{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "6467393a-96d7-44b5-bfba-ee68f1a09952",
   "metadata": {},
   "source": [
    "# ACCESS Cryosphere Community Datapool Tools (ccdtools)\n",
    "\n",
    "This notebook provides a general introduction to the ACCESS Cryosphere Community Datapool Tools (ccdtools) and its functionality. This notebook is designed to be executed on the [NCI _Gadi_ supercomputer](https://opus.nci.org.au/spaces/Help/pages/90308778/0.+Welcome+to+Gadi#id-0.WelcometoGadi-Overview)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bda508bd-92db-4625-8816-5b500599cc9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import ccdtools\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7bbfe9f-6e40-4508-9022-c747667d6840",
   "metadata": {},
   "source": [
    "## Initialise and interrogate the datapool catalogue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd8cb99d-55b1-499d-89fa-49596f680512",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialise the datapool catalogue.\n",
    "## NOTE: To build a catalog on your local machine, parse a custom YAML file to ccdtools.catalog.DataCatalog(yaml_path = \"<PATH_TO_YAML_FILE>\").\n",
    "catalog = ccdtools.catalog.DataCatalog()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8fe7ec5c-3aa6-4b41-8bc8-b3e7bbd09e6d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <div>\n",
       "            \n",
       "        <div style=\"margin-bottom: 0.75em;\">\n",
       "            <strong>ACCESS Cryosphere Data Catalogue</strong>\n",
       "            <hr style=\"border: 1px solid #ccc; margin: 0.5em 0;\">\n",
       "            Number of datasets: 16<br>\n",
       "            Rows: 65\n",
       "        </div>\n",
       "        \n",
       "            <div style=\"\n",
       "                max-height: 300px;\n",
       "                overflow-x: auto;\n",
       "                overflow-y: auto;\n",
       "                border: 1px solid #ddd;\n",
       "            \">\n",
       "                <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>display_name</th>\n",
       "      <th>description</th>\n",
       "      <th>tags</th>\n",
       "      <th>version</th>\n",
       "      <th>subdataset</th>\n",
       "      <th>path</th>\n",
       "      <th>full_path</th>\n",
       "      <th>extension</th>\n",
       "      <th>skip_lines</th>\n",
       "      <th>no_data_value</th>\n",
       "      <th>ignore_dirs</th>\n",
       "      <th>ignore_files</th>\n",
       "      <th>loader</th>\n",
       "      <th>resolutions</th>\n",
       "      <th>static_patterns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>measures_bedmachine_antarctica</td>\n",
       "      <td>BedMachine Antarctica</td>\n",
       "      <td>High-resolution bed topography and ice thickne...</td>\n",
       "      <td>[antarctica, bed topography, ice thickness, su...</td>\n",
       "      <td>v1</td>\n",
       "      <td>None</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/e...</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/e...</td>\n",
       "      <td>nc</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>measures_bedmachine_antarctica</td>\n",
       "      <td>BedMachine Antarctica</td>\n",
       "      <td>High-resolution bed topography and ice thickne...</td>\n",
       "      <td>[antarctica, bed topography, ice thickness, su...</td>\n",
       "      <td>v2</td>\n",
       "      <td>None</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/e...</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/e...</td>\n",
       "      <td>nc</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>measures_bedmachine_antarctica</td>\n",
       "      <td>BedMachine Antarctica</td>\n",
       "      <td>High-resolution bed topography and ice thickne...</td>\n",
       "      <td>[antarctica, bed topography, ice thickness, su...</td>\n",
       "      <td>v3</td>\n",
       "      <td>None</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/e...</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/e...</td>\n",
       "      <td>nc</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>bedmap</td>\n",
       "      <td>Bedmap</td>\n",
       "      <td>Gridded, geospatial, and point datasets of Ant...</td>\n",
       "      <td>[antarctica, bed topography, ice thickness, su...</td>\n",
       "      <td>v1</td>\n",
       "      <td>geospatial</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/e...</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/e...</td>\n",
       "      <td>gpkg</td>\n",
       "      <td>0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bedmap</td>\n",
       "      <td>Bedmap</td>\n",
       "      <td>Gridded, geospatial, and point datasets of Ant...</td>\n",
       "      <td>[antarctica, bed topography, ice thickness, su...</td>\n",
       "      <td>v1</td>\n",
       "      <td>points</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/e...</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/e...</td>\n",
       "      <td>csv</td>\n",
       "      <td>18</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>default</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>measures_its_live_regional_glacier_and_ice_she...</td>\n",
       "      <td>ITS_LIVE Regional Glacier and Ice Sheet Surfac...</td>\n",
       "      <td>ITS_LIVE regional glacier and ice sheet surfac...</td>\n",
       "      <td>[antarctica, greenland, ice velocity]</td>\n",
       "      <td>v2</td>\n",
       "      <td>RGI17A</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/i...</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/i...</td>\n",
       "      <td>nc</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>measures_velocity</td>\n",
       "      <td>None</td>\n",
       "      <td>[_2014-2022_]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>measures_its_live_regional_glacier_and_ice_she...</td>\n",
       "      <td>ITS_LIVE Regional Glacier and Ice Sheet Surfac...</td>\n",
       "      <td>ITS_LIVE regional glacier and ice sheet surfac...</td>\n",
       "      <td>[antarctica, greenland, ice velocity]</td>\n",
       "      <td>v2</td>\n",
       "      <td>RGI18A</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/i...</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/i...</td>\n",
       "      <td>nc</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>measures_velocity</td>\n",
       "      <td>None</td>\n",
       "      <td>[_2014-2022_]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>measures_its_live_regional_glacier_and_ice_she...</td>\n",
       "      <td>ITS_LIVE Regional Glacier and Ice Sheet Surfac...</td>\n",
       "      <td>ITS_LIVE regional glacier and ice sheet surfac...</td>\n",
       "      <td>[antarctica, greenland, ice velocity]</td>\n",
       "      <td>v2</td>\n",
       "      <td>RGI19A</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/i...</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/i...</td>\n",
       "      <td>nc</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[]</td>\n",
       "      <td>measures_velocity</td>\n",
       "      <td>None</td>\n",
       "      <td>[_2014-2022_]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>racmo2.3p2_monthly_27km_1979-2022</td>\n",
       "      <td>RACMO2.3p2 Monthly Surface Mass Balance and Cl...</td>\n",
       "      <td>Regional Atmospheric Climate Model (RACMO) ver...</td>\n",
       "      <td>[antarctica, surface mass balance, climate dat...</td>\n",
       "      <td>v1</td>\n",
       "      <td>None</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/s...</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/s...</td>\n",
       "      <td>nc</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[Height_latlon_ANT27.nc, TotIS_RACMO_ANT27_IMB...</td>\n",
       "      <td>racmo</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>racmo2.4p1_monthly_11km_1979-2023</td>\n",
       "      <td>RACMO2.4p1 Monthly Surface Mass Balance and Cl...</td>\n",
       "      <td>Regional Atmospheric Climate Model (RACMO) ver...</td>\n",
       "      <td>[antarctica, surface mass balance, climate dat...</td>\n",
       "      <td>v1</td>\n",
       "      <td>None</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/s...</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/s...</td>\n",
       "      <td>nc</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[ANT11_masks.nc]</td>\n",
       "      <td>racmo</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>65 rows × 16 columns</p>\n",
       "</div>\n",
       "            </div>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "<datapool.catalog.DataCatalog at 0x148eb654cd90>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# List all datasets within the catalog.\n",
    "## NOTE:  A raw pandas.DataFrame can be returned using catalog.datasets\n",
    "catalog"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "14e90a24-a8da-4b5a-bbdf-3f984657cdbc",
   "metadata": {},
   "source": [
    "Various functions exist to allow users to interrogate the data catalog:\n",
    "- `search()` - Search the data pool for a given key word. This searches the `dataset`, `display_name`, and `tags` fields.\n",
    "- `help()` - Retrieve a summary of dataset-specific information for a given dataset.\n",
    "- `available_versions()` - Retrieve a list of available versions for a given dataset.\n",
    "- `available_subdatasets()` - Retrieve a list of available subdataets for a given dataset.\n",
    "- `available_resolutions()` - Retrieve a list of available resolutions for a given dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38cabb46-a59d-4b61-8241-4b0ca2ed8310",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <div>\n",
       "            \n",
       "        <div style=\"margin-bottom: 0.75em;\">\n",
       "            <strong>ACCESS Cryosphere Data Catalogue</strong>\n",
       "            <hr style=\"border: 1px solid #ccc; margin: 0.5em 0;\">\n",
       "            Number of datasets: 2<br>\n",
       "            Rows: 2\n",
       "        </div>\n",
       "        \n",
       "            <div style=\"\n",
       "                max-height: 300px;\n",
       "                overflow-x: auto;\n",
       "                overflow-y: auto;\n",
       "                border: 1px solid #ddd;\n",
       "            \">\n",
       "                <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dataset</th>\n",
       "      <th>display_name</th>\n",
       "      <th>description</th>\n",
       "      <th>tags</th>\n",
       "      <th>version</th>\n",
       "      <th>subdataset</th>\n",
       "      <th>path</th>\n",
       "      <th>full_path</th>\n",
       "      <th>extension</th>\n",
       "      <th>skip_lines</th>\n",
       "      <th>no_data_value</th>\n",
       "      <th>ignore_dirs</th>\n",
       "      <th>ignore_files</th>\n",
       "      <th>loader</th>\n",
       "      <th>resolutions</th>\n",
       "      <th>static_patterns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>racmo2.3p2_monthly_27km_1979-2022</td>\n",
       "      <td>RACMO2.3p2 Monthly Surface Mass Balance and Cl...</td>\n",
       "      <td>Regional Atmospheric Climate Model (RACMO) ver...</td>\n",
       "      <td>[antarctica, surface mass balance, climate dat...</td>\n",
       "      <td>v1</td>\n",
       "      <td>None</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/s...</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/s...</td>\n",
       "      <td>nc</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[Height_latlon_ANT27.nc, TotIS_RACMO_ANT27_IMB...</td>\n",
       "      <td>racmo</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>racmo2.4p1_monthly_11km_1979-2023</td>\n",
       "      <td>RACMO2.4p1 Monthly Surface Mass Balance and Cl...</td>\n",
       "      <td>Regional Atmospheric Climate Model (RACMO) ver...</td>\n",
       "      <td>[antarctica, surface mass balance, climate dat...</td>\n",
       "      <td>v1</td>\n",
       "      <td>None</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/s...</td>\n",
       "      <td>/g/data/av17/access-nri/cryosphere-data-pool/s...</td>\n",
       "      <td>nc</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>[ANT11_masks.nc]</td>\n",
       "      <td>racmo</td>\n",
       "      <td>None</td>\n",
       "      <td>[]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "            </div>\n",
       "        </div>\n",
       "        "
      ],
      "text/plain": [
       "<datapool.catalog.DataCatalog at 0x148eb6769e90>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Search the catalog based on display_name, tags, or dataset_name. Here, explore racmo datasets\n",
    "catalog.search('racmo')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5b868000-c7df-410d-b823-121d6b1518e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: measures_its_live_regional_glacier_and_ice_sheet_surface_velocities\n",
      "\n",
      "Available versions:\n",
      "  - v1\n",
      "  - v2\n",
      "\n",
      "Tip:\n",
      "  Use catalog.help(dataset=..., version=...) for more details.\n"
     ]
    }
   ],
   "source": [
    "# The help() function provides a full overview of a given dataset\n",
    "catalog.help('measures_its_live_regional_glacier_and_ice_sheet_surface_velocities')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8487a6d8-29d1-45f8-9e0a-e0ddc0c9f0e4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Dataset: measures_its_live_regional_glacier_and_ice_sheet_surface_velocities\n",
      "\n",
      "Available versions:\n",
      "  - v1\n",
      "  - v2\n",
      "\n",
      "Version: v1\n",
      "\n",
      "Available subdatasets:\n",
      "  - ALA\n",
      "  - ANT\n",
      "  - CAN\n",
      "  - GRE\n",
      "  - HMA\n",
      "  - ICE\n",
      "  - PAT\n",
      "  - SRA\n",
      "\n",
      "Supported catalog keywords:\n",
      "  - subdataset : yes\n",
      "  - resolution : yes\n",
      "  - static  : yes\n",
      "\n",
      "Example usage:\n",
      "  catalog.load_dataset('measures_its_live_regional_glacier_and_ice_sheet_surface_velocities', version = 'v1', subdataset = '...', resolution = '...', static = True)\n"
     ]
    }
   ],
   "source": [
    "# Version-specific information about a given dataset can be obtained by specifying a given version\n",
    "catalog.help('measures_its_live_regional_glacier_and_ice_sheet_surface_velocities', version = 'v1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "0e13c638-d19b-46fc-aa4a-cc3d0d26913f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['v1']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find versions of a given dataset\n",
    "catalog.available_versions('racmo2.3p2_monthly_27km_1979-2022')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b1dc0830-166c-458a-a68c-3e38b6420dae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['geospatial', 'points', 'gridded']"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find available subdatasets for bedmap. By default, the latest version is used.\n",
    "catalog.available_subdatasets('bedmap')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2f35575b-fb71-4da6-94a9-c9bd7a500ab4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'annual': {'120m': 'G0120', '240m': 'G0240'},\n",
       " 'static': {'120m': 'G0120', '240m': 'G0240'}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get available resolutions for a given dataset/version/subdataset. If not specified, the latest version is used.\n",
    "catalog.available_resolutions('measures_its_live_regional_glacier_and_ice_sheet_surface_velocities', version = 'v1', subdataset = 'ALA')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6ee1b2c-9770-422c-b7af-410a736d3f66",
   "metadata": {},
   "source": [
    "## Loading Datasets\n",
    "\n",
    "To load a dataset, we use the `load_dataset` function. This accepts various keyword arguments, depending on the dataset. At a minimum it requires `dataset_name`.\n",
    "\n",
    "Other arguments include:\n",
    "- `version` [str] - this is the version string to be loaded (e.g. \"v1\", \"v2\", \"v3\"). By default, it uses the latest version.\n",
    "- `subdataset` [str]- this is the name of the subdataset to be loaded\n",
    "- `resolution` [str] - this is the requested resolution to be loaded, if resolutions are available for the given dataset.\n",
    "- `composite` [bool] - should the composite file be loaded, or the annual series. Default = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "250fbb46-135f-4ab4-91fb-762e57b1ed43",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.12/lib/python3.11/site-packages/distributed/diagnostics/nvml.py:14: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.\n",
      "  import pynvml\n"
     ]
    },
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       "Coordinates:\n",
       "  * y          (y) int32 53kB 3333000 3332500 3332000 ... -3332500 -3333000\n",
       "  * x          (x) int32 53kB -3333000 -3332500 -3332000 ... 3332500 3333000\n",
       "Data variables:\n",
       "    mapping    |S1 1B ...\n",
       "    mask       (y, x) int8 178MB dask.array&lt;chunksize=(1905, 1905), meta=np.ndarray&gt;\n",
       "    firn       (y, x) float32 711MB dask.array&lt;chunksize=(953, 953), meta=np.ndarray&gt;\n",
       "    surface    (y, x) float32 711MB dask.array&lt;chunksize=(953, 953), meta=np.ndarray&gt;\n",
       "    thickness  (y, x) float32 711MB dask.array&lt;chunksize=(953, 953), meta=np.ndarray&gt;\n",
       "    bed        (y, x) float32 711MB dask.array&lt;chunksize=(953, 953), meta=np.ndarray&gt;\n",
       "    errbed     (y, x) float32 711MB dask.array&lt;chunksize=(1334, 1334), meta=np.ndarray&gt;\n",
       "    source     (y, x) int8 178MB dask.array&lt;chunksize=(1905, 1905), meta=np.ndarray&gt;\n",
       "    geoid      (y, x) int16 356MB dask.array&lt;chunksize=(1334, 1334), meta=np.ndarray&gt;\n",
       "Attributes: (12/17)\n",
       "    Conventions:                 CF-1.7\n",
       "    Title:                       BedMachine Antarctica\n",
       "    Author:                      Mathieu Morlighem\n",
       "    version:                     05-Nov-2019 (v1.38)\n",
       "    nx:                          13333.0\n",
       "    ny:                          13333.0\n",
       "    ...                          ...\n",
       "    ymax:                        3333000\n",
       "    spacing:                     500\n",
       "    no_data:                     -9999.0\n",
       "    license:                     No restrictions on access or use\n",
       "    Data_citation:               Morlighem M. et al., (2019), Deep glacial tr...\n",
       "    Notes:                       Data processed at the Department of Earth Sy...</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-00a9a269-5fdb-4724-8c0e-cdf887b9d6c5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-00a9a269-5fdb-4724-8c0e-cdf887b9d6c5' 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'>y</span>: 13333</li><li><span class='xr-has-index'>x</span>: 13333</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1c55f13d-ea57-48c5-808a-488fef34b7f9' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1c55f13d-ea57-48c5-808a-488fef34b7f9' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>3333000 3332500 ... -3333000</div><input id='attrs-faa840b1-f237-49e6-9c63-627ea3b718f6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-faa840b1-f237-49e6-9c63-627ea3b718f6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2138dd85-7359-4ef2-b255-ca96c116b67f' class='xr-var-data-in' type='checkbox'><label for='data-2138dd85-7359-4ef2-b255-ca96c116b67f' title='Show/Hide data 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>long_name :</span></dt><dd>Cartesian y-coordinate</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>array([ 3333000,  3332500,  3332000, ..., -3332000, -3332500, -3333000],\n",
       "      dtype=int32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>int32</div><div class='xr-var-preview xr-preview'>-3333000 -3332500 ... 3333000</div><input id='attrs-d59e10f2-fe9e-4c75-8cf4-b618564476b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d59e10f2-fe9e-4c75-8cf4-b618564476b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c0fced1-c0cf-4a83-9d66-73952e4ca82c' class='xr-var-data-in' type='checkbox'><label for='data-5c0fced1-c0cf-4a83-9d66-73952e4ca82c' title='Show/Hide data 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>long_name :</span></dt><dd>Cartesian x-coordinate</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>meter</dd></dl></div><div class='xr-var-data'><pre>array([-3333000, -3332500, -3332000, ...,  3332000,  3332500,  3333000],\n",
       "      dtype=int32)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dc39b25d-1bc3-4a94-baae-afd13ba57059' class='xr-section-summary-in' type='checkbox'  checked><label for='section-dc39b25d-1bc3-4a94-baae-afd13ba57059' class='xr-section-summary' >Data variables: <span>(9)</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>mapping</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>|S1</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-b5f0094b-66c8-4611-b004-7e86e647cd85' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5f0094b-66c8-4611-b004-7e86e647cd85' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8be21061-2e22-4c6b-93dc-4cc9c1da35bd' class='xr-var-data-in' type='checkbox'><label for='data-8be21061-2e22-4c6b-93dc-4cc9c1da35bd' title='Show/Hide data 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>grid_mapping_name :</span></dt><dd>polar_stereographic</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>-90.0</dd><dt><span>standard_parallel :</span></dt><dd>-71.0</dd><dt><span>straight_vertical_longitude_from_pole :</span></dt><dd>0.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378273.0</dd><dt><span>inverse_flattening :</span></dt><dd>298.2794050428205</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd></dl></div><div class='xr-var-data'><pre>[1 values with dtype=|S1]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1905, 1905), meta=np.ndarray&gt;</div><input id='attrs-2ab17cdd-e09a-49f0-ae2c-b2a2f8873202' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ab17cdd-e09a-49f0-ae2c-b2a2f8873202' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-47a40cd2-5742-4c90-84ff-d06656f4b53a' class='xr-var-data-in' type='checkbox'><label for='data-47a40cd2-5742-4c90-84ff-d06656f4b53a' title='Show/Hide data 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>long_name :</span></dt><dd>mask</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>valid_range :</span></dt><dd>[0 4]</dd><dt><span>flag_values :</span></dt><dd>[0 1 2 3 4]</dd><dt><span>flag_meanings :</span></dt><dd>ocean ice_free_land grounded_ice floating_ice lake_vostok</dd><dt><span>source :</span></dt><dd>Antarctic Digital Database (rock outcrop) and Jeremie Mouginot (pers. comm., grounding lines)</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> 169.53 MiB </td>\n",
       "                        <td> 3.46 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13333, 13333) </td>\n",
       "                        <td> (1905, 1905) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 49 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> int8 numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\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>firn</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(953, 953), meta=np.ndarray&gt;</div><input id='attrs-8e88d0e0-386a-42aa-a3fe-6a33aaa1dafe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e88d0e0-386a-42aa-a3fe-6a33aaa1dafe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6144fe53-e6a0-4c88-89ee-ae06e8f23055' class='xr-var-data-in' type='checkbox'><label for='data-6144fe53-e6a0-4c88-89ee-ae06e8f23055' title='Show/Hide data 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>long_name :</span></dt><dd>firn air content</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>source :</span></dt><dd>FAC model from Ligtenberg et al., 2011, forced by RACMO2.3p2 (van Wessem et al., 2018)</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> 678.13 MiB </td>\n",
       "                        <td> 3.46 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13333, 13333) </td>\n",
       "                        <td> (953, 953) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 196 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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       "</svg>\n",
       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>surface</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(953, 953), meta=np.ndarray&gt;</div><input id='attrs-a9cfa3cb-7bc2-4486-9798-1add2645c5fa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9cfa3cb-7bc2-4486-9798-1add2645c5fa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e07dfff1-3234-450d-85d2-d2d8107b5913' class='xr-var-data-in' type='checkbox'><label for='data-e07dfff1-3234-450d-85d2-d2d8107b5913' title='Show/Hide data 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>long_name :</span></dt><dd>ice surface elevation</dd><dt><span>standard_name :</span></dt><dd>surface_altitude</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>source :</span></dt><dd>REMA (Byrd Polar and Climate Research Center and the Polar Geospatial Center)</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> 678.13 MiB </td>\n",
       "                        <td> 3.46 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13333, 13333) </td>\n",
       "                        <td> (953, 953) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 196 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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       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 678.13 MiB </td>\n",
       "                        <td> 3.46 MiB </td>\n",
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       "                    <tr>\n",
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       "                        <td> (953, 953) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 196 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>bed</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(953, 953), meta=np.ndarray&gt;</div><input id='attrs-6bcbdfbf-9168-46d2-8381-b72878b583ac' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bcbdfbf-9168-46d2-8381-b72878b583ac' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-39b48cad-789e-4fed-8a7a-54375bc35bcc' class='xr-var-data-in' type='checkbox'><label for='data-39b48cad-789e-4fed-8a7a-54375bc35bcc' title='Show/Hide data 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>long_name :</span></dt><dd>bed topography</dd><dt><span>standard_name :</span></dt><dd>bedrock_altitude</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>source :</span></dt><dd>IBCSO and Mathieu Morlighem</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> 678.13 MiB </td>\n",
       "                        <td> 3.46 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 196 chunks in 2 graph layers </td>\n",
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       "                        <th> Data type </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>errbed</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1334, 1334), meta=np.ndarray&gt;</div><input id='attrs-a27da1af-53cb-4e14-9f22-d7de2b80af44' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a27da1af-53cb-4e14-9f22-d7de2b80af44' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-37ec1a0d-ebc0-4b6b-a04f-eb1f5d8ecd06' class='xr-var-data-in' type='checkbox'><label for='data-37ec1a0d-ebc0-4b6b-a04f-eb1f5d8ecd06' title='Show/Hide data 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>long_name :</span></dt><dd>bed topography/ice thickness error</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>source :</span></dt><dd>Mathieu Morlighem</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> 678.13 MiB </td>\n",
       "                        <td> 6.79 MiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (13333, 13333) </td>\n",
       "                        <td> (1334, 1334) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 100 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>source</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>int8</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1905, 1905), meta=np.ndarray&gt;</div><input id='attrs-f524a1f8-6c19-43b5-8fdb-2aa7fdabc517' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f524a1f8-6c19-43b5-8fdb-2aa7fdabc517' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7d6a88ba-788e-40ff-ab6d-82214e954628' class='xr-var-data-in' type='checkbox'><label for='data-7d6a88ba-788e-40ff-ab6d-82214e954628' title='Show/Hide data 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>long_name :</span></dt><dd>data source</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>valid_range :</span></dt><dd>[ 1 10]</dd><dt><span>flag_values :</span></dt><dd>[ 1  2  3  4  5  6  7 10]</dd><dt><span>flag_meanings :</span></dt><dd>REMA_IBCSO mass_conservation interpolation hydrostatic streamline_diffusion gravity seismic multibeam</dd><dt><span>source :</span></dt><dd>Mathieu Morlighem</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> 169.53 MiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>geoid</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>int16</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1334, 1334), meta=np.ndarray&gt;</div><input id='attrs-9ed5d146-fe4c-4ee6-9850-aaf392419795' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9ed5d146-fe4c-4ee6-9850-aaf392419795' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d5ab71a8-4b1b-4be5-9be0-19b0e253285e' class='xr-var-data-in' type='checkbox'><label for='data-d5ab71a8-4b1b-4be5-9be0-19b0e253285e' title='Show/Hide data 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>long_name :</span></dt><dd>EIGEN-EC4 Geoid - WGS84 Ellipsoid difference</dd><dt><span>standard_name :</span></dt><dd>geoid_height_above_reference_ellipsoid</dd><dt><span>units :</span></dt><dd>meters</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>geoid :</span></dt><dd>eigen-6c4 (Forste et al 2014)</dd></dl></div><div class='xr-var-data'><table>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-703ef0bf-1edd-43f3-9db4-b0f8902b7d0d' class='xr-section-summary-in' type='checkbox'  ><label for='section-703ef0bf-1edd-43f3-9db4-b0f8902b7d0d' class='xr-section-summary' >Attributes: <span>(17)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.7</dd><dt><span>Title :</span></dt><dd>BedMachine Antarctica</dd><dt><span>Author :</span></dt><dd>Mathieu Morlighem</dd><dt><span>version :</span></dt><dd>05-Nov-2019 (v1.38)</dd><dt><span>nx :</span></dt><dd>13333.0</dd><dt><span>ny :</span></dt><dd>13333.0</dd><dt><span>Projection :</span></dt><dd>Polar Stereographic South (71S,0E)</dd><dt><span>proj4 :</span></dt><dd>+init=epsg:3031</dd><dt><span>sea_water_density (kg m-3) :</span></dt><dd>1027.0</dd><dt><span>ice_density (kg m-3) :</span></dt><dd>917.0</dd><dt><span>xmin :</span></dt><dd>-3333000</dd><dt><span>ymax :</span></dt><dd>3333000</dd><dt><span>spacing :</span></dt><dd>500</dd><dt><span>no_data :</span></dt><dd>-9999.0</dd><dt><span>license :</span></dt><dd>No restrictions on access or use</dd><dt><span>Data_citation :</span></dt><dd>Morlighem M. et al., (2019), Deep glacial troughs and stabilizing ridges unveiled beneath the margins of the Antarctic ice sheet, Nature Geoscience (accepted)</dd><dt><span>Notes :</span></dt><dd>Data processed at the Department of Earth System Science, University of California, Irvine</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 4GB\n",
       "Dimensions:    (y: 13333, x: 13333)\n",
       "Coordinates:\n",
       "  * y          (y) int32 53kB 3333000 3332500 3332000 ... -3332500 -3333000\n",
       "  * x          (x) int32 53kB -3333000 -3332500 -3332000 ... 3332500 3333000\n",
       "Data variables:\n",
       "    mapping    |S1 1B ...\n",
       "    mask       (y, x) int8 178MB dask.array<chunksize=(1905, 1905), meta=np.ndarray>\n",
       "    firn       (y, x) float32 711MB dask.array<chunksize=(953, 953), meta=np.ndarray>\n",
       "    surface    (y, x) float32 711MB dask.array<chunksize=(953, 953), meta=np.ndarray>\n",
       "    thickness  (y, x) float32 711MB dask.array<chunksize=(953, 953), meta=np.ndarray>\n",
       "    bed        (y, x) float32 711MB dask.array<chunksize=(953, 953), meta=np.ndarray>\n",
       "    errbed     (y, x) float32 711MB dask.array<chunksize=(1334, 1334), meta=np.ndarray>\n",
       "    source     (y, x) int8 178MB dask.array<chunksize=(1905, 1905), meta=np.ndarray>\n",
       "    geoid      (y, x) int16 356MB dask.array<chunksize=(1334, 1334), meta=np.ndarray>\n",
       "Attributes: (12/17)\n",
       "    Conventions:                 CF-1.7\n",
       "    Title:                       BedMachine Antarctica\n",
       "    Author:                      Mathieu Morlighem\n",
       "    version:                     05-Nov-2019 (v1.38)\n",
       "    nx:                          13333.0\n",
       "    ny:                          13333.0\n",
       "    ...                          ...\n",
       "    ymax:                        3333000\n",
       "    spacing:                     500\n",
       "    no_data:                     -9999.0\n",
       "    license:                     No restrictions on access or use\n",
       "    Data_citation:               Morlighem M. et al., (2019), Deep glacial tr...\n",
       "    Notes:                       Data processed at the Department of Earth Sy..."
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load a simple dataset\n",
    "bedmachine_v1 = catalog.load_dataset('measures_bedmachine_antarctica', version = 'v1')\n",
    "bedmachine_v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e197bfe6-a2e3-469a-8a63-d6070da80d7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
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       "     NAME    Regions Subregions TYPE  \\\n",
       "0     NaN    Islands        NaN   IS   \n",
       "1    A-Ap       East       A-Ap   GR   \n",
       "2    Ap-B       East       Ap-B   GR   \n",
       "3     B-C       East        B-C   GR   \n",
       "4    C-Cp       East       C-Cp   GR   \n",
       "5    Cp-D       East       Cp-D   GR   \n",
       "6    D-Dp       East       D-Dp   GR   \n",
       "7    Dp-E       East       Dp-E   GR   \n",
       "8    E-Ep       East       E-Ep   GR   \n",
       "9    Ep-F       West       Ep-F   GR   \n",
       "10    F-G       West        F-G   GR   \n",
       "11    G-H       West        G-H   GR   \n",
       "12   H-Hp       West       H-Hp   GR   \n",
       "13   Hp-I  Peninsula       Hp-I   GR   \n",
       "14  I-Ipp  Peninsula      I-Ipp   GR   \n",
       "15  Ipp-J  Peninsula      Ipp-J   GR   \n",
       "16  J-Jpp       West      J-Jpp   GR   \n",
       "17  Jpp-K       East      Jpp-K   GR   \n",
       "18    K-A       East        K-A   GR   \n",
       "\n",
       "                                             geometry  \n",
       "0   MULTIPOLYGON (((-217776.532 2130382.522, -2168...  \n",
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       "9   POLYGON ((-578695.182 -1285897.642, -578945.60...  \n",
       "10  POLYGON ((-1099110.89 -966140.129, -1089377.46...  \n",
       "11  POLYGON ((-1515456.671 -545301.309, -1515230.3...  \n",
       "12  POLYGON ((-1665853.445 206829.399, -1662779.24...  \n",
       "13  MULTIPOLYGON (((-1981550.026 606779.819, -1980...  \n",
       "14  POLYGON ((-2418949.539 1418837.511, -2418949.6...  \n",
       "15  POLYGON ((-1584579.956 902423, -1584579.956 90...  \n",
       "16  POLYGON ((-1187659.179 23095.994, -1199482.277...  \n",
       "17  POLYGON ((-621202.971 771276.873, -620950.97 7...  \n",
       "18  POLYGON ((-681761.563 1361890.885, -681258.113...  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load a specific subdataset. The 'type' returned depends on the dataset.\n",
    "measures_basins_imbie = catalog.load_dataset('measures_antarctic_boundaries', version = 'v2', subdataset = 'basins_imbie')\n",
    "measures_basins_imbie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "6154cd67-e1c0-40f8-91f9-706bf63da8ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Cartesian x-coordinate [meter]', ylabel='Cartesian y-coordinate [meter]'>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.12/lib/python3.11/site-packages/dask/_task_spec.py:758: FutureWarning: In a future version, xarray will not decode the variable 'date' into a timedelta64 dtype based on the presence of a timedelta-like 'units' attribute by default. Instead it will rely on the presence of a timedelta64 'dtype' attribute, which is now xarray's default way of encoding timedelta64 values.\n",
      "To continue decoding into a timedelta64 dtype, either set `decode_timedelta=True` when opening this dataset, or add the attribute `dtype='timedelta64[ns]'` to this variable on disk.\n",
      "To opt-in to future behavior, set `decode_timedelta=False`.\n",
      "  return self.func(*new_argspec, **kwargs)\n",
      "/g/data/xp65/public/apps/med_conda/envs/analysis3-25.12/lib/python3.11/site-packages/dask/_task_spec.py:758: FutureWarning: In a future version, xarray will not decode the variable 'dt' into a timedelta64 dtype based on the presence of a timedelta-like 'units' attribute by default. Instead it will rely on the presence of a timedelta64 'dtype' attribute, which is now xarray's default way of encoding timedelta64 values.\n",
      "To continue decoding into a timedelta64 dtype, either set `decode_timedelta=True` when opening this dataset, or add the attribute `dtype='timedelta64[ns]'` to this variable on disk.\n",
      "To opt-in to future behavior, set `decode_timedelta=False`.\n",
      "  return self.func(*new_argspec, **kwargs)\n"
     ]
    }
   ],
   "source": [
    "# Construct a simple figure\n",
    "\n",
    "# Coarsen bed topogrpahy for improved render time\n",
    "bed = bedmachine_v1.bed.coarsen(x = 100, y = 100, boundary = 'trim').mean()\n",
    "\n",
    "# Plot bed topography\n",
    "fig, ax = plt.subplots()\n",
    "bed.plot(ax=ax)\n",
    "\n",
    "# Add grounding line\n",
    "measures_basins_imbie.plot(ax = ax, edgecolor = 'red', facecolor = 'none')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "9bb96378-ea6e-4996-9838-f7db4536b4f5",
   "metadata": {},
   "outputs": [
    {
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       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, white)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, white) h s calc(l - 15))\n",
       "  );\n",
       "}\n",
       "\n",
       "html[theme=\"dark\"],\n",
       "html[data-theme=\"dark\"],\n",
       "body[data-theme=\"dark\"],\n",
       "body.vscode-dark {\n",
       "  --xr-font-color0: var(\n",
       "    --jp-content-font-color0,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 1))\n",
       "  );\n",
       "  --xr-font-color2: var(\n",
       "    --jp-content-font-color2,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.54))\n",
       "  );\n",
       "  --xr-font-color3: var(\n",
       "    --jp-content-font-color3,\n",
       "    var(--pst-color-text-base, rgba(255, 255, 255, 0.38))\n",
       "  );\n",
       "  --xr-border-color: var(\n",
       "    --jp-border-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 10))\n",
       "  );\n",
       "  --xr-disabled-color: var(\n",
       "    --jp-layout-color3,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 40))\n",
       "  );\n",
       "  --xr-background-color: var(\n",
       "    --jp-layout-color0,\n",
       "    var(--pst-color-on-background, #111111)\n",
       "  );\n",
       "  --xr-background-color-row-even: var(\n",
       "    --jp-layout-color1,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 5))\n",
       "  );\n",
       "  --xr-background-color-row-odd: var(\n",
       "    --jp-layout-color2,\n",
       "    hsl(from var(--pst-color-on-background, #111111) h s calc(l + 15))\n",
       "  );\n",
       "}\n",
       "\n",
       ".xr-wrap {\n",
       "  display: block !important;\n",
       "  min-width: 300px;\n",
       "  max-width: 700px;\n",
       "  line-height: 1.6;\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-obj-name,\n",
       ".xr-group-name {\n",
       "  margin-left: 2px;\n",
       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-group-name::before {\n",
       "  content: \"📁\";\n",
       "  padding-right: 0.3em;\n",
       "}\n",
       "\n",
       ".xr-group-name,\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 0 20px 0 20px;\n",
       "  margin-block-start: 0;\n",
       "  margin-block-end: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: inline-block;\n",
       "  opacity: 0;\n",
       "  height: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "  border: 2px solid transparent !important;\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:focus + label {\n",
       "  border: 2px solid var(--xr-font-color0) !important;\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",
       "}\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-top: 4px;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-group-box {\n",
       "  display: inline-grid;\n",
       "  grid-template-columns: 0px 20px auto;\n",
       "  width: 100%;\n",
       "}\n",
       "\n",
       ".xr-group-box-vline {\n",
       "  grid-column-start: 1;\n",
       "  border-right: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "  width: 0px;\n",
       "}\n",
       "\n",
       ".xr-group-box-hline {\n",
       "  grid-column-start: 2;\n",
       "  grid-row-start: 1;\n",
       "  height: 1em;\n",
       "  width: 20px;\n",
       "  border-bottom: 0.2em solid;\n",
       "  border-color: var(--xr-border-color);\n",
       "}\n",
       "\n",
       ".xr-group-box-contents {\n",
       "  grid-column-start: 3;\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",
       "  border-color: var(--xr-background-color-row-odd);\n",
       "  margin-bottom: 0;\n",
       "  padding-top: 2px;\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",
       "  border-color: var(--xr-background-color-row-even);\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",
       "  border-top: 2px dotted var(--xr-background-color);\n",
       "  padding-bottom: 20px !important;\n",
       "  padding-top: 10px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in + label,\n",
       ".xr-var-data-in + label,\n",
       ".xr-index-data-in + label {\n",
       "  padding: 0 1px;\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-data > pre,\n",
       ".xr-index-data > pre,\n",
       ".xr-var-data > table > tbody > tr {\n",
       "  background-color: transparent !important;\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",
       "\n",
       ".xr-var-attrs-in:checked + label > .xr-icon-file-text2,\n",
       ".xr-var-data-in:checked + label > .xr-icon-database,\n",
       ".xr-index-data-in:checked + label > .xr-icon-database {\n",
       "  color: var(--xr-font-color0);\n",
       "  filter: drop-shadow(1px 1px 5px var(--xr-font-color2));\n",
       "  stroke-width: 0.8px;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt; Size: 25GB\n",
       "Dimensions:        (y: 18392, x: 22896)\n",
       "Coordinates:\n",
       "  * y              (y) float64 147kB 2.26e+06 2.259e+06 ... -2.154e+06\n",
       "  * x              (x) float64 183kB -2.678e+06 -2.678e+06 ... 2.817e+06\n",
       "Data variables: (12/14)\n",
       "    chip_size_max  (y, x) float32 2GB dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;\n",
       "    count          (y, x) float32 2GB dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;\n",
       "    date           (y, x) timedelta64[ns] 3GB dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;\n",
       "    dt             (y, x) timedelta64[ns] 3GB dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;\n",
       "    ice            (y, x) float32 2GB dask.array&lt;chunksize=(288, 358), meta=np.ndarray&gt;\n",
       "    mapping        float32 4B ...\n",
       "    ...             ...\n",
       "    v              (y, x) float32 2GB dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;\n",
       "    v_err          (y, x) float32 2GB dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;\n",
       "    vx             (y, x) float32 2GB dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;\n",
       "    vx_err         (y, x) float32 2GB dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;\n",
       "    vy             (y, x) float32 2GB dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;\n",
       "    vy_err         (y, x) float32 2GB dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;\n",
       "Attributes:\n",
       "    Conventions:              CF-1.8\n",
       "    GDAL_AREA_OR_POINT:       Area\n",
       "    author:                   Alex S. Gardner, JPL/NASA\n",
       "    date_created:             05-May-2019 15:53:24\n",
       "    institution:              NASA Jet Propulsion Laboratory (JPL), Californi...\n",
       "    motion_coordinates:       map\n",
       "    motion_detection_method:  feature\n",
       "    scene_pair_type:          optical\n",
       "    title:                    autoRIFT surface velocities</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-1a8c7142-5c62-4aac-8143-5b8974aa13ab' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-1a8c7142-5c62-4aac-8143-5b8974aa13ab' 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'>y</span>: 18392</li><li><span class='xr-has-index'>x</span>: 22896</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-9b98734d-60e3-4d2f-b5af-6d3bc5735438' class='xr-section-summary-in' type='checkbox'  checked><label for='section-9b98734d-60e3-4d2f-b5af-6d3bc5735438' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.26e+06 2.259e+06 ... -2.154e+06</div><input id='attrs-c5a0754d-926e-40d3-a5fb-03394c9eed95' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c5a0754d-926e-40d3-a5fb-03394c9eed95' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c9debdd-83e9-49e9-b2f0-2d518fd2c136' class='xr-var-data-in' type='checkbox'><label for='data-9c9debdd-83e9-49e9-b2f0-2d518fd2c136' title='Show/Hide data 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>long_name :</span></dt><dd>y coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_y_coordinate</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([ 2259727.5,  2259487.5,  2259247.5, ..., -2153632.5, -2153872.5,\n",
       "       -2154112.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.678e+06 -2.678e+06 ... 2.817e+06</div><input id='attrs-f50ea7ff-3b35-4c4f-988d-994e863b1c30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f50ea7ff-3b35-4c4f-988d-994e863b1c30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-01221d1f-cece-4456-9c30-b226e812f3e7' class='xr-var-data-in' type='checkbox'><label for='data-01221d1f-cece-4456-9c30-b226e812f3e7' title='Show/Hide data 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>long_name :</span></dt><dd>x coordinate of projection</dd><dt><span>standard_name :</span></dt><dd>projection_x_coordinate</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-2678287.5, -2678047.5, -2677807.5, ...,  2816032.5,  2816272.5,\n",
       "        2816512.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d47fde41-9abb-4a1e-9361-a2f8944d92c6' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d47fde41-9abb-4a1e-9361-a2f8944d92c6' class='xr-section-summary' >Data variables: <span>(14)</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>chip_size_max</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;</div><input id='attrs-f4881fab-8f0e-49dc-8940-26598c17586c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-f4881fab-8f0e-49dc-8940-26598c17586c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9697d3cb-0786-4f29-b93b-057f9a33cbe6' class='xr-var-data-in' type='checkbox'><label for='data-9697d3cb-0786-4f29-b93b-057f9a33cbe6' title='Show/Hide data 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>grid_mapping :</span></dt><dd>mapping</dd><dt><span>long_name :</span></dt><dd>maximum_allowable_search_chip</dd><dt><span>standard_name :</span></dt><dd>chip_size_max</dd><dt><span>units :</span></dt><dd>m</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> 1.57 GiB </td>\n",
       "                        <td> 201.38 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18392, 22896) </td>\n",
       "                        <td> (144, 358) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8192 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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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>count</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;</div><input id='attrs-fc0cbbd7-e904-4b6d-92ed-7a1cd3ea9554' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fc0cbbd7-e904-4b6d-92ed-7a1cd3ea9554' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb703e0c-9de2-4e89-be4f-66e7e4d578ef' class='xr-var-data-in' type='checkbox'><label for='data-bb703e0c-9de2-4e89-be4f-66e7e4d578ef' title='Show/Hide data 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>grid_mapping :</span></dt><dd>mapping</dd><dt><span>long_name :</span></dt><dd>image_pair_count</dd><dt><span>standard_name :</span></dt><dd>count</dd><dt><span>units :</span></dt><dd>count</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> 1.57 GiB </td>\n",
       "                        <td> 201.38 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18392, 22896) </td>\n",
       "                        <td> (144, 358) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8192 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>date</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;</div><input id='attrs-cf5170fb-8675-4f56-9d86-610690a2c5be' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cf5170fb-8675-4f56-9d86-610690a2c5be' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7675d888-67f9-4f1a-81bf-62c5c57b69c5' class='xr-var-data-in' type='checkbox'><label for='data-7675d888-67f9-4f1a-81bf-62c5c57b69c5' title='Show/Hide data 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>grid_mapping :</span></dt><dd>mapping</dd><dt><span>long_name :</span></dt><dd>error_weighted_image_pair_center_date</dd><dt><span>standard_name :</span></dt><dd>date</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> 3.14 GiB </td>\n",
       "                        <td> 402.75 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18392, 22896) </td>\n",
       "                        <td> (144, 358) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8192 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> timedelta64[ns] 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>dt</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>timedelta64[ns]</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;</div><input id='attrs-8e875b63-ac8a-45b5-a9d4-b9de5184e568' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8e875b63-ac8a-45b5-a9d4-b9de5184e568' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f3bf0049-ff83-4023-97e5-404d71ffaafa' class='xr-var-data-in' type='checkbox'><label for='data-f3bf0049-ff83-4023-97e5-404d71ffaafa' title='Show/Hide data 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>grid_mapping :</span></dt><dd>mapping</dd><dt><span>long_name :</span></dt><dd>error weighted average time separation between image-pairs</dd><dt><span>standard_name :</span></dt><dd>dt</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> 3.14 GiB </td>\n",
       "                        <td> 402.75 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18392, 22896) </td>\n",
       "                        <td> (144, 358) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8192 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> timedelta64[ns] numpy.ndarray </td>\n",
       "                    </tr>\n",
       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
       "        <td>\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>ice</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(288, 358), meta=np.ndarray&gt;</div><input id='attrs-85f1a137-c402-4815-b293-8258e0fbc940' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-85f1a137-c402-4815-b293-8258e0fbc940' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b68e59e3-b37f-42a8-8f91-378e3bacea52' class='xr-var-data-in' type='checkbox'><label for='data-b68e59e3-b37f-42a8-8f91-378e3bacea52' title='Show/Hide data 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>flag_meanings :</span></dt><dd>non-ice ice</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>long_name :</span></dt><dd>ice_mask</dd><dt><span>standard_name :</span></dt><dd>ice</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> 1.57 GiB </td>\n",
       "                        <td> 402.75 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18392, 22896) </td>\n",
       "                        <td> (288, 358) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4096 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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       "  <!-- Text -->\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mapping</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-2a499c88-a3b0-413d-b7e5-a5a6248f2118' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2a499c88-a3b0-413d-b7e5-a5a6248f2118' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e76aad9a-e8a1-4e01-9a1f-dbc6fb8b21d4' class='xr-var-data-in' type='checkbox'><label for='data-e76aad9a-e8a1-4e01-9a1f-dbc6fb8b21d4' title='Show/Hide data 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>GeoTransform :</span></dt><dd>-2678407.5 240.0 0 2259847.5 0 -240.0</dd><dt><span>false_easting :</span></dt><dd>0.0</dd><dt><span>false_northing :</span></dt><dd>0.0</dd><dt><span>grid_mapping_name :</span></dt><dd>polar_stereographic</dd><dt><span>inverse_flattening :</span></dt><dd>298.257223563</dd><dt><span>latitude_of_projection_origin :</span></dt><dd>-90.0</dd><dt><span>scale_factor_at_projection_origin :</span></dt><dd>1.0</dd><dt><span>semi_major_axis :</span></dt><dd>6378137.0</dd><dt><span>spatial_epsg :</span></dt><dd>3031.0</dd><dt><span>spatial_ref :</span></dt><dd>PROJCS[&quot;WGS 84 / Antarctic Polar Stereographic&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Polar_Stereographic&quot;],PARAMETER[&quot;latitude_of_origin&quot;,-71],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;3031&quot;]]</dd><dt><span>straight_vertical_longitude_from_pole :</span></dt><dd>0.0</dd><dt><span>proj4text :</span></dt><dd>+proj=stere +lat_0=-90 +lat_ts=-71 +lon_0=0 +k=1 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs</dd><dt><span>crs_wkt :</span></dt><dd>PROJCS[&quot;WGS 84 / Antarctic Polar Stereographic&quot;,GEOGCS[&quot;WGS 84&quot;,DATUM[&quot;WGS_1984&quot;,SPHEROID[&quot;WGS 84&quot;,6378137,298.257223563,AUTHORITY[&quot;EPSG&quot;,&quot;7030&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;6326&quot;]],PRIMEM[&quot;Greenwich&quot;,0,AUTHORITY[&quot;EPSG&quot;,&quot;8901&quot;]],UNIT[&quot;degree&quot;,0.0174532925199433,AUTHORITY[&quot;EPSG&quot;,&quot;9122&quot;]],AUTHORITY[&quot;EPSG&quot;,&quot;4326&quot;]],PROJECTION[&quot;Polar_Stereographic&quot;],PARAMETER[&quot;latitude_of_origin&quot;,-71],PARAMETER[&quot;central_meridian&quot;,0],PARAMETER[&quot;scale_factor&quot;,1],PARAMETER[&quot;false_easting&quot;,0],PARAMETER[&quot;false_northing&quot;,0],UNIT[&quot;metre&quot;,1,AUTHORITY[&quot;EPSG&quot;,&quot;9001&quot;]],AXIS[&quot;Easting&quot;,EAST],AXIS[&quot;Northing&quot;,NORTH],AUTHORITY[&quot;EPSG&quot;,&quot;3031&quot;]]</dd><dt><span>standard_parallel :</span></dt><dd>-71.0</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>ocean</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(288, 358), meta=np.ndarray&gt;</div><input id='attrs-cb3a02c6-8bad-4a87-bef1-bdfd227e3834' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb3a02c6-8bad-4a87-bef1-bdfd227e3834' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c923f0ae-544d-47ae-a909-42b0a9a9b0c5' class='xr-var-data-in' type='checkbox'><label for='data-c923f0ae-544d-47ae-a909-42b0a9a9b0c5' title='Show/Hide data 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>flag_meanings :</span></dt><dd>non-ocean ocean</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>long_name :</span></dt><dd>ocean_mask</dd><dt><span>standard_name :</span></dt><dd>ocean</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> 1.57 GiB </td>\n",
       "                        <td> 402.75 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18392, 22896) </td>\n",
       "                        <td> (288, 358) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4096 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>rock</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(288, 358), meta=np.ndarray&gt;</div><input id='attrs-99957ed5-16df-4c5a-bad9-f486882ec69e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-99957ed5-16df-4c5a-bad9-f486882ec69e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-563a5557-1cfc-4622-b020-d85c1249ba51' class='xr-var-data-in' type='checkbox'><label for='data-563a5557-1cfc-4622-b020-d85c1249ba51' title='Show/Hide data 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>flag_meanings :</span></dt><dd>non-rock rock</dd><dt><span>flag_values :</span></dt><dd>[0 1]</dd><dt><span>grid_mapping :</span></dt><dd>mapping</dd><dt><span>long_name :</span></dt><dd>rock_mask</dd><dt><span>standard_name :</span></dt><dd>rock</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> 1.57 GiB </td>\n",
       "                        <td> 402.75 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18392, 22896) </td>\n",
       "                        <td> (288, 358) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4096 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>v</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;</div><input id='attrs-8d6d7c2e-3efa-4844-bc26-fa27a268669f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8d6d7c2e-3efa-4844-bc26-fa27a268669f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b5119d31-d569-46cb-b174-995481f45371' class='xr-var-data-in' type='checkbox'><label for='data-b5119d31-d569-46cb-b174-995481f45371' title='Show/Hide data 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>grid_mapping :</span></dt><dd>mapping</dd><dt><span>map_scale_corrected :</span></dt><dd>1</dd><dt><span>quality_cutoff :</span></dt><dd>7.0</dd><dt><span>standard_name :</span></dt><dd>land_ice_surface_velocity</dd><dt><span>units :</span></dt><dd>meter/year</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> 1.57 GiB </td>\n",
       "                        <td> 201.38 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18392, 22896) </td>\n",
       "                        <td> (144, 358) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8192 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",
       "        <td>\n",
       "            <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",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.57 GiB </td>\n",
       "                        <td> 201.38 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18392, 22896) </td>\n",
       "                        <td> (144, 358) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8192 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>vx</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;</div><input id='attrs-c8e11d55-63aa-4235-86be-5db115969409' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c8e11d55-63aa-4235-86be-5db115969409' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2fac8d14-d0c7-4c4e-98ee-132e14e60651' class='xr-var-data-in' type='checkbox'><label for='data-2fac8d14-d0c7-4c4e-98ee-132e14e60651' title='Show/Hide data 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>grid_mapping :</span></dt><dd>mapping</dd><dt><span>map_scale_corrected :</span></dt><dd>1</dd><dt><span>quality_cutoff :</span></dt><dd>7.0</dd><dt><span>standard_name :</span></dt><dd>land_ice_surface_x_velocity</dd><dt><span>units :</span></dt><dd>meter/year</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> 1.57 GiB </td>\n",
       "                        <td> 201.38 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18392, 22896) </td>\n",
       "                        <td> (144, 358) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8192 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>vx_err</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;</div><input id='attrs-2cb1a9dc-1fb3-4a45-bd12-51126854f43a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2cb1a9dc-1fb3-4a45-bd12-51126854f43a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b9af0ce2-d5cd-44c6-a468-488200312d2e' class='xr-var-data-in' type='checkbox'><label for='data-b9af0ce2-d5cd-44c6-a468-488200312d2e' title='Show/Hide data 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>grid_mapping :</span></dt><dd>mapping</dd><dt><span>standard_name :</span></dt><dd>x_velocity_err</dd><dt><span>units :</span></dt><dd>meter/year</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> 1.57 GiB </td>\n",
       "                        <td> 201.38 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18392, 22896) </td>\n",
       "                        <td> (144, 358) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8192 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>vy</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;</div><input id='attrs-6b1b95e3-4255-43dd-9e39-1cb50d008057' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6b1b95e3-4255-43dd-9e39-1cb50d008057' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27ea5c22-c532-49dc-9503-582f98b78f29' class='xr-var-data-in' type='checkbox'><label for='data-27ea5c22-c532-49dc-9503-582f98b78f29' title='Show/Hide data 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>grid_mapping :</span></dt><dd>mapping</dd><dt><span>map_scale_corrected :</span></dt><dd>1</dd><dt><span>quality_cutoff :</span></dt><dd>7.0</dd><dt><span>standard_name :</span></dt><dd>land_ice_surface_y_velocity</dd><dt><span>units :</span></dt><dd>meter/year</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",
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       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.57 GiB </td>\n",
       "                        <td> 201.38 kiB </td>\n",
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       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
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       "                        <td> (144, 358) </td>\n",
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       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 8192 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>vy_err</span></div><div class='xr-var-dims'>(y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(144, 358), meta=np.ndarray&gt;</div><input id='attrs-69300c96-47cd-43e0-b730-6d41f32803e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-69300c96-47cd-43e0-b730-6d41f32803e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1c293a60-641d-4c57-b650-c132fdd793c0' class='xr-var-data-in' type='checkbox'><label for='data-1c293a60-641d-4c57-b650-c132fdd793c0' title='Show/Hide data 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>grid_mapping :</span></dt><dd>mapping</dd><dt><span>standard_name :</span></dt><dd>y_velocity_err</dd><dt><span>units :</span></dt><dd>meter/year</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",
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       "                </thead>\n",
       "                <tbody>\n",
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       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.57 GiB </td>\n",
       "                        <td> 201.38 kiB </td>\n",
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       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (18392, 22896) </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2931116a-b819-4b7b-a656-7a73682abeb8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2931116a-b819-4b7b-a656-7a73682abeb8' class='xr-section-summary' >Attributes: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.8</dd><dt><span>GDAL_AREA_OR_POINT :</span></dt><dd>Area</dd><dt><span>author :</span></dt><dd>Alex S. Gardner, JPL/NASA</dd><dt><span>date_created :</span></dt><dd>05-May-2019 15:53:24</dd><dt><span>institution :</span></dt><dd>NASA Jet Propulsion Laboratory (JPL), California Institute of Technology</dd><dt><span>motion_coordinates :</span></dt><dd>map</dd><dt><span>motion_detection_method :</span></dt><dd>feature</dd><dt><span>scene_pair_type :</span></dt><dd>optical</dd><dt><span>title :</span></dt><dd>autoRIFT surface velocities</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset> Size: 25GB\n",
       "Dimensions:        (y: 18392, x: 22896)\n",
       "Coordinates:\n",
       "  * y              (y) float64 147kB 2.26e+06 2.259e+06 ... -2.154e+06\n",
       "  * x              (x) float64 183kB -2.678e+06 -2.678e+06 ... 2.817e+06\n",
       "Data variables: (12/14)\n",
       "    chip_size_max  (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>\n",
       "    count          (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>\n",
       "    date           (y, x) timedelta64[ns] 3GB dask.array<chunksize=(144, 358), meta=np.ndarray>\n",
       "    dt             (y, x) timedelta64[ns] 3GB dask.array<chunksize=(144, 358), meta=np.ndarray>\n",
       "    ice            (y, x) float32 2GB dask.array<chunksize=(288, 358), meta=np.ndarray>\n",
       "    mapping        float32 4B ...\n",
       "    ...             ...\n",
       "    v              (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>\n",
       "    v_err          (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>\n",
       "    vx             (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>\n",
       "    vx_err         (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>\n",
       "    vy             (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>\n",
       "    vy_err         (y, x) float32 2GB dask.array<chunksize=(144, 358), meta=np.ndarray>\n",
       "Attributes:\n",
       "    Conventions:              CF-1.8\n",
       "    GDAL_AREA_OR_POINT:       Area\n",
       "    author:                   Alex S. Gardner, JPL/NASA\n",
       "    date_created:             05-May-2019 15:53:24\n",
       "    institution:              NASA Jet Propulsion Laboratory (JPL), Californi...\n",
       "    motion_coordinates:       map\n",
       "    motion_detection_method:  feature\n",
       "    scene_pair_type:          optical\n",
       "    title:                    autoRIFT surface velocities"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load composite velocity data\n",
    "vel_data = catalog.load_dataset('measures_its_live_regional_glacier_and_ice_sheet_surface_velocities', version = 'v1', subdataset = 'ANT', resolution = '240m', static = True)\n",
    "vel_data"
   ]
  }
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