scalar_FD_matrix.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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      "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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      "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-71107069-69e3-49d8-a63f-675ddb0354e7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-71107069-69e3-49d8-a63f-675ddb0354e7' 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-b091cd4b-a45d-42c9-a1d4-30f4919d9a94' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b091cd4b-a45d-42c9-a1d4-30f4919d9a94' 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-1393d462-7eb8-490e-95c2-0fb3700094a4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1393d462-7eb8-490e-95c2-0fb3700094a4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a6084ce4-11a9-40c0-9a46-e91b50004b84' class='xr-var-data-in' type='checkbox'><label for='data-a6084ce4-11a9-40c0-9a46-e91b50004b84' title='Show/Hide data repr'><svg 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-9a99a1f5-ef2b-42df-8b1d-f5bf9b519baa' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-9a99a1f5-ef2b-42df-8b1d-f5bf9b519baa' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-142850b2-6026-4a1b-902c-b612de5058f2' class='xr-var-data-in' type='checkbox'><label for='data-142850b2-6026-4a1b-902c-b612de5058f2' title='Show/Hide data repr'><svg 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-4151e6fc-e338-4912-8e99-27f8a36f4c38' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4151e6fc-e338-4912-8e99-27f8a36f4c38' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7bc3ba6a-4324-434d-a53f-852cb067fad0' class='xr-var-data-in' type='checkbox'><label for='data-7bc3ba6a-4324-434d-a53f-852cb067fad0' title='Show/Hide data repr'><svg 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-41ee4b5e-febf-4aee-b030-5b6701586b2f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-41ee4b5e-febf-4aee-b030-5b6701586b2f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-08bbe5e0-313c-49e7-adc5-05445c360cc5' class='xr-var-data-in' type='checkbox'><label for='data-08bbe5e0-313c-49e7-adc5-05445c360cc5' title='Show/Hide data repr'><svg 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-a36d1096-d9f5-4ba4-886e-b31d79b45001' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a36d1096-d9f5-4ba4-886e-b31d79b45001' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65add749-73c4-4fc7-9660-a870d7d4fb57' class='xr-var-data-in' type='checkbox'><label for='data-65add749-73c4-4fc7-9660-a870d7d4fb57' title='Show/Hide data repr'><svg 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-dd87bc78-9b24-4a4b-b872-f0e9862959d5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-dd87bc78-9b24-4a4b-b872-f0e9862959d5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bb0d86ff-025f-49c2-ae00-61bd65c9d776' class='xr-var-data-in' type='checkbox'><label for='data-bb0d86ff-025f-49c2-ae00-61bd65c9d776' title='Show/Hide data repr'><svg 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-eb9f689d-db43-4a23-ab1e-5c566df3f1e3' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb9f689d-db43-4a23-ab1e-5c566df3f1e3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5dffbdd4-75da-4a02-b2d0-5ea5d9decee1' class='xr-var-data-in' type='checkbox'><label for='data-5dffbdd4-75da-4a02-b2d0-5ea5d9decee1' title='Show/Hide data repr'><svg 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-f4399b17-17b8-4581-a366-52e220d264eb' class='xr-section-summary-in' type='checkbox'  ><label for='section-f4399b17-17b8-4581-a366-52e220d264eb' 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-3d20d5c9-d5af-44ad-861c-dd617f3adbd4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3d20d5c9-d5af-44ad-861c-dd617f3adbd4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-42b62038-9f32-4d17-a94e-fb82c5f39b6c' class='xr-var-data-in' type='checkbox'><label for='data-42b62038-9f32-4d17-a94e-fb82c5f39b6c' title='Show/Hide data repr'><svg 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-485fad1f-0a71-4be2-aff9-129a8311d1cc' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-485fad1f-0a71-4be2-aff9-129a8311d1cc' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d07da55-8815-4ef9-8b7e-cdd4ae7ba1c0' class='xr-var-data-in' type='checkbox'><label for='data-8d07da55-8815-4ef9-8b7e-cdd4ae7ba1c0' title='Show/Hide data repr'><svg 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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       "            </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-d3ef30bf-df30-40b9-93a6-19aadda99b58' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d3ef30bf-df30-40b9-93a6-19aadda99b58' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5f1c6f05-8927-410b-8b59-a42c3607abcb' class='xr-var-data-in' type='checkbox'><label for='data-5f1c6f05-8927-410b-8b59-a42c3607abcb' title='Show/Hide data repr'><svg 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",
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       "                    </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-eadbe616-a053-4a88-a751-9cead5e0194b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eadbe616-a053-4a88-a751-9cead5e0194b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3b12aa4a-f41e-42af-be26-7d88526cd326' class='xr-var-data-in' type='checkbox'><label for='data-3b12aa4a-f41e-42af-be26-7d88526cd326' title='Show/Hide data repr'><svg 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",
       "                <thead>\n",
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       "                        <td> (44, 32, 33) </td>\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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       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                        <td> (44, 32, 33) </td>\n",
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       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                        <td colspan=\"2\"> float64 numpy.ndarray </td>\n",
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       "                        <td> 5.44 MiB </td>\n",
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       "                        <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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       "                        <td colspan=\"2\"> float64 numpy.ndarray </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-554432a5-1d6c-40a5-a630-b684710fcc1d' class='xr-section-summary-in' type='checkbox'  ><label for='section-554432a5-1d6c-40a5-a630-b684710fcc1d' 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-26f1cd47-0868-4ea6-a838-5c1b37ea18b7' class='xr-index-data-in' type='checkbox'/><label for='index-26f1cd47-0868-4ea6-a838-5c1b37ea18b7' 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-2edd9442-e34c-450f-bf8e-c7c38b756b29' class='xr-index-data-in' type='checkbox'/><label for='index-2edd9442-e34c-450f-bf8e-c7c38b756b29' 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-a6d47b7e-c281-4127-81f8-74758f48f81b' class='xr-index-data-in' type='checkbox'/><label for='index-a6d47b7e-c281-4127-81f8-74758f48f81b' 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-25689314-7faf-4f48-b2c6-a3d6da8716ab' class='xr-index-data-in' type='checkbox'/><label for='index-25689314-7faf-4f48-b2c6-a3d6da8716ab' 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-92fc5101-6dfd-4b20-90b6-47dfdc3f04e5' class='xr-index-data-in' type='checkbox'/><label for='index-92fc5101-6dfd-4b20-90b6-47dfdc3f04e5' 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-87ec3b9e-f9d9-4174-93f8-cff8ff15904d' class='xr-index-data-in' type='checkbox'/><label for='index-87ec3b9e-f9d9-4174-93f8-cff8ff15904d' 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-6e6047ea-160f-4fd9-b35c-bf41a84375ab' class='xr-index-data-in' type='checkbox'/><label for='index-6e6047ea-160f-4fd9-b35c-bf41a84375ab' 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-d2a997fb-eac1-4738-937c-8ffc82723e1f' class='xr-section-summary-in' type='checkbox'  ><label for='section-d2a997fb-eac1-4738-937c-8ffc82723e1f' 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": 5,
   "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": 6,
   "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": 7,
   "id": "83dc30d8-7540-47a2-bd94-d272a33e314c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "15625.0"
      ]
     },
     "execution_count": 7,
     "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": 9,
   "id": "ef49619b-bc9f-4e03-8de9-d34b0c79bdd0",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3600.0"
      ]
     },
     "execution_count": 9,
     "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": 10,
   "id": "4041b22b-451c-464b-ac9b-e13649b397b8",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "kappa = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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": 12,
   "id": "0c4044ee-c1d1-4555-8ac2-ab8844a0866d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.94912e-05,  1.47456e-05,  0.00000e+00, ...,  0.00000e+00,\n",
       "         0.00000e+00,  0.00000e+00],\n",
       "       [ 1.47456e-05, -2.94912e-05,  1.47456e-05, ...,  0.00000e+00,\n",
       "         0.00000e+00,  0.00000e+00],\n",
       "       [ 0.00000e+00,  1.47456e-05, -2.94912e-05, ...,  0.00000e+00,\n",
       "         0.00000e+00,  0.00000e+00],\n",
       "       ...,\n",
       "       [ 0.00000e+00,  0.00000e+00,  0.00000e+00, ..., -2.94912e-05,\n",
       "         1.47456e-05,  0.00000e+00],\n",
       "       [ 0.00000e+00,  0.00000e+00,  0.00000e+00, ...,  1.47456e-05,\n",
       "        -2.94912e-05,  1.47456e-05],\n",
       "       [ 0.00000e+00,  0.00000e+00,  0.00000e+00, ...,  0.00000e+00,\n",
       "         1.47456e-05, -2.94912e-05]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy\n",
    "from scipy.sparse import diags\n",
    "\n",
    "K1_coeff = kappa * del_t / (del_x**2)\n",
    "K2_coeff = kappa * del_t / (del_y**2)\n",
    "\n",
    "K1_diag = np.dot([-2]*domain_x*domain_y, K1_coeff)\n",
    "K1pN_diag = np.dot([1]*(domain_x*domain_y-domain_x), K1_coeff)\n",
    "K1mN_diag = np.dot([1]*(domain_x*domain_y-domain_x), K1_coeff)\n",
    "\n",
    "K2_diag = np.dot([-2]*domain_x*domain_y, K2_coeff)\n",
    "K2p1_diag = np.dot([1]*(domain_x*domain_y-1), K2_coeff)\n",
    "K2m1_diag = np.dot([1]*(domain_x*domain_y-1), K2_coeff)\n",
    "\n",
    "K1_diags = [K1_diag, K1pN_diag, K1mN_diag]\n",
    "K1 = diags(K1_diags, [0, domain_x, -domain_x]).toarray()\n",
    "\n",
    "K2_diags = [K2_diag, K2p1_diag, K2m1_diag]\n",
    "K2 = diags(K2_diags, [0, 1, -1]).toarray()\n",
    "\n",
    "K2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "274a6e8e-2b51-4be8-890a-d403974f7770",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.sparse as sparse #for case of 10x10 domain --- don't have enough computational power to plot for actual domain\n",
    "\n",
    "plt.figure(figsize=(12,5)).suptitle(\"Sparsity patterns of matrices K1 and K2\")\n",
    "plt.subplot(121); plt.spy(K1); plt.title(\"K1\")\n",
    "plt.subplot(122); plt.spy(K2); plt.title( \"K2\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "37b727b9-4f1a-400f-8680-d513fdc2de38",
   "metadata": {},
   "outputs": [],
   "source": [
    "U_coeff = - del_t / (2*del_x)\n",
    "V_coeff = - del_t / (2*del_y)\n",
    "\n",
    "UpN_diag = np.dot([1]*(domain_x*domain_y-domain_x), U_coeff)\n",
    "UmN_diag = np.dot([-1]*(domain_x*domain_y-domain_x), U_coeff)\n",
    "\n",
    "Vp1_diag = np.dot([1]*(domain_x*domain_y-1), V_coeff)\n",
    "Vm1_diag = np.dot([-1]*(domain_x*domain_y-1), V_coeff)\n",
    "\n",
    "U_diags = [UpN_diag, UmN_diag]\n",
    "U = diags(U_diags, [domain_x, -domain_x]).toarray()\n",
    "\n",
    "V_diags = [Vp1_diag, Vm1_diag]\n",
    "V = diags(V_diags, [1, -1]).toarray()\n",
    "\n",
    "I_diag = np.dot([1]*domain_x*domain_y, K2_coeff)\n",
    "I = diags([I_diag], [0]).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "11d70499-08c4-44d8-9cc7-ab41b19af401",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1300x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#for case of 10x10 domain --- don't have enough computational power to plot for actual domain\n",
    "\n",
    "plt.figure(figsize=(13,5)).suptitle(\"Sparsity patterns of basic matrices U, V, I\")\n",
    "plt.subplot(131); plt.spy(U); plt.title(\"U\")\n",
    "plt.subplot(132); plt.spy(V); plt.title( \"V\")\n",
    "plt.subplot(133); plt.spy(I); plt.title( \"I\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": [
    "xy_grid = np.zeros((domain_x, domain_y))\n",
    "xy_grid_shape = xy_grid.shape\n",
    "xy_grid[20:44, 20:44] = 1.0\n",
    "xy_grid[10:20, 10:54] = 0.5\n",
    "xy_grid[10:54, 10:20] = 0.5\n",
    "xy_grid[10:54, 44:54] = 0.5\n",
    "xy_grid[44:54, 10:54] = 0.5\n",
    "plt.imshow(xy_grid)\n",
    "plt.title(\"Initial condition\")\n",
    "xy_flat = xy_grid.reshape((domain_x*domain_y,1)) #create proper sin wave"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "a684f4d5-1656-472b-98c4-e85550f4ec82",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "scalar_init = xy_flat\n",
    "scalar_mat = scalar_init #look at np.diff and np.roll for the below\n",
    "\n",
    "u_flat = eddy_model_highRes.isel(lev=0).u[0].to_numpy().reshape((domain_x*domain_y,1))\n",
    "v_flat = eddy_model_highRes.isel(lev=0).v[0].to_numpy().reshape((domain_x*domain_y,1))\n",
    "\n",
    "FD_timesteps = 50 #86400\n",
    "for i in range(FD_timesteps): #range(86400)\n",
    "    Uk = U * u_flat\n",
    "    Vk = V * v_flat\n",
    "    step_mat = (I + Uk + Vk + K1 + K2)\n",
    "    scalar2 = np.matmul(step_mat, scalar_init)\n",
    "    scalar_mat = np.append(scalar_mat, scalar2, axis=1)\n",
    "    scalar_init = scalar2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18261c80-7d69-42d6-b6a3-a9b4b05d65a4",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#if scalar simulation del_t was the same as QG model\n",
    "#scalars = eddy_model_highRes.u.isel(lev=0)\n",
    "\n",
    "#for i in range(time.size):\n",
    "#    scalars[i,:,:] = scalar_mat[:,i].reshape((domain_x,domain_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "192bfb1c-e862-4abf-9382-4c826a1c1d9f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f8481dbe140>"
      ]
     },
     "execution_count": 41,
     "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": [
    "plt.imshow(scalar_mat[:,40].reshape((domain_x,domain_y)))\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "d57bf528-9ffd-43ec-8e4e-203031bb5a46",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "scalar_init2 = xy_flat\n",
    "scalar_mat2 = scalar_init2\n",
    "\n",
    "u_flat = eddy_model_highRes.isel(lev=1).u[0].to_numpy().reshape((domain_x*domain_y,1))\n",
    "v_flat = eddy_model_highRes.isel(lev=1).v[0].to_numpy().reshape((domain_x*domain_y,1))\n",
    "\n",
    "FD_timesteps = 50 #86400\n",
    "for i in range(FD_timesteps): \n",
    "    Uk = U * u_flat\n",
    "    Vk = V * v_flat\n",
    "    step_mat = (I + Uk + Vk + K1 + K2)\n",
    "    scalar2 = np.matmul(step_mat, scalar_init2)\n",
    "    scalar_mat2 = np.append(scalar_mat2, scalar2, axis=1)\n",
    "    scalar_init2 = scalar2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e606503-566c-4444-9b26-b9ed156a33ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#if scalar simulation del_t was the same as QG model\n",
    "#scalars = eddy_model_highRes.u.isel(lev=1)\n",
    "\n",
    "#for i in range(time.size):\n",
    "#    scalars2[i,:,:] = scalar_mat2[:,i].reshape((domain_x,domain_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4bf3c72a-b2ff-4fa9-9df2-5cd149a4566f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f8491f6bf40>"
      ]
     },
     "execution_count": 44,
     "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": [
    "plt.imshow(scalar_mat2[:,40].reshape((domain_x,domain_y)))\n",
    "plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "abf8529f-6cb2-4bcf-892e-9a39d37e289f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "#to make DataArray\n",
    "#if scalar simulation del_t was the same as QG model\n",
    "\n",
    "#scalars = scalars.rename('scalars_top')\n",
    "#scalars = scalars.assign_attrs(long_name='scalar values for top layer')\n",
    "#scalars = scalars.assign_attrs(units=' ')\n",
    "#scalars2 = scalars2.rename('scalars_bottom')\n",
    "#scalars2 = scalars2.assign_attrs(long_name='scalar values for bottom layer')\n",
    "#scalars2 = scalars2.assign_attrs(units=' ')\n",
    "#scalar_vals = xr.concat([scalars, scalars2], dim='lev')\n",
    "#scalar_vals = scalar_vals.assign_attrs(long_name='scalar values')\n",
    "#scalar_vals = scalar_vals.rename('scalars')\n",
    "#scalar_vals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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": 49,
   "id": "160c5322-a01e-4447-8848-ae30e6c1de40",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_fun(idT): \n",
    "    plt.imshow(scalar_mat[:,idT].reshape((domain_x,domain_y)))\n",
    "    plt.title(\"Top Layer Scalar Values\")\n",
    "    plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "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(40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "0a4eb2b3-4a7c-471a-8aa1-c075ba72d94f",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Animation at FPS=18 will last for 2.7777777777777777 seconds\n"
     ]
    }
   ],
   "source": [
    "create_animation(plot_fun, range(0, FD_timesteps), 'topLayerScalars.gif', dpi=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "bb0ef678-49d4-4101-8a51-6145a946eaab",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def plot_fun2(idT): \n",
    "    plt.imshow(scalar_mat2[:,idT].reshape((domain_x,domain_y)))\n",
    "    plt.title(\"Bottom Layer Scalar Values\")\n",
    "    plt.colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "8c7ea03a-fd60-4183-a34c-8a1ec6764622",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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flt/hhx9+wL///W9DvHXr1ujSpQvq1q2L0aNHY/LkyQgODsY///lP7NixQzu9J554Avfccw9sNpv7FOh5ERERmDVrFkaMGIFff/0Vd999Nxo0aIBffvkFO3bswC+//GK4NlZRo0aNwqlTp3DXXXehTZs2CAwMxN69e/G3v/0NAQEBeOaZZ9zzMH36dDz88MPo3bs3HnnkEcTFxeH777/Hjh07MHv2bERFReHGG2/Eyy+/jHr16qFx48bIzMzEggULUKdOnYvOhzefrSiHw4GhQ4di1qxZUEq5b9M7b8CAAZgxYwaGDBmCUaNGIS8vD6+88orhjwdJfHw8evfujfT0dNStWxeNGjXCf//7X7Ha2RtvvIH+/fujb9++GDlyJK644gr8+uuv2LNnD77++mu88847AIBOnTrh1ltvxTXXXIO6detiz549WLp0KTp37qy9L742+uGHH/Cvf/0LR44ccf9hOWHCBKxduxYLFy5030Hia6+99hoeeeQRNGzYEEFBQQgICMA//vEPjz/+AOD111/H008/jYKCArRs2RLr16+v9B/Wl1315bFRZUhZ39HR0ap9+/ZqxowZqri42KN9Xl6eGj16tEpISFBBQUGqUaNGauLEiYZ2WVlZqmvXriosLEwB8MiknTlzpkpOTlaBgYGGzNl//OMf6pprrlF2u11FR0er22+/Xe3atctj2iNGjFDh4eGGZUlNTVVXX321Id6oUSM1YMCAS/4WF/4Ov31NnjxZKaXU5s2bVefOnVVYWJiqX7++evjhh9XXX39tWI7zSkpKlMPhUP369dN+b2ZmphowYICKiYlRwcHB6oorrlADBgxQ77zzjrvN+ezmX3755ZLLoZRSGRkZ6sEHH1StW7dW0dHRKigoSCUkJKhBgwapL774wtD+448/VqmpqSo8PFyFhYWp1q1bq2nTprnfP3LkiLrrrrtU3bp1VWRkpOrXr5/67rvvVKNGjTyy+6Us6Yp+9nxf3Lp1a4WW8bd27Njhzm4/duyY4f0333xTtWjRQjkcDtWkSROVnp6uFixYoACoQ4cOudtdmPWtlFLHjx9Xd999t4qJiVHR0dFq2LBhatu2beI637Fjh/rd736nGjRooIKDg1V8fLy66aab1Ny5c91tnn32WZWSkqLq1q3rnp8nn3xSnThxwvRy1yQA1KpVq9z/f/vttxUAFR4e7vEKCgpSv/vd75RS/8u+v9hLdyfHhd933ssvv6yaN2+uVq9erXbs2KFmzZqlIiIi1Pr16z3anTp1Su3fv19lZmaqgQMHqmuvvVYVFRX57PeoSjal/KTkEdFl8MEHH+C2227DRx995JGlTkSebDYbVq1ahTvuuAPAueJCQ4cOxa5duzwuWwDnzgTFx8ejrKwMP/zww0WnW7duXUOuh/R9wLlLItHR0Vi1apW7vgMAPPzwwzhy5Ii2tnxpaSnq1q2Lf/zjH7jvvvsquMTVh6e+iXCupvmPP/6IP/zhD2jfvv0lE1iIyFOHDh3gdDqRm5uL7t27i22Cg4PRsmVLn31nWVkZysrKEBDgmW4VGBgIl8t10c8qpbRV9KyGAzURgDFjxuDzzz/Htdde6y6lSkSezp49i++//979/0OHDiErKwsxMTFo3rw5hg4divvvvx/Tp09Hhw4dcOLECWzYsAFt27at1Bmqi33flVdeiaioKKSmpuL//b//h9DQUDRq1AiZmZlYsmQJZsyYAeBcLYXzpYTr16+Po0ePYtq0ae5qjH6hmk+9ExGRnzif03Dh63zuQmlpqfrLX/6iGjdu7L7mf+edd6pvv/22Sr5PqXM5CSNHjlSJiYkqJCREtWjRQk2fPt1dwe7o0aOqf//+7jyEhg0bqiFDhqi9e/d6+3NcNrxGTUREZGG8j5qIiMjCOFATERFZWJUlk73++ut4+eWXcfz4cVx99dWYOXOmNhPwt1wuF44dO4bIyEgm9BAR+SGlFPLz85GYmGjIyPal4uJilJaWej0du91ueBaBpVTFhe8VK1ao4OBgNX/+fLV79271xBNPqPDwcPXjjz9e8rPZ2dmXvCGeL7744osv67+ys7OrYohRSilVVFSk4hsE+mQ+4+PjLV38pEqSyTp16oRrr73Wo5xiq1atcMcddyA9Pf2inz19+jTq1KmDhpP/hIAL/8LRHWBLS2CmLaC9CGATyhu7Ao0xAAgol+OuYPlLA0rlL5Xa66atNOdEpPkGAPHBprrfxOxv6Iv1Q7WDL7ZZH/Q3m+ZWW6XZxm26bVzYDgPK5C912eUv1bbXbOMBwjaum2/obimuon2qq7gYR56bglOnTiE6OlrzQe+cOXMG0dHROLS9EaIiK3/UfibfheSOP+L06dOIiory4Rz6js9PfZeWlmL79u0eT94BgD59+mDz5s2G9iUlJR43nefn5wMAAkJCLDtQ6361gDLNtO2agVp3Skhor5s2B2rySzVsoEawMRQQqPlSh2ag1rUXpg3If7xbZaB2f+QyXL6MigzwaqD2Bz5fuhMnTsDpdBpKwMXFxSEnJ8fQPj09HdHR0e6XvzzNhIiIqp9Tubx+WV2V/Rly4V9S6oJnIp83ceJEnD592v3Kzs6uqlkiIqIaxgXl9cvqfH7qu169eggMDDQcPefm5oqF1h0Oh/z4OhuMp1pMnALTndLSXu8p1VwfCjFOKLBQ/vvGGSF/aVC+3L4sSj4/HXTGeP5KN23tvAjzDcjLqTt97pPTZbr2Zk9nkn+qyj5h4jKW9tqy5rJUYLFmuwrTbIdnje3LNdt3sLB9A0B5ZMWnrZuXAM1865ZTm/vi7SWyy7gdu+DS7qoq+nmr8/kRtd1uR8eOHbF+/XqP+Pr169GlSxdffx0REVGNViX3UT/11FMYPnw4UlJS0LlzZ8ybNw8//fQTRo8eXRVfR0REtZRTKTi9uHnJm89eLlVyjfqee+7BzJkz8fzzz6N9+/bYtGkTPv74YzRq1Kgqvo6IiGqp6rhGffToUQwbNgyxsbEICwtD+/btsX379ipYunOqrDLZmDFjMGbMmKqaPBER0WV38uRJdO3aFT179sSaNWvQoEED/PDDD6hTp06VfSefR01ERH7LBQWnF5nbZo+op02bhqSkJCxcuNAda9y4caW/vyKsO1CfL+72WyaKFuiyuwNL5Ik4wzUZ22eMVwfKYuRUSXuu/KWlCXK1EsdRuxgvEdrbf9FMO0aTOX5aziyVljNA85tUaTa4r4pbkDVUZREcs0WKhM3T5dBkdxfIEymPlrcr+6/ydlVa3/iljuNypZKSK+Ta1HZN+9IG8v4m+FfjBloepckc1yynU/O7iMVUdBdKpUlcxsu+3t5idf6zZ86c8Yjr7khavXo1+vbti8GDByMzMxNXXHEFxowZg0ceeaTS83ApNbucCxERUQUkJSV5FN/Slbs+ePAg5syZg2bNmiEjIwOjR4/G448/jiVLllTZvFn3iJqIiOgSfJX1nZ2d7VHrW6zvgXNPeExJScHUqVMBAB06dMCuXbswZ84c3H///ZWej4vhETUREfktlw9eABAVFeXx0g3UCQkJaN26tUesVatW+Omnn3y8ZP/DgZqIiKiCunbtin379nnE9u/fX6W3H1v31LdQQtRMWVBd0lh5hCZJ5KQmSSTemNgVekiTBNaySIyHfxcqxouvKRTjYTvDDLGilsViW8dh+WHnJXGaBJRTxuUs1yTSVWmSGZPGapaqLCtrImkMkBPHgjTJVGV15P2B42dN8mZjeTsM22vcDkvaytt3+LfG7RsAitrI+4/QvfL+oyjZmJRmz9EkpNXVJJ2e1SSdCr+httyoNInLeAjo9DLr2+xnn3zySXTp0gVTp07F7373O3z11VeYN28e5s2bV+l5uBQeURMRkd9yKu9fZlx33XVYtWoV/vWvf6FNmzZ44YUXMHPmTAwdOrRqFhBWPqImIiK6hN9eZ67s58269dZbceutt3rxrebwiJqIiMjCeERNRER+ywUbnF4kt7j8IDGGAzUREfktlzr38ubzVmfZgdrmMmZ5mykLqs3u1pUA1JT1C99nvJfO2TFfbBuVGSnGA/qekOfl41gx7ux70hCL2FRXbFuYImeWhuyVM0uLrzBmsQeflH/YKs0GZwnRmsUXJUR9kN0NyBneZXXliYQclbOky1rK21XENnm7st1o3GYdGXXktrfI+4PgjHpivDhV3t+EbzfubwpalIht7ZpyxdoSxEI2uJlyozZ5slRJlh2oiYiILsXp5alvbz57uXCgJiIiv1UbBmpmfRMREVkYj6iJiMhvuZQNLuVF1rcXn71cOFATEZHfqg2nvi07ULsCYZi7gFL5B3UK2cna2t2a7O6IPfKTUmxdjNmcIR/UEds2GHZYjJ+ec6UYTxh3QIwfmdvUEAsaniO2DfgoXowXdikQ4yF7wg2x4kQ5IzZIqAsOAM4wTTZ4mSYbXLrA4oss4Yu1p6pRhetNlynssssTCTRRv1uX3e1sJW8nEZuN2wkARAyQt8PypQ0MsYaj5e37+Czj9g0A0b8/LMZzl8kPeygdKNwdslm+O+RsK002+DE5G7ysjnEbDyiUf29p/bjkXQdVkmUHaiIioktxIgBOL9Kt/OFOMg7URETkt5SX16gVr1ETERFVndpwjZq3ZxEREVkYj6iJiMhvOVUAnGLGakU/78OZqSKWHagDyoGAC8pSu0LkbOOgM8aVVBpvrGkNyLW7ATm7GwBCVtUxxJo/ukdseyxNzubs/8pGMZ45rrMY7/63Lw2xLVOuF9vGjjkixvF+QzF8trOxhnHIfk1d8DhNNni+JhvcRC1g7XbFbHBrqOL1cGEdf+AitfyL5c5SHi2nAYXkGCekrd2tye6OvV3erkpfTxDj3f9k3Gaznmwvtu0/a6MY3zChmxhvmSbvb/a/0coQK75T3o+Fb5GzwQuayXfBBOcas+TLIzV3exQJ60dzB0hVcMEGlxcnh13azmsdPPVNRERkYZY9oiYiIrqU2pBMxoGaiIj8lvfXqHnqm4iIiLxg2SNqV7ACLihNpy0ZGGPMVgo9JJfGc3aUH8KuKwsqJY79PDFZbDtq3rtifMmtPcX4s2uXiPFX7hxsiPVdlim2zUhLFeMRo+RSh651xpKjZ9sVi23t2XLiXVldTVKJpsSgChb+YpUn4bukMal9bUlIs9JvpWmvhHxEMyWCAcCeJyc1liQb+3PENjlhUlcSVJc01jdN3g4/H9beEHt2lbx9T+93uxgf9aG8/1gw6g4x3jzduG/au8CYYAYABf3k/V7ojkgxXpRkTMYNOikPF9L6cSndBu5755LJvHgohx9s6JYdqImIiC7F5WUJUWZ9ExERkVd4RE1ERH6rNiSTcaAmIiK/5UJAjS94woGaiIj8llPZ4PTiCVjefPZysexAHVgSgACb519J5VFyyUDHz8bFKGlZJLaNypSzHBsMOyzGpbKguuzupT06ifFFXy0V4yNa9xPj03YuMsSeuW2k2PaGRV+J8U+ny/NiG2wsMRj6pVxesLCJXIY1OE/uNuWa7NyAEuOGoHQ9ryqzwWtaedKqXB5fTVtzoGMTyspqSwTnyxMp1ZS4DdsbYvy+G+XSmuVLG4hxqSQoIGd3A8C01YsMsWfa3iy2Xbxb3h88cP1dYvyhje+J8XnjjO0bTPxRbHv83cZi/MwN8n7ScdD4G5bUk/e/Uklhm6bsK1WOZQdqIiKiS3F6mfXt5KlvIiKiquNSAXB5kUzm8oNkMp6fICIisjAeURMRkd/iqW8iIiILc8G7zO3LV+y08iw7UDtDXVChnj9h8GlNbd9EY3Zy+HehYtuAvifE+Ok5V4rx/q9sNMR0tbt12d3Dk7qK8Yxjn8rf2cKYLToh6x2x7YsP3i/GG075XowfXN7MECvsXii2Ddkv10cujpezbYM068cZatwUdHWdddngNjnhFNpLU5e7fnVVZ4JbZHlsmr2aVLsbkLO7AcBlN35poKZWfFkdeeWHHA0W46Vtjf3ZkVFHbNtw9AExnvVkezGuq98tZXiv2Sdv330T5f3B0mz5bpKRNw0X4ze9+5nxO6f0ENsGDJf3e6Gf1xPjBU1LDbHgX+TfuzxSqPUd4A/Dn/+w7EBNRER0Kd4XPLF+qhYHaiIi8lvelxC1/kBt/TkkIiKqxXhETUREfqs2PI/a9BH1pk2bMHDgQCQmJsJms+G9997zeF8phbS0NCQmJiI0NBQ9evTArl27fDW/REREbudPfXvzsjrTR9QFBQVo164dHnjgAdx1l7HW7EsvvYQZM2Zg0aJFaN68OaZMmYKbb74Z+/btQ2SkXGdbElgYgACX5w+oy/50HLUbYsXXyJnM9o9jxXjCODn7M3NcZ0Ps2bVy5qeudrcuu7tvYnsxvjR7rSE2sucwsW3PlZvF+Ecv9BDjgSPyDLGQTXLm59lWJWLcLvzeAFAaU/FawM4wTV1wTY1gKUsYAAI0WcViFrKV6ojrWKQety7L3qXZY+iy+HX1u6UM7/JI+UvtJ+SU8uJGcv+MyDLerRA4QM56Pv7aVWJ8wKyNYnx639vE+OLdywwxXXZ3xrEsMd6/hbz/mJD1vhiX7vhIMHG3BwAUa+74cBww/oYlDTR3e5yp3lrf3t9HXQMH6v79+6N///7ie0opzJw5E5MmTcKgQYMAAIsXL0ZcXByWL1+ORx991Lu5JSIiqmV8+qfEoUOHkJOTgz59+rhjDocDqamp2LxZPvIrKSnBmTNnPF5EREQV4VI2r19W59OBOicnBwAQFxfnEY+Li3O/d6H09HRER0e7X0lJSb6cJSIiqsFc/3fqu7Ivf7iPukrm0Gbz/AtFKWWInTdx4kScPn3a/crOzq6KWSIiIvJLPr09Kz4+HsC5I+uEhAR3PDc313CUfZ7D4YDD4fDlbBARUS3h/WMurX9E7dOBOjk5GfHx8Vi/fj06dOgAACgtLUVmZiamTZtmalrOMO9qfYftlOtUO/ueFONH5jYV493/9qUh9sqdg8W203YuEuNS7W5Azu4GgAeuN2bTj8zcILadO+ZuMR775x/F+JEVyYZYSepZsW3orggxXnSl8fcGAPvPcncqrWfM5g0+Ja9LqW4wAAQWyBuTU5NVLGUh6+qI+yQb3FeXuXzxnSayuwG5Hrcuyz5Qk83rDJd/xKD8itfv1mV3l8bJ2cahB+U/8EuuNfbn4I/kuz1ixx4S4/95qpsYH/3xv8W4tM3qanfrsrun7Vwvxp+5baQYb78oyxD7dHonsa1z8Ckx7viyrhgvbGLcxoPz5A2oXFj3Ll1R+CrghA1OLzZAbz57uZgeqM+ePYvvv//fLQCHDh1CVlYWYmJicOWVV2L8+PGYOnUqmjVrhmbNmmHq1KkICwvDkCFDfDrjREREtYHpgXrbtm3o2fN/T4966qmnAAAjRozAokWL8PTTT6OoqAhjxozByZMn0alTJ6xbt87UPdREREQVURtOfZuewx49ekApZXgtWrQIwLlEsrS0NBw/fhzFxcXIzMxEmzZtfD3fREREcOJ/p78r9/JOeno6bDYbxo8f74OlkVn/TwkiIiIL2rp1K+bNm4drrrmmSr/Hsg/lCCwKQMAFpyR0iUZSElNRy2KxbcQmOXkiaLh8n/eWKdcbYn2XZYptdUkfE7LeEeO6sqBS4tj8hwaJbZNf3ifGd85pK8bVIGMyneOzGLFtYbsiMR66L1SMFzWRSzqG/GhM+im+wmRCWqymPKkmwVBKbgookZNGfJJkZqUSoiaSxgDA5TBOSJe8Vx6tKfOZZy4RLORosCGmKwmqTRprIffPsO3GJEjV71exbc6bxuRKAGg7ZacY122Hj2SuNMR02/eErNViXLf/6LosS4xnpKUaYkGjcsW2al28GC9qJ+8n7dnG37ysriZxUygHi5LLdwzoq1PfFxbbutQdSWfPnsXQoUMxf/58TJkypdLfXxE8oiYiIr/lq4dyJCUleRTfSk9Pv+j3PvbYYxgwYAB69+5d5cto2SNqIiKiS1FePuZS/d9ns7OzERUV5Y5f7Gh6xYoV+Prrr7F169ZKf68ZHKiJiKjWi4qK8hiodbKzs/HEE09g3bp1CAkJuQxzxoGaiIj8mLfPlDb72e3btyM3NxcdO3b83zScTmzatAmzZ89GSUkJAgPlnI3K4kBNRER+y9snYJn9bK9evbBzp2ey4QMPPICWLVvimWee8fkgDVh4oHY6XFAXlIcMPCv/5SNlBIcckk9JFKbID0oP+EjOiowdc8QQk7ItAeCGRV+JcekB7wDQc6X86E+pLKguu3vXLPke9cBhcvZn6RpjzfXy7vli27Cv5CI1JR3lkqMRX8slR4vbG39zXYlXXba+bn0Wx8tZxVKJUqnUIeCjbHCz5Tx1zEzHB9ndABAkZHhLJT4BICRH/lFKkuX1FrZXXm+lbY19IiJL7hNSSVBAzu4GgPLrjf058L/ynQ2B9/4sxnXb1dUvfyfGpW2298rPxLa6/YFUEhTQ72/sY44bYnnvNxTbFnaW93uO/fJvXixk6wfla+6wEMr4ulyXr4To5RYZGWmoDRIeHo7Y2Ngqqxli2YGaiIjoUs4/rtKbz1sdB2oiIvJbl/vUt2Tjxo1eT+NirP+nBBERUS3GI2oiIvJbLgTA5cUxpzefvVw4UBMRkd9yKhucXpy+9uazl4tlB+qAMhsCAj1/QGeo5uH0Qr1nXTZwyF45y7GwS4E8I0IWZcQouS647qHtDad8L8Y/eqGHGI/984+GmK52ty67u/hDY3Y3ADh7nzLEHJ/UEdvaesn1kYPXyvXSAweckNt/FGucj77GmuOAvha7Lltftz6lWuLBJyv+4HvAZDa4mbrgF2OifrcvsrsBoKyucUJSLW4AKGspr4eIbfJ6sN0or2dHRh1DzEz/AfT1u6UMb2fPU2Jb3XYSMkzOBtdth/F/PmSI6bbvhCk/iHHd/kNXv1vK8Nbtx4L3hIvx4kS5EwUJd004wzTbSanQycusP/j5E8sO1ERERJdihWSyqsaBmoiI/Jby8ulZyovPXi4cqImIyG85YYPTi4dyePPZy8X6f0oQERHVYjyiJiIiv+VS3l1ndpkt81sNLDtQqyBjdq2YXQjAKWTtSrWeATkbGABCNFmRZ4Uaua51cl1w22A5w/Xg8mZiPHBEnhg/siLZEFOD5GlLtbsBObsbAIL+U8cQC7lVznB1rmggxuMfNGa4AkDe7EZiPOHxA4bYkblN5fkbLmfU62qx67JcpfVpJsMVuEiWq5DRqr3M5aNa3zah9LbLLk88UJfdravfLWR4O1vJv2vEZnk7iRggr7fypXIfajja2CeOv3aV2DZ2rNzfct40bieAXL9bl91drtlOijTblW2QnGkubbO67Vu3P3AOludFafY3Uv1ubXa3Zr9n5k4IM3dBaOvkVwGXl9eovfns5WL9OSQiIqrFLHtETUREdCku2ODyIiHMm89eLhyoiYjIb9WGymQ89U1ERGRhlj2itjmNCTS6BAUpyUFXFlKXPKFLNAoRHqx+tl2x2Db0S035y+6a8peb6onxktSzhpjjM/nB9+Xd88W4riyolDimltUX27Ydt1OMH/pTCzE+YMZGMf7JuC6GWPe/fSm23TLlejEeO+aIGJdKvAJyEqDjgFzmsqSBJsksX5NkJpTolJK9zr2hietoks9cQrcNLJb/zi6PlmfG8bPc96WyoLqksdjb5fVQ+nqCGO/+J3k9Zz3Z3hAbMGuj2PY/T3UT422nyP1z16w2hpiuJKguaaxEs10FabZDcZvVbN/Fmv2BXbP/KNLsbxzCvkm3H/NF+Vxtgpg0CV1J3SpQG5LJLDtQExERXYoLXpYQ9YNr1Nb/U4KIiKgW4xE1ERH5LeVl1rfygyNqDtREROS3+PQsIiIiC2MyWTVSAUJZRk0moZSNqCt3p8ty1JWRLI4zZlHasx1i28ImmvKkQnYmAJxtVSLGQ3dFGKfdrkhsG/ZVpBi39ZJLHUplQXXZ3T/9P7nU4egF/xbji265SYw/m7HEEHvlzsFi277LMsV4RlqqGI8YJZeulMq86rL1deuzrI4mI7bYy4zYi9HsMwKErqUrcWrPk/tyaWN5+SO2GfunriSoLru7b5q83j4f1l6MP7vK2Cem971NbDv6Y7m/zX9okBi/+uXvDLGdc9qKbXUlQXXZ3brt0CFss7rtW7c/0O0/dP1T2jfp9mNmsrsBTX/W9WVpEtY/SPUrlh2oiYiILoWnvomIiCysNpQQtf7JeSIiolqMR9REROS3eOqbiIjIwjhQVycFY91j3e8pZCOaqQsO6DNopXrPZXU1dcTzNHXE4+X6u/ajdjFedKUx+zN0X6jYtqSjscYwAASvlesGxz94yBDT1e7WZXcvTu0sxhd9tUyMj2jdzxCbtnOR2PaZ20aK8RsWfSXGP53eSYzbBp80xLS12DXZtkGa9Sn1lYAyTfasyYtLuprhLruxCHjgWXnipUI2MACE7Q2Rv/NG429VvtR4dwCgr92ty+6etnqRGH+m7c2G2OLdcv954Pq7xPgjmSvF+Nwxdxti8X829nsAOLIiWYxLtbsBObsbkLdZ3fat2x/o9h+6/Y20b9Ltx0xldwNyhrdu/yvVp9fUrKfKse5ATUREdAk8oiYiIrKw2jBQM+ubiIjIwnhETUREfkvBu3uh/eFyOgdqIiLyW7Xh1Ld1B2objFmGuj99pN/ZRF1wQJ+163QYvzSgUL5ioK0jflpTezlGTvG1/2ycyaImct3giK/lLNTAASfEeN7sRobYgBkbxba62t267O7hSV3FeMaxTw2x/i2MWb8AMCHrHTH+4oP3i/GGU74X4weXG+uUF3QvFNs6Dsi1l0vqy9m5gQXG9amCNZ3TZK1vJXcVBJQY+1x5lNx/HMeCxXhJW83yZ9QxxBqOPiC2zXqyvRiXancDcnY3AKzZZ+wTfRPl/rM0+10xPrLnMDHee+VnhthHL/QQ2waOyBPjjk31xLiufreU4a3bvnX7A209bs3+Rtw36e4+MFuLXpqMmf3vZRz7asNAzWvUREREFmbdI2oiIqJLqA1H1ByoiYjIb9WGgZqnvomIiCzM1ECdnp6O6667DpGRkWjQoAHuuOMO7Nu3z6ONUgppaWlITExEaGgoevTogV27dvl0pomIiABAKZvXL6szdeo7MzMTjz32GK677jqUl5dj0qRJ6NOnD3bv3o3w8HAAwEsvvYQZM2Zg0aJFaN68OaZMmYKbb74Z+/btQ2RkZMW/zEytbykb0URdcEBfkzlASPzVZfhq64iHVrxWLwCU1jNmi4b86BDbFreXM3mDP4oV4wmPG7N5PxnXRWz7bIacySvV7gbk7G4A6JvY3hBbmr1WbDvypuFiPPXdL8T4mik9xLhtuDGbN+RzOZO3oGmpGA8+IWdPl1dHrW+H8TuDz8j9p+QKeXnCv5Wz2223GO8QOD6rqdi2/6yNYnx6v9vF+OLdS8W4lOGdcSxL/s4Wcn+bkLVajEt3CCRM+UFsK90dAADFmjsEQvbLv6FUv1uX3a3bH2jrcev2N9K+SdffzGR3A+b2qdVc67s2PI/a1EC9dq3nznXhwoVo0KABtm/fjhtvvBFKKcycOROTJk3CoEGDAACLFy9GXFwcli9fjkcffdR3c05ERFQLeHWN+vTp0wCAmJgYAMChQ4eQk5ODPn36uNs4HA6kpqZi8+bN4jRKSkpw5swZjxcREVFFnE8m8+ZldZUeqJVSeOqpp9CtWze0adMGAJCTkwMAiIuL82gbFxfnfu9C6enpiI6Odr+SkpIqO0tERFTL1IZr1JUeqMeOHYtvv/0W//rXvwzv2WyeC66UMsTOmzhxIk6fPu1+ZWdnV3aWiIiIapxK3Uc9btw4rF69Gps2bULDhg3d8fj4eADnjqwTEhLc8dzcXMNR9nkOhwMOh5Ao5W0JUTNtL9JeTM4wW560VJNkpnnIe/ApYxJK8RXGB9MDQNhOObnF2fekGD8y15gk1P1vX4ptX7lzsBiftnORGNeVBZUSxx64/i6x7f0bPxHj88bJ7aMn/iTGC99tbIgV3VAktnUcDBHjpbEVL+lYLSVEI+WJ60qIFrWRlz84w5hkF/37w2LbDRO6ifFRH8plPnXrWSoLqksam7ZzvRh/5raRYrz9oixD7NPpncS2zsGnxLj9y7pivLCJvB0G5xk3fm1JUF3SmMkyn+K+yUf7PZYQtRZTR9RKKYwdOxYrV67Ehg0bkJyc7PF+cnIy4uPjsX79/zas0tJSZGZmoksXObOYiIiosmrDqW9TR9SPPfYYli9fjvfffx+RkZHu687R0dEIDQ2FzWbD+PHjMXXqVDRr1gzNmjXD1KlTERYWhiFDhlTJAhARUe2lvDyi9oeB2tQR9Zw5c3D69Gn06NEDCQkJ7tdbb73lbvP0009j/PjxGDNmDFJSUnD06FGsW7fO3D3UREREFlSRwl++ZuqIWqlL38Vus9mQlpaGtLS0ys4TERFRhSgAFRiaLvp5MypS+MvX+FAOIiLyWy7YYLuMlckuVfirKvjXQF1V5e58NW2z2eDF8pUHKZvX/rM8kaKWxWI8YpOctRo03Hg/+5Yp14tt+y7LFOO6bNsJWe+IcaksqC67e8GoO8R4Yvr3YnzvglZivLhfviHm2CFffilKkjN5g07Jv7kzxNgppHKOgA9LiNqN3xl4Vp54SZw8M6F7Q8V4carxt8pd1khs2zJtjxjXrbeHNr4nxqU+MSHrfbGtrr91XZYlxjPSUg2xoFG5Ylu1Ll6MF7WTtyt7tlzKt6yuUFZWuDsAqMQdAlbf79UQFxbb0t6RdIELC39VBT49i4iI/Javsr6TkpI8im+lp6dX4LuNhb+qgn8dURMREf2GS9lg88F91NnZ2YiKinLHK3I0fb7w12effVbp768IDtRERFTrRUVFeQzUl6Ir/FUVOFATEZHfUsrLrG+Tn1VKYdy4cVi1ahU2btxoKPxVFThQExGR3/K2upjZz16q8FdVqBkDtZnf+XLXvIW5TF4ACCww5viVxsoTCTkk16kuTJEffB/wkTHLNXbMEbGtlD0LADcs+kqMv/jg/WI89d0vDDFd7W5ddvf+N+Ts7tI7T4nxwC3GrPeCZqVi2+BcuTZ2eUTFazXranSbvUlTlyUeICSma2vF/6q5QyBZXv7w7cZs+NKBcq143Xponi5ng+vW803vGq/p6fqPVLsb0PdP+5jjhlje+/KpycLO8nbi2C/X0C/WZNQH5Rs7gNMhr3zTdwhU5TMOdGp4hrc35syZAwDo0aOHR3zhwoUYOXJklXxnzRioiYioVrrcR9QVKfzlaxyoiYjIb/kq69vKOFATEZHfutzJZNWBBU+IiIgsjEfURETkt84dUXtzjdqHM1NFOFCfV4X1dLWZvJrsT2eIMZs36LScVlwcL08kZK+ctVrYpcAY1GTERowy1gUHgE+ndxLjDafIGdtrpvQwxKIn/iS21dXu1mV3q81yTfOCViWGmP2YXWxbVkeT3W2mVrPZOs06uj4kbKnaWvFR8szYc+Ts9oIWxt8qQvO7Ft8pZ4Pr1luDiT+KcalPJGj6j66/6ep3SxneYr8HELxHftpRcaImu/uUvB1KGfgBZfLK90l2t6699S+3+tzlTiarDjz1TUREZGE8oiYiIr+lYP6Z0hd+3uo4UBMRkd/iqW8iIiKqVjyiJiIi/1ULzn1zoL4UX9TT1WXyaupDB5QaJ+QM19R11mShFl8hFIcGECJkuZ7V1Dt2rTPWBQcA22A58/fg8mZy++F5hljhu43FtsX98sW4VLsbkLO7AcB+1JjhXRoj10sPOqvJ5DVRq9l0Jq+OiXrxZmrFA0BpXXn5pd/qrOZ3DdetB816O65ZzwHDTxhiuv7jHHxKjCtN/5Tqd2uzuzXbSfBJeddYrtkOxfrvur2r2TsEfFW/u6by8tQ3/ODUNwdqIiLyW6xMRkRERNWKR9REROS3akPWNwdqIiLyX8rm3XVmDtQ1lK+SPjRJJWK5SCFZBdAnt+iSYaTSiI4DcrnRs+2KxXjol5qEou5yUlrI5/UMsaIbisS2jh2R8rSblYpxXVlQKXHMF0ljgCZxzFcJPybK0GpL0GqWR7f80m+l+1116yFUs97OaNZzqNAnijX9x6Hpb0Wa/mkX+rOuJKgvksYATeIYk8bIRzhQExGR36oNyWQcqImIyH/VgvuomfVNRERkYTyiJiIiv8WsbyIiIqvzg9PX3uBAXRk+KiFqJhtcV47QbDa49OD7kgZyRqw92yHGC5vIZRd12eMFTY2Zwo6DIWLboiRNScfcYDFeVkeznEKGs0+yuwF5fZpd9zompqObP19kg+t+V9160K033XoW+4Sm/+j6m65/Sv1Z6veAj7K7ATnD21f7A2aD13ocqImIyG/x1DcREZGV1YKsbw7URETkx2zw7jqA9Y+oeXsWERGRhfGImoiI/BdPfZOoOrI5TdQFB/RZq84w44SC8uWMWG1GdZ78pSX1NfWUTxgzhUtjdVnpmtrLEZrs3EL5pJCU4eyT7G5AXj9VXOvbzHf6Ihtc97vq1oNuvenWs9QndP1H19+0/VPoz1K/B3yU3Q34pk8wu7tyasFAzVPfREREFsYjaiIi8l98zCUREZF11YanZ/HUNxERkYXxiJqIiPxXLUgm40DtS1WZDW6iLjhwkWzwMuOEtDWwiyueOQ4AgQWaespCe222dohmXnTZucEVr9/tk+xuXXtfXebyxXf6IBtc+7vq7ibQrTdd9rjQJ3T9R5uxreufUha70O8BH2V3A76p/279S6XWVAuuUfPUNxERkYXxiJqIiPyWTZ17efN5q+NATURE/ovXqImIiCyM16g9zZkzB9dccw2ioqIQFRWFzp07Y82aNe73lVJIS0tDYmIiQkND0aNHD+zatcvnM+13bJqX0ry8bWvDuWQY4aUCjC+bU36pIPkVUGYTXypYiS9TbcshvlSg/DKznKZ/QzPtq5ov+oSmvfhb6X5XzXrQrjdf9Aldf9P0T7EvC8t4seWslm2WSMPUQN2wYUO8+OKL2LZtG7Zt24abbroJt99+u3swfumllzBjxgzMnj0bW7duRXx8PG6++Wbk5+dXycwTEVEtp/uDyMzL4kwN1AMHDsQtt9yC5s2bo3nz5vjrX/+KiIgIbNmyBUopzJw5E5MmTcKgQYPQpk0bLF68GIWFhVi+fHlVzT8REdVmHKj1nE4nVqxYgYKCAnTu3BmHDh1CTk4O+vTp427jcDiQmpqKzZs3a6dTUlKCM2fOeLyIiIjoHNMD9c6dOxEREQGHw4HRo0dj1apVaN26NXJycgAAcXFxHu3j4uLc70nS09MRHR3tfiUlJZmdJSIiqq14RG3UokULZGVlYcuWLfj973+PESNGYPfu3e73bTbPrAillCH2WxMnTsTp06fdr+zsbLOzREREtdX5rG9vXhZn+vYsu92Opk2bAgBSUlKwdetWvPrqq3jmmWcAADk5OUhISHC3z83NNRxl/5bD4YDD4TA7GzWDmf5hpfKkuj/vzLQ3O+2qXE5/LelYHX3CZHnSKu0TZsp8+uI3uVh7b9sSXYTXJUSVUigpKUFycjLi4+Oxfv1693ulpaXIzMxEly5dvP0aIiIig/OVybx5WZ2pI+o//vGP6N+/P5KSkpCfn48VK1Zg48aNWLt2LWw2G8aPH4+pU6eiWbNmaNasGaZOnYqwsDAMGTKkquafiIhqM2+vM/vBQG3qiPrnn3/G8OHD0aJFC/Tq1Qtffvkl1q5di5tvvhkA8PTTT2P8+PEYM2YMUlJScPToUaxbtw6RkZFVMvNERETV4fXXX0dycjJCQkLQsWNHfPrpp1X2XaaOqBcsWHDR9202G9LS0pCWlubNPBEREVnWW2+9hfHjx+P1119H165d8cYbb6B///7YvXs3rrzySp9/Hx9zSUREfssGL69R/990LqznUVJSov3OGTNm4KGHHsLDDz+MVq1aYebMmUhKSsKcOXOqZBk5UBMRkf/y0e1ZSUlJHjU90tPTxa8rLS3F9u3bPYp7AUCfPn0uWtzLG3x6FhER1XrZ2dmIiopy/1932/CJEyfgdDpNF/fyBgdqIiLyXz7K+j7/VMiKMlvcyxscqImIyH9d5tuz6tWrh8DAQMPR86WKe3mD16iJiIgqyG63o2PHjh7FvQBg/fr1VVbci0fURETkt7ytLlaZzz711FMYPnw4UlJS0LlzZ8ybNw8//fQTRo8eXfkZuQgO1NXJTD3hqqxVrGurOd9ic2q+MrDi7c20BXxUA7w66j1XperoE5pp23R1uquyT2jaizXAq7Iuuq691ftPTVENlcnuuece5OXl4fnnn8fx48fRpk0bfPzxx2jUqJEXM6LHgZqIiMikMWPGYMyYMZfluzhQExGR/6oFtb45UBMRkd+qjmvUlxuzvomIiCyMR9REROS/flMGtNKftzgO1JeDLzJLzWahmsjYdml6QUCZHHfZ5S8NKJG/1OUwpuFq2+qmrZkXpZl3MavYF5njuvZVva37ok+YyNjWZmCXy3FXsBwPKJW/1FSfENpedNrCvARo5ttU5jhQtdus9ccLa+I1aiIiIuviNWoiIiKqVjyiJiIi/8VT30RERBbm5alvDtS1jZXKfOqSfoRkrcBieSLOMDmjJvCs3L48Sq71GHzGmLFTHmlu2rp5CdDMu7Sc2oSi6ihPqlMNZT6lZEJtolaIZr0VatZbhNw+KN/YvkzTf4KE/nOxaUvz4tTMt245dUmKppLMalrJWqo2HKiJiMh/8dQ3ERGRhdWCgZpZ30RERBbGI2oiIvJbvI+aiIiIqhWPqCujqrM5pfZms7sd8pcGFhgnVB4tZ9va8+Rs29I4+Usdx+Q6kiVXlFa8rWbawb/KXbU8SpP5KyynU/Ob+CQb3Oy61/FBiUoz2d0AEFhinJAzXJOtfUb+UcpiNH3iZ/lLSxKNNWEdR+0VbgsAds20S2ON/TnotCZzXLOcAcJvApjMBq/K/cHF2lONw4GaiIj8Vy1IJuNATUREfqs2XKPmQE1ERP7NDwZbbzCZjIiIyMJ4RE1ERP6L16hJVNXZnMJ5DrPZ3UFC1jMAlNUxZsTqMnNLGxeL8bC9IWK8pG2hGA//NswQK2pTJLYN3RsqxouSjZnjAGDPkbPHS+sKmb9nNZm/vsgGr4Za377I7gaA8gjjb2U/qcn4j5czsEMPaTK2W8rrOfw743ouvkbuP2E7jf0HAIpayv3TcdjYP7V3E5ySl7PcF9ngZuqCA8zurqTacI2ap76JiIgsjEfURETkv3jqm4iIyLp46puIiIiqFY+oiYjIf/HUN4mqMLsbkDO8TWd315WzXEOOGrOky1rK2bYR2+RsW9uNJ8W4I6OO3P6WE4ZYcEY9sW1xar4YD98eKcYLWpSIcbtQN7o0Rq5p7otscFN1wS/GRP1uX2R3A4D9V+Pylwr12QEgfJ9DjDs7yustKlNebwF9jX3C/nGsPO2+cn+L2FRXjBemGPtzyF65LxdfIWexB5/U1JY3kQ1uqi44wGzwyqoFAzVPfRMREVkYj6iJiMhv1YZkMg7URETkv2rBqW8O1ERE5L84UJO4En2U9GGTc3vgshsnFGiiJCggJ40BgLNVgSEWsTlcbBsxIEeMly9tIMYbjj4gxo/PamqIRf/+sNg2d1kjMV46UJNQtFlOKDrbyphkZj8ml7ksq6NJECqUf3MVLKxoswlCOpo+pIR8t4BSeeJOTcKTtiyokDgWsUdOGrN1kddDyAd1xHiDYYfF+Ok5VxpiCePk/nNkrrH/AEDQcLl/BnwUb4gVdjH2ewAI2SP3/eJEORkzSFNy1Blm/M0DyjTlRs0mHprZ3zDBrEbiQE1ERH6L16iJiIisrBac+ubtWURERBbGI2oiIvJbPPVNRERkZbXg1LdXA3V6ejr++Mc/4oknnsDMmTMBAEopPPfcc5g3bx5OnjyJTp064e9//zuuvvpqX8xv1TGTcWk2u1uTEawtAVlsvCJRHq3J7s6RJ6ItCypkeMfefkRsW/p6ghjv/qcvxXjWk+3FeP9ZGw2xDRO6iW1bpu0R4/vfaCXGi++Us5DDtxizwQuayWUxg3PlDPnyCBPlIuVkYN+VEJXKyobI8xd0Rr6iVRovl8uUyoJqs7tX1RHjzR+V19uxNDlju/8rGw2xzHGdxbbd/yb3ty1TrhfjsWOE/vx+Q7Ht2c7ydhKyX1NyNE6TDZ5v7ABmStACPsoGZ7nRGqnS16i3bt2KefPm4ZprrvGIv/TSS5gxYwZmz56NrVu3Ij4+HjfffDPy8+VawERERJWmfPCyuEoN1GfPnsXQoUMxf/581K37vyMXpRRmzpyJSZMmYdCgQWjTpg0WL16MwsJCLF++3GczTUREBJw7WeDty+oqNVA/9thjGDBgAHr37u0RP3ToEHJyctCnTx93zOFwIDU1FZs3bxanVVJSgjNnzni8iIiI6BzT16hXrFiBr7/+Glu3bjW8l5NzrlJQXFycRzwuLg4//vijOL309HQ899xzZmeDiIioViSTmTqizs7OxhNPPIFly5YhJCRE285m8zyZoJQyxM6bOHEiTp8+7X5lZ2ebmSUiIqrFzt+e5c3L6kwdUW/fvh25ubno2LGjO+Z0OrFp0ybMnj0b+/btA3DuyDoh4X8Zw7m5uYaj7PMcDgccDrmucIVVYT1uX0xblxFsplazPU+eSElysRiP2CZnrUr1u3XZ3X3TMsX458Pai/FnVy0R49P73W6IjfrwXbHtglF3iPHm6XJW8d4FcjZ4QT9j8mLojkixbVGSnA0ddErePJwhxhVtOpNXQ3uHgJCYHqipRV4WI89M6CG51rmzo/G30tXu1mV3/zwxWYyPmiev5yW39jTEnl0r959X7hwsxvsuk/tnRlqqIRYxSq4L7lpnrAsOAGfbyduVPVveV5XVFWp9m6kVD5ivF2+V/V514xG1p169emHnzp3Iyspyv1JSUjB06FBkZWWhSZMmiI+Px/r1692fKS0tRWZmJrp06eLzmSciIqrpTB1RR0ZGok2bNh6x8PBwxMbGuuPjx4/H1KlT0axZMzRr1gxTp05FWFgYhgwZ4ru5JiIiOs+iR8WHDx/GCy+8gA0bNiAnJweJiYkYNmwYJk2aBLtdPsMl8XllsqeffhpFRUUYM2aMu+DJunXrEBkpn3YkIiKqLCuXEN27dy9cLhfeeOMNNG3aFN999x0eeeQRFBQU4JVXXqnwdLweqDdu3Ojxf5vNhrS0NKSlpXk7aSIiIr/Vr18/9OvXz/3/Jk2aYN++fZgzZ87lHaiJiIiqjY+SyS6s4eGTRGfB6dOnERMTY+oz/jVQV2XNW19MW5OaJ9VpBi5SqznfOKFSTY3hsL3ybXK2G+VazeVLGxhiutrduuzuaasXifFn2t4sxhfvXmqIPXD9XWLbhza+J8bnjZPbN5go359//N3GhtiZG4rEto6D8m9YGqup9S1k85rO5NUwc4eAU1OL3PGzvFmXtJSXPyrTeFmqwbDDYltd7W5ddvfSHp3E+KKvjH1iROt+Qktg2s5FYvyZ20aK8RsWfWWIfTpdng/bYHk7Cf3SWCseAAqbyHcIBOcZf/Ny4e4NQK4VDwBKtzc2kw1eHfu9as4G99Wp76SkJI/45MmTfX5m+IcffsCsWbMwffp0U5/j86iJiKjWy87O9qjpMXHiRG3btLQ02Gy2i762bdvm8Zljx46hX79+GDx4MB5++GFT8+ZfR9RERES/5aNT31FRUYiKiqrQR8aOHYt77733om0aN27s/vexY8fQs2dPdO7cGfPmzTM9ixyoiYjIb1VH1ne9evVQr169CrU9evQoevbsiY4dO2LhwoUICDB/IpsDNRERURU4duwYevTogSuvvBKvvPIKfvnlF/d78fFyVTwJB2oiIvJfFi4hum7dOnz//ff4/vvv0bBhQ8+vVRX/YusO1NKPXw01b6Xay7rMXG12t13+Um2t5jpOQyzkqFDsGUBp20Ix7sioI8Ybjj5giGU92V5sq6vdrcvuXrPvUzHeN7GrIbY0W84SHnnTcDF+07ufyd85pYcYDxh+whAL/Vw+VVXQtFSMB5+Qf/PyMKGuc5kmk9dsrW/jqgcAuBzG75TuDgCAkkQ5Mzn8u1AxHtDX+FudnnOl2Lb/KxvFuFS7G5CzuwFgeJKxT2Qck/tP/xZyf5uQ9Y4Yf/HB+w2xhlO+F9seXN5MjBd2l7erkP1yDf3ieOPGH3Ra3lE4QzXZ4Jra/7pscKmvaPvb5X7GweWsFGbhgXrkyJEYOXKk19Ox7kBNRER0CVauTOYrvD2LiIjIwnhETURE/svCp759hQM1ERH5LZtSsJlIzJI+b3XWHahtMCYvVGWZT10Sj/AL6ZI+dCVBdUlj5ZHyl9pPGJNQihuViG0jsuTklsABxgQhADj+2lWG2IBZG8W20/veJsYX714mxqWkMQDIOJZliPVvIZeLnJD1vhiXEoQAIMFEklCxJkHIcUD+DUvqy9mBgQXG9VPlJURLjH2oLEruP46j8uPziq+Rl9/+cawhljDOmHQIAJnjOovxZ9fKiYe6sqBS4ljfxPZi26XZa8X4yJ7DxHjPlZsNsY9e6CG2DRyRJ8ZDNsmJh2dbyduhXfjNS2Pk9ROUr0kyE5IUASCgWN5/SEmqAZqEVl2/qrLypNVcVrSmse5ATUREdCk89U1ERGRdzPomIiKiasUjaiIi8l889U1ERGRdteHUt3UHam9LiOqyu82W+RQyLp2aB8LrSjpKJUEBObsbAErjjDMZetAhti259qwYD/7ImMkLALFjDxli/3mqm9h29Mf/FuMPXH+XGNeVBZUyvKftXC+2fea2kWK8/aIsMf7p9E5i3Dn4lCHm+LKu2LawiVxyMyhP3jyk7NxqKSF6Ru4/uhKiYTvl7HZn35OG2JG5TcW23f/2pRh/5c7BYnzazkViXCoLqsvu1vW3kZkbxPjcMXcbYrF//lFse2RFshgvSZW3q9BdEWK86Erjb27/We4/pfXklRx8Sl6f5ZGau0kKhH2T5s4Ts+VJxWxwq5YQrQWsO1ATERFdCk99ExERWRdPfRMREVlZLTii5u1ZREREFsYjaiIi8mv+cPraG9YdqM3U+hbOC2izux2a7G4hgxIAyqONGZr2vIpnawNAyNFgMa6r3y1leJe0KBLbhm2Xs1BVv1/FeM6bxizXtlN2im3nPzRIjD+SuVKM62ovT8habYjpsru7LssS4xlpqWI8aFSuGFfr4g2xonbFYlt7tpxRX1ZHV3vZmP5qKnv2IrS1voWsXWeEPHFdtnFRS3n5IzYZs+GDhueIbbdMuV6M912WKcZ163lC1juGmK7/6LK7df0z+eV9htjOOW3FtmqQMeMdAByfxYjxwnbydhi6L9QQK2oib98hP8r9rfgKOVtfmz0ea9w3BZ3W1BHX3KkSUGIiG9xMXfDLWetbqXMvbz5vcTz1TUREZGHWPaImIiK6BGZ9ExERWRmzvomIiKg68YiaiIj8ls117uXN563OugO1dDrDRP1uXXZ3kCa7W1ePOyTH+BOVJMvZs2F7Q8R4adtCMR6RJddelup367K7y6/PF+OB/5WzVgPv/dkQ2zWrjdj26pe/E+NSLWUA6L3yMzH+4oP3G2K62t267G77mONiPO/9hmK8sLPxN7cf0PzeDeRs/aB8TQat0Ld0NbpNZ79qdhou4caBwEK5L0vZwADgOCz3z8IU428V8JExax4AYsccEeO69XbDoq/EuNQneq7cLLbV9TcpuxuQ+3PgMPnugNI1cWK8vLu8XYV9FSnGSzoat9mIr+Vttri9vD/Q1WLXZeuHHDKuz+J4uS9r64ibyAbX3dkg9f3LOvjx1DcRERFVJ+seURMREV0Cs76JiIisrBYUPOFATUREfotH1NUpAIYr6GbKgmqTxuqaK/NZ1tKY+BGxTU76sN2oKUeYUUeMBw44IcaDP4o1xHQlQXVJY86ep8R48YfG5JmQYcYEM0BfdjH+z4fE+Ecv9BDjCVN+MMQ+nd5JbKsrCapNGutSIMaD94QbYsWJmqQxTaKNM0yTaFMmJNrosj3M7gQ0yWcBwqw7Q+T505WRLNGVuN1r7M+63xWa9RAxSi45qlvPDad8b4jp+k/sn38U47r+KSWOSf0eAJy9T4lxxyd1xLitl7wdBq81lmE1s30DgLOvvP+QSrwCchKgtC4BfXnS4JPyECAlmZkpN6orhUuVY92BmoiI6FJqQdY3B2oiIvJbteHUN2/PIiIisjAeURMRkf9i1jcREZF11YZT35YdqG3lxixvl13+RQOFDG9tSVBNdrezlZzlGrHZmD0cMUDOcC1f2kCMNxx9QIwff+0qMR471phVnfNmsthWKgkK6LNcy4Us1yJNGUXbIDnD9cgKzbyMyBPjB5c3M8Scg43zAQBqnVy6UioJCsjZ3YCc5WomwxUwl+WqK/1puoSoZqchZdEGlMoTd2qWR1dGUvqtQjS/61nNenBp1pttsJzJLPUJXf/R9Tc1SJ62VBZUl90d9J86YjzkVnm7cq6Qt/H4B43bbN7sRmLbhMfl/cGRuU3FeNBweX8jlXnVZevr1qeZOyG0d0EI/VB3hw5VjmUHaiIiokti1jcREZF11YZT38z6JiIisjAeURMRkf9yqXMvbz5vcaaOqNPS0mCz2Txe8fH/S2hQSiEtLQ2JiYkIDQ1Fjx49sGvXLp/PNBEREYD/XaP25mVxpo+or776avznP/9x/z8w8H/ZgS+99BJmzJiBRYsWoXnz5pgyZQpuvvlm7Nu3D5GR8gPXdVzBCrggyzuwWP67ojzamOEdkiMvmlS7G5CzuwEg9vYjhljp6wli2+5/+lKMZz3ZXowPmLVRjP/nqW6GWNspO8W2u2a1EeO6+t1ShndJ93yxbdBnch3xktSzYtyxqZ4YL+5u/M3tX8r1i4vaFcvT3q+pYazJWpUyvH2S3Q3IGd667G4f1fqWvlM3f7rl0S2/9FvpftcQzXo4q1lvoZr1XCj0iRBN/9H2N03/LBf6s652ty67Wy2rL8bbjpO3w0N/amGIDZixUWz7ybguYrz73+T9x5Yp14vx2DHGfZOuFrsuW1+3PouFuvBBZzQ18UON/crlvHyjnw1eXqP22ZxUHdPXqIOCghAfH+9+1a9/rkMrpTBz5kxMmjQJgwYNQps2bbB48WIUFhZi+fLlPp9xIiKi2sD0QH3gwAEkJiYiOTkZ9957Lw4ePAgAOHToEHJyctCnTx93W4fDgdTUVGzevFk7vZKSEpw5c8bjRUREVCHnK5N587I4UwN1p06dsGTJEmRkZGD+/PnIyclBly5dkJeXh5ycczflx8V5nlqNi4tzvydJT09HdHS0+5WUlFSJxSAiotro/O1Z3rysztRA3b9/f9x1111o27YtevfujY8++ggAsHjxYncbm83zjL9SyhD7rYkTJ+L06dPuV3Z2tplZIiIiqtG8uo86PDwcbdu2xYEDB9zZ3xcePefm5hqOsn/L4XAgKirK40VERFQhzPq+uJKSEuzZswfdu3dHcnIy4uPjsX79enTo0AEAUFpaiszMTEybNs30tANKAxAQ4Pl3hK6GsT3PmI1YkixnoUZsk7McdfW7pQzvvmmZYtvPh7UX48+uWiLGp/e9TYyP/vjfhtj8hwaJba9++TsxvnNOWzEu1e/WZXcXtisS445dEWL8bKsSMS5llhY2MdaXBgB7tkOMS1mogFyTGJAznH2S3Q3IaaK6jd1Htb7F6WjmzxfZ4LrfVbcedOtNt56lPqHrP6Ga/qbrn2FfGe8wsfWS69branfrsrt/+n/GGuUAMHqBcZtddMtNYttnM+T9wSt3DhbjfZfJ+5uMtFRDLGKUvB/T1WLXZetL67OsrtzhpGct2EouXy0tm1KweXGd2ZvPXi6mBuoJEyZg4MCBuPLKK5Gbm4spU6bgzJkzGDFiBGw2G8aPH4+pU6eiWbNmaNasGaZOnYqwsDAMGTKkquafiIioRjM1UB85cgT33XcfTpw4gfr16+OGG27Ali1b0KjRuafEPP300ygqKsKYMWNw8uRJdOrUCevWrTN9DzUREVGFuKA/+1XRz18GJSUl6NSpE3bs2IFvvvkG7du3r/BnTQ3UK1asuOj7NpsNaWlpSEtLMzNZIiKiSvGXU99PP/00EhMTsWPHDtOf5UM5iIio1ruwnkdJiZwzURlr1qzBunXr8Morr1Tq8xyoiYjIf/ko6zspKcmjpkd6erpPZu/nn3/GI488gqVLlyIsTE5mvhTLPj3L5XABIZ4XD4Ly5b8rSoVM1LC9IWJb240nxXj5Ujn7U6rfrcvunrZ6kRh/pu3NYnzx7mVi/IHr7zLEHslcKbadO+ZuMR7/50Ni/MiKZENMW0tZk21bdKUmY/uoXYwXxxvXT3Cepha7JrM0KF9TZzis4vW7fZLdDciZ2b4qGOyL7/RBNrjud9WtB916061nqU/o+o+uv4XuCxXjJR2N/Tl4rVxzPP5BeTuRancDcnY3ACxO7WyILfpK3r5HtO4nxqftXCTGn7ltpBi/YdFXhtin0zuJbW2D5f2etha7kK2vW5flEUKtb9tluvALeF9d7P8+m52d7XF7sMMh38lgbtIKI0eOxOjRo5GSkoLDhw9Xajo8oiYiIr/lq8pkF9bzuNhALT1J8sLXtm3bMGvWLJw5cwYTJ070ahkte0RNRERkRWPHjsW999570TaNGzfGlClTsGXLFsOgn5KSgqFDh3pU9bwYDtREROS/fHTq24x69eqhXj35say/9dprr2HKlCnu/x87dgx9+/bFW2+9hU6d5MsUEg7URETkt2yucy9vPl9VrrzySo//R0Scy/u56qqr0LCh/OxwiWUH6sCiAAQoz0voZdFOsa3jWLAhVtJWflC6I6OOGG84+oAYz3qyvSGmKwmqSxpbs+9TMd43sasYX5r9riE2sucwsW3vlZ+J8Y9e6CHGA0fkGWKOTfJfhrqSjrqkn9IYef0EnTYmIEllKwEgoFBOm3A65L96A8pMlAX1RdKYrn11lBA1+50mksx0v6t2PWjWm249S31C13/sP8u7qaImcv+M+NqYBBk44ITYNm92IzE+YMZGMa4rCyoljg1PkrfvjGPy/qB/C3n/MSHrHTH+4oP3G2INp3wvtj24XC59WtBds588YMxOLmmgKeN7xrgubcVMf/Ilyw7UREREl1QNp74rq3HjxlCV+D4O1ERE5L+8fQKW9Z/JwduziIiIrIxH1ERE5Lf8pda3NzhQExGR//Kja9SVZdmB2hnmggr1zBgN1jzMvuSKUkMs/Fu5pqrtFjn78/ispmK8/6yNhtj0freLbRfvXirGddndGcey5O9sYSwxOCFrtdhWyvwEgIQpP4hxKfuzWJP5GbJf/g2l8o+AnMkLAM5QY+avVLYSAFSwJqtY/koo3cUbKdnYF9nduvZWKiHqg2xw3e+qXQ+69aZZz1Kf0JUnLa0nZ4OH/ChXjipub+zPwR/Fim0THpfv9vhkXBcx/myGfMeHVBZUl93dN7G9GF+avVaMj7xpuBhPffcLQ2zNlB5iW9tw490eABDyuXzHR0FT4z41+Bfj3TUAUB4plBANuIwlRGsByw7UREREl6Tg3TOlrX9AzYGaiIj8F69RExERWZmCl9eofTYnVYa3ZxEREVkYj6iJiMh/Meu7+gQWBiDAVfla30VtisS2wRlylmP07w+L8Q0Tuhlioz401uIGgAeuv0uMS7W7ATm7GwCm7VxviOkeHt9+UZYY1z1A3jn4lCFmN/HweOAiD5DX1e8WMn/FWtyAvh617txPVdbG9lX9bl+43MujaWsqyx769RxQavxSZ5g8Ed3dHsVXyP0zbKfxbgVn35Ni2yNz5bs9uv/tSzH+yp2Dxfi0nYsMMV3tbl12t27/cf/GT8T4vHHG9tETfxLbFr7bWIwX3SDvJx0HQwyxEk32fdDZaq717YJ326QfJKjz1DcREZGFWfaImoiI6FKY9U1ERGRlteAaNU99ExERWRiPqImIyH/VgiNqyw7UzlBjre+gM5pa33HGAsShe0PFtsWp+WI8d1kjMd4ybY8htmDUHWLbhza+J8Z1tXonZL0vxqUM767LssS2GWmpYjxoVK4YV+viDbGidsViW3u2XEu5rK4mu7tQPkEj1oHWZVpWRz1uK2V3m+EPv5WJbPAATaawVEsaAOw/y7uvopbG/hyxSb6zIWh4jhjfMuV6Md53WaYYl7bZCVnviG11+wNddrduf5OY/r0htndBK7FtcT95v+fYESnGi5KMGfVBJ+XfW8rWd6nLmEpdCwZqnvomIiKyMMseURMREV1SLbiPmgM1ERH5Ld6eRUREZGW8Rk1ERETVybJH1AElAQiwef4d4YzQ1AL+1bgYRcmlYtvw7XKWY+lAuRbw/jeMWZTN042Z4IBcexcAbnr3MzH+4oP3i3Gpfrcuu9s+5rgYz3u/oRgv7FxoiDn2G2sjA0CxkE0PAEH5cva90yH/ZRogTMYntbsr097btv7A7PJUQ110m1A22mWXJxJYIHeW0li59nTIIWOd6sIUY78HgICPjHdBAEDsmCNiXLcd3rDoK0NMt32nvvuFGNftP6TsbkDeN5XeeUpsG7hFznovaCbvJ4Nzjc9PKNfsf6Va/ii7jBuVSwE2L46KXdY/orbsQE1ERHRJPPVNRERE1YlH1ERE5Me8PKLWXrexDg7URETkv2rBqW/LDtSuYAVckFwSUKQpMRhlTHKw5xiTIQCgoEWJGI/YLCdbFN9pTDLTlelrMPFHMb5mSg8xnjBFThL5dHonQ0xXElSbNNalQIwH7wk3xIoTNUljpzRJY0LJQAAI0CSQiIlj1ZE0RhVXleVJIfcJKekQAJwhcn8LOi33z+J444RC9soJk7rtBJrtKmKUXHJU2mYbarZv3f4geuJPYly3v5ESx5RmP1bQSt7v2Y/ZxXhZHeNvrisRLCUBujTrkirHsgM1ERHRJbkUvDp9zaxvIiKiKqRc517efN7imPVNRERkYTyiJiIi/8VkMiIiIgvjNerqE+A0ZoGaKTFYWlcuL2g/Kmc5ntVkRYYLpfcKNA9hP/5uYzEeMPyEGD+4vJkYdw4+ZYipdXKpQ6kkKCBndwNA8RXGB8IHax4IXx5uomQgAKXrTdJkmN3tn6owG1zJSdwIKJUn4tT0z2DhbgWp3wNAiGY7OavZrlya7dA22Hh3iG77tg3PE+OFmv1HsWZ/I5UF1WZ3a/Z7pTHyfjLorPE3NFMiGPJkq0YtOKLmNWoiIiILs+wRNRER0SUpeHlE7bM5qTIcqImIyH/x1DcRERFVJ9MD9dGjRzFs2DDExsYiLCwM7du3x/bt293vK6WQlpaGxMREhIaGokePHti1a5dPZ5qIiAgA4HJ5/7I4U6e+T548ia5du6Jnz55Ys2YNGjRogB9++AF16tRxt3nppZcwY8YMLFq0CM2bN8eUKVNw8803Y9++fYiMjKzwd6lAYxaothawkI0oZS0C+ixHXc1b6cHqoTvk5ThzQ5EYD/28nhgv7i5nljq+NGZzFrUrFtvaD8g1jHX1u6UM7yrN7gbkzF9md9csvsgG1/QfXX/T9U+pP+vubNBtJw7NdnVWsx2GCttsgWb7DtHsD4o0+w+HZn8j7Zt0+zEz2d2AvE/V7X+luu1iff+qUgtOfZsaqKdNm4akpCQsXLjQHWvcuLH730opzJw5E5MmTcKgQYMAAIsXL0ZcXByWL1+ORx991DdzTUREVEuY+rtn9erVSElJweDBg9GgQQN06NAB8+fPd79/6NAh5OTkoE+fPu6Yw+FAamoqNm/eLE6zpKQEZ86c8XgRERFVyPkjam9eFmdqoD548CDmzJmDZs2aISMjA6NHj8bjjz+OJUuWAABycs49Ai4uLs7jc3Fxce73LpSeno7o6Gj3KykpqTLLQUREtZFLef+yOFMDtcvlwrXXXoupU6eiQ4cOePTRR/HII49gzpw5Hu1sNs9rR0opQ+y8iRMn4vTp0+5Xdna2yUUgIiKquUwN1AkJCWjdurVHrFWrVvjpp3MPPI+PP1de78Kj59zcXMNR9nkOhwNRUVEeLyIioopQyuX1y+pMJZN17doV+/bt84jt378fjRo1AgAkJycjPj4e69evR4cOHQAApaWlyMzMxLRp08zNmQuGLFBdJqGUjairS6vLciyro6kbnBtsiBUlyXWD7YdCxHhBU2N2JqDPLC1MNk7ffsQhti1pIKdiBgn1jgE5I7ZKs7sBOfOXtb5rFh/U+ta29UE2uO7OBt12otuudNuhtM3qtm/d/kC3/9Dtb6R9k24/Zia7G5D3qdpMbmkSl/NssvLy9HVNu0b95JNPYsuWLZg6dSq+//57LF++HPPmzcNjjz0G4Nwp7/Hjx2Pq1KlYtWoVvvvuO4wcORJhYWEYMmRIlSwAERHVYn6QTPbRRx+hU6dOCA0NRb169dx3RVWUqSPq6667DqtWrcLEiRPx/PPPIzk5GTNnzsTQoUPdbZ5++mkUFRVhzJgxOHnyJDp16oR169aZuoeaiIioJnj33XfxyCOPYOrUqbjpppuglMLOnTtNTcN0re9bb70Vt956q/Z9m82GtLQ0pKWlmZ00ERGROS4XYPPiOnMVXqMuLy/HE088gZdffhkPPfSQO96iRQtT02GtbyIi8l8+OvV9YT2PkhL52d5mfP311zh69CgCAgLQoUMHJCQkoH///qbLalv36Vk2GJNLNJcSpCQHM+VGASCgUP6bpTzC+NdW0Cn5ZyuL0SSknTAmfQBASX1NItivxumXRWuSRPI1SSJhFS8LWqVJY7r2TBqrWaqjT5hIMtMlTOq2E912pd0OhW1Wt33r9ge6/YdufyPtm3T7MTNJY4AmcczM+vHD7fjCGh6TJ0/2+szwwYMHAQBpaWmYMWMGGjdujOnTpyM1NRX79+9HTExMhabDI2oiIvJbyuXy+gUA2dnZHjU9Jk6cqP3OtLQ02Gy2i762bdsG1/9Ne9KkSbjrrrvQsWNHLFy4EDabDe+8806Fl9G6R9RERESXohS8uh/s/059m6njMXbsWNx7770XbdO4cWPk5+cDgEf9EYfDgSZNmrjrj1QEB2oiIiIT6tWrh3r15Keg/VbHjh3hcDiwb98+dOvWDQBQVlaGw4cPu+uPVAQHaiIi8l8uBdisWfAkKioKo0ePxuTJk5GUlIRGjRrh5ZdfBgAMHjy4wtPhQE1ERP5LKegzXyv6+arz8ssvIygoCMOHD0dRURE6deqEDRs2oG5d4zPMdaw7UEuXHUxklurK3dnk56dDBWuyIkuNX+oM0bQt0jzIXpNZGligydgOFR7aXixP22XXzEuZpiyo9JVVmd2ta++HWaFUCdVRVlboz2K/h3470W5Xmu1Q2mZ127duf6Dbf2j3N8K+Sbcf0+73zJQFNbN+rF+V87IJDg7GK6+8gldeeaXS07DuQE1ERHQJyqWgvDj1rfyg1jcHaiIi8l9KeIKT6c9bGwdqIiLyW7XhiJoFT4iIiCzMckfU5/+6cRUXG9+sysQUE8lnLk1iCjTl+FxOzZeWyl8qtteV+tOsQVPJI75IDjPbnslktVtVJpOZmLbuWQ665DObbhuX4rpENZfmS3XtNd8JYRvXzfflfob8+f335ThaLVclXp2+Lof8vG8rsSmLHfcfOXLEUHOViIj8T3Z2Nho2bFgl0y4uLkZycjJycnK8nlZ8fDwOHTqEkJAQH8yZ71luoHa5XDh27BgiIyORn5+PpKQkZGdnV7i0mz86c+YMl7MGqQ3LWRuWEeByVpZSCvn5+UhMTERAQNVdYS0uLkZpaanX07Hb7ZYdpAELnvoOCAhw/wVms50712KmBqs/43LWLLVhOWvDMgJczsqIjo72yXQuJiQkxNIDrK8wmYyIiMjCOFATERFZmKUHaofDgcmTJ8PhcFT3rFQpLmfNUhuWszYsI8DlJGuwXDIZERER/Y+lj6iJiIhqOw7UREREFsaBmoiIyMI4UBMREVkYB2oiIiILs/RA/frrryM5ORkhISHo2LEjPv300+qeJa9s2rQJAwcORGJiImw2G9577z2P95VSSEtLQ2JiIkJDQ9GjRw/s2rWrema2ktLT03HdddchMjISDRo0wB133IF9+/Z5tKkJyzlnzhxcc8017kpOnTt3xpo1a9zv14RlvFB6ejpsNhvGjx/vjtWE5UxLS4PNZvN4xcfHu9+vCct43tGjRzFs2DDExsYiLCwM7du3x/bt293v16RlrUksO1C/9dZbGD9+PCZNmoRvvvkG3bt3R//+/fHTTz9V96xVWkFBAdq1a4fZs2eL77/00kuYMWMGZs+eja1btyI+Ph4333wz8vPzL/OcVl5mZiYee+wxbNmyBevXr0d5eTn69OmDgoICd5uasJwNGzbEiy++iG3btmHbtm246aabcPvtt7t3ajVhGX9r69atmDdvHq655hqPeE1ZzquvvhrHjx93v3bu3Ol+r6Ys48mTJ9G1a1cEBwdjzZo12L17N6ZPn446deq429SUZa1xlEVdf/31avTo0R6xli1bqmeffbaa5si3AKhVq1a5/+9yuVR8fLx68cUX3bHi4mIVHR2t5s6dWw1z6Bu5ubkKgMrMzFRK1dzlVEqpunXrqn/84x81bhnz8/NVs2bN1Pr161Vqaqp64oknlFI1Z11OnjxZtWvXTnyvpiyjUko988wzqlu3btr3a9Ky1jSWPKIuLS3F9u3b0adPH494nz59sHnz5mqaq6p16NAh5OTkeCyzw+FAamqqXy/z6dOnAQAxMTEAauZyOp1OrFixAgUFBejcuXONW8bHHnsMAwYMQO/evT3iNWk5Dxw4gMTERCQnJ+Pee+/FwYMHAdSsZVy9ejVSUlIwePBgNGjQAB06dMD8+fPd79ekZa1pLDlQnzhxAk6nE3FxcR7xuLg4nzx71IrOL1dNWmalFJ566il069YNbdq0AVCzlnPnzp2IiIiAw+HA6NGjsWrVKrRu3bpGLeOKFSvw9ddfIz093fBeTVnOTp06YcmSJcjIyMD8+fORk5ODLl26IC8vr8YsIwAcPHgQc+bMQbNmzZCRkYHRo0fj8ccfx5IlSwDUnPVZE1nuMZe/df4xl+cppQyxmqYmLfPYsWPx7bff4rPPPjO8VxOWs0WLFsjKysKpU6fw7rvvYsSIEcjMzHS/7+/LmJ2djSeeeALr1q276KME/X05+/fv7/5327Zt0blzZ1x11VVYvHgxbrjhBgD+v4wA4HK5kJKSgqlTpwIAOnTogF27dmHOnDm4//773e1qwrLWNJY8oq5Xrx4CAwMNf8Xl5uYa/tqrKc5nmdaUZR43bhxWr16NTz75xP18caBmLafdbkfTpk2RkpKC9PR0tGvXDq+++mqNWcbt27cjNzcXHTt2RFBQEIKCgpCZmYnXXnsNQUFB7mXx9+W8UHh4ONq2bYsDBw7UmHUJAAkJCWjdurVHrFWrVu4E3Zq0rDWNJQdqu92Ojh07Yv369R7x9evXo0uXLtU0V1UrOTkZ8fHxHstcWlqKzMxMv1pmpRTGjh2LlStXYsOGDUhOTvZ4v6Ysp0QphZKSkhqzjL169cLOnTuRlZXlfqWkpGDo0KHIyspCkyZNasRyXqikpAR79uxBQkJCjVmXANC1a1fDrZL79+9Ho0aNANTsbdPvVVcW26WsWLFCBQcHqwULFqjdu3er8ePHq/DwcHX48OHqnrVKy8/PV99884365ptvFAA1Y8YM9c0336gff/xRKaXUiy++qKKjo9XKlSvVzp071X333acSEhLUmTNnqnnOK+73v/+9io6OVhs3blTHjx93vwoLC91tasJyTpw4UW3atEkdOnRIffvtt+qPf/yjCggIUOvWrVNK1YxllPw261upmrGcf/jDH9TGjRvVwYMH1ZYtW9Stt96qIiMj3fuamrCMSin11VdfqaCgIPXXv/5VHThwQP3zn/9UYWFhatmyZe42NWVZaxrLDtRKKfX3v/9dNWrUSNntdnXttde6b/HxV5988okCYHiNGDFCKXXu9ojJkyer+Ph45XA41I033qh27txZvTNtkrR8ANTChQvdbWrCcj744IPuvlm/fn3Vq1cv9yCtVM1YRsmFA3VNWM577rlHJSQkqODgYJWYmKgGDRqkdu3a5X6/JizjeR988IFq06aNcjgcqmXLlmrevHke79ekZa1J+DxqIiIiC7PkNWoiIiI6hwM1ERGRhXGgJiIisjAO1ERERBbGgZqIiMjCOFATERFZGAdqIiIiC+NATUREZGEcqImIiCyMAzUREZGFcaAmIiKysP8PSoTuwHZOtyEAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_fun2(45)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "b3b00269-d739-4073-a7e9-77b66960325b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Animation at FPS=18 will last for 2.7777777777777777 seconds\n"
     ]
    }
   ],
   "source": [
    "create_animation(plot_fun2, range(0, FD_timesteps), '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
}