code/xcontour_isoentropic_CMAM.ipynb

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    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from dask.distributed import Client, LocalCluster, get_task_stream\n",
    "import xarray as xr\n",
    "import glob\n",
    "from numba import jit, guvectorize, float32, float64, boolean"
   ]
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    "%config Completer.use_jedi = False"
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    "#sns.set_style('whitegrid')\n",
    "#plt.style.use(['seaborn-whitegrid','seaborn-poster','plotstyle.mplstyle'])\n",
    "mpl.rcParams.update({'font.size': 18})\n",
    "xr.set_options(keep_attrs=True,display_style='html')"
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    "@guvectorize(\n",
    "    [\n",
    "        (float64[:], float64[:], float64[:], boolean, float64[:]),\n",
    "        (float32[:], float32[:], float32[:], boolean, float32[:]),\n",
    "    ],\n",
    "    \"(n),(n),(m),()->(m)\",\n",
    "    nopython=True,\n",
    ")\n",
    "def _interp_1d_linear(phi, theta, target_theta_levels, mask_edges, output):\n",
    "    # if last theta value is smaller than first, assume the profile is monotonically decreasing and flip\n",
    "    if theta[-1] < theta[0]:\n",
    "        theta = np.flip(theta)\n",
    "        phi = np.flip(phi)\n",
    "\n",
    "    output[:] = np.interp(target_theta_levels, theta, phi)\n",
    "\n",
    "    if mask_edges:\n",
    "        theta_max = np.nanmax(theta)\n",
    "        theta_min = np.nanmin(theta)\n",
    "        for i in range(len(target_theta_levels)):\n",
    "            theta_lev = target_theta_levels[i]\n",
    "            if (theta_lev < theta_min) or (theta_lev > theta_max):\n",
    "                output[i] = np.nan\n",
    "\n",
    "\n",
    "def interp_1d_linear(phi, theta, target_theta_levels, mask_edges=False):\n",
    "    \"\"\"\n",
    "    Vectorized interpolation of scalar phi to isosurfaces of scalar theta\n",
    "    along the final axis.\n",
    "    Parameters\n",
    "    ----------\n",
    "    phi : array_like\n",
    "        Array of shape (..., n), scalar field to be interpolated\n",
    "    theta : array_like\n",
    "        Array of shape (..., n), scalar field which defines the isosurfaces\n",
    "    target_theta_levels : array_like\n",
    "        Array of shape (m) specificying target isosurface levels\n",
    "    mask_edges : bool, optional\n",
    "        Determines how to handle theta values that exceed the bounds of\n",
    "        target_theta_levels. If False, fill with nearest valid values. If\n",
    "        True, fill with NaNs.\n",
    "    Returns\n",
    "    -------\n",
    "    phi_interp : array\n",
    "        Array of shape (..., m) of phi interpolated to theta isosurfaces.\n",
    "    \"\"\"\n",
    "    return _interp_1d_linear(phi, theta, target_theta_levels, mask_edges)\n",
    "\n",
    "\n",
    "def linear_interpolation(\n",
    "    phi, theta, target_theta_levels, phi_dim, theta_dim, target_dim, **kwargs):\n",
    "    out = xr.apply_ufunc(\n",
    "        interp_1d_linear,\n",
    "        phi,\n",
    "        theta,\n",
    "        target_theta_levels,\n",
    "        kwargs=kwargs,\n",
    "        input_core_dims=[[phi_dim], [theta_dim], [target_dim]],\n",
    "        output_core_dims=[[target_dim]],\n",
    "        exclude_dims=set((phi_dim,)),\n",
    "        dask=\"parallelized\",\n",
    "        output_dtypes=[np.float32],\n",
    "    )\n",
    "    out[target_dim] = target_theta_levels\n",
    "    return out\n",
    "\n",
    "def cal_integral_within_contours(tracer, contour, var=None,\n",
    "                                 out_name=None, lt=True):\n",
    "    \"\"\"\n",
    "    Calculate masked variable using pre-calculated tracer contours.\n",
    "    Parameters\n",
    "    ----------\n",
    "    contour : xarray.DataArray\n",
    "        A given contour levels.\n",
    "    var  : xarray.DataArray\n",
    "        A given variable in dset.  If None, area enclosed by contour\n",
    "        will be calculated and returned\n",
    "    out_name : str\n",
    "        A given name for the returned variable.\n",
    "    lt : boolean\n",
    "        less than the given contour or greater than.\n",
    "    Returns\n",
    "    ----------\n",
    "    intVar : xarray.DataArray\n",
    "        The integral of var inside contour.  If None, area enclosed by\n",
    "        contour will be calculated and returned\n",
    "    \"\"\"\n",
    "    if var is None:\n",
    "        var = tracer - tracer + 1\n",
    "\n",
    "    if out_name is None:\n",
    "        if var is None:\n",
    "            out_name = 'area'\n",
    "        else:\n",
    "            out_name = 'int' + var.name\n",
    "    print(var.shape)\n",
    "    print(tracer.shape)\n",
    "    print(contour.shape)\n",
    "    if lt:\n",
    "        mskVar = np.where(tracer < contour, var, np.full_like(var, np.nan))#var.where(tracer < contour)\n",
    "    else:\n",
    "        mskVar = np.where(tracer > contour, var, np.full_like(var, np.nan))#var.where(tracer > contour)\n",
    "\n",
    "    intVar = grid.integrate(mskVar, ['X','Y']).rename(out_name)\n",
    "\n",
    "    return intVar\n"
   ]
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    "timescale = 20\n",
    "DJF_bool = '_DJFonly'\n",
    "root_path = '/mnt/nas4.meop2/meop40.data.model/CMAM/0A.daily/'\n",
    "root_path = '/mnt/data04/model/CMAM/0A.daily/'\n",
    "cesta = f'{root_path}composites_woSSW{DJF_bool}/'\n",
    "name = 'himalayas'\n",
    "lag = 0\n",
    "a0 = 6376.0e3 # [m]\n",
    "g = 9.81 #[m/s2 ]"
   ]
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    "https://github.com/Unidata/MetPy/issues/1479"
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     "text": [
      "/mnt/data04/model/CMAM/0A.daily/vorpot/vorpot_6hrPlev_CMAM_CMAM30-SD_r1i1p1_1979010100-1979123118.nc\n",
      "/mnt/data04/model/CMAM/0A.daily/tpot/tpot_6hrPlev_CMAM_CMAM30-SD_r1i1p1_1979010100-1979123118.nc\n"
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       "  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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:  (lat: 48, lon: 96, plev: 63, time: 1460)\n",
       "Coordinates:\n",
       "  * time     (time) object 1979-01-01 00:00:00 ... 1979-12-31 18:00:00\n",
       "  * plev     (plev) float64 1e+05 9.25e+04 8.5e+04 8e+04 ... 0.2 0.15 0.1 0.07\n",
       "  * lat      (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "  * lon      (lon) float64 0.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "Data variables:\n",
       "    vorpot   (time, plev, lat, lon) float32 ...\n",
       "    tpot     (time, plev, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    standard_name:     ertel_potential_vorticity\n",
       "    long_name:         Ertel&#x27;s Potential Vorticity\n",
       "    units:             K m2 kg-1 s-1\n",
       "    cell_methods:      time:mean\n",
       "    cell_measures:     area: areacella\n",
       "    history:           2013-05-28T07:40:04Z altered by CMOR: replaced missing...\n",
       "    associated_files:  baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation...</pre><div class='xr-wrap' hidden><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-2b8ea3d4-c106-4f84-b36b-1031091b0fe7' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2b8ea3d4-c106-4f84-b36b-1031091b0fe7' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>lat</span>: 48</li><li><span class='xr-has-index'>lon</span>: 96</li><li><span class='xr-has-index'>plev</span>: 63</li><li><span class='xr-has-index'>time</span>: 1460</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-c7f0a88d-24d3-4d99-8082-4afd4da2249b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-c7f0a88d-24d3-4d99-8082-4afd4da2249b' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1979-01-01 00:00:00 ... 1979-12-...</div><input id='attrs-192889b0-6d26-47a5-a512-c3aecaae1e54' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-192889b0-6d26-47a5-a512-c3aecaae1e54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00150d18-866d-4466-95b3-bb5d49335d01' class='xr-var-data-in' type='checkbox'><label for='data-00150d18-866d-4466-95b3-bb5d49335d01' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1979, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1979, 1, 1, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1979, 1, 1, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(1979, 12, 31, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1979, 12, 31, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1979, 12, 31, 18, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>plev</span></div><div class='xr-var-dims'>(plev)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e+05 9.25e+04 8.5e+04 ... 0.1 0.07</div><input id='attrs-a9b606a6-fddb-4d30-ac6a-e048ad1ef454' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a9b606a6-fddb-4d30-ac6a-e048ad1ef454' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3ed9c0cc-9e60-45f3-94f8-8cbc398ab530' class='xr-var-data-in' type='checkbox'><label for='data-3ed9c0cc-9e60-45f3-94f8-8cbc398ab530' 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>bounds :</span></dt><dd>plev_bnds</dd><dt><span>units :</span></dt><dd>Pa</dd><dt><span>axis :</span></dt><dd>Z</dd><dt><span>positive :</span></dt><dd>down</dd><dt><span>long_name :</span></dt><dd>pressure</dd><dt><span>standard_name :</span></dt><dd>air_pressure</dd></dl></div><div class='xr-var-data'><pre>array([1.00e+05, 9.25e+04, 8.50e+04, 8.00e+04, 7.80e+04, 7.50e+04, 7.00e+04,\n",
       "       6.50e+04, 6.00e+04, 5.00e+04, 4.50e+04, 4.00e+04, 3.50e+04, 3.00e+04,\n",
       "       2.85e+04, 2.50e+04, 2.00e+04, 1.70e+04, 1.50e+04, 1.30e+04, 1.15e+04,\n",
       "       1.00e+04, 9.00e+03, 8.00e+03, 7.00e+03, 6.00e+03, 5.00e+03, 4.00e+03,\n",
       "       3.50e+03, 3.00e+03, 2.50e+03, 2.00e+03, 1.50e+03, 1.00e+03, 7.00e+02,\n",
       "       5.00e+02, 4.00e+02, 3.00e+02, 2.00e+02, 1.50e+02, 1.00e+02, 7.00e+01,\n",
       "       5.00e+01, 4.00e+01, 3.00e+01, 2.00e+01, 1.50e+01, 1.00e+01, 7.00e+00,\n",
       "       5.00e+00, 4.00e+00, 3.00e+00, 2.00e+00, 1.50e+00, 1.00e+00, 7.00e-01,\n",
       "       5.00e-01, 4.00e-01, 3.00e-01, 2.00e-01, 1.50e-01, 1.00e-01, 7.00e-02])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.16 -83.48 ... 83.48 87.16</div><input id='attrs-21c7dd10-0e0d-4718-bfd4-d5b21037170a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-21c7dd10-0e0d-4718-bfd4-d5b21037170a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-554a7b29-f351-4c9c-a710-cb9ed8cc6735' class='xr-var-data-in' type='checkbox'><label for='data-554a7b29-f351-4c9c-a710-cb9ed8cc6735' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-87.159094, -83.478937, -79.777046, -76.070244, -72.361581, -68.652017,\n",
       "       -64.941949, -61.231573, -57.520994, -53.810274, -50.099453, -46.388558,\n",
       "       -42.677606, -38.96661 , -35.25558 , -31.544523, -27.833444, -24.122348,\n",
       "       -20.411238, -16.700118, -12.988988,  -9.277853,  -5.566714,  -1.855571,\n",
       "         1.855571,   5.566714,   9.277853,  12.988988,  16.700118,  20.411238,\n",
       "        24.122348,  27.833444,  31.544523,  35.25558 ,  38.96661 ,  42.677606,\n",
       "        46.388558,  50.099453,  53.810274,  57.520994,  61.231573,  64.941949,\n",
       "        68.652017,  72.361581,  76.070244,  79.777046,  83.478937,  87.159094])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.75 7.5 ... 348.8 352.5 356.2</div><input id='attrs-c6d8ff51-6496-48ba-9e82-f3c5b52eef8d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-c6d8ff51-6496-48ba-9e82-f3c5b52eef8d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-dc750fca-ae59-41d8-848b-4096d3548015' class='xr-var-data-in' type='checkbox'><label for='data-dc750fca-ae59-41d8-848b-4096d3548015' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   3.75,   7.5 ,  11.25,  15.  ,  18.75,  22.5 ,  26.25,  30.  ,\n",
       "        33.75,  37.5 ,  41.25,  45.  ,  48.75,  52.5 ,  56.25,  60.  ,  63.75,\n",
       "        67.5 ,  71.25,  75.  ,  78.75,  82.5 ,  86.25,  90.  ,  93.75,  97.5 ,\n",
       "       101.25, 105.  , 108.75, 112.5 , 116.25, 120.  , 123.75, 127.5 , 131.25,\n",
       "       135.  , 138.75, 142.5 , 146.25, 150.  , 153.75, 157.5 , 161.25, 165.  ,\n",
       "       168.75, 172.5 , 176.25, 180.  , 183.75, 187.5 , 191.25, 195.  , 198.75,\n",
       "       202.5 , 206.25, 210.  , 213.75, 217.5 , 221.25, 225.  , 228.75, 232.5 ,\n",
       "       236.25, 240.  , 243.75, 247.5 , 251.25, 255.  , 258.75, 262.5 , 266.25,\n",
       "       270.  , 273.75, 277.5 , 281.25, 285.  , 288.75, 292.5 , 296.25, 300.  ,\n",
       "       303.75, 307.5 , 311.25, 315.  , 318.75, 322.5 , 326.25, 330.  , 333.75,\n",
       "       337.5 , 341.25, 345.  , 348.75, 352.5 , 356.25])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b9c99541-fbf7-4ef7-992f-9b20ef8fab66' class='xr-section-summary-in' type='checkbox'  checked><label for='section-b9c99541-fbf7-4ef7-992f-9b20ef8fab66' class='xr-section-summary' >Data variables: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>vorpot</span></div><div class='xr-var-dims'>(time, plev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-0d1ebce7-3fe2-46e6-ad3d-e525baf612e1' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0d1ebce7-3fe2-46e6-ad3d-e525baf612e1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-92fc7ccd-3a07-4a5f-9d96-f1d488a0bac2' class='xr-var-data-in' type='checkbox'><label for='data-92fc7ccd-3a07-4a5f-9d96-f1d488a0bac2' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ertel_potential_vorticity</dd><dt><span>long_name :</span></dt><dd>Ertel&#x27;s Potential Vorticity</dd><dt><span>units :</span></dt><dd>K m2 kg-1 s-1</dd><dt><span>cell_methods :</span></dt><dd>time:mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2013-05-28T07:40:04Z altered by CMOR: replaced missing value flag (1e+38) with standard missing value (1e+20). 2013-05-28T07:40:04Z altered by CMOR: Inverted axis: plev.</dd><dt><span>associated_files :</span></dt><dd>baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_CMAM_CMAM30-SD_r0i0p0.nc areacella: areacella_fx_CMAM_CMAM30-SD_r0i0p0.nc</dd></dl></div><div class='xr-var-data'><pre>[423843840 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>tpot</span></div><div class='xr-var-dims'>(time, plev, lat, lon)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-fdb145ae-241f-4d9b-9094-856eef7d9cf4' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fdb145ae-241f-4d9b-9094-856eef7d9cf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3d86a49b-332d-477f-81b2-f44cc1e998be' class='xr-var-data-in' type='checkbox'><label for='data-3d86a49b-332d-477f-81b2-f44cc1e998be' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>air_potential_temperature</dd><dt><span>long_name :</span></dt><dd>Potential Temperature</dd><dt><span>units :</span></dt><dd>K</dd><dt><span>cell_methods :</span></dt><dd>time:mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2013-05-27T23:15:41Z altered by CMOR: replaced missing value flag (1e+38) with standard missing value (1e+20). 2013-05-27T23:15:41Z altered by CMOR: Inverted axis: plev.</dd><dt><span>associated_files :</span></dt><dd>baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_CMAM_CMAM30-SD_r0i0p0.nc areacella: areacella_fx_CMAM_CMAM30-SD_r0i0p0.nc</dd></dl></div><div class='xr-var-data'><pre>[423843840 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-1293c800-ec0c-4d7d-a912-cdb6cca948e4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1293c800-ec0c-4d7d-a912-cdb6cca948e4' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ertel_potential_vorticity</dd><dt><span>long_name :</span></dt><dd>Ertel&#x27;s Potential Vorticity</dd><dt><span>units :</span></dt><dd>K m2 kg-1 s-1</dd><dt><span>cell_methods :</span></dt><dd>time:mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2013-05-28T07:40:04Z altered by CMOR: replaced missing value flag (1e+38) with standard missing value (1e+20). 2013-05-28T07:40:04Z altered by CMOR: Inverted axis: plev.</dd><dt><span>associated_files :</span></dt><dd>baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_CMAM_CMAM30-SD_r0i0p0.nc areacella: areacella_fx_CMAM_CMAM30-SD_r0i0p0.nc</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:  (lat: 48, lon: 96, plev: 63, time: 1460)\n",
       "Coordinates:\n",
       "  * time     (time) object 1979-01-01 00:00:00 ... 1979-12-31 18:00:00\n",
       "  * plev     (plev) float64 1e+05 9.25e+04 8.5e+04 8e+04 ... 0.2 0.15 0.1 0.07\n",
       "  * lat      (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "  * lon      (lon) float64 0.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "Data variables:\n",
       "    vorpot   (time, plev, lat, lon) float32 ...\n",
       "    tpot     (time, plev, lat, lon) float32 ...\n",
       "Attributes:\n",
       "    standard_name:     ertel_potential_vorticity\n",
       "    long_name:         Ertel's Potential Vorticity\n",
       "    units:             K m2 kg-1 s-1\n",
       "    cell_methods:      time:mean\n",
       "    cell_measures:     area: areacella\n",
       "    history:           2013-05-28T07:40:04Z altered by CMOR: replaced missing...\n",
       "    associated_files:  baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation..."
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sel_var_ls = ['vorpot','tpot']\n",
    "ds_ls = []\n",
    "for sel_var in sel_var_ls:\n",
    "    infiles = sorted(glob.glob(f'{root_path}{sel_var}/{sel_var}_6*18.nc'))\n",
    "    infile = infiles[0]\n",
    "    print(infile)\n",
    "    ds = xr.open_dataset(infile)[sel_var]\n",
    "    #ds = xr.open_mfdataset(infile, concat_dim='time')[sel_var] # dask not supported yet\n",
    "    ds_ls.append(ds)\n",
    "    \n",
    "ds_all = xr.merge(ds_ls)\n",
    "ds_all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "336db301-4fd1-4ffc-b32a-97dcfdd99f71",
   "metadata": {
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;vorpot&#x27; (time: 1460, lat: 48, lon: 96, lvl: 45)&gt;\n",
       "array([[[[-1.00253734e-05, -1.08483503e-05, -1.19984851e-05, ...,\n",
       "          -2.62434196e-04, -2.75508612e-04, -2.88583029e-04],\n",
       "         [-1.00341333e-05, -1.08650068e-05, -1.20253402e-05, ...,\n",
       "          -2.62683896e-04, -2.75759866e-04, -2.88835836e-04],\n",
       "         [-1.00440045e-05, -1.08829530e-05, -1.20527703e-05, ...,\n",
       "          -2.62907409e-04, -2.75985060e-04, -2.89062711e-04],\n",
       "         ...,\n",
       "         [-1.00119918e-05, -1.08148401e-05, -1.19329276e-05, ...,\n",
       "          -2.61547371e-04, -2.74617714e-04, -2.87688057e-04],\n",
       "         [-1.00135519e-05, -1.08223296e-05, -1.19510561e-05, ...,\n",
       "          -2.61863819e-04, -2.74935448e-04, -2.88007077e-04],\n",
       "         [-1.00182814e-05, -1.08338201e-05, -1.19733236e-05, ...,\n",
       "          -2.62160168e-04, -2.75233149e-04, -2.88306130e-04]],\n",
       "\n",
       "        [[-1.24396973e-05, -1.32972732e-05, -1.46738935e-05, ...,\n",
       "          -2.65178291e-04, -2.78709462e-04, -2.92240632e-04],\n",
       "         [-1.25787053e-05, -1.34629177e-05, -1.48621702e-05, ...,\n",
       "          -2.65759158e-04, -2.79258351e-04, -2.92757544e-04],\n",
       "         [-1.26809498e-05, -1.35948517e-05, -1.50113760e-05, ...,\n",
       "          -2.66312085e-04, -2.79780012e-04, -2.93247939e-04],\n",
       "...\n",
       "         [ 1.27425822e-05,  1.41477780e-05,  1.57338752e-05, ...,\n",
       "           7.70979846e-04,  8.02392662e-04,  8.33805478e-04],\n",
       "         [ 1.30644075e-05,  1.44851515e-05,  1.60660600e-05, ...,\n",
       "           7.64133616e-04,  7.94665520e-04,  8.25197424e-04],\n",
       "         [ 1.34203502e-05,  1.48708793e-05,  1.64520586e-05, ...,\n",
       "           7.58816477e-04,  7.88625692e-04,  8.18434908e-04]],\n",
       "\n",
       "        [[ 1.32711801e-05,  1.47249371e-05,  1.66697855e-05, ...,\n",
       "           7.43767092e-04,  7.71391991e-04,  7.99016889e-04],\n",
       "         [ 1.33950097e-05,  1.48721040e-05,  1.68579356e-05, ...,\n",
       "           7.42402835e-04,  7.70088609e-04,  7.97774383e-04],\n",
       "         [ 1.35205584e-05,  1.50243650e-05,  1.70551479e-05, ...,\n",
       "           7.40912806e-04,  7.68676715e-04,  7.96440625e-04],\n",
       "         ...,\n",
       "         [ 1.29204586e-05,  1.43232993e-05,  1.61668129e-05, ...,\n",
       "           7.47049497e-04,  7.74588332e-04,  8.02127166e-04],\n",
       "         [ 1.30331183e-05,  1.44499159e-05,  1.63238702e-05, ...,\n",
       "           7.46098519e-04,  7.73651207e-04,  8.01203894e-04],\n",
       "         [ 1.31502418e-05,  1.45839471e-05,  1.64915833e-05, ...,\n",
       "           7.45001230e-04,  7.72582099e-04,  8.00162967e-04]]]])\n",
       "Coordinates:\n",
       "  * time     (time) object 1979-01-01 00:00:00 ... 1979-12-31 18:00:00\n",
       "  * lat      (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "  * lon      (lon) float64 0.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "  * lvl      (lvl) int64 400 410 420 430 440 450 460 ... 790 800 810 820 830 840\n",
       "Attributes:\n",
       "    standard_name:     ertel_potential_vorticity\n",
       "    long_name:         Ertel&#x27;s Potential Vorticity\n",
       "    units:             K m2 kg-1 s-1\n",
       "    cell_methods:      time:mean\n",
       "    cell_measures:     area: areacella\n",
       "    history:           2013-05-28T07:40:04Z altered by CMOR: replaced missing...\n",
       "    associated_files:  baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation...</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'vorpot'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 1460</li><li><span class='xr-has-index'>lat</span>: 48</li><li><span class='xr-has-index'>lon</span>: 96</li><li><span class='xr-has-index'>lvl</span>: 45</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-84972eee-3e8d-4e54-9803-80a9b2d1b4fa' class='xr-array-in' type='checkbox' checked><label for='section-84972eee-3e8d-4e54-9803-80a9b2d1b4fa' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-1.003e-05 -1.085e-05 -1.2e-05 ... 0.000745 0.0007726 0.0008002</span></div><div class='xr-array-data'><pre>array([[[[-1.00253734e-05, -1.08483503e-05, -1.19984851e-05, ...,\n",
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       "         [-1.00341333e-05, -1.08650068e-05, -1.20253402e-05, ...,\n",
       "          -2.62683896e-04, -2.75759866e-04, -2.88835836e-04],\n",
       "         [-1.00440045e-05, -1.08829530e-05, -1.20527703e-05, ...,\n",
       "          -2.62907409e-04, -2.75985060e-04, -2.89062711e-04],\n",
       "         ...,\n",
       "         [-1.00119918e-05, -1.08148401e-05, -1.19329276e-05, ...,\n",
       "          -2.61547371e-04, -2.74617714e-04, -2.87688057e-04],\n",
       "         [-1.00135519e-05, -1.08223296e-05, -1.19510561e-05, ...,\n",
       "          -2.61863819e-04, -2.74935448e-04, -2.88007077e-04],\n",
       "         [-1.00182814e-05, -1.08338201e-05, -1.19733236e-05, ...,\n",
       "          -2.62160168e-04, -2.75233149e-04, -2.88306130e-04]],\n",
       "\n",
       "        [[-1.24396973e-05, -1.32972732e-05, -1.46738935e-05, ...,\n",
       "          -2.65178291e-04, -2.78709462e-04, -2.92240632e-04],\n",
       "         [-1.25787053e-05, -1.34629177e-05, -1.48621702e-05, ...,\n",
       "          -2.65759158e-04, -2.79258351e-04, -2.92757544e-04],\n",
       "         [-1.26809498e-05, -1.35948517e-05, -1.50113760e-05, ...,\n",
       "          -2.66312085e-04, -2.79780012e-04, -2.93247939e-04],\n",
       "...\n",
       "         [ 1.27425822e-05,  1.41477780e-05,  1.57338752e-05, ...,\n",
       "           7.70979846e-04,  8.02392662e-04,  8.33805478e-04],\n",
       "         [ 1.30644075e-05,  1.44851515e-05,  1.60660600e-05, ...,\n",
       "           7.64133616e-04,  7.94665520e-04,  8.25197424e-04],\n",
       "         [ 1.34203502e-05,  1.48708793e-05,  1.64520586e-05, ...,\n",
       "           7.58816477e-04,  7.88625692e-04,  8.18434908e-04]],\n",
       "\n",
       "        [[ 1.32711801e-05,  1.47249371e-05,  1.66697855e-05, ...,\n",
       "           7.43767092e-04,  7.71391991e-04,  7.99016889e-04],\n",
       "         [ 1.33950097e-05,  1.48721040e-05,  1.68579356e-05, ...,\n",
       "           7.42402835e-04,  7.70088609e-04,  7.97774383e-04],\n",
       "         [ 1.35205584e-05,  1.50243650e-05,  1.70551479e-05, ...,\n",
       "           7.40912806e-04,  7.68676715e-04,  7.96440625e-04],\n",
       "         ...,\n",
       "         [ 1.29204586e-05,  1.43232993e-05,  1.61668129e-05, ...,\n",
       "           7.47049497e-04,  7.74588332e-04,  8.02127166e-04],\n",
       "         [ 1.30331183e-05,  1.44499159e-05,  1.63238702e-05, ...,\n",
       "           7.46098519e-04,  7.73651207e-04,  8.01203894e-04],\n",
       "         [ 1.31502418e-05,  1.45839471e-05,  1.64915833e-05, ...,\n",
       "           7.45001230e-04,  7.72582099e-04,  8.00162967e-04]]]])</pre></div></div></li><li class='xr-section-item'><input id='section-5d214580-5ae2-43cb-b9f2-c6add5a9492e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5d214580-5ae2-43cb-b9f2-c6add5a9492e' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1979-01-01 00:00:00 ... 1979-12-...</div><input id='attrs-2800e28b-2cbb-45e8-8598-2c3cd674d502' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2800e28b-2cbb-45e8-8598-2c3cd674d502' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f9b4b6a5-9ad6-4875-ab60-baa7f30f08d2' class='xr-var-data-in' type='checkbox'><label for='data-f9b4b6a5-9ad6-4875-ab60-baa7f30f08d2' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1979, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1979, 1, 1, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1979, 1, 1, 12, 0, 0, 0, has_year_zero=True), ...,\n",
       "       cftime.DatetimeNoLeap(1979, 12, 31, 6, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1979, 12, 31, 12, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1979, 12, 31, 18, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lat</span></div><div class='xr-var-dims'>(lat)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.16 -83.48 ... 83.48 87.16</div><input id='attrs-066cfcf9-85a9-416d-b2aa-ccde3b43d6ca' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-066cfcf9-85a9-416d-b2aa-ccde3b43d6ca' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec2afc32-8a53-4b75-bebb-6433354b3e2a' class='xr-var-data-in' type='checkbox'><label for='data-ec2afc32-8a53-4b75-bebb-6433354b3e2a' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-87.159094, -83.478937, -79.777046, -76.070244, -72.361581, -68.652017,\n",
       "       -64.941949, -61.231573, -57.520994, -53.810274, -50.099453, -46.388558,\n",
       "       -42.677606, -38.96661 , -35.25558 , -31.544523, -27.833444, -24.122348,\n",
       "       -20.411238, -16.700118, -12.988988,  -9.277853,  -5.566714,  -1.855571,\n",
       "         1.855571,   5.566714,   9.277853,  12.988988,  16.700118,  20.411238,\n",
       "        24.122348,  27.833444,  31.544523,  35.25558 ,  38.96661 ,  42.677606,\n",
       "        46.388558,  50.099453,  53.810274,  57.520994,  61.231573,  64.941949,\n",
       "        68.652017,  72.361581,  76.070244,  79.777046,  83.478937,  87.159094])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lon</span></div><div class='xr-var-dims'>(lon)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.75 7.5 ... 348.8 352.5 356.2</div><input id='attrs-22ef7005-f42e-4d88-a92e-ba26ddfc568f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-22ef7005-f42e-4d88-a92e-ba26ddfc568f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bc7dd8ee-e906-439e-ad51-ae640170876e' class='xr-var-data-in' type='checkbox'><label for='data-bc7dd8ee-e906-439e-ad51-ae640170876e' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   3.75,   7.5 ,  11.25,  15.  ,  18.75,  22.5 ,  26.25,  30.  ,\n",
       "        33.75,  37.5 ,  41.25,  45.  ,  48.75,  52.5 ,  56.25,  60.  ,  63.75,\n",
       "        67.5 ,  71.25,  75.  ,  78.75,  82.5 ,  86.25,  90.  ,  93.75,  97.5 ,\n",
       "       101.25, 105.  , 108.75, 112.5 , 116.25, 120.  , 123.75, 127.5 , 131.25,\n",
       "       135.  , 138.75, 142.5 , 146.25, 150.  , 153.75, 157.5 , 161.25, 165.  ,\n",
       "       168.75, 172.5 , 176.25, 180.  , 183.75, 187.5 , 191.25, 195.  , 198.75,\n",
       "       202.5 , 206.25, 210.  , 213.75, 217.5 , 221.25, 225.  , 228.75, 232.5 ,\n",
       "       236.25, 240.  , 243.75, 247.5 , 251.25, 255.  , 258.75, 262.5 , 266.25,\n",
       "       270.  , 273.75, 277.5 , 281.25, 285.  , 288.75, 292.5 , 296.25, 300.  ,\n",
       "       303.75, 307.5 , 311.25, 315.  , 318.75, 322.5 , 326.25, 330.  , 333.75,\n",
       "       337.5 , 341.25, 345.  , 348.75, 352.5 , 356.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lvl</span></div><div class='xr-var-dims'>(lvl)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>400 410 420 430 ... 810 820 830 840</div><input id='attrs-a2c70d83-91d6-4d1e-a5d8-6cf420bcefb6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a2c70d83-91d6-4d1e-a5d8-6cf420bcefb6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d0f84033-862b-4773-bc9b-3b9962a8706b' class='xr-var-data-in' type='checkbox'><label for='data-d0f84033-862b-4773-bc9b-3b9962a8706b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530,\n",
       "       540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670,\n",
       "       680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810,\n",
       "       820, 830, 840])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4b05f88b-6d14-4820-bf7d-e3436a4bbcb1' class='xr-section-summary-in' type='checkbox'  checked><label for='section-4b05f88b-6d14-4820-bf7d-e3436a4bbcb1' class='xr-section-summary' >Attributes: <span>(7)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ertel_potential_vorticity</dd><dt><span>long_name :</span></dt><dd>Ertel&#x27;s Potential Vorticity</dd><dt><span>units :</span></dt><dd>K m2 kg-1 s-1</dd><dt><span>cell_methods :</span></dt><dd>time:mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2013-05-28T07:40:04Z altered by CMOR: replaced missing value flag (1e+38) with standard missing value (1e+20). 2013-05-28T07:40:04Z altered by CMOR: Inverted axis: plev.</dd><dt><span>associated_files :</span></dt><dd>baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_CMAM_CMAM30-SD_r0i0p0.nc areacella: areacella_fx_CMAM_CMAM30-SD_r0i0p0.nc</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'vorpot' (time: 1460, lat: 48, lon: 96, lvl: 45)>\n",
       "array([[[[-1.00253734e-05, -1.08483503e-05, -1.19984851e-05, ...,\n",
       "          -2.62434196e-04, -2.75508612e-04, -2.88583029e-04],\n",
       "         [-1.00341333e-05, -1.08650068e-05, -1.20253402e-05, ...,\n",
       "          -2.62683896e-04, -2.75759866e-04, -2.88835836e-04],\n",
       "         [-1.00440045e-05, -1.08829530e-05, -1.20527703e-05, ...,\n",
       "          -2.62907409e-04, -2.75985060e-04, -2.89062711e-04],\n",
       "         ...,\n",
       "         [-1.00119918e-05, -1.08148401e-05, -1.19329276e-05, ...,\n",
       "          -2.61547371e-04, -2.74617714e-04, -2.87688057e-04],\n",
       "         [-1.00135519e-05, -1.08223296e-05, -1.19510561e-05, ...,\n",
       "          -2.61863819e-04, -2.74935448e-04, -2.88007077e-04],\n",
       "         [-1.00182814e-05, -1.08338201e-05, -1.19733236e-05, ...,\n",
       "          -2.62160168e-04, -2.75233149e-04, -2.88306130e-04]],\n",
       "\n",
       "        [[-1.24396973e-05, -1.32972732e-05, -1.46738935e-05, ...,\n",
       "          -2.65178291e-04, -2.78709462e-04, -2.92240632e-04],\n",
       "         [-1.25787053e-05, -1.34629177e-05, -1.48621702e-05, ...,\n",
       "          -2.65759158e-04, -2.79258351e-04, -2.92757544e-04],\n",
       "         [-1.26809498e-05, -1.35948517e-05, -1.50113760e-05, ...,\n",
       "          -2.66312085e-04, -2.79780012e-04, -2.93247939e-04],\n",
       "...\n",
       "         [ 1.27425822e-05,  1.41477780e-05,  1.57338752e-05, ...,\n",
       "           7.70979846e-04,  8.02392662e-04,  8.33805478e-04],\n",
       "         [ 1.30644075e-05,  1.44851515e-05,  1.60660600e-05, ...,\n",
       "           7.64133616e-04,  7.94665520e-04,  8.25197424e-04],\n",
       "         [ 1.34203502e-05,  1.48708793e-05,  1.64520586e-05, ...,\n",
       "           7.58816477e-04,  7.88625692e-04,  8.18434908e-04]],\n",
       "\n",
       "        [[ 1.32711801e-05,  1.47249371e-05,  1.66697855e-05, ...,\n",
       "           7.43767092e-04,  7.71391991e-04,  7.99016889e-04],\n",
       "         [ 1.33950097e-05,  1.48721040e-05,  1.68579356e-05, ...,\n",
       "           7.42402835e-04,  7.70088609e-04,  7.97774383e-04],\n",
       "         [ 1.35205584e-05,  1.50243650e-05,  1.70551479e-05, ...,\n",
       "           7.40912806e-04,  7.68676715e-04,  7.96440625e-04],\n",
       "         ...,\n",
       "         [ 1.29204586e-05,  1.43232993e-05,  1.61668129e-05, ...,\n",
       "           7.47049497e-04,  7.74588332e-04,  8.02127166e-04],\n",
       "         [ 1.30331183e-05,  1.44499159e-05,  1.63238702e-05, ...,\n",
       "           7.46098519e-04,  7.73651207e-04,  8.01203894e-04],\n",
       "         [ 1.31502418e-05,  1.45839471e-05,  1.64915833e-05, ...,\n",
       "           7.45001230e-04,  7.72582099e-04,  8.00162967e-04]]]])\n",
       "Coordinates:\n",
       "  * time     (time) object 1979-01-01 00:00:00 ... 1979-12-31 18:00:00\n",
       "  * lat      (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "  * lon      (lon) float64 0.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "  * lvl      (lvl) int64 400 410 420 430 440 450 460 ... 790 800 810 820 830 840\n",
       "Attributes:\n",
       "    standard_name:     ertel_potential_vorticity\n",
       "    long_name:         Ertel's Potential Vorticity\n",
       "    units:             K m2 kg-1 s-1\n",
       "    cell_methods:      time:mean\n",
       "    cell_measures:     area: areacella\n",
       "    history:           2013-05-28T07:40:04Z altered by CMOR: replaced missing...\n",
       "    associated_files:  baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vert_c_name = 'plev'\n",
    "isentlevs = np.arange(400,850,10)\n",
    "\n",
    "ds_out = linear_interpolation(ds_all['vorpot'], \n",
    "                              ds_all['tpot'], \n",
    "                              isentlevs,\n",
    "                              vert_c_name, \n",
    "                              vert_c_name, \n",
    "                              'lvl')\n",
    "ds_out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "da89484c-ee6c-4800-93c1-1eacfd022831",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T12:56:48.836156Z",
     "iopub.status.busy": "2022-04-13T12:56:48.835596Z",
     "iopub.status.idle": "2022-04-13T12:56:48.842930Z",
     "shell.execute_reply": "2022-04-13T12:56:48.841681Z",
     "shell.execute_reply.started": "2022-04-13T12:56:48.836090Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from xcontour import Contour2D, latitude_lengths_at, add_latlon_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f55f171e-4158-41cd-89f0-aac6c1a12e0c",
   "metadata": {},
   "source": [
    "# Using xcontour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "fded0a09-926f-4552-833b-c31f7a03ac14",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:07:54.666359Z",
     "iopub.status.busy": "2022-04-25T13:07:54.666176Z",
     "iopub.status.idle": "2022-04-25T13:07:57.029141Z",
     "shell.execute_reply": "2022-04-25T13:07:57.028236Z",
     "shell.execute_reply.started": "2022-04-25T13:07:54.666337Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.append('../../')\n",
    "#from GeoApps.ContourMethods import ContourAnalysisInLatLon\n",
    "from GeoApps.DiagnosticMethods import Dynamics\n",
    "#from GeoApps.GridUtils import add_latlon_metrics\n",
    "from xcontour import Contour2D, latitude_lengths_at, add_latlon_metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c3f92d02-7d2f-40ec-a57b-e7e6a4958327",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:08:06.747672Z",
     "iopub.status.busy": "2022-04-25T13:08:06.747179Z",
     "iopub.status.idle": "2022-04-25T13:08:06.782103Z",
     "shell.execute_reply": "2022-04-25T13:08:06.781332Z",
     "shell.execute_reply.started": "2022-04-25T13:08:06.747622Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "\n",
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       "\n",
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       "  fill: currentColor;\n",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
       "Dimensions:    (latitude: 48, longitude: 96, lvl: 45, time: 2)\n",
       "Coordinates:\n",
       "  * time       (time) object 1979-01-01 00:00:00 1979-01-01 06:00:00\n",
       "  * latitude   (latitude) float64 -87.16 -83.48 -79.78 ... 79.78 83.48 87.16\n",
       "  * longitude  (longitude) float64 0.0 3.75 7.5 11.25 ... 348.8 352.5 356.2\n",
       "  * lvl        (lvl) int64 400 410 420 430 440 450 ... 790 800 810 820 830 840\n",
       "Data variables:\n",
       "    vorpot     (time, latitude, longitude, lvl) float64 -1.003e-05 ... 0.0008737</pre><div class='xr-wrap' hidden><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-a99aa6af-102f-450c-8d0b-1e7d59420332' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-a99aa6af-102f-450c-8d0b-1e7d59420332' 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'>latitude</span>: 48</li><li><span class='xr-has-index'>longitude</span>: 96</li><li><span class='xr-has-index'>lvl</span>: 45</li><li><span class='xr-has-index'>time</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-1be39310-d2b3-48fb-98f5-dfb767007d9c' class='xr-section-summary-in' type='checkbox'  checked><label for='section-1be39310-d2b3-48fb-98f5-dfb767007d9c' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1979-01-01 00:00:00 1979-01-01 0...</div><input id='attrs-6c2abacc-f35f-42cf-85e1-e1d38a04c790' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6c2abacc-f35f-42cf-85e1-e1d38a04c790' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bbc388e2-4590-426e-9c35-aaa2068a22e0' class='xr-var-data-in' type='checkbox'><label for='data-bbc388e2-4590-426e-9c35-aaa2068a22e0' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1979, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1979, 1, 1, 6, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.16 -83.48 ... 83.48 87.16</div><input id='attrs-4e3b46b5-165e-40b2-91c8-cfe99e9965f5' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4e3b46b5-165e-40b2-91c8-cfe99e9965f5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-caa16d04-09a9-4d73-979e-7ccbd9f69a1f' class='xr-var-data-in' type='checkbox'><label for='data-caa16d04-09a9-4d73-979e-7ccbd9f69a1f' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-87.159094, -83.478937, -79.777046, -76.070244, -72.361581, -68.652017,\n",
       "       -64.941949, -61.231573, -57.520994, -53.810274, -50.099453, -46.388558,\n",
       "       -42.677606, -38.96661 , -35.25558 , -31.544523, -27.833444, -24.122348,\n",
       "       -20.411238, -16.700118, -12.988988,  -9.277853,  -5.566714,  -1.855571,\n",
       "         1.855571,   5.566714,   9.277853,  12.988988,  16.700118,  20.411238,\n",
       "        24.122348,  27.833444,  31.544523,  35.25558 ,  38.96661 ,  42.677606,\n",
       "        46.388558,  50.099453,  53.810274,  57.520994,  61.231573,  64.941949,\n",
       "        68.652017,  72.361581,  76.070244,  79.777046,  83.478937,  87.159094])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.75 7.5 ... 348.8 352.5 356.2</div><input id='attrs-78fc2040-b326-461e-92ba-0cd66979610b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-78fc2040-b326-461e-92ba-0cd66979610b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5c714999-77f0-4acd-a634-b00bab1164c3' class='xr-var-data-in' type='checkbox'><label for='data-5c714999-77f0-4acd-a634-b00bab1164c3' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   3.75,   7.5 ,  11.25,  15.  ,  18.75,  22.5 ,  26.25,  30.  ,\n",
       "        33.75,  37.5 ,  41.25,  45.  ,  48.75,  52.5 ,  56.25,  60.  ,  63.75,\n",
       "        67.5 ,  71.25,  75.  ,  78.75,  82.5 ,  86.25,  90.  ,  93.75,  97.5 ,\n",
       "       101.25, 105.  , 108.75, 112.5 , 116.25, 120.  , 123.75, 127.5 , 131.25,\n",
       "       135.  , 138.75, 142.5 , 146.25, 150.  , 153.75, 157.5 , 161.25, 165.  ,\n",
       "       168.75, 172.5 , 176.25, 180.  , 183.75, 187.5 , 191.25, 195.  , 198.75,\n",
       "       202.5 , 206.25, 210.  , 213.75, 217.5 , 221.25, 225.  , 228.75, 232.5 ,\n",
       "       236.25, 240.  , 243.75, 247.5 , 251.25, 255.  , 258.75, 262.5 , 266.25,\n",
       "       270.  , 273.75, 277.5 , 281.25, 285.  , 288.75, 292.5 , 296.25, 300.  ,\n",
       "       303.75, 307.5 , 311.25, 315.  , 318.75, 322.5 , 326.25, 330.  , 333.75,\n",
       "       337.5 , 341.25, 345.  , 348.75, 352.5 , 356.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lvl</span></div><div class='xr-var-dims'>(lvl)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>400 410 420 430 ... 810 820 830 840</div><input id='attrs-2c0be847-3118-42f7-8659-0cc096d9bce3' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2c0be847-3118-42f7-8659-0cc096d9bce3' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3dc25cff-630b-4850-8d7d-a3f648d52067' class='xr-var-data-in' type='checkbox'><label for='data-3dc25cff-630b-4850-8d7d-a3f648d52067' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530,\n",
       "       540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670,\n",
       "       680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810,\n",
       "       820, 830, 840])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5c80c0b7-3569-486f-bdb4-a59f753be1ba' class='xr-section-summary-in' type='checkbox'  checked><label for='section-5c80c0b7-3569-486f-bdb4-a59f753be1ba' class='xr-section-summary' >Data variables: <span>(1)</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>vorpot</span></div><div class='xr-var-dims'>(time, latitude, longitude, lvl)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.003e-05 -1.085e-05 ... 0.0008737</div><input id='attrs-03798adf-5d5b-4abb-8be8-ddd4865033ff' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-03798adf-5d5b-4abb-8be8-ddd4865033ff' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-f78a6dd5-88be-4889-a9c9-bae15b33cd72' class='xr-var-data-in' type='checkbox'><label for='data-f78a6dd5-88be-4889-a9c9-bae15b33cd72' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ertel_potential_vorticity</dd><dt><span>long_name :</span></dt><dd>Ertel&#x27;s Potential Vorticity</dd><dt><span>units :</span></dt><dd>K m2 kg-1 s-1</dd><dt><span>cell_methods :</span></dt><dd>time:mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2013-05-28T07:40:04Z altered by CMOR: replaced missing value flag (1e+38) with standard missing value (1e+20). 2013-05-28T07:40:04Z altered by CMOR: Inverted axis: plev.</dd><dt><span>associated_files :</span></dt><dd>baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_CMAM_CMAM30-SD_r0i0p0.nc areacella: areacella_fx_CMAM_CMAM30-SD_r0i0p0.nc</dd></dl></div><div class='xr-var-data'><pre>array([[[[-1.00253734e-05, -1.08483503e-05, -1.19984851e-05, ...,\n",
       "          -2.62434196e-04, -2.75508612e-04, -2.88583029e-04],\n",
       "         [-1.00341333e-05, -1.08650068e-05, -1.20253402e-05, ...,\n",
       "          -2.62683896e-04, -2.75759866e-04, -2.88835836e-04],\n",
       "         [-1.00440045e-05, -1.08829530e-05, -1.20527703e-05, ...,\n",
       "          -2.62907409e-04, -2.75985060e-04, -2.89062711e-04],\n",
       "         ...,\n",
       "         [-1.00119918e-05, -1.08148401e-05, -1.19329276e-05, ...,\n",
       "          -2.61547371e-04, -2.74617714e-04, -2.87688057e-04],\n",
       "         [-1.00135519e-05, -1.08223296e-05, -1.19510561e-05, ...,\n",
       "          -2.61863819e-04, -2.74935448e-04, -2.88007077e-04],\n",
       "         [-1.00182814e-05, -1.08338201e-05, -1.19733236e-05, ...,\n",
       "          -2.62160168e-04, -2.75233149e-04, -2.88306130e-04]],\n",
       "\n",
       "        [[-1.24396973e-05, -1.32972732e-05, -1.46738935e-05, ...,\n",
       "          -2.65178291e-04, -2.78709462e-04, -2.92240632e-04],\n",
       "         [-1.25787053e-05, -1.34629177e-05, -1.48621702e-05, ...,\n",
       "          -2.65759158e-04, -2.79258351e-04, -2.92757544e-04],\n",
       "         [-1.26809498e-05, -1.35948517e-05, -1.50113760e-05, ...,\n",
       "          -2.66312085e-04, -2.79780012e-04, -2.93247939e-04],\n",
       "...\n",
       "         [ 1.61889752e-05,  1.83772058e-05,  2.13542638e-05, ...,\n",
       "           8.40966677e-04,  8.82420335e-04,  9.25655034e-04],\n",
       "         [ 1.61856771e-05,  1.83987099e-05,  2.14008803e-05, ...,\n",
       "           8.42278996e-04,  8.83320290e-04,  9.26696781e-04],\n",
       "         [ 1.61582615e-05,  1.84018354e-05,  2.14291298e-05, ...,\n",
       "           8.42201593e-04,  8.82680854e-04,  9.26109555e-04]],\n",
       "\n",
       "        [[ 1.38810452e-05,  1.59087935e-05,  1.87213877e-05, ...,\n",
       "           7.89083531e-04,  8.30371281e-04,  8.73565471e-04],\n",
       "         [ 1.38757750e-05,  1.58983781e-05,  1.87179570e-05, ...,\n",
       "           7.88858593e-04,  8.29970330e-04,  8.73154441e-04],\n",
       "         [ 1.38655711e-05,  1.58829645e-05,  1.87088622e-05, ...,\n",
       "           7.88435270e-04,  8.29351537e-04,  8.72503618e-04],\n",
       "         ...,\n",
       "         [ 1.38638598e-05,  1.59062362e-05,  1.86938214e-05, ...,\n",
       "           7.88453151e-04,  8.30104865e-04,  8.73172673e-04],\n",
       "         [ 1.38753271e-05,  1.59130152e-05,  1.87096586e-05, ...,\n",
       "           7.88887741e-04,  8.30449246e-04,  8.73586330e-04],\n",
       "         [ 1.38809962e-05,  1.59137876e-05,  1.87187423e-05, ...,\n",
       "           7.89096218e-04,  8.30535341e-04,  8.73714468e-04]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-e11cdeee-c998-487a-af51-25f1908fd6b4' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-e11cdeee-c998-487a-af51-25f1908fd6b4' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (latitude: 48, longitude: 96, lvl: 45, time: 2)\n",
       "Coordinates:\n",
       "  * time       (time) object 1979-01-01 00:00:00 1979-01-01 06:00:00\n",
       "  * latitude   (latitude) float64 -87.16 -83.48 -79.78 ... 79.78 83.48 87.16\n",
       "  * longitude  (longitude) float64 0.0 3.75 7.5 11.25 ... 348.8 352.5 356.2\n",
       "  * lvl        (lvl) int64 400 410 420 430 440 450 ... 790 800 810 820 830 840\n",
       "Data variables:\n",
       "    vorpot     (time, latitude, longitude, lvl) float64 -1.003e-05 ... 0.0008737"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#dset=isent_data_xr.rename({'lat':\"latitude\", \"lon\": \"longitude\"}).metpy.dequantify()\n",
    "dset=ds_out.rename({'lat':\"latitude\", \"lon\": \"longitude\"})[:2].to_dataset()#.metpy.dequantify()\n",
    "dset"
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    },
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   "source": [
    "dset, grid = add_latlon_metrics(dset)\n",
    "tracer = dset.vorpot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "df1d4fd5-e3c9-4136-ab43-99ea542e92c7",
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    {
     "data": {
      "text/plain": [
       "<xgcm.Grid>\n",
       "Y Axis (not periodic, boundary='extend'):\n",
       "  * center   latitude --> left\n",
       "  * left     latitude_left --> center\n",
       "T Axis (not periodic, boundary=None):\n",
       "  * center   time\n",
       "X Axis (periodic, boundary=None):\n",
       "  * center   longitude --> left\n",
       "  * left     longitude_left --> center"
      ]
     },
     "execution_count": 49,
     "metadata": {},
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    "grid"
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       "  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-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",
       "  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",
       "  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-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-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",
       "  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:         (latitude: 48, latitude_left: 48, longitude: 96, longitude_left: 96, lvl: 45, time: 2)\n",
       "Coordinates: (12/18)\n",
       "  * time            (time) object 1979-01-01 00:00:00 1979-01-01 06:00:00\n",
       "  * latitude        (latitude) float64 -87.16 -83.48 -79.78 ... 83.48 87.16\n",
       "  * longitude       (longitude) float64 0.0 3.75 7.5 11.25 ... 348.8 352.5 356.2\n",
       "  * lvl             (lvl) int64 400 410 420 430 440 450 ... 800 810 820 830 840\n",
       "  * longitude_left  (longitude_left) float64 -1.875 1.875 5.625 ... 350.6 354.4\n",
       "  * latitude_left   (latitude_left) float64 -89.0 -85.32 -81.63 ... 81.63 85.32\n",
       "    ...              ...\n",
       "    dxV             (latitude_left, longitude_left) float64 7.284e+03 ... 3.4...\n",
       "    dyU             (latitude_left, longitude_left) float64 4.092e+05 ... 4.1...\n",
       "    rA              (latitude, longitude) float64 8.453e+09 ... 1.397e+10\n",
       "    rAw             (latitude, longitude_left) float64 8.458e+09 ... 8.483e+09\n",
       "    rAs             (latitude_left, longitude) float64 2.981e+09 ... 1.393e+10\n",
       "    rAz             (latitude_left, longitude_left) float64 2.981e+09 ... 1.3...\n",
       "Data variables:\n",
       "    vorpot          (time, latitude, longitude, lvl) float64 -1.003e-05 ... 0...</pre><div class='xr-wrap' hidden><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-dba4a6a8-a9f0-44e9-aca4-0db0b835f967' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-dba4a6a8-a9f0-44e9-aca4-0db0b835f967' 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'>latitude</span>: 48</li><li><span class='xr-has-index'>latitude_left</span>: 48</li><li><span class='xr-has-index'>longitude</span>: 96</li><li><span class='xr-has-index'>longitude_left</span>: 96</li><li><span class='xr-has-index'>lvl</span>: 45</li><li><span class='xr-has-index'>time</span>: 2</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3499b8dd-9e97-42c1-a3bd-ae76d732773d' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3499b8dd-9e97-42c1-a3bd-ae76d732773d' class='xr-section-summary' >Coordinates: <span>(18)</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'>object</div><div class='xr-var-preview xr-preview'>1979-01-01 00:00:00 1979-01-01 0...</div><input id='attrs-d81ebbd0-61ae-424d-b6cb-7292ecdbfa87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d81ebbd0-61ae-424d-b6cb-7292ecdbfa87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-71a7ab7c-fddf-418a-b03d-fa96b2256e8a' class='xr-var-data-in' type='checkbox'><label for='data-71a7ab7c-fddf-418a-b03d-fa96b2256e8a' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array([cftime.DatetimeNoLeap(1979, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "       cftime.DatetimeNoLeap(1979, 1, 1, 6, 0, 0, 0, has_year_zero=True)],\n",
       "      dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude</span></div><div class='xr-var-dims'>(latitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-87.16 -83.48 ... 83.48 87.16</div><input id='attrs-2f482ff7-6d62-45d6-97c0-a8f113e30f59' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2f482ff7-6d62-45d6-97c0-a8f113e30f59' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-00f63cbc-d15f-4c2a-a612-ee289e0c1150' class='xr-var-data-in' type='checkbox'><label for='data-00f63cbc-d15f-4c2a-a612-ee289e0c1150' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([-87.159094, -83.478937, -79.777046, -76.070244, -72.361581, -68.652017,\n",
       "       -64.941949, -61.231573, -57.520994, -53.810274, -50.099453, -46.388558,\n",
       "       -42.677606, -38.96661 , -35.25558 , -31.544523, -27.833444, -24.122348,\n",
       "       -20.411238, -16.700118, -12.988988,  -9.277853,  -5.566714,  -1.855571,\n",
       "         1.855571,   5.566714,   9.277853,  12.988988,  16.700118,  20.411238,\n",
       "        24.122348,  27.833444,  31.544523,  35.25558 ,  38.96661 ,  42.677606,\n",
       "        46.388558,  50.099453,  53.810274,  57.520994,  61.231573,  64.941949,\n",
       "        68.652017,  72.361581,  76.070244,  79.777046,  83.478937,  87.159094])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude</span></div><div class='xr-var-dims'>(longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 3.75 7.5 ... 348.8 352.5 356.2</div><input id='attrs-ba68f4e8-3d23-483f-9c50-2d4a88f80f03' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ba68f4e8-3d23-483f-9c50-2d4a88f80f03' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3579001b-ca3b-4efd-aaa9-8ec095e997b9' class='xr-var-data-in' type='checkbox'><label for='data-3579001b-ca3b-4efd-aaa9-8ec095e997b9' 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>bounds :</span></dt><dd>lon_bnds</dd><dt><span>units :</span></dt><dd>degrees_east</dd><dt><span>axis :</span></dt><dd>X</dd><dt><span>long_name :</span></dt><dd>longitude</dd><dt><span>standard_name :</span></dt><dd>longitude</dd></dl></div><div class='xr-var-data'><pre>array([  0.  ,   3.75,   7.5 ,  11.25,  15.  ,  18.75,  22.5 ,  26.25,  30.  ,\n",
       "        33.75,  37.5 ,  41.25,  45.  ,  48.75,  52.5 ,  56.25,  60.  ,  63.75,\n",
       "        67.5 ,  71.25,  75.  ,  78.75,  82.5 ,  86.25,  90.  ,  93.75,  97.5 ,\n",
       "       101.25, 105.  , 108.75, 112.5 , 116.25, 120.  , 123.75, 127.5 , 131.25,\n",
       "       135.  , 138.75, 142.5 , 146.25, 150.  , 153.75, 157.5 , 161.25, 165.  ,\n",
       "       168.75, 172.5 , 176.25, 180.  , 183.75, 187.5 , 191.25, 195.  , 198.75,\n",
       "       202.5 , 206.25, 210.  , 213.75, 217.5 , 221.25, 225.  , 228.75, 232.5 ,\n",
       "       236.25, 240.  , 243.75, 247.5 , 251.25, 255.  , 258.75, 262.5 , 266.25,\n",
       "       270.  , 273.75, 277.5 , 281.25, 285.  , 288.75, 292.5 , 296.25, 300.  ,\n",
       "       303.75, 307.5 , 311.25, 315.  , 318.75, 322.5 , 326.25, 330.  , 333.75,\n",
       "       337.5 , 341.25, 345.  , 348.75, 352.5 , 356.25])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>lvl</span></div><div class='xr-var-dims'>(lvl)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>400 410 420 430 ... 810 820 830 840</div><input id='attrs-0e34a5b1-e90d-4c97-98d7-8d08e408879a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0e34a5b1-e90d-4c97-98d7-8d08e408879a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8d01733c-7b46-42d8-bb50-d51831868009' class='xr-var-data-in' type='checkbox'><label for='data-8d01733c-7b46-42d8-bb50-d51831868009' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530,\n",
       "       540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670,\n",
       "       680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810,\n",
       "       820, 830, 840])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>longitude_left</span></div><div class='xr-var-dims'>(longitude_left)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.875 1.875 5.625 ... 350.6 354.4</div><input id='attrs-e4407a72-52bc-4f3b-a4cf-8e37d9255f87' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-e4407a72-52bc-4f3b-a4cf-8e37d9255f87' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d1f74920-d4bc-4308-aa60-e4bf1608f94a' class='xr-var-data-in' type='checkbox'><label for='data-d1f74920-d4bc-4308-aa60-e4bf1608f94a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>X</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd></dl></div><div class='xr-var-data'><pre>array([ -1.875,   1.875,   5.625,   9.375,  13.125,  16.875,  20.625,  24.375,\n",
       "        28.125,  31.875,  35.625,  39.375,  43.125,  46.875,  50.625,  54.375,\n",
       "        58.125,  61.875,  65.625,  69.375,  73.125,  76.875,  80.625,  84.375,\n",
       "        88.125,  91.875,  95.625,  99.375, 103.125, 106.875, 110.625, 114.375,\n",
       "       118.125, 121.875, 125.625, 129.375, 133.125, 136.875, 140.625, 144.375,\n",
       "       148.125, 151.875, 155.625, 159.375, 163.125, 166.875, 170.625, 174.375,\n",
       "       178.125, 181.875, 185.625, 189.375, 193.125, 196.875, 200.625, 204.375,\n",
       "       208.125, 211.875, 215.625, 219.375, 223.125, 226.875, 230.625, 234.375,\n",
       "       238.125, 241.875, 245.625, 249.375, 253.125, 256.875, 260.625, 264.375,\n",
       "       268.125, 271.875, 275.625, 279.375, 283.125, 286.875, 290.625, 294.375,\n",
       "       298.125, 301.875, 305.625, 309.375, 313.125, 316.875, 320.625, 324.375,\n",
       "       328.125, 331.875, 335.625, 339.375, 343.125, 346.875, 350.625, 354.375])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>latitude_left</span></div><div class='xr-var-dims'>(latitude_left)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-89.0 -85.32 -81.63 ... 81.63 85.32</div><input id='attrs-d0f271e9-2a94-478d-a85f-c35523748003' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d0f271e9-2a94-478d-a85f-c35523748003' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-225faf29-4bbb-4076-9277-e30b5b26d376' class='xr-var-data-in' type='checkbox'><label for='data-225faf29-4bbb-4076-9277-e30b5b26d376' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd></dl></div><div class='xr-var-data'><pre>array([-88.999173, -85.319016, -81.627991, -77.923645, -74.215913, -70.506799,\n",
       "       -66.796983, -63.086761, -59.376283, -55.665634, -51.954864, -48.244006,\n",
       "       -44.533082, -40.822108, -37.111095, -33.400052, -29.688984, -25.977896,\n",
       "       -22.266793, -18.555678, -14.844553, -11.133421,  -7.422283,  -3.711142,\n",
       "         0.      ,   3.711142,   7.422283,  11.133421,  14.844553,  18.555678,\n",
       "        22.266793,  25.977896,  29.688984,  33.400052,  37.111095,  40.822108,\n",
       "        44.533082,  48.244006,  51.954864,  55.665634,  59.376283,  63.086761,\n",
       "        66.796983,  70.506799,  74.215913,  77.923645,  81.627991,  85.319016])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dxG</span></div><div class='xr-var-dims'>(latitude_left, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.284e+03 7.284e+03 ... 3.403e+04</div><input id='attrs-a70f5301-5eb4-483c-8222-e68dbcbcfd39' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a70f5301-5eb4-483c-8222-e68dbcbcfd39' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b35cbcf-5496-4e62-a57f-ef88874b13dc' class='xr-var-data-in' type='checkbox'><label for='data-8b35cbcf-5496-4e62-a57f-ef88874b13dc' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>axis :</span></dt><dd>Y</dd><dt><span>c_grid_axis_shift :</span></dt><dd>-0.5</dd></dl></div><div class='xr-var-data'><pre>array([[ 7283.56494045,  7283.56494045,  7283.56494045, ...,\n",
       "         7283.56494045,  7283.56494045,  7283.56494045],\n",
       "       [34029.94192128, 34029.94192128, 34029.94192128, ...,\n",
       "        34029.94192128, 34029.94192128, 34029.94192128],\n",
       "       [60714.21736169, 60714.21736169, 60714.21736169, ...,\n",
       "        60714.21736169, 60714.21736169, 60714.21736169],\n",
       "       ...,\n",
       "       [87241.42629553, 87241.42629553, 87241.42629553, ...,\n",
       "        87241.42629553, 87241.42629553, 87241.42629553],\n",
       "       [60714.21736169, 60714.21736169, 60714.21736169, ...,\n",
       "        60714.21736169, 60714.21736169, 60714.21736169],\n",
       "       [34029.94192128, 34029.94192128, 34029.94192128, ...,\n",
       "        34029.94192128, 34029.94192128, 34029.94192128]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dyG</span></div><div class='xr-var-dims'>(latitude, longitude_left)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.092e+05 4.092e+05 ... 4.104e+05</div><input id='attrs-1de3f8ee-bc74-4d67-8d36-7f8f7aef051c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1de3f8ee-bc74-4d67-8d36-7f8f7aef051c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-98c8c3ed-4985-4623-ba92-57a3c79fef91' class='xr-var-data-in' type='checkbox'><label for='data-98c8c3ed-4985-4623-ba92-57a3c79fef91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[409227.73240926, 409227.73240926, 409227.73240926, ...,\n",
       "        409227.73240926, 409227.73240926, 409227.73240926],\n",
       "       [410436.07712458, 410436.07712458, 410436.07712458, ...,\n",
       "        410436.07712458, 410436.07712458, 410436.07712458],\n",
       "       [411917.42383929, 411917.42383929, 411917.42383929, ...,\n",
       "        411917.42383929, 411917.42383929, 411917.42383929],\n",
       "       ...,\n",
       "       [411917.42383929, 411917.42383929, 411917.42383929, ...,\n",
       "        411917.42383929, 411917.42383929, 411917.42383929],\n",
       "       [410436.07712458, 410436.07712458, 410436.07712458, ...,\n",
       "        410436.07712458, 410436.07712458, 410436.07712458],\n",
       "       [410436.07712458, 410436.07712458, 410436.07712458, ...,\n",
       "        410436.07712458, 410436.07712458, 410436.07712458]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dxC</span></div><div class='xr-var-dims'>(latitude, longitude_left)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.067e+04 2.067e+04 ... 2.067e+04</div><input id='attrs-bf03cf4e-9d0b-4697-84a1-9003e681c04d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-bf03cf4e-9d0b-4697-84a1-9003e681c04d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ba68716a-c4d5-4b1d-948e-48a338163642' class='xr-var-data-in' type='checkbox'><label for='data-ba68716a-c4d5-4b1d-948e-48a338163642' 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>bounds :</span></dt><dd>lat_bnds</dd><dt><span>units :</span></dt><dd>degrees_north</dd><dt><span>axis :</span></dt><dd>Y</dd><dt><span>long_name :</span></dt><dd>latitude</dd><dt><span>standard_name :</span></dt><dd>latitude</dd></dl></div><div class='xr-var-data'><pre>array([[20667.41071127, 20667.41071127, 20667.41071127, ...,\n",
       "        20667.41071127, 20667.41071127, 20667.41071127],\n",
       "       [47357.37762385, 47357.37762385, 47357.37762385, ...,\n",
       "        47357.37762385, 47357.37762385, 47357.37762385],\n",
       "       [74007.70009553, 74007.70009553, 74007.70009553, ...,\n",
       "        74007.70009553, 74007.70009553, 74007.70009553],\n",
       "       ...,\n",
       "       [74007.70009553, 74007.70009553, 74007.70009553, ...,\n",
       "        74007.70009553, 74007.70009553, 74007.70009553],\n",
       "       [47357.37762385, 47357.37762385, 47357.37762385, ...,\n",
       "        47357.37762385, 47357.37762385, 47357.37762385],\n",
       "       [20667.41071127, 20667.41071127, 20667.41071127, ...,\n",
       "        20667.41071127, 20667.41071127, 20667.41071127]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dyC</span></div><div class='xr-var-dims'>(latitude_left, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.092e+05 4.092e+05 ... 4.092e+05</div><input id='attrs-86b923e3-c472-4ee2-9b1e-71166c19698d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-86b923e3-c472-4ee2-9b1e-71166c19698d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c8d95d1f-4923-4818-b79c-8c215d35edf0' class='xr-var-data-in' type='checkbox'><label for='data-c8d95d1f-4923-4818-b79c-8c215d35edf0' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[409227.73240926, 409227.73240926, 409227.73240926, ...,\n",
       "        409227.73240926, 409227.73240926, 409227.73240926],\n",
       "       [409227.73240926, 409227.73240926, 409227.73240926, ...,\n",
       "        409227.73240926, 409227.73240926, 409227.73240926],\n",
       "       [411644.42183989, 411644.42183989, 411644.42183989, ...,\n",
       "        411644.42183989, 411644.42183989, 411644.42183989],\n",
       "       ...,\n",
       "       [412190.4258387 , 412190.4258387 , 412190.4258387 , ...,\n",
       "        412190.4258387 , 412190.4258387 , 412190.4258387 ],\n",
       "       [411644.42183989, 411644.42183989, 411644.42183989, ...,\n",
       "        411644.42183989, 411644.42183989, 411644.42183989],\n",
       "       [409227.73240926, 409227.73240926, 409227.73240926, ...,\n",
       "        409227.73240926, 409227.73240926, 409227.73240926]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dxF</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.066e+04 2.066e+04 ... 3.403e+04</div><input id='attrs-2bb89b51-17f1-4846-83e1-bc755627cf83' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2bb89b51-17f1-4846-83e1-bc755627cf83' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-79f29cf7-d5e1-42b4-b346-76ec0a605340' class='xr-var-data-in' type='checkbox'><label for='data-79f29cf7-d5e1-42b4-b346-76ec0a605340' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[20656.75343086, 20656.75343086, 20656.75343086, ...,\n",
       "        20656.75343086, 20656.75343086, 20656.75343086],\n",
       "       [47372.07964148, 47372.07964148, 47372.07964148, ...,\n",
       "        47372.07964148, 47372.07964148, 47372.07964148],\n",
       "       [73977.82182861, 73977.82182861, 73977.82182861, ...,\n",
       "        73977.82182861, 73977.82182861, 73977.82182861],\n",
       "       ...,\n",
       "       [73977.82182861, 73977.82182861, 73977.82182861, ...,\n",
       "        73977.82182861, 73977.82182861, 73977.82182861],\n",
       "       [47372.07964148, 47372.07964148, 47372.07964148, ...,\n",
       "        47372.07964148, 47372.07964148, 47372.07964148],\n",
       "       [34029.94192128, 34029.94192128, 34029.94192128, ...,\n",
       "        34029.94192128, 34029.94192128, 34029.94192128]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dyF</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.092e+05 4.092e+05 ... 4.104e+05</div><input id='attrs-ab3cf472-58b5-49be-ab8a-c554e7579984' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ab3cf472-58b5-49be-ab8a-c554e7579984' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7388f5f6-ba5a-4afa-b796-645e7d69ba02' class='xr-var-data-in' type='checkbox'><label for='data-7388f5f6-ba5a-4afa-b796-645e7d69ba02' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[409227.73240926, 409227.73240926, 409227.73240926, ...,\n",
       "        409227.73240926, 409227.73240926, 409227.73240926],\n",
       "       [410436.07712458, 410436.07712458, 410436.07712458, ...,\n",
       "        410436.07712458, 410436.07712458, 410436.07712458],\n",
       "       [411917.42383929, 411917.42383929, 411917.42383929, ...,\n",
       "        411917.42383929, 411917.42383929, 411917.42383929],\n",
       "       ...,\n",
       "       [411917.42383929, 411917.42383929, 411917.42383929, ...,\n",
       "        411917.42383929, 411917.42383929, 411917.42383929],\n",
       "       [410436.07712458, 410436.07712458, 410436.07712458, ...,\n",
       "        410436.07712458, 410436.07712458, 410436.07712458],\n",
       "       [410436.07712458, 410436.07712458, 410436.07712458, ...,\n",
       "        410436.07712458, 410436.07712458, 410436.07712458]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dxV</span></div><div class='xr-var-dims'>(latitude_left, longitude_left)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7.284e+03 7.284e+03 ... 3.403e+04</div><input id='attrs-d04260a7-724d-418b-8ab9-67b8580df9d9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d04260a7-724d-418b-8ab9-67b8580df9d9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3fa98f76-cd2e-44bb-a4e8-609d0f1e54a3' class='xr-var-data-in' type='checkbox'><label for='data-3fa98f76-cd2e-44bb-a4e8-609d0f1e54a3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[ 7283.56494045,  7283.56494045,  7283.56494045, ...,\n",
       "         7283.56494045,  7283.56494045,  7283.56494045],\n",
       "       [34029.94192128, 34029.94192128, 34029.94192128, ...,\n",
       "        34029.94192128, 34029.94192128, 34029.94192128],\n",
       "       [60714.21736169, 60714.21736169, 60714.21736169, ...,\n",
       "        60714.21736169, 60714.21736169, 60714.21736169],\n",
       "       ...,\n",
       "       [87241.42629553, 87241.42629553, 87241.42629553, ...,\n",
       "        87241.42629553, 87241.42629553, 87241.42629553],\n",
       "       [60714.21736169, 60714.21736169, 60714.21736169, ...,\n",
       "        60714.21736169, 60714.21736169, 60714.21736169],\n",
       "       [34029.94192128, 34029.94192128, 34029.94192128, ...,\n",
       "        34029.94192128, 34029.94192128, 34029.94192128]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dyU</span></div><div class='xr-var-dims'>(latitude_left, longitude_left)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>4.092e+05 4.092e+05 ... 4.104e+05</div><input id='attrs-14dff274-58f3-4535-a3f6-4e594f84a1bb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-14dff274-58f3-4535-a3f6-4e594f84a1bb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec011415-7b01-48a0-86a7-fded45e6b326' class='xr-var-data-in' type='checkbox'><label for='data-ec011415-7b01-48a0-86a7-fded45e6b326' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[409227.73240926, 409227.73240926, 409227.73240926, ...,\n",
       "        409227.73240926, 409227.73240926, 409227.73240926],\n",
       "       [409831.90476692, 409831.90476692, 409831.90476692, ...,\n",
       "        409831.90476692, 409831.90476692, 409831.90476692],\n",
       "       [411176.75048193, 411176.75048193, 411176.75048193, ...,\n",
       "        411176.75048193, 411176.75048193, 411176.75048193],\n",
       "       ...,\n",
       "       [412105.69439972, 412105.69439972, 412105.69439972, ...,\n",
       "        412105.69439972, 412105.69439972, 412105.69439972],\n",
       "       [411176.75048193, 411176.75048193, 411176.75048193, ...,\n",
       "        411176.75048193, 411176.75048193, 411176.75048193],\n",
       "       [410436.07712458, 410436.07712458, 410436.07712458, ...,\n",
       "        410436.07712458, 410436.07712458, 410436.07712458]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rA</span></div><div class='xr-var-dims'>(latitude, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.453e+09 8.453e+09 ... 1.397e+10</div><input id='attrs-349fba68-63e0-4b05-a46d-50750fb243cf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-349fba68-63e0-4b05-a46d-50750fb243cf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a4676a1e-3774-4499-aa1e-fa685ee4a29f' class='xr-var-data-in' type='checkbox'><label for='data-a4676a1e-3774-4499-aa1e-fa685ee4a29f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[8.45331637e+09, 8.45331637e+09, 8.45331637e+09, ...,\n",
       "        8.45331637e+09, 8.45331637e+09, 8.45331637e+09],\n",
       "       [1.94432105e+10, 1.94432105e+10, 1.94432105e+10, ...,\n",
       "        1.94432105e+10, 1.94432105e+10, 1.94432105e+10],\n",
       "       [3.04727538e+10, 3.04727538e+10, 3.04727538e+10, ...,\n",
       "        3.04727538e+10, 3.04727538e+10, 3.04727538e+10],\n",
       "       ...,\n",
       "       [3.04727538e+10, 3.04727538e+10, 3.04727538e+10, ...,\n",
       "        3.04727538e+10, 3.04727538e+10, 3.04727538e+10],\n",
       "       [1.94432105e+10, 1.94432105e+10, 1.94432105e+10, ...,\n",
       "        1.94432105e+10, 1.94432105e+10, 1.94432105e+10],\n",
       "       [1.39671159e+10, 1.39671159e+10, 1.39671159e+10, ...,\n",
       "        1.39671159e+10, 1.39671159e+10, 1.39671159e+10]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rAw</span></div><div class='xr-var-dims'>(latitude, longitude_left)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>8.458e+09 8.458e+09 ... 8.483e+09</div><input id='attrs-0efb8f65-167e-458e-b47a-afa631c8936d' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-0efb8f65-167e-458e-b47a-afa631c8936d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-30e46b07-7c1a-420d-9de7-21fa38e3dbad' class='xr-var-data-in' type='checkbox'><label for='data-30e46b07-7c1a-420d-9de7-21fa38e3dbad' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[8.45767762e+09, 8.45767762e+09, 8.45767762e+09, ...,\n",
       "        8.45767762e+09, 8.45767762e+09, 8.45767762e+09],\n",
       "       [1.94371763e+10, 1.94371763e+10, 1.94371763e+10, ...,\n",
       "        1.94371763e+10, 1.94371763e+10, 1.94371763e+10],\n",
       "       [3.04850612e+10, 3.04850612e+10, 3.04850612e+10, ...,\n",
       "        3.04850612e+10, 3.04850612e+10, 3.04850612e+10],\n",
       "       ...,\n",
       "       [3.04850612e+10, 3.04850612e+10, 3.04850612e+10, ...,\n",
       "        3.04850612e+10, 3.04850612e+10, 3.04850612e+10],\n",
       "       [1.94371763e+10, 1.94371763e+10, 1.94371763e+10, ...,\n",
       "        1.94371763e+10, 1.94371763e+10, 1.94371763e+10],\n",
       "       [8.48265098e+09, 8.48265098e+09, 8.48265098e+09, ...,\n",
       "        8.48265098e+09, 8.48265098e+09, 8.48265098e+09]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rAs</span></div><div class='xr-var-dims'>(latitude_left, longitude)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.981e+09 2.981e+09 ... 1.393e+10</div><input id='attrs-4e1f85bb-98c0-4b8f-ba97-6485c15d09f8' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4e1f85bb-98c0-4b8f-ba97-6485c15d09f8' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de99a2a6-119f-4911-a4e5-9cbb4e378d5f' class='xr-var-data-in' type='checkbox'><label for='data-de99a2a6-119f-4911-a4e5-9cbb4e378d5f' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.98063676e+09, 2.98063676e+09, 2.98063676e+09, ...,\n",
       "        2.98063676e+09, 2.98063676e+09, 2.98063676e+09],\n",
       "       [1.39259960e+10, 1.39259960e+10, 1.39259960e+10, ...,\n",
       "        1.39259960e+10, 1.39259960e+10, 1.39259960e+10],\n",
       "       [2.49926689e+10, 2.49926689e+10, 2.49926689e+10, ...,\n",
       "        2.49926689e+10, 2.49926689e+10, 2.49926689e+10],\n",
       "       ...,\n",
       "       [3.59600807e+10, 3.59600807e+10, 3.59600807e+10, ...,\n",
       "        3.59600807e+10, 3.59600807e+10, 3.59600807e+10],\n",
       "       [2.49926689e+10, 2.49926689e+10, 2.49926689e+10, ...,\n",
       "        2.49926689e+10, 2.49926689e+10, 2.49926689e+10],\n",
       "       [1.39259960e+10, 1.39259960e+10, 1.39259960e+10, ...,\n",
       "        1.39259960e+10, 1.39259960e+10, 1.39259960e+10]])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>rAz</span></div><div class='xr-var-dims'>(latitude_left, longitude_left)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.981e+09 2.981e+09 ... 1.397e+10</div><input id='attrs-bd34b773-265d-4f67-823e-0441f4e2cfdf' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-bd34b773-265d-4f67-823e-0441f4e2cfdf' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6c0e0553-6b6b-4755-b700-5e09853c2d36' class='xr-var-data-in' type='checkbox'><label for='data-6c0e0553-6b6b-4755-b700-5e09853c2d36' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[2.98063676e+09, 2.98063676e+09, 2.98063676e+09, ...,\n",
       "        2.98063676e+09, 2.98063676e+09, 2.98063676e+09],\n",
       "       [1.39465559e+10, 1.39465559e+10, 1.39465559e+10, ...,\n",
       "        1.39465559e+10, 1.39465559e+10, 1.39465559e+10],\n",
       "       [2.49642746e+10, 2.49642746e+10, 2.49642746e+10, ...,\n",
       "        2.49642746e+10, 2.49642746e+10, 2.49642746e+10],\n",
       "       ...,\n",
       "       [3.59526886e+10, 3.59526886e+10, 3.59526886e+10, ...,\n",
       "        3.59526886e+10, 3.59526886e+10, 3.59526886e+10],\n",
       "       [2.49642746e+10, 2.49642746e+10, 2.49642746e+10, ...,\n",
       "        2.49642746e+10, 2.49642746e+10, 2.49642746e+10],\n",
       "       [1.39671159e+10, 1.39671159e+10, 1.39671159e+10, ...,\n",
       "        1.39671159e+10, 1.39671159e+10, 1.39671159e+10]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-6f9aff0d-a8c2-48d1-8690-dd9fd19d13ae' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6f9aff0d-a8c2-48d1-8690-dd9fd19d13ae' class='xr-section-summary' >Data variables: <span>(1)</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>vorpot</span></div><div class='xr-var-dims'>(time, latitude, longitude, lvl)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-1.003e-05 -1.085e-05 ... 0.0008737</div><input id='attrs-ab78d7cd-1936-4f2e-9aa9-c04210478ee9' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ab78d7cd-1936-4f2e-9aa9-c04210478ee9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-27b4930a-8939-422f-9459-7c51e990bcb7' class='xr-var-data-in' type='checkbox'><label for='data-27b4930a-8939-422f-9459-7c51e990bcb7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>standard_name :</span></dt><dd>ertel_potential_vorticity</dd><dt><span>long_name :</span></dt><dd>Ertel&#x27;s Potential Vorticity</dd><dt><span>units :</span></dt><dd>K m2 kg-1 s-1</dd><dt><span>cell_methods :</span></dt><dd>time:mean</dd><dt><span>cell_measures :</span></dt><dd>area: areacella</dd><dt><span>history :</span></dt><dd>2013-05-28T07:40:04Z altered by CMOR: replaced missing value flag (1e+38) with standard missing value (1e+20). 2013-05-28T07:40:04Z altered by CMOR: Inverted axis: plev.</dd><dt><span>associated_files :</span></dt><dd>baseURL: http://cmip-pcmdi.llnl.gov/CMIP5/dataLocation gridspecFile: gridspec_atmos_fx_CMAM_CMAM30-SD_r0i0p0.nc areacella: areacella_fx_CMAM_CMAM30-SD_r0i0p0.nc</dd></dl></div><div class='xr-var-data'><pre>array([[[[-1.00253734e-05, -1.08483503e-05, -1.19984851e-05, ...,\n",
       "          -2.62434196e-04, -2.75508612e-04, -2.88583029e-04],\n",
       "         [-1.00341333e-05, -1.08650068e-05, -1.20253402e-05, ...,\n",
       "          -2.62683896e-04, -2.75759866e-04, -2.88835836e-04],\n",
       "         [-1.00440045e-05, -1.08829530e-05, -1.20527703e-05, ...,\n",
       "          -2.62907409e-04, -2.75985060e-04, -2.89062711e-04],\n",
       "         ...,\n",
       "         [-1.00119918e-05, -1.08148401e-05, -1.19329276e-05, ...,\n",
       "          -2.61547371e-04, -2.74617714e-04, -2.87688057e-04],\n",
       "         [-1.00135519e-05, -1.08223296e-05, -1.19510561e-05, ...,\n",
       "          -2.61863819e-04, -2.74935448e-04, -2.88007077e-04],\n",
       "         [-1.00182814e-05, -1.08338201e-05, -1.19733236e-05, ...,\n",
       "          -2.62160168e-04, -2.75233149e-04, -2.88306130e-04]],\n",
       "\n",
       "        [[-1.24396973e-05, -1.32972732e-05, -1.46738935e-05, ...,\n",
       "          -2.65178291e-04, -2.78709462e-04, -2.92240632e-04],\n",
       "         [-1.25787053e-05, -1.34629177e-05, -1.48621702e-05, ...,\n",
       "          -2.65759158e-04, -2.79258351e-04, -2.92757544e-04],\n",
       "         [-1.26809498e-05, -1.35948517e-05, -1.50113760e-05, ...,\n",
       "          -2.66312085e-04, -2.79780012e-04, -2.93247939e-04],\n",
       "...\n",
       "         [ 1.61889752e-05,  1.83772058e-05,  2.13542638e-05, ...,\n",
       "           8.40966677e-04,  8.82420335e-04,  9.25655034e-04],\n",
       "         [ 1.61856771e-05,  1.83987099e-05,  2.14008803e-05, ...,\n",
       "           8.42278996e-04,  8.83320290e-04,  9.26696781e-04],\n",
       "         [ 1.61582615e-05,  1.84018354e-05,  2.14291298e-05, ...,\n",
       "           8.42201593e-04,  8.82680854e-04,  9.26109555e-04]],\n",
       "\n",
       "        [[ 1.38810452e-05,  1.59087935e-05,  1.87213877e-05, ...,\n",
       "           7.89083531e-04,  8.30371281e-04,  8.73565471e-04],\n",
       "         [ 1.38757750e-05,  1.58983781e-05,  1.87179570e-05, ...,\n",
       "           7.88858593e-04,  8.29970330e-04,  8.73154441e-04],\n",
       "         [ 1.38655711e-05,  1.58829645e-05,  1.87088622e-05, ...,\n",
       "           7.88435270e-04,  8.29351537e-04,  8.72503618e-04],\n",
       "         ...,\n",
       "         [ 1.38638598e-05,  1.59062362e-05,  1.86938214e-05, ...,\n",
       "           7.88453151e-04,  8.30104865e-04,  8.73172673e-04],\n",
       "         [ 1.38753271e-05,  1.59130152e-05,  1.87096586e-05, ...,\n",
       "           7.88887741e-04,  8.30449246e-04,  8.73586330e-04],\n",
       "         [ 1.38809962e-05,  1.59137876e-05,  1.87187423e-05, ...,\n",
       "           7.89096218e-04,  8.30535341e-04,  8.73714468e-04]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5cf9ebcf-5af2-4d1b-ab98-faafefdd1c89' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5cf9ebcf-5af2-4d1b-ab98-faafefdd1c89' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:         (latitude: 48, latitude_left: 48, longitude: 96, longitude_left: 96, lvl: 45, time: 2)\n",
       "Coordinates: (12/18)\n",
       "  * time            (time) object 1979-01-01 00:00:00 1979-01-01 06:00:00\n",
       "  * latitude        (latitude) float64 -87.16 -83.48 -79.78 ... 83.48 87.16\n",
       "  * longitude       (longitude) float64 0.0 3.75 7.5 11.25 ... 348.8 352.5 356.2\n",
       "  * lvl             (lvl) int64 400 410 420 430 440 450 ... 800 810 820 830 840\n",
       "  * longitude_left  (longitude_left) float64 -1.875 1.875 5.625 ... 350.6 354.4\n",
       "  * latitude_left   (latitude_left) float64 -89.0 -85.32 -81.63 ... 81.63 85.32\n",
       "    ...              ...\n",
       "    dxV             (latitude_left, longitude_left) float64 7.284e+03 ... 3.4...\n",
       "    dyU             (latitude_left, longitude_left) float64 4.092e+05 ... 4.1...\n",
       "    rA              (latitude, longitude) float64 8.453e+09 ... 1.397e+10\n",
       "    rAw             (latitude, longitude_left) float64 8.458e+09 ... 8.483e+09\n",
       "    rAs             (latitude_left, longitude) float64 2.981e+09 ... 1.393e+10\n",
       "    rAz             (latitude_left, longitude_left) float64 2.981e+09 ... 1.3...\n",
       "Data variables:\n",
       "    vorpot          (time, latitude, longitude, lvl) float64 -1.003e-05 ... 0..."
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5df3e38a-1106-495f-aa61-73cfcddb0557",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:08:09.829427Z",
     "iopub.status.busy": "2022-04-25T13:08:09.828804Z",
     "iopub.status.idle": "2022-04-25T13:08:09.873497Z",
     "shell.execute_reply": "2022-04-25T13:08:09.872334Z",
     "shell.execute_reply.started": "2022-04-25T13:08:09.829356Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "grdS = Dynamics(dset, grid).cal_squared_gradient(tracer)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "64db4b82-bafc-4125-846d-a4ae64ea8e2d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:21:59.403900Z",
     "iopub.status.busy": "2022-04-25T13:21:59.403295Z",
     "iopub.status.idle": "2022-04-25T13:21:59.425622Z",
     "shell.execute_reply": "2022-04-25T13:21:59.424594Z",
     "shell.execute_reply.started": "2022-04-25T13:21:59.403834Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Construct an analysis class using PV as the tracer\n",
    "N  = 121           # increase the contour number may get non-monotonic A(q) relation\n",
    "increase = True    # Y-index increases with latitude (sometimes not)\n",
    "lt = True          # northward of PV contours (larger than) is inside the contour\n",
    "                   # change this should not change the result of Keff, but may alter\n",
    "                   # the values at boundaries\n",
    "dtype = np.float32 # use float32 to save memory\n",
    "undef = -9.99e8    # for maskout topography if present\n",
    "\n",
    "# initialize a Contour2D analysis class using grid and tracer\n",
    "analysis = Contour2D(grid, tracer,\n",
    "                     dims={'X':'longitude','Y':'latitude'},\n",
    "                     dimEq={'Y':'latitude'},\n",
    "                     increase=increase,\n",
    "                     lt=lt)\n",
    "\n",
    "\n",
    "# This should be called first to initialize contours from minimum value\n",
    "# to maximum value (within lat/lon dims) using `N` contours.\n",
    "N   = 121\n",
    "ctr = analysis.cal_contours(N)#.metpy.dequantify()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "057ecc05-d0a7-473a-b871-6bda1faaf924",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:22:06.983012Z",
     "iopub.status.busy": "2022-04-25T13:22:06.982543Z",
     "iopub.status.idle": "2022-04-25T13:22:06.996269Z",
     "shell.execute_reply": "2022-04-25T13:22:06.995104Z",
     "shell.execute_reply.started": "2022-04-25T13:22:06.982964Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "mask = xr.where(tracer!=undef, 1, 0).astype(dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "891034da-5eb9-4584-9475-9c44bffb94f1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:22:07.834283Z",
     "iopub.status.busy": "2022-04-25T13:22:07.833682Z",
     "iopub.status.idle": "2022-04-25T13:22:09.849835Z",
     "shell.execute_reply": "2022-04-25T13:22:09.849075Z",
     "shell.execute_reply.started": "2022-04-25T13:22:07.834214Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "case 1: increase & lt\n"
     ]
    }
   ],
   "source": [
    "table   = analysis.cal_area_eqCoord_table(mask) # A(Yeq) table\n",
    "area    = analysis.cal_integral_within_contours(ctr).rename('intArea')\n",
    "intgrdS = analysis.cal_integral_within_contours(ctr, integrand=grdS).rename('int')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "da0a21d0-8497-4810-89a9-fb8c3f3fe333",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:08:28.019668Z",
     "iopub.status.busy": "2022-04-25T13:08:28.018825Z",
     "iopub.status.idle": "2022-04-25T13:08:28.291048Z",
     "shell.execute_reply": "2022-04-25T13:08:28.289916Z",
     "shell.execute_reply.started": "2022-04-25T13:08:28.019599Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Input \u001b[0;32mIn [15]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m table   \u001b[38;5;241m=\u001b[39m analysis\u001b[38;5;241m.\u001b[39mcal_area_eqCoord_table_hist(mask) \u001b[38;5;66;03m# A(Yeq) table\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m area    \u001b[38;5;241m=\u001b[39m \u001b[43manalysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcal_integral_within_contours_hist\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctr\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mrename(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mintArea\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      3\u001b[0m intgrdS \u001b[38;5;241m=\u001b[39m analysis\u001b[38;5;241m.\u001b[39mcal_integral_within_contours_hist(ctr, integrand\u001b[38;5;241m=\u001b[39mgrdS)\u001b[38;5;241m.\u001b[39mrename(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mintgr\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[0;32m~/.conda/envs/moje/lib/python3.9/site-packages/xcontour-0.0.1-py3.9.egg/xcontour/core.py:439\u001b[0m, in \u001b[0;36mContour2D.cal_integral_within_contours_hist\u001b[0;34m(self, contour, tracer, integrand)\u001b[0m\n\u001b[1;32m    436\u001b[0m \u001b[38;5;66;03m# replacing nan with 0 in weights, as weights cannot have nan\u001b[39;00m\n\u001b[1;32m    437\u001b[0m wei \u001b[38;5;241m=\u001b[39m wei\u001b[38;5;241m.\u001b[39mfillna(\u001b[38;5;241m0.\u001b[39m)\n\u001b[0;32m--> 439\u001b[0m CDF \u001b[38;5;241m=\u001b[39m \u001b[43m_histogram\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtracer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontour\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdimVs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwei\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlt\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    441\u001b[0m \u001b[38;5;66;03m# ensure that the contour index is increasing\u001b[39;00m\n\u001b[1;32m    442\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m CDF[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontour\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;241m<\u001b[39m CDF[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcontour\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m]:\n",
      "File \u001b[0;32m~/.conda/envs/moje/lib/python3.9/site-packages/xcontour-0.0.1-py3.9.egg/xcontour/core.py:892\u001b[0m, in \u001b[0;36m_histogram\u001b[0;34m(var, bins, dim, weights, lt)\u001b[0m\n\u001b[1;32m    889\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    890\u001b[0m     wt \u001b[38;5;241m=\u001b[39m weights\n\u001b[0;32m--> 892\u001b[0m bincrease \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m \u001b[38;5;28;01mif\u001b[39;00m ctr[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m<\u001b[39m ctr[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[1;32m    894\u001b[0m \u001b[38;5;66;03m# add a bin so that the result has the same length of contour\u001b[39;00m\n\u001b[1;32m    895\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m bincrease:\n",
      "\u001b[0;31mValueError\u001b[0m: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()"
     ]
    }
   ],
   "source": [
    "table   = analysis.cal_area_eqCoord_table_hist(mask) # A(Yeq) table\n",
    "area    = analysis.cal_integral_within_contours_hist(ctr).rename('intArea')\n",
    "intgrdS = analysis.cal_integral_within_contours_hist(ctr, integrand=grdS).rename('intgr')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "db00a110-8862-437c-a556-9ec89f2c7c3e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:22:12.314114Z",
     "iopub.status.busy": "2022-04-25T13:22:12.313779Z",
     "iopub.status.idle": "2022-04-25T13:22:12.339071Z",
     "shell.execute_reply": "2022-04-25T13:22:12.337960Z",
     "shell.execute_reply.started": "2022-04-25T13:22:12.314071Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "latEq   = table.lookup_coordinates(area).rename('latEq')\n",
    "Lmin    = latitude_lengths_at(latEq).rename('Lmin')\n",
    "dintSdA = analysis.cal_gradient_wrt_area(intgrdS, area).rename('dintSdA')\n",
    "dqdA    = analysis.cal_gradient_wrt_area(ctr, area).rename('dqdA')\n",
    "Leq2    = analysis.cal_sqared_equivalent_length(dintSdA, dqdA).rename('Leq2')\n",
    "nkeff   = analysis.cal_normalized_Keff(Leq2, Lmin).rename('nkeff')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "76d07b51-ae5d-46c7-b688-1165820d9787",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:08:43.141988Z",
     "iopub.status.busy": "2022-04-25T13:08:43.141464Z",
     "iopub.status.idle": "2022-04-25T13:08:43.524474Z",
     "shell.execute_reply": "2022-04-25T13:08:43.522976Z",
     "shell.execute_reply.started": "2022-04-25T13:08:43.141925Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f42781fd3d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "latEq[0].plot.contourf(x = 'contour')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "94002c88-d206-45dc-8000-686280c43559",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:22:14.450668Z",
     "iopub.status.busy": "2022-04-25T13:22:14.450076Z",
     "iopub.status.idle": "2022-04-25T13:22:14.745938Z",
     "shell.execute_reply": "2022-04-25T13:22:14.744963Z",
     "shell.execute_reply.started": "2022-04-25T13:22:14.450600Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = np.log(nkeff[0]).plot.contourf(figsize=(10, 5), levels=16, vmin=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9059c26e-84bd-4341-89ac-7247fbfec8d2",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:30:53.428066Z",
     "iopub.status.busy": "2022-04-25T13:30:53.427473Z",
     "iopub.status.idle": "2022-04-25T13:30:53.440197Z",
     "shell.execute_reply": "2022-04-25T13:30:53.439512Z",
     "shell.execute_reply.started": "2022-04-25T13:30:53.428000Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-90., -89., -88., -87., -86., -85., -84., -83., -82., -81., -80.,\n",
       "       -79., -78., -77., -76., -75., -74., -73., -72., -71., -70., -69.,\n",
       "       -68., -67., -66., -65., -64., -63., -62., -61., -60., -59., -58.,\n",
       "       -57., -56., -55., -54., -53., -52., -51., -50., -49., -48., -47.,\n",
       "       -46., -45., -44., -43., -42., -41., -40., -39., -38., -37., -36.,\n",
       "       -35., -34., -33., -32., -31., -30., -29., -28., -27., -26., -25.,\n",
       "       -24., -23., -22., -21., -20., -19., -18., -17., -16., -15., -14.,\n",
       "       -13., -12., -11., -10.,  -9.,  -8.,  -7.,  -6.,  -5.,  -4.,  -3.,\n",
       "        -2.,  -1.,   0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,\n",
       "         9.,  10.,  11.,  12.,  13.,  14.,  15.,  16.,  17.,  18.,  19.,\n",
       "        20.,  21.,  22.,  23.,  24.,  25.,  26.,  27.,  28.,  29.,  30.,\n",
       "        31.,  32.,  33.,  34.,  35.,  36.,  37.,  38.,  39.,  40.,  41.,\n",
       "        42.,  43.,  44.,  45.,  46.,  47.,  48.,  49.,  50.,  51.,  52.,\n",
       "        53.,  54.,  55.,  56.,  57.,  58.,  59.,  60.,  61.,  62.,  63.,\n",
       "        64.,  65.,  66.,  67.,  68.,  69.,  70.,  71.,  72.,  73.,  74.,\n",
       "        75.,  76.,  77.,  78.,  79.,  80.,  81.,  82.,  83.,  84.,  85.,\n",
       "        86.,  87.,  88.,  89.,  90.], dtype=float32)"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preLats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "b6af9559-9054-44ca-ab0f-96dbeabbcf5b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:22:36.544637Z",
     "iopub.status.busy": "2022-04-25T13:22:36.544031Z",
     "iopub.status.idle": "2022-04-25T13:22:36.619434Z",
     "shell.execute_reply": "2022-04-25T13:22:36.618330Z",
     "shell.execute_reply.started": "2022-04-25T13:22:36.544569Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#%% combined the results\n",
    "# results in contour space\n",
    "ds_contour = xr.merge([ctr, area, intgrdS, latEq, dintSdA, dqdA, Leq2, Lmin, nkeff])\n",
    "\n",
    "# interpolate from contour space to equivalent-latitude space\n",
    "preLats = np.linspace(-90, 90, 181).astype(dtype)\n",
    "# results in latEq space\n",
    "ds_latEq = analysis.interp_to_dataset(preLats, latEq, ds_contour)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "31de7eea-a423-4529-b756-e551863bda8e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:17:06.155368Z",
     "iopub.status.busy": "2022-04-25T13:17:06.154810Z",
     "iopub.status.idle": "2022-04-25T13:17:06.161593Z",
     "shell.execute_reply": "2022-04-25T13:17:06.160939Z",
     "shell.execute_reply.started": "2022-04-25T13:17:06.155328Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3.348"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(3.125-2.679)/2+3.125"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5a2d62e4-6e5c-4729-b33e-ca132ed7b10d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:22:39.192837Z",
     "iopub.status.busy": "2022-04-25T13:22:39.190545Z",
     "iopub.status.idle": "2022-04-25T13:22:39.482332Z",
     "shell.execute_reply": "2022-04-25T13:22:39.481146Z",
     "shell.execute_reply.started": "2022-04-25T13:22:39.192718Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.contour.QuadContourSet at 0x7f42770eae20>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.log(ds_latEq.nkeff[0]).plot.contourf(\n",
    "    figsize=(10, 5), \n",
    "    levels=16, \n",
    "    vmin=0,\n",
    "    vmax = 3.348\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4d4b7576-864a-4e49-b0d7-c0b1419385f0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:10:04.270194Z",
     "iopub.status.busy": "2022-04-25T13:10:04.269072Z",
     "iopub.status.idle": "2022-04-25T13:10:04.298648Z",
     "shell.execute_reply": "2022-04-25T13:10:04.297604Z",
     "shell.execute_reply.started": "2022-04-25T13:10:04.270117Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ds_latEq' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [20]\u001b[0m, in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0m np\u001b[38;5;241m.\u001b[39mlog(\u001b[43mds_latEq\u001b[49m\u001b[38;5;241m.\u001b[39mnkeff[\u001b[38;5;241m0\u001b[39m])\u001b[38;5;241m.\u001b[39mplot\u001b[38;5;241m.\u001b[39mcontourf(x \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnew\u001b[39m\u001b[38;5;124m'\u001b[39m, figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m5\u001b[39m), levels\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m16\u001b[39m, vmin\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0\u001b[39m)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ds_latEq' is not defined"
     ]
    }
   ],
   "source": [
    "np.log(ds_latEq.nkeff[0]).plot.contourf(x = 'new', figsize=(10, 5), levels=16, vmin=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "3b363b25-a93a-4701-a997-8e6b848f1363",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-13T13:05:45.243742Z",
     "iopub.status.busy": "2022-04-13T13:05:45.243164Z",
     "iopub.status.idle": "2022-04-13T13:05:45.338457Z",
     "shell.execute_reply": "2022-04-13T13:05:45.337599Z",
     "shell.execute_reply.started": "2022-04-13T13:05:45.243691Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "#%% combined the results\n",
    "# results in contour space\n",
    "ds_contour = xr.merge([ctr, area, intgrdS, latEq, dintSdA, dqdA, Leq2, Lmin, nkeff])\n",
    "\n",
    "# interpolate from contour space to equivalent-latitude space\n",
    "preLats = np.linspace(-90, 90, 181).astype(dtype)\n",
    "# results in latEq space\n",
    "ds_latEq = analysis.interp_to_dataset(preLats, latEq, ds_contour)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "44ed2d2d-e70d-4821-b20c-9aa5d75eb15c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:12:56.883254Z",
     "iopub.status.busy": "2022-04-25T13:12:56.882591Z",
     "iopub.status.idle": "2022-04-25T13:12:56.914530Z",
     "shell.execute_reply": "2022-04-25T13:12:56.913455Z",
     "shell.execute_reply.started": "2022-04-25T13:12:56.883202Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cal_integral_within_contours() got an unexpected keyword argument 'out_name'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [25]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Calculate various diagnostics defined in contour-based coordinates\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m area    \u001b[38;5;241m=\u001b[39m \u001b[43manalysis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcal_integral_within_contours\u001b[49m\u001b[43m(\u001b[49m\u001b[43mctr\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout_name\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mintArea\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m intgrdS \u001b[38;5;241m=\u001b[39m analysis\u001b[38;5;241m.\u001b[39mcal_integral_within_contours(ctr, grdS, out_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mintgrdS\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m latEq   \u001b[38;5;241m=\u001b[39m analysis\u001b[38;5;241m.\u001b[39mcal_equivalent_coords(area)\n",
      "\u001b[0;31mTypeError\u001b[0m: cal_integral_within_contours() got an unexpected keyword argument 'out_name'"
     ]
    }
   ],
   "source": [
    "# Calculate various diagnostics defined in contour-based coordinates\n",
    "area    = analysis.cal_integral_within_contours(ctr, out_name='intArea')\n",
    "intgrdS = analysis.cal_integral_within_contours(ctr, grdS, out_name='intgrdS')\n",
    "latEq   = analysis.cal_equivalent_coords(area)\n",
    "dgrdSdA = analysis.cal_gradient_wrt_area(intgrdS, area)\n",
    "dqdA    = analysis.cal_gradient_wrt_area(ctr, area)\n",
    "Leq2    = analysis.cal_sqared_equivalent_length(dgrdSdA, dqdA)\n",
    "Lmin    = analysis.cal_minimum_possible_length(latEq)\n",
    "nkeff   = analysis.cal_normalized_Keff(Leq2, Lmin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3fae6bb7-2ad7-49df-bed4-487db018420e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:12:22.448002Z",
     "iopub.status.busy": "2022-04-25T13:12:22.447649Z",
     "iopub.status.idle": "2022-04-25T13:12:22.471824Z",
     "shell.execute_reply": "2022-04-25T13:12:22.470822Z",
     "shell.execute_reply.started": "2022-04-25T13:12:22.447969Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'dgrdSdA' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [24]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Collect all these as a xarray.Dataset defined on N contours\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m origin \u001b[38;5;241m=\u001b[39m xr\u001b[38;5;241m.\u001b[39mmerge([ctr, area, intgrdS, latEq, \u001b[43mdgrdSdA\u001b[49m, dqdA, Leq2, Lmin, nkeff])\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(origin)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'dgrdSdA' is not defined"
     ]
    }
   ],
   "source": [
    "\n",
    "# Collect all these as a xarray.Dataset defined on N contours\n",
    "origin = xr.merge([ctr, area, intgrdS, latEq, dgrdSdA, dqdA, Leq2, Lmin, nkeff])\n",
    "\n",
    "print(origin)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2dc33deb-c1fc-412f-9c4a-7e030d9188bb",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T11:10:00.385097Z",
     "iopub.status.busy": "2021-12-07T11:10:00.382318Z"
    },
    "tags": []
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "fe4a34ad-9d0e-40d1-aa99-092e6b721a0d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-06T15:34:02.802366Z",
     "iopub.status.busy": "2021-12-06T15:34:02.801710Z",
     "iopub.status.idle": "2021-12-06T15:34:03.394305Z",
     "shell.execute_reply": "2021-12-06T15:34:03.393226Z",
     "shell.execute_reply.started": "2021-12-06T15:34:02.802307Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = np.log(origin.nkeff[:]).plot.contourf(figsize=(10, 5), levels=16, vmin=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "5acb69b7-2ee0-4d17-90b9-a4894544f4f8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-04-25T13:10:54.503289Z",
     "iopub.status.busy": "2022-04-25T13:10:54.502456Z",
     "iopub.status.idle": "2022-04-25T13:10:54.534934Z",
     "shell.execute_reply": "2022-04-25T13:10:54.533646Z",
     "shell.execute_reply.started": "2022-04-25T13:10:54.503219Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'origin' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Input \u001b[0;32mIn [21]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m preLats \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mlinspace(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m90\u001b[39m, \u001b[38;5;241m90\u001b[39m, \u001b[38;5;241m181\u001b[39m)\n\u001b[0;32m----> 2\u001b[0m interp  \u001b[38;5;241m=\u001b[39m analysis\u001b[38;5;241m.\u001b[39minterp_to_dataset(preLats, latEq, \u001b[43morigin\u001b[49m)\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28mprint\u001b[39m(interp)\n\u001b[1;32m      6\u001b[0m \u001b[38;5;66;03m# Select those levels above ground (400k above)\u001b[39;00m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'origin' is not defined"
     ]
    }
   ],
   "source": [
    "preLats = np.linspace(-90, 90, 181)\n",
    "interp  = analysis.interp_to_dataset(preLats, latEq, origin)\n",
    "\n",
    "print(interp)\n",
    "\n",
    "# Select those levels above ground (400k above)\n",
    "p = np.log(interp.nkeff[:]).plot.contourf(figsize=(10, 5), levels=16, vmin=0)\n",
    "ax = p.ax#[0]\n",
    "isent_data_xr['ua'].mean('lon').plot.contour(x = 'lat', ax = ax, colors = ('k'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "78b4eabf-2f13-4d82-a00a-8ea6eb79003b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T11:43:58.399225Z",
     "iopub.status.busy": "2021-12-07T11:43:58.398469Z",
     "iopub.status.idle": "2021-12-07T11:44:03.497243Z",
     "shell.execute_reply": "2021-12-07T11:44:03.495793Z",
     "shell.execute_reply.started": "2021-12-07T11:43:58.399159Z"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.QuadMesh at 0x7fe30b952ee0>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ds_out[0].mean('lon').plot(x = 'lat')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "a8ef65eb-ca73-44b2-be7f-4de579ddfd80",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-12-07T13:33:18.655835Z",
     "iopub.status.busy": "2021-12-07T13:33:18.655073Z",
     "iopub.status.idle": "2021-12-07T13:33:18.674432Z",
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       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;isentropic_level&#x27; (isentropic_level: 45)&gt;\n",
       "array([400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530,\n",
       "       540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670,\n",
       "       680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810,\n",
       "       820, 830, 840])\n",
       "Coordinates:\n",
       "  * isentropic_level  (isentropic_level) int64 400 410 420 430 ... 820 830 840\n",
       "    time              object 1979-01-01 00:00:00\n",
       "Attributes:\n",
       "    units:     kelvin\n",
       "    positive:  up</pre><div class='xr-wrap' hidden><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'isentropic_level'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>isentropic_level</span>: 45</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-b94e025b-0818-4bdb-a079-fff6d35e53ab' class='xr-array-in' type='checkbox' checked><label for='section-b94e025b-0818-4bdb-a079-fff6d35e53ab' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>400 410 420 430 440 450 460 470 ... 770 780 790 800 810 820 830 840</span></div><div class='xr-array-data'><pre>array([400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530,\n",
       "       540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670,\n",
       "       680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810,\n",
       "       820, 830, 840])</pre></div></div></li><li class='xr-section-item'><input id='section-49042236-09e1-472e-9da2-e5dcb800d9e4' class='xr-section-summary-in' type='checkbox'  checked><label for='section-49042236-09e1-472e-9da2-e5dcb800d9e4' class='xr-section-summary' >Coordinates: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>isentropic_level</span></div><div class='xr-var-dims'>(isentropic_level)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>400 410 420 430 ... 810 820 830 840</div><input id='attrs-d7cd1ae5-923c-4550-8ed7-8b48613c6e5e' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d7cd1ae5-923c-4550-8ed7-8b48613c6e5e' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-013e0377-c825-48bd-bac4-4e4fc79aa199' class='xr-var-data-in' type='checkbox'><label for='data-013e0377-c825-48bd-bac4-4e4fc79aa199' 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>kelvin</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div><div class='xr-var-data'><pre>array([400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530,\n",
       "       540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670,\n",
       "       680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810,\n",
       "       820, 830, 840])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>time</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1979-01-01 00:00:00</div><input id='attrs-610bc9a7-1e62-4d6b-a362-3034f23b704c' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-610bc9a7-1e62-4d6b-a362-3034f23b704c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7c5c1a74-555d-481a-868e-4a652c6fd27b' class='xr-var-data-in' type='checkbox'><label for='data-7c5c1a74-555d-481a-868e-4a652c6fd27b' 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>bounds :</span></dt><dd>time_bnds</dd><dt><span>axis :</span></dt><dd>T</dd><dt><span>long_name :</span></dt><dd>time</dd><dt><span>standard_name :</span></dt><dd>time</dd><dt><span>_metpy_axis :</span></dt><dd>time</dd></dl></div><div class='xr-var-data'><pre>array(cftime.DatetimeNoLeap(1979, 1, 1, 0, 0, 0, 0, has_year_zero=True),\n",
       "      dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-2011cbb7-83e9-47d5-b820-5cd348add455' class='xr-section-summary-in' type='checkbox'  checked><label for='section-2011cbb7-83e9-47d5-b820-5cd348add455' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>kelvin</dd><dt><span>positive :</span></dt><dd>up</dd></dl></div></li></ul></div></div>"
      ],
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       "<xarray.DataArray 'isentropic_level' (isentropic_level: 45)>\n",
       "array([400, 410, 420, 430, 440, 450, 460, 470, 480, 490, 500, 510, 520, 530,\n",
       "       540, 550, 560, 570, 580, 590, 600, 610, 620, 630, 640, 650, 660, 670,\n",
       "       680, 690, 700, 710, 720, 730, 740, 750, 760, 770, 780, 790, 800, 810,\n",
       "       820, 830, 840])\n",
       "Coordinates:\n",
       "  * isentropic_level  (isentropic_level) int64 400 410 420 430 ... 820 830 840\n",
       "    time              object 1979-01-01 00:00:00\n",
       "Attributes:\n",
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       "    positive:  up"
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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "interp.vorpot.plot.contourf(figsize=(10, 5), levels=16, vmin=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8de4c050-de75-4b40-9cdc-60ea7cd818c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Construct an analysis class using PV as the tracer\n",
    "analysis = ContourAnalysisInLatLon(dset, tracer, grid)\n",
    "\n",
    "# This should be called first to initialize contours from minimum value\n",
    "# to maximum value (within lat/lon dims) using `N` contours.\n",
    "N   = 121\n",
    "ctr = analysis.cal_contours(N, dims=['latitude', 'longitude']).metpy.dequantify()\n",
    "\n",
    "# Calculate various diagnostics defined in contour-based coordinates\n",
    "area    = analysis.cal_integral_within_contours(ctr, out_name='intArea')\n",
    "intgrdS = analysis.cal_integral_within_contours(ctr, grdS, out_name='intgrdS')\n",
    "latEq   = analysis.cal_equivalent_coords(area)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}