scalar_FD_roll.ipynb

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    "import numpy as np #note that periodic BCs have yet to be added in\n",
    "import xarray as xr\n",
    "import fsspec\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pyqg \n",
    "import pyqg.diagnostic_tools as tools\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline"
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      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
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     "text": [
      "Exception ignored in: 'pyfftw.pyfftw._fftw_plan_with_nthreads_null'\n",
      "Traceback (most recent call last):\n",
      "  File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/pyqg/model.py\", line 216, in __init__\n",
      "    PseudoSpectralKernel.__init__(self, nz, ny, nx, ntd,\n",
      "RuntimeError: Undefined plan with nthreads. This is a bug\n"
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      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
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      "Exception ignored in: 'pyfftw.pyfftw._fftw_plan_with_nthreads_null'\n",
      "Traceback (most recent call last):\n",
      "  File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/pyqg/model.py\", line 216, in __init__\n",
      "    PseudoSpectralKernel.__init__(self, nz, ny, nx, ntd,\n",
      "RuntimeError: Undefined plan with nthreads. This is a bug\n"
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;31mRuntimeError\u001b[0m: Undefined plan with nthreads. This is a bug"
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      "Exception ignored in: 'pyfftw.pyfftw._fftw_plan_with_nthreads_null'\n",
      "Traceback (most recent call last):\n",
      "  File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/pyqg/model.py\", line 216, in __init__\n",
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      "RuntimeError: Undefined plan with nthreads. This is a bug\n"
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      "Exception ignored in: 'pyfftw.pyfftw._fftw_plan_with_nthreads_null'\n",
      "Traceback (most recent call last):\n",
      "  File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/pyqg/model.py\", line 216, in __init__\n",
      "    PseudoSpectralKernel.__init__(self, nz, ny, nx, ntd,\n",
      "RuntimeError: Undefined plan with nthreads. This is a bug\n"
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      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
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     "text": [
      "Exception ignored in: 'pyfftw.pyfftw._fftw_plan_with_nthreads_null'\n",
      "Traceback (most recent call last):\n",
      "  File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/pyqg/model.py\", line 216, in __init__\n",
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      "RuntimeError: Undefined plan with nthreads. This is a bug\n"
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      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
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     "text": [
      "Exception ignored in: 'pyfftw.pyfftw._fftw_plan_with_nthreads_null'\n",
      "Traceback (most recent call last):\n",
      "  File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/pyqg/model.py\", line 216, in __init__\n",
      "    PseudoSpectralKernel.__init__(self, nz, ny, nx, ntd,\n",
      "RuntimeError: Undefined plan with nthreads. This is a bug\n"
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      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
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      "Exception ignored in: 'pyfftw.pyfftw._fftw_plan_with_nthreads_null'\n",
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      "    PseudoSpectralKernel.__init__(self, nz, ny, nx, ntd,\n",
      "RuntimeError: Undefined plan with nthreads. This is a bug\n"
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      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;31mRuntimeError\u001b[0m: Undefined plan with nthreads. This is a bug"
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     "text": [
      "Exception ignored in: 'pyfftw.pyfftw._fftw_plan_with_nthreads_null'\n",
      "Traceback (most recent call last):\n",
      "  File \"/srv/conda/envs/notebook/lib/python3.10/site-packages/pyqg/model.py\", line 216, in __init__\n",
      "    PseudoSpectralKernel.__init__(self, nz, ny, nx, ntd,\n",
      "RuntimeError: Undefined plan with nthreads. This is a bug\n",
      "INFO:  Logger initialized\n"
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    "eddy_model = pyqg.QGModel(nx=64, dt=3600.0, tmax=311040000.0, twrite=10000, tavestart=155520000.0) #defaults match up to Ross paper \n",
    "#twrite : Interval for cfl writeout (units: number of timesteps)\n",
    "#tavestart : Start time for averaging (units: model time)"
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      "INFO: Step: 10000, Time: 3.60e+07, KE: 1.96e-05, CFL: 0.009\n",
      "INFO: Step: 20000, Time: 7.20e+07, KE: 4.81e-04, CFL: 0.045\n",
      "INFO: Step: 30000, Time: 1.08e+08, KE: 5.06e-04, CFL: 0.045\n",
      "INFO: Step: 40000, Time: 1.44e+08, KE: 4.84e-04, CFL: 0.045\n",
      "INFO: Step: 50000, Time: 1.80e+08, KE: 4.61e-04, CFL: 0.043\n",
      "INFO: Step: 60000, Time: 2.16e+08, KE: 4.65e-04, CFL: 0.063\n",
      "INFO: Step: 70000, Time: 2.52e+08, KE: 4.64e-04, CFL: 0.047\n",
      "INFO: Step: 80000, Time: 2.88e+08, KE: 4.92e-04, CFL: 0.047\n"
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       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (time: 87, lev: 2, y: 64, x: 64, l: 64, k: 33, lev_mid: 1)\n",
       "Coordinates:\n",
       "  * time               (time) float64 0.0 3.6e+06 7.2e+06 ... 3.06e+08 3.096e+08\n",
       "  * lev                (lev) int64 1 2\n",
       "  * lev_mid            (lev_mid) float64 1.5\n",
       "  * x                  (x) float64 7.812e+03 2.344e+04 ... 9.766e+05 9.922e+05\n",
       "  * y                  (y) float64 7.812e+03 2.344e+04 ... 9.766e+05 9.922e+05\n",
       "  * l                  (l) float64 0.0 6.283e-06 ... -1.257e-05 -6.283e-06\n",
       "  * k                  (k) float64 0.0 6.283e-06 ... 0.0001948 0.0002011\n",
       "Data variables: (12/32)\n",
       "    q                  (time, lev, y, x) float64 8.821e-07 ... -1.423e-06\n",
       "    u                  (time, lev, y, x) float64 0.0 0.0 ... -0.00861 -0.007082\n",
       "    v                  (time, lev, y, x) float64 0.0 0.0 ... -0.002291 -0.0035\n",
       "    ufull              (time, lev, y, x) float64 0.025 0.025 ... -0.007082\n",
       "    vfull              (time, lev, y, x) float64 0.0 0.0 ... -0.002291 -0.0035\n",
       "    qh                 (time, lev, l, k) complex128 (0.002521111233473065+0j)...\n",
       "    ...                 ...\n",
       "    ENSgenspec         (time, l, k) float64 nan nan nan ... -7.318e-52 9.523e-63\n",
       "    ENSfrictionspec    (time, l, k) float64 nan nan ... -5.582e-51 -2.619e-60\n",
       "    APEgenspec         (time, l, k) float64 nan nan nan ... -1.647e-43 2.143e-54\n",
       "    APEflux            (time, l, k) float64 nan nan nan ... 6.009e-29 1.176e-33\n",
       "    KEflux             (time, l, k) float64 nan nan nan ... 2.422e-27 4.291e-32\n",
       "    APEgen             (time) float64 nan nan nan ... 6.475e-11 6.456e-11\n",
       "Attributes: (12/23)\n",
       "    pyqg:beta:       1.5e-11\n",
       "    pyqg:delta:      0.25\n",
       "    pyqg:del2:       0.8\n",
       "    pyqg:dt:         3600.0\n",
       "    pyqg:filterfac:  23.6\n",
       "    pyqg:L:          1000000.0\n",
       "    ...              ...\n",
       "    pyqg:tc:         0\n",
       "    pyqg:tmax:       311040000.0\n",
       "    pyqg:twrite:     10000\n",
       "    pyqg:W:          1000000.0\n",
       "    title:           pyqg: Python Quasigeostrophic Model\n",
       "    reference:       https://pyqg.readthedocs.io/en/latest/index.html</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-a4903de1-5821-4c3d-814f-5d1d9fd4b2be' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a4903de1-5821-4c3d-814f-5d1d9fd4b2be' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 87</li><li><span class='xr-has-index'>lev</span>: 2</li><li><span class='xr-has-index'>y</span>: 64</li><li><span class='xr-has-index'>x</span>: 64</li><li><span class='xr-has-index'>l</span>: 64</li><li><span class='xr-has-index'>k</span>: 33</li><li><span class='xr-has-index'>lev_mid</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-7ecb6e4e-0ee6-40c4-9da7-7832c9cafc92' class='xr-section-summary-in' type='checkbox'  checked><label for='section-7ecb6e4e-0ee6-40c4-9da7-7832c9cafc92' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.6e+06 ... 3.06e+08 3.096e+08</div><input id='attrs-d6b3ee1e-35e8-4f34-8e0f-2afc4ae743e7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d6b3ee1e-35e8-4f34-8e0f-2afc4ae743e7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0404f6b-a579-4a70-9376-cb125b65a9ee' class='xr-var-data-in' type='checkbox'><label for='data-a0404f6b-a579-4a70-9376-cb125b65a9ee' title='Show/Hide data repr'><svg 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>model time</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>array([0.000e+00, 3.600e+06, 7.200e+06, 1.080e+07, 1.440e+07, 1.800e+07,\n",
       "       2.160e+07, 2.520e+07, 2.880e+07, 3.240e+07, 3.600e+07, 3.960e+07,\n",
       "       4.320e+07, 4.680e+07, 5.040e+07, 5.400e+07, 5.760e+07, 6.120e+07,\n",
       "       6.480e+07, 6.840e+07, 7.200e+07, 7.560e+07, 7.920e+07, 8.280e+07,\n",
       "       8.640e+07, 9.000e+07, 9.360e+07, 9.720e+07, 1.008e+08, 1.044e+08,\n",
       "       1.080e+08, 1.116e+08, 1.152e+08, 1.188e+08, 1.224e+08, 1.260e+08,\n",
       "       1.296e+08, 1.332e+08, 1.368e+08, 1.404e+08, 1.440e+08, 1.476e+08,\n",
       "       1.512e+08, 1.548e+08, 1.584e+08, 1.620e+08, 1.656e+08, 1.692e+08,\n",
       "       1.728e+08, 1.764e+08, 1.800e+08, 1.836e+08, 1.872e+08, 1.908e+08,\n",
       "       1.944e+08, 1.980e+08, 2.016e+08, 2.052e+08, 2.088e+08, 2.124e+08,\n",
       "       2.160e+08, 2.196e+08, 2.232e+08, 2.268e+08, 2.304e+08, 2.340e+08,\n",
       "       2.376e+08, 2.412e+08, 2.448e+08, 2.484e+08, 2.520e+08, 2.556e+08,\n",
       "       2.592e+08, 2.628e+08, 2.664e+08, 2.700e+08, 2.736e+08, 2.772e+08,\n",
       "       2.808e+08, 2.844e+08, 2.880e+08, 2.916e+08, 2.952e+08, 2.988e+08,\n",
       "       3.024e+08, 3.060e+08, 3.096e+08])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2</div><input id='attrs-def4fa87-4189-4113-971f-623225cdf5f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-def4fa87-4189-4113-971f-623225cdf5f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-702cf63b-4ea6-4cd7-a10b-d04b06bb2ffb' class='xr-var-data-in' type='checkbox'><label for='data-702cf63b-4ea6-4cd7-a10b-d04b06bb2ffb' title='Show/Hide data repr'><svg 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>vertical levels</dd></dl></div><div class='xr-var-data'><pre>array([1, 2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev_mid</span></div><div class='xr-var-dims'>(lev_mid)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5</div><input id='attrs-4b22665b-0f22-4ecc-a000-0e4fb52e4307' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b22665b-0f22-4ecc-a000-0e4fb52e4307' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-919c62f1-ecd4-4b70-8327-d912e924b2da' class='xr-var-data-in' type='checkbox'><label for='data-919c62f1-ecd4-4b70-8327-d912e924b2da' title='Show/Hide data repr'><svg 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>vertical level interface</dd></dl></div><div class='xr-var-data'><pre>array([1.5])</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'>7.812e+03 2.344e+04 ... 9.922e+05</div><input id='attrs-003026b9-18cc-4e9b-843b-c9a585e23a39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-003026b9-18cc-4e9b-843b-c9a585e23a39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a54fc98d-d401-4d1f-9328-ab865241b2ff' class='xr-var-data-in' type='checkbox'><label for='data-a54fc98d-d401-4d1f-9328-ab865241b2ff' title='Show/Hide data repr'><svg 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>real space grid points in the x direction</dd><dt><span>units :</span></dt><dd>grid point</dd></dl></div><div class='xr-var-data'><pre>array([  7812.5,  23437.5,  39062.5,  54687.5,  70312.5,  85937.5, 101562.5,\n",
       "       117187.5, 132812.5, 148437.5, 164062.5, 179687.5, 195312.5, 210937.5,\n",
       "       226562.5, 242187.5, 257812.5, 273437.5, 289062.5, 304687.5, 320312.5,\n",
       "       335937.5, 351562.5, 367187.5, 382812.5, 398437.5, 414062.5, 429687.5,\n",
       "       445312.5, 460937.5, 476562.5, 492187.5, 507812.5, 523437.5, 539062.5,\n",
       "       554687.5, 570312.5, 585937.5, 601562.5, 617187.5, 632812.5, 648437.5,\n",
       "       664062.5, 679687.5, 695312.5, 710937.5, 726562.5, 742187.5, 757812.5,\n",
       "       773437.5, 789062.5, 804687.5, 820312.5, 835937.5, 851562.5, 867187.5,\n",
       "       882812.5, 898437.5, 914062.5, 929687.5, 945312.5, 960937.5, 976562.5,\n",
       "       992187.5])</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'>7.812e+03 2.344e+04 ... 9.922e+05</div><input id='attrs-a624b199-bd8d-4296-815f-6861e1d27a80' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a624b199-bd8d-4296-815f-6861e1d27a80' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c636543-75ff-4217-9fd3-5d5cfab28374' class='xr-var-data-in' type='checkbox'><label for='data-9c636543-75ff-4217-9fd3-5d5cfab28374' title='Show/Hide data repr'><svg 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>real space grid points in the y direction</dd><dt><span>units :</span></dt><dd>grid point</dd></dl></div><div class='xr-var-data'><pre>array([  7812.5,  23437.5,  39062.5,  54687.5,  70312.5,  85937.5, 101562.5,\n",
       "       117187.5, 132812.5, 148437.5, 164062.5, 179687.5, 195312.5, 210937.5,\n",
       "       226562.5, 242187.5, 257812.5, 273437.5, 289062.5, 304687.5, 320312.5,\n",
       "       335937.5, 351562.5, 367187.5, 382812.5, 398437.5, 414062.5, 429687.5,\n",
       "       445312.5, 460937.5, 476562.5, 492187.5, 507812.5, 523437.5, 539062.5,\n",
       "       554687.5, 570312.5, 585937.5, 601562.5, 617187.5, 632812.5, 648437.5,\n",
       "       664062.5, 679687.5, 695312.5, 710937.5, 726562.5, 742187.5, 757812.5,\n",
       "       773437.5, 789062.5, 804687.5, 820312.5, 835937.5, 851562.5, 867187.5,\n",
       "       882812.5, 898437.5, 914062.5, 929687.5, 945312.5, 960937.5, 976562.5,\n",
       "       992187.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>l</span></div><div class='xr-var-dims'>(l)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 6.283e-06 ... -6.283e-06</div><input id='attrs-fb84670e-268c-4938-acfb-b41debc303c6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb84670e-268c-4938-acfb-b41debc303c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b7c8d155-8f56-4314-8822-8523f2b99dd4' class='xr-var-data-in' type='checkbox'><label for='data-b7c8d155-8f56-4314-8822-8523f2b99dd4' title='Show/Hide data repr'><svg 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>spectal space grid points in the l direction</dd><dt><span>units :</span></dt><dd>meridional wavenumber</dd></dl></div><div class='xr-var-data'><pre>array([ 0.000000e+00,  6.283185e-06,  1.256637e-05,  1.884956e-05,\n",
       "        2.513274e-05,  3.141593e-05,  3.769911e-05,  4.398230e-05,\n",
       "        5.026548e-05,  5.654867e-05,  6.283185e-05,  6.911504e-05,\n",
       "        7.539822e-05,  8.168141e-05,  8.796459e-05,  9.424778e-05,\n",
       "        1.005310e-04,  1.068142e-04,  1.130973e-04,  1.193805e-04,\n",
       "        1.256637e-04,  1.319469e-04,  1.382301e-04,  1.445133e-04,\n",
       "        1.507964e-04,  1.570796e-04,  1.633628e-04,  1.696460e-04,\n",
       "        1.759292e-04,  1.822124e-04,  1.884956e-04,  1.947787e-04,\n",
       "       -2.010619e-04, -1.947787e-04, -1.884956e-04, -1.822124e-04,\n",
       "       -1.759292e-04, -1.696460e-04, -1.633628e-04, -1.570796e-04,\n",
       "       -1.507964e-04, -1.445133e-04, -1.382301e-04, -1.319469e-04,\n",
       "       -1.256637e-04, -1.193805e-04, -1.130973e-04, -1.068142e-04,\n",
       "       -1.005310e-04, -9.424778e-05, -8.796459e-05, -8.168141e-05,\n",
       "       -7.539822e-05, -6.911504e-05, -6.283185e-05, -5.654867e-05,\n",
       "       -5.026548e-05, -4.398230e-05, -3.769911e-05, -3.141593e-05,\n",
       "       -2.513274e-05, -1.884956e-05, -1.256637e-05, -6.283185e-06])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 6.283e-06 ... 0.0002011</div><input id='attrs-b7325bf2-d0f5-4e7e-a9bb-66cd2894930d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b7325bf2-d0f5-4e7e-a9bb-66cd2894930d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80c0007b-633e-411c-b40a-fa6d7851b264' class='xr-var-data-in' type='checkbox'><label for='data-80c0007b-633e-411c-b40a-fa6d7851b264' title='Show/Hide data repr'><svg 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>spectal space grid points in the k direction</dd><dt><span>units :</span></dt><dd>zonal wavenumber</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 6.283185e-06, 1.256637e-05, 1.884956e-05, 2.513274e-05,\n",
       "       3.141593e-05, 3.769911e-05, 4.398230e-05, 5.026548e-05, 5.654867e-05,\n",
       "       6.283185e-05, 6.911504e-05, 7.539822e-05, 8.168141e-05, 8.796459e-05,\n",
       "       9.424778e-05, 1.005310e-04, 1.068142e-04, 1.130973e-04, 1.193805e-04,\n",
       "       1.256637e-04, 1.319469e-04, 1.382301e-04, 1.445133e-04, 1.507964e-04,\n",
       "       1.570796e-04, 1.633628e-04, 1.696460e-04, 1.759292e-04, 1.822124e-04,\n",
       "       1.884956e-04, 1.947787e-04, 2.010619e-04])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-cf668e41-736f-4cf6-a14c-9db0df1ef890' class='xr-section-summary-in' type='checkbox'  ><label for='section-cf668e41-736f-4cf6-a14c-9db0df1ef890' class='xr-section-summary' >Data variables: <span>(32)</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>q</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.821e-07 6.087e-08 ... -1.423e-06</div><input id='attrs-bfa63084-a853-42b8-bd2a-68bb61c385f2' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bfa63084-a853-42b8-bd2a-68bb61c385f2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab65730d-ba70-448e-8fea-15642769fbca' class='xr-var-data-in' type='checkbox'><label for='data-ab65730d-ba70-448e-8fea-15642769fbca' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s^-1</dd><dt><span>long_name :</span></dt><dd>potential vorticity in real space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 8.82129184e-07,  6.08666452e-08,  8.78161281e-07, ...,\n",
       "           4.97510154e-07,  3.84435847e-07,  7.89969641e-07],\n",
       "         [ 8.20778447e-07,  1.11493553e-07,  8.64111991e-07, ...,\n",
       "           5.88518893e-07,  3.73938753e-07,  7.70048730e-07],\n",
       "         [ 8.93383458e-07,  8.07749308e-08,  9.22354864e-07, ...,\n",
       "           5.09415125e-07,  4.23016328e-07,  8.21399276e-07],\n",
       "         ...,\n",
       "         [ 8.61119575e-07,  1.25312871e-07,  8.42051438e-07, ...,\n",
       "           5.33748516e-07,  4.19951605e-07,  7.57653504e-07],\n",
       "         [ 8.97413390e-07,  1.08929865e-07,  9.31279983e-07, ...,\n",
       "           5.88661423e-07,  3.51254147e-07,  7.95860285e-07],\n",
       "         [ 8.86867453e-07,  5.59440931e-08,  9.25945327e-07, ...,\n",
       "           5.18323545e-07,  3.40303537e-07,  8.09196040e-07]],\n",
       "\n",
       "        [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "...\n",
       "         [-3.09260364e-06,  1.31401890e-06,  7.13990688e-06, ...,\n",
       "          -2.61036902e-06, -6.11170848e-07, -2.51023941e-06],\n",
       "         [ 7.57303675e-06,  8.67724242e-06,  7.77813792e-06, ...,\n",
       "          -1.99699141e-06,  4.23804772e-06,  6.49568592e-06],\n",
       "         [ 9.55819073e-06,  1.26958483e-05,  1.05982465e-05, ...,\n",
       "          -7.52835497e-07,  3.00759536e-06,  5.87060110e-06]],\n",
       "\n",
       "        [[-2.18351790e-06, -2.48254225e-06, -2.02631999e-06, ...,\n",
       "          -4.72763777e-07, -1.01070912e-06, -1.60998634e-06],\n",
       "         [-2.06028272e-06, -2.25576031e-06, -1.71953288e-06, ...,\n",
       "          -4.96964131e-07, -8.99667700e-07, -1.53249627e-06],\n",
       "         [-1.42222080e-06, -1.50975155e-06, -1.35612539e-06, ...,\n",
       "          -9.09250676e-07, -1.19288051e-06, -1.42640292e-06],\n",
       "         ...,\n",
       "         [-6.28368134e-08, -1.22403339e-06, -1.33797138e-06, ...,\n",
       "           1.68587510e-06,  1.63246843e-06,  1.17917013e-06],\n",
       "         [-1.12845122e-06, -1.63389105e-06, -1.49744787e-06, ...,\n",
       "           6.61400859e-07,  1.84182643e-07, -3.79886413e-07],\n",
       "         [-1.82541802e-06, -2.13354694e-06, -1.95647333e-06, ...,\n",
       "          -4.83189501e-07, -1.02682414e-06, -1.42278479e-06]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>u</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... -0.00861 -0.007082</div><input id='attrs-7176b8c9-503e-4ae7-b3d2-52cc0bbd491b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7176b8c9-503e-4ae7-b3d2-52cc0bbd491b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-35c2a524-264b-46bc-a5da-53aadf2bc23d' class='xr-var-data-in' type='checkbox'><label for='data-35c2a524-264b-46bc-a5da-53aadf2bc23d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>zonal velocity anomaly</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         ...,\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],\n",
       "\n",
       "        [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "...\n",
       "         [ 8.71630153e-02,  8.25180806e-02,  3.81059551e-02, ...,\n",
       "          -9.20937611e-03,  1.70818715e-02,  5.37294220e-02],\n",
       "         [ 6.69245460e-02,  6.37951857e-02,  3.00313312e-02, ...,\n",
       "           9.91410326e-03,  2.17620645e-02,  4.53760929e-02],\n",
       "         [ 3.05004310e-02,  4.99914223e-02,  4.05782580e-02, ...,\n",
       "           2.14004886e-02, -4.53873232e-03,  2.18096155e-03]],\n",
       "\n",
       "        [[-9.61798961e-04,  5.21202266e-04,  5.88051725e-04, ...,\n",
       "           2.18001474e-03, -1.25671504e-03, -2.37552749e-03],\n",
       "         [-2.30100533e-03, -3.48003238e-03, -5.86325388e-03, ...,\n",
       "           2.65428037e-03, -7.82171799e-04, -2.22989722e-03],\n",
       "         [-6.12977642e-03, -7.54649618e-03, -9.66955824e-03, ...,\n",
       "           5.60432822e-04, -3.58369864e-03, -5.58368100e-03],\n",
       "         ...,\n",
       "         [-5.95040177e-05,  5.28662743e-03,  5.82386540e-03, ...,\n",
       "          -3.81850882e-03, -5.60263064e-03, -5.06817437e-03],\n",
       "         [-1.75774276e-03,  3.47553068e-03,  2.80358808e-03, ...,\n",
       "          -1.21352261e-02, -1.30735855e-02, -9.24911813e-03],\n",
       "         [-2.23357422e-03,  1.63650269e-03,  2.07965190e-03, ...,\n",
       "          -5.99467325e-03, -8.61042074e-03, -7.08221615e-03]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>v</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... -0.002291 -0.0035</div><input id='attrs-1d21a9f9-4672-4c53-85c6-33bb2e41dbe7' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1d21a9f9-4672-4c53-85c6-33bb2e41dbe7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c71574d-7359-471a-9881-c52d5b17155a' class='xr-var-data-in' type='checkbox'><label for='data-0c71574d-7359-471a-9881-c52d5b17155a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>meridional velocity anomaly</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         ...,\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],\n",
       "\n",
       "        [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "...\n",
       "         [-1.61087793e-02, -5.10827160e-02, -5.58942172e-02, ...,\n",
       "          -4.46534383e-02, -1.73180892e-02,  5.02121728e-03],\n",
       "         [-2.82634851e-02, -2.33396917e-02, -4.82940960e-03, ...,\n",
       "          -6.73736084e-02, -4.51014543e-02, -2.87047716e-02],\n",
       "         [-4.92400837e-02, -1.81582431e-02,  2.87935326e-02, ...,\n",
       "          -5.08378465e-02, -4.73668046e-02, -5.21171485e-02]],\n",
       "\n",
       "        [[-7.72188633e-03, -6.62706253e-03, -3.91487256e-03, ...,\n",
       "           2.57542369e-03, -2.98562674e-04, -5.42365163e-03],\n",
       "         [-8.34499512e-03, -6.03439090e-03, -2.22650205e-03, ...,\n",
       "           5.95465520e-03,  2.43492102e-03, -5.37025929e-03],\n",
       "         [-7.64407193e-03, -3.65175884e-03,  3.03037149e-04, ...,\n",
       "           1.00459584e-02,  5.42470538e-03, -4.83150887e-03],\n",
       "         ...,\n",
       "         [ 9.44332172e-03,  1.53577519e-04, -9.50702489e-03, ...,\n",
       "          -6.05723526e-03, -1.69536032e-03,  7.11795798e-03],\n",
       "         [ 2.18599061e-03, -2.65923905e-03, -6.63265937e-03, ...,\n",
       "          -4.26116250e-03, -1.91962692e-03,  1.92934487e-03],\n",
       "         [-4.27543292e-03, -4.86834471e-03, -4.53338145e-03, ...,\n",
       "          -1.13144459e-03, -2.29077223e-03, -3.50022343e-03]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ufull</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.025 0.025 ... -0.00861 -0.007082</div><input id='attrs-7790ae4a-1ebc-4656-be17-287be684331c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7790ae4a-1ebc-4656-be17-287be684331c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-185e9111-7821-4a3e-81d8-2f836098f4ba' class='xr-var-data-in' type='checkbox'><label for='data-185e9111-7821-4a3e-81d8-2f836098f4ba' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>zonal full velocities in real space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 2.50000000e-02,  2.50000000e-02,  2.50000000e-02, ...,\n",
       "           2.50000000e-02,  2.50000000e-02,  2.50000000e-02],\n",
       "         [ 2.50000000e-02,  2.50000000e-02,  2.50000000e-02, ...,\n",
       "           2.50000000e-02,  2.50000000e-02,  2.50000000e-02],\n",
       "         [ 2.50000000e-02,  2.50000000e-02,  2.50000000e-02, ...,\n",
       "           2.50000000e-02,  2.50000000e-02,  2.50000000e-02],\n",
       "         ...,\n",
       "         [ 2.50000000e-02,  2.50000000e-02,  2.50000000e-02, ...,\n",
       "           2.50000000e-02,  2.50000000e-02,  2.50000000e-02],\n",
       "         [ 2.50000000e-02,  2.50000000e-02,  2.50000000e-02, ...,\n",
       "           2.50000000e-02,  2.50000000e-02,  2.50000000e-02],\n",
       "         [ 2.50000000e-02,  2.50000000e-02,  2.50000000e-02, ...,\n",
       "           2.50000000e-02,  2.50000000e-02,  2.50000000e-02]],\n",
       "\n",
       "        [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "...\n",
       "         [ 1.12163015e-01,  1.07518081e-01,  6.31059551e-02, ...,\n",
       "           1.57906239e-02,  4.20818715e-02,  7.87294220e-02],\n",
       "         [ 9.19245460e-02,  8.87951857e-02,  5.50313312e-02, ...,\n",
       "           3.49141033e-02,  4.67620645e-02,  7.03760929e-02],\n",
       "         [ 5.55004310e-02,  7.49914223e-02,  6.55782580e-02, ...,\n",
       "           4.64004886e-02,  2.04612677e-02,  2.71809615e-02]],\n",
       "\n",
       "        [[-9.61798961e-04,  5.21202266e-04,  5.88051725e-04, ...,\n",
       "           2.18001474e-03, -1.25671504e-03, -2.37552749e-03],\n",
       "         [-2.30100533e-03, -3.48003238e-03, -5.86325388e-03, ...,\n",
       "           2.65428037e-03, -7.82171799e-04, -2.22989722e-03],\n",
       "         [-6.12977642e-03, -7.54649618e-03, -9.66955824e-03, ...,\n",
       "           5.60432822e-04, -3.58369864e-03, -5.58368100e-03],\n",
       "         ...,\n",
       "         [-5.95040177e-05,  5.28662743e-03,  5.82386540e-03, ...,\n",
       "          -3.81850882e-03, -5.60263064e-03, -5.06817437e-03],\n",
       "         [-1.75774276e-03,  3.47553068e-03,  2.80358808e-03, ...,\n",
       "          -1.21352261e-02, -1.30735855e-02, -9.24911813e-03],\n",
       "         [-2.23357422e-03,  1.63650269e-03,  2.07965190e-03, ...,\n",
       "          -5.99467325e-03, -8.61042074e-03, -7.08221615e-03]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vfull</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... -0.002291 -0.0035</div><input id='attrs-00dc5c81-c680-43ef-870f-da6fd6615026' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-00dc5c81-c680-43ef-870f-da6fd6615026' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79467ff2-3ab6-4846-844f-d249864ad383' class='xr-var-data-in' type='checkbox'><label for='data-79467ff2-3ab6-4846-844f-d249864ad383' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>meridional full velocities in real space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         ...,\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],\n",
       "\n",
       "        [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "...\n",
       "         [-1.61087793e-02, -5.10827160e-02, -5.58942172e-02, ...,\n",
       "          -4.46534383e-02, -1.73180892e-02,  5.02121728e-03],\n",
       "         [-2.82634851e-02, -2.33396917e-02, -4.82940960e-03, ...,\n",
       "          -6.73736084e-02, -4.51014543e-02, -2.87047716e-02],\n",
       "         [-4.92400837e-02, -1.81582431e-02,  2.87935326e-02, ...,\n",
       "          -5.08378465e-02, -4.73668046e-02, -5.21171485e-02]],\n",
       "\n",
       "        [[-7.72188633e-03, -6.62706253e-03, -3.91487256e-03, ...,\n",
       "           2.57542369e-03, -2.98562674e-04, -5.42365163e-03],\n",
       "         [-8.34499512e-03, -6.03439090e-03, -2.22650205e-03, ...,\n",
       "           5.95465520e-03,  2.43492102e-03, -5.37025929e-03],\n",
       "         [-7.64407193e-03, -3.65175884e-03,  3.03037149e-04, ...,\n",
       "           1.00459584e-02,  5.42470538e-03, -4.83150887e-03],\n",
       "         ...,\n",
       "         [ 9.44332172e-03,  1.53577519e-04, -9.50702489e-03, ...,\n",
       "          -6.05723526e-03, -1.69536032e-03,  7.11795798e-03],\n",
       "         [ 2.18599061e-03, -2.65923905e-03, -6.63265937e-03, ...,\n",
       "          -4.26116250e-03, -1.91962692e-03,  1.92934487e-03],\n",
       "         [-4.27543292e-03, -4.86834471e-03, -4.53338145e-03, ...,\n",
       "          -1.13144459e-03, -2.29077223e-03, -3.50022343e-03]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>qh</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>(0.002521111233473065+0j) ... (-...</div><input id='attrs-6bebafb5-4f40-441d-a4ea-e1c302581c08' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6bebafb5-4f40-441d-a4ea-e1c302581c08' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b220e330-b3bb-4024-a4d5-e09789cd4b90' class='xr-var-data-in' type='checkbox'><label for='data-b220e330-b3bb-4024-a4d5-e09789cd4b90' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s^-1</dd><dt><span>long_name :</span></dt><dd>potential vorticity in spectral space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 2.52111123e-03+0.00000000e+00j,\n",
       "           1.52854943e-04+1.44574042e-04j,\n",
       "           5.17498936e-05+5.66081528e-05j, ...,\n",
       "          -2.05918824e-05-1.16802262e-04j,\n",
       "          -4.44060564e-05+4.20515567e-05j,\n",
       "           2.05071634e-04+0.00000000e+00j],\n",
       "         [ 3.70557205e-08-1.72834490e-06j,\n",
       "          -6.84940404e-07-2.88585473e-06j,\n",
       "           3.06830174e-08-1.40018144e-07j, ...,\n",
       "          -2.87667892e-06+1.67027053e-06j,\n",
       "           3.13904315e-07+1.55325038e-06j,\n",
       "          -1.57666381e-06+1.91329098e-06j],\n",
       "         [-4.27335461e-07+2.56852418e-08j,\n",
       "          -2.11953536e-06-1.28282633e-06j,\n",
       "          -9.05993857e-07-1.07294556e-06j, ...,\n",
       "          -3.41219016e-07+2.62934375e-07j,\n",
       "           1.96396929e-06-1.73220711e-06j,\n",
       "          -4.54425463e-07-1.12696613e-06j],\n",
       "         ...,\n",
       "         [-1.60725908e-06-7.79116729e-07j,\n",
       "...\n",
       "           1.03966638e-24-2.19416622e-24j],\n",
       "         ...,\n",
       "         [-4.23666538e-04-7.46887343e-06j,\n",
       "          -3.76013549e-05-1.18731503e-04j,\n",
       "          -8.71085873e-05+7.42248592e-05j, ...,\n",
       "          -1.21319190e-16-1.14150042e-16j,\n",
       "           1.87667224e-19-1.39415920e-20j,\n",
       "          -8.52320342e-25+3.98864419e-24j],\n",
       "         [-8.03239544e-05-1.92329338e-05j,\n",
       "           1.63810577e-04-3.52909917e-06j,\n",
       "          -9.09726807e-05+1.17684333e-05j, ...,\n",
       "          -4.65017631e-16+3.95540692e-16j,\n",
       "          -3.82509764e-19-8.61554028e-20j,\n",
       "          -2.91420385e-24-5.69547461e-24j],\n",
       "         [-1.26970365e-04-1.97640022e-04j,\n",
       "          -1.59762731e-04+5.71353161e-06j,\n",
       "           1.48006161e-04+1.45319727e-04j, ...,\n",
       "          -1.60554650e-15-4.21380457e-16j,\n",
       "           2.63675246e-19+1.51470322e-19j,\n",
       "          -9.47815344e-24+6.04573287e-24j]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uh</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>0j ... (-2.215733036399299e-21+1...</div><input id='attrs-d156713c-cec5-406d-8fc5-9b7731fed3df' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d156713c-cec5-406d-8fc5-9b7731fed3df' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-669ab402-ef20-412a-9b5a-2e85a44b58bb' class='xr-var-data-in' type='checkbox'><label for='data-669ab402-ef20-412a-9b5a-2e85a44b58bb' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>zonal velocity anomaly in spectral space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j],\n",
       "         ...,\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "...\n",
       "          -2.06316062e-21+1.20231366e-20j],\n",
       "         ...,\n",
       "         [ 2.03581900e+00+4.10503678e+00j,\n",
       "          -5.76566241e+00-9.47688604e+00j,\n",
       "          -2.19990379e+00+7.46697213e+00j, ...,\n",
       "           5.39631736e+00-5.24598094e+00j,\n",
       "          -1.24552598e+01-4.89088766e+00j,\n",
       "          -2.47425650e-21+1.24936278e-20j],\n",
       "         [-4.76595723e+00-3.24722238e+00j,\n",
       "          -2.63476041e+00-1.11370830e+01j,\n",
       "          -3.45070261e+00+3.23130715e+00j, ...,\n",
       "          -4.87544199e-01-6.51103788e+00j,\n",
       "          -1.31096093e+01-1.41479041e+00j,\n",
       "          -2.32360964e-21+1.43374996e-20j],\n",
       "         [-6.40915087e+00-6.31180986e+00j,\n",
       "           1.90280795e+00-3.40334167e+00j,\n",
       "          -2.25379297e+00-3.19978794e-01j, ...,\n",
       "          -3.38729899e+00-4.82273684e+00j,\n",
       "          -4.32464676e+00+3.31395291e+00j,\n",
       "          -2.21573304e-21+1.52465389e-20j]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>vh</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>0j ... (1.365241643969694e-19+2....</div><input id='attrs-60b7ea19-ff2a-49f4-bb37-3cc99a9fa2fc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-60b7ea19-ff2a-49f4-bb37-3cc99a9fa2fc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b4c6a8c-db6c-4ee4-be2e-0d4798990b59' class='xr-var-data-in' type='checkbox'><label for='data-8b4c6a8c-db6c-4ee4-be2e-0d4798990b59' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>meridional velocity anomaly in spectral space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j],\n",
       "         ...,\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "...\n",
       "           2.71772537e-19+4.10704205e-20j],\n",
       "         ...,\n",
       "         [ 7.58515292e-01+1.26069709e+00j,\n",
       "           2.96318451e+00+1.09636933e+00j,\n",
       "           5.51334835e+00-5.21463800e+00j, ...,\n",
       "           1.60299440e+00-1.50507398e-01j,\n",
       "          -1.81025339e+00+2.29953123e+00j,\n",
       "           4.74205962e-20+6.29946070e-21j],\n",
       "         [-2.37725446e+00+1.31997830e+00j,\n",
       "           1.67842458e+00+2.07011032e+00j,\n",
       "           3.97686660e+00-3.33453943e+00j, ...,\n",
       "           2.79508697e+00+8.08938785e-01j,\n",
       "          -3.83781966e-01+1.58023333e-01j,\n",
       "           8.97949316e-20+1.38267813e-20j],\n",
       "         [-3.48352789e+00+1.19258118e+00j,\n",
       "           3.85131314e-01+2.08332492e+00j,\n",
       "           2.39633292e+00+8.39302763e-01j, ...,\n",
       "           3.10132916e+00+1.19949064e+00j,\n",
       "           2.26440760e-01-1.27984041e+00j,\n",
       "           1.36524164e-19+2.09446410e-20j]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ph</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>0j ... (2.3356262016399966e-16-4...</div><input id='attrs-6a0242aa-c775-4ccd-92f7-f21aaed8156b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6a0242aa-c775-4ccd-92f7-f21aaed8156b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0852931a-2af6-400a-973e-c924ce53b5bd' class='xr-var-data-in' type='checkbox'><label for='data-0852931a-2af6-400a-973e-c924ce53b5bd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m^2 s^-1</dd><dt><span>long_name :</span></dt><dd>streamfunction in spectral space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j],\n",
       "         ...,\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "...\n",
       "          -6.58727197e-17+2.34160027e-16j],\n",
       "         ...,\n",
       "         [-1.35414421e+05+7.71604725e+04j,\n",
       "          -2.88879036e+04+3.35588028e+04j,\n",
       "          -1.01377514e+04+5.24004887e+04j, ...,\n",
       "          -1.26231090e-08+4.86596694e-09j,\n",
       "           7.62378705e-12-5.78376483e-12j,\n",
       "           1.51040682e-16-2.28436833e-16j],\n",
       "         [-3.63468172e+04+1.06362531e+05j,\n",
       "          -5.80131518e+04-4.79178192e+04j,\n",
       "          -1.39526568e+04+2.01701524e+04j, ...,\n",
       "           3.91477710e-08-7.39887817e-08j,\n",
       "           6.07943124e-12+2.42855834e-11j,\n",
       "           2.54119288e-16+5.00719670e-16j],\n",
       "         [ 2.23714142e+05+2.41302405e+05j,\n",
       "           1.70938922e+05+2.87930492e+04j,\n",
       "           1.06547050e+04-7.81606267e+04j, ...,\n",
       "          -4.95177062e-08+3.22554087e-08j,\n",
       "           1.33816271e-11-2.43253298e-11j,\n",
       "           2.33562620e-16-4.69283342e-16j]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dqhdt</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>complex128</div><div class='xr-var-preview xr-preview'>0j ... (-3.063243095847373e-12+1...</div><input id='attrs-8cc78386-caef-4c01-980c-248543113778' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cc78386-caef-4c01-980c-248543113778' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d64d48c-7a26-4a9f-82c3-b8868831dfbf' class='xr-var-data-in' type='checkbox'><label for='data-3d64d48c-7a26-4a9f-82c3-b8868831dfbf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s^-2</dd><dt><span>long_name :</span></dt><dd>previous partial derivative of potential vorticity wrt. time in spectral space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j],\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j, ...,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j,\n",
       "           0.00000000e+00+0.00000000e+00j],\n",
       "         ...,\n",
       "         [ 0.00000000e+00+0.00000000e+00j,\n",
       "...\n",
       "           5.99094474e-13-1.27347951e-12j],\n",
       "         ...,\n",
       "         [-3.99016557e-11-2.34025896e-11j,\n",
       "           4.49275141e-11+5.83020313e-12j,\n",
       "          -4.42423951e-11+7.06736836e-11j, ...,\n",
       "          -6.21615964e-13-5.98931145e-13j,\n",
       "           4.18398906e-12-3.12426795e-13j,\n",
       "          -1.31648519e-12+6.18117644e-12j],\n",
       "         [-6.55417103e-13+2.01952387e-11j,\n",
       "          -1.34416704e-11-6.94612558e-12j,\n",
       "          -1.26705232e-12+1.94960223e-11j, ...,\n",
       "          -1.34582748e-12+1.14614836e-12j,\n",
       "          -3.94838169e-12-8.87295279e-13j,\n",
       "          -1.69280846e-12-3.30594892e-12j],\n",
       "         [ 2.12908009e-12-8.03726072e-12j,\n",
       "           5.47910465e-13+1.89302821e-11j,\n",
       "           4.69430926e-11-4.25677325e-11j, ...,\n",
       "          -3.31680440e-12-8.64661868e-13j,\n",
       "           1.72289333e-12+9.98443399e-13j,\n",
       "          -3.06324310e-12+1.95567580e-12j]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Ubg</span></div><div class='xr-var-dims'>(time, lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.025 0.0 0.025 ... 0.0 0.025 0.0</div><input id='attrs-7d41f91a-ed05-4ba9-865b-14ca472c64dc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-7d41f91a-ed05-4ba9-865b-14ca472c64dc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe7d208c-de85-4ca5-a40e-4ef52b22a75d' class='xr-var-data-in' type='checkbox'><label for='data-fe7d208c-de85-4ca5-a40e-4ef52b22a75d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m s^-1</dd><dt><span>long_name :</span></dt><dd>background zonal velocity</dd></dl></div><div class='xr-var-data'><pre>array([[0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "...\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ],\n",
       "       [0.025, 0.   ]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Qy</span></div><div class='xr-var-dims'>(time, lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.039e-10 -7.222e-12 ... -7.222e-12</div><input id='attrs-cb8e6c54-5157-4e9a-8fe7-2e26df5e3cbb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-cb8e6c54-5157-4e9a-8fe7-2e26df5e3cbb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6dc64d92-15f6-4cd8-8c5b-b8516df6c611' class='xr-var-data-in' type='checkbox'><label for='data-6dc64d92-15f6-4cd8-8c5b-b8516df6c611' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m^-1 s^-1</dd><dt><span>long_name :</span></dt><dd>background potential vorticity gradient</dd></dl></div><div class='xr-var-data'><pre>array([[ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "...\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12],\n",
       "       [ 1.03888889e-10, -7.22222222e-12]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dqdt</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 ... -3.249e-14 -1.078e-13</div><input id='attrs-fb431773-7663-43b3-8bfc-4796066e8178' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fb431773-7663-43b3-8bfc-4796066e8178' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-96878369-6b79-49b0-8053-d7418572fa0f' class='xr-var-data-in' type='checkbox'><label for='data-96878369-6b79-49b0-8053-d7418572fa0f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s^-2</dd><dt><span>long_name :</span></dt><dd>previous partial derivative of potential vorticity wrt. time in real space</dd></dl></div><div class='xr-var-data'><pre>array([[[[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         ...,\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00]],\n",
       "\n",
       "        [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "         [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "           0.00000000e+00,  0.00000000e+00,  0.00000000e+00],\n",
       "...\n",
       "         [-7.35878930e-12, -2.09059502e-12, -3.61300316e-12, ...,\n",
       "           1.97902073e-12,  1.07003862e-11,  2.81101845e-12],\n",
       "         [ 1.43138922e-11,  3.75093355e-12,  4.02189541e-12, ...,\n",
       "          -9.77533326e-12,  3.80620321e-13,  1.49486370e-11],\n",
       "         [-1.69413476e-11,  6.79799078e-12,  1.07888757e-11, ...,\n",
       "          -3.81644980e-12, -1.33825543e-11, -2.38192707e-11]],\n",
       "\n",
       "        [[-1.28533514e-13, -2.47665742e-13, -2.91360014e-13, ...,\n",
       "           2.56932663e-13,  3.51470763e-13,  1.57119065e-13],\n",
       "         [-3.57761729e-14, -1.32978573e-13, -3.73608255e-14, ...,\n",
       "           7.94587724e-14,  1.61981135e-13,  3.84998473e-14],\n",
       "         [ 1.42948877e-13, -8.19899170e-14,  1.09637472e-13, ...,\n",
       "           5.82348570e-14,  3.34262019e-13,  1.73406762e-13],\n",
       "         ...,\n",
       "         [ 9.17580976e-13,  6.56113594e-13, -3.95766821e-14, ...,\n",
       "          -7.65292184e-13, -9.08926931e-13, -1.41911566e-13],\n",
       "         [ 9.47049404e-14,  7.02907596e-14, -3.22271387e-13, ...,\n",
       "          -8.02977872e-13, -7.69105989e-13, -3.62230739e-13],\n",
       "         [-1.69287158e-13, -2.13163429e-13, -2.29943232e-13, ...,\n",
       "           8.31079077e-14, -3.24932524e-14, -1.07754414e-13]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>p</span></div><div class='xr-var-dims'>(time, lev, y, x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 0.0 0.0 ... 79.42 52.91 7.272</div><input id='attrs-c3c922e0-ddf1-41a7-93ce-5ccc47583153' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c3c922e0-ddf1-41a7-93ce-5ccc47583153' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e9c98067-484f-44e6-b07b-7eb2dc986b57' class='xr-var-data-in' type='checkbox'><label for='data-e9c98067-484f-44e6-b07b-7eb2dc986b57' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m^2 s^-1</dd><dt><span>long_name :</span></dt><dd>streamfunction in real space</dd></dl></div><div class='xr-var-data'><pre>array([[[[    0.        ,     0.        ,     0.        , ...,\n",
       "              0.        ,     0.        ,     0.        ],\n",
       "         [    0.        ,     0.        ,     0.        , ...,\n",
       "              0.        ,     0.        ,     0.        ],\n",
       "         [    0.        ,     0.        ,     0.        , ...,\n",
       "              0.        ,     0.        ,     0.        ],\n",
       "         ...,\n",
       "         [    0.        ,     0.        ,     0.        , ...,\n",
       "              0.        ,     0.        ,     0.        ],\n",
       "         [    0.        ,     0.        ,     0.        , ...,\n",
       "              0.        ,     0.        ,     0.        ],\n",
       "         [    0.        ,     0.        ,     0.        , ...,\n",
       "              0.        ,     0.        ,     0.        ]],\n",
       "\n",
       "        [[    0.        ,     0.        ,     0.        , ...,\n",
       "              0.        ,     0.        ,     0.        ],\n",
       "         [    0.        ,     0.        ,     0.        , ...,\n",
       "              0.        ,     0.        ,     0.        ],\n",
       "         [    0.        ,     0.        ,     0.        , ...,\n",
       "              0.        ,     0.        ,     0.        ],\n",
       "...\n",
       "         [    9.78027828,  -534.27285882, -1419.20132712, ...,\n",
       "            611.08262997,    94.84994173,    43.64440053],\n",
       "         [-1274.26631787, -1698.84737243, -1924.483455  , ...,\n",
       "            604.61272682,  -287.68895693,  -841.04940001],\n",
       "         [-1996.71267871, -2565.40351792, -2470.65919783, ...,\n",
       "            351.81696468,  -407.26703056, -1179.00821896]],\n",
       "\n",
       "        [[  -29.11382433,  -144.26608476,  -228.07484151, ...,\n",
       "            101.04284017,   124.45208223,    79.00950684],\n",
       "         [   -9.12477293,  -125.29800508,  -190.74794243, ...,\n",
       "             55.11165296,   131.29275858,   107.24130047],\n",
       "         [   56.8299338 ,   -34.6660928 ,   -59.22963895, ...,\n",
       "             29.72822238,   165.56457454,   167.66095328],\n",
       "         ...,\n",
       "         [ -102.41413111,   -16.54426496,   -99.40325731, ...,\n",
       "           -219.64159786,  -290.0284053 ,  -247.45552749],\n",
       "         [  -88.43427696,   -88.12783785,  -166.65969646, ...,\n",
       "            -76.53803166,  -129.41007015,  -128.14211469],\n",
       "         [  -53.9046204 ,  -125.69955409,  -201.18546374, ...,\n",
       "             79.42446009,    52.91028787,     7.27164653]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Ensspec</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 1.55e-44 5.696e-54</div><input id='attrs-64b4e350-bd67-4e08-8bf5-f1350e4b5437' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64b4e350-bd67-4e08-8bf5-f1350e4b5437' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-142f8b22-0671-4ed8-be11-a9b05508f045' class='xr-var-data-in' type='checkbox'><label for='data-142f8b22-0671-4ed8-be11-a9b05508f045' title='Show/Hide data repr'><svg 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>enstrophy spectrum</dd><dt><span>units :</span></dt><dd>s^-2</dd></dl></div><div class='xr-var-data'><pre>array([[[[           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         ...,\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan]],\n",
       "\n",
       "        [[           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "...\n",
       "         [6.12983933e-14, 1.14310615e-13, 9.12603625e-14, ...,\n",
       "          3.01975357e-34, 1.55034162e-41, 4.76577288e-51],\n",
       "         [3.18122251e-14, 3.18409187e-14, 4.03794160e-14, ...,\n",
       "          6.79794212e-34, 5.11812458e-41, 1.80912860e-50],\n",
       "         [2.01244693e-14, 2.28300899e-14, 3.18471069e-14, ...,\n",
       "          1.09415086e-33, 8.15881697e-41, 3.17153609e-50]],\n",
       "\n",
       "        [[0.00000000e+00, 1.00956075e-15, 7.30290547e-16, ...,\n",
       "          2.70346017e-37, 1.88026604e-44, 7.91393029e-54],\n",
       "         [1.24813577e-15, 1.57904179e-15, 1.14625912e-15, ...,\n",
       "          2.00076380e-37, 1.61407043e-44, 6.00992581e-54],\n",
       "         [1.73888053e-15, 1.64083824e-15, 1.40072257e-15, ...,\n",
       "          1.02763815e-37, 6.47841772e-45, 2.45075739e-54],\n",
       "         ...,\n",
       "         [2.57669487e-15, 4.62548750e-15, 3.34253538e-15, ...,\n",
       "          4.21287677e-38, 1.59025618e-45, 3.98086126e-55],\n",
       "         [1.73888053e-15, 1.62045221e-15, 1.79295217e-15, ...,\n",
       "          1.15426605e-37, 6.90869634e-45, 2.44043412e-54],\n",
       "         [1.24813577e-15, 1.39038378e-15, 1.73725316e-15, ...,\n",
       "          2.18736031e-37, 1.54980684e-44, 5.69635222e-54]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KEspec</span></div><div class='xr-var-dims'>(time, lev, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 1.174e-36 4.458e-46</div><input id='attrs-39d1217e-fbda-4fab-9240-32f0b673b5b5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-39d1217e-fbda-4fab-9240-32f0b673b5b5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbe104b2-d319-4427-a0c7-006aa9afa359' class='xr-var-data-in' type='checkbox'><label for='data-fbe104b2-d319-4427-a0c7-006aa9afa359' title='Show/Hide data repr'><svg 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>kinetic energy spectrum</dd><dt><span>units :</span></dt><dd>m^2 s^-2</dd></dl></div><div class='xr-var-data'><pre>array([[[[           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         ...,\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan]],\n",
       "\n",
       "        [[           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "         [           nan,            nan,            nan, ...,\n",
       "                     nan,            nan,            nan],\n",
       "...\n",
       "         [2.26970960e-06, 4.35504398e-06, 4.08498074e-06, ...,\n",
       "          6.99534099e-27, 3.40202277e-34, 9.91946487e-44],\n",
       "         [5.80112933e-07, 7.66763191e-07, 1.44293566e-06, ...,\n",
       "          1.58188519e-26, 1.12785260e-33, 3.78044718e-43],\n",
       "         [9.99920101e-08, 1.49563703e-07, 6.18064943e-07, ...,\n",
       "          2.55331289e-26, 1.80267231e-33, 6.64589126e-43]],\n",
       "\n",
       "        [[0.00000000e+00, 5.47267565e-09, 5.61188098e-08, ...,\n",
       "          2.38814538e-29, 1.31190366e-36, 4.78470883e-46],\n",
       "         [5.26889771e-08, 4.07604555e-08, 1.03957595e-07, ...,\n",
       "          1.76466628e-29, 1.20553951e-36, 4.27020618e-46],\n",
       "         [1.26746848e-07, 1.74920971e-07, 1.68473277e-07, ...,\n",
       "          1.03408028e-29, 6.05854417e-37, 2.13739673e-46],\n",
       "         ...,\n",
       "         [2.78236974e-07, 3.86557112e-07, 3.18070309e-07, ...,\n",
       "          4.88207003e-30, 1.90952891e-37, 5.05979885e-47],\n",
       "         [1.26746848e-07, 1.53458376e-07, 2.48950523e-07, ...,\n",
       "          1.14757056e-29, 6.48169238e-37, 2.01796256e-46],\n",
       "         [5.26889771e-08, 3.29117290e-08, 1.11625658e-07, ...,\n",
       "          2.00671073e-29, 1.17361257e-36, 4.45807057e-46]]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EKEdiss</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 5.27e-11 5.275e-11</div><input id='attrs-fdf898de-552a-4fa3-8676-9091eab28281' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdf898de-552a-4fa3-8676-9091eab28281' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c061208-d948-4e11-a64c-071fd5861b70' class='xr-var-data-in' type='checkbox'><label for='data-0c061208-d948-4e11-a64c-071fd5861b70' title='Show/Hide data repr'><svg 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>total energy dissipation by bottom drag</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([           nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "       4.88125451e-11, 4.99578106e-11, 5.16531874e-11, 5.36607672e-11,\n",
       "       5.34473995e-11, 5.31409184e-11, 5.27291387e-11, 5.22614385e-11,\n",
       "       5.21696884e-11, 5.19933055e-11, 5.19349916e-11, 5.23227199e-11,\n",
       "       5.26946922e-11, 5.27670311e-11, 5.28503767e-11, 5.29834806e-11,\n",
       "       5.29566380e-11, 5.29939727e-11, 5.31582506e-11, 5.31017486e-11,\n",
       "       5.28738102e-11, 5.27027811e-11, 5.23531823e-11, 5.21686582e-11,\n",
       "       5.21881697e-11, 5.21558381e-11, 5.21256663e-11, 5.20056226e-11,\n",
       "       5.19439458e-11, 5.19662556e-11, 5.20357050e-11, 5.21752738e-11,\n",
       "       5.22417952e-11, 5.22104429e-11, 5.20526458e-11, 5.19981040e-11,\n",
       "       5.20436912e-11, 5.22012717e-11, 5.24397413e-11, 5.25449557e-11,\n",
       "       5.26687785e-11, 5.27011752e-11, 5.27528485e-11])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KEfrictionspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan ... -5.433e-43 -2.064e-52</div><input id='attrs-c49cd563-9955-485c-b836-cac90e6e8df6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c49cd563-9955-485c-b836-cac90e6e8df6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ad5d8bd8-895e-462c-a9b3-a66f291e6954' class='xr-var-data-in' type='checkbox'><label for='data-ad5d8bd8-895e-462c-a9b3-a66f291e6954' title='Show/Hide data repr'><svg 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>total energy dissipation spectrum by bottom drag</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "...\n",
       "        [-1.25565573e-13, -1.80936920e-13, -1.50253906e-13, ...,\n",
       "         -2.26647655e-36, -8.80001507e-44, -2.32782770e-53],\n",
       "        [-5.83132864e-14, -7.13643633e-14, -1.17625110e-13, ...,\n",
       "         -5.27563007e-36, -2.95233983e-43, -9.13733464e-53],\n",
       "        [-2.22907022e-14, -1.49100903e-14, -5.24538446e-14, ...,\n",
       "         -9.42201557e-36, -5.39018270e-43, -2.03739092e-52]],\n",
       "\n",
       "       [[-0.00000000e+00, -2.53362992e-15, -2.59807642e-14, ...,\n",
       "         -1.10561579e-35, -6.07358917e-43, -2.21512880e-52],\n",
       "        [-2.43928888e-14, -1.88704605e-14, -4.81282080e-14, ...,\n",
       "         -8.16969902e-36, -5.58116573e-43, -1.97693465e-52],\n",
       "        [-5.86787209e-14, -8.09814127e-14, -7.79963883e-14, ...,\n",
       "         -4.78737806e-36, -2.80486361e-43, -9.89529191e-53],\n",
       "        ...,\n",
       "        [-1.28812589e-13, -1.78960481e-13, -1.47253830e-13, ...,\n",
       "         -2.26020314e-36, -8.84035506e-44, -2.34248448e-53],\n",
       "        [-5.86787209e-14, -7.10450900e-14, -1.15254134e-13, ...,\n",
       "         -5.31279266e-36, -3.00076431e-43, -9.34235949e-53],\n",
       "        [-2.43928888e-14, -1.52368140e-14, -5.16782148e-14, ...,\n",
       "         -9.29026801e-36, -5.43335677e-43, -2.06390835e-52]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>EKE</span></div><div class='xr-var-dims'>(time, lev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 0.002138 5.697e-05</div><input id='attrs-decf0a4d-c6be-46ec-9686-05c7b3178eb1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-decf0a4d-c6be-46ec-9686-05c7b3178eb1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b22e407f-b427-4d5b-a504-d45e44bb36b7' class='xr-var-data-in' type='checkbox'><label for='data-b22e407f-b427-4d5b-a504-d45e44bb36b7' title='Show/Hide data repr'><svg 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>mean eddy kinetic energy</dd><dt><span>units :</span></dt><dd>m^2 s^-2</dd></dl></div><div class='xr-var-data'><pre>array([[           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "       [           nan,            nan],\n",
       "...\n",
       "       [2.12477834e-03, 5.63425114e-05],\n",
       "       [2.12072834e-03, 5.63635841e-05],\n",
       "       [2.11821197e-03, 5.63286657e-05],\n",
       "       [2.11676160e-03, 5.62960799e-05],\n",
       "       [2.11484670e-03, 5.61664319e-05],\n",
       "       [2.11319479e-03, 5.60998205e-05],\n",
       "       [2.11306115e-03, 5.61239152e-05],\n",
       "       [2.11288194e-03, 5.61989211e-05],\n",
       "       [2.11380534e-03, 5.63496564e-05],\n",
       "       [2.11460495e-03, 5.64215000e-05],\n",
       "       [2.11405129e-03, 5.63876392e-05],\n",
       "       [2.11261839e-03, 5.62172173e-05],\n",
       "       [2.11217177e-03, 5.61583117e-05],\n",
       "       [2.11420248e-03, 5.62075462e-05],\n",
       "       [2.11818777e-03, 5.63777343e-05],\n",
       "       [2.12302130e-03, 5.66352830e-05],\n",
       "       [2.12748891e-03, 5.67489153e-05],\n",
       "       [2.13201492e-03, 5.68826448e-05],\n",
       "       [2.13567269e-03, 5.69176335e-05],\n",
       "       [2.13774391e-03, 5.69734410e-05]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Dissspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan ... -2.573e-27 -4.668e-32</div><input id='attrs-3723014d-238e-4ee4-b8e5-aec0dc44a57b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3723014d-238e-4ee4-b8e5-aec0dc44a57b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42bf3ff3-cbae-42ee-95bf-1e74bbc410bd' class='xr-var-data-in' type='checkbox'><label for='data-42bf3ff3-cbae-42ee-95bf-1e74bbc410bd' title='Show/Hide data repr'><svg 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>Spectral contribution of filter dissipation to total energy</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "...\n",
       "        [-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -8.03070075e-24, -1.64100417e-27, -3.29700303e-32],\n",
       "        [-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -1.00551949e-23, -2.51588026e-27, -4.58830555e-32],\n",
       "        [-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -1.16082308e-23, -2.54811720e-27, -4.66052649e-32]],\n",
       "\n",
       "       [[-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -1.18425464e-23, -2.23790761e-27, -4.29204565e-32],\n",
       "        [-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -1.01792391e-23, -2.40642563e-27, -4.60883080e-32],\n",
       "        [-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -1.01692047e-23, -2.27134862e-27, -5.64390005e-32],\n",
       "        ...,\n",
       "        [-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -8.04473909e-24, -1.65348836e-27, -3.34090065e-32],\n",
       "        [-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -1.01182312e-23, -2.53637128e-27, -4.75388513e-32],\n",
       "        [-0.00000000e+00, -0.00000000e+00, -0.00000000e+00, ...,\n",
       "         -1.15644031e-23, -2.57309026e-27, -4.66765549e-32]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ENSDissspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan ... -1.067e-34 -2.051e-39</div><input id='attrs-ab119277-ed69-4b54-98e5-54ec927de93c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab119277-ed69-4b54-98e5-54ec927de93c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7fd2fd16-5c6e-4532-a078-2bc2d1a79532' class='xr-var-data-in' type='checkbox'><label for='data-7fd2fd16-5c6e-4532-a078-2bc2d1a79532' title='Show/Hide data repr'><svg 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>Spectral contribution of filter dissipation to barotropic enstrophy</dd><dt><span>units :</span></dt><dd>s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "...\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -3.16092044e-31, -6.85591443e-35, -1.45945044e-39],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -3.93812942e-31, -1.04625066e-34, -2.02227667e-39],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -4.53196445e-31, -1.05655615e-34, -2.04794833e-39]],\n",
       "\n",
       "       [[ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -4.61872447e-31, -9.26949655e-35, -1.88481490e-39],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -3.97434304e-31, -9.97650286e-35, -2.02544664e-39],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -3.98353194e-31, -9.44477728e-35, -2.48786054e-39],\n",
       "        ...,\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -3.16647294e-31, -6.90807963e-35, -1.47890157e-39],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -3.96276122e-31, -1.05475261e-34, -2.09525760e-39],\n",
       "        [ 0.00000000e+00,  0.00000000e+00,  0.00000000e+00, ...,\n",
       "         -4.51500772e-31, -1.06690978e-34, -2.05104257e-39]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>paramspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... -0.0 -0.0 -0.0</div><input id='attrs-0389666d-77b6-4694-b5b7-c21ce1d1fc51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0389666d-77b6-4694-b5b7-c21ce1d1fc51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c2e3e868-1e3e-4557-acfc-45e76c7e602a' class='xr-var-data-in' type='checkbox'><label for='data-c2e3e868-1e3e-4557-acfc-45e76c7e602a' title='Show/Hide data repr'><svg 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>Spectral contribution of subgrid parameterization (if present)</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\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><li class='xr-var-item'><div class='xr-var-name'><span>entspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 3.226e-42 1.273e-51</div><input id='attrs-947292e6-0f60-460f-b760-5533aeec2c30' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-947292e6-0f60-460f-b760-5533aeec2c30' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-920b5297-1ee0-41a6-9037-3c47f1b24a55' class='xr-var-data-in' type='checkbox'><label for='data-920b5297-1ee0-41a6-9037-3c47f1b24a55' title='Show/Hide data repr'><svg 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>barotropic enstrophy spectrum</dd><dt><span>units :</span></dt><dd>m s^-2</dd></dl></div><div class='xr-var-data'><pre>array([[[           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        ...,\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "...\n",
       "        [1.56425997e-16, 2.94980024e-16, 3.36169117e-16, ...,\n",
       "         1.20016026e-35, 6.09339049e-43, 1.87178983e-52],\n",
       "        [2.51089063e-17, 4.09228880e-17, 1.15090782e-16, ...,\n",
       "         2.68287201e-35, 2.00034805e-42, 6.90379917e-52],\n",
       "        [1.94284812e-18, 2.68213045e-18, 2.85858941e-17, ...,\n",
       "         4.38711187e-35, 3.19434154e-42, 1.27040109e-51]],\n",
       "\n",
       "       [[1.51538893e-14, 1.88120951e-19, 1.05521297e-17, ...,\n",
       "         5.01311220e-35, 3.27228566e-42, 1.40106805e-51],\n",
       "        [2.06432255e-18, 3.28421102e-18, 2.39985471e-17, ...,\n",
       "         3.83656563e-35, 3.03689504e-42, 1.25029097e-51],\n",
       "        [2.52352344e-17, 4.13962491e-17, 8.10158063e-17, ...,\n",
       "         2.69272612e-35, 1.81281944e-42, 8.43935295e-52],\n",
       "        ...,\n",
       "        [1.62790744e-16, 2.91101850e-16, 3.29163590e-16, ...,\n",
       "         1.20184912e-35, 6.13936469e-43, 1.89555706e-52],\n",
       "        [2.52352344e-17, 4.07173087e-17, 1.12677261e-16, ...,\n",
       "         2.70122472e-35, 2.01823504e-42, 7.15246263e-52],\n",
       "        [2.06432255e-18, 2.71143311e-18, 2.80468560e-17, ...,\n",
       "         4.36462348e-35, 3.22583974e-42, 1.27343179e-51]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>paramspec_APEflux</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... -0.0 -0.0 -0.0</div><input id='attrs-64ecdea2-cebe-4863-b946-1bc1a41a8aa9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-64ecdea2-cebe-4863-b946-1bc1a41a8aa9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9f15d3c6-239f-483c-a878-079d0874318a' class='xr-var-data-in' type='checkbox'><label for='data-9f15d3c6-239f-483c-a878-079d0874318a' title='Show/Hide data repr'><svg 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>total additional APE flux due to subgrid parameterization</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\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><li class='xr-var-item'><div class='xr-var-name'><span>paramspec_KEflux</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan nan ... 0.0 0.0 0.0 0.0</div><input id='attrs-4b724116-2a9f-4d40-be42-cedc01d7df75' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b724116-2a9f-4d40-be42-cedc01d7df75' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-67e8edfc-9615-4a85-a6b5-68782e002a4a' class='xr-var-data-in' type='checkbox'><label for='data-67e8edfc-9615-4a85-a6b5-68782e002a4a' title='Show/Hide data repr'><svg 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>total additional KE flux due to subgrid parameterization</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        ...,\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan]],\n",
       "\n",
       "       [[nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\n",
       "        [nan, nan, nan, ..., nan, nan, nan],\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><li class='xr-var-item'><div class='xr-var-name'><span>ENSflux</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 1.067e-34 2.051e-39</div><input id='attrs-c286df3a-927d-4446-b94a-448bbc77fa93' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c286df3a-927d-4446-b94a-448bbc77fa93' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fc105ed-1b43-42f7-9b3b-3fca342f0144' class='xr-var-data-in' type='checkbox'><label for='data-3fc105ed-1b43-42f7-9b3b-3fca342f0144' title='Show/Hide data repr'><svg 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>barotropic enstrophy flux</dd><dt><span>units :</span></dt><dd>s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "...\n",
       "        [-2.79152876e-23, -2.86390149e-22, -2.30101552e-22, ...,\n",
       "          3.16112789e-31,  6.85642715e-35,  1.45953331e-39],\n",
       "        [-1.23117628e-22,  1.81700566e-23, -6.14040117e-23, ...,\n",
       "          3.93840844e-31,  1.04632676e-34,  2.02241637e-39],\n",
       "        [ 3.60009616e-23,  5.57798812e-23,  1.44786228e-22, ...,\n",
       "          4.53219774e-31,  1.05662078e-34,  2.04807192e-39]],\n",
       "\n",
       "       [[ 0.00000000e+00, -1.15833633e-23,  2.55402526e-23, ...,\n",
       "          4.61906041e-31,  9.27022236e-35,  1.88494409e-39],\n",
       "        [ 3.93939926e-23,  9.84904775e-24, -1.30541767e-23, ...,\n",
       "          3.97457055e-31,  9.97712882e-35,  2.02559735e-39],\n",
       "        [-1.15699619e-22, -2.16110725e-23, -3.14779218e-23, ...,\n",
       "          3.98391163e-31,  9.44559100e-35,  2.48803780e-39],\n",
       "        ...,\n",
       "        [ 7.75711536e-23, -3.06000970e-22, -2.18859190e-22, ...,\n",
       "          3.16668968e-31,  6.90854968e-35,  1.47898615e-39],\n",
       "        [-1.15699619e-22,  1.34795354e-23, -6.15057291e-23, ...,\n",
       "          3.96302784e-31,  1.05482353e-34,  2.09539074e-39],\n",
       "        [ 3.93939926e-23,  5.43994701e-23,  1.02144108e-22, ...,\n",
       "          4.51526972e-31,  1.06698170e-34,  2.05117141e-39]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ENSgenspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan ... -7.318e-52 9.523e-63</div><input id='attrs-bbd2f651-cf5e-41cf-aa5c-57f8dfd88841' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bbd2f651-cf5e-41cf-aa5c-57f8dfd88841' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff550687-01ce-4db1-bb14-36014fb7ae98' class='xr-var-data-in' type='checkbox'><label for='data-ff550687-01ce-4db1-bb14-36014fb7ae98' title='Show/Hide data repr'><svg 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>the spectrum of the rate of generation of barotropic enstrophy</dd><dt><span>units :</span></dt><dd>s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "...\n",
       "        [ 0.00000000e+00,  5.91578074e-23,  5.76528889e-24, ...,\n",
       "          1.01291656e-45, -1.58455728e-52, -4.94602906e-62],\n",
       "        [ 0.00000000e+00, -1.59043603e-23, -2.94752432e-23, ...,\n",
       "          4.68341128e-45, -2.16364861e-52, -2.19624952e-62],\n",
       "        [ 0.00000000e+00, -6.24391799e-23, -9.21345419e-23, ...,\n",
       "         -7.33145528e-45, -8.20770888e-52,  8.18526660e-63]],\n",
       "\n",
       "       [[ 0.00000000e+00, -9.20293453e-24, -1.88082407e-23, ...,\n",
       "          6.49705361e-45,  4.56354536e-52, -8.40869209e-80],\n",
       "        [ 0.00000000e+00, -2.13523582e-23,  2.67616913e-23, ...,\n",
       "          5.43737822e-45,  4.96620798e-53, -2.17537263e-61],\n",
       "        [ 0.00000000e+00,  2.11240168e-23, -2.67554879e-23, ...,\n",
       "          4.61109270e-45, -1.25047969e-52, -6.73919826e-62],\n",
       "        ...,\n",
       "        [ 0.00000000e+00,  6.55846383e-23,  8.18510881e-24, ...,\n",
       "          8.82227911e-46, -1.54230264e-52, -4.63660551e-62],\n",
       "        [ 0.00000000e+00, -2.19705600e-23, -3.12121239e-23, ...,\n",
       "          4.37024725e-45, -1.62587410e-52, -3.22040286e-62],\n",
       "        [ 0.00000000e+00, -6.47863242e-23, -9.23165879e-23, ...,\n",
       "         -7.89142695e-45, -7.31804198e-52,  9.52306931e-63]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ENSfrictionspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan ... -5.582e-51 -2.619e-60</div><input id='attrs-12ea7e07-dece-4c25-8d0e-43503544fc55' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-12ea7e07-dece-4c25-8d0e-43503544fc55' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1abd22a4-20f3-418b-95ec-d54f73134365' class='xr-var-data-in' type='checkbox'><label for='data-1abd22a4-20f3-418b-95ec-d54f73134365' title='Show/Hide data repr'><svg 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>the spectrum of the rate of dissipation of barotropic enstrophy due to bottom friction</dd><dt><span>units :</span></dt><dd>s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "...\n",
       "        [ 7.59431911e-23,  1.79266135e-22,  1.49815159e-22, ...,\n",
       "         -1.65004375e-44, -5.09600313e-52, -1.44387400e-61],\n",
       "        [ 9.68015921e-24,  2.26806913e-23,  4.20398865e-23, ...,\n",
       "         -4.42478687e-44, -2.05912997e-51, -8.44532393e-61],\n",
       "        [-2.24165805e-24, -4.98321162e-24,  4.22093234e-24, ...,\n",
       "         -9.25410025e-44, -5.57094269e-51, -2.54845348e-60]],\n",
       "\n",
       "       [[ 0.00000000e+00, -2.87860389e-24,  8.01158048e-25, ...,\n",
       "         -1.15221466e-43, -7.46180046e-51, -3.08840454e-60],\n",
       "        [-3.90947257e-24, -7.01349881e-24, -3.22438209e-24, ...,\n",
       "         -8.13721521e-44, -6.56445021e-51, -2.54686545e-60],\n",
       "        [ 9.92415895e-24,  2.47777474e-24,  3.92436002e-23, ...,\n",
       "         -3.12562051e-44, -2.19450842e-51, -6.27506890e-61],\n",
       "        ...,\n",
       "        [ 8.31857685e-23,  1.76355130e-22,  1.46420116e-22, ...,\n",
       "         -1.62844418e-44, -5.03725184e-52, -1.42857314e-61],\n",
       "        [ 9.92415895e-24,  2.23517057e-23,  4.08349751e-23, ...,\n",
       "         -4.41346837e-44, -2.12085199e-51, -8.22862320e-61],\n",
       "        [-3.90947257e-24, -5.36596951e-24,  2.92121950e-24, ...,\n",
       "         -9.06672483e-44, -5.58152599e-51, -2.61853748e-60]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>APEgenspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan ... -1.647e-43 2.143e-54</div><input id='attrs-2721c4b9-47d7-45eb-aa6b-83c778815c2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2721c4b9-47d7-45eb-aa6b-83c778815c2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f6631683-1512-4db7-a006-dc3ec67de129' class='xr-var-data-in' type='checkbox'><label for='data-f6631683-1512-4db7-a006-dc3ec67de129' title='Show/Hide data repr'><svg 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>the spectrum of the rate of generation of available potential energy</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "...\n",
       "        [ 0.00000000e+00,  1.33105067e-14,  1.29719000e-15, ...,\n",
       "          2.27906226e-37, -3.56525388e-44, -1.11285654e-53],\n",
       "        [ 0.00000000e+00, -3.57848107e-15, -6.63192971e-15, ...,\n",
       "          1.05376754e-36, -4.86820937e-44, -4.94156143e-54],\n",
       "        [ 0.00000000e+00, -1.40488155e-14, -2.07302719e-14, ...,\n",
       "         -1.64957744e-36, -1.84673450e-43,  1.84168498e-54]],\n",
       "\n",
       "       [[ 0.00000000e+00, -2.07066027e-15, -4.23185415e-15, ...,\n",
       "          1.46183706e-36,  1.02679771e-43, -1.89195572e-71],\n",
       "        [ 0.00000000e+00, -4.80428059e-15,  6.02138055e-15, ...,\n",
       "          1.22341010e-36,  1.11739680e-44, -4.89458842e-53],\n",
       "        [ 0.00000000e+00,  4.75290379e-15, -6.01998478e-15, ...,\n",
       "          1.03749586e-36, -2.81357931e-44, -1.51631961e-53],\n",
       "        ...,\n",
       "        [ 0.00000000e+00,  1.47565436e-14,  1.84164948e-15, ...,\n",
       "          1.98501280e-37, -3.47018094e-44, -1.04323624e-53],\n",
       "        [ 0.00000000e+00, -4.94337599e-15, -7.02272788e-15, ...,\n",
       "          9.83305630e-37, -3.65821672e-44, -7.24590642e-54],\n",
       "        [ 0.00000000e+00, -1.45769229e-14, -2.07712323e-14, ...,\n",
       "         -1.77557106e-36, -1.64655945e-43,  2.14269060e-54]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>APEflux</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 6.009e-29 1.176e-33</div><input id='attrs-4b40b5ca-534e-4c80-be5f-9f39a1549b3d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4b40b5ca-534e-4c80-be5f-9f39a1549b3d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d6348072-07f8-4597-b934-a570e2edbef8' class='xr-var-data-in' type='checkbox'><label for='data-d6348072-07f8-4597-b934-a570e2edbef8' title='Show/Hide data repr'><svg 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>spectral flux of available potential energy</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        ...,\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan]],\n",
       "\n",
       "       [[            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "        [            nan,             nan,             nan, ...,\n",
       "                     nan,             nan,             nan],\n",
       "...\n",
       "        [-9.07965447e-14, -2.12633531e-13, -1.79900790e-13, ...,\n",
       "          2.49147160e-25,  7.07844285e-29,  1.55737362e-33],\n",
       "        [-3.31590069e-14, -1.84992929e-14, -4.50463454e-14, ...,\n",
       "          2.68727605e-25,  9.23761036e-29,  1.94489112e-33],\n",
       "        [ 9.83528125e-15,  9.80413187e-15,  2.27310319e-14, ...,\n",
       "          2.09922652e-25,  5.91089512e-29,  1.19712728e-33]],\n",
       "\n",
       "       [[-0.00000000e+00, -1.14007711e-15,  6.05041885e-15, ...,\n",
       "          1.65854099e-25,  3.34228166e-29,  6.99613525e-34],\n",
       "        [ 1.22508665e-14,  6.15231291e-15,  9.38951356e-15, ...,\n",
       "          2.19553492e-25,  5.60451632e-29,  1.18341218e-33],\n",
       "        [-3.15266495e-14,  2.86498421e-15, -3.66422714e-14, ...,\n",
       "          3.18491190e-25,  7.40785241e-29,  2.36878617e-33],\n",
       "        ...,\n",
       "        [-8.19514322e-14, -2.12611630e-13, -1.74000810e-13, ...,\n",
       "          2.53979319e-25,  7.14327629e-29,  1.56902844e-33],\n",
       "        [-3.15266495e-14, -1.81035927e-14, -4.42426712e-14, ...,\n",
       "          2.67031725e-25,  9.27003063e-29,  2.02781364e-33],\n",
       "        [ 1.22508665e-14,  1.04136249e-14,  1.49802449e-14, ...,\n",
       "          2.11046009e-25,  6.00855620e-29,  1.17617517e-33]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>KEflux</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 2.422e-27 4.291e-32</div><input id='attrs-962c8e01-050d-42d8-8345-f39db4f112bc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-962c8e01-050d-42d8-8345-f39db4f112bc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-638fc176-2e6b-4c4a-a1d2-63539d5e40e2' class='xr-var-data-in' type='checkbox'><label for='data-638fc176-2e6b-4c4a-a1d2-63539d5e40e2' title='Show/Hide data repr'><svg 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>spectral flux of kinetic energy</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([[[           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        ...,\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan]],\n",
       "\n",
       "       [[           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "        [           nan,            nan,            nan, ...,\n",
       "                    nan,            nan,            nan],\n",
       "...\n",
       "        [2.24597443e-13, 3.72273603e-13, 3.12896034e-13, ...,\n",
       "         7.10917252e-24, 1.37298888e-27, 2.66738655e-32],\n",
       "        [6.70621353e-14, 9.94719988e-14, 1.58284627e-13, ...,\n",
       "         9.24406004e-24, 2.21319672e-27, 3.86865051e-32],\n",
       "        [2.00461121e-14, 1.67409339e-14, 6.25940360e-14, ...,\n",
       "         1.11765606e-23, 2.39997336e-27, 4.28303794e-32]],\n",
       "\n",
       "       [[0.00000000e+00, 4.82345621e-16, 2.59234941e-14, ...,\n",
       "         1.16775541e-23, 2.20466006e-27, 4.22237860e-32],\n",
       "        [2.04859510e-14, 1.34540924e-14, 3.47304899e-14, ...,\n",
       "         9.78533627e-24, 2.26719663e-27, 4.24307233e-32],\n",
       "        [6.70340648e-14, 7.55668859e-14, 1.15758186e-13, ...,\n",
       "         9.30411802e-24, 2.02113647e-27, 4.76953338e-32],\n",
       "        ...,\n",
       "        [2.45636780e-13, 3.63387207e-13, 3.05913879e-13, ...,\n",
       "         7.10817748e-24, 1.38038431e-27, 2.70492417e-32],\n",
       "        [6.70340648e-14, 9.62168111e-14, 1.54683346e-13, ...,\n",
       "         9.31103824e-24, 2.23119730e-27, 4.00736006e-32],\n",
       "        [2.04859510e-14, 1.61558408e-14, 6.00798388e-14, ...,\n",
       "         1.11330930e-23, 2.42161714e-27, 4.29131799e-32]]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>APEgen</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 6.475e-11 6.456e-11</div><input id='attrs-d1489c3a-8c7b-400d-84eb-9245b4654d34' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d1489c3a-8c7b-400d-84eb-9245b4654d34' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cb59831-d025-4f98-9d67-e40ac3868e50' class='xr-var-data-in' type='checkbox'><label for='data-7cb59831-d025-4f98-9d67-e40ac3868e50' title='Show/Hide data repr'><svg 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>total available potential energy generation</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><pre>array([           nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "                  nan,            nan,            nan,            nan,\n",
       "       6.34425494e-11, 6.54395681e-11, 6.70926477e-11, 6.66109152e-11,\n",
       "       6.40405443e-11, 6.33695758e-11, 6.36028637e-11, 6.40699794e-11,\n",
       "       6.43281202e-11, 6.43169236e-11, 6.47496070e-11, 6.54998246e-11,\n",
       "       6.53919153e-11, 6.52939303e-11, 6.52407854e-11, 6.44910895e-11,\n",
       "       6.43796164e-11, 6.44973398e-11, 6.50160026e-11, 6.53565076e-11,\n",
       "       6.48395327e-11, 6.35820065e-11, 6.30562134e-11, 6.29877462e-11,\n",
       "       6.32006903e-11, 6.32672199e-11, 6.35699101e-11, 6.33380340e-11,\n",
       "       6.31595850e-11, 6.30717153e-11, 6.32056090e-11, 6.34479625e-11,\n",
       "       6.35859613e-11, 6.35054378e-11, 6.35682147e-11, 6.35319693e-11,\n",
       "       6.38371455e-11, 6.42162400e-11, 6.46542735e-11, 6.48371434e-11,\n",
       "       6.47469698e-11, 6.47538237e-11, 6.45632696e-11])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3f778f5f-9af6-491c-baa2-e4ac9c2ce23e' class='xr-section-summary-in' type='checkbox'  ><label for='section-3f778f5f-9af6-491c-baa2-e4ac9c2ce23e' class='xr-section-summary' >Indexes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-1c05f089-fcca-46f7-9ae5-e7e81af6f96c' class='xr-index-data-in' type='checkbox'/><label for='index-1c05f089-fcca-46f7-9ae5-e7e81af6f96c' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([        0.0,   3600000.0,   7200000.0,  10800000.0,  14400000.0,\n",
       "        18000000.0,  21600000.0,  25200000.0,  28800000.0,  32400000.0,\n",
       "        36000000.0,  39600000.0,  43200000.0,  46800000.0,  50400000.0,\n",
       "        54000000.0,  57600000.0,  61200000.0,  64800000.0,  68400000.0,\n",
       "        72000000.0,  75600000.0,  79200000.0,  82800000.0,  86400000.0,\n",
       "        90000000.0,  93600000.0,  97200000.0, 100800000.0, 104400000.0,\n",
       "       108000000.0, 111600000.0, 115200000.0, 118800000.0, 122400000.0,\n",
       "       126000000.0, 129600000.0, 133200000.0, 136800000.0, 140400000.0,\n",
       "       144000000.0, 147600000.0, 151200000.0, 154800000.0, 158400000.0,\n",
       "       162000000.0, 165600000.0, 169200000.0, 172800000.0, 176400000.0,\n",
       "       180000000.0, 183600000.0, 187200000.0, 190800000.0, 194400000.0,\n",
       "       198000000.0, 201600000.0, 205200000.0, 208800000.0, 212400000.0,\n",
       "       216000000.0, 219600000.0, 223200000.0, 226800000.0, 230400000.0,\n",
       "       234000000.0, 237600000.0, 241200000.0, 244800000.0, 248400000.0,\n",
       "       252000000.0, 255600000.0, 259200000.0, 262800000.0, 266400000.0,\n",
       "       270000000.0, 273600000.0, 277200000.0, 280800000.0, 284400000.0,\n",
       "       288000000.0, 291600000.0, 295200000.0, 298800000.0, 302400000.0,\n",
       "       306000000.0, 309600000.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-ff72aace-0401-48a6-8b9d-5ce6f10b570a' class='xr-index-data-in' type='checkbox'/><label for='index-ff72aace-0401-48a6-8b9d-5ce6f10b570a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2], dtype=&#x27;int64&#x27;, name=&#x27;lev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev_mid</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-3fb7bcf9-84ed-4e90-ba70-dbd1efd74ecd' class='xr-index-data-in' type='checkbox'/><label for='index-3fb7bcf9-84ed-4e90-ba70-dbd1efd74ecd' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1.5], dtype=&#x27;float64&#x27;, name=&#x27;lev_mid&#x27;))</pre></div></li><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-1cb88855-f648-40cf-a435-167b2650e593' class='xr-index-data-in' type='checkbox'/><label for='index-1cb88855-f648-40cf-a435-167b2650e593' 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([  7812.5,  23437.5,  39062.5,  54687.5,  70312.5,  85937.5, 101562.5,\n",
       "       117187.5, 132812.5, 148437.5, 164062.5, 179687.5, 195312.5, 210937.5,\n",
       "       226562.5, 242187.5, 257812.5, 273437.5, 289062.5, 304687.5, 320312.5,\n",
       "       335937.5, 351562.5, 367187.5, 382812.5, 398437.5, 414062.5, 429687.5,\n",
       "       445312.5, 460937.5, 476562.5, 492187.5, 507812.5, 523437.5, 539062.5,\n",
       "       554687.5, 570312.5, 585937.5, 601562.5, 617187.5, 632812.5, 648437.5,\n",
       "       664062.5, 679687.5, 695312.5, 710937.5, 726562.5, 742187.5, 757812.5,\n",
       "       773437.5, 789062.5, 804687.5, 820312.5, 835937.5, 851562.5, 867187.5,\n",
       "       882812.5, 898437.5, 914062.5, 929687.5, 945312.5, 960937.5, 976562.5,\n",
       "       992187.5],\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-e5ee5913-5c39-4b4f-9cab-96720b765be8' class='xr-index-data-in' type='checkbox'/><label for='index-e5ee5913-5c39-4b4f-9cab-96720b765be8' 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([  7812.5,  23437.5,  39062.5,  54687.5,  70312.5,  85937.5, 101562.5,\n",
       "       117187.5, 132812.5, 148437.5, 164062.5, 179687.5, 195312.5, 210937.5,\n",
       "       226562.5, 242187.5, 257812.5, 273437.5, 289062.5, 304687.5, 320312.5,\n",
       "       335937.5, 351562.5, 367187.5, 382812.5, 398437.5, 414062.5, 429687.5,\n",
       "       445312.5, 460937.5, 476562.5, 492187.5, 507812.5, 523437.5, 539062.5,\n",
       "       554687.5, 570312.5, 585937.5, 601562.5, 617187.5, 632812.5, 648437.5,\n",
       "       664062.5, 679687.5, 695312.5, 710937.5, 726562.5, 742187.5, 757812.5,\n",
       "       773437.5, 789062.5, 804687.5, 820312.5, 835937.5, 851562.5, 867187.5,\n",
       "       882812.5, 898437.5, 914062.5, 929687.5, 945312.5, 960937.5, 976562.5,\n",
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       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>l</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-99fc799a-d427-4b68-8c53-f06c03b5016e' class='xr-index-data-in' type='checkbox'/><label for='index-99fc799a-d427-4b68-8c53-f06c03b5016e' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                    0.0,   6.283185307179587e-06,\n",
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       "       -0.00020106192982974677, -0.00019477874452256718,\n",
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       "       -0.00011309733552923256, -0.00010681415022205297,\n",
       "       -0.00010053096491487339,   -9.42477796076938e-05,\n",
       "        -8.796459430051421e-05,  -8.168140899333462e-05,\n",
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       "       -3.7699111843077517e-05, -3.1415926535897935e-05,\n",
       "       -2.5132741228718347e-05, -1.8849555921538758e-05,\n",
       "       -1.2566370614359173e-05,  -6.283185307179587e-06],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;l&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>k</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-7fabfc6d-374c-480f-a283-e570aeaeff1f' class='xr-index-data-in' type='checkbox'/><label for='index-7fabfc6d-374c-480f-a283-e570aeaeff1f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                   0.0,  6.283185307179587e-06, 1.2566370614359173e-05,\n",
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       "      dtype=&#x27;float64&#x27;, name=&#x27;k&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-a7fab065-61ff-44cf-94cc-ed3554e5ecff' class='xr-section-summary-in' type='checkbox'  ><label for='section-a7fab065-61ff-44cf-94cc-ed3554e5ecff' class='xr-section-summary' >Attributes: <span>(23)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>pyqg:beta :</span></dt><dd>1.5e-11</dd><dt><span>pyqg:delta :</span></dt><dd>0.25</dd><dt><span>pyqg:del2 :</span></dt><dd>0.8</dd><dt><span>pyqg:dt :</span></dt><dd>3600.0</dd><dt><span>pyqg:filterfac :</span></dt><dd>23.6</dd><dt><span>pyqg:L :</span></dt><dd>1000000.0</dd><dt><span>pyqg:M :</span></dt><dd>4096</dd><dt><span>pyqg:nk :</span></dt><dd>33</dd><dt><span>pyqg:nl :</span></dt><dd>64</dd><dt><span>pyqg:ntd :</span></dt><dd>1</dd><dt><span>pyqg:nx :</span></dt><dd>64</dd><dt><span>pyqg:ny :</span></dt><dd>64</dd><dt><span>pyqg:nz :</span></dt><dd>2</dd><dt><span>pyqg:rd :</span></dt><dd>15000.0</dd><dt><span>pyqg:rek :</span></dt><dd>5.787e-07</dd><dt><span>pyqg:taveint :</span></dt><dd>86400.0</dd><dt><span>pyqg:tavestart :</span></dt><dd>155520000.0</dd><dt><span>pyqg:tc :</span></dt><dd>0</dd><dt><span>pyqg:tmax :</span></dt><dd>311040000.0</dd><dt><span>pyqg:twrite :</span></dt><dd>10000</dd><dt><span>pyqg:W :</span></dt><dd>1000000.0</dd><dt><span>title :</span></dt><dd>pyqg: Python Quasigeostrophic Model</dd><dt><span>reference :</span></dt><dd>https://pyqg.readthedocs.io/en/latest/index.html</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (time: 87, lev: 2, y: 64, x: 64, l: 64, k: 33, lev_mid: 1)\n",
       "Coordinates:\n",
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       "  * lev                (lev) int64 1 2\n",
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       "  * y                  (y) float64 7.812e+03 2.344e+04 ... 9.766e+05 9.922e+05\n",
       "  * l                  (l) float64 0.0 6.283e-06 ... -1.257e-05 -6.283e-06\n",
       "  * k                  (k) float64 0.0 6.283e-06 ... 0.0001948 0.0002011\n",
       "Data variables: (12/32)\n",
       "    q                  (time, lev, y, x) float64 8.821e-07 ... -1.423e-06\n",
       "    u                  (time, lev, y, x) float64 0.0 0.0 ... -0.00861 -0.007082\n",
       "    v                  (time, lev, y, x) float64 0.0 0.0 ... -0.002291 -0.0035\n",
       "    ufull              (time, lev, y, x) float64 0.025 0.025 ... -0.007082\n",
       "    vfull              (time, lev, y, x) float64 0.0 0.0 ... -0.002291 -0.0035\n",
       "    qh                 (time, lev, l, k) complex128 (0.002521111233473065+0j)...\n",
       "    ...                 ...\n",
       "    ENSgenspec         (time, l, k) float64 nan nan nan ... -7.318e-52 9.523e-63\n",
       "    ENSfrictionspec    (time, l, k) float64 nan nan ... -5.582e-51 -2.619e-60\n",
       "    APEgenspec         (time, l, k) float64 nan nan nan ... -1.647e-43 2.143e-54\n",
       "    APEflux            (time, l, k) float64 nan nan nan ... 6.009e-29 1.176e-33\n",
       "    KEflux             (time, l, k) float64 nan nan nan ... 2.422e-27 4.291e-32\n",
       "    APEgen             (time) float64 nan nan nan ... 6.475e-11 6.456e-11\n",
       "Attributes: (12/23)\n",
       "    pyqg:beta:       1.5e-11\n",
       "    pyqg:delta:      0.25\n",
       "    pyqg:del2:       0.8\n",
       "    pyqg:dt:         3600.0\n",
       "    pyqg:filterfac:  23.6\n",
       "    pyqg:L:          1000000.0\n",
       "    ...              ...\n",
       "    pyqg:tc:         0\n",
       "    pyqg:tmax:       311040000.0\n",
       "    pyqg:twrite:     10000\n",
       "    pyqg:W:          1000000.0\n",
       "    title:           pyqg: Python Quasigeostrophic Model\n",
       "    reference:       https://pyqg.readthedocs.io/en/latest/index.html"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# after initial run, generate snapshots of model\n",
    "\n",
    "def generate_snapshots(model):\n",
    "    snapshots = []\n",
    "    snapshots.append(model.to_dataset())\n",
    "    for _ in model.run_with_snapshots(tsnapint=1000*model.dt):\n",
    "        snapshots.append(model.to_dataset())\n",
    "    return xr.concat(snapshots, dim='time')\n",
    "\n",
    "eddy_model_highRes = generate_snapshots(eddy_model)\n",
    "eddy_model_highRes"
   ]
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  {
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   "execution_count": 4,
   "id": "7e84451a-3354-46dc-a511-a7610d9674b9",
   "metadata": {
    "tags": []
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   "outputs": [
    {
     "data": {
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       "<xarray.backends.zarr.ZarrStore at 0x7f88972298c0>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# write data to scratch bucket after initial run\n",
    "\n",
    "import os\n",
    "import xarray as xr\n",
    "SCRATCH_BUCKET = os.environ['SCRATCH_BUCKET'] \n",
    "eddy_model_highRes.to_zarr('gs://leap-scratch/panalluri/scalar/eddy_lowRes.zarr')  # write data"
   ]
  },
  {
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   "id": "d60fc617-5c7c-466b-91b9-6528013e1f27",
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   },
   "outputs": [
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       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:            (time: 87, l: 64, k: 33, lev: 2, y: 64, x: 64, lev_mid: 1)\n",
       "Coordinates:\n",
       "  * k                  (k) float64 0.0 6.283e-06 ... 0.0001948 0.0002011\n",
       "  * l                  (l) float64 0.0 6.283e-06 ... -1.257e-05 -6.283e-06\n",
       "  * lev                (lev) int64 1 2\n",
       "  * lev_mid            (lev_mid) float64 1.5\n",
       "  * time               (time) float64 0.0 3.6e+06 7.2e+06 ... 3.06e+08 3.096e+08\n",
       "  * x                  (x) float64 7.812e+03 2.344e+04 ... 9.766e+05 9.922e+05\n",
       "  * y                  (y) float64 7.812e+03 2.344e+04 ... 9.766e+05 9.922e+05\n",
       "Data variables: (12/32)\n",
       "    APEflux            (time, l, k) float64 dask.array&lt;chunksize=(44, 32, 33), meta=np.ndarray&gt;\n",
       "    APEgen             (time) float64 dask.array&lt;chunksize=(87,), meta=np.ndarray&gt;\n",
       "    APEgenspec         (time, l, k) float64 dask.array&lt;chunksize=(44, 32, 33), meta=np.ndarray&gt;\n",
       "    Dissspec           (time, l, k) float64 dask.array&lt;chunksize=(44, 32, 33), meta=np.ndarray&gt;\n",
       "    EKE                (time, lev) float64 dask.array&lt;chunksize=(87, 2), meta=np.ndarray&gt;\n",
       "    EKEdiss            (time) float64 dask.array&lt;chunksize=(87,), meta=np.ndarray&gt;\n",
       "    ...                 ...\n",
       "    u                  (time, lev, y, x) float64 dask.array&lt;chunksize=(44, 1, 32, 32), meta=np.ndarray&gt;\n",
       "    ufull              (time, lev, y, x) float64 dask.array&lt;chunksize=(44, 1, 32, 32), meta=np.ndarray&gt;\n",
       "    uh                 (time, lev, l, k) complex128 dask.array&lt;chunksize=(44, 1, 32, 17), meta=np.ndarray&gt;\n",
       "    v                  (time, lev, y, x) float64 dask.array&lt;chunksize=(44, 1, 32, 32), meta=np.ndarray&gt;\n",
       "    vfull              (time, lev, y, x) float64 dask.array&lt;chunksize=(44, 1, 32, 32), meta=np.ndarray&gt;\n",
       "    vh                 (time, lev, l, k) complex128 dask.array&lt;chunksize=(44, 1, 32, 17), meta=np.ndarray&gt;\n",
       "Attributes: (12/23)\n",
       "    pyqg:L:          1000000.0\n",
       "    pyqg:M:          4096\n",
       "    pyqg:W:          1000000.0\n",
       "    pyqg:beta:       1.5e-11\n",
       "    pyqg:del2:       0.8\n",
       "    pyqg:delta:      0.25\n",
       "    ...              ...\n",
       "    pyqg:tavestart:  155520000.0\n",
       "    pyqg:tc:         0\n",
       "    pyqg:tmax:       311040000.0\n",
       "    pyqg:twrite:     10000\n",
       "    reference:       https://pyqg.readthedocs.io/en/latest/index.html\n",
       "    title:           pyqg: Python Quasigeostrophic Model</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-88076b7e-4b70-4179-a3a6-2c5b97b9f398' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-88076b7e-4b70-4179-a3a6-2c5b97b9f398' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 87</li><li><span class='xr-has-index'>l</span>: 64</li><li><span class='xr-has-index'>k</span>: 33</li><li><span class='xr-has-index'>lev</span>: 2</li><li><span class='xr-has-index'>y</span>: 64</li><li><span class='xr-has-index'>x</span>: 64</li><li><span class='xr-has-index'>lev_mid</span>: 1</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-4cafb717-0527-4c5b-9964-8cca536f5abd' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4cafb717-0527-4c5b-9964-8cca536f5abd' class='xr-section-summary' >Coordinates: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>k</span></div><div class='xr-var-dims'>(k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 6.283e-06 ... 0.0002011</div><input id='attrs-6f2c1f8e-7346-44dd-ba48-4b5d6af08b7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6f2c1f8e-7346-44dd-ba48-4b5d6af08b7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c4872601-95ff-441d-a7e2-bb77ad9b04a7' class='xr-var-data-in' type='checkbox'><label for='data-c4872601-95ff-441d-a7e2-bb77ad9b04a7' title='Show/Hide data repr'><svg 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>spectal space grid points in the k direction</dd><dt><span>units :</span></dt><dd>zonal wavenumber</dd></dl></div><div class='xr-var-data'><pre>array([0.000000e+00, 6.283185e-06, 1.256637e-05, 1.884956e-05, 2.513274e-05,\n",
       "       3.141593e-05, 3.769911e-05, 4.398230e-05, 5.026548e-05, 5.654867e-05,\n",
       "       6.283185e-05, 6.911504e-05, 7.539822e-05, 8.168141e-05, 8.796459e-05,\n",
       "       9.424778e-05, 1.005310e-04, 1.068142e-04, 1.130973e-04, 1.193805e-04,\n",
       "       1.256637e-04, 1.319469e-04, 1.382301e-04, 1.445133e-04, 1.507964e-04,\n",
       "       1.570796e-04, 1.633628e-04, 1.696460e-04, 1.759292e-04, 1.822124e-04,\n",
       "       1.884956e-04, 1.947787e-04, 2.010619e-04])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>l</span></div><div class='xr-var-dims'>(l)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 6.283e-06 ... -6.283e-06</div><input id='attrs-5b0972f6-681d-4828-954a-44d54ad9cca1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-5b0972f6-681d-4828-954a-44d54ad9cca1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9e52bd1e-e3c2-4614-a372-b518fb1f99ad' class='xr-var-data-in' type='checkbox'><label for='data-9e52bd1e-e3c2-4614-a372-b518fb1f99ad' title='Show/Hide data repr'><svg 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>spectal space grid points in the l direction</dd><dt><span>units :</span></dt><dd>meridional wavenumber</dd></dl></div><div class='xr-var-data'><pre>array([ 0.000000e+00,  6.283185e-06,  1.256637e-05,  1.884956e-05,\n",
       "        2.513274e-05,  3.141593e-05,  3.769911e-05,  4.398230e-05,\n",
       "        5.026548e-05,  5.654867e-05,  6.283185e-05,  6.911504e-05,\n",
       "        7.539822e-05,  8.168141e-05,  8.796459e-05,  9.424778e-05,\n",
       "        1.005310e-04,  1.068142e-04,  1.130973e-04,  1.193805e-04,\n",
       "        1.256637e-04,  1.319469e-04,  1.382301e-04,  1.445133e-04,\n",
       "        1.507964e-04,  1.570796e-04,  1.633628e-04,  1.696460e-04,\n",
       "        1.759292e-04,  1.822124e-04,  1.884956e-04,  1.947787e-04,\n",
       "       -2.010619e-04, -1.947787e-04, -1.884956e-04, -1.822124e-04,\n",
       "       -1.759292e-04, -1.696460e-04, -1.633628e-04, -1.570796e-04,\n",
       "       -1.507964e-04, -1.445133e-04, -1.382301e-04, -1.319469e-04,\n",
       "       -1.256637e-04, -1.193805e-04, -1.130973e-04, -1.068142e-04,\n",
       "       -1.005310e-04, -9.424778e-05, -8.796459e-05, -8.168141e-05,\n",
       "       -7.539822e-05, -6.911504e-05, -6.283185e-05, -5.654867e-05,\n",
       "       -5.026548e-05, -4.398230e-05, -3.769911e-05, -3.141593e-05,\n",
       "       -2.513274e-05, -1.884956e-05, -1.256637e-05, -6.283185e-06])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev</span></div><div class='xr-var-dims'>(lev)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1 2</div><input id='attrs-b1246d0b-b8bb-4fe2-8c1d-25db16976a53' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b1246d0b-b8bb-4fe2-8c1d-25db16976a53' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5b3dd038-fbd6-4c50-9d47-73902da2104b' class='xr-var-data-in' type='checkbox'><label for='data-5b3dd038-fbd6-4c50-9d47-73902da2104b' title='Show/Hide data repr'><svg 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>vertical levels</dd></dl></div><div class='xr-var-data'><pre>array([1, 2])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lev_mid</span></div><div class='xr-var-dims'>(lev_mid)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1.5</div><input id='attrs-ce322561-ffb3-4488-a8c9-9db880624b23' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ce322561-ffb3-4488-a8c9-9db880624b23' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-323b8532-ccc0-4c7d-942d-47e673eadb42' class='xr-var-data-in' type='checkbox'><label for='data-323b8532-ccc0-4c7d-942d-47e673eadb42' title='Show/Hide data repr'><svg 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>vertical level interface</dd></dl></div><div class='xr-var-data'><pre>array([1.5])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.6e+06 ... 3.06e+08 3.096e+08</div><input id='attrs-48201194-59d6-4a24-a9bb-bfcdfae0db7f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-48201194-59d6-4a24-a9bb-bfcdfae0db7f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ab63711e-3a4d-4fbd-9789-c706adc12e7a' class='xr-var-data-in' type='checkbox'><label for='data-ab63711e-3a4d-4fbd-9789-c706adc12e7a' title='Show/Hide data repr'><svg 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>model time</dd><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>array([0.000e+00, 3.600e+06, 7.200e+06, 1.080e+07, 1.440e+07, 1.800e+07,\n",
       "       2.160e+07, 2.520e+07, 2.880e+07, 3.240e+07, 3.600e+07, 3.960e+07,\n",
       "       4.320e+07, 4.680e+07, 5.040e+07, 5.400e+07, 5.760e+07, 6.120e+07,\n",
       "       6.480e+07, 6.840e+07, 7.200e+07, 7.560e+07, 7.920e+07, 8.280e+07,\n",
       "       8.640e+07, 9.000e+07, 9.360e+07, 9.720e+07, 1.008e+08, 1.044e+08,\n",
       "       1.080e+08, 1.116e+08, 1.152e+08, 1.188e+08, 1.224e+08, 1.260e+08,\n",
       "       1.296e+08, 1.332e+08, 1.368e+08, 1.404e+08, 1.440e+08, 1.476e+08,\n",
       "       1.512e+08, 1.548e+08, 1.584e+08, 1.620e+08, 1.656e+08, 1.692e+08,\n",
       "       1.728e+08, 1.764e+08, 1.800e+08, 1.836e+08, 1.872e+08, 1.908e+08,\n",
       "       1.944e+08, 1.980e+08, 2.016e+08, 2.052e+08, 2.088e+08, 2.124e+08,\n",
       "       2.160e+08, 2.196e+08, 2.232e+08, 2.268e+08, 2.304e+08, 2.340e+08,\n",
       "       2.376e+08, 2.412e+08, 2.448e+08, 2.484e+08, 2.520e+08, 2.556e+08,\n",
       "       2.592e+08, 2.628e+08, 2.664e+08, 2.700e+08, 2.736e+08, 2.772e+08,\n",
       "       2.808e+08, 2.844e+08, 2.880e+08, 2.916e+08, 2.952e+08, 2.988e+08,\n",
       "       3.024e+08, 3.060e+08, 3.096e+08])</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'>7.812e+03 2.344e+04 ... 9.922e+05</div><input id='attrs-83218976-8d44-48c5-8c48-d097700daa12' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-83218976-8d44-48c5-8c48-d097700daa12' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7460fbd5-9736-4704-9cb9-6d87ccf9616c' class='xr-var-data-in' type='checkbox'><label for='data-7460fbd5-9736-4704-9cb9-6d87ccf9616c' title='Show/Hide data repr'><svg 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>real space grid points in the x direction</dd><dt><span>units :</span></dt><dd>grid point</dd></dl></div><div class='xr-var-data'><pre>array([  7812.5,  23437.5,  39062.5,  54687.5,  70312.5,  85937.5, 101562.5,\n",
       "       117187.5, 132812.5, 148437.5, 164062.5, 179687.5, 195312.5, 210937.5,\n",
       "       226562.5, 242187.5, 257812.5, 273437.5, 289062.5, 304687.5, 320312.5,\n",
       "       335937.5, 351562.5, 367187.5, 382812.5, 398437.5, 414062.5, 429687.5,\n",
       "       445312.5, 460937.5, 476562.5, 492187.5, 507812.5, 523437.5, 539062.5,\n",
       "       554687.5, 570312.5, 585937.5, 601562.5, 617187.5, 632812.5, 648437.5,\n",
       "       664062.5, 679687.5, 695312.5, 710937.5, 726562.5, 742187.5, 757812.5,\n",
       "       773437.5, 789062.5, 804687.5, 820312.5, 835937.5, 851562.5, 867187.5,\n",
       "       882812.5, 898437.5, 914062.5, 929687.5, 945312.5, 960937.5, 976562.5,\n",
       "       992187.5])</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'>7.812e+03 2.344e+04 ... 9.922e+05</div><input id='attrs-412d17fe-d63c-4d7d-9dc3-b5c1adc71d66' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-412d17fe-d63c-4d7d-9dc3-b5c1adc71d66' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-691166a1-814b-48be-9b2a-a4e80601f68d' class='xr-var-data-in' type='checkbox'><label for='data-691166a1-814b-48be-9b2a-a4e80601f68d' title='Show/Hide data repr'><svg 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>real space grid points in the y direction</dd><dt><span>units :</span></dt><dd>grid point</dd></dl></div><div class='xr-var-data'><pre>array([  7812.5,  23437.5,  39062.5,  54687.5,  70312.5,  85937.5, 101562.5,\n",
       "       117187.5, 132812.5, 148437.5, 164062.5, 179687.5, 195312.5, 210937.5,\n",
       "       226562.5, 242187.5, 257812.5, 273437.5, 289062.5, 304687.5, 320312.5,\n",
       "       335937.5, 351562.5, 367187.5, 382812.5, 398437.5, 414062.5, 429687.5,\n",
       "       445312.5, 460937.5, 476562.5, 492187.5, 507812.5, 523437.5, 539062.5,\n",
       "       554687.5, 570312.5, 585937.5, 601562.5, 617187.5, 632812.5, 648437.5,\n",
       "       664062.5, 679687.5, 695312.5, 710937.5, 726562.5, 742187.5, 757812.5,\n",
       "       773437.5, 789062.5, 804687.5, 820312.5, 835937.5, 851562.5, 867187.5,\n",
       "       882812.5, 898437.5, 914062.5, 929687.5, 945312.5, 960937.5, 976562.5,\n",
       "       992187.5])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-9a28f0f2-4152-43d4-89f5-4edea2c1b8af' class='xr-section-summary-in' type='checkbox'  ><label for='section-9a28f0f2-4152-43d4-89f5-4edea2c1b8af' class='xr-section-summary' >Data variables: <span>(32)</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>APEflux</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44, 32, 33), meta=np.ndarray&gt;</div><input id='attrs-396d11c6-08b0-4ada-84a1-dfae74f3688f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-396d11c6-08b0-4ada-84a1-dfae74f3688f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-adb998f4-4d10-4470-a000-0fcb3a98650a' class='xr-var-data-in' type='checkbox'><label for='data-adb998f4-4d10-4470-a000-0fcb3a98650a' title='Show/Hide data repr'><svg 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>spectral flux of available potential energy</dd><dt><span>units :</span></dt><dd>m^2 s^-3</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> 1.40 MiB </td>\n",
       "                        <td> 363.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (87, 64, 33) </td>\n",
       "                        <td> (44, 32, 33) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 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>APEgen</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(87,), meta=np.ndarray&gt;</div><input id='attrs-253ee9b9-5bdd-4538-9b06-1adb95406c3b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-253ee9b9-5bdd-4538-9b06-1adb95406c3b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c9fdb4a9-894c-4a7b-a78d-0f40de1681da' class='xr-var-data-in' type='checkbox'><label for='data-c9fdb4a9-894c-4a7b-a78d-0f40de1681da' title='Show/Hide data repr'><svg 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>total available potential energy generation</dd><dt><span>units :</span></dt><dd>m^2 s^-3</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> 696 B </td>\n",
       "                        <td> 696 B </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (87,) </td>\n",
       "                        <td> (87,) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 1 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                </tbody>\n",
       "            </table>\n",
       "        </td>\n",
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       "        </td>\n",
       "    </tr>\n",
       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>APEgenspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44, 32, 33), meta=np.ndarray&gt;</div><input id='attrs-e577af0c-3c99-4a83-847e-91c74c0a411e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e577af0c-3c99-4a83-847e-91c74c0a411e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c99da28a-1912-4e5c-b5a2-1288bc74dc75' class='xr-var-data-in' type='checkbox'><label for='data-c99da28a-1912-4e5c-b5a2-1288bc74dc75' title='Show/Hide data repr'><svg 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>the spectrum of the rate of generation of available potential energy</dd><dt><span>units :</span></dt><dd>m^2 s^-3</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> 1.40 MiB </td>\n",
       "                        <td> 363.00 kiB </td>\n",
       "                    </tr>\n",
       "                    \n",
       "                    <tr>\n",
       "                        <th> Shape </th>\n",
       "                        <td> (87, 64, 33) </td>\n",
       "                        <td> (44, 32, 33) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>Dissspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44, 32, 33), meta=np.ndarray&gt;</div><input id='attrs-2ad1ab49-b09d-40bf-b88f-cf5af88981a3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2ad1ab49-b09d-40bf-b88f-cf5af88981a3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a0e97e82-18ef-482f-8e4a-77dd2faf1330' class='xr-var-data-in' type='checkbox'><label for='data-a0e97e82-18ef-482f-8e4a-77dd2faf1330' title='Show/Hide data repr'><svg 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>Spectral contribution of filter dissipation to total energy</dd><dt><span>units :</span></dt><dd>m^2 s^-3</dd></dl></div><div class='xr-var-data'><table>\n",
       "    <tr>\n",
       "        <td>\n",
       "            <table style=\"border-collapse: collapse;\">\n",
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       "        <td>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 1.40 MiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (87, 64, 33) </td>\n",
       "                        <td> (44, 32, 33) </td>\n",
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       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "</table></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ENSgenspec</span></div><div class='xr-var-dims'>(time, l, k)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(44, 32, 33), meta=np.ndarray&gt;</div><input id='attrs-31f9cb55-c5d5-4525-936c-ae9d9f9e4d43' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-31f9cb55-c5d5-4525-936c-ae9d9f9e4d43' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8cebc1e9-b777-4bae-8bb4-4b2da2ed967a' class='xr-var-data-in' type='checkbox'><label for='data-8cebc1e9-b777-4bae-8bb4-4b2da2ed967a' title='Show/Hide data repr'><svg 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>the spectrum of the rate of generation of barotropic enstrophy</dd><dt><span>units :</span></dt><dd>s^-3</dd></dl></div><div class='xr-var-data'><table>\n",
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       "                        <td> 1.40 MiB </td>\n",
       "                        <td> 363.00 kiB </td>\n",
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       "                        <td> (44, 32, 33) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4 chunks in 2 graph layers </td>\n",
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       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                    <tr>\n",
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       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "    <tr>\n",
       "        <td>\n",
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       "                        <td> 5.44 MiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (87, 2, 64, 64) </td>\n",
       "                        <td> (44, 1, 32, 32) </td>\n",
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       "                        <th> Bytes </th>\n",
       "                        <td> 1.40 MiB </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (87, 64, 33) </td>\n",
       "                        <td> (44, 32, 33) </td>\n",
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       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 4 chunks in 2 graph layers </td>\n",
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       "                        <th> Shape </th>\n",
       "                        <td> (87, 2, 64, 33) </td>\n",
       "                        <td> (44, 1, 32, 17) </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Dask graph </th>\n",
       "                        <td colspan=\"2\"> 16 chunks in 2 graph layers </td>\n",
       "                    </tr>\n",
       "                    <tr>\n",
       "                        <th> Data type </th>\n",
       "                        <td colspan=\"2\"> complex128 numpy.ndarray </td>\n",
       "                    </tr>\n",
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       "</table></div></li></ul></div></li><li class='xr-section-item'><input id='section-5e51e985-9cba-4451-85a6-d6ffb40ac5e9' class='xr-section-summary-in' type='checkbox'  ><label for='section-5e51e985-9cba-4451-85a6-d6ffb40ac5e9' class='xr-section-summary' >Indexes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>k</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-78c46491-f6c5-4985-a2b3-0d62c911919b' class='xr-index-data-in' type='checkbox'/><label for='index-78c46491-f6c5-4985-a2b3-0d62c911919b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                   0.0,  6.283185307179587e-06, 1.2566370614359173e-05,\n",
       "       1.8849555921538758e-05, 2.5132741228718347e-05, 3.1415926535897935e-05,\n",
       "       3.7699111843077517e-05, 4.3982297150257105e-05,  5.026548245743669e-05,\n",
       "        5.654866776461628e-05,  6.283185307179587e-05,  6.911503837897546e-05,\n",
       "        7.539822368615503e-05,  8.168140899333462e-05,  8.796459430051421e-05,\n",
       "         9.42477796076938e-05, 0.00010053096491487339, 0.00010681415022205297,\n",
       "       0.00011309733552923256, 0.00011938052083641215, 0.00012566370614359174,\n",
       "       0.00013194689145077133, 0.00013823007675795092,  0.0001445132620651305,\n",
       "       0.00015079644737231007, 0.00015707963267948965, 0.00016336281798666924,\n",
       "       0.00016964600329384883, 0.00017592918860102842,   0.000182212373908208,\n",
       "        0.0001884955592153876, 0.00019477874452256718, 0.00020106192982974677],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;k&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>l</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-5c5d2eeb-da77-4471-88f6-37e200da6176' class='xr-index-data-in' type='checkbox'/><label for='index-5c5d2eeb-da77-4471-88f6-37e200da6176' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                    0.0,   6.283185307179587e-06,\n",
       "        1.2566370614359173e-05,  1.8849555921538758e-05,\n",
       "        2.5132741228718347e-05,  3.1415926535897935e-05,\n",
       "        3.7699111843077517e-05,  4.3982297150257105e-05,\n",
       "         5.026548245743669e-05,   5.654866776461628e-05,\n",
       "         6.283185307179587e-05,   6.911503837897546e-05,\n",
       "         7.539822368615503e-05,   8.168140899333462e-05,\n",
       "         8.796459430051421e-05,    9.42477796076938e-05,\n",
       "        0.00010053096491487339,  0.00010681415022205297,\n",
       "        0.00011309733552923256,  0.00011938052083641215,\n",
       "        0.00012566370614359174,  0.00013194689145077133,\n",
       "        0.00013823007675795092,   0.0001445132620651305,\n",
       "        0.00015079644737231007,  0.00015707963267948965,\n",
       "        0.00016336281798666924,  0.00016964600329384883,\n",
       "        0.00017592918860102842,    0.000182212373908208,\n",
       "         0.0001884955592153876,  0.00019477874452256718,\n",
       "       -0.00020106192982974677, -0.00019477874452256718,\n",
       "        -0.0001884955592153876,   -0.000182212373908208,\n",
       "       -0.00017592918860102842, -0.00016964600329384883,\n",
       "       -0.00016336281798666924, -0.00015707963267948965,\n",
       "       -0.00015079644737231007,  -0.0001445132620651305,\n",
       "       -0.00013823007675795092, -0.00013194689145077133,\n",
       "       -0.00012566370614359174, -0.00011938052083641215,\n",
       "       -0.00011309733552923256, -0.00010681415022205297,\n",
       "       -0.00010053096491487339,   -9.42477796076938e-05,\n",
       "        -8.796459430051421e-05,  -8.168140899333462e-05,\n",
       "        -7.539822368615503e-05,  -6.911503837897546e-05,\n",
       "        -6.283185307179587e-05,  -5.654866776461628e-05,\n",
       "        -5.026548245743669e-05, -4.3982297150257105e-05,\n",
       "       -3.7699111843077517e-05, -3.1415926535897935e-05,\n",
       "       -2.5132741228718347e-05, -1.8849555921538758e-05,\n",
       "       -1.2566370614359173e-05,  -6.283185307179587e-06],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;l&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-aebc4578-3982-4d90-b764-0583b407cef4' class='xr-index-data-in' type='checkbox'/><label for='index-aebc4578-3982-4d90-b764-0583b407cef4' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1, 2], dtype=&#x27;int64&#x27;, name=&#x27;lev&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>lev_mid</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4301098c-5f72-4cc3-b6cc-4ec9472bb73a' class='xr-index-data-in' type='checkbox'/><label for='index-4301098c-5f72-4cc3-b6cc-4ec9472bb73a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([1.5], dtype=&#x27;float64&#x27;, name=&#x27;lev_mid&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>time</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-d179dd4b-d90c-403e-a407-e1851f8753b5' class='xr-index-data-in' type='checkbox'/><label for='index-d179dd4b-d90c-403e-a407-e1851f8753b5' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([        0.0,   3600000.0,   7200000.0,  10800000.0,  14400000.0,\n",
       "        18000000.0,  21600000.0,  25200000.0,  28800000.0,  32400000.0,\n",
       "        36000000.0,  39600000.0,  43200000.0,  46800000.0,  50400000.0,\n",
       "        54000000.0,  57600000.0,  61200000.0,  64800000.0,  68400000.0,\n",
       "        72000000.0,  75600000.0,  79200000.0,  82800000.0,  86400000.0,\n",
       "        90000000.0,  93600000.0,  97200000.0, 100800000.0, 104400000.0,\n",
       "       108000000.0, 111600000.0, 115200000.0, 118800000.0, 122400000.0,\n",
       "       126000000.0, 129600000.0, 133200000.0, 136800000.0, 140400000.0,\n",
       "       144000000.0, 147600000.0, 151200000.0, 154800000.0, 158400000.0,\n",
       "       162000000.0, 165600000.0, 169200000.0, 172800000.0, 176400000.0,\n",
       "       180000000.0, 183600000.0, 187200000.0, 190800000.0, 194400000.0,\n",
       "       198000000.0, 201600000.0, 205200000.0, 208800000.0, 212400000.0,\n",
       "       216000000.0, 219600000.0, 223200000.0, 226800000.0, 230400000.0,\n",
       "       234000000.0, 237600000.0, 241200000.0, 244800000.0, 248400000.0,\n",
       "       252000000.0, 255600000.0, 259200000.0, 262800000.0, 266400000.0,\n",
       "       270000000.0, 273600000.0, 277200000.0, 280800000.0, 284400000.0,\n",
       "       288000000.0, 291600000.0, 295200000.0, 298800000.0, 302400000.0,\n",
       "       306000000.0, 309600000.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;time&#x27;))</pre></div></li><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-d0848d73-08ce-482b-8763-61d1d83acbef' class='xr-index-data-in' type='checkbox'/><label for='index-d0848d73-08ce-482b-8763-61d1d83acbef' 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([  7812.5,  23437.5,  39062.5,  54687.5,  70312.5,  85937.5, 101562.5,\n",
       "       117187.5, 132812.5, 148437.5, 164062.5, 179687.5, 195312.5, 210937.5,\n",
       "       226562.5, 242187.5, 257812.5, 273437.5, 289062.5, 304687.5, 320312.5,\n",
       "       335937.5, 351562.5, 367187.5, 382812.5, 398437.5, 414062.5, 429687.5,\n",
       "       445312.5, 460937.5, 476562.5, 492187.5, 507812.5, 523437.5, 539062.5,\n",
       "       554687.5, 570312.5, 585937.5, 601562.5, 617187.5, 632812.5, 648437.5,\n",
       "       664062.5, 679687.5, 695312.5, 710937.5, 726562.5, 742187.5, 757812.5,\n",
       "       773437.5, 789062.5, 804687.5, 820312.5, 835937.5, 851562.5, 867187.5,\n",
       "       882812.5, 898437.5, 914062.5, 929687.5, 945312.5, 960937.5, 976562.5,\n",
       "       992187.5],\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-b065ab64-01fe-4d5e-8b70-26e8c532a57f' class='xr-index-data-in' type='checkbox'/><label for='index-b065ab64-01fe-4d5e-8b70-26e8c532a57f' 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([  7812.5,  23437.5,  39062.5,  54687.5,  70312.5,  85937.5, 101562.5,\n",
       "       117187.5, 132812.5, 148437.5, 164062.5, 179687.5, 195312.5, 210937.5,\n",
       "       226562.5, 242187.5, 257812.5, 273437.5, 289062.5, 304687.5, 320312.5,\n",
       "       335937.5, 351562.5, 367187.5, 382812.5, 398437.5, 414062.5, 429687.5,\n",
       "       445312.5, 460937.5, 476562.5, 492187.5, 507812.5, 523437.5, 539062.5,\n",
       "       554687.5, 570312.5, 585937.5, 601562.5, 617187.5, 632812.5, 648437.5,\n",
       "       664062.5, 679687.5, 695312.5, 710937.5, 726562.5, 742187.5, 757812.5,\n",
       "       773437.5, 789062.5, 804687.5, 820312.5, 835937.5, 851562.5, 867187.5,\n",
       "       882812.5, 898437.5, 914062.5, 929687.5, 945312.5, 960937.5, 976562.5,\n",
       "       992187.5],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b75b1f47-a855-49b7-8e05-08add330a4f0' class='xr-section-summary-in' type='checkbox'  ><label for='section-b75b1f47-a855-49b7-8e05-08add330a4f0' class='xr-section-summary' >Attributes: <span>(23)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>pyqg:L :</span></dt><dd>1000000.0</dd><dt><span>pyqg:M :</span></dt><dd>4096</dd><dt><span>pyqg:W :</span></dt><dd>1000000.0</dd><dt><span>pyqg:beta :</span></dt><dd>1.5e-11</dd><dt><span>pyqg:del2 :</span></dt><dd>0.8</dd><dt><span>pyqg:delta :</span></dt><dd>0.25</dd><dt><span>pyqg:dt :</span></dt><dd>3600.0</dd><dt><span>pyqg:filterfac :</span></dt><dd>23.6</dd><dt><span>pyqg:nk :</span></dt><dd>33</dd><dt><span>pyqg:nl :</span></dt><dd>64</dd><dt><span>pyqg:ntd :</span></dt><dd>1</dd><dt><span>pyqg:nx :</span></dt><dd>64</dd><dt><span>pyqg:ny :</span></dt><dd>64</dd><dt><span>pyqg:nz :</span></dt><dd>2</dd><dt><span>pyqg:rd :</span></dt><dd>15000.0</dd><dt><span>pyqg:rek :</span></dt><dd>5.787e-07</dd><dt><span>pyqg:taveint :</span></dt><dd>86400.0</dd><dt><span>pyqg:tavestart :</span></dt><dd>155520000.0</dd><dt><span>pyqg:tc :</span></dt><dd>0</dd><dt><span>pyqg:tmax :</span></dt><dd>311040000.0</dd><dt><span>pyqg:twrite :</span></dt><dd>10000</dd><dt><span>reference :</span></dt><dd>https://pyqg.readthedocs.io/en/latest/index.html</dd><dt><span>title :</span></dt><dd>pyqg: Python Quasigeostrophic Model</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:            (time: 87, l: 64, k: 33, lev: 2, y: 64, x: 64, lev_mid: 1)\n",
       "Coordinates:\n",
       "  * k                  (k) float64 0.0 6.283e-06 ... 0.0001948 0.0002011\n",
       "  * l                  (l) float64 0.0 6.283e-06 ... -1.257e-05 -6.283e-06\n",
       "  * lev                (lev) int64 1 2\n",
       "  * lev_mid            (lev_mid) float64 1.5\n",
       "  * time               (time) float64 0.0 3.6e+06 7.2e+06 ... 3.06e+08 3.096e+08\n",
       "  * x                  (x) float64 7.812e+03 2.344e+04 ... 9.766e+05 9.922e+05\n",
       "  * y                  (y) float64 7.812e+03 2.344e+04 ... 9.766e+05 9.922e+05\n",
       "Data variables: (12/32)\n",
       "    APEflux            (time, l, k) float64 dask.array<chunksize=(44, 32, 33), meta=np.ndarray>\n",
       "    APEgen             (time) float64 dask.array<chunksize=(87,), meta=np.ndarray>\n",
       "    APEgenspec         (time, l, k) float64 dask.array<chunksize=(44, 32, 33), meta=np.ndarray>\n",
       "    Dissspec           (time, l, k) float64 dask.array<chunksize=(44, 32, 33), meta=np.ndarray>\n",
       "    EKE                (time, lev) float64 dask.array<chunksize=(87, 2), meta=np.ndarray>\n",
       "    EKEdiss            (time) float64 dask.array<chunksize=(87,), meta=np.ndarray>\n",
       "    ...                 ...\n",
       "    u                  (time, lev, y, x) float64 dask.array<chunksize=(44, 1, 32, 32), meta=np.ndarray>\n",
       "    ufull              (time, lev, y, x) float64 dask.array<chunksize=(44, 1, 32, 32), meta=np.ndarray>\n",
       "    uh                 (time, lev, l, k) complex128 dask.array<chunksize=(44, 1, 32, 17), meta=np.ndarray>\n",
       "    v                  (time, lev, y, x) float64 dask.array<chunksize=(44, 1, 32, 32), meta=np.ndarray>\n",
       "    vfull              (time, lev, y, x) float64 dask.array<chunksize=(44, 1, 32, 32), meta=np.ndarray>\n",
       "    vh                 (time, lev, l, k) complex128 dask.array<chunksize=(44, 1, 32, 17), meta=np.ndarray>\n",
       "Attributes: (12/23)\n",
       "    pyqg:L:          1000000.0\n",
       "    pyqg:M:          4096\n",
       "    pyqg:W:          1000000.0\n",
       "    pyqg:beta:       1.5e-11\n",
       "    pyqg:del2:       0.8\n",
       "    pyqg:delta:      0.25\n",
       "    ...              ...\n",
       "    pyqg:tavestart:  155520000.0\n",
       "    pyqg:tc:         0\n",
       "    pyqg:tmax:       311040000.0\n",
       "    pyqg:twrite:     10000\n",
       "    reference:       https://pyqg.readthedocs.io/en/latest/index.html\n",
       "    title:           pyqg: Python Quasigeostrophic Model"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import xarray as xr\n",
    "eddy_model_highRes = xr.open_zarr(\"gs://leap-scratch/panalluri/scalar/eddy_lowRes.zarr\")  # load example data\n",
    "eddy_model_highRes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "37bbda03-b4fe-4f31-97c7-38b3a6ef8d68",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "u_lev0 = eddy_model_highRes.u.isel(lev=0)\n",
    "v_lev0 = eddy_model_highRes.v.isel(lev=0)\n",
    "u_lev1 = eddy_model_highRes.u.isel(lev=1)\n",
    "v_lev1 = eddy_model_highRes.v.isel(lev=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c3b97d03-69e9-4791-bbd2-1abff1f5d62e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "xvals = eddy_model_highRes.x\n",
    "yvals = eddy_model_highRes.y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "83dc30d8-7540-47a2-bd94-d272a33e314c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15625.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del_x = np.diff(xvals).mean()\n",
    "del_y = np.diff(yvals).mean()\n",
    "del_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ef49619b-bc9f-4e03-8de9-d34b0c79bdd0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3600.0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del_t = np.diff(eddy_model_highRes.time).mean()\n",
    "time = u_lev0.time\n",
    "del_t = del_t / 1000\n",
    "del_t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "de87e27b-7b87-41d3-b55b-6def214e9c69",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "domain_size = u_lev0[0].shape\n",
    "domain_x = domain_size[0]\n",
    "domain_y = domain_size[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e8b76b7a-d957-4e00-8117-9fd44dbb790e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#scalar initial condition\n",
    "\n",
    "xy_grid = np.zeros((domain_x, domain_y))\n",
    "xy_grid[20:44, 20:44] = 1.0\n",
    "xy_grid[15:20, 15:49] = 0.5\n",
    "xy_grid[15:49, 15:20] = 0.5\n",
    "xy_grid[15:49, 44:49] = 0.5\n",
    "xy_grid[44:49, 15:49] = 0.5\n",
    "plt.imshow(xy_grid)\n",
    "plt.title(\"Initial condition\")\n",
    "scalar_init = xy_grid "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "33b53c24-6518-4821-a069-fab9c36c05ba",
   "metadata": {},
   "source": [
    "# Top layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "caf7d92a-a955-4a14-aded-ca9a36569619",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "U = eddy_model_highRes.isel(lev=0).u[-1]\n",
    "V = eddy_model_highRes.isel(lev=0).v[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "8b1f2a54-830c-4445-84c4-5ee5cb0aee46",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(121)\n",
    "U.plot()\n",
    "\n",
    "plt.subplot(122)\n",
    "V.plot()\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a684f4d5-1656-472b-98c4-e85550f4ec82",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def RHS(U, V, C, dx, dy, k):\n",
    "    \n",
    "    dCdx = (np.roll(C, -1, axis=1) - np.roll(C, 1, axis=1) )/2/dx\n",
    "    dCdy = (np.roll(C, -1, axis=0) - np.roll(C, 1, axis=0) )/2/dy\n",
    "    \n",
    "    d2Cdx2 = (np.roll(C, -1, axis=1) - 2*np.roll(C, 0, axis=1) + np.roll(C, 1, axis=1) )/(dx**2)\n",
    "    d2Cdy2 = (np.roll(C, -1, axis=0) - 2*np.roll(C, 0, axis=0) + np.roll(C, 1, axis=0) )/(dy**2)\n",
    "    \n",
    "    RHS = -(U*dCdx + V*dCdy) + k*(d2Cdx2 + d2Cdy2)\n",
    "    \n",
    "    return RHS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0a6401f1-f56b-4f66-9cd7-87b94cf1fe19",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.096e-09"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1/(15625**2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "872a7643-a8e0-44bd-b722-af28533f74c5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Time loop \n",
    "dt = 3600. \n",
    "dt_scalar = 10*dt\n",
    "dx =  -7812.5 +  23437.5\n",
    "dy = dx\n",
    "\n",
    "Nt = 100\n",
    "domain_x = 64\n",
    "domain_y = 64\n",
    "\n",
    "k = 300\n",
    "\n",
    "scalar_mat = np.zeros((Nt+3, domain_x, domain_y), dtype='float64')\n",
    "\n",
    "# intialize\n",
    "scalar_mat[0,:,:] = scalar_init\n",
    "# first timestep from Euler\n",
    "scalar_mat[1, :, :] = scalar_mat[0, :, :] + dt_scalar* RHS(U,V, scalar_mat[0, :, :], dx, dy, k)\n",
    "# second timestep from AB2\n",
    "scalar_mat[2, :, :] = scalar_mat[1, :, :] + 0.5*dt_scalar* ( 3*RHS(U,V, scalar_mat[1, :, :], dx, dy, k) - RHS(U,V, scalar_mat[0, :, :], dx, dy, k) )\n",
    "\n",
    "#AB3 timestepping\n",
    "for i in range(0,Nt): \n",
    "    \n",
    "    scalar_mat[i+3, :, :] = scalar_mat[i+2, :, :] + (1/12)*dt_scalar* ( 23*RHS(U,V, scalar_mat[i+2, :, :], dx, dy, k) - 16*RHS(U,V, scalar_mat[i+1, :, :], dx, dy, k) + 5*RHS(U,V, scalar_mat[i, :, :], dx, dy, k) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "8c0321f6-58ff-4fb0-af36-a6b8b415863a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "scalar_mat_xr = xr.DataArray(scalar_mat, dims=['time', 'y', 'x'], coords={'x': eddy_model_highRes.x, 'y':eddy_model_highRes.y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "34df923d-2e5d-46ba-af12-18bf417d1c5f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f794e3599f0>"
      ]
     },
     "execution_count": 70,
     "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"
    }
   ],
   "source": [
    "scalar_mat_xr.isel(time=-1).plot(cmap='magma')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6d1978a-909d-4f4f-91e7-62f3888f8eef",
   "metadata": {},
   "source": [
    "# Bottom layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e7624e2b-eb98-4536-a4fe-897e0a8bc26d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "U = eddy_model_highRes.isel(lev=1).u[-1]\n",
    "V = eddy_model_highRes.isel(lev=1).v[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "1eb14f13-435a-48c9-9602-8f39a35b1069",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "\n",
    "plt.subplot(121)\n",
    "U.plot()\n",
    "\n",
    "plt.subplot(122)\n",
    "V.plot()\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "d57bf528-9ffd-43ec-8e4e-203031bb5a46",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Time loop \n",
    "dt = 3600. \n",
    "dt_scalar = 10*dt\n",
    "dx =  -7812.5 +  23437.5\n",
    "dy = dx\n",
    "\n",
    "Nt = 100\n",
    "domain_x = 64\n",
    "domain_y = 64\n",
    "\n",
    "k = 250\n",
    "\n",
    "scalar_mat2 = np.zeros((Nt+3, domain_x, domain_y), dtype='float64')\n",
    "\n",
    "# intialize\n",
    "scalar_mat2[0,:,:] = scalar_init\n",
    "# first timestep from Euler\n",
    "scalar_mat2[1, :, :] = scalar_mat2[0, :, :] + dt_scalar* RHS(U,V, scalar_mat2[0, :, :], dx, dy, k)\n",
    "# second timestep from AB2\n",
    "scalar_mat2[2, :, :] = scalar_mat2[1, :, :] + 0.5*dt_scalar* ( 3*RHS(U,V, scalar_mat2[1, :, :], dx, dy, k) - RHS(U,V, scalar_mat2[0, :, :], dx, dy, k) )\n",
    "\n",
    "#AB3 timestepping\n",
    "for i in range(0,Nt): \n",
    "    \n",
    "    scalar_mat2[i+3, :, :] = scalar_mat2[i+2, :, :] + (1/12)*dt_scalar* ( 23*RHS(U,V, scalar_mat2[i+2, :, :], dx, dy, k) - 16*RHS(U,V, scalar_mat2[i+1, :, :], dx, dy, k) + 5*RHS(U,V, scalar_mat2[i, :, :], dx, dy, k) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8c2848c3-78bd-4230-aa06-35259af377c5",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "scalar_mat2_xr = xr.DataArray(scalar_mat2, dims=['time', 'y', 'x'], coords={'x': eddy_model_highRes.x, 'y':eddy_model_highRes.y})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "872ef2e3-c717-4e7c-9efa-6d498388bb4c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7f794dd80fd0>"
      ]
     },
     "execution_count": 79,
     "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"
    }
   ],
   "source": [
    "scalar_mat2_xr.isel(time=-1).plot(cmap='magma')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "f29edcdd-ace9-4bf4-bba5-1588dc853b4a",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Specific for animation\n",
    "# Can be put in a separate script\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import os\n",
    "from PIL import Image\n",
    "from mpl_toolkits.axes_grid1 import make_axes_locatable\n",
    "#import cmocean\n",
    "\n",
    "# Thanks to Pavel for this function :) \n",
    "def create_animation(fun, idx, filename='my-animation.gif', dpi=200, FPS=18, loop=0):\n",
    "    '''\n",
    "    See https://pythonprogramming.altervista.org/png-to-gif/\n",
    "    fun(i) - a function creating one snapshot, has only one input:\n",
    "        - number of frame i\n",
    "    idx - range of frames, i in idx\n",
    "    FPS - frames per second\n",
    "    filename - animation name\n",
    "    dpi - set 300 or so to increase quality\n",
    "    loop - number of repeats of the gif\n",
    "    '''\n",
    "    frames = []\n",
    "    for i in idx:\n",
    "        fun(i)\n",
    "        plt.savefig('.frame.png', dpi=dpi, bbox_inches='tight')\n",
    "        plt.close()\n",
    "        frames.append(Image.open('.frame.png').convert('RGB'))\n",
    "        print(f'Frame {i} is created', end='\\r')\n",
    "    os.system('rm .frame.png')\n",
    "    # How long to persist one frame in milliseconds to have a desired FPS\n",
    "    duration = 1000 / FPS\n",
    "    print(f'Animation at FPS={FPS} will last for {len(idx)/FPS} seconds')\n",
    "    frames[0].save(\n",
    "        filename, format='GIF',\n",
    "        append_images=frames[1:],\n",
    "        save_all=True,\n",
    "        duration=duration,\n",
    "        loop=loop)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "160c5322-a01e-4447-8848-ae30e6c1de40",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_fun(idT): \n",
    "    scalar_mat_xr.isel(time=idT).plot(cmap='magma')\n",
    "    plt.title(\"Top Layer Scalar Values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "ea3a2797-f62c-4f68-9988-5f2c78536ee1",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fun(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "0a4eb2b3-4a7c-471a-8aa1-c075ba72d94f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Animation at FPS=18 will last for 5.722222222222222 seconds\n"
     ]
    }
   ],
   "source": [
    "create_animation(plot_fun, range(0, 103), 'topLayerScalars.gif', dpi=100) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "bb0ef678-49d4-4101-8a51-6145a946eaab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_fun2(idT): \n",
    "    scalar_mat2_xr.isel(time=idT).plot(cmap='magma')\n",
    "    plt.title(\"Bottom Layer Scalar Values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "8c7ea03a-fd60-4183-a34c-8a1ec6764622",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fun2(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b3b00269-d739-4073-a7e9-77b66960325b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Animation at FPS=18 will last for 5.722222222222222 seconds\n"
     ]
    }
   ],
   "source": [
    "create_animation(plot_fun2, range(0, 103), 'bottomLayerScalars.gif', dpi=100) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d444f04-aa92-41c6-9129-af74f2ecf3cf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.10.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}