code/reproduce_FigS1.ipynb

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    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Import-libraries\" data-toc-modified-id=\"Import-libraries-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Import libraries</a></span></li><li><span><a href=\"#Load-data\" data-toc-modified-id=\"Load-data-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Load data</a></span></li><li><span><a href=\"#Process-data\" data-toc-modified-id=\"Process-data-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Process data</a></span><ul class=\"toc-item\"><li><span><a href=\"#Spatial-average\" data-toc-modified-id=\"Spatial-average-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Spatial average</a></span></li></ul></li><li><span><a href=\"#Visualization\" data-toc-modified-id=\"Visualization-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Visualization</a></span><ul class=\"toc-item\"><li><span><a href=\"#Meridional-and-zonal-components-together\" data-toc-modified-id=\"Meridional-and-zonal-components-together-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>Meridional and zonal components together</a></span></li><li><span><a href=\"#Meridional-component-only\" data-toc-modified-id=\"Meridional-component-only-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>Meridional component only</a></span></li></ul></li></ul></div>"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-22T07:58:39.880078Z",
     "start_time": "2020-07-22T07:58:33.779717Z"
    }
   },
   "outputs": [],
   "source": [
    "import xarray as xr\n",
    "import xarray.ufuncs as xrf\n",
    "from averaging import xarray_average\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import xclim as xc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-27T19:25:32.540398Z",
     "start_time": "2020-04-27T19:25:32.406731Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "numpy   1.17.3\n",
      "seaborn 0.9.0\n",
      "xarray  0.14.1\n",
      "pandas  1.0.1\n",
      "CPython 3.6.9\n",
      "IPython 7.1.1\n",
      "\n",
      "compiler   : GCC 8.4.0\n",
      "system     : Linux\n",
      "release    : 4.15.0-96-generic\n",
      "machine    : x86_64\n",
      "processor  : x86_64\n",
      "CPU cores  : 8\n",
      "interpreter: 64bit\n"
     ]
    }
   ],
   "source": [
    "%reload_ext watermark\n",
    "%watermark --iversions -v -m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-22T08:20:02.809554Z",
     "start_time": "2020-07-22T08:20:02.805954Z"
    }
   },
   "outputs": [],
   "source": [
    "var_name = 'accelogwn'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-22T08:07:41.215539Z",
     "start_time": "2020-07-22T08:01:52.393327Z"
    }
   },
   "outputs": [
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       "</style><div class='xr-wrap'><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-2b6f5049-3641-44d9-b77b-5d502eefd42f' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-2b6f5049-3641-44d9-b77b-5d502eefd42f' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span>bnds</span>: 2</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'>plev</span>: 63</li><li><span class='xr-has-index'>time</span>: 46720</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-39bd6ac6-b17e-45fc-a6eb-e5e0a5faac7e' class='xr-section-summary-in' type='checkbox'  checked><label for='section-39bd6ac6-b17e-45fc-a6eb-e5e0a5faac7e' 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'>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-eb7e89cd-1ee3-46a8-acd5-f08ec348be8f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-eb7e89cd-1ee3-46a8-acd5-f08ec348be8f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-c5505101-bf59-47be-8994-246562427cd7' class='xr-var-data-in' type='checkbox'><label for='data-c5505101-bf59-47be-8994-246562427cd7' 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><pre class='xr-var-data'>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></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-a38736aa-d600-4397-98e7-151d5149c3d6' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a38736aa-d600-4397-98e7-151d5149c3d6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0c6c3065-8382-47e9-886a-7a368222f11d' class='xr-var-data-in' type='checkbox'><label for='data-0c6c3065-8382-47e9-886a-7a368222f11d' 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><pre class='xr-var-data'>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></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-2472b53d-0280-48b7-9228-71ec159f366a' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-2472b53d-0280-48b7-9228-71ec159f366a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8f081d8f-f443-4062-a94b-c04d3f00d47b' class='xr-var-data-in' type='checkbox'><label for='data-8f081d8f-f443-4062-a94b-c04d3f00d47b' 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><pre class='xr-var-data'>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></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>time</span></div><div class='xr-var-dims'>(time)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>1979-01-01 00:00:00 ... 2010-12-31 18:00:00</div><input id='attrs-d5d8beb1-75b1-4bea-9607-9bece9d3fc51' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-d5d8beb1-75b1-4bea-9607-9bece9d3fc51' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ff1615a1-44f1-49d7-868d-2bf604159149' class='xr-var-data-in' type='checkbox'><label for='data-ff1615a1-44f1-49d7-868d-2bf604159149' 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><pre class='xr-var-data'>array([cftime.DatetimeNoLeap(1979-01-01 00:00:00),\n",
       "       cftime.DatetimeNoLeap(1979-01-01 06:00:00),\n",
       "       cftime.DatetimeNoLeap(1979-01-01 12:00:00), ...,\n",
       "       cftime.DatetimeNoLeap(2010-12-31 06:00:00),\n",
       "       cftime.DatetimeNoLeap(2010-12-31 12:00:00),\n",
       "       cftime.DatetimeNoLeap(2010-12-31 18:00:00)], dtype=object)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-6765e002-ee8c-4595-bcd6-6d37bae5e8f2' class='xr-section-summary-in' type='checkbox'  checked><label for='section-6765e002-ee8c-4595-bcd6-6d37bae5e8f2' class='xr-section-summary' >Data variables: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>time_bnds</span></div><div class='xr-var-dims'>(time, bnds)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1460, 2), meta=np.ndarray&gt;</div><input id='attrs-6369df3d-759e-4c9a-a60d-92d36e62dbf4' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6369df3d-759e-4c9a-a60d-92d36e62dbf4' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6cab9d3a-3665-4bbe-9753-3a9080e84757' class='xr-var-data-in' type='checkbox'><label for='data-6cab9d3a-3665-4bbe-9753-3a9080e84757' 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><pre class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
       "<table>\n",
       "  <thead>\n",
       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr><th> Bytes </th><td> 747.52 kB </td> <td> 23.36 kB </td></tr>\n",
       "    <tr><th> Shape </th><td> (46720, 2) </td> <td> (1460, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 96 Tasks </td><td> 32 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> object </td><td> numpy.ndarray </td></tr>\n",
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       "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>plev_bnds</span></div><div class='xr-var-dims'>(time, plev, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1460, 63, 2), meta=np.ndarray&gt;</div><input id='attrs-e43a3513-cf5c-45f3-bd74-76bf47df6f09' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e43a3513-cf5c-45f3-bd74-76bf47df6f09' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ddf85d73-3059-4b11-9f15-336df7aea265' class='xr-var-data-in' type='checkbox'><label for='data-ddf85d73-3059-4b11-9f15-336df7aea265' 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><pre class='xr-var-data'><table>\n",
       "<tr>\n",
       "<td>\n",
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       "    <tr><td> </td><th> Array </th><th> Chunk </th></tr>\n",
       "  </thead>\n",
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       "    <tr><th> Bytes </th><td> 47.09 MB </td> <td> 1.47 MB </td></tr>\n",
       "    <tr><th> Shape </th><td> (46720, 63, 2) </td> <td> (1460, 63, 2) </td></tr>\n",
       "    <tr><th> Count </th><td> 128 Tasks </td><td> 32 Chunks </td></tr>\n",
       "    <tr><th> Type </th><td> float64 </td><td> numpy.ndarray </td></tr>\n",
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       "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>lat_bnds</span></div><div class='xr-var-dims'>(time, lat, bnds)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>dask.array&lt;chunksize=(1460, 48, 2), meta=np.ndarray&gt;</div><input id='attrs-5ae2dd3f-5eff-472f-b1c0-429670a1cee2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5ae2dd3f-5eff-472f-b1c0-429670a1cee2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fe6ae111-297f-4d8e-bf62-93e71f74f08f' class='xr-var-data-in' type='checkbox'><label for='data-fe6ae111-297f-4d8e-bf62-93e71f74f08f' 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><pre class='xr-var-data'><table>\n",
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       "</table></pre></li><li class='xr-var-item'><div class='xr-var-name'><span>accelogwn</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'>dask.array&lt;chunksize=(1460, 63, 48, 96), meta=np.ndarray&gt;</div><input id='attrs-a66872da-9a5f-4709-b50a-99eb80b58c2b' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-a66872da-9a5f-4709-b50a-99eb80b58c2b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-57c14f7d-8825-4205-ba24-01da50286780' class='xr-var-data-in' type='checkbox'><label for='data-57c14f7d-8825-4205-ba24-01da50286780' 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>tendency_of_northward_wind_due_to_orographic_gravity_wave_drag</dd><dt><span>long_name :</span></dt><dd>Tendency Of Northward Wind Due To Orographic Gravity Wave Drag</dd><dt><span>units :</span></dt><dd>m s-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>2013-05-27T23:53:57Z altered by CMOR: replaced missing value flag (1e+38) with standard missing value (1e+20). 2013-05-27T23:53:57Z 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><pre class='xr-var-data'><table>\n",
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       "owner of all intellectual property rights (including copyright) that may exist in this Data \n",
       "product. You (as &quot;The Licensee&quot;) are hereby granted a non-exclusive, non-assignable, \n",
       "non-transferable unrestricted licence to use this data product for any purpose including \n",
       "the right to share these data with others and to make value-added and derivative \n",
       "products from it. This licence is not a sale of any or all of the owner&#x27;s rights.\n",
       "2) NO WARRANTY - This Data product is provided &quot;as-is&quot;; it has not been designed or \n",
       "prepared to meet the Licensee&#x27;s particular requirements. Environment Canada makes no \n",
       "warranty, either express or implied, including but not limited to, warranties of \n",
       "merchantability and fitness for a particular purpose. In no event will Environment Canada \n",
       "be liable for any indirect, special, consequential or other damages attributed to the \n",
       "Licensee&#x27;s use of the Data product.</dd><dt><span>product :</span></dt><dd>output</dd><dt><span>experiment :</span></dt><dd>CMAM 30-year specified dynamics run</dd><dt><span>frequency :</span></dt><dd>6hr</dd><dt><span>creation_date :</span></dt><dd>2013-05-27T23:53:57Z</dd><dt><span>history :</span></dt><dd>2013-05-27T23:53:57Z CMOR rewrote data to comply with CF standards and CMAM30 requirements.</dd><dt><span>Conventions :</span></dt><dd>CF-1.4</dd><dt><span>project_id :</span></dt><dd>CMAM30</dd><dt><span>table_id :</span></dt><dd>Table 6hrPlev (10 April 2013) 303ca12ce2a31d5584f739b38f636ae1</dd><dt><span>title :</span></dt><dd>CMAM model output prepared for CMAM30 CMAM 30-year specified dynamics run</dd><dt><span>parent_experiment :</span></dt><dd>N/A</dd><dt><span>modeling_realm :</span></dt><dd>atmos</dd><dt><span>realization :</span></dt><dd>1</dd><dt><span>cmor_version :</span></dt><dd>2.8.2</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.Dataset>\n",
       "Dimensions:    (bnds: 2, lat: 48, lon: 96, plev: 63, time: 46720)\n",
       "Coordinates:\n",
       "  * lon        (lon) float64 0.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "  * lat        (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "  * plev       (plev) float64 1e+05 9.25e+04 8.5e+04 8e+04 ... 0.2 0.15 0.1 0.07\n",
       "  * time       (time) object 1979-01-01 00:00:00 ... 2010-12-31 18:00:00\n",
       "Dimensions without coordinates: bnds\n",
       "Data variables:\n",
       "    time_bnds  (time, bnds) object dask.array<chunksize=(1460, 2), meta=np.ndarray>\n",
       "    plev_bnds  (time, plev, bnds) float64 dask.array<chunksize=(1460, 63, 2), meta=np.ndarray>\n",
       "    lat_bnds   (time, lat, bnds) float64 dask.array<chunksize=(1460, 48, 2), meta=np.ndarray>\n",
       "    lon_bnds   (time, lon, bnds) float64 dask.array<chunksize=(1460, 96, 2), meta=np.ndarray>\n",
       "    accelogwn  (time, plev, lat, lon) float32 dask.array<chunksize=(1460, 63, 48, 96), meta=np.ndarray>\n",
       "Attributes:\n",
       "    institution:            CCCma (Canadian Centre for Climate Modelling and ...\n",
       "    institute_id:           CCCma\n",
       "    experiment_id:          CMAM30-SD\n",
       "    source:                 CMAM 2013\n",
       "    model_id:               CMAM\n",
       "    forcing:                N/A\n",
       "    parent_experiment_id:   N/A\n",
       "    parent_experiment_rip:  N/A\n",
       "    branch_time:            0.0\n",
       "    contact:                cccma_info@ec.gc.ca\n",
       "    comment:                The CMAM30 team gratefully acknowledges the Canad...\n",
       "    references:             http://www.cccma.ec.gc.ca/models\n",
       "    initialization_method:  1\n",
       "    physics_version:        1\n",
       "    tracking_id:            bc122c60-a2eb-4cdb-b934-5124c6772033\n",
       "    CCCma_runid:            cmam20_abc001\n",
       "    CCCma_data_licence:     1) GRANT OF LICENCE - The Government of Canada (E...\n",
       "    product:                output\n",
       "    experiment:             CMAM 30-year specified dynamics run\n",
       "    frequency:              6hr\n",
       "    creation_date:          2013-05-27T23:53:57Z\n",
       "    history:                2013-05-27T23:53:57Z CMOR rewrote data to comply ...\n",
       "    Conventions:            CF-1.4\n",
       "    project_id:             CMAM30\n",
       "    table_id:               Table 6hrPlev (10 April 2013) 303ca12ce2a31d5584f...\n",
       "    title:                  CMAM model output prepared for CMAM30 CMAM 30-yea...\n",
       "    parent_experiment:      N/A\n",
       "    modeling_realm:         atmos\n",
       "    realization:            1\n",
       "    cmor_version:           2.8.2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inpath = '/mnt/4data/CMAM/0A.daily/'\n",
    "infiles = f'{inpath}{var_name}/{var_name}_6hrPlev_CMAM_CMAM30-SD_r1i1p1_*18.nc'\n",
    "ds = xr.open_mfdataset(infiles, concat_dim='time', combine = 'by_coords', parallel = True)\n",
    "ds"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Process data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-22T08:17:47.890556Z",
     "start_time": "2020-07-22T08:10:19.295688Z"
    }
   },
   "outputs": [],
   "source": [
    "time_mask = (ds.time.dt.month == 12) | (ds.time.dt.month == 1) | (ds.time.dt.month == 2)\n",
    "ds_sel = ds[var_name].sel(time = time_mask, plev = 7000)\n",
    "ds_sel = ds_sel.resample(time = '1D').mean('time').load()\n",
    "\n",
    "sel_dict_ea = dict(lon =slice(110,145), lat=slice(30,48)) \n",
    "sel_dict_hi = dict(lon =slice(70,102.5), lat=slice(20,40)) \n",
    "sel_dict_wa = dict(lon =slice(-125+360,-102.5+360), lat=slice(27.5,52)) \n",
    "\n",
    "sel_dict_ls = {'Himalayas': sel_dict_hi, \\\n",
    "               'West America': sel_dict_wa, \\\n",
    "               'East Asia': sel_dict_ea}\n",
    "weights = xrf.cos(xrf.deg2rad(ds_sel.lat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-22T08:19:05.703506Z",
     "start_time": "2020-07-22T08:19:05.668301Z"
    }
   },
   "outputs": [
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       "  margin-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-obj-type {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-sections {\n",
       "  padding-left: 0 !important;\n",
       "  display: grid;\n",
       "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
       "}\n",
       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-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, 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><div class='xr-wrap'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'accelogwn'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>time</span>: 11680</li><li><span class='xr-has-index'>lat</span>: 48</li><li><span class='xr-has-index'>lon</span>: 96</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-cd288b6b-d248-481e-ab52-dcb2ec5cfdfd' class='xr-array-in' type='checkbox' ><label for='section-cd288b6b-d248-481e-ab52-dcb2ec5cfdfd' 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>2.2820075e-07 1.723767e-07 ... -1.8452792e-14 -1.8452792e-14</span></div><pre class='xr-array-data'>array([[[ 2.2820075e-07,  1.7237670e-07,  2.2476610e-07, ...,\n",
       "          2.0871891e-08,  5.3706735e-08,  1.6687891e-07],\n",
       "        [ 4.9990467e-07,  9.0303661e-07,  8.0997467e-07, ...,\n",
       "          2.7404079e-07,  9.4123033e-07,  1.7986617e-06],\n",
       "        [ 1.4095470e-07,  3.0932753e-07,  1.5927787e-07, ...,\n",
       "          8.2788823e-08,  2.4745464e-07,  1.1475254e-07],\n",
       "        ...,\n",
       "        [-5.4866498e-09,  9.9014272e-09,  3.1816526e-07, ...,\n",
       "          3.7391653e-14,  3.7391653e-14,  3.7391653e-14],\n",
       "        [ 3.7391653e-14,  3.7391653e-14,  3.7391653e-14, ...,\n",
       "          3.7391653e-14,  3.7391653e-14,  3.7391653e-14],\n",
       "        [ 3.7391653e-14,  3.7391653e-14,  3.7391653e-14, ...,\n",
       "          3.7391653e-14,  3.7391653e-14,  3.7391653e-14]],\n",
       "\n",
       "       [[ 5.8412922e-07,  4.9615170e-07,  5.5010315e-07, ...,\n",
       "          5.5014013e-08,  1.2216869e-07,  4.4100787e-07],\n",
       "        [ 1.1130800e-07,  3.2509938e-08,  7.9702431e-08, ...,\n",
       "          4.0093141e-08,  8.5367176e-08,  6.1114342e-08],\n",
       "        [ 4.3343899e-08,  1.3571875e-07,  1.6334330e-07, ...,\n",
       "         -8.9398127e-15, -8.9398127e-15, -8.9398127e-15],\n",
       "        ...,\n",
       "        [ 2.4721546e-14,  2.2425416e-08,  2.4721546e-14, ...,\n",
       "          4.2582491e-07,  5.5933154e-11,  3.2202237e-11],\n",
       "        [ 2.4721546e-14,  2.4721546e-14,  2.4721546e-14, ...,\n",
       "          2.4721546e-14,  4.5281126e-09,  3.4304895e-10],\n",
       "        [ 2.4721546e-14,  2.4721546e-14,  2.4721546e-14, ...,\n",
       "          2.4721546e-14,  2.4721546e-14,  2.4721546e-14]],\n",
       "\n",
       "       [[ 5.6582721e-08,  5.6006026e-08,  1.3524247e-07, ...,\n",
       "          6.8411936e-08,  3.1539678e-08,  1.5547753e-08],\n",
       "        [ 1.7679049e-08,  1.2308277e-08,  4.7914114e-08, ...,\n",
       "          2.5059044e-10,  1.6550533e-09,  3.9506465e-10],\n",
       "        [ 1.8223456e-07,  3.4573486e-07,  6.6030857e-08, ...,\n",
       "          2.0853793e-08,  1.0231162e-07,  6.6652660e-08],\n",
       "        ...,\n",
       "        [ 5.1740859e-14,  5.9454212e-08, -5.1493735e-14, ...,\n",
       "          2.3100100e-08,  1.9548596e-08,  3.8046549e-11],\n",
       "        [-1.4394576e-14, -1.4394576e-14, -1.4394576e-14, ...,\n",
       "         -1.4394576e-14, -1.4394576e-14, -1.4394576e-14],\n",
       "        [-1.4394576e-14, -1.4394576e-14, -1.4394576e-14, ...,\n",
       "         -1.4394576e-14, -1.4394576e-14, -1.4394576e-14]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[-7.9900699e-14, -7.9900699e-14, -7.9900699e-14, ...,\n",
       "         -7.9900699e-14, -7.9900699e-14, -7.9900699e-14],\n",
       "        [ 5.2627733e-09,  5.8057936e-09,  9.5810087e-08, ...,\n",
       "         -7.9900699e-14, -7.9900699e-14, -7.9900699e-14],\n",
       "        [ 6.1124226e-08,  1.9384451e-07,  2.2775976e-07, ...,\n",
       "         -7.9900699e-14,  1.6873566e-08,  2.0152529e-11],\n",
       "        ...,\n",
       "        [-1.8954177e-14, -1.8954177e-14, -1.8954177e-14, ...,\n",
       "         -1.8954177e-14, -1.8954177e-14, -1.8954177e-14],\n",
       "        [-1.8954177e-14, -1.8954177e-14, -1.8954177e-14, ...,\n",
       "         -1.8954177e-14, -1.8954177e-14, -1.8954177e-14],\n",
       "        [-1.8954177e-14, -1.8954177e-14, -1.8954177e-14, ...,\n",
       "         -1.8954177e-14, -1.8954177e-14, -1.8954177e-14]],\n",
       "\n",
       "       [[ 8.4172883e-14,  8.4172883e-14,  8.4172883e-14, ...,\n",
       "          8.4172883e-14,  8.4172883e-14,  8.4172883e-14],\n",
       "        [ 3.1083520e-08,  1.8426405e-08,  3.3105695e-07, ...,\n",
       "          8.4172883e-14,  8.4172883e-14,  8.4172883e-14],\n",
       "        [ 2.0985009e-07,  5.6425063e-07,  4.7628276e-07, ...,\n",
       "          8.4172883e-14,  6.4802258e-08,  7.3497097e-10],\n",
       "        ...,\n",
       "        [ 8.4172883e-14,  8.4172883e-14,  8.4172883e-14, ...,\n",
       "          1.2431730e-08,  8.4172883e-14,  8.4172883e-14],\n",
       "        [ 8.4172883e-14,  8.4172883e-14,  8.4172883e-14, ...,\n",
       "          8.4172883e-14,  8.4172883e-14,  8.4172883e-14],\n",
       "        [ 8.4172883e-14,  8.4172883e-14,  8.4172883e-14, ...,\n",
       "          8.4172883e-14,  8.4172883e-14,  8.4172883e-14]],\n",
       "\n",
       "       [[ 2.9070903e-14,  2.9070903e-14,  2.9070903e-14, ...,\n",
       "         -1.9500131e-08, -2.7725728e-09,  2.9070903e-14],\n",
       "        [ 5.9755769e-09,  1.3630184e-08,  4.1049795e-08, ...,\n",
       "          2.9070903e-14,  2.9070903e-14,  2.9070903e-14],\n",
       "        [ 3.5483378e-08,  7.5100345e-08,  3.9613177e-08, ...,\n",
       "          2.9070903e-14,  1.9969487e-08,  2.9070903e-14],\n",
       "        ...,\n",
       "        [ 1.7565687e-09,  9.3384314e-09,  2.0815274e-07, ...,\n",
       "         -1.8452792e-14, -1.8452792e-14, -1.8452792e-14],\n",
       "        [-1.8452792e-14, -1.8452792e-14, -1.8452792e-14, ...,\n",
       "         -1.8452792e-14, -1.8452792e-14, -1.8452792e-14],\n",
       "        [-1.8452792e-14, -1.8452792e-14, -1.8452792e-14, ...,\n",
       "         -1.8452792e-14, -1.8452792e-14, -1.8452792e-14]]], dtype=float32)</pre></div></li><li class='xr-section-item'><input id='section-93c81359-2b69-42d5-a713-5a3b3a73c852' class='xr-section-summary-in' type='checkbox'  checked><label for='section-93c81359-2b69-42d5-a713-5a3b3a73c852' 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 ... 2010-12-31 00:00:00</div><input id='attrs-73032e9a-50b6-46cd-9935-aaa5e9e6641a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-73032e9a-50b6-46cd-9935-aaa5e9e6641a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e830699-87bc-4f1a-a1c4-b0ef14c004e6' class='xr-var-data-in' type='checkbox'><label for='data-6e830699-87bc-4f1a-a1c4-b0ef14c004e6' 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><pre class='xr-var-data'>array([cftime.DatetimeNoLeap(1979-01-01 00:00:00),\n",
       "       cftime.DatetimeNoLeap(1979-01-02 00:00:00),\n",
       "       cftime.DatetimeNoLeap(1979-01-03 00:00:00), ...,\n",
       "       cftime.DatetimeNoLeap(2010-12-29 00:00:00),\n",
       "       cftime.DatetimeNoLeap(2010-12-30 00:00:00),\n",
       "       cftime.DatetimeNoLeap(2010-12-31 00:00:00)], dtype=object)</pre></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-fe6730a4-50b3-4306-8224-aecb432e9861' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-fe6730a4-50b3-4306-8224-aecb432e9861' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-a02bf50c-daed-49b0-84ff-da7111c16d89' class='xr-var-data-in' type='checkbox'><label for='data-a02bf50c-daed-49b0-84ff-da7111c16d89' 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><pre class='xr-var-data'>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></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-b5feb17b-e108-4eb2-9c00-2040710b4b9d' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-b5feb17b-e108-4eb2-9c00-2040710b4b9d' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-515bb5cb-727b-45e4-bd6d-30c405233928' class='xr-var-data-in' type='checkbox'><label for='data-515bb5cb-727b-45e4-bd6d-30c405233928' 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><pre class='xr-var-data'>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></li><li class='xr-var-item'><div class='xr-var-name'><span>plev</span></div><div class='xr-var-dims'>()</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>7e+03</div><input id='attrs-954e64da-44ae-4e23-9ff3-094e468b980f' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-954e64da-44ae-4e23-9ff3-094e468b980f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b6255ebb-704b-4ff6-86a7-6e83a8c06452' class='xr-var-data-in' type='checkbox'><label for='data-b6255ebb-704b-4ff6-86a7-6e83a8c06452' 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><pre class='xr-var-data'>array(7000.)</pre></li></ul></div></li><li class='xr-section-item'><input id='section-174886d8-5c79-4076-bfc7-e677bdbac132' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-174886d8-5c79-4076-bfc7-e677bdbac132' 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.DataArray 'accelogwn' (time: 11680, lat: 48, lon: 96)>\n",
       "array([[[ 2.2820075e-07,  1.7237670e-07,  2.2476610e-07, ...,\n",
       "          2.0871891e-08,  5.3706735e-08,  1.6687891e-07],\n",
       "        [ 4.9990467e-07,  9.0303661e-07,  8.0997467e-07, ...,\n",
       "          2.7404079e-07,  9.4123033e-07,  1.7986617e-06],\n",
       "        [ 1.4095470e-07,  3.0932753e-07,  1.5927787e-07, ...,\n",
       "          8.2788823e-08,  2.4745464e-07,  1.1475254e-07],\n",
       "        ...,\n",
       "        [-5.4866498e-09,  9.9014272e-09,  3.1816526e-07, ...,\n",
       "          3.7391653e-14,  3.7391653e-14,  3.7391653e-14],\n",
       "        [ 3.7391653e-14,  3.7391653e-14,  3.7391653e-14, ...,\n",
       "          3.7391653e-14,  3.7391653e-14,  3.7391653e-14],\n",
       "        [ 3.7391653e-14,  3.7391653e-14,  3.7391653e-14, ...,\n",
       "          3.7391653e-14,  3.7391653e-14,  3.7391653e-14]],\n",
       "\n",
       "       [[ 5.8412922e-07,  4.9615170e-07,  5.5010315e-07, ...,\n",
       "          5.5014013e-08,  1.2216869e-07,  4.4100787e-07],\n",
       "        [ 1.1130800e-07,  3.2509938e-08,  7.9702431e-08, ...,\n",
       "          4.0093141e-08,  8.5367176e-08,  6.1114342e-08],\n",
       "        [ 4.3343899e-08,  1.3571875e-07,  1.6334330e-07, ...,\n",
       "         -8.9398127e-15, -8.9398127e-15, -8.9398127e-15],\n",
       "        ...,\n",
       "        [ 2.4721546e-14,  2.2425416e-08,  2.4721546e-14, ...,\n",
       "          4.2582491e-07,  5.5933154e-11,  3.2202237e-11],\n",
       "        [ 2.4721546e-14,  2.4721546e-14,  2.4721546e-14, ...,\n",
       "          2.4721546e-14,  4.5281126e-09,  3.4304895e-10],\n",
       "        [ 2.4721546e-14,  2.4721546e-14,  2.4721546e-14, ...,\n",
       "          2.4721546e-14,  2.4721546e-14,  2.4721546e-14]],\n",
       "\n",
       "       [[ 5.6582721e-08,  5.6006026e-08,  1.3524247e-07, ...,\n",
       "          6.8411936e-08,  3.1539678e-08,  1.5547753e-08],\n",
       "        [ 1.7679049e-08,  1.2308277e-08,  4.7914114e-08, ...,\n",
       "          2.5059044e-10,  1.6550533e-09,  3.9506465e-10],\n",
       "        [ 1.8223456e-07,  3.4573486e-07,  6.6030857e-08, ...,\n",
       "          2.0853793e-08,  1.0231162e-07,  6.6652660e-08],\n",
       "        ...,\n",
       "        [ 5.1740859e-14,  5.9454212e-08, -5.1493735e-14, ...,\n",
       "          2.3100100e-08,  1.9548596e-08,  3.8046549e-11],\n",
       "        [-1.4394576e-14, -1.4394576e-14, -1.4394576e-14, ...,\n",
       "         -1.4394576e-14, -1.4394576e-14, -1.4394576e-14],\n",
       "        [-1.4394576e-14, -1.4394576e-14, -1.4394576e-14, ...,\n",
       "         -1.4394576e-14, -1.4394576e-14, -1.4394576e-14]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[-7.9900699e-14, -7.9900699e-14, -7.9900699e-14, ...,\n",
       "         -7.9900699e-14, -7.9900699e-14, -7.9900699e-14],\n",
       "        [ 5.2627733e-09,  5.8057936e-09,  9.5810087e-08, ...,\n",
       "         -7.9900699e-14, -7.9900699e-14, -7.9900699e-14],\n",
       "        [ 6.1124226e-08,  1.9384451e-07,  2.2775976e-07, ...,\n",
       "         -7.9900699e-14,  1.6873566e-08,  2.0152529e-11],\n",
       "        ...,\n",
       "        [-1.8954177e-14, -1.8954177e-14, -1.8954177e-14, ...,\n",
       "         -1.8954177e-14, -1.8954177e-14, -1.8954177e-14],\n",
       "        [-1.8954177e-14, -1.8954177e-14, -1.8954177e-14, ...,\n",
       "         -1.8954177e-14, -1.8954177e-14, -1.8954177e-14],\n",
       "        [-1.8954177e-14, -1.8954177e-14, -1.8954177e-14, ...,\n",
       "         -1.8954177e-14, -1.8954177e-14, -1.8954177e-14]],\n",
       "\n",
       "       [[ 8.4172883e-14,  8.4172883e-14,  8.4172883e-14, ...,\n",
       "          8.4172883e-14,  8.4172883e-14,  8.4172883e-14],\n",
       "        [ 3.1083520e-08,  1.8426405e-08,  3.3105695e-07, ...,\n",
       "          8.4172883e-14,  8.4172883e-14,  8.4172883e-14],\n",
       "        [ 2.0985009e-07,  5.6425063e-07,  4.7628276e-07, ...,\n",
       "          8.4172883e-14,  6.4802258e-08,  7.3497097e-10],\n",
       "        ...,\n",
       "        [ 8.4172883e-14,  8.4172883e-14,  8.4172883e-14, ...,\n",
       "          1.2431730e-08,  8.4172883e-14,  8.4172883e-14],\n",
       "        [ 8.4172883e-14,  8.4172883e-14,  8.4172883e-14, ...,\n",
       "          8.4172883e-14,  8.4172883e-14,  8.4172883e-14],\n",
       "        [ 8.4172883e-14,  8.4172883e-14,  8.4172883e-14, ...,\n",
       "          8.4172883e-14,  8.4172883e-14,  8.4172883e-14]],\n",
       "\n",
       "       [[ 2.9070903e-14,  2.9070903e-14,  2.9070903e-14, ...,\n",
       "         -1.9500131e-08, -2.7725728e-09,  2.9070903e-14],\n",
       "        [ 5.9755769e-09,  1.3630184e-08,  4.1049795e-08, ...,\n",
       "          2.9070903e-14,  2.9070903e-14,  2.9070903e-14],\n",
       "        [ 3.5483378e-08,  7.5100345e-08,  3.9613177e-08, ...,\n",
       "          2.9070903e-14,  1.9969487e-08,  2.9070903e-14],\n",
       "        ...,\n",
       "        [ 1.7565687e-09,  9.3384314e-09,  2.0815274e-07, ...,\n",
       "         -1.8452792e-14, -1.8452792e-14, -1.8452792e-14],\n",
       "        [-1.8452792e-14, -1.8452792e-14, -1.8452792e-14, ...,\n",
       "         -1.8452792e-14, -1.8452792e-14, -1.8452792e-14],\n",
       "        [-1.8452792e-14, -1.8452792e-14, -1.8452792e-14, ...,\n",
       "         -1.8452792e-14, -1.8452792e-14, -1.8452792e-14]]], dtype=float32)\n",
       "Coordinates:\n",
       "  * time     (time) object 1979-01-01 00:00:00 ... 2010-12-31 00:00:00\n",
       "  * lon      (lon) float64 0.0 3.75 7.5 11.25 15.0 ... 345.0 348.8 352.5 356.2\n",
       "  * lat      (lat) float64 -87.16 -83.48 -79.78 -76.07 ... 79.78 83.48 87.16\n",
       "    plev     float64 7e+03"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds_sel#.resample(time = '1D').mean('time')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-22T08:20:05.482676Z",
     "start_time": "2020-07-22T08:20:05.193089Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Himalayas\n",
      "West America\n",
      "East Asia\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>accelogwn</th>\n",
       "      <th>g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1979-01-01 00:00:00</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>71.25</td>\n",
       "      <td>3.230639e-09</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1979-01-01 00:00:00</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>75.00</td>\n",
       "      <td>3.230639e-09</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1979-01-01 00:00:00</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>78.75</td>\n",
       "      <td>3.230639e-09</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1979-01-01 00:00:00</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>82.50</td>\n",
       "      <td>4.196809e-01</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1979-01-01 00:00:00</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>86.25</td>\n",
       "      <td>8.470623e-03</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time        lat    lon     accelogwn          g\n",
       "0  1979-01-01 00:00:00  20.411238  71.25  3.230639e-09  Himalayas\n",
       "1  1979-01-01 00:00:00  20.411238  75.00  3.230639e-09  Himalayas\n",
       "2  1979-01-01 00:00:00  20.411238  78.75  3.230639e-09  Himalayas\n",
       "3  1979-01-01 00:00:00  20.411238  82.50  4.196809e-01  Himalayas\n",
       "4  1979-01-01 00:00:00  20.411238  86.25  8.470623e-03  Himalayas"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_ls = []\n",
    "g_ls = []\n",
    "for hs_name in sel_dict_ls.keys():\n",
    "    print(hs_name)\n",
    "    sel_dict = sel_dict_ls[hs_name]\n",
    "    ds_sel_hs = ds_sel.sel(**sel_dict)\n",
    "    #ds_sel_hs = xarray_average(ds_sel_hs, dim = ['lat', 'lon'], weights = weights)#.load()\n",
    "    #ds_sel_hs.name = 'accelogw'\n",
    "    ts = ds_sel_hs.dropna('time').to_series().reset_index()\n",
    "    #sys.exit()\n",
    "    g_ls += [hs_name]*ts.shape[0]    \n",
    "    ts_ls.append(ts)\n",
    "\n",
    "df = pd.concat(ts_ls)\n",
    "df['g'] = g_ls\n",
    "df[var_name] = df[var_name]*24*3600\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-22T08:20:23.494856Z",
     "start_time": "2020-07-22T08:20:23.203225Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>accelogwn</th>\n",
       "      <th>g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1979-01-01 00:00:00</td>\n",
       "      <td>-0.088828</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1979-01-02 00:00:00</td>\n",
       "      <td>-0.145551</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1979-01-03 00:00:00</td>\n",
       "      <td>-0.038588</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1979-01-04 00:00:00</td>\n",
       "      <td>-0.049557</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1979-01-05 00:00:00</td>\n",
       "      <td>0.358900</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  time  accelogwn          g\n",
       "0  1979-01-01 00:00:00  -0.088828  Himalayas\n",
       "1  1979-01-02 00:00:00  -0.145551  Himalayas\n",
       "2  1979-01-03 00:00:00  -0.038588  Himalayas\n",
       "3  1979-01-04 00:00:00  -0.049557  Himalayas\n",
       "4  1979-01-05 00:00:00   0.358900  Himalayas"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_ls = []\n",
    "g_ls = []\n",
    "for hs_name in sel_dict_ls.keys():\n",
    "    sel_dict = sel_dict_ls[hs_name]\n",
    "    ds_sel_hs = ds_sel.sel(**sel_dict)\n",
    "    ds_sel_hs = xarray_average(ds_sel_hs, dim = ['lat', 'lon'], weights = weights)#.load()\n",
    "    ds_sel_hs.name = var_name\n",
    "    ts = ds_sel_hs.dropna('time').to_series().reset_index()\n",
    "    g_ls += [hs_name]*ts.shape[0]    \n",
    "    ts_ls.append(ts)\n",
    "\n",
    "df2 = pd.concat(ts_ls)\n",
    "df2['g'] = g_ls\n",
    "df2[var_name] = df2[var_name]*24*3600\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-22T08:55:34.753873Z",
     "start_time": "2020-07-22T08:55:34.722968Z"
    }
   },
   "outputs": [],
   "source": [
    "inpath = '/mnt/4data/CMAM/0A.daily/'\n",
    "ds_zonal = xr.open_dataset(f'{inpath}accelogw/accelogw_daily_CMAM_CMAM30-SD_r1i1p1_19790101-20101231_70hPa.nc')\n",
    "time_mask = (ds_zonal.time.dt.month == 12) | (ds_zonal.time.dt.month == 1) | (ds_zonal.time.dt.month == 2)\n",
    "ds_sel_zonal = ds_zonal['accelogw'].sel(time = time_mask)\n",
    "weights = xrf.cos(xrf.deg2rad(ds_sel_zonal.lat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-22T08:56:21.596965Z",
     "start_time": "2020-07-22T08:56:20.660004Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Himalayas\n",
      "West America\n",
      "East Asia\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>accelogw</th>\n",
       "      <th>g</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>71.25</td>\n",
       "      <td>5.009432e-09</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>75.00</td>\n",
       "      <td>5.009432e-09</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>78.75</td>\n",
       "      <td>5.009432e-09</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>82.50</td>\n",
       "      <td>-2.866767e-01</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>20.411238</td>\n",
       "      <td>86.25</td>\n",
       "      <td>-5.888247e-03</td>\n",
       "      <td>Himalayas</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        time        lat    lon      accelogw          g\n",
       "0 1979-01-01  20.411238  71.25  5.009432e-09  Himalayas\n",
       "1 1979-01-01  20.411238  75.00  5.009432e-09  Himalayas\n",
       "2 1979-01-01  20.411238  78.75  5.009432e-09  Himalayas\n",
       "3 1979-01-01  20.411238  82.50 -2.866767e-01  Himalayas\n",
       "4 1979-01-01  20.411238  86.25 -5.888247e-03  Himalayas"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ts_ls = []\n",
    "g_ls = []\n",
    "for hs_name in sel_dict_ls.keys():\n",
    "    print(hs_name)\n",
    "    sel_dict = sel_dict_ls[hs_name]\n",
    "    ds_sel_hs_zonal = ds_sel_zonal.sel(**sel_dict)\n",
    "    ts = ds_sel_hs_zonal.dropna('time').to_series().reset_index()\n",
    "    #sys.exit()\n",
    "    g_ls += [hs_name]*ts.shape[0]    \n",
    "    ts_ls.append(ts)\n",
    "\n",
    "df_zonal = pd.concat(ts_ls)\n",
    "df_zonal['g'] = g_ls\n",
    "df_zonal['accelogw'] = df_zonal['accelogw']*24*3600\n",
    "df_zonal.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meridional and zonal components together"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-22T09:40:13.173398Z",
     "start_time": "2020-07-22T09:39:55.189846Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Himalayas 0.5554687690286121\n",
      "West America 0.46725125923226224\n",
      "East Asia -0.6054369687347316\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(-80,41)\n",
    "xticks = np.arange(-80,45,10)\n",
    "g = sns.FacetGrid(df_zonal, row=\"g\", hue=\"g\", aspect=5, palette=pal)\n",
    "g.map(sns.distplot, 'accelogw', norm_hist = True, kde=False, rug=False, bins = bins)\n",
    "\n",
    "dist_name = 'laplace'#'cauchy'#'norm'\n",
    "for i, hs_name in enumerate(sel_dict_ls.keys()):\n",
    "    ax = g.axes[i][0]\n",
    "    \n",
    "    arr = df[var_name][df['g'] == hs_name]\n",
    "    arr2 = df_zonal['accelogw'][df['g'] == hs_name]\n",
    "    print(hs_name, np.corrcoef(arr, arr2)[0][1])\n",
    "    arr.index.name = 'time'\n",
    "    arr = arr.to_xarray()\n",
    "    sns.distplot(arr, norm_hist = True, kde=False, rug=False, \\\n",
    "             bins = bins, ax = ax)\n",
    "    \"\"\"\n",
    "    ind = xc.indices.generic.fit(arr, dist = dist_name).compute()\n",
    "    dist = xc.indices.generic.get_dist(dist_name)\n",
    "    mean, var, skew, kurt = dist.stats(*ind.values, moments='mvsk')\n",
    "    textstr = '\\n'.join((\n",
    "        'Mean=%.2f' % (mean, ),\n",
    "        'Variance=%.2f' % (var, ),\n",
    "        'Skewness=%.2f' % (skew, ),\n",
    "        'Kurtosis=%.2f' % (kurt, )))\n",
    "    ax.text(0.7, 0.8, textstr, fontsize=14, verticalalignment='top', \\\n",
    "            color = 'C0', transform=ax.transAxes)  \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "    dist = xc.indices.generic.get_dist(dist_name)\n",
    "    ax.plot(bins,dist.pdf(bins, *ind.values), color = 'C0')\n",
    "    \n",
    "    arr = df[var_name][df['g'] == hs_name]\n",
    "    arr.index.name = 'time'\n",
    "    arr = arr.to_xarray()\n",
    "\n",
    "    ind = xc.indices.generic.fit(arr, dist = dist_name).compute()\n",
    "    dist = xc.indices.generic.get_dist(dist_name)\n",
    "    ax.plot(bins, dist.pdf(bins, *ind.values), color = pal[i])    \n",
    "    mean, var, skew, kurt = dist.stats(*ind.values, moments='mvsk')\n",
    "    textstr = '\\n'.join((\n",
    "        'Mean=%.2f' % (mean, ),\n",
    "        'Variance=%.2f' % (var, ),\n",
    "        'Skewness=%.2f' % (skew, ),\n",
    "        'Kurtosis=%.2f' % (kurt, )))\n",
    "    ax.text(0.2, 0.8, textstr, fontsize=14, verticalalignment='top', \\\n",
    "            color = pal[i], transform=ax.transAxes)  \n",
    "    \"\"\"\n",
    "    \n",
    "    ax.tick_params(axis=\"y\",direction=\"out\", pad=10, labelsize = 15)\n",
    "    ax.tick_params(axis=\"x\",direction=\"out\", pad=10, labelsize = 15)\n",
    "    ax.xaxis.set_minor_locator(plt.MultipleLocator(5))\n",
    "    ax.set_ylim(0,0.9)\n",
    "    ax.set_yscale('log')\n",
    "    \n",
    "def label(x, color, label):\n",
    "    ax = plt.gca()\n",
    "    ax.text(0.025, .55, label, fontweight=\"bold\", color=color,\n",
    "            ha=\"left\", va=\"center\", transform=ax.transAxes)\n",
    "\n",
    "\n",
    "g.map(label, 'accelogw')\n",
    "g.set(xticks=xticks)\n",
    "# Remove axes details that don't play well with overlap\n",
    "g.set_titles(\"\")\n",
    "#g.set(yscale=\"log\")\n",
    "g.axes[nh-1][0].set_xlabel('OGWD [m/s/day]', fontsize = 20)\n",
    "g.axes[nh-2][0].set_ylabel('Occurrence density', fontsize = 20)\n",
    "plt.savefig('merOGWD_distribution_hotspots_DJFonly.pdf', bbox_inches='tight') "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Meridional component only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-07-22T08:52:45.383249Z",
     "start_time": "2020-07-22T08:52:40.714674Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(1.3039843749999989, 0.5, 'Occurrence density')"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bins = np.arange(-40,41)\n",
    "xticks = np.arange(-40,45,5)\n",
    "g = sns.FacetGrid(df, row=\"g\", hue=\"g\", aspect=5, palette=pal)\n",
    "g.map(sns.distplot, var_name, norm_hist = True, kde=False, rug=False, bins = bins)\n",
    "\n",
    "dist_name = 'laplace'#'cauchy'#'norm'\n",
    "for i, hs_name in enumerate(sel_dict_ls.keys()):\n",
    "    ax = g.axes[i][0]\n",
    "    \n",
    "    arr = df2[var_name][df2['g'] == hs_name]\n",
    "    arr.index.name = 'time'\n",
    "    arr = arr.to_xarray()\n",
    "    sns.distplot(arr, norm_hist = True, kde=False, rug=False, \\\n",
    "             bins = bins, ax = ax)\n",
    "    \"\"\"\n",
    "    ind = xc.indices.generic.fit(arr, dist = dist_name).compute()\n",
    "    dist = xc.indices.generic.get_dist(dist_name)\n",
    "    mean, var, skew, kurt = dist.stats(*ind.values, moments='mvsk')\n",
    "    textstr = '\\n'.join((\n",
    "        'Mean=%.2f' % (mean, ),\n",
    "        'Variance=%.2f' % (var, ),\n",
    "        'Skewness=%.2f' % (skew, ),\n",
    "        'Kurtosis=%.2f' % (kurt, )))\n",
    "    ax.text(0.7, 0.8, textstr, fontsize=14, verticalalignment='top', \\\n",
    "            color = 'C0', transform=ax.transAxes)  \n",
    "    \n",
    "    \n",
    "    \n",
    "\n",
    "    dist = xc.indices.generic.get_dist(dist_name)\n",
    "    ax.plot(bins,dist.pdf(bins, *ind.values), color = 'C0')\n",
    "    \n",
    "    arr = df[var_name][df['g'] == hs_name]\n",
    "    arr.index.name = 'time'\n",
    "    arr = arr.to_xarray()\n",
    "\n",
    "    ind = xc.indices.generic.fit(arr, dist = dist_name).compute()\n",
    "    dist = xc.indices.generic.get_dist(dist_name)\n",
    "    ax.plot(bins, dist.pdf(bins, *ind.values), color = pal[i])    \n",
    "    mean, var, skew, kurt = dist.stats(*ind.values, moments='mvsk')\n",
    "    textstr = '\\n'.join((\n",
    "        'Mean=%.2f' % (mean, ),\n",
    "        'Variance=%.2f' % (var, ),\n",
    "        'Skewness=%.2f' % (skew, ),\n",
    "        'Kurtosis=%.2f' % (kurt, )))\n",
    "    ax.text(0.2, 0.8, textstr, fontsize=14, verticalalignment='top', \\\n",
    "            color = pal[i], transform=ax.transAxes)  \n",
    "    \"\"\"\n",
    "    \n",
    "    ax.tick_params(axis=\"y\",direction=\"out\", pad=10, labelsize = 15)\n",
    "    ax.tick_params(axis=\"x\",direction=\"out\", pad=10, labelsize = 15)\n",
    "    ax.xaxis.set_minor_locator(plt.MultipleLocator(5))\n",
    "    ax.set_ylim(0,0.9)\n",
    "    ax.set_yscale('log')\n",
    "    \n",
    "def label(x, color, label):\n",
    "    ax = plt.gca()\n",
    "    ax.text(0.025, .55, label, fontweight=\"bold\", color=color,\n",
    "            ha=\"left\", va=\"center\", transform=ax.transAxes)\n",
    "\n",
    "\n",
    "g.map(label, var_name)\n",
    "g.set(xticks=xticks)\n",
    "# Remove axes details that don't play well with overlap\n",
    "g.set_titles(\"\")\n",
    "#g.set(yscale=\"log\")\n",
    "g.axes[nh-1][0].set_xlabel('meridional OGWD [m/s/day]', fontsize = 20)\n",
    "g.axes[nh-2][0].set_ylabel('Occurrence density', fontsize = 20)"
   ]
  }
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