Access_ISMAR-CNR.ipynb

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   "cell_type": "markdown",
   "id": "9e847ef4-584e-491e-bbc9-b071ae2d489d",
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   "source": [
    "### How to access DOORS cubes based on model data by ISMAR-CNR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84c4be6b-3016-4ea4-972a-460805e63fc3",
   "metadata": {},
   "source": [
    "This notebook serves to explain how to access data cubes that have been created based on model data provided by ISMAR-CNR for the Black Sea region. The cubes are accessed using the xcube package. To access the data, the first step is to set up a store that establishes the connection to the s3 bucket hosting the data."
   ]
  },
  {
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   "id": "dcf8804f-c91e-4b49-ad9e-6c22e9f494bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "from xcube.core.store import new_data_store"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f3c26a33-6dac-4d90-8b6b-b89a5fb5d809",
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   "source": [
    "ismar_model_store = new_data_store(\n",
    "    \"s3\",\n",
    "    root=\"doors-cubes/model-data/ISMAR-CNR\",\n",
    "    max_depth=1\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "200f3187-5b76-450f-a036-705e4a5e3ace",
   "metadata": {},
   "source": [
    "Next, we can list the data cubes that are stored in this bucket. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "32506203-ead1-463c-a567-0b63d15f41a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['west2018_01_01_2020_reg_hydro_0.01.levels',\n",
       " 'west2018_01_01_2020_reg_ts_0.01.levels']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(ismar_model_store.get_data_ids())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80647075-a2e6-48fa-878b-c9cb3e4e77c8",
   "metadata": {},
   "source": [
    "We can get a description of the data and open the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00587de3-272d-4dda-add5-5c47fd4b3a71",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/json": {
       "attrs": {
        "Conventions": "CF-1.4",
        "acknowledgment": "DOORS project",
        "comment": "Data restriction: for academic research use only",
        "contact": "email: georg.umgiesser@ismar.cnr.it",
        "contributor_name": "ISMAR-CNR",
        "contributor_url": "http://www.ismar.cnr.it/",
        "creator_email": "info@brockmann-consult.de",
        "creator_name": "Brockmann Consult GmbH",
        "creator_url": "www.brockmann-consult.de",
        "date_modified": "2023-02-08T19:45:40",
        "doors_cube_gen_version": "0.1",
        "history": "created on 2022-11-22 14:49:30 MET",
        "institution": "ISMAR-CNR, Venice, Italy",
        "project": "DOORS",
        "recipe": "https://github.com/bcdev/doors-recipes/cubegen/ISMAR-CNR",
        "references": "Model info: http://www.ismar.cnr.it/shyfem",
        "source": "Model data produced by SHYFEM at ISMAR-CNR",
        "time_coverage_end": "2020-01-02T00:00:00",
        "time_coverage_start": "2020-01-01T00:00:00",
        "time_period": "3H",
        "title": "Western BS Hydro 3D Characterization"
       },
       "bbox": [
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       "coords": {
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         "attrs": {
          "axis": "Y",
          "standard_name": "latitude",
          "units": "degrees_north"
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         "dims": [
          "lat"
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         "dtype": "float32",
         "name": "lat"
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        "level": {
         "dims": [
          "level"
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         "dtype": "float64",
         "name": "level"
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        "lon": {
         "attrs": {
          "axis": "X",
          "standard_name": "longitude",
          "units": "degrees_east"
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         "dims": [
          "lon"
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         "name": "lon"
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        "time": {
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         "dims": [
          "time"
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         "dtype": "datetime64[ns]",
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       "data_id": "west2018_01_01_2020_reg_hydro_0.01.levels",
       "data_type": "mldataset",
       "data_vars": {
        "lev": {
         "attrs": {
          "axis": "Z",
          "description": "bottom of vertical layers",
          "positive": "down",
          "standard_name": "depth",
          "units": "m"
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          75
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         "dims": [
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        "total_depth": {
         "attrs": {
          "description": "total depth at data grid nodes",
          "standard_name": "sea_floor_depth_below_sea_surface",
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          "valid_min": -100
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         "chunks": [
          503,
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         "dims": [
          "lat",
          "lon"
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         "dtype": "float32",
         "name": "total_depth"
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        "u_velocity": {
         "attrs": {
          "standard_name": "eastward_sea_water_velocity",
          "units": "m s-1",
          "valid_max": 10,
          "valid_min": -10
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         "dims": [
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         "dtype": "float32",
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        "v_velocity": {
         "attrs": {
          "standard_name": "northward_sea_water_velocity",
          "units": "m s-1",
          "valid_max": 10,
          "valid_min": -10
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         "dims": [
          "time",
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          "lat",
          "lon"
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         "dtype": "float32",
         "name": "v_velocity"
        },
        "water_level": {
         "attrs": {
          "standard_name": "water_surface_height_above_reference_datum",
          "units": "m",
          "valid_max": 10,
          "valid_min": -10
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          3,
          503,
          541
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         "dims": [
          "time",
          "lat",
          "lon"
         ],
         "dtype": "float32",
         "name": "water_level"
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       },
       "dims": {
        "lat": 503,
        "level": 75,
        "lon": 541,
        "time": 9
       },
       "num_levels": 1,
       "time_period": "0D",
       "time_range": [
        "2019-12-31",
        "2020-01-02"
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      },
      "text/plain": [
       "<xcube.core.store.descriptor.MultiLevelDatasetDescriptor at 0x7fe1e31362b0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ismar_model_store.describe_data('west2018_01_01_2020_reg_hydro_0.01.levels')"
   ]
  },
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   "cell_type": "code",
   "execution_count": 5,
   "id": "5d0367b8-fddf-4e0b-b7d5-c3c8521d9818",
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    {
     "data": {
      "text/plain": [
       "<xcube.core.store.fs.impl.mldataset.FsMultiLevelDataset at 0x7fe23839eaf0>"
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     "execution_count": 5,
     "metadata": {},
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   ],
   "source": [
    "ds_hydro = ismar_model_store.open_data('west2018_01_01_2020_reg_hydro_0.01.levels')\n",
    "ds_hydro"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "662caab2-c9b9-4b18-bfab-d112467ec4c5",
   "metadata": {},
   "source": [
    "For purposes of better visualisation, the data is provided as a MultiLevelDataset. We may access the underlying dataset like this:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 6,
   "id": "7a664d43-470b-49de-aa47-de3f76d1b059",
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       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:      (lat: 503, level: 75, lon: 541, time: 9)\n",
       "Coordinates:\n",
       "  * lat          (lat) float32 41.98 41.99 42.0 42.01 ... 46.97 46.98 46.99 47.0\n",
       "  * level        (level) float64 0.0 0.01 0.02 0.03 0.04 ... 0.71 0.72 0.73 0.74\n",
       "  * lon          (lon) float32 27.4 27.41 27.42 27.43 ... 32.77 32.78 32.79 32.8\n",
       "  * time         (time) datetime64[ns] 2020-01-01 ... 2020-01-02\n",
       "Data variables:\n",
       "    lev          (level) float32 dask.array&lt;chunksize=(75,), meta=np.ndarray&gt;\n",
       "    total_depth  (lat, lon) float32 dask.array&lt;chunksize=(503, 541), meta=np.ndarray&gt;\n",
       "    u_velocity   (time, level, lat, lon) float32 dask.array&lt;chunksize=(3, 75, 503, 541), meta=np.ndarray&gt;\n",
       "    v_velocity   (time, level, lat, lon) float32 dask.array&lt;chunksize=(3, 75, 503, 541), meta=np.ndarray&gt;\n",
       "    water_level  (time, lat, lon) float32 dask.array&lt;chunksize=(3, 503, 541), meta=np.ndarray&gt;\n",
       "Attributes: (12/21)\n",
       "    Conventions:             CF-1.4\n",
       "    acknowledgment:          DOORS project\n",
       "    comment:                 Data restriction: for academic research use only\n",
       "    contact:                 email: georg.umgiesser@ismar.cnr.it\n",
       "    contributor_name:        ISMAR-CNR\n",
       "    contributor_url:         http://www.ismar.cnr.it/\n",
       "    ...                      ...\n",
       "    references:              Model info: http://www.ismar.cnr.it/shyfem\n",
       "    source:                  Model data produced by SHYFEM at ISMAR-CNR\n",
       "    time_coverage_end:       2020-01-02T00:00:00\n",
       "    time_coverage_start:     2020-01-01T00:00:00\n",
       "    time_period:             3H\n",
       "    title:                   Western BS Hydro 3D Characterization</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-954230a3-ceb4-4edc-8f07-e089f69ac4ed' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-954230a3-ceb4-4edc-8f07-e089f69ac4ed' 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'>lat</span>: 503</li><li><span class='xr-has-index'>level</span>: 75</li><li><span class='xr-has-index'>lon</span>: 541</li><li><span class='xr-has-index'>time</span>: 9</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-5485661f-9045-4183-aef0-fdc532f6b04b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5485661f-9045-4183-aef0-fdc532f6b04b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>41.98 41.99 42.0 ... 46.99 47.0</div><input id='attrs-499b7ae0-6bb0-46d0-8917-c56dfd73397b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-499b7ae0-6bb0-46d0-8917-c56dfd73397b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b839fc0a-665a-407d-bea2-39065c4e2620' class='xr-var-data-in' type='checkbox'><label for='data-b839fc0a-665a-407d-bea2-39065c4e2620' title='Show/Hide data 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>axis :</span></dt><dd>Y</dd><dt><span>standard_name :</span></dt><dd>latitude</dd><dt><span>units :</span></dt><dd>degrees_north</dd></dl></div><div class='xr-var-data'><pre>array([41.98    , 41.989998, 42.      , ..., 46.98    , 46.989998, 47.      ],\n",
       "      dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>level</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.01 0.02 ... 0.72 0.73 0.74</div><input id='attrs-1e9b19eb-35e4-4d8e-b087-81d26bb57ef3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1e9b19eb-35e4-4d8e-b087-81d26bb57ef3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-631e9236-b468-4e09-954d-82b56f236c56' class='xr-var-data-in' type='checkbox'><label for='data-631e9236-b468-4e09-954d-82b56f236c56' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([0.  , 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 , 0.11,\n",
       "       0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 , 0.21, 0.22, 0.23,\n",
       "       0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 , 0.31, 0.32, 0.33, 0.34, 0.35,\n",
       "       0.36, 0.37, 0.38, 0.39, 0.4 , 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47,\n",
       "       0.48, 0.49, 0.5 , 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59,\n",
       "       0.6 , 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 , 0.71,\n",
       "       0.72, 0.73, 0.74])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>27.4 27.41 27.42 ... 32.79 32.8</div><input id='attrs-e608addd-1ee2-4130-b8d5-5a1afa9860bd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e608addd-1ee2-4130-b8d5-5a1afa9860bd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b54e0872-9fc0-4fa2-a823-eda00ac05521' class='xr-var-data-in' type='checkbox'><label for='data-b54e0872-9fc0-4fa2-a823-eda00ac05521' title='Show/Hide data 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>axis :</span></dt><dd>X</dd><dt><span>standard_name :</span></dt><dd>longitude</dd><dt><span>units :</span></dt><dd>degrees_east</dd></dl></div><div class='xr-var-data'><pre>array([27.4 , 27.41, 27.42, ..., 32.78, 32.79, 32.8 ], dtype=float32)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>datetime64[ns]</div><div class='xr-var-preview xr-preview'>2020-01-01 ... 2020-01-02</div><input id='attrs-fd1a106f-13b5-44b1-ba30-234c810dc433' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fd1a106f-13b5-44b1-ba30-234c810dc433' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ebac34c-cb92-4c3c-98f3-ced7622281c3' class='xr-var-data-in' type='checkbox'><label for='data-8ebac34c-cb92-4c3c-98f3-ced7622281c3' title='Show/Hide data 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>axis :</span></dt><dd>T</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([&#x27;2020-01-01T00:00:00.000000000&#x27;, &#x27;2020-01-01T03:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-01T06:00:00.000000000&#x27;, &#x27;2020-01-01T09:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-01T12:00:00.000000000&#x27;, &#x27;2020-01-01T15:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-01T18:00:00.000000000&#x27;, &#x27;2020-01-01T21:00:00.000000000&#x27;,\n",
       "       &#x27;2020-01-02T00:00:00.000000000&#x27;], dtype=&#x27;datetime64[ns]&#x27;)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2d3b4505-cb87-4f78-b89c-290baadd5682' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2d3b4505-cb87-4f78-b89c-290baadd5682' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>lev</span></div><div class='xr-var-dims'>(level)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(75,), meta=np.ndarray&gt;</div><input id='attrs-a4a7467c-d3bd-4dcf-a6c2-7b1120025595' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a4a7467c-d3bd-4dcf-a6c2-7b1120025595' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35ea1a17-6025-4e55-acb4-9baf06bb23c3' class='xr-var-data-in' type='checkbox'><label for='data-35ea1a17-6025-4e55-acb4-9baf06bb23c3' title='Show/Hide data 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>axis :</span></dt><dd>Z</dd><dt><span>description :</span></dt><dd>bottom of vertical layers</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>standard_name :</span></dt><dd>depth</dd><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 300 B </td>\n",
       "                        <td> 300 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (75,) </td>\n",
       "                        <td> (75,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> 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>total_depth</span></div><div class='xr-var-dims'>(lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(503, 541), meta=np.ndarray&gt;</div><input id='attrs-326d8781-6bf6-4a7b-8034-6ba094cc4ebe' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-326d8781-6bf6-4a7b-8034-6ba094cc4ebe' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0132481b-a4fe-442a-88e0-682a5a38f20c' class='xr-var-data-in' type='checkbox'><label for='data-0132481b-a4fe-442a-88e0-682a5a38f20c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>description :</span></dt><dd>total depth at data grid nodes</dd><dt><span>standard_name :</span></dt><dd>sea_floor_depth_below_sea_surface</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>10000.0</dd><dt><span>valid_min :</span></dt><dd>-100.0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table>\n",
       "                <thead>\n",
       "                    <tr>\n",
       "                        <td> </td>\n",
       "                        <th> Array </th>\n",
       "                        <th> Chunk </th>\n",
       "                    </tr>\n",
       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 1.04 MiB </td>\n",
       "                        <td> 1.04 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (503, 541) </td>\n",
       "                        <td> (503, 541) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 2 Tasks </td>\n",
       "                        <td> 1 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> 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>u_velocity</span></div><div class='xr-var-dims'>(time, level, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 75, 503, 541), meta=np.ndarray&gt;</div><input id='attrs-b889f886-b268-4343-9b06-642d34f92165' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b889f886-b268-4343-9b06-642d34f92165' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6fa32f53-b1ca-458c-be7b-f0892f1b471d' class='xr-var-data-in' type='checkbox'><label for='data-6fa32f53-b1ca-458c-be7b-f0892f1b471d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>eastward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>valid_max :</span></dt><dd>10.0</dd><dt><span>valid_min :</span></dt><dd>-10.0</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Bytes </th>\n",
       "                        <td> 700.70 MiB </td>\n",
       "                        <td> 233.57 MiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (9, 75, 503, 541) </td>\n",
       "                        <td> (3, 75, 503, 541) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 4 Tasks </td>\n",
       "                        <td> 3 Chunks </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v_velocity</span></div><div class='xr-var-dims'>(time, level, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 75, 503, 541), meta=np.ndarray&gt;</div><input id='attrs-3ae9bd71-78da-487c-bd2a-1f9de805b73b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3ae9bd71-78da-487c-bd2a-1f9de805b73b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-95c3cec4-60f6-4cb8-9245-d68b1b995698' class='xr-var-data-in' type='checkbox'><label for='data-95c3cec4-60f6-4cb8-9245-d68b1b995698' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>northward_sea_water_velocity</dd><dt><span>units :</span></dt><dd>m s-1</dd><dt><span>valid_max :</span></dt><dd>10.0</dd><dt><span>valid_min :</span></dt><dd>-10.0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                </thead>\n",
       "                <tbody>\n",
       "                    \n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 700.70 MiB </td>\n",
       "                        <td> 233.57 MiB </td>\n",
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       "                        <th> Shape </th>\n",
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       "                        <td> (3, 75, 503, 541) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
       "                        <td> 4 Tasks </td>\n",
       "                        <td> 3 Chunks </td>\n",
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       "                    <tr>\n",
       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
       "                    <td> numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>water_level</span></div><div class='xr-var-dims'>(time, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(3, 503, 541), meta=np.ndarray&gt;</div><input id='attrs-0c6043c5-5af5-44e0-a112-f7983a3a9eca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0c6043c5-5af5-44e0-a112-f7983a3a9eca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ced07b5c-e5dc-4595-b392-038584f2d7d6' class='xr-var-data-in' type='checkbox'><label for='data-ced07b5c-e5dc-4595-b392-038584f2d7d6' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>water_surface_height_above_reference_datum</dd><dt><span>units :</span></dt><dd>m</dd><dt><span>valid_max :</span></dt><dd>10.0</dd><dt><span>valid_min :</span></dt><dd>-10.0</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 9.34 MiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (9, 503, 541) </td>\n",
       "                        <td> (3, 503, 541) </td>\n",
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       "                    <tr>\n",
       "                        <th> Count </th>\n",
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       "                    <th> Type </th>\n",
       "                    <td> float32 </td>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-2b6b2601-10b1-412a-a00f-62f4b7967ada' class='xr-section-summary-in' type='checkbox'  ><label for='section-2b6b2601-10b1-412a-a00f-62f4b7967ada' class='xr-section-summary' >Indexes: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>lat</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-52d7438f-db0f-4c2e-858b-aac2c5cd1f61' class='xr-index-data-in' type='checkbox'/><label for='index-52d7438f-db0f-4c2e-858b-aac2c5cd1f61' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([ 41.97999954223633,  41.98999786376953,               42.0,\n",
       "                42.0099983215332,  42.02000045776367, 42.029998779296875,\n",
       "              42.040000915527344,  42.04999923706055, 42.060001373291016,\n",
       "               42.06999969482422,\n",
       "              ...\n",
       "               46.90999984741211,  46.91999816894531,  46.93000030517578,\n",
       "              46.939998626708984,  46.95000076293945, 46.959999084472656,\n",
       "              46.970001220703125,  46.97999954223633,  46.98999786376953,\n",
       "                            47.0],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lat&#x27;, length=503))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>level</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e47cd41d-75b8-486e-9121-1f3324eb242c' class='xr-index-data-in' type='checkbox'/><label for='index-e47cd41d-75b8-486e-9121-1f3324eb242c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([                0.0,                0.01,                0.02,\n",
       "                             0.03,                0.04,                0.05,\n",
       "                             0.06,                0.07,                0.08,\n",
       "                             0.09,                 0.1,                0.11,\n",
       "                             0.12,                0.13,                0.14,\n",
       "                             0.15,                0.16,                0.17,\n",
       "                             0.18,                0.19,                 0.2,\n",
       "                             0.21,                0.22,                0.23,\n",
       "                             0.24,                0.25,                0.26,\n",
       "                             0.27,                0.28,                0.29,\n",
       "                              0.3,                0.31,                0.32,\n",
       "                             0.33,                0.34, 0.35000000000000003,\n",
       "                             0.36,                0.37,                0.38,\n",
       "                             0.39,                 0.4, 0.41000000000000003,\n",
       "                             0.42,                0.43,                0.44,\n",
       "                             0.45,                0.46, 0.47000000000000003,\n",
       "                             0.48,                0.49,                 0.5,\n",
       "                             0.51,                0.52,                0.53,\n",
       "                             0.54,                0.55,                0.56,\n",
       "               0.5700000000000001,                0.58,                0.59,\n",
       "                              0.6,                0.61,                0.62,\n",
       "                             0.63,                0.64,                0.65,\n",
       "                             0.66,                0.67,                0.68,\n",
       "               0.6900000000000001,  0.7000000000000001,                0.71,\n",
       "                             0.72,                0.73,                0.74],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;level&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lon</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-c8f3e841-3568-4c32-a787-946a7ff498de' class='xr-index-data-in' type='checkbox'/><label for='index-c8f3e841-3568-4c32-a787-946a7ff498de' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Float64Index([27.399999618530273,  27.40999984741211, 27.420000076293945,\n",
       "               27.43000030517578, 27.440000534057617,  27.44999885559082,\n",
       "              27.459999084472656, 27.469999313354492, 27.479999542236328,\n",
       "              27.489999771118164,\n",
       "              ...\n",
       "              32.709999084472656, 32.720001220703125,  32.72999954223633,\n",
       "               32.73999786376953,              32.75,  32.76000213623047,\n",
       "               32.77000045776367, 32.779998779296875, 32.790000915527344,\n",
       "               32.79999923706055],\n",
       "             dtype=&#x27;float64&#x27;, name=&#x27;lon&#x27;, length=541))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d072f6d4-b0cc-4e9c-93e7-5987ed5b518f' class='xr-index-data-in' type='checkbox'/><label for='index-d072f6d4-b0cc-4e9c-93e7-5987ed5b518f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(DatetimeIndex([&#x27;2020-01-01 00:00:00&#x27;, &#x27;2020-01-01 03:00:00&#x27;,\n",
       "               &#x27;2020-01-01 06:00:00&#x27;, &#x27;2020-01-01 09:00:00&#x27;,\n",
       "               &#x27;2020-01-01 12:00:00&#x27;, &#x27;2020-01-01 15:00:00&#x27;,\n",
       "               &#x27;2020-01-01 18:00:00&#x27;, &#x27;2020-01-01 21:00:00&#x27;,\n",
       "               &#x27;2020-01-02 00:00:00&#x27;],\n",
       "              dtype=&#x27;datetime64[ns]&#x27;, name=&#x27;time&#x27;, freq=None))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-c816892f-59ea-4214-9273-090878d19690' class='xr-section-summary-in' type='checkbox'  ><label for='section-c816892f-59ea-4214-9273-090878d19690' class='xr-section-summary' >Attributes: <span>(21)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>acknowledgment :</span></dt><dd>DOORS project</dd><dt><span>comment :</span></dt><dd>Data restriction: for academic research use only</dd><dt><span>contact :</span></dt><dd>email: georg.umgiesser@ismar.cnr.it</dd><dt><span>contributor_name :</span></dt><dd>ISMAR-CNR</dd><dt><span>contributor_url :</span></dt><dd>http://www.ismar.cnr.it/</dd><dt><span>creator_email :</span></dt><dd>info@brockmann-consult.de</dd><dt><span>creator_name :</span></dt><dd>Brockmann Consult GmbH</dd><dt><span>creator_url :</span></dt><dd>www.brockmann-consult.de</dd><dt><span>date_modified :</span></dt><dd>2023-02-08T19:45:40</dd><dt><span>doors_cube_gen_version :</span></dt><dd>0.1</dd><dt><span>history :</span></dt><dd>created on 2022-11-22 14:49:30 MET</dd><dt><span>institution :</span></dt><dd>ISMAR-CNR, Venice, Italy</dd><dt><span>project :</span></dt><dd>DOORS</dd><dt><span>recipe :</span></dt><dd>https://github.com/bcdev/doors-recipes/cubegen/ISMAR-CNR</dd><dt><span>references :</span></dt><dd>Model info: http://www.ismar.cnr.it/shyfem</dd><dt><span>source :</span></dt><dd>Model data produced by SHYFEM at ISMAR-CNR</dd><dt><span>time_coverage_end :</span></dt><dd>2020-01-02T00:00:00</dd><dt><span>time_coverage_start :</span></dt><dd>2020-01-01T00:00:00</dd><dt><span>time_period :</span></dt><dd>3H</dd><dt><span>title :</span></dt><dd>Western BS Hydro 3D Characterization</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:      (lat: 503, level: 75, lon: 541, time: 9)\n",
       "Coordinates:\n",
       "  * lat          (lat) float32 41.98 41.99 42.0 42.01 ... 46.97 46.98 46.99 47.0\n",
       "  * level        (level) float64 0.0 0.01 0.02 0.03 0.04 ... 0.71 0.72 0.73 0.74\n",
       "  * lon          (lon) float32 27.4 27.41 27.42 27.43 ... 32.77 32.78 32.79 32.8\n",
       "  * time         (time) datetime64[ns] 2020-01-01 ... 2020-01-02\n",
       "Data variables:\n",
       "    lev          (level) float32 dask.array<chunksize=(75,), meta=np.ndarray>\n",
       "    total_depth  (lat, lon) float32 dask.array<chunksize=(503, 541), meta=np.ndarray>\n",
       "    u_velocity   (time, level, lat, lon) float32 dask.array<chunksize=(3, 75, 503, 541), meta=np.ndarray>\n",
       "    v_velocity   (time, level, lat, lon) float32 dask.array<chunksize=(3, 75, 503, 541), meta=np.ndarray>\n",
       "    water_level  (time, lat, lon) float32 dask.array<chunksize=(3, 503, 541), meta=np.ndarray>\n",
       "Attributes: (12/21)\n",
       "    Conventions:             CF-1.4\n",
       "    acknowledgment:          DOORS project\n",
       "    comment:                 Data restriction: for academic research use only\n",
       "    contact:                 email: georg.umgiesser@ismar.cnr.it\n",
       "    contributor_name:        ISMAR-CNR\n",
       "    contributor_url:         http://www.ismar.cnr.it/\n",
       "    ...                      ...\n",
       "    references:              Model info: http://www.ismar.cnr.it/shyfem\n",
       "    source:                  Model data produced by SHYFEM at ISMAR-CNR\n",
       "    time_coverage_end:       2020-01-02T00:00:00\n",
       "    time_coverage_start:     2020-01-01T00:00:00\n",
       "    time_period:             3H\n",
       "    title:                   Western BS Hydro 3D Characterization"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(ds_hydro.base_dataset)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10acb85a-2203-4682-910f-a3024dd142d3",
   "metadata": {},
   "source": [
    "We can then visualise a single image ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "6a7e41d6-47a8-4669-80c6-2616f245ff9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fe1e256cb80>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_hydro.base_dataset.u_velocity.isel(level=0, time=0).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6b95a460-0eea-47e3-ab32-8b9d3300bc54",
   "metadata": {},
   "source": [
    "... or a time series."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "77be1b84-0de1-43ea-892d-06e136896455",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<xarray.plot.facetgrid.FacetGrid at 0x7fe1e25962e0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 11 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_hydro.base_dataset.u_velocity.isel(level=0).plot.imshow(col='time', col_wrap=5)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}