880-efc4-4e04-bd11-c608b890e1e4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-12b0e880-efc4-4e04-bd11-c608b890e1e4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-962b84ac-9468-498a-a7de-8a80f4265b27' class='xr-var-data-in' type='checkbox'><label for='data-962b84ac-9468-498a-a7de-8a80f4265b27' 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> 124.24 MiB </td>\n",
       "                        <td> 1.00 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (186, 912, 768) </td>\n",
       "                        <td> (24, 228, 192) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 128 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\n",
       "        <svg width=\"185\" height=\"194\" style=\"stroke:rgb(0,0,0);stroke-width:1\" >\n",
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       "  <text x=\"85.286360\" y=\"164.760044\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" >768</text>\n",
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       "  <text x=\"12.380022\" y=\"152.380022\" font-size=\"1.0rem\" font-weight=\"100\" text-anchor=\"middle\" transform=\"rotate(45,12.380022,152.380022)\">186</text>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-87303745-8448-4fba-95fb-1c5e3d5d8581' class='xr-section-summary-in' type='checkbox'  ><label for='section-87303745-8448-4fba-95fb-1c5e3d5d8581' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4527d9e4-0f73-4156-a9f9-7e6430908064' class='xr-index-data-in' type='checkbox'/><label for='index-4527d9e4-0f73-4156-a9f9-7e6430908064' 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([             0.000256, 0.0022559999999999998,              0.004256,\n",
       "        0.006255999999999999,              0.008256,              0.010256,\n",
       "                    0.012256,              0.014256,              0.016256,\n",
       "        0.018255999999999998,\n",
       "       ...\n",
       "         0.30125599999999997,   0.30225599999999997,   0.30325599999999997,\n",
       "         0.30425599999999997,   0.30525599999999997,              0.306256,\n",
       "                    0.307256,              0.308256,              0.309256,\n",
       "                    0.310256],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=186))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-0268ef3e-24a6-4d9e-9ad9-2fe97ebd9626' class='xr-section-summary-in' type='checkbox'  ><label for='section-0268ef3e-24a6-4d9e-9ad9-2fe97ebd9626' class='xr-section-summary' >Attributes: <span>(48)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CLASS :</span></dt><dd>IMAGE</dd><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_INDEXED</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>board_temp :</span></dt><dd>0.0</dd><dt><span>bottom :</span></dt><dd>1024</dd><dt><span>camera :</span></dt><dd></dd><dt><span>ccd_temp :</span></dt><dd>0.0</dd><dt><span>codex :</span></dt><dd>JP2</dd><dt><span>date_time :</span></dt><dd>2013-09-25T13:05:51Z</dd><dt><span>depth :</span></dt><dd>8</dd><dt><span>description :</span></dt><dd>Photron bullet camera A</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;height&#x27;, &#x27;width&#x27;]</dd><dt><span>exposure :</span></dt><dd>250.0</dd><dt><span>file_format :</span></dt><dd>IPX-1</dd><dt><span>file_name :</span></dt><dd>rba030420.ipx</dd><dt><span>filter :</span></dt><dd></dd><dt><span>format :</span></dt><dd>IPX</dd><dt><span>gain :</span></dt><dd>[4.0, 0.0]</dd><dt><span>hbin :</span></dt><dd>0</dd><dt><span>height :</span></dt><dd>912</dd><dt><span>is_color :</span></dt><dd>0</dd><dt><span>left :</span></dt><dd>129</dd><dt><span>lens :</span></dt><dd></dd><dt><span>mds_name :</span></dt><dd>None</dd><dt><span>n_frames :</span></dt><dd>186</dd><dt><span>name :</span></dt><dd>rba</dd><dt><span>offset :</span></dt><dd>[0.0, 0.0]</dd><dt><span>orientation :</span></dt><dd>0</dd><dt><span>pre_exp :</span></dt><dd>0.0</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>3</dd><dt><span>right :</span></dt><dd>896</dd><dt><span>shape :</span></dt><dd>[186, 912, 768]</dd><dt><span>shot :</span></dt><dd>30420</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Image</dd><dt><span>source :</span></dt><dd>rba</dd><dt><span>strobe :</span></dt><dd>0</dd><dt><span>taps :</span></dt><dd>0</dd><dt><span>top :</span></dt><dd>113</dd><dt><span>trigger :</span></dt><dd>-0.10000000149011612</dd><dt><span>uda_name :</span></dt><dd>RBA</dd><dt><span>units :</span></dt><dd>pixels</dd><dt><span>uuid :</span></dt><dd>4a073a8f-aa2d-5deb-95df-91f93194c494</dd><dt><span>vbin :</span></dt><dd>0</dd><dt><span>version :</span></dt><dd>-1</dd><dt><span>view :</span></dt><dd>Hl07 floor mount + FFC2 + 25mm lens + CII filter</dd><dt><span>width :</span></dt><dd>768</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 130MB\n",
       "Dimensions:  (time: 186, height: 912, width: 768)\n",
       "Coordinates:\n",
       "  * time     (time) float64 1kB 0.000256 0.002256 0.004256 ... 0.3093 0.3103\n",
       "Dimensions without coordinates: height, width\n",
       "Data variables:\n",
       "    data     (time, height, width) uint8 130MB dask.array<chunksize=(24, 228, 192), meta=np.ndarray>\n",
       "Attributes: (12/48)\n",
       "    CLASS:           IMAGE\n",
       "    IMAGE_SUBCLASS:  IMAGE_INDEXED\n",
       "    IMAGE_VERSION:   1.2\n",
       "    board_temp:      0.0\n",
       "    bottom:          1024\n",
       "    camera:          \n",
       "    ...              ...\n",
       "    units:           pixels\n",
       "    uuid:            4a073a8f-aa2d-5deb-95df-91f93194c494\n",
       "    vbin:            0\n",
       "    version:         -1\n",
       "    view:            Hl07 floor mount + FFC2 + 25mm lens + CII filter\n",
       "    width:           768"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = catalog.level1.shots(url=url, group='rba')\n",
    "dataset = dataset.to_dask()\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x37423ed10>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(dataset.data[-10], cmap='gray')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### RBB\n",
    "\n",
    "RBB contains the data from Photron bullet camera B, which is looking at the central column. \n",
    "\n",
    "A Photron Bullet Camera provides high-speed, high-resolution imaging of fast transient events in the plasma. Its ability to capture detailed images of plasma instabilities, turbulence, and disruptions makes it essential for understanding and controlling plasma behavior, ultimately aiding in the pursuit of sustained nuclear fusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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       "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-header {\n",
       "  padding-top: 6px;\n",
       "  padding-bottom: 6px;\n",
       "  margin-bottom: 4px;\n",
       "  border-bottom: solid 1px var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-header > div,\n",
       ".xr-header > ul {\n",
       "  display: inline;\n",
       "  margin-top: 0;\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-obj-type,\n",
       ".xr-array-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
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       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
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       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
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       "  z-index: 1;\n",
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       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
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       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".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: 82MB\n",
       "Dimensions:  (time: 286, height: 448, width: 640)\n",
       "Coordinates:\n",
       "  * time     (time) float64 2kB 1.6e-05 0.002016 0.004016 ... 0.308 0.309 0.31\n",
       "Dimensions without coordinates: height, width\n",
       "Data variables:\n",
       "    data     (time, height, width) uint8 82MB dask.array&lt;chunksize=(72, 112, 160), meta=np.ndarray&gt;\n",
       "Attributes: (12/48)\n",
       "    CLASS:           IMAGE\n",
       "    IMAGE_SUBCLASS:  IMAGE_INDEXED\n",
       "    IMAGE_VERSION:   1.2\n",
       "    board_temp:      0.0\n",
       "    bottom:          680\n",
       "    camera:          \n",
       "    ...              ...\n",
       "    units:           pixels\n",
       "    uuid:            10ed506a-3ac4-5e62-8a6b-25a7abfc3171\n",
       "    vbin:            0\n",
       "    version:         -1\n",
       "    view:            photron HM10 + Dalpha filter\n",
       "    width:           640</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-d83b7cde-13ad-48bc-ba03-bf4eea8b3dbb' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d83b7cde-13ad-48bc-ba03-bf4eea8b3dbb' 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>: 286</li><li><span>height</span>: 448</li><li><span>width</span>: 640</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-035fa4d3-c228-4c67-811b-b79e99b6c4d6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-035fa4d3-c228-4c67-811b-b79e99b6c4d6' class='xr-section-summary' >Coordinates: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.6e-05 0.002016 ... 0.309 0.31</div><input id='attrs-84fed784-2453-446d-9e20-ef471f341713' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-84fed784-2453-446d-9e20-ef471f341713' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a42cd3d-0374-4b79-bd03-a7d75321c7f0' class='xr-var-data-in' type='checkbox'><label for='data-1a42cd3d-0374-4b79-bd03-a7d75321c7f0' 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>s</dd></dl></div><div class='xr-var-data'><pre>array([1.60000e-05, 2.01600e-03, 4.01600e-03, ..., 3.08016e-01, 3.09016e-01,\n",
       "       3.10016e-01])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e54bce30-7206-443f-8444-83655f7e5c85' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e54bce30-7206-443f-8444-83655f7e5c85' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>data</span></div><div class='xr-var-dims'>(time, height, width)</div><div class='xr-var-dtype'>uint8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(72, 112, 160), meta=np.ndarray&gt;</div><input id='attrs-09f0df31-b416-47f7-835c-19e99d99d4ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-09f0df31-b416-47f7-835c-19e99d99d4ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7e9e0aa1-3dba-4f6e-ad3d-5a5d7c4a7c7f' class='xr-var-data-in' type='checkbox'><label for='data-7e9e0aa1-3dba-4f6e-ad3d-5a5d7c4a7c7f' 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> 78.20 MiB </td>\n",
       "                        <td> 1.23 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (286, 448, 640) </td>\n",
       "                        <td> (72, 112, 160) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 64 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> uint8 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "    </tr>\n",
       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-12f63ac5-7800-4625-a3a1-0c78e923a184' class='xr-section-summary-in' type='checkbox'  ><label for='section-12f63ac5-7800-4625-a3a1-0c78e923a184' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8b800a2a-6e3b-4329-bf68-6f2786285833' class='xr-index-data-in' type='checkbox'/><label for='index-8b800a2a-6e3b-4329-bf68-6f2786285833' 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([             1.6e-05,             0.002016,             0.004016,\n",
       "                   0.006016, 0.008015999999999999, 0.010015999999999999,\n",
       "       0.012015999999999999, 0.014015999999999999,             0.016016,\n",
       "                   0.018016,\n",
       "       ...\n",
       "                   0.301016,             0.302016,             0.303016,\n",
       "                   0.304016,             0.305016,             0.306016,\n",
       "                   0.307016,             0.308016,             0.309016,\n",
       "        0.31001599999999996],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;, length=286))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6e9e9bb6-9759-4e7b-9607-3f4b3742a73f' class='xr-section-summary-in' type='checkbox'  ><label for='section-6e9e9bb6-9759-4e7b-9607-3f4b3742a73f' class='xr-section-summary' >Attributes: <span>(48)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>CLASS :</span></dt><dd>IMAGE</dd><dt><span>IMAGE_SUBCLASS :</span></dt><dd>IMAGE_INDEXED</dd><dt><span>IMAGE_VERSION :</span></dt><dd>1.2</dd><dt><span>board_temp :</span></dt><dd>0.0</dd><dt><span>bottom :</span></dt><dd>680</dd><dt><span>camera :</span></dt><dd></dd><dt><span>ccd_temp :</span></dt><dd>0.0</dd><dt><span>codex :</span></dt><dd>JP2</dd><dt><span>date_time :</span></dt><dd>2013-09-25T12:50:53Z</dd><dt><span>depth :</span></dt><dd>8</dd><dt><span>description :</span></dt><dd>Photron bullet camera B</dd><dt><span>dims :</span></dt><dd>[&#x27;time&#x27;, &#x27;height&#x27;, &#x27;width&#x27;]</dd><dt><span>exposure :</span></dt><dd>10.0</dd><dt><span>file_format :</span></dt><dd>IPX-1</dd><dt><span>file_name :</span></dt><dd>rbb030420.ipx</dd><dt><span>filter :</span></dt><dd></dd><dt><span>format :</span></dt><dd>IPX</dd><dt><span>gain :</span></dt><dd>[4.0, 0.0]</dd><dt><span>hbin :</span></dt><dd>0</dd><dt><span>height :</span></dt><dd>448</dd><dt><span>is_color :</span></dt><dd>0</dd><dt><span>left :</span></dt><dd>193</dd><dt><span>lens :</span></dt><dd></dd><dt><span>mds_name :</span></dt><dd>None</dd><dt><span>n_frames :</span></dt><dd>286</dd><dt><span>name :</span></dt><dd>rbb</dd><dt><span>offset :</span></dt><dd>[0.0, 0.0]</dd><dt><span>orientation :</span></dt><dd>0</dd><dt><span>pre_exp :</span></dt><dd>0.0</dd><dt><span>quality :</span></dt><dd>Not Checked</dd><dt><span>rank :</span></dt><dd>3</dd><dt><span>right :</span></dt><dd>832</dd><dt><span>shape :</span></dt><dd>[286, 448, 640]</dd><dt><span>shot :</span></dt><dd>30420</dd><dt><span>shot_id :</span></dt><dd>30420</dd><dt><span>signal_type :</span></dt><dd>Image</dd><dt><span>source :</span></dt><dd>rbb</dd><dt><span>strobe :</span></dt><dd>0</dd><dt><span>taps :</span></dt><dd>0</dd><dt><span>top :</span></dt><dd>233</dd><dt><span>trigger :</span></dt><dd>-0.10000000149011612</dd><dt><span>uda_name :</span></dt><dd>RBB</dd><dt><span>units :</span></dt><dd>pixels</dd><dt><span>uuid :</span></dt><dd>10ed506a-3ac4-5e62-8a6b-25a7abfc3171</dd><dt><span>vbin :</span></dt><dd>0</dd><dt><span>version :</span></dt><dd>-1</dd><dt><span>view :</span></dt><dd>photron HM10 + Dalpha filter</dd><dt><span>width :</span></dt><dd>640</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 82MB\n",
       "Dimensions:  (time: 286, height: 448, width: 640)\n",
       "Coordinates:\n",
       "  * time     (time) float64 2kB 1.6e-05 0.002016 0.004016 ... 0.308 0.309 0.31\n",
       "Dimensions without coordinates: height, width\n",
       "Data variables:\n",
       "    data     (time, height, width) uint8 82MB dask.array<chunksize=(72, 112, 160), meta=np.ndarray>\n",
       "Attributes: (12/48)\n",
       "    CLASS:           IMAGE\n",
       "    IMAGE_SUBCLASS:  IMAGE_INDEXED\n",
       "    IMAGE_VERSION:   1.2\n",
       "    board_temp:      0.0\n",
       "    bottom:          680\n",
       "    camera:          \n",
       "    ...              ...\n",
       "    units:           pixels\n",
       "    uuid:            10ed506a-3ac4-5e62-8a6b-25a7abfc3171\n",
       "    vbin:            0\n",
       "    version:         -1\n",
       "    view:            photron HM10 + Dalpha filter\n",
       "    width:           640"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataset = catalog.level1.shots(url=url, group='rbb')\n",
    "dataset = dataset.to_dask()\n",
    "dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x38ab54710>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(dataset.data[-10], cmap='gray')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "mast-book",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}