plotfile2xarray_example.ipynb

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    "import yt\n",
    "yt.set_log_level(0) # why do you talk so much, yt?\n",
    "import xarray\n",
    "\n",
    "import matplotlib.pyplot as plt"
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    "def plotfile2xarray(plotfiles):\n",
    "    dss_yt = yt.load(plotfiles)\n",
    "\n",
    "    #pull the field list from the first file -- assume they are all the same\n",
    "    field_list = dss_yt[0].field_list\n",
    "    \n",
    "    # create a list of xarray datasets, one for each plotfile\n",
    "    xars = []\n",
    "    for ds_yt in dss_yt:\n",
    "        adf = ds_yt.covering_grid(level=0,left_edge=ds_yt.domain_left_edge, dims=ds_yt.domain_dimensions)\n",
    "        xars.append(adf.to_xarray(fields=field_list).transpose()) # transpose to get dims in standard (reverse) order\n",
    "    \n",
    "    ds = xarray.concat(xars, dim='ocean_time') # concatenate the xarray datasets over 'ocean_time'\n",
    "    keys = [f[1] for f in field_list] # correct the keys to remove the prepended 'boxlib'\n",
    "    \n",
    "    return ds.rename_vars(dict(zip(field_list, keys)))"
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     "text": [
      "CPU times: user 2 s, sys: 357 ms, total: 2.36 s\n",
      "Wall time: 3.94 s\n"
     ]
    }
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   "source": [
    "%%time\n",
    "plotfiles = \"./plt?????/\"\n",
    "ds = plotfile2xarray(plotfiles)"
   ]
  },
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:     (x: 100, y: 300, z: 50, ocean_time: 6)\n",
       "Coordinates:\n",
       "  * x           (x) float64 500.0 1.5e+03 2.5e+03 ... 9.75e+04 9.85e+04 9.95e+04\n",
       "  * y           (y) float64 500.0 1.5e+03 2.5e+03 ... 2.985e+05 2.995e+05\n",
       "  * z           (z) float64 -49.5 -48.5 -47.5 -46.5 ... -3.5 -2.5 -1.5 -0.5\n",
       "Dimensions without coordinates: ocean_time\n",
       "Data variables:\n",
       "    salt        (ocean_time, z, y, x) float64 35.0 35.0 35.0 ... 73.46 73.46\n",
       "    temp        (ocean_time, z, y, x) float64 9.126 9.125 9.127 ... 13.99 13.99\n",
       "    x_velocity  (ocean_time, z, y, x) float64 0.0 0.0 0.0 ... -0.317 -0.317\n",
       "    y_velocity  (ocean_time, z, y, x) float64 0.0 0.0 0.0 ... -0.02132 -0.02133\n",
       "    z_velocity  (ocean_time, z, y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-cb05a598-43a6-4ed7-a877-5082d8d86ee5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cb05a598-43a6-4ed7-a877-5082d8d86ee5' 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'>x</span>: 100</li><li><span class='xr-has-index'>y</span>: 300</li><li><span class='xr-has-index'>z</span>: 50</li><li><span>ocean_time</span>: 6</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4d9b0076-6162-4369-af7a-4719f213088f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4d9b0076-6162-4369-af7a-4719f213088f' class='xr-section-summary' >Coordinates: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>500.0 1.5e+03 ... 9.85e+04 9.95e+04</div><input id='attrs-cfea2aa1-d59c-493f-ba25-29a220b3cf48' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cfea2aa1-d59c-493f-ba25-29a220b3cf48' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a5df63b2-0ee3-40ab-8ae2-1ed8a0fcb451' class='xr-var-data-in' type='checkbox'><label for='data-a5df63b2-0ee3-40ab-8ae2-1ed8a0fcb451' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1 code_length</dd></dl></div><div class='xr-var-data'><pre>array([  500.,  1500.,  2500.,  3500.,  4500.,  5500.,  6500.,  7500.,  8500.,\n",
       "        9500., 10500., 11500., 12500., 13500., 14500., 15500., 16500., 17500.,\n",
       "       18500., 19500., 20500., 21500., 22500., 23500., 24500., 25500., 26500.,\n",
       "       27500., 28500., 29500., 30500., 31500., 32500., 33500., 34500., 35500.,\n",
       "       36500., 37500., 38500., 39500., 40500., 41500., 42500., 43500., 44500.,\n",
       "       45500., 46500., 47500., 48500., 49500., 50500., 51500., 52500., 53500.,\n",
       "       54500., 55500., 56500., 57500., 58500., 59500., 60500., 61500., 62500.,\n",
       "       63500., 64500., 65500., 66500., 67500., 68500., 69500., 70500., 71500.,\n",
       "       72500., 73500., 74500., 75500., 76500., 77500., 78500., 79500., 80500.,\n",
       "       81500., 82500., 83500., 84500., 85500., 86500., 87500., 88500., 89500.,\n",
       "       90500., 91500., 92500., 93500., 94500., 95500., 96500., 97500., 98500.,\n",
       "       99500.])</pre></div></li><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'>500.0 1.5e+03 ... 2.995e+05</div><input id='attrs-b049fd65-fb72-4b52-8a4d-49ceb4bd5978' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b049fd65-fb72-4b52-8a4d-49ceb4bd5978' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e68da52b-fe4d-407d-b0f0-56d7778253a1' class='xr-var-data-in' type='checkbox'><label for='data-e68da52b-fe4d-407d-b0f0-56d7778253a1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1 code_length</dd></dl></div><div class='xr-var-data'><pre>array([   500.,   1500.,   2500., ..., 297500., 298500., 299500.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-49.5 -48.5 -47.5 ... -1.5 -0.5</div><input id='attrs-98dbfa4c-07cb-42df-a3cd-d8f523e93cbd' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-98dbfa4c-07cb-42df-a3cd-d8f523e93cbd' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7a35f318-d7f3-4ffe-9849-1077bf81b143' class='xr-var-data-in' type='checkbox'><label for='data-7a35f318-d7f3-4ffe-9849-1077bf81b143' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1 code_length</dd></dl></div><div class='xr-var-data'><pre>array([-49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-82ddee01-a06e-430b-b2e9-9e150c5be393' class='xr-section-summary-in' type='checkbox'  checked><label for='section-82ddee01-a06e-430b-b2e9-9e150c5be393' 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>salt</span></div><div class='xr-var-dims'>(ocean_time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>35.0 35.0 35.0 ... 73.46 73.46</div><input id='attrs-8b8af355-cea0-44e2-b294-593a0ba67b8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8b8af355-cea0-44e2-b294-593a0ba67b8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-19500e4f-a2b9-4158-9a4d-b358f2b6ba95' class='xr-var-data-in' type='checkbox'><label for='data-19500e4f-a2b9-4158-9a4d-b358f2b6ba95' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1 dimensionless</dd></dl></div><div class='xr-var-data'><pre>array([[[[35.        , 35.        , 35.        , ..., 35.        ,\n",
       "          35.        , 35.        ],\n",
       "         [35.13      , 35.13      , 35.13      , ..., 35.13      ,\n",
       "          35.13      , 35.13      ],\n",
       "         [35.26      , 35.26      , 35.26      , ..., 35.26      ,\n",
       "          35.26      , 35.26      ],\n",
       "         ...,\n",
       "         [73.61      , 73.61      , 73.61      , ..., 73.61      ,\n",
       "          73.61      , 73.61      ],\n",
       "         [73.74      , 73.74      , 73.74      , ..., 73.74      ,\n",
       "          73.74      , 73.74      ],\n",
       "         [73.87      , 73.87      , 73.87      , ..., 73.87      ,\n",
       "          73.87      , 73.87      ]],\n",
       "\n",
       "        [[35.        , 35.        , 35.        , ..., 35.        ,\n",
       "          35.        , 35.        ],\n",
       "         [35.13      , 35.13      , 35.13      , ..., 35.13      ,\n",
       "          35.13      , 35.13      ],\n",
       "         [35.26      , 35.26      , 35.26      , ..., 35.26      ,\n",
       "          35.26      , 35.26      ],\n",
       "...\n",
       "         [73.09743176, 73.09743373, 73.09743063, ..., 73.09742858,\n",
       "          73.09742997, 73.09743081],\n",
       "         [73.23330025, 73.23331192, 73.2333156 , ..., 73.23325547,\n",
       "          73.23327206, 73.23328711],\n",
       "         [73.46464408, 73.46464388, 73.46464385, ..., 73.46463048,\n",
       "          73.46463601, 73.46464124]],\n",
       "\n",
       "        [[34.97670127, 34.97670088, 34.97670137, ..., 34.97670283,\n",
       "          34.97670061, 34.97670159],\n",
       "         [35.01107814, 35.01108093, 35.01108138, ..., 35.01108109,\n",
       "          35.01107776, 35.01107657],\n",
       "         [35.05368455, 35.05368112, 35.05367843, ..., 35.05368078,\n",
       "          35.0536827 , 35.05368483],\n",
       "         ...,\n",
       "         [73.08891981, 73.08892058, 73.08891801, ..., 73.08891499,\n",
       "          73.08891695, 73.08891853],\n",
       "         [73.21718833, 73.21719801, 73.21720042, ..., 73.21714966,\n",
       "          73.21716522, 73.21717822],\n",
       "         [73.45570194, 73.45570201, 73.45570025, ..., 73.45569085,\n",
       "          73.45569537, 73.45569925]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>temp</span></div><div class='xr-var-dims'>(ocean_time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.126 9.125 9.127 ... 13.99 13.99</div><input id='attrs-17f977ba-5fe8-4423-ab73-bc9fe8b97060' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-17f977ba-5fe8-4423-ab73-bc9fe8b97060' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-897443da-7b05-4441-8b75-dc14024d11a0' class='xr-var-data-in' type='checkbox'><label for='data-897443da-7b05-4441-8b75-dc14024d11a0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1 dimensionless</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 9.12586442,  9.12512004,  9.12710291, ...,  9.12405713,\n",
       "           9.12668576,  9.12758913],\n",
       "         [ 9.12525949,  9.1259654 ,  9.12652643, ...,  9.12563375,\n",
       "           9.12544853,  9.12400714],\n",
       "         [ 9.1250576 ,  9.123775  ,  9.12334466, ...,  9.12562139,\n",
       "           9.12611245,  9.12456075],\n",
       "         ...,\n",
       "         [ 9.12532108,  9.12341063,  9.12594379, ...,  9.12580152,\n",
       "           9.12463728,  9.12493619],\n",
       "         [ 9.12340884,  9.12422185,  9.12415466, ...,  9.1259687 ,\n",
       "           9.12573651,  9.12551937],\n",
       "         [ 9.12564603,  9.1261783 ,  9.12333879, ...,  9.12621996,\n",
       "           9.12608609,  9.12492365]],\n",
       "\n",
       "        [[ 9.36723184,  9.36653174,  9.36839663, ...,  9.36553207,\n",
       "           9.36800431,  9.36885393],\n",
       "         [ 9.36666289,  9.36732681,  9.36785445, ...,  9.36701488,\n",
       "           9.36684069,  9.36548506],\n",
       "         [ 9.36647302,  9.36526673,  9.36486199, ...,  9.36700327,\n",
       "           9.36746511,  9.36600572],\n",
       "...\n",
       "         [13.95823474, 13.95823285, 13.95823937, ..., 13.95822001,\n",
       "          13.95822316, 13.95822931],\n",
       "         [13.94164781, 13.94164411, 13.94164288, ..., 13.94166443,\n",
       "          13.94166261, 13.94165719],\n",
       "         [13.97950948, 13.97951047, 13.97950796, ..., 13.97951243,\n",
       "          13.97950997, 13.97950825]],\n",
       "\n",
       "        [[13.95990564, 13.95990074, 13.95991374, ..., 13.95990692,\n",
       "          13.95990793, 13.95991592],\n",
       "         [13.9558444 , 13.95585316, 13.95585153, ..., 13.95583125,\n",
       "          13.95582521, 13.95582871],\n",
       "         [13.94494039, 13.94493238, 13.9449256 , ..., 13.94493552,\n",
       "          13.94493375, 13.94493628],\n",
       "         ...,\n",
       "         [13.98548237, 13.98548189, 13.98548348, ..., 13.98547869,\n",
       "          13.9854795 , 13.98548103],\n",
       "         [13.98095925, 13.9809582 , 13.98095792, ..., 13.98096387,\n",
       "          13.98096333, 13.98096183],\n",
       "         [13.99027331, 13.99027354, 13.99027297, ..., 13.99027412,\n",
       "          13.99027352, 13.99027306]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_velocity</span></div><div class='xr-var-dims'>(ocean_time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... -0.317 -0.317</div><input id='attrs-a1848912-7b00-4aba-80cc-0c9d87c63499' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a1848912-7b00-4aba-80cc-0c9d87c63499' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9ca7db8a-5e3e-4016-a0d5-da8d68ec5042' class='xr-var-data-in' type='checkbox'><label for='data-9ca7db8a-5e3e-4016-a0d5-da8d68ec5042' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1 cm/s</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         ...,\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ]],\n",
       "\n",
       "        [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "...\n",
       "         [-0.39276602, -0.39276527, -0.39276663, ..., -0.39278601,\n",
       "          -0.39277633, -0.39276524],\n",
       "         [-0.3961425 , -0.39614358, -0.39614828, ..., -0.39616554,\n",
       "          -0.39615703, -0.39614862],\n",
       "         [-0.3097785 , -0.30978645, -0.30979168, ..., -0.30977789,\n",
       "          -0.30977961, -0.30977883]],\n",
       "\n",
       "        [[-0.03782539, -0.03782817, -0.03783398, ..., -0.03782832,\n",
       "          -0.03783066, -0.03782542],\n",
       "         [-0.08618159, -0.08618595, -0.08619018, ..., -0.08619951,\n",
       "          -0.08619534, -0.08618393],\n",
       "         [-0.15232616, -0.15234343, -0.15235503, ..., -0.15235055,\n",
       "          -0.15234115, -0.15232637],\n",
       "         ...,\n",
       "         [-0.39920876, -0.39920823, -0.39920932, ..., -0.39922941,\n",
       "          -0.39921941, -0.3992081 ],\n",
       "         [-0.40887528, -0.40887766, -0.408883  , ..., -0.40889364,\n",
       "          -0.40888578, -0.40887926],\n",
       "         [-0.31700485, -0.31701312, -0.31701885, ..., -0.31700222,\n",
       "          -0.31700467, -0.31700478]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_velocity</span></div><div class='xr-var-dims'>(ocean_time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... -0.02132 -0.02133</div><input id='attrs-b78c03c4-7f5a-4c4b-aa5a-c6e7f06a229d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b78c03c4-7f5a-4c4b-aa5a-c6e7f06a229d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b0d60727-f4f6-4405-acb8-89db9d48256a' class='xr-var-data-in' type='checkbox'><label for='data-b0d60727-f4f6-4405-acb8-89db9d48256a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1 cm/s</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         ...,\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ]],\n",
       "\n",
       "        [[ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "         [ 0.        ,  0.        ,  0.        , ...,  0.        ,\n",
       "           0.        ,  0.        ],\n",
       "...\n",
       "         [-0.08003022, -0.08002978, -0.08003286, ..., -0.08007516,\n",
       "          -0.08005761, -0.08004618],\n",
       "         [-0.0618961 , -0.06190985, -0.06193307, ..., -0.06187765,\n",
       "          -0.06187376, -0.0618812 ],\n",
       "         [-0.02248756, -0.02249804, -0.02250647, ..., -0.02246363,\n",
       "          -0.02246946, -0.02247781]],\n",
       "\n",
       "        [[-0.02538424, -0.02538407, -0.02538442, ..., -0.02538758,\n",
       "          -0.02539088, -0.0253877 ],\n",
       "         [-0.10201933, -0.10201884, -0.10202855, ..., -0.10204985,\n",
       "          -0.10204407, -0.10203066],\n",
       "         [-0.18430195, -0.1843024 , -0.18430803, ..., -0.18432488,\n",
       "          -0.18431468, -0.18430327],\n",
       "         ...,\n",
       "         [-0.08148425, -0.08148376, -0.08148635, ..., -0.08152898,\n",
       "          -0.08151159, -0.08150023],\n",
       "         [-0.06200173, -0.06201525, -0.06203733, ..., -0.06198305,\n",
       "          -0.06197939, -0.06198698],\n",
       "         [-0.02133737, -0.02134767, -0.02135556, ..., -0.02131344,\n",
       "          -0.02131934, -0.0213277 ]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>z_velocity</span></div><div class='xr-var-dims'>(ocean_time, z, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0 0.0</div><input id='attrs-6183cb8a-6aa0-49f2-8784-8eaea30f4747' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6183cb8a-6aa0-49f2-8784-8eaea30f4747' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1e8cd5ad-65ee-48f8-a056-1356eebd5b3b' class='xr-var-data-in' type='checkbox'><label for='data-1e8cd5ad-65ee-48f8-a056-1356eebd5b3b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>1 cm/s</dd></dl></div><div class='xr-var-data'><pre>array([[[[0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         ...,\n",
       "...\n",
       "         ...,\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.]],\n",
       "\n",
       "        [[0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         ...,\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.],\n",
       "         [0., 0., 0., ..., 0., 0., 0.]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5ed16b41-ae1d-4cfc-9019-07722e56bb3f' class='xr-section-summary-in' type='checkbox'  ><label for='section-5ed16b41-ae1d-4cfc-9019-07722e56bb3f' class='xr-section-summary' >Indexes: <span>(3)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e3692637-c7d6-4077-9931-deca18494de3' class='xr-index-data-in' type='checkbox'/><label for='index-e3692637-c7d6-4077-9931-deca18494de3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([  500.0,  1500.0,  2500.0,  3500.0,  4500.0,  5500.0,  6500.0,  7500.0,\n",
       "        8500.0,  9500.0, 10500.0, 11500.0, 12500.0, 13500.0, 14500.0, 15500.0,\n",
       "       16500.0, 17500.0, 18500.0, 19500.0, 20500.0, 21500.0, 22500.0, 23500.0,\n",
       "       24500.0, 25500.0, 26500.0, 27500.0, 28500.0, 29500.0, 30500.0, 31500.0,\n",
       "       32500.0, 33500.0, 34500.0, 35500.0, 36500.0, 37500.0, 38500.0, 39500.0,\n",
       "       40500.0, 41500.0, 42500.0, 43500.0, 44500.0, 45500.0, 46500.0, 47500.0,\n",
       "       48500.0, 49500.0, 50500.0, 51500.0, 52500.0, 53500.0, 54500.0, 55500.0,\n",
       "       56500.0, 57500.0, 58500.0, 59500.0, 60500.0, 61500.0, 62500.0, 63500.0,\n",
       "       64500.0, 65500.0, 66500.0, 67500.0, 68500.0, 69500.0, 70500.0, 71500.0,\n",
       "       72500.0, 73500.0, 74500.0, 75500.0, 76500.0, 77500.0, 78500.0, 79500.0,\n",
       "       80500.0, 81500.0, 82500.0, 83500.0, 84500.0, 85500.0, 86500.0, 87500.0,\n",
       "       88500.0, 89500.0, 90500.0, 91500.0, 92500.0, 93500.0, 94500.0, 95500.0,\n",
       "       96500.0, 97500.0, 98500.0, 99500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-de83c635-5ad3-4f9f-9945-1674956abf54' class='xr-index-data-in' type='checkbox'/><label for='index-de83c635-5ad3-4f9f-9945-1674956abf54' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([   500.0,   1500.0,   2500.0,   3500.0,   4500.0,   5500.0,   6500.0,\n",
       "         7500.0,   8500.0,   9500.0,\n",
       "       ...\n",
       "       290500.0, 291500.0, 292500.0, 293500.0, 294500.0, 295500.0, 296500.0,\n",
       "       297500.0, 298500.0, 299500.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;, length=300))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-77a4e4f9-1946-4965-bcfa-c79899c01935' class='xr-index-data-in' type='checkbox'/><label for='index-77a4e4f9-1946-4965-bcfa-c79899c01935' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-49.5, -48.5, -47.5, -46.5, -45.5, -44.5, -43.5, -42.5, -41.5, -40.5,\n",
       "       -39.5, -38.5, -37.5, -36.5, -35.5, -34.5, -33.5, -32.5, -31.5, -30.5,\n",
       "       -29.5, -28.5, -27.5, -26.5, -25.5, -24.5, -23.5, -22.5, -21.5, -20.5,\n",
       "       -19.5, -18.5, -17.5, -16.5, -15.5, -14.5, -13.5, -12.5, -11.5, -10.5,\n",
       "        -9.5,  -8.5,  -7.5,  -6.5,  -5.5,  -4.5,  -3.5,  -2.5,  -1.5,  -0.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;z&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cdb1812e-f5b5-4340-92aa-101fa59a0089' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-cdb1812e-f5b5-4340-92aa-101fa59a0089' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:     (x: 100, y: 300, z: 50, ocean_time: 6)\n",
       "Coordinates:\n",
       "  * x           (x) float64 500.0 1.5e+03 2.5e+03 ... 9.75e+04 9.85e+04 9.95e+04\n",
       "  * y           (y) float64 500.0 1.5e+03 2.5e+03 ... 2.985e+05 2.995e+05\n",
       "  * z           (z) float64 -49.5 -48.5 -47.5 -46.5 ... -3.5 -2.5 -1.5 -0.5\n",
       "Dimensions without coordinates: ocean_time\n",
       "Data variables:\n",
       "    salt        (ocean_time, z, y, x) float64 35.0 35.0 35.0 ... 73.46 73.46\n",
       "    temp        (ocean_time, z, y, x) float64 9.126 9.125 9.127 ... 13.99 13.99\n",
       "    x_velocity  (ocean_time, z, y, x) float64 0.0 0.0 0.0 ... -0.317 -0.317\n",
       "    y_velocity  (ocean_time, z, y, x) float64 0.0 0.0 0.0 ... -0.02132 -0.02133\n",
       "    z_velocity  (ocean_time, z, y, x) float64 0.0 0.0 0.0 0.0 ... 0.0 0.0 0.0"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bf84e4b7-6408-46d3-a4b5-4a8118d97754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ds['salt'].isel(ocean_time=-1, z=-1).plot(ax=ax)\n",
    "ax.set_aspect(1.0)"
   ]
  },
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